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Restoring Faith in the Promise of Healing: Affirmative Presence as a Care Ethic for Post-Covid Medical Distrust
No full textThis study draws on both an affirmative biopolitical framework and a pastoral framework to propose an effective way of navigating medical distrust from communities found online and throughout the world. I engage with Roberto Esposito’s theorization of ‘community’ and ‘immunity’ in states of exceptional crises to introduce a critique of biopolitical governance by emerging online communities. Through a case study analyzing coded Reddit thread posts worldwide, I explore how these communities perceive and react to the COVID-19 response efforts by the medical field. In giving voice to these perspectives and offering a responsive care ethic of affirmative presence, I argue that restoring faith in the promise of medicine is not only possible, but practically achievable – in both the clinic and the public sphere. In my work, I hope to contribute to improved healthcare communication, management, and administration methods, as well as highlight the utility of affirmative presence in addressing the various forms of skepticism and distrust of conventional healthcare
Large-scale atomistic simulation at near-quantum accuracy with equivariant machine learning
No full textSince the first digital computers were built, scientists have programmed them to make concrete predictions about real-world systems by numerically solving the relevant physical laws that govern the behavior of those systems. Many of these efforts have focused on simulating the behavior of the atoms that make up materials and chemicals.
As computational power has grown, these simulations have shifted from using simple qualitative models of idealized interatomic interactions to sophisticated techniques for solving the quantum physics of realistic systems of electrons and atomic nuclei. These first-principles models offer a remarkable degree of accuracy and predictive power, but their computational cost also imposes severe limitations on the kinds of length- and time-scales that can be simulated.
Machine learning models of interatomic interactions---called machine learning interatomic potentials (MLIPs)---can be used as fast approximations of these accurate but costly quantum calculations. In principle, MLIPs can enable much longer and larger simulations to be run at near-quantum accuracy.
This thesis presents algorithms and implementations that can realize this promise through "equivariant" machine learning techniques that leverage the symmetries of the underlying physics. The first half discusses the equivariant MLIP NequIP, with its significantly improved data efficiency, accuracy, generalization, and robustness. The second half presents the development of the novel Allegro architecture. Allegro makes it possible to scale up equivariant machine learning: this ability is demonstrated through comprehensive performance experiments that apply a powerful Allegro model trained on a large dataset of more than 1M quantum calculations to biomolecular systems of realistic sizes of up to 44M atoms while taking advantage of up to 5120 GPUs
Modulators of sensitivity to KDM5 inhibition in basal breast cancer
No full textWe previously identified KDM5B, encoding a histone H3 lysine 4 (H3K4) demethylase, as an oncogene in luminal breast cancer associated with endocrine resistance. Here we describe that KDM5A is frequently amplified and overexpressed in basal breast tumors and is associated with chemotherapy resistance. To better understand the biological relevance of KDM5 activity in basal breast cancer, we performed whole-genome CRISPR knockout viability screens in a KDM5A- amplified basal breast cancer line -/+ KDM5 inhibition (KDM5i). We found that loss of the transcription factor ZBTB7A and core SAGA complex increased sensitivity to KDM5i, whereas knockout of RHO-GTPases led to resistance. Integrated ChIP-seq and RNA-seq analyses revealed that ZBTB7A and KDM5s co-localize at promoters with high H3K4me3 signal and that half of KDM5A binding is dependent on ZBTB7A. This loss in KDM5A binding upon ZBTB7A knockout is associated with decreased gene expression, indicating a role in gene activation at these sites. Interestingly, ZBTB7A is associated with both gene activation and repression depending on whether it binds to promoter or non-promoter regions, respectively. ZBTB7A knockout has a pleiotropic effect on the transcriptional response to KDM5i, in which it modulates the KDM5i- induced innate immune signaling and NF-kB target gene expression programs. Additionally, ZBTB7A knockout and KDM5i coordinate to alter cell state, in which KDM5i leads to a loss in basal-like gene expression and ZBTB7A knockout induces mesenchymal-like gene expression. Together, this work furthers our understanding of KDM5-mediated gene regulation in basal breast cancer and identifies key pathways that mediate sensitivity to KDM5 inhibition, such as the ZBTB7A transcription factor, SAGA complex, and RHO-GTPases.Medical SciencesMedical Science
The Compositional Poetics of the Book of Isaiah’s Nations Cycle: Recursive Symmetry and Analogy in Isaiah 13–27
No full textIn this dissertation, I seek to understand and elucidate the organizing principles and argument of the collection of prophecies about foreign nations in Isaiah 13–27. The study is oriented toward the compositional poetics of the collection, the way in which scribal tradents fashioned discrete and diverse oracles into a literary collection. The dissertation foregrounds the nature and function of literary structure in the collection. To signal this object of study, I propose a change in terminology from speaking of the Oracles against the Nations, which implies a focus on prophetic speech rather than prophetic literature, to the Nations Cycle. I argue that the book of Isaiah’s Nations Cycle is organized according to the principle of recursive symmetry, expressed particularly through concentric triads at multiple textual levels. The symmetrical patterning of the cycle’s structure invites a comparative reading strategy in which individual passages are read on analogy with others. This is essential for understanding the cycle’s arguments regarding the relationship between the past and the future and between Judah and the nations.
In the introductory chapter, I survey the history of scholarship on the Oracles against the Nations in general. I then articulate my own approach by defining the object of study as the Nations Cycle and describing the methodology of compositional poetics. I conclude the introduction by surveying previous studies of the shape and/or shaping of Isaiah 13–27. Chapters 2–4 constitute a close reading of the three units that comprise Isaiah 13–20 (13–14, 15–18, 19–20). The fifth chapter builds on chapters 2–4 and explores concurrent macrostructures in an ever-widening scope, beginning with Isaiah 13–20, expanding to Isaiah 13–23, and concluding with Isaiah 13–27 as a whole. The structures and the reading strategies they imply are foundational for tracing the vision of the Nations Cycle of the book of Isaiah regarding the future of the nations and the analogical relationship between the Assyrian crisis and the day of Yhwh
Homeward Bound: How Migrants Seek Out Familiar Climates
No full textThis paper introduces the concept of “climate matching” as a driver of migration and establishes several new results. First, we show that climate strongly predicts the spatial distribution of immigrants in the US, both historically (1880) and more recently (2015), whereby movers select destinations with climates similar to their place of origin. Second, we analyze historical flows of German, Norwegian, and domestic migrants in the US and document that climate sorting also holds within countries. Third, we exploit variation in the long-run change in average US climate from 1900 to 2019 and find that migration increased more between locations whose climate converged. Fourth, we verify that results are not driven by the persistence of ethnic networks or other confounders, and provide evidence for two complementary mechanisms: climate-specific human capital and climate as amenity. Fifth, we back out the value of climate similarity by: i) exploiting the Homestead Act, a historical policy that changed relative land prices; and, ii) examining the relationship between climate mismatch and mortality. Finally, we project how climate change shapes the geography of US population growth by altering migration patterns, both historically and into the 21st century.Version of Recor
Contextual Learning on Graphs for Precision Medicine
No full textPrecision medicine requires reasoning over interconnected data across multiple modalities to tailor medical decisions based on the context of individual patients. Graphs---or networks---are universal descriptors for systems of interacting elements, and deep learning on biomedical graphs has facilitated advancements in medicine, including accelerated disease gene prioritization and drug target identification. However, existing graph-based models are context-free: unable to adjust their outputs based on the contexts in which they operate. This PhD dissertation innovates two fundamental contextual learning algorithms, SHEPHERD and PINNACLE, to tackle medical questions for which patient and cell type contexts, respectively, are important. SHEPHERD addresses the challenge of low sample sizes among rare diseases by infusing patient data with external biomedical knowledge. It considers individual patients as unique subgraphs in a rare disease knowledge graph to learn patient-specific contexts derived from relationships such as genotype-phenotype and disease-gene associations, phenotype ontology, and genetic pathways. SHEPHERD's contextualized patient representations are optimized for multi-faceted rare disease diagnosis: performing causal gene discovery, retrieving "patients-like-me" with the same causal gene or disease, and providing interpretable characterizations of novel disease presentations. PINNACLE leverages cell-type-specific gene expression as well as cellular and tissue organization to resolve the role of a protein depending on the cell type context. It generates unique protein representations for every cell type context using cell-type-specific protein interaction networks constructed from single-cell transcriptomic atlases, and enforces the global organization of these representations with a metagraph of cell type communication and tissue hierarchy. PINNACLE's context-aware protein representations enable the analysis of drug effects across cell type contexts and the prediction of therapeutic targets in a cell-type-specific manner. Overall, this PhD dissertation pioneers the development of contextualized models to empower precision medicine, from rare disease diagnosis to drug discovery.Medical SciencesMedical Science
THE RELATIONSHIP BETWEEN SLEEP AND SALIVARY AND SERUM IMMUNOMETABOLIC BIOMARKERS
No full textBackground and Objectives: Sleep plays a vital role in maintaining health and well-being. Poor sleep contributes to several adverse health outcomes due to hormonal and metabolic disorders that alters molecular processes that drive cellular immune activation and induce the secretion of immunometabolic biomarkers. Most studies use blood samples for their investigations, in this study we tested both salivary and serum samples to study the effect of sleep variables on immunometabolic biomarkers. There is established evidence on the relationship between poor sleep behavior and inflammatory and metabolic biomarkers. In this study, our aims were to determine the relationship between sleep timing (bedtime), sleep debt and social jetlag with levels of nine salivary and serum immunometabolic biomarkers (C-Reactive Protein (CRP), Interleukin-6 (IL-6), Interleukin-8 (IL-8), Interleuken-10 (IL-10), Vascular Endothelial Growth Factor (VEGF), Monocyte Chemoattractant Protein-1 (MCP-1), adiponectin, leptin, and insulin). Materials and Methodology: Data were collected from 352 adolescents, 16-18 years old and enrolled in Kuwait’s public high schools. Nine biomarkers were measured in saliva and serum supernatants using multiplex magnetic bead panels on a Luminex 200™ system. For statistical analyses, we conducted mixed effect linear regression models to account for school variable as a random effect to study the association between biomarkers as our outcome variables and sleep variables as our exposure variables, controlling for confounders (age, sex, medical history, nap, screen time, negative dietary habits). We also investigated the role of Nap, BMI and Waist circumference (WC) in these associations. Results: After controlling for confounding variables in all our models we found that (bedtime) sleep timing was statistically significantly associated with an overall elevation for salivary IL-6 and serum (CRP, IL-6, IL-10, MCP-1 and insulin) and reduction in serum IL-8 biomarkers. BMI and WC played a major rule in attenuating some of these associations as confounding variables, for example, leptin biomarker with bedtime. In addition, we displayed a mediation effect of BMI for the associations between bedtime and serum (CRP, IL-6, and insulin). Adolescents who exhibited catch-up sleep on free days (weekends) showed a statistically significant positive association with salivary IL-6 and serum (CRP and IL-6) and negative association with serum IL-8 biomarkers. For students with social jetlag (SJL), only salivary biomarkers (IL-6, VEGF and leptin) showed a statistically significant reduction with increase in SJL. Conclusion: This change of biomarker levels and directions with sleep timing behaviors support the hypothesis that late bedtime and disrupted circadian rhythm promote local and systemic inflammation that can trigger chronic metabolic diseases. Our findings highlight the importance of late bedtime as a strong predictor of oral and systemic inflammation, indexed by salivary and serum biomarkers
Defining the Regulation and Function of Hepatic mTORC1 Signaling
No full textThe protein kinase complex mechanistic target of rapamycin complex 1 (mTORC1) is at the center of an anabolic signaling pathway that is dysregulated in metabolic disease, as well as in human cancers and tumor syndromes. Mediators and targets of mTORC1 signaling have been characterized in cell culture models, but the regulation and function of mTORC1 in response to specific physiological cues and in specific tissue contexts has not been thoroughly examined. Utilizing knowledge gained from in vitro studies, we have developed a novel genetic mouse model to investigate the specific role of insulin-PI3K-AKT signaling in the physiological regulation and function of hepatic mTORC1.
By analyzing mTORC1 signaling in liver tissue under a variety of experimental paradigms, including insulin and glucose treatment and fasting and refeeding, we demonstrated that the dominant mechanism of hepatic mTORC1 induction by insulin is AKT-mediated phosphorylation of the TSC complex, a critical negative regulator of mTORC1. However, we found that hepatic mTORC1 can be induced by feeding in a manner independent of this insulin-mediated mechanism. Specifically, we found that dietary protein was critical for hepatic mTORC1 activation by feeding and dominant over insulin signaling, and we present evidence that amino acids are sufficient to induce mTORC1 in primary hepatocytes. The reliance on insulin signaling for the activation of hepatic mTORC1 was increased in response to feeding a high carbohydrate, low protein diet. These data suggest a model whereby dietary composition impacts the contribution of insulin in the activation of mTORC1 in the liver. Strikingly, insulin-induced hepatic mTORC1 played a minimal role in impaired glucose homeostasis in diet-induced obese mice.
To provide insight into the physiological functions of hepatic mTORC1, we defined novel targets of mTOR kinase activity through liver quantitative phosphoproteomics. We identified growth factor receptor bound protein 7 (Grb7) as a novel target of insulin-induced mTOR kinase activity. Our data suggests Grb7 inhibits insulin signaling in primary mouse hepatocytes and human hepatoma cells and may mediate mTORC1-driven feedback inhibition of insulin signaling, with a potential role in hepatic insulin resistance.
Collectively, this study defines a hierarchy of signals regulating physiological mTORC1 signaling for the first time and identifies a set of physiological mTOR substrates that can be exploited to modulate mTORC1 function in physiological or pathological settings
Dynamic computational phenotyping of human cognition
No full textComputational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioral data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. In order to identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception, and decision making. To examine the influence of state effects, each week participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.Version of Recor
Bioinspired Slippery Solutions for Marine Fouling Prevention
No full textRecent decades have seen an uptake in the development of non-toxic fouling prevention treatments, especially for marine applications. This trend is driven by technological innovation as a deeper understanding of material and adhesion science allows for the design of more effective repellent surfaces. Additionally, an ever-tighter regulatory framework makes the state-of-the-art biocidal coating technologies ever less desirable.
A major challenge for the development of novel, non-toxic adhesion prevention coatings is the size of the available design space, with an innumerable number of possible coating systems and materials combinations. As there is still no broad mechanistic understanding for the specific factors that govern a coatings biofouling prevention performance, the enormous number of possible approaches creates the need for a reliable screening and down-selection tool.
In the framework of this thesis a simple bioassay, based on the adhesion of biofilms of the green algae Chlamydomonas reinhardtii, is developed to explore the design space of the Slippery Liquid Infused Porous Surfaces (SLIPS) as biofouling prevention treatments. Using this novel screening tool, we were able to identify several highly promising SLIPS coating solutions out of dozens of candidate systems. The initial screening study results were followed up with an extensive series of field studies which did not only demonstrate that specific SLIPS treatments have the biofouling prevention performance and longevity to function in the ocean for up to two years, but also allowed to investigate the mechanisms behind their efficacy.
The potential that is offered by effective bioassay application for the identification of promising coating systems, the unraveling of the underlying performance mechanisms and the detection of interesting material properties and phenomena is demonstrated and discussed in this thesis