Dissertations

Photoactive materials spark interest for areas such as solar energy conversion, photo-catalytic energy production, efficient light displays, or control of quantum-mechanical spin phenomena by light. This dissertation work centers on two classes of materials, chalcogenides and metal halide perovskites, chosen for their promise in light-interactive applications. Current research on these novel semiconductors is focused on overcoming challenges in detailed material design. Tuning strategies, including manipulation of chemical composition, dimensionality, and structural distortions, stand as exciting opportunities for modulating electronic and spin properties. These avenues for advancement necessitate a deep understanding of the complex physics that underlies material behavior, calling for density functional theory (DFT) simulations to capture intricate electronic structures and complex particle interactions.

This dissertation work therefore employs DFT simulations to focus specifically on electronic structure and defects in efforts to strengthen our understanding of structure-property relationships in chalcogenide and perovskite semiconductors. The ability to tune energy band gaps and spin splitting via Se-alloying in the 3D chalcogenide, CuPbSbS3 is demonstrated. Next, transferred symmetry breaking from chiral organics to inorganic-sublattices, and resultant impact to electronic and spin properties is reported in hybrid organic-inorganic metal-halides. Following this, DFT strategies are employed to study H-bonding in a 2D hybrid perovskite, (2-BrPEA)2PbI4, uncovering (i) strategies to improve H-bonding analyses and (ii) formation mechanisms of spin-related properties. Finally, the potential for electronic doping via introduction of impurities in the 2D hybrid perovskite, PEA2PbI4, is examined. DFT calculations uncover the most promising candidates for extrinsic n- and p-type dopants, alongside formation mechanisms of defect complexes and compensating defects.

Why do local and state governments across the United States dole out billions of dollars in tax cuts, grants, and other financial incentives regularly to attract potential businesses, often with promises of low-wage labor, lax economic and environmental regulations, and other overtly pro-business policies that are often a detriment to public welfare? And more importantly, why are all of these policies accepted as normative practice within U.S. economic policy? This dissertation, “Economic Development at What Cost? The Fantus Company, Financial Incentives, and Working-Class Communities, 1926-1996,” addresses these questions by tracing the origins of this practice and its development throughout the twentieth century through an exploration of one entity’s history: the Fantus Company. The Fantus Company, the first site selection consulting company in the U.S., acted as a broker on behalf of both local and state governments and private companies to facilitate economic development agreements. The Fantus Company, through their advising, furthered the competition between states and localities in the quest for economic success in finding employers for their jurisdictions which ultimately depleted their financial resources.My research follows the Fantus Company from 1926 to 1996 and demonstrates how Felix Fantus, a Chicago-based industrial real estate entrepreneur, launched his enterprise in 1926 when he realized he could monetize his knowledge to drive factory relocation for profit. By 1936, in conjunction with Mississippi Governor Hugh Lawson White, Fantus helped craft the Balance Agriculture with Industry (BAWI) program, the first statewide economic development incentive program for this desperately poor former cotton plantation state. Fantus achieved such national stature by the mid-twentieth century that the Kennedy administration recommended its services to chronically impoverished communities from rural Appalachia to urban coastal metropoles that had qualified for assistance from the federal Area Redevelopment Administration. The consulting firm negotiated some of its largest economic development incentives from 1976 through 1992 when they were hired by seven different automotive manufacturers, recommending the mostly foreign automakers move to Southern and Midwestern towns in exchange for these record-setting economic development packages. Ultimately, this dissertation shows how this normative practice of large financial incentive packages and pro-business policies was purposefully crafted and propagated by site selection consultants, with the Fantus Company being the most influential actor because of its longevity and wide-ranging reach.

This dissertation considers the crisis of industrial manufacturing and onset of protracted economic stagnation in the 1970s as a problem of political and narrative form. The onset of what economic historian Robert Brenner calls “the long downturn” in 1973 marks the end of modern economic growth and the closure of political horizons premised on the expansion of industrial capital. Drawing on social movement history and recent developments in Marxist literary theory, I consider a series of novels by politically committed U.S. authors who return to the epochal break of the 1970s to narrate the emergence of our historical present. Examining novels by Leslie Feinberg, John Edgar Wideman, and Rachel Kushner alongside other novels and cultural artifacts of the long 1970s, I find that they delimit the terrain of the present by staging the onset of deindustrialization as the exhaustion of earlier genres of political literature and modes of organization.

G protein-coupled receptors (GPCRs) are targets for almost a third of all FDA-approved drugs. Canonically, GPCRs are known to signal through both heterotrimeric G proteins and β-arrestins. Traditionally these pathways were viewed as largely separable since G proteins primarily initiate downstream signaling while β-arrestins function to regulate receptor desensitization and trafficking in addition to modulating their own signaling. However more recent studies have provided evidence for an integrated role of G protein and β-arrestins in GPCRs signaling through the formation of G protein:β-arrestin:Receptor “megaplexes”. Additionally, G proteins and βarrs were found to associate across a panel of receptors. This association was surprisingly specific for inhibitory G protein (Gαi), even among receptors which do not classically signal through Gαi and suggested that this may be a novel non-canonical method of GPCR signaling. The mechanism required for such interaction and the biochemical and physiological significance remains unknown. To elucidate how this novel complex forms downstream of GPCRs, this dissertation focuses on uncovering mechanisms associated with Gαi: β-arrestin association. Utilizing biochemical and biosensor-based approaches, we show a large portion of selectivity for Gαi in these complexes lies within the N-terminal helical domain. Additionally, we detail the requirements for complex formation where β-arrestin translocation to the plasma membrane is sufficient for complex assembly and, surprisingly, G protein activation and recruitment via GPCRs is dispensable. These findings delineate a novel mechanism for GPCR signaling through β-arrestin recruitment to the plasma membrane that results in complex formation and signaling through novel signaling cascades.

Political consumerism appears to have become the most common form of political participation outside of voting in North America and Western Europe. Nearly half of U.S. adults report using their purchasing power to punish or reward firm behavior. However, studies that actually observe political consumerism are rare and tend to focus on narrow cases. Does political consumerism represent a significant political risk to firms?

In this dissertation, I examine three foundational questions about the political risk implications of environment-related political consumerism by leveraging 13 years of barcode-level data on grocery purchases by approximately 60,000 U.S. households. First, I estimate how environment-related reputational risk incidents affect aggregate household purchasing behavior and whether the implicated firms adjust their pricing strategies to mitigate the short-term revenue impact of risk exposure. Next, I study who the political consumers are by analyzing which political and demographic characteristics explain participation. Finally, to explain why households participate, I analyze how key risk incident attributes affect observed political consumerism.

I find that environment-related risk incidents modestly reduce firm revenue in the short run and that firms choose not to leverage three plausible pricing strategies that could mitigate their revenue loss. Differences in household participation primarily reflect differences in issue preferences, but surveys appear to underestimate the impact of household income. Partisan preferences have a substantively large and plausibly causal effect on participation. Incidents related to global environmental issues lead to more political consumerism than local pollution incidents, but participants may be more sensitive to local pollution incidents that occur in their home market.

Acoustofluidics is an emerging field which utilizes acoustic waves to generate a multitude of microfluidic functionalities such as precise particle manipulation and liquid handling. Thanks to the high biocompatibility of ultrasound, this technique allows for gentle, noninvasive control over fragile biological material, making it ideally suited for applications in the life sciences and medicine. However, as the size of target particles decreases, it becomes increasingly challenging to generate enough force for reliable and precise manipulation. This dissertation showcases the development of two acoustofluidic platforms aimed at overcoming these challenges. In the first part, I will detail the development of an acoustofluidic centrifuge, which utilizes acoustic waves to drive the rotation of a microfluidic disk to separate exosomes from plasma with high yield and purity. The acoustofluidic disk is integrated with an amplification-free nanoplasmonic assay, creating a fully integrated sample-to-answer diagnostic assay for colorectal cancer with 95.8% sensitivity and 100% specificity. In the second part, I present an acoustofluidic picoinjector that enables precise and controllable injection of picoliter volume reagents or biological samples into microfluidic droplets, broadening the utility of droplet microfluidics for a myriad of applications in chemical and biological research such as single-cell analysis and nanoparticle synthesis.

This dissertation argues that post-1980 postcolonial novels reinvent allegory as a narrative-cum-visual form to reimagine the nation in a globalized, neoliberal world. Postcolonial novels have long been read by literary critics as national allegories, their narratives of private lives interpreted as signs of a people’s collective march towards redemption in historical time. This form is taken to have flourished during the movements for independence when postcolonial societies, specifically their newly forming elites, posited nation-building as the utopian horizon of individual struggles. But I find that increasingly, postcolonial novels undermine the older allegorical form. J.M. Coetzee’s Waiting for the Barbarians (1980), Wilson Harris’ The Carnival Trilogy (1985-1990), and Zadie Smith’s White Teeth (2000) fashion damned worlds in which the actions of characters resist their narrativization into a shared horizon of redemption. I contend that these novels are still allegories, only of a different kind. They assume an aesthetic of critical redemption akin to that of Baroque allegory, which Walter Benjamin, in The Origin of German Tragic Drama, describes as a method for accessing providential meaning in a world from which God has disappeared. These novels pursue redemption through their critique of the now defunct “god” of the sovereign nation as the protector of a people’s unfolding destiny. They register in their narrative fragmentation postcolonial societies’ loss of a collective sense of history in a globalized, neoliberal world. But they also display in the sheer configuration of narrative fragments, captured in an image and read as an allegorical sign unto itself, the historical time experienced by subaltern populations who could never be assimilated into the nation’s teleological progress to begin with. This dissertation identifies the emergence of a new allegorical form in postcolonial literature that invites readers to survey the ruins of the old bourgeois narrative of nationalism for signs of alternative forms of redemptive horizons.

My dissertation offers a whole new method of reading for history in postcolonial novels, one that follows a novel’s development as both a narrative and an image. Each chapter focuses on one of the above four novels, examining its characteristic syntax of narrative fragmentation – parataxis, concretization, and digression, respectively. If this syntax interrupts a novel’s narrative continuity and obscures its narrative history, it simultaneously makes itself visible over time as something to be looked at; it becomes visible as the visual configuration of narrative fragments. Each novel, ultimately, displays in its syntax the substance of its allegorical message. I argue that while my method finds its most adequate object in post-1980 novels, it affords a new insight into the redemptive, utopian impulse of novels from an earlier time that also register in their narrative fragmentation a critique of the nation. I provide Claude Simon’s The Flanders Road (1960) as an example. Coetzee, Harris, and Smith have all cited the Nouveau Roman as a stylistic influence. If these authors inherit Simon’s narrative techniques, their novels, in turn, retroactively make visible the allegorical form of Simon’s 1960 novel.

Abnormal tissue growth can result from a variety of factors, including hereditary and carcinogen-induced mechanisms. Overall abnormal tissue development can be described with a step-wise process that is initiated by a single cell mutation which eventually evolves to metastatic invasive cancer. Between the appearance of single mutated cells to its development into in situ cancer tumors, there is a wide time window for early detection and intervention, which can significantly alter the course of the disease and reduce morbidity and mortality. The gold standard for many different abnormal tissue(sickle cell disease, breast cancer etc.) detection is histopathological evaluation. However, histological diagnosis methods can be time-consuming, labor-intensive and expensive. As an alternative, quantitative phase imaging (QPI) is a label-free microscopy technique that provides height information of thin, transparent objects. Without the need of extraneous dyes, QPI offers nanoscale sensitivity to cell morphology and structural changes. Previous studies have shown that a QPI-based imaging modality coupled with microfluidics and machine learning, termed holographic cytometry (HC), is an effective tool for identifying unique biophysical traits of red blood cells(RBCs). In this dissertation, initial works begin with investigating HC’s abilities in identifying abnormal cells in simple homogenous samples such as breast cancer cell lines and proceed to expand the tool’s use for heterogenous samples. Through further hardware development, a novel portable high throughput interferometric chamber (InCh) system, which can screen for abnormal cells in point-of-care settings, is presented in chapter 5. The findings on the implementation of HC to distinguish carcinogen-exposed cells from normal cells and cancer cells are presented in chapter 3. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data that is obtained with HC, are able to build single-cell-analysis-based biophysical phenotype profiles of the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. Different from lab-grown cell lines, human cell samples are a heterogeneous mixture consisting of both healthy, moderately healthy and unhealthy. In the case of sickle cell disease(SCD), only a portion of RBCs are sickle-shaped while majority of a blood sample may still be disc-shaped. To address this issue, a selective search algorithm, based on previous biophysical profiling methods with HC, has been developed to single out any abnormal RBCs within a biopsy sample. By identifying the defining features of sickled red blood cells (RBCs), the algorithm constructs a comprehensive profile that distinguishes between diseased RBCs in sickle cell disease (SCD) and normal RBCs. This profiling enables differentiation between SCD patients and healthy individuals, facilitating better diagnosis and treatment planning. Despite the many benefits of applying the HC system for cytological sample evaluation, it has, to this day, been a difficult modality to implement in clinical settings due to its extreme susceptibility to environmental vibrations, high maintenance, and high build costs. A new design of high throughput QPI has been constructed in this work to counter the issues with the current HC system. Customized microfluidic structures act as beamsplitter, enabling the design of a near common-path interferometric imaging system and eliminating much of the optical components required in the previous HC system design. This new system demands fewer optical components, requires less alignment, and is resistant to environmental vibrations. By combining this newly developed hardware with the software algorithms from the previous two experiments in this thesis, new possibilities for point-of-care clinical applications are unlocked.

Marine bioinorganic chemistry is an emerging field that focuses on how inorganic elements impact biological functions within marine organisms. Exploration of heavy metals within marine environments is the connection that holds the world presented herein together. The work begins with an examination of Interspeed 640, a popular antifouling (AF) coating that utilizes cuprous oxide (Cu2O) as the primary biocide, to understand the rationale behind its declining efficacy. This effort was approached in two distinct ways: 1) by designing an assay to characterize the impact of environmental conditions on surface composition and copper (Cu) release to define the interface primary colonizing bacterial species interact with, and 2) by describing the bacterial communities involved in primary colonization to understand acquired Cu tolerance. First, a newly developed and highly tunable lab-scale assay was utilized to measure the impact of environmental conditions on surface composition and Cu release. This method was utilized to describe the behavior of Interspeed 640 in an inert environment (double-distilled, 18.2 MΩ water, (ddH2O)) and under conditions which may facilitate biofilm growth (tryptic soy broth, (TSB)). We utilized x-ray photoelectron spectroscopy (XPS) to characterize the coating surface and inductively coupled plasma optical emission spectroscopy (ICP-OES) to measure Cu release over a 14-day period. Under both immersion conditions, XPS analysis of the coating surface showed signs of hydrolysis. Coated substrates immersed in TSB demonstrated an increase in both nitrogen and copper as compared to those in water. Additionally, coated substrates immersed for 14 days in TSB released approximately 500% more Cu than those immersed in water. Next, polyvinyl chloride panels either left blank or coated with Interspeed 640 were deployed at three field sites to collect, isolate, and identify bacterial species involved in the early stages of biofouling. We have created a library of primary colonizing bacteria for all three field sites and, for one site, have completed a preliminary assessment of bacterial Cu tolerance capabilities using broth microdilution assays. These results indicate that Cu tolerance is not unique to species isolated from coated panels. Further investigations into the interplay between metal ions and marine bacteria continue in the following section where Marinobacter atlanticus is evaluated for potential applications in bioremediation. M. atlanticus can produce small amounts of electricity and this ability is likely dependent upon labile metal ion concentration and mineral cycling. We hypothesized that M. atlanticus may be a promising candidate for applications in heavy metal remediation or as a biosensor. We utilized broth microdilution assays to expose M. atlanticus to a concentration range of essential and non-essential metal ions. M. atlanticus demonstrated a profound ability to grow in the presence of metal concentrations that significantly exceed those found in native environments. In a metal detoxification assay, where the ability of M. atlanticus to remove metal ions from solution was assessed, we found that this bacterium is unable to significantly reduce metal concentrations. In one final shift of focus, we provide an examination of bioactive molecules isolated from evolutionarily resilient organisms via an investigation of the Clavanin family of antimicrobial peptides. Prior literature on these peptides indicates the possession of broad-spectrum antimicrobial activity. We focused on the antimicrobial of ClavA against fungal pathogens Candida albicans and Cryptococcus neoformans and the bacterium Escherichia coli strain BW25113; ClavC was tested against C. albiancs and E. coli. Overall, both peptides exhibited enhanced antimicrobial activity under acidic (pH 5.5) and, in most cases, ClavC was more potent than ClavA. When supplemental Cu or Zn were introduced, significant metal modulated activity increases were seen between ClavA and Cu at pH 5.5 against C. albicans and C. neoformans. Conversely, the combination of ClavC and Cu at pH 5.5 seemed to inhibit peptide activity.

Latinx sexually minoritized men (SMM) in the United States face a disproportionately high burden of HIV. Despite overall progress in reducing HIV infections among various groups, new HIV infections among Latinx SMM continue to rise. This increase persists even during a decade that has introduced highly effective HIV prevention tools, such as once-daily medication for HIV pre-exposure prophylaxis (PrEP), which is up to 99% effective at preventing HIV. Despite the availability of these prevention options, Latinx SMM are not equally benefiting from these strategies, exacerbating HIV inequities within this group. Individual behaviors do not fully account for the HIV inequities experienced by Latinx SMM, underscoring an urgent need to identify social and structural determinants of health such as socioeconomic status, access to care, and stigma that contribute to the burden of HIV in this group. To date, research focused on the unique experiences and determinants of health among Latinx SMM remains limited. Furthermore, the threat of fraud to online research by survey bots, potentially compromising the integrity of data collected through online research, presents a novel structural barrier to advancing HIV research.In this dissertation a health equity lens was applied to conduct three studies to explore the social and structural determinants of HIV prevention among Latinx SMM. A systematic review was conducted to explore barriers and facilitators of engagement in the PrEP continuum of care among Latinx SMM and transgender women (Chapter 2). This review demonstrated that social and structural factors such as PrEP stigma, access to healthcare, and xenophobic policies which have led to a widespread fear of deportation among Latinx groups influence engagement in the PrEP continuum of care among both groups. A qualitative descriptive study of 15 Latinx SMM revealed that participants viewed sexual health promotion as an integral part of their broader physical and emotional well-being, with a significant focus on protecting themselves from HIV (Chapter 3). Social and cultural stigma, driven by patriarchal stereotypes (i.e., Machismo) and heterosexism, acted as barriers to self-acceptance and HIV self-protection. Despite these challenges, participants overcame these barriers and took ownership of their sexual health and HIV self-protection. An online survey conducted to examine the relationships between intersecting oppression and HIV prevention behaviors, including PrEP use, was compromised by survey bots. In Chapter 4, an analysis of bot detection strategies revealed that 98% of the responses showed indications of being fraudulent and likely generated by survey bots. Findings from this dissertation underscore the need to expand the focus of interventions beyond individual behavior change to include social and structural determinants of health. Future research is needed to continue identifying effective strategies for mitigating these issues and ensuring the reliability of online data collection, as well as to further explore the social and structural determinants that impact HIV prevention among Latinx SMM. Addressing these challenges will be vital for developing equitable, structurally responsive HIV prevention interventions for Latinx SMM.

How do racial ideologies influence how Mexican immigrants think about race, racism, and inequality once in the U.S. and what are the political implications? To answer these questions, I employ a historical and comparative approach to provide an ideological framework to understand the relationship between Mexican immigrants’ racial viewpoints and Republican partisanship. I focus on two dominant racial ideologies that I assert are opposite sides of the same coin: mestizaje in Mexico and color blindness in the U.S. Overall, this dissertation makes three core arguments. First, despite differences in the history of race and racism in both countries, both mestizaje in Mexico and colorblindness in the United States provide an ideological roadmap to rationalize away racial inequalities and racism. Second, as migrants motivated by the American Dream to immigrate and coming from a context where there is little to no perceived discourse around race and racism, Mexicans are predisposed to being receptive to color-blind messages in the U.S. Third, despite the Republican Party and Donald Trump being associated with tough immigration policies and anti-immigrant attitudes, Mexicans who embrace color-blindness may view support for the party and Trump as unproblematic. Anchoring these three arguments is my mestizo color-blind theory which posits that Mexicans uphold mestizaje and color-blind ideological frames when they believe there are social, economic, and/or psychological incentives to do so. To advance our knowledge on how racial ideologies come to shape and influence Mexican’s understanding of race, racism, and racial inequalities and its association with Mexican Republican partisanship and views of Trump, I conduct a multi-method research project. I conducted semi-structured one-on-one interviews with Mexican immigrants in Durham, NC and Philadelphia, PA. Through an inductive approach to my interviews, I illustrate how both racial ideologies are prevalent in Mexican immigrant racial discourse. Additionally, I conduct regression analysis using the 2020 Collaborative Multiracial Post-Election Survey (CMPS) to assess the relationship between Mexicans’ responses to color-blind questions and both Mexican partisanship and views of Donald Trump. The interview findings revealed roughly four broad categories that Mexican immigrants fall into while navigating mestizaje and color blindness: 1) Mexicans who internalize mestizaje in Mexico but challenge colorblind frames in the U.S. 2) Mexicans who internalize both mestizaje and color-blindness. 3) Mexicans who challenge mestizaje in Mexico but internalize color-blind frames in the U.S. 4) Mexicans who challenge both mestizaje and color-blindness. The quantitative analysis using CMPS data produced mixed results. Of the three color-blind variables I used to assess their association with Republican partisanship and views of Trump among Mexican immigrants, two measures produced statistically significant, positive results with Republican partisanship and having a favorable view of Trump, respectively. Ultimately, my findings suggest that Mexican immigrants are influenced by both mestizaje and color-blind racial ideologies and that they can have a relationship with Mexican immigrant politics in the U.S.

Ultrasound is a dominant modality in medical imaging. The use of ultrasound continues to increase, and improved methods and new applications continue to be developed. Given the prevalence of ultrasound and its increasing use, streamlining scans, making imaging more straightforward for operators, and improving the quality of the acquisitions that are performed would have significant impact. One task currently standing in contrast to these goals is the adjustment of imaging parameters. Ideally, imaging parameters should be adjusted with each patient and imaging view to optimize quality, yielding the greatest likelihood for successful diagnosis. However, instead there is a reliance on preset imaging parameter settings that perform well on average, but do not necessarily optimize an individual scan. This work explores a method for automated adjustment of two imaging parameters, frequency and acoustic output, achieving optimal settings for one or both parameters during B-mode and color Doppler imaging without needing operator intervention. Frequency presents a tradeoff between data quality and imaging resolution. Acoustic output settings have a tradeoff between data quality and acoustic exposure levels. Acoustic output can be described by the Mechanical Index (MI), which is based on the pulse pressure, or Thermal Index (TI), a measure of tissue heating. Following the ALARA (As Low As Reasonably Achievable) principle, acoustic output should be kept as low as possible to minimize any risks associated with imaging. Implementing the adaptive parameter selection methods explored here in clinical settings could make ultrasound more accessible for operators, increase its availability to patients, and allow for the continued growth of this modality.The method explored here for frequency and acoustic output adjustment relies on real-time assessment of temporal signal-to-noise ratio (SNR). Temporal SNR describes the quality of the received data and is directly related to frequency and acoustic output levels. Higher frequencies experience greater attenuation leading to lower signal levels, while decreasing acoustic output leads to less intense receive echo signals. A human observer study was performed with simulated ultrasound data to assess at what temporal SNR noise becomes imperceptible during B-mode imaging. This threshold SNR was determined to be 28 dB for typical imaging conditions. An imaging sequence was implemented on a clinical ultrasound system that measured SNR during live imaging and performed real-time acoustic output adjustment, targeting the 28 dB SNR threshold. Adjustments minimally impacted the system operator, while automatically enforcing the ALARA principle. The recommended acoustic output level ranged from 0.1-0.4 MI, significantly lower than the 0.8-1.4 MI typically used by default on clinical systems. To extend the work done in B-mode imaging, similar adaptive adjustment methods were evaluated during color Doppler imaging. Both frequency and acoustic output were considered for adaptive adjustment in color Doppler. A simulation study was performed to assess velocity estimation accuracy under a variety of imaging settings and temporal SNR levels. At low SNR levels, velocity estimation performance was poor. Increasing SNR to 5 dB led to low levels of bias in the velocity estimate. With further SNR increases to 15-20 dB, the standard deviation in the velocity estimates across the vessel were near the noise-free minimum. These results were validated in phantom imaging experiments. In an in vivo clinical test, sweeps of acoustic output and frequency were performed in five volunteers. The SNR and color Doppler velocity estimates were calculated for each frequency and acoustic output combination. The highest frequency achieving SNR at or above the target level was recommended for imaging. From there, acoustic output was reduced to meet the target SNR threshold. Relative to their values for a potential default condition of mid-level (2.5 MHz) frequency imaging with 100% transmit power, MI was reduced 24-45% and TI reduced 68-69%. These results further supported the simulation and phantom findings, indicating SNR could guide the in vivo adjustment of frequency and acoustic output for color Doppler. The results of these studies demonstrate the promise of adaptive frequency and acoustic output adjustment and encourage the exploration of additional adaptive imaging methods. Demonstrating that these techniques could be implemented on existing clinical systems makes clear that temporal SNR guided adaptive imaging has the potential to assist operators in the short-term, while offering opportunity for further expanded benefits with continued development.

The prevalence of single combustible tobacco product use has decreased among adolescents and young adults (AYAs) in recent years, but the introduction of alternative tobacco products has led to increases in dual- or poly-use, especially among AYAs. Understanding susceptibility to multiple tobacco product (MTP) use is crucial, as it predicts initiation and subsequent use. Mental distress, including stress and mental health symptoms, may influence susceptibility and harm perceptions, yet this area remains underexplored in the literature. This dissertation aims to fill this gap by examining the influence of mental distress on susceptibility to MTP use among AYAs and whether it alters harm perceptions. By addressing these questions, this research aims to develop effective prevention techniques to prevent substance use initiation and reduce susceptibility among vulnerable populations, ultimately improving long-term health outcomes.Chapter 1 of this dissertation further introduces the background and context of this critical issue at the intersection of mental health and tobacco uptake among the AYA population in the United States, setting the stage for understanding the multifaceted relationship between mental health, stress, tobacco use, and susceptibility among AYAs, and laying a strong foundation for the subsequent chapters' detailed exploration and analysis. The theoretical framework guiding the dissertation is introduced to provide a structured lens through which to examine the complex interplay of factors influencing tobacco use behaviors among AYAs. This framework incorporates theories from psychology, public health, and dependence studies, offering a comprehensive approach to understanding the pathways and mechanisms that contribute to susceptibility and initiation of tobacco product use in this vulnerable population. Chapter 2 was a systematic scoping review which employed the Joanna Briggs Institute (JBI) method and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist to explore factors associated with multiple tobacco product (MTP) susceptibility, uptake, and use among U.S. adolescents and young adults. A comprehensive search strategy in PsycINFO and PubMed databases yielded 52 eligible articles published from 2013 onwards. Data extraction focused on demographic, individual, familial, social, environmental, and societal factors influencing MTP use. Key findings revealed higher susceptibility to MTP use among older adolescents and males, with Hispanic and Non-Hispanic Black adolescents also exhibiting elevated susceptibility to MTP use. Factors such as intentions to use tobacco, peer and parental tobacco use, and limited exposure to pro-smoking media were associated with susceptibility to MTP use. Additionally, e-cigarette use was linked to subsequent cigarette smoking initiation, while experimentation with MTP was influenced by parental use and tobacco marketing exposure. Progression to MTP use involved concurrent use of multiple products, with sociodemographic characteristics and environmental influences playing significant roles. These findings underscore the complex interplay of factors shaping tobacco use behaviors among adolescents and young adults, highlighting areas for further research and targeted interventions. Chapter 3 employed two pilot studies to examine the relationship between mental health factors (perceived stress, anxiety, and depression) and susceptibility to hookah tobacco smoking among adolescents and young adults; examining differences based on susceptibility status and exploring the predictive role of these factors while considering known risk factors and covariates. Statistical analyses using were conducted across two studies to explore demographic characteristics and predictor variables related to susceptibility and smoking status among young adults. Chi-square tests, t-tests (Study 1), and ANOVAs (Study 2) were utilized for comparisons, alongside bivariate logistic regression and proportional logistic regression for relationship exploration. Findings from Study 1 revealed that perceived stress was a significant predictor, with susceptible young adults reporting higher stress levels, indicating a potential link to susceptibility to hookah tobacco smoking (HTS). Peer acceptability and harm perceptions also emerged as influential factors. Study 2 showed differences in psychological distress levels between non-susceptible individuals and hookah users, highlighting the role of psychological factors in HTS behaviors. A positive linear trend in stress, anxiety, and depression levels from susceptibility status transitions (i.e., from non-susceptible, to susceptible, to use) underscored the need for comprehensive interventions targeting multiple risk factors. The consistent predictive power of perceived stress emphasizes its importance in addressing susceptibility to HTS among young adults. These findings advocate for tailored interventions addressing psychological, social, and demographic factors collectively to reduce susceptibility and prevent HTS initiation effectively. Chapter 4 utilized a national dataset (the Population Assessment on Tobacco and Health) to explore longitudinal links between multiple tobacco product (MTP) susceptibility, mental health symptoms, product harm perceptions and initiation of MTP use among AYAs in the US. We first examined whether increased externalizing and internalizing symptoms predicted changes in susceptibility to MTP use. Longitudinal parallel-process (LPP) modeling was used, involving multilevel modeling (MLM) and structural equations modeling (SEM) considering age group as the major predictor. We also examined whether harm perceptions mediated any relationships between mental distress and susceptibility, adjusting for covariates. We then examined if harm perceptions predicted MTP uptake. Cox-proportional hazard regression analyzed relationships between harm perceptions and MTP uptake. A linear regression model estimated longitudinal relationships between harm perceptions and susceptibility status. Mediation analysis determined if susceptibility status mediated the relationship between harm perceptions and MTP uptake. Lastly, as an exploratory analysis, we examined the interaction between mental distress and susceptibility on MTP uptake. We utilized intercepts and slopes for each variable in analyses to provide insights into how these factors evolve over time. Among a sample of AYAs who reported never smoking cigarettes, e-cigarettes, hookah tobacco, nor cigars (traditional, filtered, and cigarillos) at Wave 1, we found that non-susceptible individuals had higher perceived harm and lower mental health symptoms than susceptible individuals. Susceptibility was highest during the transition from ages 12-14 to 15-17, highlighting a critical period for intervention. Young adults showed higher initiation rates of MTP by Wave 6, emphasizing the need for targeted interventions during the transition to adulthood. Unexpectedly, higher initial levels of mental distress were associated with reduced susceptibility to MTP use, challenging conventional assumptions. Perceptions of harm mediated these relationships variably, indicating complex pathways influencing MTP use initiation. Further investigation using Cox-proportional hazard regression models confirmed that higher initial and increasing perceptions of harm over time were protective against MTP initiation. However, contrary to expectations, higher initial harm perceptions were associated with increased susceptibility initially. Initial susceptibility partially mediated the relationship between perceptions of harm and MTP initiation, highlighting nuanced interactions. The study suggests that while mental distress influences susceptibility and perceptions of harm, its direct moderation effect on MTP initiation was not significant. These findings underscore the importance of developmental stages and social contexts in shaping MTP use behaviors, advocating for tailored interventions during critical transitions to mitigate tobacco use among AYAs. Chapter 5 concludes the dissertation by synthesizing its findings from all chapters, discussing their implications and recommendations for future research.

This dissertation investigates esoteric philosophy (the deliberate concealment of philosophical positions) in Rome. It does so through case studies of three Roman philosophers: Cicero, Lucretius, and Plutarch. Until now, there has not been a full-length general study of esoteric philosophy in Greco-Roman antiquity. The presence or absence, however, of deliberate concealment in philosophical texts greatly determines how one should read them. In arguing that Roman philosophers were aware of this phenomenon and practiced it themselves, this dissertation thus suggests a new way of reading Roman philosophy. First, a new typology of esoteric philosophy creates a framework for interpreting the discourse around and practice of esotericism. Prominent reasons for esotericism include reflections on ontology and epistemology as well as pedagogical and political motives (e.g., avoiding persecution or critiquing the powerful). The study begins with an argument that esoteric philosophy is thematic in Cicero’s De natura deorum and key in resolving central interpretative questions about the dialogue. Then, an assessment of esoteric philosophy in Lucretius’ De rerum natura reveals a contemporary perspective on the topic rooted in a different genre and in the Epicurean rather than Platonic philosophic tradition. The final chapters treat Plutarch’s De audiendis poetis, a work which lays bare the nexus between philosophy, poetry, and esotericism. This study offers esoteric readings of these philosophers by taking their own statements on esotericism as the starting point for interpretation as a control against anachronism. The interpretations also situate esoteric philosophy in aspects of philosophy particular to the Roman context, including its tenuous status as a Greek import, tension with Roman religion, relationship to Roman oratory, and response to the political pressures of the Late Republic and Principate. The methods and results of this dissertation may serve as the basis for future research into esoteric philosophy in other philosophers in antiquity and beyond.

Protein modifications modulate nearly every aspect of cell biology in organisms ranging from Archaea to Eukaryotes. The earliest evidence of covalent protein modifications was found in the early 20th century by studying the amino acid composition of proteins by chemical hydrolysis. These discoveries challenged what defined a canonical amino acid. The advent and rapid adoption of mass spectrometry-based proteomics in the latter part of the 20th century enabled a veritable explosion in the number of known protein modifications, with over 500 discrete modifications counted today. Now, new computational tools in data science, machine learning, and artificial intelligence are poised to allow researchers to make significant progress discovering new protein modifications and determining their function. Lysine acetylation is one of the most well-known post translational modifications. Acetylation is not limited to lysine with acetylation of serine and threonine having also been reported in the literature. Lysine acetylation is known to occur both enzymatically and non-enzymatically. Cysteine is a reactive amino acid central to the catalytic activities of many enzymes. Given the highly reactive nature of the cysteine side-chain, non-enzymatic acetylation of cysteine would be expected to be more favorable than non-enzymatic acetylation of lysine. Cysteine is also a common target of post-translational modifications (PTMs), such as palmitoylation. This long-chain acyl PTM can modify cysteine residues and induce changes in protein sub-cellular localization. Transfer of an acetyl moiety from the side-chain of cysteine to the side-chain of lysine has been shown in vitro. Cysteine side-chain acetylation has never been shown in vivo. We hypothesized that cysteine could also be modified by short-chain acyl groups, such as cysteine S-acetylation. To test this, we developed sample preparation and non-targeted mass spectrometry protocols to analyze the mouse liver proteome for cysteine acetylation. Our findings revealed hundreds of sites of cysteine acetylation across multiple tissue types, revealing a previously uncharacterized cysteine acetylome. The cysteine acetylome shows distinct patterns in different sub-cellular compartments and is most abundant in the cytoplasm. Cysteine acetylation is present in all tissue types tested and has tissue-specific acetylome patterns. Metabolic stress led to targeted changes in the cysteine acetylome of BAT. Acetylation of the active site cysteines of GAPDH led to a sharp reduction in activity. This study uncovers a novel aspect of cysteine biochemistry, highlighting short-chain modifications alongside known long-chain acyl PTMs. These findings enrich our understanding of the landscape of acyl modifications and suggest new research directions in enzyme activity regulation and cellular signaling in metabolism.

Retinal ganglion cells (RGCs) are projection neurons of the retina that are a nexus for integrating light signals originating from retinal photoreceptors and transmitting them to the visual processing centers of the brain. RGCs have a highly polarized morphology broadly divided into somatodendritic and axonal compartments. The drastically dissimilar structures and functions of these compartments implies that they face different bioenergetic and other physiological demands. As RGCs are known to be uniquely sensitive to dysfunction of mitochondria, it is believed that these organelles are key to maintaining RGC homeostasis and survival. Differences in mitochondrial biology are likely to be tailored to the specific physiological needs of each RGC compartment.This dissertation focuses on identifying fundamental differences between RGC mitochondria in the somatodendritic and axonal compartments. Compartment-specific functional disparities between mitochondria may not only highlight unique physiological demands inherent to these compartments but also play a pathophysiological role in the development of RGC dysfunction and death in various optic neuropathies. We hypothesized that compartmental differences in mitochondrial biology would be reflected by dissimilarities in mitochondrial protein composition and therefore be amenable to interrogation using proteomics. We describe an optimized protocol to isolate intact mitochondria separately from mouse RGC somatodendritic and axonal compartments by immunoprecipitating labeled mitochondria from novel RGC MitoTag mice. These genetically modified mice express a cytosol-facing GFP tag specifically on RGC mitochondria, allowing for GFP-based immunoprecipitation of mitochondria from the RGC somatodendritic compartment in the retina and from the axonal compartment in the optic nerve. Using liquid chromatography-mass spectrometry techniques, we identified several hundred proteins in the RGC somatodendritic and axonal mitochondrial immunoprecipitates, including a number of proteins highly enriched or exclusively identified in either compartment. To validate these findings, we further analyzed three mitochondrial proteins with distinct compartmental enrichment profiles: superoxide dismutase (SOD2), enriched in the RGC somatodendritic compartment; sideroflexin-3 (SFXN3), expressed equally in both compartments; and trifunctional enzyme subunit α (HADHA), enriched in the RGC axonal compartment. The expression and localization profiles of these proteins within RGCs were assessed using immunofluorescence techniques and compared to the protein abundance data obtained in the proteomics analysis. We subsequently explored RGC axon-specific metabolic programs after the identification of several enzymes involved in long-chain fatty acid catabolism as being enriched RGC axonal mitochondria. Metabolite profiling of mitochondria obtained in a compartment-specific manner from RGC MitoTag mice revealed an abundance of long-chain fatty acylcarnitine molecules primed for fatty acid oxidation in axonal mitochondria. Building from this finding, we performed RGC-specific genetic ablation of carnitine palmitoyltransferase 1, thereby depriving RGC mitochondria of the ability to import fatty acylcarnitine molecules to be used as metabolic substrates for the generation of cellular ATP. When assessing RGC abundance in the setting of chronic impairment of mitochondrial fatty acid import, there appeared to be a subtle trend towards decreased RGC survival in the mutant mice. Our findings provide clues to compartment-specific distinctions in the roles of RGC mitochondria. While compartmentalized energy gradients have been previously identified in other neuronal populations, our work represents the first to pursue metabolic differences within a type of retinal neuron. Exploring how several optic nerve disease states impact mitochondrial proteomic differences in RGCs is the subject of ongoing work in the lab, with preliminary results described here.

The big data tendency influences how people think and inspires potential research directions. Recent feats of machine learning have seized collective attention because of its profound performance in conducting big data analysis including text analysis and image processing. Machine learning is also a popular topic in clinical medicine to implement analysis on electronic health records and medical image data, which traditional statistics model is not adequate for. However, we realize that machine learning is not panacea and its defects such as loss of interpretability and excess selection may restrict its application. And we must also recognize that for many clinical prediction analyses, the simpler approach-generalized linear model is enough for what we need.

In this dissertation, we propose to use standard regression methods, without any penalizing approach, combined with a stepwise variable selection procedure to overcome the over-selection issue of popular machine learning methods. For model validation, we propose a permutation approach to estimate the performance of various validation methods. Finally, we propose a repeated sieving approach, extending the standard regression methods with stepwise variable selection, to handle high dimensional modeling.

When a normal metal is placed in a good electrical contact to a superconductor, Cooper pairs leak across the interface inducing pair correlations into the normal metal. This corresponds to Andreev scattering of electrons into holes at the interface. With long-range phase coherence, this enables charge interconversion effects. A conventional Andreev scattering is a retroreflection where the hole retraces the electron's trajectory in time-reverse. In graphene, however, a specular Andreev reflection (SAR) is possible where the reflected hole, instead, maintains the forward progression of the electron along the interface (similar to light reflection in a mirror). Thus, confining SAR (e.g. by a second boundary) leads to multiple Andreev reflections in a propagating coherent electron-hole superposition, an Andreev mode. In this dissertation, we consider another variation of this: by breaking time-reversal by quantum Hall effect, the trajectory is also forward progressing due to the cyclotron orbits "skipping" along the interface, the chiral Andreev interface mode. We call the Andreev process in such one-way propagating boundary channels, a chiral Andreev reflection (chAR). In this dissertation, chAR is studied in lattice and continuum models of disorder-free graphene on the lowest Hall plateau coupled to a generic superconducting contact.

At first, we show possibility of transport interference of two conjugate Andreev modes upon varying diverse control parameters, and study particularities of distinct graphene-superconductor boundaries in the transport pattern and surface spectrum. This is done in tight-binding as well as more generically via a parametric family of boundary conditions. We then study more concrete models for the interface properties and parameter dependences of the proximity effect in the chAR. Lastly, we study scattering properties of the same in graphene nanostructures with superconductor coupling of varied interface transparency and geometry, which are to be of experimental relevance.

Graphene Fermi velocity makes a strong proximity effect possible despite large sharp potential step from superconductor Fermi wave mismatch unlike in systems of single lattice type. Thus, the strong effect is compatible with metallicity of the superconductor and is only weakly sensitive to graphene parameters. Single-channel chAR is found to be stronger and more robust to reduced transparency interfaces compared to the conventional multimode Andreev retroreflection at the same graphene doping. Intervalley scattering in nanostructures is important and allows for significant electron-hole conversion in weak transparency junctions even despite suppressed interface pairing. At this weak transparency, in nanostructures with two intervalley corners, an interference of chAR with up to full conversion amplitude is shown. The interferometer in the proposed nanostructure should be fairly available in a range of atomistic corners without much finetuning.

Coal combustion residuals (i.e., coal ash) are the solid wastes generated from burning coal for electricity and primarily comprise bottom ash, fly ash, and flue gas desulfurization sludges. Coal ash represents one of the largest solid waste streams in the United States. While approximately half of the coal ash generated in the U.S. is re-purposed for beneficial applications such as construction materials, the rest is discarded, mostly in unlined landfilled and ponds adjacent to coal fired power plants. Collectively disposal sites in the U.S. host more than 2 billion tons of coal ash that have accumulated over multiple decades of coal power generation. Coal ash disposal sites pose a threat to local water quality due to leaching of toxins and uncontrolled releases of ash residual resulting from impoundment failures. Despite their environmental risks, these disposal sites also represent an untapped resource for raw materials for supplementary cements and other infrastructure material applications. The chemical composition of coal ash (particularly fly ash and bottom ash) has not been routinely monitored in landfills and surface impoundments, resulting in insufficient knowledge to assess the reuse potential and toxicity of coal ash at these disposal sites. This dissertation addresses this knowledge gap by investigating the complex chemical composition of coal ash and evaluating its reuse potential. The research is structured into three chapters, each focusing on different aspects of the coal ash challenge, from the evaluation of the resource availability to the toxicity of this waste product. As a first step towards understanding the scope of legacy coal ash at disposal sites managed by power plants, one can first evaluate the various sources of coal that has been burned at power stations over the last decades and to use this information to assess the quantity and quality coal ash generated. To this end, Chapter 2 describes the compilation of a 50-year coal supply chain dataset for major U.S. power stations, integrating data on coal purchases, consumption, and regional supplies of coal described in a variety of databases hosted by U.S. federal agencies. Detailed records of coal purchases by power stations across the United States from 1973 to 2022 are included. Geographic inconsistencies of power stations were corrected by comparing coal purchase patterns across different energy databases. Discrepancies due to missing reports from power stations were addressed using a Compound Annual Growth Rate (CAGR) algorithm, resulting in a valuable database for future research on coal usage and coal combustion residuals reuse in the U.S. With this historical coal supply chain record, Chapter 3 details the development, testing and application of a model to predict the concentrations of major and minor elements in coal fly ash at each U.S. power station based on annual coal purchase records from power stations. This model, employed multivariate linear regression and Bayesian regression methods and was trained with chemical compositional data from the U.S. Geological Survey’s COALQUAL database and elemental contents that were measured in 696 coal ash samples from previous studies. The models indicate that the chemical composition of coal fly ash is significantly impacted by the coal source, rather than the year trend and other combustion parameters. The Bayesian model, utilizing prior knowledge of data from COALQUAL, effectively predicts fly ash elemental contents with relatively small sample sizes (<40 samples) and provides a a method to quantify uncertainties for the prediction historical fly ash composition produced annually at each major power station. This approach leverages prior knowledge of coal element distributions to estimate element concentrations in coal ash, providing guidelines for ash quality at disposal sites across the U.S. and the market potential for recycling efforts. Chapter 4 evaluates the toxicity of coal ash, focusing on the leachability of trace toxic elements like arsenic and selenium, which influence reuse potential. Leaching experiments with 52 fly ash samples from 15 different U.S. power plants representing coal feedstocks from three major domestic regions were conducted. The mobilization potential of As and Se in fly ash was assessed based on standardized leaching protocols. Multivariate and lasso regression analyses explored correlations between leachable As and Se contents and characteristics such as major element contents, loss on ignition, and pH. The results indicate that major elements (Fe, Ca, Al) can serve as predictor variables for the leaching potential of As, but not Se. Loss on ignition and pH were not significant predictive variables. The regression models showed relatively strong fits for leachable As (R2 = 0.78 for both models) compared to leachable Se (R2 = 0.49). These findings suggest that correlation models combined with on-site elemental analysis with portable analyzers could enable a screening method for leachable As in coal ash. Overall, this dissertation examined the recyclability, toxicity, and leachability of coal ash through the development of predictive models and protocols for assessing coal ash quality and leaching potential of toxins at disposal sites. The research contributes to a national dataset on the coal supply chain of power plants and coal ash residuals at disposal sites, thereby promoting sustainable management practices.

Jail leasing is the practice by which states enter into contractual agreements with local governments to house individuals under the state’s jurisdiction in local jails. This dissertation is an attempt to understand this practice and its effects on individuals and communities. In the introductory chapter, I briefly explain the history of the jail as a carceral space and then explain jail leasing in the context of mass incarceration in the US. The focus of the second chapter is the location of the jail-leased population across and within states. I show the by-state distribution of the leasing population in 1999 and 2019, as well as the 2019 within-state (i.e., by county) distribution for the nine states for which data are publicly available. In chapter three, I test the effects of jail leasing on recidivism outcomes. I do so using data from six US states over a period of 20 years, 2000-2019. Using matching techniques, I display Kaplan-Meier curves to compare the survival rates of release cohorts leaving prison or jail and Cox Proportional Hazard model results to show estimates of the effects of facility type on recidivism risk. I find that overall, the recidivism risk is higher for those individuals released from jails, but that the difference in risk varies greatly across states. I also show the results of a cost-benefit analysis meant to determine whether differences in recidivism further support or undermine efforts by the state to reduce spending on incarceration. Chapter four explores what, if any, differences in mortality risk exist for the jail-leased population compared to the general jail and prison populations. Using data on deaths that occurred to individuals while they were experiencing incarceration in the US between 2013 and 2019, and data on the composition of the US corrections population over the same time period, I employ traditional demographic methods in order to be able to make the best possible comparisons of these mortality risks. The results suggest that the mortality risk for the jail-leased population is the lowest of the three populations compared in the analyses. In the conclusion chapter, I describe the ways in which jail leasing should and should not be considered a potential tool for decarceration.