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.

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.

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.

Targeted therapeutics are a class of chemotherapeutics that block specific cellular functions in order to inhibit tumor proliferation. One such targeted therapeutics is a class of drugs known as tyrosine-kinase inhibitors (TKIs) which work by inhibiting the human epithelial growth factor. However, TKIs, like many cancer therapeutics, can fail eventually. This is known as drug resistance. Drug combinations or "cocktails" are a way to combat this issue, where drugs work together by attacking different angles of tumor proliferation. I aim to quantify the differences in intracellular concentration of cancer drugs by combining a bulk cell viability assay and microscopic data. For this project, I will use four different drugs representative of separate drug classes: lapatinib and afatinib, two different types of TKIs; Verapamil, a calcium-channel inhibitor, and Chloroquine, a drug that inhibits the progress of the cell cycle. First, to find the optimal concentration between combinations of different drugs, I ascertained the bulk differences in cell survivability using the MTT assay, a biochemical assay that measures the optimal inhibition of drug concentrations. Then, at and around the optimal inhibitory concentration, we will quantify the TKI uptake of different drug combinations. This is done at a single-cell level using the novel imaging technique of Stimulated Raman Scattering (SRS) microscopy, allowing us to determine the optimal drug combination that would increase the uptake of TKIs in cells. Results of SRS show that cells treated with drug combinations show higher TKI uptake when compared to cells treated with only one TKI. I predict that this particularly stands between drugs of a different class and is concentration specific. Combining the cell viability assay and a non-invasive microscopy technique, we can better understand the mechanisms behind how non-typical cancer therapeutics can be used with cancer treatments to lessen drug resistance, streamlining future therapeutic developments.

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.

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.