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Dead Stones and Dumb Trees: Old English Logics of Dispossession
No full textThis dissertation brings Indigenous critical theory to bear on Old English archives. In so doing, it reconciles a longstanding problem faced by scholars of Old English: of how to recognize the subject of Old English poetry by methods that withstand interpellation into the modern episteme.
There is a double motion to this approach. On the one hand, I show how the logic of Old English poetic affect becomes clearer when analyzed through Indigenous conceptions of sovereignty as a matrix or root system rather an individual right. Against the nationalist formulation of subjecthood, in which “recognition” catalyzes sovereignty, Indigenous critics assert a sovereignty generated by affective attachment. While “recognition” implies identification with an intelligible Other, affective bonds are formed by the ongoing invocation of the Other, which does not depend on an intelligible response. By this analytic, we can read the Old English poetics of binding as a function of, rather than constraint on, sovereignty. I show how this function of poetics is at work in the lyrics of the Exeter Book (Chapter 1) and the medicinal remedies of the Lacnunga (Chapter 2), which invocatively bind “I” with “you” to formulate sovereign homes and bodies.
On the other hand, however, I argue that Old English poetic manuscripts enact a colonial conversion of the affective subject. Since there is no distinction between theory and poetry in the Old English archive, and since this archive actively teaches its reader how to read, I argue that we can detect in it “logics of dispossession” in the sense of geographer Mishuana Goeman when she writes that all colonial regimes “obfuscate the power of the land to possess us” (“Land as Life,” 79). Put another way, Old English poetic manuscripts perform a conversion that I call mind-binding: they reify intelligibility as the mark of responsiveness and teach readers to bind themselves to singular truth.
As the Exeter lyrics motivate the practice of binding oneself to an eternal home with God, they turn their readers away from “unfamiliar” bird-song and wolf-calls (Chapter 1). Meanwhile, the dialogic poem Solomon and Saturn I purports to make the healing power of plants intelligible by alphabetic code and thereby teaches that invocation of the plants is superfluous to herbal medicine (Chapter 2). Finally, the Old English Daniel stages the heathen king Nebuchadnezzar’s recognition of himself in a magnificent world-tree. The text expressly fixes a typological reading of the tree as a sign of the true cross, yet simultaneously reveals a concern that readers might become bound to the tree qua person (Chapter 3).
This “new” approach to premodern affect is, more precisely, a reconfiguration of very old interpretive methods. In a coda, I foreground the presence of the Sámi in the Old English and Old Norse archives, which troubles the historical narrative of first contact between Indigenous and European peoples as a modern event. The project of reading European medieval archives with Indigenous critical theory is not anachronistic, but rather urges alternative historiographies by making legible latent authorities and sovereignties
Integrative systems analysis of the influence of tissue niches on human immune cells
No full textA preponderance of the body’s immune cells resides and functions within tissue, rather than in circulation. Murine models suggest the tissue microenvironment drives tissue-specific molecular profiles in immune cells; however, the restricted availability of healthy human tissue samples for research has limited our understanding of how various lineages and subsets of human immune cells are impacted by their tissue contexts. Here, we employ a systems approach to profile RNA, surface protein and immune receptor expression of >1.5 million immune cells isolated from human organ donor blood, lymphoid tissues, and mucosal barriers. Using these data, we define transcriptomic signatures of tissue-residency, identify tissue- and subset-specific trends with age, and probe hypotheses regarding how tissue-resident populations are maintained throughout life.
While transcriptomic data has proven invaluable for relatively unbiased profiling of heterogeneous cellular states, transcriptomic profiles cannot always delineate the immunological subsets defined by decades of surface proteome profiling. Multimodal sequencing technology, such as Cellular Indexing of Transcriptomes and Epitopes (CITE)-seq enables simultaneous profiling of single-cell transcriptomes and surface proteomes, offering potential to greatly improve cell type annotation accuracy. Here, we propose and evaluate algorithmic advancements in batch-correction (landmark registration) and joint-classification (Multi-Modal Classifier Hierarchy; MMoCHi) of these data, together facilitating accurate, granular annotation of immune subsets, such as discriminating CD4+ and CD8+ memory T cell subsets, various populations of cytotoxic lymphocytes, and rare subsets of dendritic cells.
We next explored molecular signatures associated with tissue-specific populations to understand the impacts of tissue on human immune cells. This high-quality annotation allowed us to distinguish between molecular programs underlying tissue-adaptation in immune cells and tissue-differences brought about by shifts in immune subset composition. We identify signatures of tissue-residency affecting expression of adhesion molecules, metabolism pathways, as well as soluble mediators and receptors of immune signaling. By relating these tissue-specific signatures across these diverse immune lineages (T cells, NK cells, ILCs, B cells, macrophages, etc.), or specific subsets (e.g., memory T cell subsets, niche-specific populations of macrophages) we identify broadly shared molecular programs suggestive of conserved regulatory mechanisms driving tissue-specific processes. As a consequence of this tissue-residency, we identified that age-associated effects were manifested in specific lineages and sites as revealed by macrophages in mucosal sites, B cells in lymphoid organs, and specific T and NK cell subsets across blood and tissues.
Finally, in addition to evaluating these molecular signatures, we also leveraged molecular profiling and immune receptor sequencing to understand maintenance of two major tissue-resident immune lineages, macrophages and T cells. Examining macrophage heterogeneity in tissues, we identify evidence for a dynamic equilibrium of monocyte-replenishment and self-renewal at homeostasis. T cell compartmentalization across tissues indicated that localized expansion and in situ differentiation of resident memory T cell subsets occur upon antigen encounter in barrier tissues.
Together, our results reveal tissue-specific signatures of immune homeostasis throughout the body, providing a molecular basis for immune variation from which to better understand immune pathologies across the human lifespan
From Promise to Performance: Reforming Blended Finance for Scale
No full textBlended finance has the potential to help close the sustainable development financing gap. Strategically combining public and philanthropic capital to unlock private investment, it has proven to be an effective tool to de-risk impact investments. But without bold, structural reform, it risks remaining a niche tool – promising in theory, underperforming in practice. Drawing on over 65 expert interviews and in-depth analysis, this report from the Columbia Center on Sustainable Investment (CCSI) maps the systemic barriers preventing blended finance from operating at scale, and outlines a practical path forward. With bold leadership and coordinated reform, blended finance can become a mainstream channel for sustainable investment at scale
-adic L-functions for -ordinary Hida families on unitary groups
No full textWe construct a -adic L-function for -ordinary Hida families of cuspidal automorphic representations on a unitary group G. The main new idea of our work is to incorporate the theory of Schneider-Zink types for the Levi quotient of , to allow for the possibility of higher ramification at primes dividing p, into the study of (-adic) modular forms and automorphic representations on G. For instance, we describe the local structure of such a -ordinary automorphic representation at using these types, allowing us to analyze the geometry of -ordinary Hida families.
Furthermore, these types play a crucial role in the construction of certain Siegel Eisenstein series designed to be compatible with such Hida families in two specific ways : Their Fourier coefficients can be p-adically interpolated into a -adic Eisenstein measure on +1 variables and, via the doubling method of Garrett and Piatetski--Shapiro-Rallis, the corresponding zeta integrals yield special values of standard -functions. Here, is the rank of the Levi quotient of . Lastly, the doubling method is reinterpreted algebraically as a pairing between modular forms on , whose nebentype are types, and viewed as the evaluation of our -adic -function at classical points of a -ordinary Hida family
Understanding Child Skill Development by Examining Longitudinal Educational Intervention Impacts
No full textA central focus of much psychological research is identifying the extent to which childhood experiences and skills shape variation in life course trajectories. Grounded in developmental theory, educational interventions are often designed with the goal of changing long-term functioning through targeting children’s skills. Contrary to theory, growing evidence from the intervention evaluation literature suggests that (1) initial program impacts on children's cognitive skills fade and, (2) interventions can yield long-run impacts on adult outcomes nonetheless. In this dissertation, I examined the longitudinal impacts of educational interventions. Through this work, I aimed to progress our understanding of when and how exogenous changes to children’s skills shape subsequent development.
Using meta-analytic techniques and three datasets, I observed that short-term fadeout is ubiquitous; initial intervention impacts faded considerably in the years after interventions ended. Regardless, interventions generated long-run impacts on adult outcomes. Social-emotional skill persistence appeared unlikely to explain these dynamics; intervention impacts on social-emotional skills faded in the years after interventions ended. I found promising support for the hypotheses proposed by the Large Interconnected Network Theory (LINT), suggesting that skill-to-skill transfer processes may underlie the emergence of network-level intervention impacts that ultimately impact adult functioning. I discussed these findings in the context of existing conceptualizations of skill building and suggested future directions for research
System-Level Design in the Era of Brain-Computer Interfaces
No full textBrain-computer interfaces (BCIs) hold significant potential to not only transform healthcare, by enabling individuals with neurological impairments to interact with the world, but also to enhance computational models and technology, opening new avenues for human-computer interaction and advancing the capabilities of artificial intelligence (AI) and machine learning (ML). However, BCI systems must overcome several obstacles to transition from research lab experiments into real-world applications.
One major challenge is the need for modern BCI systems to be mobile, wireless, and include an implanted system-on-chip (SoC) that interfaces with the brain. This requirement introduces safety concerns and imposes physical constraints on BCI systems. A second challenge is that BCI systems must integrate an ever-increasing number of sensors to support large-scale data acquisition (DAQ), enabling a better understanding of the brain. Consequently, these systems must handle growing volumes of neural data, pushing wireless transmission data rates, power consumption, and overall feasibility of implant-based BCI systems to their limits. To address these challenges, one potential solution is to integrate computation for BCI applications in specialized hardware close to the neural data source, a common approach in I/O-bound systems. Nonetheless, this solution introduces its own set of complexities, as BCI applications often rely on computationally intensive machine-learning models that must be optimized in order to be executed in real time, while meeting the physical constraints of implant-based BCI systems.
For this reason, understanding the full structure of the BCI system, including all of its components, is key for future development in the BCI field. In addition to the implanted SoC that transmits the neural data, implant-based BCI systems also incorporate a mobile, wearable SoC that receives the transmitted data. This wearable SoC must meet the physical constraints of wearable devices, which are less stringent than those of implanted devices. As long as transmission data rates between the two SoCs are sufficient, the wearable SoC can execute BCI applications either fully or in collaboration with the implanted SoC, enabling the design of practical BCI systems capable of running meaningful applications for public use.
The field of BCI is multidisciplinary and holds transformative potential for both medicine and technology. To fully realize this potential, computer architects and engineers must understand the general system-level structure of BCI systems, as well as their constraints and components. Thus, my thesis is that to unlock the full potential of the BCI field, BCI system development must be properly defined and standardized, with a clear target BCI system, an understanding of its constraints, and a distinction between three overlapping time domains that emphasize different levels of research and development, with progress continuing concurrently in each domain.
I support this thesis by presenting a three-dimensional approach to restructuring system-level design in the BCI field. Each dimension corresponds to a time domain, representing an independent area of research and development: (1) Pre-BCI – designing implant-based BCI systems that support large-scale data acquisition and wireless communication; (2) Intra-BCI – developing methodologies to integrate real-time computation and complete BCI applications into BCI systems; and (3) Post-BCI – specializing computational data flows to interact seamlessly with the biological brain.
To guide system development in these domains, I discuss the unique constraints of modern BCI systems, relying on trends in machine learning and neural interface design. I define the self-contained BCI system as our target system, capable of large-scale DAQ, high-throughput data transmission and application-level computation. I also explore potential future trends, including the use of brain-inspired neuromorphic computing as an intermediate step in the computational flow. Most importantly, I demonstrate how development can proceed concurrently across all three dimensions to overcome the key obstacles in BCI system development and accelerate the realization of fully functional BCI systems for real-world BCI applications
On the Hodge Structures of Global Smoothings of Normal Crossing Varieties
No full textLet : ⭢ be a one-parameter semistable degeneration of -dimensional compact complex manifolds. Assume that each component of the central fiber ₀ is Kähler. Then, we provide a criterion for a general fiber to satisfy the ̄-lemma and a formula to compute the Hodge index on the middle cohomology of the general fiber in terms of the topological conditions/invariants on the central fiber.
We apply our theorem to several examples, including the global smoothing of -fold ODPs, Hashimoto-Sano's non-Kähler Calabi-Yau threefolds, and Sano's non-Kähler Calabi-Yau -folds. To deal with the last example, we also prove a Lefschetz-type theorem for the cohomology of the fiber product of two Lefschetz fibrations over ¹ with disjoint critical locus
Mutation Agnostic Genome Editing Strategies for Treating Neuroretinal Degenerations
No full textInherited retinal disorders (IRDs) represent a collection of highly genetically heterogeneous neurodegenerative disorders including retinitis pigmentosa that ultimately result in blindness. While there has been substantial preclinical progress in precision medicines for more common forms of retinal degenerations, therapeutic development for rare mutations unfortunately remains handicapped by burdensome developmental costs. Currently, there is only one FDA approved treatment for retinitis pigmentosa, which addresses a highly narrow subset of patients with mutations specifically in the RPE65 gene. For the remaining >100,000 patients in the United States alone, there exists no treatment.
To develop commercially viable therapeutics with the potential for real world applications, we have focused our research efforts on two key fronts: 1) a metabolic reprogramming strategy aimed at treating the universal metabolic dysregulation that underlines disease progression and occurs regardless of genetic background and 2) an epidemiologically informed gene editing approach capable of addressing the most common forms of retinitis pigmentosa.
Naturally, the retina’s metabolic ecosystem relies on the highly glycolytic photoreceptors (PRs) to produce a carbon substrate that the supportive RPE can consume as an alternative carbon source to glucose. However, during retinal degeneration, these cells can become metabolically uncoupled, resulting in the RPE consuming glucose that is essential for PR survival and effectively starving the distal neuroretina.
To drive glycolysis in the PRs and provide more lactate to the RPE, thus restoring metabolic homeostasis, we have targeted hypoxia-inducible factors (HIFs)—well characterized regulators of cell metabolism that are abundantly expressed in the retina. By ablating their negatively regulating binding partner, Von Hippel-Lindau (VHL), specifically in the photoreceptors of disease-modeling mice, we demonstrate a HIF-dependent metabolic reprogramming that drives glycolysis, preserves cells, and improves functionality in a preclinical model of retinitis pigmentosa. Furthermore, we uncovered evidence that suggests a crosstalk mechanism from PRs to RPE where genetically unperturbed RPE cells experience altered metabolism that is essential for therapeutic effect.
While metabolic reprogramming has the potential to treat a wide range of diseases and thus overcome the financial barriers of rare disease drug development, these approaches do not address the underlying genetic defects causing degeneration and may not be curative. To this end, we have developed an AAV-based, therapeutic prime editing strategy generalized to treat multiple mutations in the most implicated IRD gene—rhodopsin (RHO). By capitalizing on a highly heterozygous SNP found within the 5’ untranslated region (UTR), we can selectively silence the mutant allele via a prime editing mediated deletion, leaving the wildtype allele intact to support basic biological function. To assess our optimized therapeutic in vivo, we report a humanized mouse model of retinitis pigmentosa possessing the targeted SNP in addition to the clinically relevant, heterozygous P347L dominant negative mutation that phenocopies clinical presentation of the disease.
Our findings demonstrate two unique and commercially viable therapeutic strategies that can address a wide base of retinitis pigmentosa patients for whom there is no current treatment, while also providing a framework for therapeutics capable of expanding to the neurodegenerations of the CNS and beyond
Methods to Reduce Selection and Confounding Bias in Observational and Clinical Studies
No full textThis dissertation explores statistical methods to enhance the robustness and reliability of inference in observational and clinical studies, with a focus on mitigating selection and confounding biases.
First, we address challenges in survey inference due to sampling bias introduced by disruptions from the COVID-19 pandemic. Using data from an ongoing survey study of people living with HIV in New York City, we assess the impact of recruitment bias on estimates of HIV viral suppression and mental and physical wellness. To correct for deviations in sample representation we propose a model-based approach for survey inference when only the marginal distributions of the population characteristics referred to in this text as an adaptation of multilevel regression with poststratification (MRP) approach. Our findings demonstrate that the MRP adaptation approach improves inference by adjusting for selection bias introduced during the COVID-19 pandemic.
Next, we investigate a causal inference challenge involving extreme positivity violations in an observational study of prenatal anesthesia exposure. The goal is to estimate the causal effect of mothers receiving anesthesia during pregnancy on the diagnosis of disruptive or internalizing behavioral disorders (DIBD) in children, separate from the confounding effects of surgery. Since anesthesia and surgery are deterministically linked, standard methods fail to disentangle their effects. To address this, we employ the separable effects model of Robins and Richardson (2010), which isolates the direct effect of anesthesia by blocking pathways through variables that fully mediate the effect of surgery on DIBD.
Finally, we examine generalizability and transportability methods in a multi-study, multi-outcome setting, focusing on medication for opioid use disorder (MOUD) and its impact on family/social status. In this application, only one clinical trial directly measured the outcome of interest, limiting statistical power. We develop semi-parametric estimation techniques that leverage additional post-exposure variable proxies across harmonized clinical trials from the National Institute on Drug Abuse (NIDA) Clinical Trials Network (CTN). These methods leverage additional post-exposure variables to help gain insight into previously underpowered outcomes.
Together, these projects contribute to the development of statistical methods that improve the validity and interpretability of findings in complex real-world data settings, advancing both survey methodology and causal inference in clinical and observational research
Building Resilience to Hate in K-12 Schools and Universities across the United States of America
No full textWith radicalization and hate-fueled violence becoming an increasingly visible and urgent problem across the United States of America, the Department of Homeland Security has proposed a whole-of-society approach to violence prevention. While this dissertation supports the espousal of a whole-of-society approach to violence prevention, the argument lays necessary emphasis on K-12 schools and universities as vital sites for the development of our collective resilience to hate to fortify democracy in America.
Using Amra Sabic-El-Rayess' Educational Displacement Model of radicalization as the theoretical foundation informing the analysis herein, I outline the strengths of the model and illustrate the promising results of its ongoing application, which I co-lead, through federally funded research and programming in K-12 schools and universities.
In addition, I utilize case studies involving formerly radicalized students to exemplify the way Educational Displacement, if unaddressed, can lead to radicalization and, in turn, hate-fueled violence. Following this, I propose a Resilience to Hate ("R2H") framework constituted by key civic practices such as non-violence and approaches to teaching and learning such as a connection-first pedagogy to guide the design and responsiveness of learning environments in classrooms, schools, and communities.
The fundamental lesson emergent from this dissertation is that it is through the empowerment of all key stakeholders in K-12 schools and universities that the twinned aims of preventing Educational Displacement in learning environments and fortifying democracy as a way of life in the United States of America becomes an actionable vision rather than a fragile hope