VictorJuiz/thesis huggingface dataset:
Contrast agent diffusion as a CTa biomarker for articular cartilage health
Keywords: Mechanical Engineering.

Osteoarthritis (OA) is a degenerative joint disease that affects 12% of the adult population of the United States and costs the health care system 5-80 billion per year in direct and indirect costs. Currently, there is no cure for OA. One of the challenges of developing effective treatments for OA is that it cannot be detected early enough to intervene before irreversible soft tissue damage has occurred. In this dissertation, apparent diffusivity of a contrast agent measured using computed tomography arthrography (CTa) scans is introduced as a potential biomarker for early-stage cartilage degeneration. While in theory, apparent diffusivity will be elevated in cartilage with early signs of degradation compared to healthy cartilage, it is not a perfectly quantitative measurement. This is because cartilage responds to osmotic changes in its environment, so interstitial fluid flow is also occurring, which can accelerate or slow down solute transport. The first aim of this dissertation (Chapter 3) seeks to answer the question of how quantitative apparent diffusivity is by measuring it in enzymatically degraded cartilage explants and healthy, unaltered ones in the presence of different contrast agent concentrations and boundary conditions. Simulations were used to predict the effects of imaging protocol variation on diffusivity measurements with a large range of resolutions and scan times. Aims 2, 3, and 4 of this dissertation are steps towards translating this method for eventual in vivo use. In Aim 2 (Chapter 4), two different methods for making 3D voxel-wise apparent diffusivity measurements are presented. They are used to analyze a bovine explant dataset consisting of enzymatically degraded and unaltered cartilage samples to confirm that voxel-wise measurements show increases in apparent diffusivity in damaged tissue, like the bulk measurements did in Aim 1. Aim 3 (Chapter 5) details a method for identifying the cartilage surface in areas where it is in contact with cartilage or menisci, a step towards overcoming the challenge of accurate cartilage segmentation in CTa datasets. In Aim 4 (Chapter 6), simulations are used to study the effects of contrast agent solutions on cartilage mechanical properties. Solute size, bath ionic strength, and bath osmolarity are altered individually, which is not possible in the laboratory, and their effect on indentation force is quantified. The work presented in this dissertation could enable CTa-based detection of cartilage degeneration, expand access to early OA detection, and greatly increase treatment options in conjunction with the development of new interventions
Controlling the immunotransmitter cGAMP with chemical biology
Keywords: Chemistry.

cGAMP is a second messenger that mediates innate immune response to cancer, infection, and cellular damage. cGAMP synthesis is triggered by a danger signal common to all these pathologies, the presence of DNA in the cytosol. Downstream, cGAMP activates the Stimulator of Interferon Genes (STING) protein which eventually leads to the production of interferon to combat these threats. Originally, cGAMP was thought to function exclusively intracellularly, stuck inside the cell where it was produced. Then, the realization that cGAMP's only hydrolase ENPP1 was extracellular hinted that cGAMP, too, might travel outside of its originating cell. However, the field lacked tools to test this hypothesis. This dissertation builds chemical biology tools and uses them to define and control cGAMP's role as an extracellular immune signal, or immunotransmitter. Chapter 1 contextualizes the role of cGAMP as an immunotransmitter in terms of the entire cGAMP-STING pathway. Chapter 2 demonstrates that cGAMP is constantly exported from cancer cells and activates host anti-cancer immunity. Chapter 3 chronicles the development of best-in-class ENPP1 inhibitors that block extracellular cGAMP degradation, functioning as both chemical tools and investigational cancer drug candidates. Chapter 4 engineers an ENPP1-based tool that selectively enhances extracellular cGAMP in vivo and shows that cGAMP acts as an immunotransmitter in pathologies beyond cancer, including viral infection and cellular damage. Chapter 5 looks ahead to the trajectory of the cGAMP immunotransmitter field. This work pioneers cGAMP's extracellular immunotransmitter role in innate immunity and takes first steps to harness it therapeutically
Convex optimization and implicit differentiation methods for control and estimation
Keywords: Electrical Engineering.

This disseration covers five applications of convex optimization and implicit differentiation methods to the fields of control and estimation. In the first chapter, we consider the problem of efficiently computing the derivative of the solution map of a convex cone program, when it exists. In the second chapter, we consider the problem of fitting the parameters in a Kalman smoother to data. In the third chapter, we propose a method for tuning parameters in convex optimization-based control policies. In the fourth chapter, we consider the problem of predicting the covariance of a zero mean Gaussian vector, based on another feature vector. In the fifth and final chapter, we describe a method for fitting a Markov chain, with a state transition matrix that depends on a feature vector, to data that can include missing values
Convex optimization methods for adaptive radiation therapy
Keywords: Electrical Engineering.

Cancer radiation therapy involves fundamental tensions between tumor coverage and damage to surrounding healthy tissue. In this dissertation, we present an optimization-based framework for adaptive radiation treatment planning. We focus on two specific planning challenges: (1) satisfying dose-volume constraints and (2) handling nonlinear patient health dynamics. For each situation, we show how to formulate the treatment planning problem as a nonconvex optimization problem and obtain a good estimate of the solution by solving a series of convex approximations. We demonstrate the effectiveness of our method on several clinical examples. Finally, we release an open-source Python library that implements our method using generic convex solvers. The last part of this dissertation concerns applications of optimization beyond radiation therapy. We develop a domain-specific language (DSL) for formulating and solving a broad class of convex optimization problems. Then, we describe an implementation of our DSL in R, a popular programming language for statistical modeling. Our resulting software package, CVXR, allows users to construct optimization problems in a natural mathematical syntax. CVXR automatically verifies the problem's convexity and converts it into the standard form required by a specific solver. We illustrate CVXR's modeling framework with a variety of examples drawn from statistics, engineering, and radiation treatment planning
Cooperative terrain-relative navigation
Keywords: Aeronautics and Astronautics.

This thesis introduces a new method to improve localization performance for teams of vehicles navigating cooperatively. When fusing measurements between multiple vehicles, the structure of the cooperative navigation network inherently introduces correlation between them, causing many traditional filter architectures to overconverge and become inconsistent. The algorithm presented in this thesis addresses this correlation and properly fuses measurements, allowing improved performance over other existing methods while still guaranteeing consistency. When restricted to linear, Gaussian systems, the covariance recovers 99% of the performance of an ideal centralized filter in some tests. Additionally, a proof is presented to guarantee that the algorithm is consistent under standard Kalman filter assumptions. The algorithm is also extended to apply to nonlinear systems, losing the guarantees of consistency (as with all Kalman filters) but achieving good performance in practice. This allowed the method to be tested in a laboratory experiment with real-world sensors. Finally, this thesis further extends the algorithm to apply to non-parametric particle filters, allowing for full cooperative Terrain-Relative Navigation (TRN) with multi-modal position estimates. This is demonstrated in simulation, where cooperative TRN is shown to provide a 63% reduction in localization error over standard single-vehicle TRN for one example mission, reducing the average error from 23.7m to 8.7m for a vehicle over flat terrain. The cooperative TRN algorithm is also demonstrated using field data from a team of Long-Range Autonomous Underwater Vehicles in Monterey Bay. In offline testing, the cooperative TRN method was able to correctly find the position of a vehicle when its own individual TRN filter was unable to converge. This demonstrates that the cooperative TRN algorithm is effective with real-world robotic systems, increasing localization accuracy and enabling new missions involving navigation in flat, unmapped, or changed terrain
Corporate Power and the CEO Pay Ratio
Keywords: Public Policy Program
CEO Pay Ratio
CEO Compensation
Corporate governance
Executives > Salaries, etc.
Income distribution
Income inequality
Business dynamism
Employment concentration
Corporate power
Stanford University
Humanities and Sciences
Public Policy Program
Text
Thesis

The increase of the CEO pay ratio has been a concerning development. This increase has occurred during a period of industrial transformation, decreasing business dynamism, and growing market and employment concentration. The number of public firms is decreasing, but those at the top are increasing in size and represent a growing proportion of GDP. These changes are intrinsically linked to a rise in corporate power. Using a large panel dataset of US firms between 1992 and 2020, this study finds that these trends play a significant role in the growth of the CEO pay ratio. Specifically, the number of publicly listed firms is negatively linked with the CEO pay ratio, the firm’s employment share of its industry’s workforce is positively linked, and being among the largest firms by number of employees is positively linked. The impact of the latter on the CEO pay ratio has been increasing over time. While not every factor behind these trends is identified in this study, it offers insights into the potential determinants and the role of the CEO pay ratio as a driver of income inequality in the context of increased corporate power.
Corporeal Piety and Descended Icon: Constructing a New Understanding of the Narthex
Keywords: Narthex
corporeality
Theodore, Studites, Saint, 759-826
iconoclasm
monastic reformation
Middle Byzantine period
Hosios Loukas (Monastery : Voiōtia, Greece)
synergism
Ritual
mosaic
Text
Thesis

This thesis proposes a reappraisal of the narthex as an architectural unit with a unique decorative, functional, and ideological program that differs from the liturgically-oriented organization of the nave and the sanctuary. On the example of the Middle Byzantine monastic church of Hosios Loukas, the analysis shows how the narthex developed an original arsenal of representational practices in response to the theological legacies and practical demands of two major events of the period – the icon controversy and the monastic reform movement. A close reading of the primary sources (writings by iconophile authors and texts of monastic foundation documents) demonstrates how the values of corporeality, synergism, and empathy – while commonly omitted from the discussion of the Middle Byzantine theological climate – arose as guiding principles of the post-iconoclast approaches to the image-beholder interaction. Against this background, the narthex became uniquely suited to represent the humanity of Christ. Representations of Christ-as-Man drive pathways of relatability, compassion, and corporeality that legitimize the beholder as an equal participant in dialogue with the divine. Through a face to face dialogue predicated on the value of synergism, the monastic community engages in ritual performances that not simply imitate, but rather complete Biblical events of the surrounding pictorial program.
COVID-19 Quarantine Fatigue in California: How Does the Quarantine Fatigue Effect Differ by Income?
Keywords:  Public Policy Program
Stanford University
Humanities and Sciences
Public Policy Program
COVID-19
prevalence elasticity
California
stay-at-home
quarantine fatigue
Thesis

Harsh, mandatory restrictions to control the spread of COVID-19 are difficult to comply with and may lose their effectiveness over time. Given the disproportionate impact of the COVID-19 pandemic on low-income populations, it is important to evaluate how the effectiveness of COVID-19 restrictions changes over time, particularly for low-income populations. This study looks at changes in social distancing over time in the state of California. After categorizing COVID-19 restrictions in California into levels of different severity, this paper uses linear regression analysis to determine how much additional social distancing is associated with more severe COVID-19 restrictions. This study then tracks these values over time between low-income, middle-income, and high-income California counties. The results show that severe COVID-19 restrictions lose their effectiveness over time, relative to more moderate restrictions, providing evidence for the quarantine fatigue effect. This effect was found in low-income, middle-income, and high-income counties, but low-income counties experienced this effect at a faster rate than high-income counties. This is likely driven in large part by the inability for many low-wage, high-risk essential workers to work remotely. Therefore, more targeted interventions that address the specific needs of low-income populations are necessary.
Crime and impunity : three essays on the drug war
Keywords: Political Science.

Across Latin America, the emergence of criminal conflict has become a primary security concern. In Mexico, the focus of this dissertation, the federal government deployed the military to enforce the law, but countries across Latin America have seen the emergence and entrenchment of criminal groups organized around the drug trade. This dissertation will explore three interrelated topics. First, it will examine the effect of this conflict on constraints on the government's security forces and examine a sweeping institutional reform that succeeded in limiting these abuses. Second, it will explore the relationship between this conflict and local state capacity. Third, it will explore the ways that cartels build up their own capacity to control their agents and deter rivals
CRISPR screens reveal lipid modulators of the G protein-coupled receptor smoothened
Keywords: Biochemistry.

The Hedgehog (Hh) signaling pathway is a GPCR-associated signaling pathway critical for vertebrate development and adult tissue homeostasis. Numerous questions remain about how the activity and localization of Smoothened (Smo)—a core Hh pathway component and GPCR—is controlled and modulated. The advent of CRISPR represents an opportunity to use high-throughput approaches to uncover novel modulators of Smo and Hh signaling as a whole. Here I present the results of a CRISPR screen of lipid-related genes that uncovered several lipid metabolic pathways that influence Hh signaling activity through separate specific effects on Smo. I present evidence that sphingomyelin synthesis and cholesterol synthesis both impact Hh signaling by modulating Smo activity, a discovery that also provides insight into a central question remaining about the mechanisms of vertebrate Hh signaling: how can cholesterol, an abundant lipid in the plasma membrane, act as the endogenous ligand for Smo as has been hypothesized? Using toxin-based fluorescent sensors of membrane lipids, we established that ciliary sphingomyelin limits cholesterol accessibility in that organelle. We postulate that this small pool of accessible cholesterol—rather than the total membrane cholesterol—is responsible for activating Smo and driving Hh signaling downstream of Patched1. I also validate another prospective negative regulator of Hh signaling suggested by our screen: the N-glycan synthesis pathway. Protein N-glycosylation can have great functional importance, but the impact of N-glycosylation remains poorly understood for many proteins. I demonstrate that inhibition of this general pathway has a specific effect on Hh pathway activity and on Smo in particular, potentiating signaling by increasing Smo accumulation at the ciliary membrane in response to Hh ligand. Intriguingly, the impact of N-glycan synthesis inhibition on Smo is not a result of hypoglycosylation of Smo itself, but rather the result of loss of glycosylation on an alternative protein. Our CRISPR screen thus sheds light on the distinct impacts of three lipid-related pathways on Smo behavior and Hh signaling, suggesting that our screening methodology may be useful to interrogate the relationship between lipid metabolic networks and GPCR signaling more generally
Cryogenic transmission electron microscopy for next-generation batteries
Keywords: Materials Science and Engineering.

Lithium-ion batteries are common in everyday life, while being strategically critical to the decarbonization and electrification of transportation. To meet higher range demands and lower costs per kWh of storage, higher energy density battery chemistries relying on new anode chemistries such as silicon or lithium metal are needed. These new chemistries are attractive for commercialization, but their deployment is hindered by a lack of understanding of their degradation and failure modes, which are dominated by a poorly understood structure on the anode surface called the solid-electrolyte interphase (SEI). Recent breakthroughs in cryogenic transmission electron microscopy (cryo-TEM) now enable the characterization of this highly reactive and radiation sensitive structure, allowing new insight and understanding to be developed towards practical silicon and lithium metal anodes. My PhD dissertation entails the use of cryo-TEM to gain functional insight into the degradation of these materials, which may provide guidance and design rules for practical next-generation lithium battery chemistries. In Chapter 1, I will give an overview of modern lithium-ion batteries, their history and shortcomings, and motivate the need for higher capacity silicon and lithium metal anodes. Chapter 2 will introduce the technique of transmission electron microscopy, an immensely powerful technique for the structural and chemical characterization of materials with atomic resolution, along with the need for cryogenic stabilization when used with lithiated anode materials. Chapter 3 will show how cryo-TEM can be used to derive new insight into the failure modes of the silicon anode, along with the working mechanism of electrolyte additives. Chapter 4 will move beyond lithium-ion chemistries, where I will show how cryo-TEM can be used to refine the SEI nanostructure of the Li metal anode beyond conventional models derived from surface analysis techniques such as x-ray photoelectron spectroscopy. In Chapter 5, I will investigate the capacity loss of the lithium metal anode during storage, a critical parameter for electric vehicles, and use cryo-TEM to elucidate the nanoscopic origins of the rapid capacity loss during storage. Finally, in Chapter 6, I will conclude the dissertation with broader insights gained from my studies and an outlook for the battery field, and how cryo-TEM can fit within the suite of modern battery characterization tools
The cultural basis of achievement motivation
Keywords: Education.

What motivates students to achieve is a question that educators, parents, and policy makers in every country seek to answer. A scientific understanding of this question can help us designing interventions that foster student achievement and unleash the potential of all members of a society, regardless of their economic, racial/ethnic, and cultural background. However, for decades, scientific research has approached this question from a single perspective—a perspective that derives from data accumulated largely from WEIRD (Western, Educated, Industrialized, Rich and Democratic) samples. Not until recently have researchers begun to understand the power of culture—that what moves people to action depends on the culture they identify with or live in. Research from multiple disciplines in individualistic Western societies across decades converges on the finding that students' independent "self"—personal preference, agency, and choice drive their actions and educational outcomes. By contrast, societies grounded in collectivism, which includes the majority of societies outside North America and Western Europe, are often animated by cultural models of agency that encourage the construal of one's self as interdependent with close others and as a part of a social whole. In this dissertation, I present evidence on how societies' differential levels of emphasis on cultural individualism shapes the relationship between students' passion and achievement, explains the prevailing gender gap in STEM (Science, Technology, Engineering, and Mathematics), and shed lights on training global and fair Artificial Intelligence (AI). Implications for appreciating diverse forms of achievement motivation across cultures and building meritocratic educational systems are discussed
Cuts Optimization and Machine Learning Models for Dark Photon Signal-Background Discrimination with the ATLAS Detector
Keywords:  Department of Physics
particle physics
dark photons
machine learning
ATLAS experiment
data analysis
Text
Thesis

Massless dark photons are theorized to mediate long-range forces between dark particles. They could lead to new and observable signatures in Higgs boson decays at the Large Hadron Collider (LHC). The discovery of dark photons would provide evidence of Beyond the Standard Model (BSM) physics and could help solve small-scale structure formation problems in cosmology. In this thesis, we consider the ZH production mechanism to search for a newly-predicted decay of the Higgs boson into a photon (γ) and a massless dark photon (γD) with a target final state of Z(→l+l−)H(→γγD). We use Monte-Carlo simulation samples corresponding to the total integrated luminosity of pp collisions at √s=13 TeV collected by the ATLAS detector in 2015-2018. First, we implement rectangular cuts optimization to obtain a baseline signal region (SR) selection. Next, we explore the use of boosted decision trees (BDTs) and neural networks to train a classifier for signal-background discrimination. BDTs using the Gradient Boosting algorithm with the xgBoost implementation yield the best performance with 96% accuracy and an area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.97. We then compare the performance of the BDT to the rectangular cuts optimization through evaluating the approximate median significance (AMS) metric on the simulation samples. We find that cutting on the BDT output score distribution has a much higher AMS than the rectangular cuts optimization (∼10 compared to ∼1), thus significantly improving the sensitivity of this analysis for dark matter detection.