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Protein-based Probes to Investigate Functions of UCH37/UCHL5
No full textThe 26S proteasome degrades proteins through a tightly regulated process dependent on the type of ubiquitin (Ub) modifications on substrates. Ub can form various polyubiquitin chains by attaching itself to one of its seven lysine residues or the N-terminal methionine, creating diverse chain architectures. Among these, branched Ub chains—where a single Ub molecule is modified at two distinct sites—are particularly potent in signaling for degradation. UCH37, a deubiquitinase (DUB) associated with the proteasome, is recruited by RPN13. Our lab has demonstrated that the UCH37-RPN13 complex specifically cleaves K48-linked chains at branch points, thereby facilitating substrate degradation.
This thesis first discusses how UCH37 selectively interacts with K48-linked chains and distinguishes between branched and non-branched ubiquitin chains. We employed several biophysical techniques, including photo-crosslinking, HDX-MS, and NMR. Our findings reveal a non-canonical S1 ubiquitin-binding site on the α5-α6 motif of UCH37, essential for binding and debranching K48-linked chains, different from the reported canonical S1 mono-ubiquitin binding site. Additionally, we developed nanobodies that bind to the canonical and non-canonical S1 ubiquitin-binding sites through directed evolution and studied how they differentially affect UCH37 activities. Finally, we described the synthesis and reactivity of a new set of Ub ABPs based on branched trimers, and how we use it as a tool for structural determination of UCH37-branched Ub3 complex, as well as probes for profiling proteins preferentially interacting with branched Ub chains.Doctor of Philosophy (Ph.D.
Cognitive Reflection and Religious Belief: A Test of Two Models
No full textExisting research suggests a negative correlation between reflective thinking and religious belief. The dual process model (DPM) posits that reflection diminishes religious belief by limiting intuitive decisions. In contrast, the expressive rationality model (ERM) argues that reflection serves an identity-protective function by bolstering rather than modifying preexisting beliefs. Although the current literature tends to favor the DPM, many studies suffer from unbalanced samples. To avoid this limitation, we recruited comparably large number of participants for both religious believers (n = 580) and non-believers (n = 594) and observed the relationship between reflection and two measures of religious belief: belief in God and disbelief in evolution. Our findings corroborate the negative associations found between higher levels of reflection and both types of belief, independent of religious affiliation. Our results align with the broader literature, supporting the DPM but not the ERM
ADVANCING PRECISION HEALTH WITH CLINICAL FOUNDATION MODELS
No full textThe integration of Artificial Intelligence (AI) in healthcare promises unprecedented improvements in patient care, yet its full potential, especially in precision health, remains underutilized due to significant challenges in transforming real-world data into real-world evidence. This dissertation explores the development and application of clinical foundation models (FMs), specifically Clinical Language Models (CLaMs) and Foundation Models for Electronic Health Records (FEHRs), which are pretrained on extensive clinical narratives and structured records. The research presents innovations in several key areas:
Accurate information extraction from clinical notes: We analyzed baseline CLaMs performance in the task of extracting diagnostic code information from clinical notes. We identified two key issues in these CLaMs: their imprecision in extracting rare diseases due to the lack of training data, and their difficulties with recognizing synonyms due to model’s inadequate medical knowledge. To address these issues, we developed a generative knowledge-injected prompt-based fine-tuned transformer, achieving state-of-the-art accuracy. Accurate information extraction paves the road for better-informed decisions during clinical diagnoses.
Enhanced quality of patient health assessments: Inferring clinical diagnosis to generate an assessment is a crucial step during the patient encounter. However, there is limited research on generating clinical diagnoses in a free text format. Hence, we propose a new task of generating full-length patient health assessments. We applied CLaM to this task and found that it tend to generate factually incorrect responses. To improve the generated assessment quality, we combined the CLaM with the medical knowledge graph. By reducing the incidence of misleading information generated during the assessment process, our CLaM supports clinicians in making better-informed decisions.
Predictive modeling of complex disease interrelations: We developed TransformEHR, a generative transformer FEHR pretrained on a vast dataset of 6.5 million patient electronic health records. With visit level pretraining objective, TransformEHR is designed for predicting complex interrelations among diseases. The high performance in predicting intentional self-harm shows the potential of TransformEHR in building effective clinical intervention systems. TransformEHR is also generalizable and can be easily fine-tuned for clinical prediction tasks with limited data.
HealthMamba are multi-purpose long context extraction and prediction engines: Traditional transformer-based FMs have limited performance in long EHR and struggle to extract information from locations distant from the end of the document (position bias). To mitigate this issue, we developed HealthMamba, a Mamba-based FM that addresses the issue of long medical history. HealthMamba uses selective scan algorithm allowing the model to selectively propagate or forget information along the sequence length depending on the input token. With prefix prompt, HealthMamba significantly outperforms transformer-based FMs in 7 clinical information extraction tasks and patient outcome prediction tasks. Notably, it demonstrated less position bias compared to GPT-4, maintaining effectiveness across all parts of EHRs.
By training advanced generative clinical FMs on large-scale healthcare data, this dissertation demonstrates AI’s role in enhancing precision health for more personalized and effective healthcare solutions. The findings underscore the potential of AI to transform medical data analysis and patient care, setting a path towards a future where healthcare is increasingly driven by intelligent and automated systems to support healthcare providers.This research was supported by the National Science Foundation under award 2124126. The work was also in part supported by the National Institutions of Health R01DA045816 and R01MH125027. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation and National Institutes of Health.Doctor of Philosophy (Ph.D.
R code associated with the paper "A quantitative classification of the geography of non-native flora in the United States"
No full textFilename: R code Fig 3 correlation plots
Short description: R code used to plot correlations between dimensions of commonness and identify outliers (e.g., species with higher than expected abundance, which pose a higher risk of becoming invasive). The output of this code is presented in Figure 3.
Filename: R code Fig 5 species area curves
R code used to calculate species area curves to estimate total numbers of non-native plant species as well as total numbers of abundant non-native plant species within each U.S. Level III ecoregion. The output of this code is presented in Appendix S5 and visualized in Figure 5
Evaluate the stability of synthesized allicin and its reactivity with endogenous compounds in garlic
No full textThe study aimed to prepare allicin in large quantities and evaluate the effects of different conditions and endogenous substances (polyphenols and free amino acids) in garlic on its stability. The optimized synthesis conditions of allicin (DADS:HAc = 2:9, H2O2:DADS = 2:1.6, reacted for 6.5 h) were established based on response surface design. Allicin with 92.57% purity was obtained by high-speed counter-current chromatography. The results of allicin degradation experiment indicated that the degradation rate of allicin accelerated with the increases of initial concentration and temperature, the degradation process of allicin was good fit with two first-order kinetics (R2 > 0.97). The stability of allicin was higher in pH 2–5.8 than in pH 8–9. Apigenin, myricetin, and quercetin combined with peroxidase enhanced the stability of allicin. Cysteine, arginine, and histidine could react with allicin. These findings provide insights for optimizing for allicin storage and food processing applications
Evaluation of novel fungicides (FRAC groups 7, 9, 12) for managing cranberry fruit rot
No full textCranberry fruit rot (CFR) is a major disease complex that significantly impacts cranberry crops, leading to substantial yield losses. Over the past decade, CFR has become increasingly problematic, particularly in high-yielding and newer cultivars, with reported losses ranging from 50% to 100%. Additionally, the cranberry industry faces increasing restrictions on the use of broad-spectrum fungicides, such as chlorothalonil and mancozeb, necessitating the exploration of alternative management strategies. This study, conducted from 2021 to 2024 at the University of Massachusetts-Amherst Cranberry Station, evaluated novel fungicides from FRAC Groups 7, 9, and 12. The active ingredients—benzovindiflupyr, pydiflumetofen, cyprodinil, and fludioxonil—were tested individually and in combination with azoxystrobin (FRAC 11). The efficacy of these fungicides in reducing CFR incidence and improving yield was assessed on cranberry cultivars ‘Demoranville’, ‘Ben Lear,’ and ‘Stevens’ with applications made at early, mid, and late bloom stages. Significant differences in fruit rot incidence and yield were observed in 2021, 2023 and 2024. Treatments containing pydiflumetofen, pydiflumetofen & fludioxonil, and benzovindiflupyr, when applied in combination with azoxystrobin, consistently resulted in lower rot incidence and higher yields. The treatment containing cyprodinil & fludioxonil plus azoxystrobin, tested only in 2021, also resulted in lower rot incidence and higher yield. These findings highlight the potential of novel fungicides from FRAC Groups 7, 9, and 12 as effective alternatives for CFR management. Their use could diversify the CFR management toolkit, mitigate fungicide resistance, and reduce environmental impacts, addressing the challenges posed by increasing fungicide regulations
Cognitive Reflection and Religious Belief: A Test of Two Models
No full textExisting research suggests a negative correlation between reflective thinking and religious belief. The dual process model (DPM) posits that reflection diminishes religious belief by limiting intuitive decisions. In contrast, the expressive rationality model (ERM) argues that reflection serves an identity-protective function by bolstering rather than modifying preexisting beliefs. Although the current literature tends to favor the DPM, many studies suffer from unbalanced samples. To avoid this limitation, we recruited comparably large number of participants for both religious believers (n = 580) and non-believers (n = 594) and observed the relationship between reflection and two measures of religious belief: belief in God and disbelief in evolution. Our findings corroborate the negative associations found between higher levels of reflection and both types of belief, independent of religious affiliation. Our results align with the broader literature, supporting the DPM but not the ERM
Transcriptional Regulation of Chromatin Motion Drives Nuclear Blebbing, Defined by Decreased DNA Density
No full textTranscription of DNA into RNA is fundamental to cellular function, influencing not only gene expression but also the biophysical properties of the nucleus. Transcriptional activity has been linked to micron-scale chromatin motion, nuclear blebbing, and, in some cases, nuclear rupture. Nuclear blebbing is a hallmark of human diseases, including various cancers and age-related disorders, which also exhibit significant changes in transcriptional activity. Inhibition of RNA polymerase II has been shown to suppress nuclear blebbing, while modeling suggests that chromatin motion can drive nuclear deformations. However, the precise relationship between transcription, chromatin dynamics, and nuclear blebbing remains unclear.
Chapter 1 of this thesis, an already published manuscript primarily written by my PI, examines the definition and composition of nuclear blebs. My contributions to this work included data collection, analysis, and manuscript editing. A key finding from this study is that decreased DNA density provides a more consistent indicator of nuclear blebs than the absence of lamin B1, which varies across conditions and cell types.
Building on these findings, Chapter 2 presents my independent research investigating the role of transcriptional regulation in nuclear blebbing and chromatin motion. To disentangle the effects of transcriptional activity from its biochemical output, I modulated transcription through serum starvation and stimulation and live-cell imaged nuclear morphology using NLS-GFP and randomly incorporated Cy3-dNTPs. Serum starvation, which reduces but does not eliminate transcription, led to a drastic suppression of nuclear blebbing and chromatin motion. Conversely, serum stimulation of starved cells rapidly restored transcription, nuclear blebbing, and chromatin motion within a few hours. Further, live imaging of randomly labeled dNTPs revealed that chromatin motion is necessary for nuclear bleb stability.
Overall, this work demonstrates that transcription-driven chromatin motion provides a mechanistic basis for nuclear blebbing. By establishing decreased DNA density as a reliable indicator of nuclear blebs, these findings contribute to a clearer framework for studying nuclear architecture and its links to transcriptional regulation and disease-associated nuclear abnormalities.This work was supported by NIH NIGMS grant Maximizing Investigators' Research Award R35GM154928, the Institute of Applied Life Sciences Midi-grant (190227), and by the Center for 3D Structure and Physics of the Genome 4DN2 grant (1UM1HG011536).Master of Science (M.S.
Implementation of an Exercise Education Program in a Community Center for People with COPD
No full textChronic obstructive pulmonary disease (COPD) significantly impacts patients' quality of life and exercise capacity. This quality improvement project evaluated the effects of an educational intervention on exercise adherence and health-related quality of life (HRQoL) among individuals with COPD. A pre-and post-intervention design was implemented with 18 participants from a community recreation center in southeast Virginia. The intervention consisted of a 60-minute education session and biweekly check-ins via phone over two months. Exercise adherence was measured via self-reported step counts, and HRQoL was assessed using the Clinical COPD Questionnaire (CCQ). The Wilcoxon signed-rank test was used for data analysis. Significant improvements were observed in exercise adherence (median increase from 3314 to 4103.5 steps/day, Z = -3.724, p < .001), CCQ Total Score (median decrease from 3.75 to 2.3665, Z = -3.724, p < .001), and CCQ Symptoms Domain (median decrease from 3.625 to 2.625, Z = -3.724, p < .001). The intervention increased exercise adherence and improved HRQoL, demonstrating the efficacy of exercise education for patients with COPD. Nurses can adopt similar interventions to enhance COPD patient outcomes.Doctor of Nursing Practice, Family Nurse Practione
A Geopolitical Economy Analysis of China and India’s Approaches to Transnational Data Governance
No full textRecent literature on the behavior of rising powers in digital trade and data governance highlights their discourses of data sovereignty and desire to preserve domestic policy autonomy. This article contributes to the literature by employing a political economy lens that shifts the focus from the nation-state/inter-state framework towards the dynamics of state–capital relations, allowing for a more historical and contextual understanding of the geopolitics of data governance in emerging economies. Using China and India—two of the largest emerging economies—as comparative cases, and drawing on secondary data from government documents and other sources, the article argues that the interplay between the state’s interests in promoting security and development objectives and the commercial interests of domestic firms, global Big Tech companies, and transnational capital in data commercialization and market expansion has shaped the two countries’ respective trajectory of data governance over the past three decades. These developments are deeply embedded in each country’s distinctive political economic and geopolitical contexts. As a result, key policy developments in digital governance that might appear to be driven primarily by geopolitics may instead have deeper roots in evolving state–business relations