Dartmouth Institute for Health Policy and Clinical Practice
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Violent Tenderness
Full text linkThis thesis concerns itself with a single question: “What is love?” Specifically, it constitutes an account of love as a failed failure. The first chapter proposes a conceptualization of love as a subject-to-subject relation, through sustained engagement with the work of Alain Badiou. Specifically, the nature of the love relation as an intersubjective construction is articulated through a critique and reworking of Badiou’s conceptualization of love as an evental phenomenon. The second chapter explores the failed failure of love in relation to language, wherein the lovers’ discourse is understood to be linguistic play, operating according to the principles of Bataillean general economy, as a circuit of excess production and unproductive expenditure. The third chapter concerns itself with the failed failure of love in relation to the Lacanian death drive, interrogating the role of enjoyment and jouissance in the psychical economy of the amorous subject. The final gesture of this thesis is an account of love’s immense political potential in its singular ability to annul the Law and bring about a new ethics
Hollow Choices: The Choice Neighborhood Initiative (CNI) and the Performance of Participation
Full text linkSince HUD Secretary Henry Cisneros\u27 pledge in 1995 to “end public housing as we know it, ” the United States has seen the rapid depletion of public housing stock. Alongside this mass demolition, a new redevelopment paradigm emerged: a community-oriented planning model that requires resident participation. HUD’ s shift toward participatory planning began with HOPE VI in 1992, which sought private investment to demolish public housing, and was formalized in 2010 through the Choice Neighborhoods Initiative (CNI). This thesis examines a CNI planning phase—a stage overlooked in existing scholarship, which largely stresses post-occupancy outcomes like displacement, (im)mobility, socioeconomic integration, and gentrification. In contrast, this research offers one of the first interview-based case studies of a CNI planning process. Grounded in resident testimony and planning meeting observations, the project analyzes Hollow Choice, a CNI effort to redevelop the Charles F. Greene Homes, a Section 8 high-rise complex in Bridgeport, Connecticut’ s Hollow neighborhood. The research asks: How do disparate stakeholders—tenants, community members, and local planners—understand Hollow Choice ’ s participatory planning process? How do these accounts reveal the governing logics of participation under neoliberal austerity? By shifting focus from outcomes to process, this work highlights how instead of blueprints, resident participation itself becomes the project’ s central product. Contrasting vibrant representations of Hollow Choice with residents \u27 persistent struggles reveals participation as performance, a creative strategy to stage empowerment through devaluation and privatization. More broadly, this study broadens the analysis of choicelessness and tokenism, from the individual level of the consultant, to broader structural imperatives imposed by the federal state
EXISTING IN CONTEXT: THE EFFECT OF THE SOCIAL ENVIRONMENT ON INDIVIDUALS\u27 FRIENDSHIPS, BELIEFS, AND WELL-BEING
No full textDo the people we interact with shape how we behave and think? Across three aims, we provide evidence that our social environment – the set of people with whom we interact – affects the resiliency of our friendships, the way we think about ourselves, and our well-being. In Aim 1, we investigated the predictors of whether friendships formed within structured study groups persisted after those groups dissolved. Drawing on five cohorts of longitudinal social network data, we tested the relative contributions of individual similarity—across both visible demographic traits and less visible personality characteristics—and the proportion of shared friends between dyad members. Although our hypotheses and initial findings supported personality similarity as a predictor of tie persistence, this effect did not replicate in a larger dataset. Instead, across both datasets, the most consistent and robust predictor of friendship persistence was the proportion of shared friends. In Aim 2, we asked whether conversation partners’ self-views might mutually evolve. Using four-person round-robin conversation networks, we found that participants tended to have more similar self-views post-conversation than pre-conversation, an effect we term “inter-self alignment.” Further, the more two partners’ self-views aligned, the more they enjoyed their conversation and were inclined to interact again. In Aim 3, we provide evidence that social diversity – spending time with different types of conversation partners – predicts greater positive affect and reduced social isolation and that this relationship exists above and beyond the effects of time spent in conversation, time spent with close others, and experiential diversity. We also identified personality-based predictors of social diversity, finding that emotional stability was predictive of social diversity during the COVID pandemic, but that extraversion was the strongest predictor after the pandemic. We also found that the relationship between social diversity and well-being was mediated by participants\u27 perceptions of their personal social networks’ expansiveness. All together, we demonstrate that the people who surround us profoundly mold our behavior and psychology
Mechanisms of Macrophage Metabolic Activation and Paracrine WNT Signaling in the Pathogenesis of Systemic Sclerosis
No full textSystemic sclerosis (SSc) is a chronic autoimmune disease of unknown etiology. We have previously shown that macrophages (MØs) play a critical role in its pathogenesis; however, the mechanisms driving MØ activation in SSc remain poorly understood. This dissertation investigates the molecular signaling pathways and biochemical mechanisms underlying profibrotic MØ activity in SSc.
First, I identify a novel MØ-specific effect of mycophenolate mofetil (MMF), the standard-of-care treatment for SSc. While MMF is a well characterized lymphostatic, my findings demonstrate that its active metabolite, mycophenolic acid (MPA), is also capable of directly inhibiting myeloid viability and profibrotic MØ activation by suppressing de novo purine synthesis. These findings are novel, and suggest that the efficacy of MMF in SSc patients may result in part from its direct effect on myeloid cells.
Additionally, I characterize WNT5A as a key mediator of MØ-fibroblast crosstalk in SSc. Recombinant WNT5A induces profibrotic MØ activation through pSTAT3 signaling, while fibroblast-derived exosomal WNT5A modulates MØ oxidative metabolism via FZD5 signaling to promote profibrotic function. MØ-specific inhibition of oxidative metabolism or FZD5 was sufficient to block MØ-mediated activation of SSc fibroblasts in 2D culture models of SSc skin. These findings suggest that metabolic reprogramming is central to SSc MØ pathology.
Overall, this work highlights aberrant MØ metabolism as a driver of SSc pathogenesis and suggests that targeting MØ bioenergetics, including purine metabolism and noncanonical WNT-mediated metabolic signaling, may represent novel therapeutic avenues for SSc treatment
The Effects of Pandemic School Closures on Standardized Testing Outcomes: Evidence From Connecticut
Full text linkDuring the Covid-19 pandemic, schools around the nation closed to prevent the virus’s spread. Following these closures, students’ performance on standardized exams worsened. Using a two-way fixed effect model with district and year fixed effects, I examine if the use of non-traditional schooling modes (i.e. hybrid and virtual) during the pandemic is associated with lower ELA and Math proficiency rates in Connecticut public schools. I find that Connecticut public school districts’ Math and ELA pass rates on the annual Smarter Balanced exam significantly decreased as they used higher levels of virtual and hybrid education. The magnitude of this decrease was higher for the math subject test compared to the ELA test. Lastly, these results hold when weighting observations by enrollment
The methane clumped isotope signatures of the acetoclastic and methylotrophic methanogenesis pathways
Full text linkMethane is a potent greenhouse gas and critical energy source for the global economy. Atmospheric methane concentrations play a critical role in global warming, while mitigating methane emissions may provide route to short-term reduction of warming effects. In order to track rates and sources of emissions, it is first necessary to understand the sources and sinks of methane; yet, there are challenges in distinguishing abiotic (water/rock), thermogenic, and biogenic methane. Traditional approaches of using the bulk isotopes of carbon (δ13C) and hydrogen (δD) of methane have limitations due to overlapping signatures and sensitivity to substrate isotope composition. In the last decade, multiply-substituted or “clumped” isotopologues of methane (13CH3D and 12CH2D2) have emerged as additional tools to track methane producing and consuming reactions. It is estimated that biological sources produce more than two thirds of global methane emissions. However, there is still a relatively poor understanding of how biological processes affect clumped isotope fractionation. Here, I investigate how methanogen strain, substrate, and temperature influence methane bulk and clumped isotope signatures. I cultured strains Methanosarcina barkeri and Methanosarcina acetivorans on either acetate, methanol, or monomethylamine (MMA) at either 25°C or 35°C and measured the bulk and clumped isotope compositions of product methane produced over time. I find that the bulk isotope values can reach enriched signatures typically associated with abiotic processes, but herein reflecting closed system distillation. The carbon isotope fractionation factors also show high variance compared to previous literature, reinforcing the limitations of relying on bulk isotopes alone to determine methane origin. Importantly, I contribute new clumped isotope measurements of methane derived from acetate and MMA. These data suggest that methanogenesis pathway, rather than temperature or strain, exerts a dominant control on clumped isotope composition. Overall, my findings can help reveal trends in environmental methane data that have complex formation histories. This study has implications for interpreting methane cycling in the environment and understanding the future impact of methane emissions on Earth’s climate
A Pigment-Inclusive, Bilayered, Tissue-Engineered Skin Substitute as a Model for Autologous Skin Grafting
No full textCurrent standard-of-care for large skin defects involves autologous grafting of skin from another site on the patient’s body. This then leaves the patient with multiple sites of surgery and, while this method achieves closure and restores barrier function, scar tissue formation can result in contracture along with loss of appropriate function and appearance. This may result in patient disfigurement secondary to poor restoration of native color, texture, and mechanical properties of the newly “healed” skin at both the donor and recipient graft sites.
Tissue engineered skin substitutes (TESSs) have been explored as an alternative to skin grafting, providing a method to augment wound healing and minimize scarring. Various cell-free and cellularized TESSs have been studied in the literature; however, none have utilized a composite, bilayered electrospun/hydrogel construct with patient-derived cells to achieve accurate pigmentation that is consistent with native patient tissue.
The research described in this thesis works toward addressing this clinical need. Here we present:
1. Characterization and biocompatibility assessment of a bilayered construct
2. Isolation of patient-derived melanocytes, keratinocytes, and fibroblasts
3. Construction of a preliminary cell-seeded, bilayered TESS model that demonstrates proof-of-concept for a pigmented, patient-derived TESS
Ongoing work is directed towards utilizing this model to seed patient-derived epidermal (keratinocytes and melanocytes) and dermal (fibroblasts) cells within the bilayered construct to recapitulate the patient’s native pigmentation and texture, providing an alternative to tissue grafting
Explorations of Amplified Feedback in Quantum Circuits
Full text linkThe Josephson Traveling Wave Parametric Amplifier (TWPA) has emerged as a key technology for high-fidelity qubit readout in superconducting quantum computing. By leveraging the nonlinear inductance of an array of Josephson Junctions, the TWPA enables broadband, near-quantum-limited amplification with minimal added noise, significantly improving the signal-to-noise ratio in qubit measurements. Unlike traditional resonant parametric amplifiers, which suffer from bandwidth constraints, the traveling wave design of the TWPA allows for wideband operation, making it particularly suited for multiplexed readout of both simple qubits and large-scale quantum processors.
In this thesis, we explore how the TWPA can be integrated into a feedback loop with a 3D microwave cavity acting as a parametrically driven nonlinear oscillator. Rather than focusing on amplification alone, we use the TWPA to stabilize the photon number in the oscillator by providing directional gain and engineered dissipation. This configuration enables preparation of non-classical states of the cavity, with the loop dynamics supporting strong squeezing and nonlinear interactions reminiscent of Kerr-like behavior. We have estimated the system to have a Kerr nonlinearity of around 1.3 MHz, much higher than non-loop experiments which measure nonlinearity on the order of tens of kilohertz.
We use Wigner tomography to reconstruct the quantum states generated in the oscillator. Unlike many traditional approaches, we do not rely on an ancillary probe (transmon) qubit to extract aspects of the state like the photon number parity; instead, we read out the cavity state directly. While this method simplifies the experimental setup, it is inefficient, as it requires repeated measurements over many quadrature angles to reconstruct the full Wigner function, and there is a significant amount of dissipation in the loop. This inefficiency leads to slower data acquisition and increased susceptibility to decoherence and drift during measurement. A potential solution is to use a higher-quality cavity with reduced photon loss, which would improve coherence and stability over the tomography timescale. Alternatively, coupling our setup to a transmon qubit in future designs could enable fast, parity-sensitive measurements and more efficient quantum state reconstruction
Monte Carlo Stellar Evolution Models and Their Application on Globular Clusters
Full text linkStellar evolution models serve as fundamental tools in astronomy, essential for interpreting the interiors and evolutionary stages of stars, and have wide applications in astronomy, from searching for exoplanets to estimating the age of the universe. This thesis explores the inherent uncertainties arising from the complex physics used in stellar evolution models. By adopting a Monte Carlo approach to vary over 20 key stellar evolution parameters, we quantify their impact on model predictions, highlighting substantial influences on age, luminosity, and temperature. Through a detailed analysis of Milky Way globular clusters (GCs), this work rigorously investigates the uncertainties inherent in stellar models. Using high-precision photometric data from the Hubble Space Telescope Advanced Camera for Surveys, we apply innovative statistical methods to fully utilize information contained in the color-magnitude diagram to achieve robust and precise absolute age determinations. Our results, validated through comparisons with detached eclipsing binaries and calibration stars, yield a pioneering absolute age-metallicity relation for Galactic GCs, consistent with cosmological age constraints from Planck observations. Recognizing the limitations posed by traditional grid-based stellar evolution databases, this thesis also introduces the Dartmouth Stellar Evolution Emulator (DSEE). Leveraging advanced machine learning techniques, particularly normalizing flows, DSEE efficiently emulates continuous stellar evolution models trained on an unprecedentedly extensive and comprehensive dataset comprising millions of evolved stellar models. This emulator achieves rapid, high-precision interpolation and extrapolation of stellar evolutionary models across extensive parameter spaces, setting a new unified benchmark for stellar evolution models. Ultimately, this work advances our capacity to exploit large observational datasets, with potential for transformative applications in stellar populations, exoplanet characterization, galaxy evolution, and cosmology