Mutations in phosphodiesterase 6 (PDE6) underlie photoreceptor degeneration through cyclic guanosine monophosphate (cGMP) accumulation, triggering a series of down-stream processes, which eventually kill photoreceptors. We hypothesize that knocking out inosine monophosphate dehydrogenase 1 (IMPDH1), the rate-limiting enzyme in de novo guanine synthesis, will rescue cell death caused by PDE6 mutations. Supporting evidence from a mouse mutant model (rd10) suggests that inhibiting IMPDH1 pharmacologically delays photoreceptor degeneration (Yang, 2020). Our procedure for this experiment is as follows. Fish heterozygous for impdh1a and pde6c mutations are mated to produce fish that are double homozygotes. Fish homozygous for mutations in both the cone-specific pde6c and the impdh1a genes are genotyped and embedded for histological analysis of the retina. Histology is examined on days 3,5, and 7 post-fertilization (dpf) for cone degeneration. To date, we have genotyped our two mutant lines. Normally, pde6c^-/- fish have severe cone photoreceptor degeneration at 5 dpf and impdh1a^-/- fish show no signs of photoreceptor degeneration even as adults. If degeneration is rescued, the double knockout larvae should retain similar photoreceptor nuclei counts to wildtype fish at all time points. Demonstrating that IMPDH1 inhibition rescues PDE6 deficiency would provide proof-of-concept for the therapeutic potential of IMPDH1 targeted inhibition for the treatment of photoreceptor degeneration due to cGMP imbalance.

Millions of people in the United States of America are suffering from substance use disorders (SUD); of particular concern is opioid use disorder (OUD) due to the high risk of overdose and death. This epidemic is a public health crisis that impacts people in every community. OUD is especially harmful to vulnerable populations who face arduous challenges in accessing treatment. People with chronic pain or complex comorbid conditions, housing-unstable communities, and the under/uninsured are all particularly vulnerable to OUD. Safe and effective medical treatments are available to mitigate overdose risk, but accessing them can be bureaucratically challenging for providers to navigate, which results in the relegation of OUD treatment to separate specialty facilities. This fragmentation of care impedes access and decreases provider follow-up. Substance users seeking help often face undue burdens in the form of strict sobriety requirements or incremental prescriptions necessitating frequent clinic visits. Madison Clinic at Harborview Medical Center provides care for people living with HIV and AIDS. This population is particularly vulnerable to medical stigma and bias, job instability, homelessness, chronic pain, and substance use. Using a qualitative descriptive method, we conducted a thematic analysis of semi-structured interviews to explore the thoughts, opinions, and perspectives of providers at the Madison Clinic and people living with HIV or AIDS using substances regarding the integration of medical treatments for substance use into the primary care setting. Interview transcripts were analyzed through a mix of inductive and deductive coding. Ideally, this integration would help remove barriers and alleviate the complexity of coordinating fragmented care. We anticipate co-locating routine care and treatment for opioid use simultaneously may lead to better adherence to medication regimens and increased patient and provider satisfaction with care. This research will influence the creation of a healthcare model for integrating substance use treatment into primary care settings.

Coccidioidomycosis, also known as Valley Fever (VF) is caused by the fungus Coccidioides. Pigtail macaques (PTMs) bred at the Washington National Primate Research Center (WaNPRC) in Mesa, AZ are naturally infected with Coccidioides and are similar to humans in their physiology, symptoms, and immune responses. Populations with a weakened immune system, notably older individuals, are at risk for severe complications from infection. Additionally, there is evidence that males have a higher incidence of VF than females in endemic areas. I characterized the immune responses in a PTM model across age and sex to better understand how VF affects the immune response of these populations. Forty-two PTMs (2.25-19.24 years, 3.66-18.29 kg, 37 female, 5 male) at the WaNPRC were sampled for blood. The frequencies of immune cell subsets in whole blood were characterized by flow cytometry and compared for significant differences based on age and sex. I analyzed sex-based differences with Brown-Forsythe and Welch ANOVA t-tests and found no statistically significant differences. For age-based differences, we used a simple linear regression to analyze differences by age in immune cell subsets. We found that old PTMs (10.07-19.24 years) have higher activation of CD8+ T cells, myeloid dendritic cells, intermediate monocytes, and higher frequency of γΔ T cells and CD4+ γΔ T cells than young PTMs (2.25-9.69 years). Young PTMs have a higher frequency of CD45+ granulocytes, PD-1 High CD8+ T cells, plasmacytoid dendritic cells, and NK cells. By correlating older PTMs with higher immune cell activation, and younger PTMs with higher immune cell frequency, we have a better understanding of how a vaccine or treatment could be developed to support older individuals, who are at greater risk of severe infection.

Following the COVID-19 pandemic in March 2020, mental health has become a prominent issue in the lives of many as reports of depression, anxiety, and other psychological distress increase. However, due to the sudden and drastic decline in collective mental health, resources including access to therapy and other treatments have been highly in demand. This has caused a shortage with facilities that offer psychiatric and psychological care being overbooked and unavailable. Using a dataset that observed mental health during the COVID-19 pandemic in US households, we utilized a hierarchical Bayesian model to analyze the reported rates of those who took prescription medication for their mental health within the last four weeks for 51 different locations (50 US states including Washington D.C.) across 12 time periods (from August 2020 to March 2021). We used the rstan package in R to implement this model using Markov Chain Monte Carlo (MCMC) methods. Our model applies a partial-pooled model that allows data from different US states to inform others in the case that there are not a sufficient amount of data points or high variance. In our analysis, we were able to conclude that Bayesian modeling is useful for removing noise from data, as when we analyzed the prescription usage rates per state for fewer time periods, our model was able to correct for uncertainty in the given data and give a more accurate reflection of the true rates. The model did not influence the results as significantly when using data across all given time periods. Despite these findings, our hierarchical Bayesian model did correct for the reported variation between the different average rates across the different US states by helping distinguish between signal and noise in the data. Our analysis provides an alternative approach to statistics that allows for analyses tonot only utilize current data, but also considers prior information to create a more informed posterior conclusion.

The Allan Hill region of Antarctica has produced the oldest ice core samples ever recovered, which provide insights into Earth’s climate history prior to the existing 800,000 year ice core record. However, the highly disturbed nature of this ice complicates straightforward dating and interpretation. Understanding the scales of preserved climate records in this old ice will enable deeper insights into the variability of climate over the last 2 million years. Here I study a section of ice from ALHIC1901, an ice core recovered from the Allan Hills in 2019. This section has three parallel sets of water isotope measurements, and they all show a small but significant dip. However, the cause of this dip remains unclear. The goal of this study is to test whether this isotope change could be a glacial-interglacial transition preserved for 1.3 million years, or whether diffusion should have eliminated any climate signal. To investigate this question, I apply a simple water isotope diffusion model that takes as inputs temperature, an initial water isotope profile, and a thinning history, and provides as an output the resulting water isotope profile after a given number of years. I identify the range of possible temperature, thinning, and initial water isotope signals for this ice. I use these as inputs for the diffusion model, and compare the results to the ice core record to evaluate if the observed water isotope signal can be climatically driven. Constraining the cause of this water isotope signal will improve our understanding of fine scale paleoclimate proxy changes in the extremely old Allan Hills ice cores, enabling new insights into past climate variability.

Vaccines have successfully reduced global infectious disease burden, but there is room to improve vaccination technologies. Because many pathogens infect at mucosal sites, a goal of new vaccines is to promote strong mucosal and systemic antibody and T-cell responses. Integrated fiber microneedle devices (iFMN) are a novel oral vaccination method that may achieve this goal. These devices are patches with a polymer backfill matrix and multiple >1 mm pyramidal needles that penetrate immune cell-rich mucosal tissue in the mouth, inducing immune responses at draining lymph nodes. To test the hypothesis that priming with iFMN delivery of a DNA vaccine increases mucosal and systemic antibody responses after systemic booster immunization with the same vaccine, male rhesus macaques (n=6) were primed with an iFMN delivery of a DNA vaccine encoding Influenza A Virus (IAV) Nucleoprotein (NP) at weeks (0) and (6). The macaques then received a single boost of the same NP DNA vaccine at week (12) using the proven delivery modality of Gene Gun epidermal delivery (GG). Mucosal secretions (including bronchoalveolar lavage, saliva, and nasal/tracheal swabs) and serum were collected 2-4 weeks before and after each immunization. I conducted enzyme-linked immunosorbent assays (ELISAs) to quantify antigen-specific IgG and IgA binding antibody at each timepoint. To characterize the priming effect of iFMN oral delivery on systemic and mucosal antibody responses, I compared these animals’ responses to macaques (n=8) previously immunized with a single GG dose of the same NP DNA vaccine. The iFMN-primed animals had robust post-GG boost NP-specific IgG responses in serum but these responses were not significantly higher than for macaques boosted solely with GG DNA. These results demonstrate that iFMN delivery did not effectively prime for robust systemic and mucosal antibody responses. Additional experiments will be done to confirm these findings.

Old (> 4 million years ago) ice drilled at the Allan Hills, Antarctica, can help us understand how Earth’s atmosphere has changed in the past. The bubbles trap bits of the atmosphere when they form, which can be analyzed to see what the climate was like. However, the preservation of this old ice depends on ice flow dynamics, possibly including localized shearing (one side of the ice is getting pulled faster than the other), that are difficult to observe. Bubbles in the ice become elongated when the ice around them deforms from strain. Over time, surface tension processes tend to restore bubbles to spherical. Thus, they can indicate the directions of recent or ongoing strain in the ice. We analyze thin/thick section images taken from four samples of Allan Hills ice. The images include information on grain size (size of individual ice crystals) and bubble size, shape, and distribution. We use the Segment Every Grain (SEG) model, a Python package based on the Segment Anything Model developed by Meta, to automatically calculate the sizes and shapes of bubbles in an image. We validate this method by comparing the values it returns with those obtained using another segmenting software, ImageJ, and manually calculated measurements. We can see if automated calculations are reliable enough to use regularly. So far, the SEG model has analyzed one image and it has made mostly correct bubble identification. The data shows that most bubbles are either elongated and small area, or round and large area. It is expected that the SEG and ImageJ models are close to humans in accuracy. The bubble orientations that we measure show the predominant directions of strain in the ice. Future work will use these data along with models of bubble elongation to estimate the strain rates at the Allan Hills.

In the context of computer-aided design, researchers have studied how to reconstruct an input geometry in CAD by decomposing it into CAD primitives. Such reconstruction is useful for creating CAD designs for manufacturing applications. What we want to study is also object decomposition but towards a different goal: understanding object affordances and interactability. For example, a handle of a basket can be grasped or hung from a sticky hook, and we recognize this affordance or functionality because it has a certain shape (e.g. hook or rod). Prior research has identified eight types of shape primitives that are common in everyday objects, but the existing tagging process requires a high degree of modeling expertise. We aim to create a more automatic and easy-to-use tagging tool. Our proposed research is to develop user-in-the-loop methods for tagging shape primitives given an object geometry. This takes advantage of human intuition for how objects function and interact. We start with building an interface, where users sketch over the input mesh to indicate the region for fitting and select the type of primitive to be fit. On top of this, we plan to crop the selected mesh data to generate a reduced mesh that encompasses only the area selected by the user. Finally, we utilize differentiable rendering techniques to automatically optimize the shape parameters of user-selected primitives to fit our reduced mesh data. With this tagging tool, we can enable more people without modeling expertise to tag objects. Data generated with this tool can support future research that studies object affordances with learning, as well as improve applications in robotics, product design, and assembly design like FabHacks.

Ferrate is an effective technology for water treatment applications because of its capabilities as an oxidant, coagulant, and disinfectant. Furthermore, ferrate is an environmentally benign chemical derived from a ubiquitous mineral on the Earth’s surface. However, ferrate rapid reduction to ferric species reduces its oxidation capacity. Ferrate-coated sand has been proposed as a better deployable method for ferrate in water treatment applications. Sand has a high composition (>80%) of silica (SiO₂) which has been demonstrated to stabilize ferrate reactivity and increase its oxidation capacity. A previous study on the treatment of phenol, a common surface water contaminant, showed that ferrate-coated sand was better at degrading phenol than ferrate only (in the absence of sand). However, the study was conducted in pure water matrices. Here, we are evaluating the oxidation of phenol by ferrate-coated sand in the presence of effluent organic matter and trace metals (i.e. copper). Organic matter is ubiquitous in the environment and can impact contaminant remediation efficiency. Studies have detected trace metals in surface waters which can pose environmental and health risks. Through batch tests, we observed that effluent organic matter hinders the stability of the ferrate-coated media and reduces its oxidation capacity. The results of this study will provide information about the ferrate-coated sand reactivity and capacity for the treatment of complex water matrices.

Quantum dots are nanometer scale semiconductor particles that have been extensively studied over the past decade. Colloidal quantum dots are dispersed in solution, and so can be easily deposited on a surface. This allows them to act as highly versatile quantum sensors. I am studying cadmium selenide quantum dots doped with manganese (Mn:CdSe). They possess a spin of 5/2, meaning they have six spin states, each corresponding to a different quantized energy. These six energies can be probed with photoluminescence spectroscopy, and theoretically appear as six distinct peaks in the spectrum. This allows us to use spectral analysis to read the spin state of a dot. Due to the Zeeman effect, the spin state energies are sensitive to applied magnetic fields. A simple sensing procedure first initializes the spin state, allows it to evolve under some magnetic field, and reads out the final spin state. My work focuses on the initialization and readout of the spin. For this purpose, I previously built a monochromator to characterize the quantum dots under pulsed excitation at various wavelengths, power, and temperature. I am measuring their properties using photon counting correlation measurements, photoluminescence spectra, and lifetime measurements. The goal of these results is to characterize the properties of these Mn:CdSe quantum dots to lay the groundwork for their development as a highly sensitive quantum sensor.

The emergence of induced Pluripotent Stem Cells (iPSCs) have allowed researchers to better study the effects of various diseases and mutations on fetal development. One such way of accomplishing this is the breakthrough of the organoid: a complex, iPSC-derived, 3D structure, that provides biologically relevant models for human systems. Lung Organoids (LO) were developed through this technology. However, the current LO models utilize mature lung phenotypes, which do not consider progenitor stages that may be critical for fetal development and the understanding of diseases that may affect this development in utero. The goal of this project is to provide characterization to the early stages of iPSC LO development: the Embryoid Body (EB) and Anterior Foregut (AFE). Using a previously established protocol, the LOs were fixed with 4% paraformaldehyde (PFA) on day 4 (EB stage) and day 6 (AFE stage), then analyzed with immunofluorescence analysis of the corresponding fetal lung (FL) development markers. 135 day old FL tissue sections were used as a positive control. The markers used to establish characterization were SOX17, a marker for the early endoderm germ layer, OCT4, an iPSC marker for pluripotency, and ECAD, a marker for tissue layer separation and cell migration. We hypothesized that all markers would appear in the EB stage, and the AFE stage would experience an upregulation in SOX17 and downregulation in OCT4 and ECAD. My results confirmed an upregulation of 133% for SOX17 and a downregulation of OCT4 by 58% from the EB to AFE stages. Lastly, as hypothesized, ECAD was present in EBs, but not in AFE. In conclusion, the LO stages proved to be similar to developmental stages of in utero development. Further analysis could help with new disease and mutation models for early development in utero, helping prevent devastating outcomes.

Quantum point defects are imperfections in a lattice that occur exclusively at or around a single point. In zinc oxide (ZnO), such imperfections can arise when implanted atoms replace zinc atoms adjacent to vacant lattice sites. This substitution leads to unpaired electrons, contained in the potential well of the vacant site, that act as standalone atomic systems. Such systems are often utilized in quantum sensing or employed as qubits; their effectiveness in these roles is qualified by their spin, optical, and charge properties, uniquely determined by the lattice material. Ab initio simulations have predicted promising spin defect behavior in ZnO implanted with vanadium, niobium, and titanium ions. Theorized properties of such defects, such as magnetic insensitivity, would be exceedingly useful for applications as a robust and coherent qubit. Therefore, the goal of this project is to create, observe, and eventually characterize these novel defects to determine their effectiveness for quantum information applications. To do so, I first determine the photoluminescent spectral profile of non-implanted ZnO samples using confocal fluorescence microscopes across various temperatures and excitation wavelengths. Then, we introduce the substituent atoms to the samples with an ion beam. After, we anneal the sample, subjecting it to high temperatures and prompting the ions within to move throughout the lattice and reattach themselves near vacancies, creating the defect. I then fluoresce the implanted sample to prompt a transition between the energy levels of the defect. If it is present, the defect’s relaxation emits a photon of a characteristic frequency unique to the defect. Thus, comparing pre and post-implantation spectra of the sample’s photoluminescence allows us to confirm the existence of the defect, gain insight into its structure, and, in later projects, examine its optical, electronic, and magnetic properties.

The Mid-Pleistocene Transition (MPT) was a major climatic shift in Earth’s history occurring between 1.2 and 0.7 million years ago. During the MPT, Earth’s glacial cycles shifted from a high frequency (~40 kyr), low amplitude cadence to a low frequency (~100 kyr), high amplitude cadence which has dominated since the MPT. While we are able to observe the MPT in benthic 𝛿¹⁸O records, our current ice core record only extends back 800 kyr and does not include a preservation of the entire MPT. COLDEX, a multi-institution collaboration, is seeking to find a region in Antarctica where a continuous deep ice core may preserve the MPT in order to better understand the underlying mechanisms that caused it. Ice at this depth, however, is subject to much different conditions than the ice cores that comprise our current record. My study aims to analyze how atmospheric gases, namely CO₂ and the 𝛿O₂/N₂ ratio (used to identify precessional cycles for dating ice cores), diffuse in Antarctic ice of 1 to 1.5 million years old. I focused on the COLDEX survey region between the South Pole and Dome A. I employed two models: 1) a one-dimensional steady state model which calculates the temperature and age of the ice with respect to depth, and 2) a gas-diffusion model which uses the temperature- and age-depth relations to calculate the amplitude of the gas signals in the ice through time. The input parameters for these models are measured using aerial radar, provided by COLDEX, or interpolated accordingly. So far, I have found that CO₂ is relatively well preserved in the region, while the 𝛿O₂/N₂ ratio is much less well preserved. This suggests that finding an ideal region for a deep ice core drill site, on the basis of gas diffusion, may be difficult.

Flying insect robots (FIRs), owing to their minuscule weight and size, offer unparalleled advantages in terms of material cost and scalability. However, their size introduces control hurdles, notably high-speed dynamics, restricted power, and payload capacities. While there have been notable advancements in developing lightweight sensors, often drawing inspiration from biological systems, the challenge remains in executing controlled flight without external feedback. We introduce Tiny Sense, a novel avionics system tailored for FIRs, encompassing an integrated sensor package — an inertial measurement unit, a pressure sensor, and an optical flow sensor. Coupled with a Kalman Filter (KF), this system weighs a mere 78.4 mg, drawing 15 mW of power. This is lighter and more power-efficient than previous sensor suites of the same capabilities. Our system uses a global-shutter camera as an optical flow sensor to collect pixel intensities for accurate optical flow calculations at 100 Hz. We collected raw data from the Tiny Sense by attaching it to a Crazyflie quadcopter and tested the KF by comparing its results to the measurements from the Crazyflie. We will continue to integrate the Tiny Sense with sub-gram FIRs and are currently working on mounting it to a 74-mg RoboFly. Our sensor suite allows even smaller FIRs to be able to achieve autonomous control.

Tissues like the meniscus, a wedge-shaped pad of connective tissue found in the knee, are fibrous and have complex architecture that regenerates poorly and undergoes active mechanical stimulation which modifies cell signaling and tissue health. In vitro models are beneficial for characterizing these interactions as they create a controlled environment where single variables can be altered. We previously used the J1 Mechanoculture bioreactor to apply strain on a fibrous polymer scaffold laden with primary meniscal cells and observed nonsignificant variances between testing groups with mock injury vs. no injury. Applied strain was modeled after physiological strain levels, ~10%. Based on the minimal changes in cell behavior observed in mock injury samples, it is likely that the mock injuries in conjunction with the applied strain did not induce comparable plastic deformation to that experienced post injury within the native meniscus. We hypothesize that increasing strain and applied force to achieve plastic deformation within the electrospun samples will create a fibrotic and apoptotic response like that in vivo. Ongoing work is analyzing how the bioreactor will interact with unaligned electrospun polymer samples with no cells present. This will demonstrate the optimal parameters to instigate a significant material response. By inducing significant changes to scaffold material properties and underlying structure, it is more likely cells with demonstrate fibrotic and apoptotic responses in vitro mimicking immediate cell reactions to meniscal injuries in vivo. This response will be assessed by assaying for fibrosis through αSMA activation and apoptosis by caspase-3 activation. On the conclusion of this study, we expect that greater applied stress and associated strain will cause more plastic deformation within the polymer scaffold. This can be applied to an in vitro meniscus injury model to better understand the response of primary meniscal cells to stress in an environment with disrupted mechanics.

The acceleration of antimicrobial resistance (AMR) in pathogens and commensal organisms is an emerging global health crisis due to the overuse and misuse of antimicrobial drugs. In addition, it is unknown how other factors such as a changing climate may impact AMR. We utilized wastewater surveillance to investigate if the diversity of antimicrobial resistant genes (ARGs) in influent wastewater is associated with rainfall. We utilized our workflow for the detection of specific ARGs in the greater Seattle area, contributing to AMR stewardship efforts. I conducted qPCR, PCR, and nanopore sequencing of CTX-M genes, an antimicrobial resistance gene, extracted from influent wastewater from wastewater treatments plants servicing the Seattle area. The purpose of this approach is to assess diversity of AMR gene alleles with high accuracy to contribute to the surveillance of AMR genes in populations. I hypothesize that higher rainfall—typically occurring from October to March—leads to a lower diversity of AMR genes due to increased dilution and decreased potential for horizontal gene transfer between organisms. During the dry season—April to September—I hypothesize we will find more unique alleles of AMR genes. In this poster, I will present the results of my statistical analyses investigating the relationships between ARG abundance, ARG diversity, and rainfall The utility of contextualizing the diversity of targeted antimicrobial resistance genes can inform clinical practices that benefit the health of populations.