As a student of both Religion and International Relations, I am intrigued and perplexed by the presence – or absence – of sanctity within international systems. My senior capstone, which I conceived of and researched myself, studies the Tlingit potlatch and its use of the sacred in international relations between the Sitka Tlingit Nation and the Russian Empire (prior to 1867), as well as the United States government (after 1867). Using secondary sources, the anthropological and historical work of Dr. Sergei Kan, and definitions of the sacred as discussed in the works of Mircea Eliade, I ask several questions to evaluate international relations theory. Firstly, what is the sacred and how does it connect us? Secondly, how was the sacred used in the Tlingit potlatch, and how did the Russians and Americans differ in their interactions within this space? Finally, how might a closer look at the potlatch help me understand one form of Indigenous international relations and how can the use of the sacred challenge assumptions made by predominantly white western international relations theorists? The Tlingit potlatch served as the primary space for international relations through the prescribed roles of “host” and “guest.” My research has led me to conclude that the Russian Orthodox Church leaders accepted their role as guests, thereby maintaining their sovereignty and the sovereignty of the Tlingit, and by extension, preserving international relations between the two groups. American Presbyterians, however, sought to conquer and control Tlingit sanctity, effectively dissolving the potlatch, Tlingit sovereignty, and the possibility of maintaining international relations between the communities. With these discoveries in mind, I ask, “What might Tlingit Elders tell us about how to conduct international relations, and what do we do with that?” By asking this question, I attempt to elevate Indigenous voices in the field of international relations and revolutionize the ways in which international diplomacy is conducted by allowing space for empathy, sanctity, and trust.

SARS-CoV-2 spreads across the globe, infecting more than 128 million people and claiming over 2.7 million lives with an absence of definitive treatment up to date. Therefore, there is an immediate need to develop treatments fighting against the COVID-19 global pandemic. The goal of my project is to generate and assess an innovative treatment for the SARS-COV-2 virus infection. Our treatment formulates computationally designed proteins, and we want to evaluate its therapeutic effects using human induced pluripotent stem cell (h-iPSC) derived cell lines and organoids. The designed protein is a combinatorial cage (mosaic cage) containing spike binders previously shown to significantly inhibit SARS-CoV-2 viral infection and F-domains that were shown to activate the Tie2 pathway. The Tie2 pathway is a key regulator of vascular stability, where active Tie2 can strengthen cell-cell junctions and enhances endothelial cell survival, thus enhancing blood vessel stability. We hypothesize that the designed protein would neutralize the spike protein to block viral entry and activate the Tie2 pathway to alleviate sepsis in COVID-19 infected patients. We will test spike-binding activity and determine the activation level of the Tie2 pathway of this mosaic cage in iPSC-derived spike-overexpressing endothelial cells. We expect to measure a strong spike-binding affinity of designed proteins and strong downstream pathway signals pAKT, pERK, pFAK in designed protein-treated iPSC-derived endothelial cells. We also plan to test the mosaic cage’s activities using Kidney Organoids. If our hypothesis is correct, we will apply the findings clinically for their potential intranasal administration as a COVID-19 therapeutic.

Over the past fifty years, survival rates for most cancers have risen as innovative treatments have been developed. However, Diffuse Intrinsic Pontine Glioma (DIPG), a rare pediatric brainstem tumor, has seen no such improvement and remains one of the deadliest cancers. DIPGs are genetically distinguished from adult gliomas by a lysine-to-methionine mutation in a variant of histone H3 called H3.3 (H3.3K27M), found in 80% of DIPG tumors. This mutation is absent in canonical H3.1 and H3.2, yet it triggers a global reduction in the levels of polycomb repressive complex 2 (PRC2) mediated H3K27me3 (tri-methylation) marks, which is traditionally considered a driving event in tumorigenesis. However, genome-wide studies of H3K27me3 marks in DIPG cells have revealed that this global loss of H3.3K27me3 is accompanied by a sharp increase in H3K27me3 repressive marks at certain genes. These trimethylation spikes represent regions with high transcriptional repression and may be key in understanding and treating DIPG. Our lab has previously described the inhibition of PRC2 using a computationally designed protein EBdCas9. Using a complementary guide-RNA tiled to the promoter region of a gene, I can target EBdCas9 to that specific region on the genome, remove existing H3K27me3 marks, and increase gene transcription. Identification of candidate genes which are repressed in DIPG cells under these H3K27me3 spikes remains an ongoing project that I am working on. Results of one target, tumor-suppressor gene p16, demonstrate that I can transfect primary DIPG cells with EBdCas9 plasmid and a p16-specific gRNA to trigger a 20-fold increase in p16 expression. I hypothesize that restoration of critical cell-cycle genes like p16 will reduce DIPG viability and offer potential therapeutic targets. I’m currently working to improve transfection efficiency in order to increase p16 expression, and future steps include in-vivo testing of this system, either via mouse model or a brainstem organoid.

The theory of quantum mechanics has been well-developed over the last hundred years. However, its application is limited by the computational power of modern computers. With the rise of Big Data and Artificial Intelligence, a new door is opening to us to untangle the fascinating world of quantum mechanics. In our lab, we use Diffusion Monte Carlo (DMC), a statistical simulation to solve molecular vibration and rotation problems. It is remarkably accurate and versatile, making it suited for notoriously difficult systems, like protonated methane (CH₅⁺). Yet, it requires millions of potential energy evaluations before quality results can be acquired, which often takes unrealistic amounts of time. In this work, we use TensorFlow, a neural network training framework developed by Google, with full Graphics processing unit (GPU)-acceleration support, to considerably speed up the evaluation of the potential energies needed for the DMC calculations. We started by running a small-scale conventional DMC simulation to collect a set of molecular configurations and their corresponding potential energies, which are then fed into a 3-layer deep neural network on Tensorflow with carefully-selected parameters. Once finished training, the neural network can replace the conventional potential energy evaluation method used in DMC to greatly expedite the process. We tested this model on water(H₂O), protonated methane(CH₅⁺) and water dimer((H₂O)₂), and was able to achieve a 15-fold acceleration, with less than 0.01% error compared to conventional methods. Our future goal is to further optimize the neural network to make it even faster and more accurate, then apply it to larger systems which were unsolvable before due to their computationally intractable time.

Water clusters play a significant role in a variety of processes such as those pertaining to the atmosphere and biological systems, and studies of water clusters have suggested that they could help us learn more about hydrogen bonding. We first must understand the energetics and trends of isolated water molecules in order to better comprehend the spectroscopic properties of water clusters. Afterwards, we can look at water clusters and observe how the energetics and patterns change due to the interactions with other water molecules. We are studying the coupling among vibrations in water molecules and how they are reflected in the spectra. The discrete variable representation (DVR), a method used to solve the Schrödinger equation, was implemented to generate the water spectrum as well as energies and wave functions. The DVR results show that the theoretical intensities are consistent with the experimental results. These results contribute to our goal of analyzing the spectra of more complicated water cluster systems. Diffusion Monte Carlo (DMC) is a different method that allows us to explore larger systems and is used in the analysis of the coupling in assemblies that contain multiple water molecules.

Long-term air pollution exposure can have detrimental effects on respiratory, cardiovascular, and cerebrovascular health, increasing mortality risk. Ultrafine particles (UFPs) are nanoscale air pollution particles with aerodynamic diameters under 100 nm. Growing evidence suggests that UFPs can deteriorate brain health by crossing the blood-brain barrier that protects the brain from toxins and pathogens. Historically, people of color and low income communities have been more likely to be exposed to higher concentrations of air pollution, but disparities in UFP exposure specifically have not been studied. This study used linear regression modeling to estimate the association of UFP exposure with race-ethnicity and income in the greater Seattle area. We observed that 83% of the census block groups in the study area were predominantly non-Hispanic white, and household incomes ranged from $9,000 to $250,000 with a median income of $71,000. Estimates of annual average UFP concentrations were modeled from mobile monitoring measurements during the year 2019. UFP concentrations ranged from 2,000 to 14,000 pt/cm3 with a mean of 6,000 pt/cm3. We found that UFP concentration decreased by 177 pt/cm3 (95%CI: 160, 194) for every 10% increase in household income. For every 10 percentage point increase in non-Hispanic white population, UFP concentration decreased by 449 pt/cm3 (95%CI: 410, 488). These results indicate disparities in UFP exposure due to both race-ethnicity and income in the Seattle area. Further analyses investigated this relationship in detail, by specific income and racial-ethnic groups, and compared the relative effects of race-ethnicity and income on UFP exposure. This study can help inform future studies on UFPs and public policies that promote healthier, more equitable communities.

Over the last few years, there has been a dramatic increase in wildfires and their severity. Wildfire is a natural phenomenon that needs to happen to regenerate life within ecosystems. It kills off the old growth, allowing new growth to flourish in its place. Previously, scientists and political officials understood wildfire as an enemy of the forest, partly because forests were valued as commodities. Wildfire has its benefits but has devastating affects on communities. This study examines wildfire in the wilderness urban interface (WUI) within the Cascades Mountain Range, Washington. This study has three parts. First, I mapped the WUI over two decades in the Cascades Mountain Range, Washington. Second, I created a spatial index to depict risk levels across the study area. Last, I met with stakeholders to understand the current practices of wildfire fighting and the needs of the community. This research found significant growth in the WUI, interesting variation in wildfire risk across the Cascades, and conflicting interests among the stakeholders, ranging from privileging forest ecology to valuing the economy. These insights were gained by using geospatial techniques. Wildfires aren’t going away, and we need to understand how communities will be impacted and can prepare for the future.

 The Cascades mountains of the Pacific Northwest are highly vulnerable to the impacts of climate change, and increases in temperature have led to decreased snow and an uncertain future. A reduced snowpack has significant ramifications for the evolving ski and backcountry Winter recreation industry in Washington state. My research considers a changing natural landscape and its hydrological processes in the face of global climate change, from the lens of backcountry recreation. I use geospatial analysis to quantify the extent to which the snowpack of the Cascades has been impacted by temperature increases using data from snowpack telemetry sites and remotely sensed hydrologic data, and models its future state given predicted climate scenarios. I discuss the dynamics of winter backcountry recreation including increased usage, the effects of the COVID-19 pandemic, avalanche awareness and risk, and the existential threat to Pacific Northwest Winter recreation when mountain snowfall becomes rain.