FAS Theses and Dissertations

Selective 1,2-hydrosilylation of 1,3-dienes is a challenging problem to solve for transition metal catalysis. Butadiene, specifically, would be a useful substrate because 3-butenylsilane products have promise as superior coupling reagents for hybrid organic/inorganic materials synthesis. In this thesis, we describe the first selective 1,2-hydrosilylation of conjugated dienes, including butadiene.

During the 1824 Confederation of the Equator revolt in northeastern Brazil, a middle-class junta rose to power and waged a war against the monarchy in Rio, journalists participating in creating pedagogical ideological pamphlets, actively engaging in battles, and governing until the revolt was suppressed after a few months. Despite the short duration of this rebel government, the Confederation of the Equator and its ideology have been addressed in cultural production by authors and painters associated with different political movements in the nineteenth, twentieth and early twenty-first centuries, including Jõao Cabral de Melo Neto, Carlos Queiroz Telles, Antônio Parreiras, Gustavo Barroso, Cícero Dias, Murillo La Greca, Heloneida Studart, José Pimentel and Câmara Cascudo. This thesis discusses the history of the event, the content of the message of these thinkers, the spread of their ideas over the course of the nineteenth century and the deployment of the Confederation and its ideas by cultural producers in order to prove that this event has had an impact on the arts in Brazil.

This thesis studies several aspects of the arithmetic of elliptic curves. In particular, we explore the prediction of the Birch and Swinnerton-Dyer conjecture when the 2-Selmer group has rank one.

For certain elliptic curves $E/\mathbb{Q}: y^2=F(x)$ with additive reduction at 2, we determine their 2-Selmer ranks in terms of the 2-rank of the class group of the cubic field $L=\mathbb{Q}[x]/F(x)$. We then interpret this result as a mod 2 congruence between the Hasse-Weil $L$-function of $E$ and a degree two Artin $L$-function associated to the cubic field $L$.

When the class number of $L$ is odd, the Birch and Swinnerton-Dyer conjecture predicts that $E$ should have rank one over $\mathbb{Q}(i)$. To construct such a point on $E$, we study Heegner points on Shimura curves with non-maximal level at a prime $p$ ramified in the quaternion algebra (in the special case when $p = 2$). These curves have a $p$-adic uniformization by a tame \etale covering of Drinfeld's $p$-adic half-plane. We use the covering to describe the geometry of their reduction mod $p$ and compute the N'eron model of their Jacobians.

For certain elliptic curves $E/\mathbb{Q}$ with good or multiplicative reduction at 2, we study their 2-Selmer groups over imaginary quadratic fields using the method of level raising of modular forms mod $p=2$. We prove a parity result (predicted by the Birch and Swinnerton-Dyer conjecture) for 2-Selmer ranks. We also show that there is an obstruction for lowering the 2-Selmer ranks, revealing a different phenomenon compared to odd $p$.

Isumi lacks the trappings that are often associated with ‘a city’. No high-rises adorn its skyline, no central business district hums 24/7 with energy generated by foot traffic and commerce, and no cultural hub serves as a common ground for discourse among locals and visitors. What it possesses—be it a library, a public hall, a government office…—it often possesses in threes. For, just a few years ago, Isumi was not a city, but rather three towns, towns that were themselves made up of many yet smaller settlements consolidated over the preceding decades. Despite this diffuse and disparate disposition, the people of Isumi are united in an ongoing process, the reimaging of the area post-gappei (municipal merger) and its transformation into a quasi-bedroom community for those wishing to maintain one foot in the metropole of Tokyo, and another in the rural periphery of contemporary Japan. For locals and newcomers alike, this process, while implying integration and linkage, also connotes a collision of history, economy and culture that necessitates a (re)imagining of community. Conducted over two years spent working alongside local, non-profit organizations whose stated mission was the fostering of ‘community’, the research upon which this study is based illustrates cases in which urban to rural migration has prompted bidirectional experiences of marginalization and integration. In an effort to reshape experience in a manner that serves both personal and communal goals, participation in local social movements tasked with ‘revitalizing community’ has grown and become a key factor in determining this rural city’s bricolage community identity and future.

A valuable testing ground for exploring new features of string theory and quantum gravity is the duality between c=1 string theory and the c=1 matrix quantum mechanics, which has been explored for over 30 years. In this thesis, we use efficient numerical techniques to evaluate Liouville correlation functions and to compute scattering amplitudes of closed strings in string perturbation theory, resolving several previous puzzles in the perturbative duality dictionary. In addition to this, we present a worldsheet formalism for computing all non-perturbative corrections to these amplitudes, which require including disconnected worldsheet diagrams with ZZ-instanton boundary conditions. By matching these contributions against the dual matrix model, we propose the non-perturbative completion of c=1 string theory. We further extend the duality by introducing new degrees of freedom known as long strings, which from the worldsheet description are given by open strings on FZZT branes in a limit where the FZZT branes decouple and the open strings are infinitely stretched. The first few tree-level scattering amplitudes of these objects are computed, and show an impressive agreement with the corresponding amplitudes computed in the dual U(N) matrix quantum mechanics, where long strings are given by states in non-singlet sectors of the U(N) symmetry group.

The ability to manufacture human tissues that replicate the spatial, mechano-chemical, and temporal aspects of biological tissues would enable myriad applications, including drug screening, disease modeling, and tissue repair and regeneration. However, given the complexity of human tissues, this is a daunting challenge. Current biofabrication methods are unable to fully recapitulate the form and function of human tissues, which are composed of multiple cell types, extracellular matrices, and pervasive vasculature.

My Ph.D. thesis focuses on advancing the capabilities of human tissue fabrication. Specifically, we demonstrate a multimaterial bioprinting method capable of producing 1D, 2D, and 3D vascularized tissue constructs by co-printing ECM, cell-laden, and fugitive inks. After these heterogeneous tissue constructs are printed and infilled with ECM, they are cooled to 4˚C to remove the fugitive ink leaving behind a pervasive network that is subsequently lined with endothelial cells. These constructs are ~ 1 mm in thickness and can be sustained for up to 14 days via rocking-based flow through their vasculature. We then created a new extracellular matrix that enabled the fabrication of tissues that exceed 1 cm in thickness that are perfused on a microfluidic chip for long time periods (> 6 weeks). To demonstrate functionality, growth factors are perfused via the vasculature to differentiate stem cells toward an osteogenic lineage in situ. Finally, we created renal proximal tubules (a sub-unit of kidney tissue) by this approach. Specifically, we constructed 3D tubules circumscribed by renal proximal tubule epithelial cells (PTECs). The PTECs form confluent, leak-tight epithelial monolayers that exhibit primary cilia and expresses Na+/K+ ATPase, Aquaporin 1, and K-cadherin. The combination of 3D geometry and on-chip perfusable nature gives rise to enhanced, polarized PTEC phenotypes that develop an enhanced brush border, basement membrane protein deposition, basolateral interdigitations, enhanced cell height, megalin expression, and albumin uptake relative to 2D controls.

In summary, this multimaterial 3D bioprinting platform enables production of engineered human tissue constructs in which multiple cell types and vasculature are programmably placed within extracellular matrices. These 3D tissues may find potential applications in drug screening, disease models, and ultimately, tissue engineering and regenerative medicine.

Soft robotics offer advantages over their rigid counterparts due to the intrinsic softness of their consisting materials, soft robotic matter. When equipped with programmable shape morphing and controllable function, soft robotics are best qualified for interaction with delicate objects, exploration of unknown terrains, and large, impact-resistant deformations. Towards this goal, new materials and fabrication methods are needed to create actuators with programmable shape- morphing behavior akin to human muscles. Liquid crystal elastomers (LCEs) are soft materials comprised of anisotropic liquid crystal mesogen molecules, which when aligned, give rise to reversible contraction with high energy density when heated above their nematic-to-isotropic transition temperature (TNI). However, the ability to produce LCE actuators with programmed director alignment in arbitrary, bulk forms is a grand challenge. The focus of my Ph.D. thesis is to create programmable LCE actuators through the integration of design, synthesis, and multi-material 3D printing methods. Towards this goal, solvent-free, oligomeric LCE inks were synthesized that incorporate rigid mesogens along their backbone as well as photopolymerizable groups at the chain ends. By varying the molecular composition of these oligomeric species, LCE inks with the appropriate viscoelastic response were designed for high operating temperature-direct ink writing (HOT-DIW), an extrusion-based 3D printing method. By tailoring polymer backbone and crosslinking chemistries of our LCE inks, their TNI could be varied from 92°C to 127°C after printing and UV cross-linking, and enable custom thermal response. We further demonstrated that patterned LCEs with programmed director alignment along the print path were produced when printing in the nematic phase. These 3D LCEs exhibit large reversible contractility and high specific energy density. Our integrated approach allows for prescribed LCE alignment in arbitrary geometric motifs. Building on this seminal advance, we created untethered soft robotic matter that repeatedly shape-morphs and self-propels in response to thermal stimuli through passive control. Specifically, we designed and printed active LCE hinges with orthogonal director alignment that interconnect rigid polymeric tiles. These hinges can be programmed as mountain or valley folds to produce reversible active origami structures. Moreover, in a single structure, we programmed hinges made of LCEs with disparate TNI to enable sequential folding and demonstrated untethered, reversible sequential folding in soft, active origami for the first time. We further demonstrated a self- compacting prism with a modular geometric locking mechanism capable of sequential folding with three temperature-specific, stable configurations. To enable the informed design of untethered robotic matter, LCE hinge bending angle and torque can be prescribed by geometry and LCE chemistry. We then exploited their exemplary performance by programming LCE hinges into the “rollbot”, an exemplar self-propelling structure with passive control. Specifically, we designed a pentagonal prism with low TNI LCE hinges and propellers with high TNI LCE hinges, informed by our torque and bending angle characterization, enabling reversible reconfiguration and self- propulsion across a heated surface. To expand upon these capabilities, ewe developed a novel method of 3D printing aligned LCE filaments with embedded, coaxial liquid metal by co-extrusion of LCE and liquid metal through a core-shell nozzle. Our innervated LCE (iLCE) fibers are electrothermally heated well above TNI with programmable and predictable heat generation through the core of the filament, which resulted in large, prescriptible contractile strains akin to those of our neat 3D printed LCEs. The iLCE fibers enable self-sensing of actuation through the resulting change of resistance with respect to actuation strain, where a change of resistance is directly predictable from strain. Moreover, our iLCEs exhibited reliable reversible actuation and considerable work output, which combined with self-sensing capabilities allows for closed loop control. Specifically, our actuators automatically reach target resistance and strain values rapidly and repeatedly despite large bias load perturbations. As a final demonstration, we patterned iLCEs with a spiral printpath to demonstrate programmable 3D shape morphing. Analogous to iLCE fibers, these spiral iLCEs were electrothermally heated, exhibited self-sensing, and were regulated with closed loop control. In summary, we have developed a new platform for creating soft robotic matter through the design, synthesis, and assembly of LCE inks, which can be seamlessly integrated with structural, sensing, and functional materials. Our platform may be harnessed for applications including soft robotics, reconfigurable electronics, adaptable structures, and well beyond.

We attack the question of BPS occupancy in a wide class of 4d N = 2 quantum field theories. We first review the Seiberg-Witten approach to finding the low energy Wilsonian effective action actions of such theories. In particular, we analyze the case of Gaiotto theories, which provide a large number of non-trivial examples in a unified framework. We then turn to understanding the massive BPS spectrum of such theories, and in particular their relation to BPS quivers. We present a purely 4d characterization of BPS quivers, and explain how a quiver's representation theory encodes the solution to the BPS occupancy problem. Next, we derive a so called mutation method, based on exploiting quiver dualities, to solve the quiver's representation theory. This method makes previously intractable calculations nearly trivial in many examples. As a particular highlight, we apply our methods to understand strongly coupled chambers in ADE SYM gauge theories with matter. Following this, we turn to the general story of quivers for theories of the Gaiotto class. We present a geometric approach to attaining quivers for the rank 2 theories, leading to a very elegant solution which includes a specification of quiver superpotentials. Finally, we solve these theories by an unrelated method based on gauging flavor symmetries in their various dual weakly coupled Lagrangian descriptions. After seeing that this method agrees in the rank 2 case, we will apply our new approach to the case of rank n.

“A Moraliste Moment” examines eighteenth-century French moraliste writing: aphoristic texts studying human nature and social and political behavior. While scholars have usually treated the late seventeenth century as the zenith of moraliste writing’s quality and influence in France, I explore why there was a second flourishing of moraliste writing during the eighteenth century, and what this moment’s significance was for our understanding of moral thought in the French Enlightenment. I study a corpus of moraliste texts by four authors spanning from the 1740s to the 1820s: the Marquis de Vauvenargues; Nicolas Chamfort; Antoine de Rivarol; and Joseph Joubert. I further compare these texts to the works of seventeenth-century moralistes, as well as to the those of eighteenth-century philosophes. I argue that the eighteenth-century moralistes chose to return to the aphorism genre, creating a new “moraliste moment,” in order to elaborate an alternative ethical perspective to that of the Enlightenment philosophes. I contend that, influenced by their seventeenth-century predecessors, eighteenth-century moralistes use the genre to present a negative moral critique of their society, and to espouse a contrarian, pessimistic view of human nature and of the possibility of political reform that contrasts with the greater optimism of Enlightenment philosophes. In Chapter One I argue that eighteenth-century moralistes chose to write in this genre in order to use its concern with the limitations of moral language, and its valorization of clarity, novelty, and piquancy, to focus on negative moral critiques of their society’s vices and hypocrisies, without offering positive, didactic ethical guidance. Chapter Two seeks to understand what eighteenth-century moralistes believe is the source of ethical ideas. While seventeenth-century moralistes shared similar theological and philosophical beliefs, eighteenth-century moralistes adopt a broad range of positions in these domains. However, I contend that eighteenth-century moralistes are ultimately united by their belief that the sources of humans’ ethical impulses lie in nature, the passions, and the heart. In Chapter Three, I examine the eighteenth-century moralistes’ ideas about the morality of revolution. I maintain that despite the eighteenth-century moralistes’ extremely varied political positions, all are undergirded by the same political impulse: each moraliste claims the mantle of the lone, principled contrarian, steadfastly defending the morally correct point of view against a more powerful opponent, be this the monarch or the mass of the people. Each eighteenth-century moraliste believes he represents reason, justice, and the righteous minority, even as each moraliste has different ideas about the moral permissibility of revolt against the government. This dissertation thus argues that eighteenth-century moraliste writing elaborates an alternative form and content of ethical writing in the Enlightenment era. The moralistes counter the philosophes’ optimism about the goodness of human nature and the possibility of political reform by offering a negative, critical moral discourse that attacks their society’s hypocrisy and that claims that only a few individuals of strong character may retain their moral clarity amid society’s corruption.

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This dissertation studies the inception of nonprofit microcredit institutions in the late Middle Ages and their evolution into instruments of early modern state finance. These institutions, called monti di pietà, were initially designed by Franciscan friars in fifteenth-century Italy as sources of low-interest, collateralized loans to the poor and promoted as an alternative to Jewish usury. As they spread throughout Catholic Europe, the Mediterranean, and Latin America, they adopted a wide array of new functions, including managing interest-bearing savings accounts, issuing bonds, and lending to states. Gradually, they became so central to the functioning of the local financial systems that many of them were co-opted by the increasingly centralized and powerful states in which they were based. This dissertation explains how this process unfolded between the fifteenth and the eighteenth centuries, with occasional incursions into the nineteenth century.

This dissertation traces the professional and intellectual development of Warren Brodey (b. 1924). Trained as a psychiatrist and psychoanalyst, Brodey was one of the pioneers of family therapy but eventually abandoned his promising career in Georgetown to become an MIT-affiliated cybernetician, a hippie inventor, and, eventually, a Maoist political activist (first in Norway and subsequently in China). Brodey’s unlikely transition attests to a greater porousness between disciplines and domains in the “closed-world” America of the 1960s – a porousness which proved congruent with the multi-faceted, ambiguous, and open-ended nature of the cybernetics project itself. Brodey’s unusual trajectory reveals the subtle yet important role played by those on the margins of the cybernetics movement. Throughout his transition, Brodey built on insights gained in his medical practice to articulate and operationalize concepts such as “intelligent environments” and “soft architecture” which would later be taken up by architects, designers, ecologists, and adherents of the appropriate technology movement. Brodey’s initial background as a therapist endowed him with a particularly strong interest in the subjective and embodied dimensions of how humans experience technologies and are, in turn, shaped by them. Contrary to the established historiographic narrative that presents cybernetics and its conceptual apparatus of feedback loops, homeostasis, and control mechanisms as an attempt to disembody and virtualize human experience while subjecting it to a new rationality rooted in a quest for communicative objectivity, Brodey’s work reveals a desire to deploy that very conceptual apparatus to re-embody and enrich human perception, all the while questioning the discourse of expertise and objectivity prevalent among the more academically inclined practitioners of cybernetics.

This dissertation develops and applies Bayesian methods to model the multi-dimensional structure of social determinants of health (SDoH) at the neighborhood level. It progresses from applied probabilistic clustering of SDoH to identify profiles and their association with health outcomes, to methodological advances in mixture modeling for proportional data with feature selection, and finally to an integrative probabilistic framework that captures domain-specific signals that highlight neighborhoods of actionable priorities for resource allocation or targeted interventions. Together, these contributions provide a flexible and interpretable tools for understanding complex social environments and informing targeted public health interventions.

Chapter 1 introduces the motivation for modeling SDoH as a multidimensional construct and provides an overview of the proposed methodological framework.

Chapter 2 examines the relationship between neighborhood conditions and endometrial cancer care using a model-based clustering approach. A Bayesian multivariate Bernoulli mixture model is applied to American Community Survey data to identify distinct neighborhood SDoH profiles across Massachusetts. These profiles are then linked to patient-level data from the Massachusetts Cancer Registry using Bayesian logistic regression to assess associations with guideline-concordant care. The results demonstrate how multidimensional neighborhood profiles can serve as interpretable exposures, revealing disparities in care and highlighting opportunities for targeted interventions.

Chapter 3 introduces SAMBA-Mix a salient multivariate Beta mixture model for clustering proportional SDoH data. The model leverages a multivariate beta likelihood to preserve the natural scale of bounded variables and incorporates feature saliency to identify determinants that drive cluster separation. This approach improves interpretability and robustness by allowing cluster formation to be guided by the most informative variables while reducing the influence of irrelevant features. Using data from the U.S. Agency for Healthcare Research and Quality (AHRQ) SDoH database, we demonstrate the utility of the model for deriving and characterizing neighborhood-level SDoH profiles in Massachusetts.

Chapter 4 presents BMISS (Bayesian Multi-dimensional Integrative Social Structure), an
unsupervised hierarchical framework for modeling neighborhood SDoH. Rather than collapsing information into a single index, BMISS produces domain-specific probability profiles that characterize domain-specific priority signals capturing patterns of co-occurring social determinants relevant for targeted resource allocation. By jointly modeling feature-level relationships and latent dependence structures, the framework provides a more refined and interpretable representation of neighborhood conditions, supporting more informed and context-specific decision-making. In an application to data from the AHRQ SDoH database, the model identifies distinct multidimensional profiles of priority signals across neighborhoods in Massachusetts and New York.

Chapter 5 provides concluding remarks and avenues for future research.

Reinforcement learning algorithms have found utility in a number of digital interventions, such as in mobile health (mHealth), wherein N users are followed and sequentially treated over T timesteps with the aim of optimizing a health outcome. In the precision medicine paradigm, the aim of the intervention (algorithm) is to learn a personalized, optimal treatment policy for each of the N users that optimizes the health outcome of interest. Learning such a policy can be prohibitively slow due to sparse and noisy data for each user; in practice, users can disengage from the intervention before a good policy is learned.

To address this problem, we aim to pool information across users to speed learning while preserving individualized treatment. However, pooling data across dissimilar users can lead to disastrous treatment decisions and outcomes. These challenges underscore the need for rigorous, model-based approaches to defining similarity across users before pooling.

We model the N-user mHealth setting with a contextual bandit environment and formalize similarity across users using a Dirichlet Process mixture model. We offer a variant of blocked Gibbs sampling to infer clusters among users; we further propose DPMM-Pooling, an integrated intervention algorithm to learn clusters among users and share data within clusters, in order to speed the learning of optimal and individualized treatment policies.

We evaluate DPMM-Pooling in simulated mHealth settings, across a range of parameters such as the number of ground-truth clusters, noise in observed outcomes, and the time of pooling. We find and analyze key bias-variance tradeoffs in pooling pertaining to parameters of the environment. We also find that DPMM-Pooling is relatively robust to likely forms of mild and extreme model misspecification. Finally, we outline the implications of our results for the design of pooling-based mHealth interventions in practice.

Gender dysphoria is a mental health condition characterized by clinically significant distress or impairment associated with marked gender incongruence, a feeling of discordance between one’s gender identity and biological sex. Gender incongruence and dysphoria prevalence have risen substantially in recent years, particularly in adolescent females. My dissertation aims to understand the etiology of these phenomena through a historical overview of clinical gender dysphoria, case studies of gender reassignment in populations with disorders of sex development, theories of the neurobiology of sexual development, and four empirical studies examining gender incongruence across early adolescent development. My empirical chapters make use of the Adolescent Brain Cognitive Development (ABCD) Study sample, a demographically diverse longitudinal sample of 11,864 youths in the United States followed from ages 9-10 years in 2016-2018 to ages 14-15 years in 2021-2023. Chapter 1 reports developmental trajectories of self- and parent-reported gender incongruence, gender expression, and gender identity across five timepoints. While gender expression was stable with age, gender incongruence and transgender or nonbinary (TNB) self-identification increased with age, particularly in females. At baseline, 0.5% of males and 1.1% of females identified as TNB. By Wave 5, 1.2% of males and 9.6% of females identified as TNB. Females were also more likely to report gender incongruence and elevated gender nonconformity (i.e., sex-atypical gender expression). TNB youth reported elevated gender nonconformity across all ages, but showed increased gender incongruence only at later timepoints. Parents reported lower gender incongruence across all measures. Chapter 2 makes use of ABCD’s large sample of 1,970 same-sex twins to estimate the heritability of gender incongruence. Gender incongruence was more common in females, and concordance was higher in female twin pairs than male twin pairs. Binary probandwise concordance for TNB gender identity was 46% among monozygotic pairs compared to 13% among dizygotic pairs. ACE models revealed that genetic factors explained 47% of the variance of gender incongruence, with the remaining variance explained by non-shared environmental factors. Estimates had large standard errors, and thus high uncertainty, but suggest a large genetic contribution to gender incongruence in adolescents. Chapter 3 examined concurrent and longitudinal cross-lagged associations between self-reported gender incongruence and a range of biopsychosocial variables known or hypothesized to be associated with gender dysphoria: internalizing symptoms, autism spectrum traits, sexual orientation, pubertal timing, body mass index, family conflict, peer victimization, cyberbullying, screen time, and number of TNB friends. All predictors of interest were significantly associated with gender incongruence except family conflict and number of TNB friends. Cross-lagged models tested competing hypotheses between the theory of rapid-onset gender dysphoria (ROGD) and minority stress theory on the directionality of these associations. Higher internalizing symptoms and screen time predicted later gender incongruence, but not vice versa, in females across all timepoints, while other associations were mixed. Longitudinal results largely favored predictions made by ROGD over minority stress theory. Chapter 4 aimed to document predictors of longitudinal persistence and desistance of TNB gender identity within the subsample of 670 TNB adolescents, and to test whether a three-cluster solution of latent gender incongruence classes resembled a hypothesized typology distinguishing between classical gender dysphoria, autogynephilia, and ROGD. Finite mixture regression identified three latent profiles of gender incongruence, but these appeared to describe mild, moderate, and pronounced subtypes of ROGD rather than the triune typology. Participants in the “mild” cluster were exclusively nonbinary (as opposed to transgender-identified), had a 100% longitudinal desistance rate, sex-typical gender expression, and low levels of internalizing symptoms and social stress. Conversely, participants in the “pronounced” cluster had the highest rates of transgender identification, nonheterosexual orientation, internalizing symptoms, and social stress, and the highest longitudinal persistence rates at 45%. Biopsychosocial correlates of gender incongruence largely did not predict longitudinal TNB persistence or desistance; the strongest predictors were gender identity and sexual orientation. I conclude by proposing a new biopsychosocial model of adolescent gender dysphoria, synthesizing these findings on adolescent gender incongruence with existing clinical and neurobiological theories of gender dysphoria. This model accounts for recent rises in nonbinary identification among adolescents without gender dysphoria symptoms by introducing a subtype of gender incongruence not captured in existing typologies, characterized by identity exploration, low persistence rates, and sex-typical gender expression and psychosocial adjustment. Sociocultural and neuroendocrine pathways to gender incongruence are discussed.

Despite over a century of work on hemoglobinopathies, among them sickle cell disease, a complete understanding of hemoglobin switching and factors regulating hemoglobin expression remains incomplete even as the first CRISPR/Cas9 gene therapy has obtained approvals for the treatment of transfusion-dependent β-thalassemia and sickle cell disease. DDX6, an RNA helicase and P-body nucleator associated with miRNA-mediated RNA decay and was identified as an HbF repressor in a genome-wide CRISPR screen performed in our lab. I performed a CRISPR tiling screen on DDX6 using SpRY-ABE8e-Cas9 to achieve near base-pair resolution and identify a well-tolerated variant that induces fetal hemoglobin expression, useful to enable the further study of the role of DDX6 in regulation of fetal hemoglobin. I studied factors regulating the heterochronically silenced m6A RNA-binding protein IGF2BP1 which has been identified as a positive regulator of fetal hemoglobin expression by suppressing BCL11A expression. In contrast to this observation, post-transcriptional control by IGF2BP1 generally acts through improving the translation and mRNA stability of IGF2BP1 target mRNAs in an m6A-dependent manner. To better characterize the IGF2BP1-mediated mechanism responsible for control of fetal hemoglobin expression, I assessed whether BCL11A was required for IGF2BP1-mediated regulation of fetal hemoglobin and discovered a direct BCL11A-independent mechanism. I developed a luminescent transgenic reporter to disentangle the BCL11A¬¬-dependent and newly identified BCL11A-independent mechanisms and used it discovered that IGF2BP1 directly supports the translation of HBG mRNA in a manner requiring an intact stop codon-proximal m6A motif. In addition to this BCL11A-independent mechanism, I identified that the BCL11A-dependent mechanism relies on IGF2BP1-mediated post-transcriptional control of HIC2, which indirectly supports fetal hemoglobin expression via suppression of BCL11A. This work establishes a strategy for near-base pair resolution CRISPR base editing screening and validation of variants of interest in a novel fetal hemoglobin reporter cell line. This gene, DDX6, is likely involved in post-transcriptional control of factors regulating fetal hemoglobin. Additionally, I characterize a heretofore misunderstood BCL11A-dependent mechanism for IGF2BP1 in regulating fetal hemoglobin expression while simultaneously identifying a novel direct effect of IGF2BP1 on the fetal hemoglobin RNA, a strategy with potential to be exploited for the improved expression of lentiviral gene therapy vectors delivering HBB for sickle cell disease or transfusion dependent β-thalassemia.

Depth from defocus is a prominent area of computer vision research, yet unlike many other areas of the field, there is no dedicated dataset or general library for training and testing depth from defocus applications. As a result, researchers have had to manually capture data, which takes a tremendous amount of patience and precision, or they have resorted to approximations of real-world data using self-designed simulation pipelines---this simulated data is often simplistic or lacking in physical accuracy, which can adversely affect the performance of depth from defocus algorithms in deployment.

This thesis proposes a new pipeline for generating simulated data for depth from defocus, one that uses physics-based rendering and has controllable defocus that can be calibrated to the optical dimensions of any physical system. This new pipeline promises to deliver all the efficiency of synthetic data generation, to generate data that is faithful to real-world physics, and to be generalizable across different depth from defocus applications while also maintaining flexibility in its specificity for different scene and object types.

Here, I explore the power of physics-based rendering for generating realistically defocused images and I present experiments and demonstrations to showcase its functionalities, capabilities, and limitations when it comes to generating synthetic data for depth from defocus research.

Understanding how brains evolve requires a system in which selective history, brain size and structure, skull morphology, and behavioral specialization can be measured directly, dissociated from one another, and compared across differentiable lineages. The domestic dog (Canis familiaris) provides exactly this. Through a combination of natural and artificial selection, dogs have evolved extraordinary variation in skull morphology, brain anatomy, and behavior within a single species, and they can be studied using the same neuroimaging methods applied to humans. In this dissertation, I use dogs as a living model system to test biological principles of brain evolution that are directly relevant to the human lineage. In Chapter 2, I show that variation in skull shape significantly predicts regional gray matter volume, demonstrating that selection on skull morphology can reshape brain organization. In Chapter 3, I characterize widespread neuroanatomical asymmetry in the canine brain and find that lateralization does not scale with brain size, suggesting it is not simply a byproduct of encephalization but may instead reflect selection on lateralized behaviors. In Chapter 4, I compare modern breed dogs, which were intensively selected for cooperative learning, with premodern dogs that underwent far less artificial selection. Modern breeds show cortical expansion linked to trainability, while premodern dogs show amygdala enlargement linked to fear. In Chapter 5, I extend this comparison to white matter and find that modern breeds exhibit greater fractional anisotropy, altered connectome topology, and fractional anisotropy patterns that predict trainability. In Chapter 6, I compare Labrador retrievers that vary in breeding lineage and training history and find evidence for experience-dependent plasticity in working dogs but no innate neuroanatomical differences between non-working dogs from different lineages, suggesting that selection may favor neuroplasticity itself rather than canalization of specific skills. Altogether, this dissertation demonstrates that even over short evolutionary timescales, selection can substantially reshape brain structure, and that the domestic dog offers a powerful window into the principles governing brain evolution in species that shares our anthropogenic niche.

A Capitalizing City charts the ways some people of African descent in Dar es Salaam, the commercial and industrial capital of Tanzania, have attempted, historically, to either integrate into the modern capitalist system or produce an alternative global economy through business, against the racial odds inherent in the world economic order. After the birth of global capitalism in the 16th century, the continent of Africa experienced the twin tragedies of slavery and colonialism, both of which limited the birth and growth of big black business worldwide. However, Africa’s entrepreneurial elite and their businesses managed to emerge during a highly exclusive first global economy of the long 19th century and some managed to endure the transition to – or even succeed in – a relatively inclusive second global economy of the long 20th century. The conventional story is that Africans were spectators in, or victims of, the rise of capitalism and spread of capitalist ventures. While acknowledging the role of capital in devaluing African labor through primitive accumulation, this dissertation also argues that there is a section of Africans – the entrepreneurial elite – who capitalized on both the colonial and socialist moments to become relatively successful. By first looking at how merchant capital penetrated the East African coast, the dissertation unpacks how capitalist relations between traders, financiers, laborers and administrators led to the construction of Dar es Salaam as a commercial and industrial capital of Tanzania after overtaking Bagamoyo and Zanzibar. As a capitalizing city, Dar es Salaam became the melting pot for the making of an African entrepreneurial elite who, in the process, also participated in its commercialization and industrialization. It is during this phase, the dissertation underscores, that they forged bourgeoisie sensibilities and embraced the culture of capitalism that seemed crucial for succeeding in big business. The dissertation also notes that the ascent of African intellectuals of capital, especially during the neoliberal transition from socialism to postsocialism, played a significant role in legitimizing capitalism in a country whose state had once eschewed privately-owned big business. By refocusing on Africans who proactively participated in the birth and growth of capitalism through business on the continent, this dissertation highlights how the emphasis on social histories of the marginalization of the downtrodden can overlook the plights and agencies of those deemed privileged. The history of capital in Africa’s commercial and industrial capitals, such as Dar es Salaam City, is not only about the African laboring masses; it is also about aspiring African captains of industry and commerce who had to contend with the intersectionality of class, race and gender to find a seat at the table of global capital.

There has been great clinical interest in the concept of ‘critical windows’ or ‘sensitive periods’ of environmental exposures. The concept of a critical window, defined formally in this dissertation, refers to a specific time period during which an individual is more susceptible to developing an outcome in response to a particular exposure than at other times. The statistical methods used to identify these critical windows have several limitations, and applied researchers are often limited to using methods developed for different research questions which can lead to bias, inflated Type I error rates, and low power. Studies on environmental exposures are almost always observational by necessity, and it remains an ongoing challenge to interpret results as the causal effect of intervening on the exposure rather than merely as an association between the exposure and the outcome. Because the methods currently in use have not been previously examined through a causal inference lens, results across studies are difficult to compare, even if the same covariates are used. This dissertation seeks to combine these two areas of interest by proposing a framework for the identification of critical windows from a causal inference perspective. Throughout this work, we demonstrate how different methods should be employed to answer subtly different research questions and compare our methods to existing approaches through simulations where appropriate. In Chapter 1, we introduce our novel flexible CAusaL Identification of Critical windOws - Modified Treatment Policy (CALICO-MTP) framework, extending previous work on using a dose modification scheme to estimate the causal effect of continuous exposures. We propose dividing the concept of critical window identification into three distinct research questions, each addressed with different approaches. These questions are: 1) Curve estimation: what does the exposure-outcome relationship look like over time? 2) Hypothesis testing: is there any time window during which there is an effect of intervening on the exposure? and 3) Window selection: after determining that there is a causal relationship, what is the critical window for that exposure? For the first question, we propose a curve estimation strategy to yield results similar to those of the commonly used distributed lag model (DLM). For the second, we propose estimating the effect of intervening on all biologically plausible windows and combining the p-values using the Aggregated Cauchy Association Test (ACAT), a p-value combination method that accounts for strong correlations between test statistics. For the third, we discuss strategies for selecting the window once the global null has been rejected. We apply these methods to a dataset from Beth Israel Deaconess Medical Center (BIDMC) and compare them to previous results regarding the effect of Nitrogen Dioxide (NO2) exposure on the 32-40 week fetal head circumference as measured by ultrasound45, and we present a novel visualization for the causal effect of intervening on time intervals.

In Chapter 2, we present a variant of this framework, CALICO-ADRF, that explicitly models nonlinear dose-response relationships by estimating the Average Dose Response Function (ADRF) for each time window. This nonlinear relationship is particularly relevant for environmental exposures such as metals, where some are necessary minerals at low exposures but act as toxins at high exposure levels, and temperature, which may exhibit a thresholding effect for certain outcomes. We use a scalar test statistic that is the integrated squared derivative of the estimated ADRF to perform global hypothesis testing with Type I error control and improved power compared to the methods of Chapter 1 for biologically-plausible nonlinear dose-response curves. We demonstrate these results looking at the effect of maternal prenatal temperature exposure and birthweight for full-term deliveries in the same BIDMC cohort.

In Chapter 3, we present a discussion of causal inference concepts specifically tailored to the methods most commonly used for time-varying environmental exposures, offering a novel perspec- tive for researchers. We present a framework through which the target estimand of different mod- eling approaches can be compared, improving the ability to draw meaningful and comparable conclusions across studies. We explore the different estimands that these methods target and illustrate when these estimands align or diverge depending on the underlying causal structure of the exposure. Finally, we provide guidance for researchers on how to appropriately align their methodological choices with their research questions.