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Identification and characterization of a lipopolysaccharide binding, anti-inflammatory protein from Parabacteroides distasonis
No full text2/28/2026 12:00:00 AM
Assessing the impact of institutional and sociodemographic factors on treatment and outcomes in head and neck cancer
Full text link2026Disparities in head and neck cancer (HNC) outcomes reflect the complex interplay of social, institutional, and biological factors that influence access to care, treatment delivery, and survival. This dissertation examines structural and clinical determinants of inequities in cancer care through three population-based studies using data from the National Cancer Database (NCDB). Collectively, these studies evaluate the effects of healthcare system characteristics, global crises, and evolving treatment paradigms on outcomes among HNC patients. The first study assessed the association between hospital safety-net burden and survival among patients with head, neck, and thyroid cancers. Although crude analyses showed worse survival in hospitals with higher safety-net burden, the association was largely explained by patient and treatment characteristics. Using propensity score–based methods, safety-net burden was not independently associated with overall survival, indicating that differences in outcomes reflect patient mix and access-related disparities rather than hospital performance itself.
The second study evaluated the impact of the COVID-19 pandemic on diagnosis, treatment, and care delivery for HNC patients in the United States. The pandemic year (2020) showed a 17% relative decline in new cancer diagnoses. Among treated patients, there was a small increase in advanced-stage presentation and a shorter time from diagnosis to treatment initiation, while treatment utilization (surgery, radiation, chemotherapy) and radiation discontinuation were essentially unchanged compared with the pre-pandemic period. While baseline disparities by race, income, and hospital type persisted, the pandemic did not substantially exacerbate these inequities.
The third study assessed reduced-dose radiation (50–<66 Gy) versus standard dose (66–70 Gy) for HPV-positive oropharyngeal cancer. After inverse probability weighting and quantitative bias analysis for smoking, overall survival was comparable across dose groups. Survival patterns varied by treatment modality: reduced dose appeared beneficial in surgical/adjuvant settings and less favorable in definitive chemoradiation. No survival disparities were detected by race or income.
Together, these studies provide evidence that institutional context, systemic disruptions, and treatment innovations intersect to shape cancer care outcomes. The findings emphasize the need for equity-centered policies that strengthen safety-net infrastructure, ensure resilience during public health crises, and judicious implementation of emerging treatment innovations such as de-escalation therapies within an appropriate clinical context
The value of school environmental monitoring in a time of change
No full text2026Each day, ~50 million children in the U.S. attend K–12 public schools during a critical period of development. Optimal classroom environmental conditions support student well-being and learning. However, aging school buildings, outdated mechanical systems, limited school indoor environmental policies, and constrained budgets are slowing improvements in classroom indoor environmental quality (IEQ). These challenges can be exacerbated by climate change and have potential lasting implications for students’ future opportunities. Real-time classroom IEQ data can help schools identify environmental problems, prioritize upgrades, advocate for funding, and inform heat preparedness plans. However, the vast and complex nature of IEQ data may prevent stakeholders from unlocking its full potential.This dissertation employed a mixed-methods approach to expanding the value of IEQ monitoring in schools with a climate lens, in collaboration with a large public school district in the U.S.
First, we characterized school thermal variability by air conditioning (AC) and floor level using traditional and novel heat metrics, analyzing minute-level temperature data from a first-of-its-kind network of thousands of monitors. Our findings emphasize the critical need for mechanical cooling in classrooms and exemplify how IEQ data can inform school heat preparedness plans.
Next, we quantified the effects of floor level, AC status, and roof albedo on classroom temperature using Bayesian multilevel models, and estimated hypothetical changes in test scores resulting from climate adaptations. Our findings suggest that central cooling systems are more effective at maintaining comfortable indoor conditions and highlight the learning benefits of moving students to lower-level classrooms, installing light-colored roofs, and upgrading AC systems.
Lastly, we conducted interviews with staff members in school districts pioneering IEQ monitoring to identify barriers and motivators for leveraging this data. We learned how this data supports their work and identified limited environmental health and data literacy as obstacles to fully capitalizing on this wealth of information. Suggested solutions to bridge this knowledge gap included artificial intelligence (AI) tools and partnerships between schools and academic researchers.
Together, these findings highlight the transformative potential of classroom IEQ monitoring to expose disparities in indoor environmental conditions, enhance student well-being and learning, and inform school climate resilience plans.2028-01-14T00:00:00
Essays on bank heterogeneity and monetary policy
No full text2024In this dissertation, I study how bank heterogeneity and the marginal propensity to lend affect the transmission of monetary policy. In the first chapter, I develop a banking model with heterogeneous banks to study how heterogeneity in marginal propensities to lend and responses of deposits to monetary shocks affect the monetary transmission to bank lending. The marginal propensity to lend (MPL) measures how much lending increases after an idiosyncratic one unit increase in deposits. Banks face financial frictions to substitute deposits with wholesale funding, which exposes bank lending to idiosyncratic deposit shocks. When banks are heterogeneous in the degree of financial frictions they face, the aggregate response of bank lending to monetary shocks depends on a deposit heterogeneity channel, which comes from the covariance of MPLs and responses of deposits to monetary shocks. I use U.S. bank-level data to calibrate the model and I find that heterogeneity in the degree of financial frictions dampens monetary policy by at least 17%.
In the second chapter, I study how heterogeneity in the volatility of deposit withdrawal shocks affects the monetary transmission to bank lending. I develop a general equilibrium model where banks differ in their size and small banks are endowed with a riskier distribution of deposit withdrawal shocks, consistent with the data. In the model, small banks experience a larger decline in deposits and lending after an increase in the policy rate. Moreover, bank size heterogeneity dampens monetary policy. I use U.S. bank-level data and I find that banks at the 90th percentile of the withdrawal risk distribution reduce lending by an extra 1% and deposits by an extra 0.7-0.9% relative to banks at the 10th percentile after a monetary shock that raises the Fed funds rate by 100 basis points. Moreover, aggregate lending falls by 0.9% due to withdrawal risk.
In the third chapter, I study the role of MPLs in the transmission of monetary policy in a general equilibrium model. I incorporate banks into a standard New Keynesian DSGE model. Banks face frictions to substitute deposits with wholesale
funding. I use U.S. bank-level data to calibrate the model and I find that higher financial frictions that increase the aggregate MPL by 66% amplify the response of bank lending and investment to monetary shocks by 11% and 16%, respectively. Moreover, if the sensitivity of the marginal cost of funds also increases, the loan pass-through increases by 20%, which amplifies the response of bank lending and investment by 31% and 54%, respectively. Higher MPLs do not amplify the response of production in the short run but they do at longer horizons, due to the decline in investment
Exploring the development of callous-unemotional behaviors in preschoolers: a behavioral genetic approach
No full text2025Callous-unemotional behaviors (CU), characterized as low emotional sensitivity, impaired empathy, and a lack of guilt and remorse, are associated with negative developmental outcomes. The present dissertation project explored genetic and environmental contributions to: (1) the development of CU; (2) links between CU and parenting (i.e., parental positivity and negativity); and (3) links between CU, irritability, and externalizing problems in preschoolers. Studies 1 and 2 included 123 monozygotic and 187 dizygotic twin pairs at ages 3, 4 and 5. Study 3 included 264 monozygotic and 350 dizygotic twin pairs at age 3.It was hypothesized that genetic and nonshared environmental influences would contribute to change—both in terms of age-to-age instability and within-individual absolute-level patterns of change (Study 1). As predicted, instability in CU was due to both genetic and nonshared environmental influences; however, variation in developmental trajectories across the preschool period was primarily due to nonshared environmental influences.
In Study 2, it was hypothesized that CU would be associated with parental positivity and negativity within and across age, and that genetic influences would mediate these associations. In contrast to the predictions, only parental negativity was associated with CU, and these associations were environmentally mediated.
Study 3 tested the hypothesis that there would be common and unique genetic covariances between externalizing problems, CU, and irritability. This hypothesis was supported. There were common genetic, shared and nonshared environmental factors operating across all three domains. In addition, there were unique genetic and nonshared environmental links from CU to externalizing problems, and from irritability to externalizing problems. There were also genetic and nonshared environmental influences specific to externalizing problems.
Although CU is a genetically influenced trait, this research reveals the importance of environmental mechanisms on the development of early CU, on links between CU and parenting, and on associations between CU and related behavior problems. This has the potential to inform prognoses of early behavioral problems and avenues for intervention
Detecting spontaneous symmetry breaking and bound states in (1+1)-dimensional bosonic models with lightcone conformal truncation
No full text2024Lightcone Conformal Truncation (LCT) is a relatively new Hamiltonian truncation method that proposes viewing QFTs as deformations of solvable conformal field theories (CFTs). LCT stands out from other truncation methods in fully preserving conformal invariance and probing the infinite volume limit directly. The output of the method is a discretized version of the spectrum of the underlying continuum theory, which can subsequently be used to calculate other QFT observables. We apply the method to two bosonic scalar field theories, with a quartic/sextic potential described by two dimensionless couplings. The theories exhibit textbook Landau-Ginzburg first order phase transitions at weak coupling, providing an ideal laboratory for the method to probe observables at criticality. The ⁴ theory requires the ℤ₂ symmetry to be broken by a ³ term to access the SSB phase, while the ⁶ theory requires no explicit breaking. We identify a method to detect a phase transition with our method and we present novel, quantitative, non-perturbative estimates for the shape of the phase transition lines and furthermore, we detect and quantify the emergence and weak coupling dependence of the bound states in the spectrum of the theories
Accelerating large scale Graph Neural Network training using SmartSSD
No full text2024Graph Neural Network (GNN) is a type of neural network that processes data structured as graphs. It excels at capturing complex relationships and dependencies between nodes within the graph. GNNs are increasingly significant, with numerous applications in the real world, such as social network analysis, recommendation systems, and node or graph classification. However, as graphs grow in size, they can exceed available memory, necessitating on-disk sampling for large-scale GNNs. In this work, we propose a novel system using SmartSSD to perform sampling for large-scale Graph Neural Networks. We designed three different SmartSSD-based kernels to perform neighborhood sampling. The Random Read Sampler simulates the random read process of in-memory sampling on the SSD, aggregates the sampled results on an FPGA, and then sends them back to the host in batches. The Sequential Read Sampler transfers the edge file in chunks to the FPGA memory and scans the chunk to draw samples.
The Streaming Sampler, an optimization of the Sequential Read Sampler, employs an improved edge file chunk format to enable parallel sampling on the FPGA, thereby accelerating the sampling process. We discuss the design and implementation of all three samplers and demonstrate their sampling time consumption in the experiment. For the largest graph dataset, Yahoo, that we tested, our optimized kernels achieve a speedup factor of 3.6 over the baseline
Essays on the economic impacts of perceptions of risk, sentiment and acquisitions
No full text2024In this dissertation, I present three distinct essays in asset pricing and macroeconomics that can be read independent of each other. In the first chapter, I estimate a factor pricing model from firm disclosures. Using the latest techniques from natural language processing and machine learning, I isolate risk factors of firms from their disclosures. I then decompose these risk factors into those that are shared with other firms and those that are unshared risk factors. This sharing of risk naturally leads to the formation of networks of firms connected by the factors affecting them. From these networks, I build measures of risk at the firm level. I find that these text-based measures of shared risk can be used to price securities in US financial markets and retain significance even in the presence of established factors. In the second chapter (joint with Marialuz Moreno Badia, Kevin Gallagher, Lei Guo, and Derry Wijaya), we adapt state-of-the-art techniques from Natural Language Processing to construct two new media-based Chinese economic sentiment indices from a large corpus of English and Chinese newspapers. We demonstrate that differences in perception matter for economic outcomes. Our sentiment classification models improve the accuracy of lexicon approaches by a factor of two. Consistent with the agenda setting theory in the communications field, we find that news flow from the English to the Chinese media, but the latter tends to be more positive. Moreover, the perception gap between Chinese and English newspapers has widened in recent years. Evidence from a structural VAR suggests that positive sentiment shocks foreshadow increases in China’s policy rates and asset returns, as well as global commodity prices. The impact of shocks to the English-media sentiment on domestic policy variables is magnified by shocks to the Chinese-media sentiment index. No such amplification is found for financial variables and commodity prices. The goal of this chapter is to understand Mergers and acquisitions (M&A) through the lens of investment at the firm-level. Specifically, M&A can be considered a form of organizational capital built through intangible investments. To demonstrate the nature of this form of investment, I show that acquisitions have the following characteristics: (a) have a strong positive relationship with firm sales; (b) are procyclical at the macro level; (c) are lumpy. Building upon a standard general equilibrium model of firm investment, I structurally estimate the model from micro-foundations. I then develop economic insights for the role of acquisitions in the macroeconomy. Reduction of costs associated with acquisitions can increase the value of firms by around 4%
Advances in optical trapping beyond biophysics: combining force and optical spectroscopies under diverse chemical conditions
No full text2024Optical tweezers (OT) have revolutionized the study of molecular biology as recognized in 2018 by the Nobel prize in Physics to Arthur Ashkin, the inventor of the technique. OT allow selective single particle manipulation and control in solution at the focus of an optical microscope and in combination with other optical spectroscopies. These techniques have been used to apply forces to single molecules in order to measure dynamics and energetics of protein folding, motor protein translocation and RNA structural transitions, to name just a few. Yet, the capacity of OT for molecular mechanistic studies for chemistry and materials applications is vastly underexplored. The subject of this thesis is to develop approaches for expanding the capability of OT outside of the biological domain. In Chapter 2 we discuss address one of the main obstacles to applying OT to study synthetic molecular mechanisms. Standard OT probes made from silica or polystyrene are incompatible with trapping in organic solvents for solution phase chemistry or with force detected absorption spectroscopies. Here, we demonstrate optical trapping of gold nanoparticles in both aqueous and organic conditions using a custom OT and darkfield instrument which can uniquely measure force and scattering spectra of single gold nanoparticles (Au NPs) simultaneously. Our work reveals that standard models of trapping developed for aqueous conditions cannot account for the trends observed in different media here. We determine that higher pushing forces mitigate the increase in trapping force in higher index organic solvents and lead to axial displacement of the particle which can be controlled through trap intensity. This work develops a new model framework incorporating axial forces for understanding nanoparticle dynamics in an optical trap. These results establish the combined darkfield OT with Au NPs as an effective OT probe for single molecule and single particle spectroscopy experiments, with three-dimensional nanoscale control over NP location. Chapter 3 demonstrates an all-optical method using an optical tweezer to perform chemistry on a single particle in solution. Specifically, we controllably and selectively grow high quality zeolitic imidazolate framework (ZIF) nanoshells on the surface of a single gold nanoparticle (AuNPs) and monitor the growth via darkfield spectroscopy. Our single particle approach allows us to localize an individual NP within a microscope slide chamber containing ZIF precursors at the focus of an optical microscope and initiate growth through localized heating without affecting the bulk system. Darkfield spectroscopy is used to characterize changes to the localized surface plasmon resonance (LSPR) of the AuNP resulting from refractive index changes as the ZIF crystal grows on the surface. We show that the procedure can be generalized to grow various types of ZIF crystals, such as ZIF-8, ZIF-11, and a previously undocumented ZIF variety. Utilizing both computational models and experimental methods, we identify the thickness of ZIF layers to be self-limiting to ∼50 nm or less, depending on the trapping laser power. Critically, the refractive index of the shells here was fou nd to be above 1.6, indicating the formation of high-density crystals, previously accessible only through slow atomic layer deposition and not through a bulk heating process. The single particle method developed here opens the door for bottom-up controllable growth of custom nanostructures with tunable optical properties. Chapter 4 of the thesis introduces an approach to studying and controlling gold nanoparticle (AuNP) dimers suspended in solution using optical trapping. The primary objective is to control inter-particle separation in AuNP dimers using OT and to leverage the plasmon scattering resonance signature to measure it in situ. This functionality is crucial for applications in nanophotonics, nanoelectronics, and biosensing, where accurate distance control between nanoparticles can lead to the development of highly sensitive sensors and devices. We use the custom optical trapping instrument described in Chapter 1 that combines an inverted optical microscope with darkfield (DF) illumination, allowing for the manipulation and imaging of metallic nanoparticles. We show that the dimer long axis aligns with the trapping laser polarization, allowing for control of dimers in solution. When the long axis of the dimer is parallel to the excitation polarization, the dimer scattering resonance is maximized, enabling more precise spectroscopic analysis. The shifts in dimer wavelengths with changing inter-particle separation are modeled computationally, leading to the development of a plasmon ruler equation for our system, which allows for conversion between optical wavelength shifts and distance. We form dimers using van der Waals sticking between polymer coated AuNPs. We find that dimers formed with distinct molecular tethers differing distributions of inter-particle distances which depend on molecule length and structure. Furthermore, by tuning the power of the OT laser, we can modulate the temperature at the dimer surface, causing the release of inter-molecular interactions and a gradual separation of the dimer particles. This experiment is the first demonstration of control over dimer geometry in solution. By tracking the evolution of the plasmon spectra during heating and separation, we are able to distinguish between the classical capacitive coupling regime captured by the plasmon ruler relationship and the charge transfer plasmon (CTP) which arises from quantum tunneling in the dimer gap. The methodology established here provides unprecedented control over dimer geometry which can be leveraged for applications in plasmon-driven catalysis, surface-enhanced spectroscopy and charge transfer studies
The ICU resilience project: establishing occupational therapy practitioners' role in addressing patient mental health in the intensive care unit
Full text link2025Patients in the intensive care unit (ICU) live through harrowing life experiences that can lead to lasting physical, cognitive, and psychosocial changes impacting survivors' quality of life called Post-Intensive Care Syndrome (PICS). Research on strategies to address mental health concerns such as depression, anxiety, and post-traumatic stress disorder within early rehabilitation is still emerging. Occupational therapy practitioners (OTPs) feel most comfortable addressing physical and cognitive impairments in the ICU despite the profession's foundational roots in mental health treatment. This doctoral project aims to address OTPs' limited knowledge and training of their role in addressing mental health concerns in the ICU by developing an evidence-based multimedia website entitled The ICU Resilience Project: Your Occupational Therapy Resource and Toolkit. This website aims to enhance clinicians' understanding of the prevalence of mental health issues following ICU admission and their impact on survivors' quality of life. It seeks to expand knowledge of screening tools, assessments, and treatment strategies within the scope of occupational therapy practice, ultimately boosting the confidence of OTPs in effectively implementing these intervention approaches into daily practice. By embracing this role in the ICU, the hope is for OTPs to assist in decreasing the prevalence of lasting mental health conditions experienced by ICU survivors and increase overall ICU survivor’s quality of life.Keywords: occupational therapy, intensive care unit, mental health intervention, post-intensive care syndrome, psychosocial treatmen