Menopause is a significant life transition that is experienced differently across cultures, with each cultural background shaping unique beliefs, expectations, and responses to menopausal symptoms. Among Somali and Indonesian older adults with a uterus, cultural factors play a particularly influential role in shaping the menopause experience, potentially affecting symptom recognition, coping strategies, and interaction with healthcare providers. The purpose of this qualitative study is to 1) explore menopausal symptoms, perceptions, beliefs, and experiences of aging and menopause among Somali and Indonesian older adults, and 2) examine how cultural factors shape their understanding of menopause, as well as how they interpret and manage the symptoms associated with this stage of life. We conducted a cross-sectional qualitative study using semi-structured interviews with 5 participants with a uterus: 2 Somali and 3 Indonesian individuals. We recruited participants through community networks within Somali and Indonesian populations living in Washington state. We included individuals who are 1) 60 years or older and 2) undergoing or have gone through menopause. We chose older adults for their reflective perspective, offering holistic insight into the long-term impact of menopause and their ability to share cultural wisdom and experiences. Our findings will inform healthcare providers in delivering culturally sensitive care to support individuals going through menopause in these communities. Additionally, this knowledge can inform the development of targeted interventions and educational tools that help bridge cultural gaps in menopause understanding, thereby promoting holistic, culturally sensitive, and inclusive care for older adults with menopausal symptoms in these communities. Addressing these deficits can lead to improved care for Somali and Indonesian adults with a uterus while also benefiting women more broadly by promoting a more comprehensive and inclusive approach to menopause support.

Cardiac Electrophysiologists (EP) use various devices to diagnose and treat heart rhythm disorders. Unfortunately, these devices can be defective and are subject to recalls by the U.S. Food and Drug Administration (FDA) to protect the patient. This study aims to better understand the rates of these recalls from 2014 to 2024 and thereby evaluate the safety of these devices, compare the safety vis a vis previous decades, and assess the safety of new devices as they are introduced for clinical use. We evaluated the number of FDA advisories related to implantable cardiac devices from 2014 – 2024; total the number of devices recalled under these advisories, determining the most prevalent malfunctions, and analyzing trends compared to rates from 1990-2000. The data shows that Permanent Pacemakers and Implantable Cardioverter Defibrillators (ICD)  have become safer compared to the past, although more units were affected per device advisory. Newer technologies of Leadless Pacemakers, Subcutaneous-ICDs, Implantable Loop Recorders, and Cardiac Contractility Modulation have been impacted by malfunctions and their trend will be critical to follow over the years to come. The findings of this study provide information regarding the safety of these technologies that are valuable to both EP cardiologists and the millions of American patients who live with these devices.

Large Language Models (LLMs) have demonstrated remarkable performance in code generation and problem-solving, but their effectiveness varies across problem domains and complexities. In this study, we assess the problem-solving capabilities of different LLMs on a subset of LeetCode problems, categorized by difficulty, topic, and computational complexity. Through a systematic analysis of LLM performance across question topics—such as dynamic programming, matrix, and binary search—we identify trends in their strengths and weaknesses. Our findings reveal that LLMs excel at problems likely included in their training data but struggle significantly with novel or unseen problems. By evaluating their performance across different algorithmic domains, we offer insights into the potential limitations in specific algorithmic domains and implications for AI-assisted coding, competitive programming, and software engineering education. 

Human Rhinovirus (HRV) is one of the primary causes of mild upper respiratory infections and is the most common infectious agent which affects billions of humans globally. To most healthy individuals, this illness causes mild symptoms. However, in populations who have immunosuppression, comorbidities, or predisposition health issues, this virus can cause severe symptoms which can lead to possible hospitalizations and even an increased mortality rate. Given the lack of approved therapeutics for this disease, our project aims to prepare a target Phenylpropenoid, which is an organic molecule that has been previously isolated from the plant Bupleurum fruticosum and has reported antiviral qualities against HRV. Our synthetic approach toward the target compound involves a three-stage process: synthesizing a phenylpropenol fragment, preparing a bis-enoate fragment, and combining the two through esterification to access the target phenylpropenoid. We have successfully prepared the phenylpropenol fragment and are working toward accessing the bis-enoate fragment for examination in the final esterification.  Our findings will enable preparation of derivatives to assess in bioactivity studies that may provide valuable insights for future target design.

The external fertilization and transparent embryos of zebrafish make them an informative model of vertebrate embryonic development from the 1-cell stage. In this study, we examine the impact of de novo GTP synthesis on the formation of the embryonic somites, which are embryonic cells which develop into segmented blocks of muscle that run the length of the body. We hypothesize the de novo GTP synthesis is required for the correct patterning of somite borders in zebrafish embryos, and that this process facilitates the formation of a vertebrate body plan. Inosine monophosphate dehydrogenase 2 (IMPDH2) is the enzyme which catalyzes the conversion of inosine monophosphate (IMP) towards the de novo synthesis of GTP instead of ATP. To test the impact of de novo GTP synthesis on somite formation, we inhibited IMPDH2 function with mycophenolic acid (MPA) both before and after somite formation began. MPA caused stronger defects in the somite morphology and embryonic body shape when added to embryos before somite formation began, earlier in development. We performed in situ hybridization against xirp2a to assess the effect of inhibiting IMPDH2 function on the formation and patterning of the somite borders. MPA treatment decreased the definition of somite borders we could observe in the posterior tail. Inhibiting IMPDH2 with MPA produced somites with smooth, round borders instead of the chevron-shape typical of zebrafish. We next conducted immunohistochemistry against IMPDH2 to examine the expression and localization of this enzyme in embryonic cells when GTP conditions are low. In MPA-treated embryos, we observed increased expression of IMPDH2 across the entire embryo. We will next explore how GTP abundance affects activity of the clock, a mechanism which synchronizes gene expression of embryonic cells.

In vivo genome editing of hematopoietic stem cells (HSCs) offers a promising approach for treating hemoglobinopathies and HIV/AIDS. The Lieber Lab has developed helper-dependent adenoviral (HDAd) vectors that preferentially transduce primitive HSCs in mobilized CD46-transgenic mice, humanized mice, and rhesus macaques following intravenous injection. However, off-target transduction (including other blood cell lineages and various organs) remains a critical challenge, potentially compromising safety. Moreover, the comparison of PGK and the relatively strong Ef1α promoters revealed that the editor expression level influences editing outcomes, especially in multiplex editing approaches. To address these limitations, the project’s goal is to engineer a highly active HSC-specific promoter that maximizes on-target gene editing while minimizing off-target effects, improving both the safety and efficacy of HDAd-based therapies. I first generated GFP reporter plasmids containing roughly 2 kb of proximal promoter sequence from five genes highly expressed in HSCs: CD164, cKit, DSG2, PROM1, and PROCR. These constructs were introduced into human CD34⁺ cells via nucleofection, and the cKit and PROCR promoters showed the strongest GFP expression in the HSC-enriched (CD34⁺/CD45RA⁻/CD90⁺) subset. To further enhance promoter activity, we linked the top-performing promoters to distal HSC enhancers that, according to ENCODE/Hi-C analyses from Dr. David Hawkin’s Lab, regulate cKit (2 enhancers), CD164 (4 enhancers), and PROM1 (2 enhancers). Engineered promoter-enhancer constructs yielded 3- to 4-fold higher GFP expression than Ef1α in CD34⁺/CD45RA⁻/CD90⁺ cells, with the cKit promoter + CD164-3 and PROM1 enhancers showing the highest activity. Building on these findings, we have incorporated these HSC-specific promoter–enhancers into helper-dependent adenoviral (HDAd) vectors driving an ABE8e-base editor for γ-globin reactivation. Ongoing work is focused on evaluating the specificity and efficacy of these HDAd vectors in humanized mice and CD46/βYAC-transgenic mice, with the ultimate goal of achieving safer, more effective in vivo genome editing in HSCs.

The Molecular Case Network (MCN) connects biology and chemistry educators and fosters collaboration to create narratives that enhance interdisciplinary teaching and learning while providing support for their professional development. This research presents a case study of 10 MCN participants who were interviewed about their experiences creating interdisciplinary educational case studies to implement in their courses. These lessons aid the teaching of the content material and technology in a real-world context. These educators are considered expert participants and bring diverse perspectives and experience, allowing them to contribute unique ideas and create multifaceted case studies. Our objectives are to articulate the definition of molecular storytelling through the instructors’ perspective, gauge the instructors' confidence in the interdisciplinary teaching of biochemistry, and examine their self-efficacy in implementing the molecular case study with technology. We used a thematic analysis when looking at the interviews to identify common sentiments and refine them into themes to better understand the relationship between the participants' experiences. Through analysis of the interviews with the participants, we found major themes about the usefulness of technology and the importance of a strong leader in building confidence in both the technology and the writing of the case studies. We explore each unique narrative to find what motivates, excites, frustrates, and ultimately leads to the success of each instructor. By exploring these areas, we aim to provide insights that can help improve education in the relevant scientific disciplines. By understanding the factors that influence the success of these educators we hope to inform the MCN to help strengthen and expand their success long term.

Chlorpyrifos (CPF) is a widely used organophosphate pesticide effective in controlling agricultural pests by inhibiting acetylcholinesterase, leading to the accumulation of acetylcholine and continuous nerve stimulation. CPF exposure has been linked to increasing autism risk and gut microbiome dysbiosis. However, the underlying mechanism linking CPF to autism remains unclear, and the role of the gut microbiome in CPF-induced neurodevelopmental toxicity remains elusive. Using a high-throughput social behavior assay, we found that embryonic exposure to CPF caused lasting social deficits in zebrafish. We then screened seven common gut microbiome metabolites and found that butyrate effectively rescued CPF-induced social deficits. Butyrate is a known inhibitor of histone deacetylases (HDACs). We discovered that valproic acid, an inhibitor of Class I and IIa HDACs, phenocopied butyrate’s rescue effects. Meanwhile, trichostatin A, an inhibitor of Class I, II, and IV HDACs, and nicotinamide, an inhibitor of Class III HDACs, did not. We are currently conducting multi-omics analyses; including metagenomics, metabolomics, RNA sequencing, and CUT & RUN, to further elucidate the mechanisms underlying CPF’s neurodevelopmental toxicity and butyrate's rescue effects. In the long run, our work will help to uncover how CPF exposure contributes to autism risk and to inspire new therapeutic approaches for alleviating autism-related social deficits.

This study aims to understand users’ needs in designing a mobile application that assists Cardiopulmonary Resuscitation (CPR). This study is designed through a two-stage online interview, beginning with a screening survey that asks about participants’ occupations and experience with CPR, followed by the main interview that asks about their challenges when performing CPR and their opinions on the most efficient type of feedback. Eight participants who completed the survey included CPR instructors, paramedics, firefighters, and medical students with experience ranging from 3 to 20 years. The participants rated maintaining compression depth as more challenging (7.63 out of 10) compared to maintaining compression rate (5.25 out of 10) when performing CPR, with 10 representing the highest level of difficulty. Participants also reported that visual feedback would be more beneficial for compression depth and auditory feedback for compression rate. Therefore, these findings suggest that an effective assistive CPR device should have real-time visual feedback to aid in-depth consistency while auditory cues support rhythm accuracy similar to a metronome. By leveraging these findings, the proposed application has the potential to enhance CPR effectiveness, improve user confidence, and ultimately increase patient survival rates.

Bernini's David (1623) attempted and—in some capacities—succeeded in breaking the barrier between the viewer and the art. David's narrative is only completed through audience participation and then furthered through dialog with the other works displayed in the same space. Bernini's motivation for creating is key to understanding his incessant cultivation of technical skill and his drive to push the boundaries of possibility in Baroque sculpture. The work functions both alone and as a key sculpture in the oeuvre of Bernini. Framing the work with analysis from Wittkower, Wallace, and Lavin, I dissect what made David different from Bernini's earlier sculpture and how David became the precursor for his later work.

Laboratory automation has demonstrated great potential in accelerating the discovery and optimization of new materials. However, the lack of low cost high-throughput characterization has been a limiting factor in the development of autonomous self-driving labs. To address this, we developed an open-source 3D-printable robotic framework that can be integrated with an ocean optics spectrometer probe designed to measure materials properties in a high-throughput fashion. The device is low-cost, easy to construct and fully compatible with the Opentron (OT-2) automated liquid handler. The system operates on a printer-gantry system that moves the spectrometer probe across a laboratory plate as scanning progresses. We aim to achieve scanning speeds of 1 second per well, allowing a standard 48 well laboratory plate to be completed in under 1 minute – a significant improvement over current times achieved with human testing. Additionally, we outline potential applications for the system through the characterization of perovskite semiconductors for energy-efficient lighting and discuss the challenges of fully integrating this device into a completely autonomous workflow. Despite its current limitations, by facilitating high throughput characterization through affordable, open-source technologies, this device enables materials researchers in underserved regions to accelerate progress in key areas such as green technology development. 

3D perovskites have enormous potential for optoelectronic applications such as light-emitting devices, photodetectors and lasers, due to tunable optical properties. Achieving precise control over their characteristics, specifically color purity, can be costly to discover because of their highly nonlinear behavior.  In this work, machine learning (ML) will be employed to explore the synthesis parameter space and target perovskite films with desired RGB values. By varying the annealing time and composition of the MAPbIBr₂ perovskite while fixing other synthesis parameters the film’s optical response can be adjusted. Using Bayesian Optimization, a data-driven approach will be established based on experimental feedback for precisely tuning the perovskite. This synthesis framework is designed for easy adaptation to other synthetic spaces requiring precise material control. This research aims to accelerate ML-driven design of perovskites while enhancing our understanding of their nonlinear synthesis space.

Advancements in HIV prevention include pre-exposure prophylaxis strategies (PrEP), which are not as effective for women due to poor partitioning of antiretrovirals (ARVs) to the female reproductive tract. Integrating ARV-releasing reservoirs with intrauterine devices (IUDs) offers a strategy for local sustained delivery to overcome the partitioning issue. Our lab investigates reservoirs containing polymer-drug conjugates (drugamers), where the HIV integrase inhibitor raltegravir (RAL) is covalently attached to a polymer through a hydrolyzable linker. A previously characterized RAL-polymer exhibited release over 30 days, which is insufficient for the targeted 1-3 years of IUD-mediated delivery. To address this kinetic problem, the drugamer linker chemistry was modified from an ester to an acetal carbonate. Since the rate-determining step of the acetal carbonate linker hydrolysis does not depend on the acidic RAL hydroxyl (pKa = 6.6), it was hypothesized that this acetal carbonate linker will slow the RAL release rate as opposed to the ester linker. An acetal carbonate-linked monomer of RAL was synthesized and led to a 30-fold reduction in hydrolysis rate. The corresponding drugamer was then synthesized via RAFT polymerization and characterized via NMR. In hydrophilic media, RAL released from the novel polymer significantly slower than in the current lab polymer, showing potential for lengthened duration of action in in vivo models. Future work includes measuring release from RAL-polymer in a matrix device for future IUD incorporation, assessing potential polymer cytotoxicity, and evaluating release rates in mouse models. These findings lay the groundwork for the development of long-acting formulations for sustained HIV prevention.

Speech technology is often evaluated under idealized conditions that privilege certain speaker profiles: native English speakers in optimal acoustic environments. This approach overlooks the reality that English, as a global lingua franca, is spoken by billions of non-native speakers. Similarly, speakers with speech disorders face potential exclusion. Accurate phonemic transcription is crucial both for analyzing speech patterns in post-stroke aphasia and Computer-Assisted Pronunciation Training (CAPT). We evaluate automatic phonemic transcription under realistic conditions, including varied noise levels, L2 accents, and speech variations. We find that standard models perform suboptimal under realistic conditions, and that applying vocabulary refinement and data augmentation improves error rates by 12-28 percentage points. To demonstrate the viability of our phonemic transcription models, we develop Machine Aided Pronunciation Learning via Entertainment (MAPLE). MAPLE maintains real-time performance on consumer devices, demonstrating the practical applicability of robust socioculturally-aware phonemic transcription in educational environments.

Following spinal cord injury (SCI), respiratory function is often impaired due to limited respiratory muscle function. Decreased respiratory function can lead to breathlessness, impaired coughing, reduced exercise tolerance, and increased respiratory infection risks. Previous studies have shown that transcutaneous spinal cord stimulation (tSCS) at cervical and lower thoracic levels can increase vital capacity by targeting respiratory and abdominal muscles in individuals with cervical SCI. This case series study aims to evaluate the effects of tSCS combined with arm crank exercise on respiratory function after SCI. We recruited three individuals with cervical motor-complete SCI, who were randomly assigned to the active tSCS or sham stimulation group. Two participants underwent 24 training sessions with active tSCS. One participant completed 24 training sessions training with sham stimulation. Spirometry was conducted with real-time tSCS at baseline at different spinal locations. Spirometry was also conducted without real-time tSCS before and after 24 training sessions to assess the long-term effect. Forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and peak expiratory flow (PEF) were measured. Out of all locations tested, T6-T7 showed the largest improvement across all spirometry parameters. Participants in the active tSCS group showed improvements in all parameters after 24 sessions. The participant in the sham group showed decreased PEF. The data collected thus far suggests that tSCS may modulate the spinal neural network responsible for respiratory function. Furthermore, tSCS combined with exercise has potential to improve respiratory function in people living with SCI. A larger sample size is necessary to evaluate the long-term efficacy of this novel non-invasive therapy on respiratory function to improve health after SCI.

Multiple System Atrophy (MSA) is a fatal neurodegenerative disease caused by alpha-syn deposition in the brain and spinal cord. This results in severely declined autonomic and motor functions.  In rare cases of MSA, there is pure autonomic system failure, only including dysregulation of blood pressure (BP) control and pelvic organ functions including bowel movement. Blood pressure changes could be extremely dramatic, with uncontrolled drops below 60 mmHg and elevation sometimes over 250 mmHg, resulting in the inability to even stand for more than one minute without feeling faint. Overall, this greatly impacts an individual’s quality of life and mortality. On average, life expectancy after MSA diagnosis is about 6 to 10 years, though this can vary based on factors such as age at onset and symptom severity. Currently, treatment options primarily focus on mitigating symptoms. This case study reports the effect of non-invasive transcutaneous spinal cord stimulation, using on-skin electrodes, on cardiovascular and bowel function. We recruited a male in his 60’s with MSA diagnosed 15 years ago, showing pure autonomic system failure. We measured both acute and long-term effects of stimulation on blood pressure by monitoring continuous BP during stimulation and also had the patient maintain a 24-hour blood pressure log pre- and post-stimulation. Upon examining the data that I analyzed, cervical spinal cord stimulation elevated blood pressure more than thoracic or lumbar stimulation. The participant also recorded his bowel management and stool quality for 5-7 days before and after the sessions. Spinal cord stimulation initiated bowel movements immediately after the intervention. Further research is warranted to better understand the effects of cervical spinal stimulation on blood pressure regulation and bowel function.

Girls in Afghanistan have been denied their rights numerous times in history, with restrictions on clothing, work, movement, education, and more. In this project, I explore the persistence of Afghan girls' fight for freedom through underground education and provide a timeline reflecting work on the underground education system. This opens the discussion on what more can be done by powerful global organizations in the context of ever decreasing rights for Afghan women brought upon the events of the Taliban takeover in 2021. My research highlights the brave and selfless Afghans who have committed themselves to empower Afghan girls through underground education by hearing their stories. The goal here is to counteract the continuous turn of limitations on Afghan women's rights that are holding back prosperity in Afghanistan. This examination of underground education and the people who make it possible paves the way for positive intervention. This research will primarily be based on qualitative data and some data visualization.

HIV incidence among adolescent girls and young women (AGYW) is disproportionately high in East and Southern Africa, highlighting the urgent need for effective HIV prevention counseling for this population. Our study addresses critical gaps in HIV prevention for AGYW in Kenya, particularly regarding HIV education and engagement in prevention services, by leveraging the trusted role of nurses to support AGYW and overcome barriers related to stigma, misinformation, and interpersonal dynamics. Nurse navigators are a promising intervention within family planning clinics, though accessing contraception via pharmacies is common among AGYW in Kenya. Prior pilot studies show the feasibility and acceptability of delivering HIV Pre-Exposure Prophylaxis (PrEP) within a pharmacy with PrEP-trained nurses. The purpose of this current study is to evaluate the effectiveness of integrating nurse navigators and long-acting PrEP agents on PrEP uptake and continuation among AGYW seeking contraception at pharmacies in Kisumu, Kenya. A 2-armed non-blinded cluster randomized control trial is being conducted in Kisumu among AGYW (n=1900) seeking contraception at pharmacies. The study randomized 20 pharmacies to receive (n=10) or not receive (n=10) nurse navigators to support PrEP delivery. We anticipate that the implementation of nurse navigators and long-acting PrEP agents in pharmacy settings will significantly improve access to and engagement with HIV PrEP among HIV-negative AGYW in Kenya. Additionally, the study will qualitatively evaluate the acceptability, feasibility, and client satisfaction of delivering long-acting PrEP agents within pharmacies with nurse navigators to support PrEP use. While the study is ongoing, our preliminary data illustrates positive experiences among AGYW with nurse navigators, including feelings of safety, trust, and educational improvement. This study can confirm the efficacy of nurse navigators in improving engagement in health management and education for AGYW in Kenya. Further exploration of the effectiveness of nurse navigators in service to other populations should be conducted. 

Individuals with spinal cord injury (SCI) often experience reduced exercise capacity due to impaired cardiovascular control, which limits their participation in rehabilitation and daily activities. Although epidural spinal cord stimulation (eSCS) has demonstrated efficacy in restoring activity tolerance, its invasive nature and high cost hinder its widespread clinical adoption. To overcome these limitations, this research aims to develop a non-invasive, closed-loop transcutaneous spinal cord stimulation (tSCS) system that automatically adjusts stimulation levels based on real-time physiological signals. As a validation study for the hypothesis that exercise tolerance can be modulated using tSCS with activity dependent stimulation intensities, electrocardiogram and photoplethysmography data were collected from four SCI participants during exercise. I processed these cardiovascular signals using Fast Fourier Transform for heart rate variability (HRV) analysis in Python. I am also involved in developing a predictive machine learning model responsible for controlling tSCS intensity to improve exercise tolerance. It estimates exercise tolerance metrics, such as oxygen consumption volume, based on the HRV parameters. In the system, data are transmitted via Bluetooth Low Energy (BLE) protocols from physiological monitoring units to a processing unit, after on-board computation it then performs automatic adjustment of stimulation intensity. I have established a stable BLE connection within the system, and the final integrated system is anticipated to enhance rehabilitation outcomes by improving cardiovascular control during exercise and providing a clinically viable method to restore exercise capacity in individuals with SCI. Future studies will focus on optimizing algorithm efficiency for real-time performance and validating the system through clinical trials to further assess its impact on rehabilitation outcomes.

Previous literature has identified the relationship between the incidence of concussion and sports played. Further delineations occur between collision versus non-collision sports, contact versus non-contact sports, and athlete gender. We aimed to examine the relationship between incidence of concussion and sport played, and if incidence correlated with increased level of contact or with differences in gender. We undertook this study to better understand why there may be a rise in concussion incidence, especially in non-contact sports and any differences in concussion incidence in sex comparable sports. The cross-sectional study design uses a large data set collected on collegiate athletic teams at universities within the PAC-12 Conference between 2016 and 2022. Each concussion was diagnosed based on the clinical assessment of the evaluating sports medicine physician. The uniqueness of this data is due to diversity in population, broad location of subjects, and sport. I will analyze concussion incidence data from all PAC-12 athletes using descriptive statistics to describe the population and incidence rates to measure differences across the various groups. We believe that our findings will show women’s sports have a higher rate of concussion than men’s. Athletes participating in collision sports such as football may not necessarily be more likely to sustain a concussion than those who play limited or non-contact sports. There has been an increase in reporting, although it is unclear whether the rise is due to an increase in reporting, actual incidence, or a little of both. The information from this study will be useful in determining where additional concussion education and prevention measures should be directed. 

An estimated 4.3% of children in the United States have disabilities, a prevalence that has been steadily rising in recent years. Children with motor disabilities often face significant delays in achieving independent mobility, with many lacking access to powered mobility devices during critical early developmental stages. Early access to these devices fosters exploration, social interaction, and cognitive development, highlighting the need for timely assessment and intervention strategies. Discussions with practicing pediatricians highlight their strong interest in data-driven tools to better inform clinical decisions to support mobility, independence, and timely care. This study leverages the Permobil Explorer Mini, a powered mobility device for young children, to evaluate motor, cognitive, and social-emotional development through the Assessment for Learning Powered Mobility (ALP). Preliminary data collected from nine participants reveal correlations between key parameters and developmental progress, demonstrating the potential of data-driven approaches to enhance therapeutic outcomes. A classification model is being developed to predict ALP scores by identifying relevant features and refining model performance. Current accuracy is 50%, with efforts underway to address data sparsity through expanded data collection and validation. Additionally, a diagnostic interface is being designed to integrate the classification model, providing data visualization and tracking for pediatric clinicians. Built using React and optimized for tablets, the system incorporates AWS for secure storage and Python for preprocessing and model application. Iterative feedback from clinicians ensures usability and clinical relevance, aiming to improve diagnostic accuracy and inform therapeutic decision-making in pediatric care. The final product will undergo real-world testing in pediatric hospitals to evaluate the effectiveness of the interface and classification model.

Pseudomonas aeruginosa commonly colonizes cystic fibrosis (CF) lungs, causing persistent infections even under novel CFTR modulator therapies such as elexacaftor-tezacaftor-ivacaftor (ETI). While antibiotic resistance and patient-specific factors partly explain this persistence, bacterial adaptation to post-ETI conditions likely plays a critical role. Previous findings of functional shifts in bacterial variants point to underlying genotypic changes, yet the genomic basis for P. aeruginosa’s persistence remains insufficiently defined. This work aims to identify the genetic adaptations enabling P. aeruginosa to persist in CF lungs despite the improved airway environment afforded by ETI. We developed a method combining temporal allele frequency shifts and cross-patient recurrence to identify selection. My preliminary analysis revealed algG, a gene involved in alginate biosynthesis, as a promising candidate showing multiple signatures of positive selection. First, algG mutations increased in frequency across two-thirds of sampled individuals. Second, the phylogenetic analysis demonstrated the parallel evolution of algG mutations within individual hosts. Third, statistical testing showed significant enrichment for non-synonymous mutations in algG, indicating protein-altering changes are favored. I am extending this work by developing null models to quantify the significance of observed parallel evolution both within and between hosts, and using protein structural prediction to evaluate the functional impact of identified mutations. This research provides novel insights into bacterial adaptation mechanisms during CF treatment and may guide the development of more effective therapies targeting P. aeruginosa persistence. The findings will enhance our understanding of pathogen evolution within human hosts and have implications for improving treatment outcomes for CF patients. 

As the effects of climate change continue to worsen, plant species face environmental challenges such as heat stress and drought. Research in plant physiology and how it can be affected by climate change is becoming extremely important, especially when it comes to planning for future conservation efforts. Xylem, a vascular plant tissue that transports water and minerals from the roots up throughout the plant, is a crucial part of plant physiology, particularly when it comes to studying the effects of a changing climate and limited water availability. Xylem staining is a simple and effective way to examine the structure of the xylem by severing plant stems, placing one end in dyed solution, and placing the other end into a staining manifold system made up of pressurized tubing that will draw the solution up the xylem much like a living stem would draw water up from the ground. The dye solution stains the stem and make it clear how fast the xylem is moving water up the stem, or how many active xylem there are, which is extremely useful when observing drought stress effects or potential resistance. However, most xylem staining methodologies were created years ago and are complicated to construct and use, which keeps many labs, including ours, from being able to access them. I am taking an existing xylem staining design and streamlining it using more affordable parts, then writing up a standard operating procedure for the lab on how to use it. Using my manifold, I expect to stain samples from ongoing projects in the lab, take and analyze images, and observe any potential changes in xylem physiology due to drought conditioning or other treatments. Overall, my project makes xylem staining more accessible for my lab and other labs to use for research projects in the future. 

This project aims to enhance EEG source localization by addressing electrode misplacement, which can possibly lead to errors in brain activity reconstruction. We developed a optimization algorithm on the quasi-static electromagnetic model to optimize electrode positions. Using the multipole expansion method, our model minimizes discrepancies between recorded and predicted EEG signals. Our work has applicability to many clinical scenarios, like stroke activity localization, and can enhance existing brain activity reconstruction protocols.

Robotic manipulation systems operating in diverse, dynamic environments must exhibit three critical abilities: generalization to unseen scenarios, multitask interaction, and spatial memory. While significant progress has been made in robotic manipulation, existing approaches often fall short in addressing memory-dependent tasks and generalization to complex environmental variations. To bridge this gap, we introduce SAM2Act, a multi-view robotic transformer that leverages multi-resolution upsampling and visual representations from large-scale foundation models. SAM2Act achieves a state-of-the-art average success rate of 86.8% across 18 tasks in the RLBench benchmark, and demonstrates robust generalization on The Colosseum benchmark, with only a 4.3% performance drop under diverse environmental perturbations. Building on this foundation, we propose SAM2Act+, a memory-augmented architecture inspired by SAM2, which incorporates a memory bank and attention mechanism for spatial memory. To address the need for evaluating memory-dependent tasks, we introduce MemoryBench, a novel benchmark designed to assess spatial memory and action recall in robotic manipulation. SAM2Act+ achieves competitive performance on MemoryBench, significantly outperforming existing approaches and pushing the boundaries of memory-enabled robotic systems. Project page: sam2act.github.io.

The use of data visualizations in qualitative research varies widely across disciplines, yet there is little consensus on how these visuals are constructed, evaluated, or effectively integrated. This project employs a data-driven literature review to systematically explore these differences and examine the broader intersection of qualitative research and data visualization. We analyze existing studies from qualitative research journals and evaluate them through the Grammar of Graphics framework. Rather than establishing a rigid standard, this research develops a systematic approach to assess and enhance how qualitative data visualizations are used. By mapping various qualitative fields along a spectrum, we identify key factors—such as disciplinary norms, methodological choices, and technological advancements—that influence the adoption and presentation of data visuals. The produced framework does not merely classify the presence of visualizations but examines their function, effectiveness, and alignment with different epistemological stances. Ultimately, this study aims to improve the clarity, accessibility, and impact of qualitative findings by providing a structured understanding of how data visualizations are utilized. By systematically mapping these variations, this study not only reveals the diverse ways qualitative research engages with visualization but also provides a foundation for more intentional and impactful integration, ensuring that visual tools enhance both the interpretability and communicative power of qualitative findings across disciplines. This study is ongoing, and we will present preliminary findings and their implications on the relationship between qualitative research and data visualization.

Vigilance refers to one’s sustained attentiveness over time. While the conceptual model and the measurement of vigilance decrement has been identified in laboratory settings, limited studies have focused on vigilance decrement within the healthcare field, specifically on medical residents tasked with providing quality care to patients over the course of long shifts and often with little sleep. We aim to investigate the effect of sleep deprivation in medical residents using data recorded from physiological sensors. Twelve medical residents enrolled at the University of Washington Medical Center completed two tasks: the Psychomotor Vigilance Task (PVT), in which participants press a button when a red dot appears on a screen, and the Electrocardiogram (ECG) Reading Task, where participants view ECG readings and determine if they display signs of Myocardial Infarction (MI). They completed each task twice; once with more than 6 hours of sleep, and once with less than 5 hours. Over each 15-minute task, we measure the participant's eye movements and physiological signals including heart rate and skin conductivity.  Additionally, we surveyed participants on the quality of their sleep from the previous two nights and their general anxiety levels through multiple questionnaires including the Pittsburgh sleep quality index. This project is currently in its data collection and analysis phase; our next steps include understanding and analyzing the relationship between variables. The findings from this study will eventually help create an intervention to alert residents when their vigilance is too low to encourage taking a break to retain better focus. 

Alzheimer’s Disease (AD) is characterized by Amyloid β (Aβ) plaques, clumps of misfolded proteins which interfere with neural function, leading to cognitive decline. Despite being the most common form of dementia, exact causes and accessible early markers for AD remain elusive. Recent literature has suggested that deficits in risky decision-making appear before memory loss, making them a potential early marker. My project investigated how risky decision-making and corticolimbic circuit activity is impacted by Aβ pathology in 5XFAD mice, an established model of AD. Mice were lowered to 85% weight for motivation to forage, and then habituated to a rectangular nest area separated with a sliding door from the foraging arena. This was followed by baseline trials over four days where mice foraged for a short, medium, and long-distance pellet each day. On the fifth day, mice procuring the long-distance pellet encountered a weasel on wheels which surged forwards, simulating a predatory threat. Mice were given three minutes to procure the pellet. Ninety minutes after the encounter, mice were euthanized and brains extracted. Brain tissue was immunostained for c-fos, a molecular indicator of neural activity, to compare neural "snapshots" of control and 5XFAD mice who encountered/did not encounter the weasel. Brain regions to compare included the amygdala, prefrontal cortex, and hippocampus, responsible for processing fear, decision-making, and spatial information, respectively. The study ended when mice were eleven months old. In line with previous studies, it is expected that 5XFAD mice will have more weasel trial pellet retrieval attempts and exhibit differences in c-fos activity in brain regions of interest. These findings could help confirm deficits in risky decision-making as an early marker of AD, significant due to the scarcity of early markers.

Interfacility transport (IFT) is essential when a patient's care needs exceed the capabilities of their current hospital. However, pediatric patients face a disproportionately high demand for IFT due to the specialized nature of pediatric care. Pediatric IFT is a complicated and risky process with adverse events occurring in up to 70% of critical care ambulance transports even with a highly trained team. This project aims to explore pediatric transport stakeholder workflows, decision making, technology, and communication to identify potential areas for improvement. In this phase of the project, we conducted semi-structured interviews with key stakeholders, including Medical Control Physicians (MCPs), Referring Providers (RPs), and Pediatric Critical Care Transport Teams (PCCT). Participants were asked to describe their roles, tasks, decision-making processes, and communication strategies throughout the transport process. The goal of the study is to qualitatively analyze these interviews to uncover key themes and insights. For methods, we employed NVivo qualitative analysis software to analyze data from 16 interviews. Participants were contacted and voluntarily agreed to take part in the study. Through this research, we aim to gain a deeper understanding of stakeholder experiences in the IFT process, which will inform future efforts to improve pediatric transport practices.

Polymer networks, materials comprised of interconnected polymer chains, have been the subject of research interest for decades and have, particularly in recent years, found use cases in a variety of applications. Despite their broad use cases these materials are limited by their inherent tendency toward brittleness. One strategy for increasing the toughness of polymer networks is to introduce mechanochemically reactive groups in the crosslinks of a network instead of in the load-bearing primary polymer chains. Previously reported scissile crosslinkers have typically relied on strained ring structures or unusually weak covalent bonds for selective bond scission, introducing challenges such as difficult synthetic procedures and high design complexity. My collaborators at Johns Hopkins University have developed a novel, synthetically accessible crosslinker design that allows for selective mechanochemical bond scission via the replacement of a single carbon atom with silicon. They demonstrated that this scissile crosslinker doubles the toughness of a polymer network prepared by controlled polymerization. In my project I incorporated this crosslinker into a liquid resin compatible with free radical vat photopolymerization, 3D printed this new material, and mechanically characterized it through tensile testing. My work demonstrated that the same toughening effect occurs on polymer networks that are much less controlled and that this strategy for network toughening is compatible with 3D printing, which allows for the fabrication of more complex constructs. In conjunction with the expedient synthesis of this new crosslinker my project demonstrates that this approach to network toughening has the potential for large-scale applications.

Copy number variations (CNVs) of the 16p11.2 (BP4-BP5) genomic locus are closely associated with neurodevelopmental disorders such as autism spectrum disorder (ASD) and schizophrenia. Interestingly, 16p11.2 CNV deletion and duplication carriers exhibit some opposing phenotypes, with deletion associated with macrocephaly and obesity, and duplication with microcephaly and decreased body mass index. To identify the molecular mechanism underlying 16p11.2 CNVs, we differentiated patient-derived stem cells into neural progenitor cells (NPCs) as a model system for early neurodevelopment. Quantitative tandem mass tag (TMT) proteomics identified proteins that are phosphorylated differently between NPCs from carriers of a 16p11.2 CNV and NPCs from unaffected individuals. Notably, the differentially phosphorylated proteins found were enriched in primary cilia and centrosomal function, which is relevant for neurodevelopment. Through immunocytochemistry on the NPCs using a primary cilium specific antibody, the lab found that deletion and duplication had opposing effect on the cilia length. Deletion carriers had increased cilial length and duplication carriers had decreased cilial length. To identify which of the 30 known genes involved in 16p11.2 are drivers of these changes, knockdown and overexpression screens determined thousand and one kinase 2 (TAOK2) to be the most significant in cilia length. Using immunofluorescence assays, I found that intraflagellar transport protein 88 (IFT88), accumulates at the cilia tip in TAOK2 knockout NPCs, indicating disrupted transport within the cilia. IFT88 is a key regulator of Sonic hedgehog (Shh) within primary cilia and Shh is also a key regulator of neurodevelopment. Therefore, to understand the functional relevance of these findings on ciliary length, I performed quantitative PCR to measure changes in Shh activity. Since our findings so far demonstrate disrupted ciliary transport, I expect differences in Shh activity between wild-type and knockout TAOK2 NPCs. These investigations build our understanding of 16p11.2 CNVs and the mechanisms that implicate them in neurodevelopmental disorders.

This research explores the effects of water deficit treatments on the leaf structure and efficiency of photosynthesis of Miscanthus sinensis 'Bandwidth,' a grass popular in landscaping and known for its variegated leaves, an alternating green and yellow banding pattern found along each leaf blade. Previous research on Miscanthus suggests that leaf variegation can influence the efficiency of photosynthesis, however, the impacts of variegation in Miscanthus under water stress remains underexplored. By measuring chlorophyll concentrations, leaf areas, and photosynthetic efficiency in both the green and yellow regions of the leaves, this research evaluates how water stress affects the plant’s overall performance. Measurements are compared between the green and yellow portions of the leaves across high and low water treatment groups to better understand the impact of water deficit on the plant's overall performance. The preliminary results indicate that water availability affects total leaf area, the ratio of green to yellow area, chlorophyll content in both yellow and green sections, and photosynthetic performance, as measured by stomatal conductance of gas exchange and the performance of photosynthetic components in the leaves under both high and low water treatments. This research is part of the University of Washington’s Climate Ready Landscape Plants project, which aims to promote sustainable landscaping practices and urban resilience strategies in response to climate change. The results from Miscanthus can continue to encourage sustainable landscaping, urban resilience, and maintaining biodiversity by examining plant adaptability under drought-like conditions.

A microclimate is a relatively small area wherein temperature, humidity, and sunlight differ significantly from the surrounding macroclimate. Though there is a broad assumption of microclimate variation in greenhouse environments, there is little formal data quantifying this variation. Additionally, in the service of scientific rigor and productive efficiency, plants are often placed together at high density, but there is a lack of research on the impact of plant spatial density within the greenhouse environment. To assess greenhouse microclimate variation, I am investigating how climatic variables such as temperature, humidity, and light vary over the length of one table. In quantifying these variables, I am measuring photosynthetic photon flux density (PPFD), relative humidity, and temperature data from 11 sampling points. To quantify the effect of density on plant physiology, I am measuring stomatal conductance of Populus trichocarpa over four different density treatments. I am also measuring relative humidity and temperature within and above each experimental setup. Treatments include high, medium, low, and zero density. I expect to see considerable variation in light and humidity within the greenhouse, as fading lights and an evaporative cooling system create very heterogeneous conditions. I expect plant density to have a small effect on stomatal conductance as climate control likely has a larger effect on variables like CO2 and light availability than plant density alone. The findings of this research have the potential to uncover useful insights into microclimatic variation with applications in the horticultural, agricultural, and forest product industries.

Organs-on-a-chip (OOAC) are biomimetic systems that replicate the physiological environments of human organs at a micro-scale. They are gaining industry acceptance due to their ability to control critical parameters including shear stress, concentration gradients, and cell-biofluid interactions. By mimicking the behavior of human organs, OOACs are transforming how pharmacokinetics, physiological, and toxicological studies are performed, offering a more relevant model than animal-based studies. Our studies focus on how drugs and toxins affect the human kidney, a crucial organ for processing medications and filtering out harmful compounds. A key component of kidney OOACs is a hydrogel, which provides a structural scaffold and a biological substrate for cells. The hydrogel consists of rat tail Collagen I (Col-I) and specialized cell culture media (PTEC and 199 (10x)). The media mimics the extracellular fluids that surround kidney cells in the body, providing a more realistic environment for cell growth/interaction. Collagen IV (Col-IV) is the most abundant protein in kidney tissue but lacks structural rigidity. A combination of these materials is crucial for achieving a more accurate representation of kidney structure and function. While adding more matrix to the hydrogel improves the model’s ability to replicate the native environment, it is challenging to maintain structural stability, hence the need for a stabilizing agent. The aim of this project is to determine the proximal tubule epithelial cell (PTEC) viability of a mixed collagen I and IV matrix. At this stage, we have shifted from determining optimal collagen ratios to evaluating cell viability. By refining these models with optimized kidney extracellular matrices, the Kelly-Yeung lab aims to develop OOAC systems that better predict how drugs, toxins, and diseases impact human kidneys. This progress will lead to more effective and personalized treatments, as well as a reduction in reliance on animal testing. 

Latines are the largest minority group in the United States but only represent 8% of clinical trial participants. This underrepresentation is due to significant barriers the Latine population faces including, but is not limited to language differences, limited access to insurance coverage, socioeconomic, urban-rural divide, immigration status concerns, and mistrust of research. Increasing Latine participation in clinical trials is an essential step in reducing health disparities. This study aims to explore the impact these barriers have on Latine participation in clinical trials and determine ways to address them. We are utilizing a mixed-method approach to conduct research - we are analyzing existing literature data, conducting qualitative interviews, and gathering survey data. Initial findings have suggested that language barriers are a main contributing factor to lack of representation, as many Latine participants report difficulties with reading and understanding consent forms in addition to other legal documents and speaking with healthcare professionals. Insurance coverage presented another significant barrier, as individuals without insurance are less likely to participate in clinical research due to the burden of out-of-pocket expenses. Additionally, there is also hesitancy with sharing personal information, as immigration status was a major concern, with individuals fearing deportation or detention. In order to address the challenges that impact Latine participation in cancer research it is important to set up culturally competent outreach programs, provide language and community health resources, and advance policy changes to ensure equitable participation. This study emphasizes the urgency for inclusive clinical research methods in order to reduce health inequalities and improve healthcare outcomes for Latine populations.

During neonatal inter-facility transport there is a critical need to accurately measure heart rate. The electrocardiogram (ECG)  signals are particularly noisy during transport due to factors such as road noise and infant movement. This inaccuracy leads to false alarms from patient monitors when the measured heart rate values fall out of range. The Pan-Tompkins algorithm is commonly used to measure heart rate from ECG signals but frequently fails under these conditions. This project introduces a novel variation of the Pan-Tompkins algorithm, using the derivative of the ECG signal with additional filters specifically designed to target transport-related noise in neonatal ECGs. We test this modified Pan-Tompkins against the traditional Pan-Tompkins on neonatal transport data to determine if it is more effective for neonatal transport. Each algorithm is applied to a common set of ECG signal patterns taken from a real neonatal transport. The different patterns are classified as clean, somewhat noisy, or very noisy. Each algorithm will be evaluated on Sensitivity and Positive Predictability for each pattern. This research will help save the lives of neonates by reducing false alarms, which will in turn reduce alarm fatigue for providers and draw their attention only when it is truly necessary.

Fat, oil, and grease (FOG) in residential wastewater presents significant environmental challenges, contributing to the formation of fatbergs that disrupt wastewater systems, increase treatment costs, and heighten public health risks. Traditional methods, like commercial enzymes, are only temporarily effective and require constant maintenance. The goal of this research is to develop Engineered Living Materials (ELMs) comprising a yeast strain, Yarrowia lipolytica, within polymeric matrices for sustained FOG degradation. Y. lipolytica is known for its ability to efficiently degrade hydrophobic FOG components due to its diverse lipase enzyme expression. I encapsulated engineered Y. lipolytica strains in UV-cured poly(ethylene glycol) diacrylate (PEGDA) hydrogels. The findings showed sustained lipase activity and robust cell growth, confirmed by enzyme assays and confocal microscopy. However, over 28 days, significant degradation of the PEGDA-based ELMs occurred, likely due to the breakdown of ester bonds by lipolytic enzymes. To address this, I switched to a thiol-ene polymer network composed of tetra-PEG-allyl and PEG-dithiol, which is expected to resist degradation more effectively. I confirmed the viability and lipase production in these thiol-ene ELMs using the same methods. Varying polymer chain lengths in the thiol-ene network influenced Y. lipolytica growth patterns and morphology, including a shift toward hyphal growth—a filamentous form typical of its dimorphic nature. These changes were influenced by the polymer network’s architecture and material stiffness. Moving forward, I will investigate how hyphal growth impacts FOG degradation and assess the long-term mechanical properties of these thiol-ene ELMs. I expect these ELMs to remain stable over time and reduce FOG concentrations in simulated wastewater. Ultimately, this research aims to provide a sustainable solution for wastewater treatment, addressing the environmental, economic, and infrastructural impacts of fatbergs.