VictorJuiz/thesis huggingface dataset:
Coupling between protein stability and catalytic activity determines pathogenicity of glucose-6-phosphate dehydrogenase (G6PD) variants [electronic resource]
Keywords: Chemical and Systems Biology.

G6PD deficiency, an enzymopathy affecting 7% of the world population, is caused by over 160 different amino acid variants in glucose-6-phosphate dehydrogenase (G6PD). This essential enzyme plays a critical role in maintaining redox homeostasis, and while variants that reduce activity are protective against malaria, complete loss of G6PD activity is lethal. G6PD deficiency is a major risk factor for hyperbilirubinemia and kernicterus, and may contribute to many health conditions including neurodegenerative diseases, heart disease, diabetes, and aging. The clinical presentation of G6PD deficiency is diverse, likely due to the broad distribution of variants across the protein and the potential for multidimensional biochemical effects -- previously characterized G6PD variants have been shown to affect catalytic activity, thermostability, and protein folding. However, the relationship between the structural, biochemical, and phenotypic effects of a G6PD variant remains largely unexplored. Recent developments in the fields of bioinformatics and genome sequencing have allowed us to combine existing phenotypic, biochemical, and genomic information about G6PD to develop new hypotheses about its evolution, structure, and function. In this study, we use existing databases of characterized and uncharacterized G6PD variants to interpret the importance of various structural regions of G6PD. Using biochemical analyses, we identify a trade-off between protein stability and catalytic activity as a major determinant of a G6PD variant's clinical phenotype. Additionally, we examine the evolution of G6PD using a recently developed sequence coevolution analysis method. We identify three coevolving sectors of amino acids that are enriched in different classes of G6PD variants; these three sectors also correspond to functionally characterized structural regions. Based on two sectors that span multiple structural regions, we develop novel hypotheses about conformational and allosteric regulation of G6PD. This work expands the current understanding of the structural and biochemical underpinnings of G6PD variant pathogenicity, and suggests a promising avenue for correcting G6PD deficiency by targeting essential structural features of G6PD.
CRISPR adaptation to RNA [electronic resource]
Keywords: Chemical and Systems Biology.

Cells use a variety of mechanisms to mitigate the threat of genetic invasions and to prevent the propagation of parasitic information. A family of prokaryotic adaptive immune systems associated with Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) arrays has been shown to protect cell populations from "selfish DNA" such as plasmids and viruses. CRISPR-mediated immunity begins with an "adaptation" phase in which the host acquires short sequence segments (spacers) from the genome of the infectious agent. The spacers are stored within CRISPR arrays in the host genome and serve as heritable molecular memories of the invader. This information is used by CRISPR-associated (Cas) nucleases in the subsequent "interference" phase to identify and disrupt future infections by the same invader. Most CRISPR-Cas systems can be phylogenetically classified into three major types. Type I and Type II systems acquire spacers from (and subsequently interfere with) DNA sequences, while Type III systems additionally target RNA sequences for degradation. In the absence of any known mechanisms for CRISPR adaptation to RNA, this was believed to be an auxiliary mode of attack against DNA parasites. We discovered that some Type III systems can acquire spacers directly from RNA, suggesting that these CRISPR-Cas systems could learn to fend off RNA parasites as well. This system represents, to our knowledge, the first identified cellular process in which sequence information is actively acquired from RNA and deposited (as DNA) into the genome at the behest of the host cell. Moreover, direct acquisition of RNA spacers into CRISPR arrays represents a novel and uniquely sequence-agnostic mode among the handful of known mechanisms for reverse flow of genetic information from RNA into DNA genomes, including reverse transcription machineries associated with retroviruses, telomeres, retrotransposons, mobile group II introns, and phage diversity generating elements.
Cyber risk analysis for a smart grid [electronic resource] : how smart is smart enough?
Keywords: Management Science and Engineering.

As electric sector stakeholders make the decision to upgrade traditional power grid architectures by incorporating smart grid technologies and new intelligent components, the benefits of added connectivity must be weighed against the risk of increased exposure to cyberattacks. Therefore, decision makers must ask: how smart is smart enough? This dissertation presents a probabilistic risk analysis (PRA) framework to this problem, involving systems analysis, stochastic modeling, economic analysis, and decision analysis to quantify the overall benefit and risk facing the network and ultimately help decision makers formally assess tradeoffs and set priorities given limited resources. Central to this approach is a new Bayes-adaptive network security model based on a reformulation of the classic "multi-armed bandits" problem, where instead of projects with uncertain probabilities of success, a network defender faces network nodes that can be attacked at uncertain Poisson-distributed rates. This new technique, which by similarity we call "multi-node bandits, " takes a dynamic approach to cybersecurity investment, exploring how network defenders can optimally allocate cyber defense teams among nodes in their network. In effect, this strategy involves taking teams that traditionally respond to cyber breaches after they occur, and instead employing them in a proactive manner for defensive and information gathering purposes. We apply this model to a case study of an electric utility considering the degree to which to integrate demand response technology into their smart grid network, jointly identifying both the optimal level of connectivity and the optimal strategy for the sequential allocation of cybersecurity resources. Additional analytical and empirical results demonstrate the extension of the model to handling a range of practical network security applications, including sensitivity analysis to organization-specific security factors, settings with dynamic or dependent rates of attack, or handling defense teams as imperfect detectors of cyberattacks.
Dam them All: On the Failure of Dams and Dam Building Frameworks
Keywords: thesis
dams
dam
water
resource conflict
climate change
global warming
lesotho
south africa
International Relations
IR
History
Cavallo
Thesis

My thesis research concerns the relationship between resource scarcity and conflict, with a particular focus on South Africa and Lesotho's international water agreement, the Lesotho Highlands Water Project. The LHWP, as with many large dam projects, has been fraught with problems and the politics surrounding it have been contentious. My research details and analyzes the various factors that have hampered the LHWP, with particular emphasis on the role of private interests, financial incentives, poor infrastructure planning and water management. My findings ultimately argue that it is the framework through which we consider and approve dam projects that is flawed, and leads to pervasive flaws in their commissioning and construction. The known impacts of global climate change, including global warming and the increasing unpredictability of temperature and precipitation, both contribute to and compound these factors. As a result, the political repercussions of large dam projects like the LHWP will become increasingly salient and demand political responses.
Data sampling and constrained reconstruction for high-dimensional MRI [electronic resource]
Keywords: Electrical Engineering.

Magnetic resonance imaging (MRI) is a powerful and flexible modality for medical imaging. Data samples in MRI are acquired in the spatial frequency domain (k-space) through a time-consuming and inherently sequential process, and consequently, the scan time is proportional to the number of acquired samples. Many clinical MRI applications require data acquisition with additional dimensions, such as channels in a phased-array receive coil in parallel MRI, time in dynamic contrast-enhanced (DCE) MRI, or multiple slices excited in imaging near metallic implants, which introduce additional acquisition requirements. Modern higher-dimensional MRI applications have potential to overcome scan time limitations with constrained image reconstruction methods that exploit dependencies between auxiliary dimensions. Two major issues are that the required inversions can be ill-conditioned depending on k-space sampling patterns and may require explicit knowledge of inter-dimensional dependencies that is hard to obtain in practice. Thus, many applications compromise robustness to motion, temporal resolution, or spatial resolution. This thesis introduces new computational methods for k-space sampling and constrained reconstruction that push the current limits of high-dimensional MRI. To address the sampling problem, a general relationship between k-space sampling and problem conditioning is described using differential domain analysis, a tool that has roots in computer graphics. The theory is then used for on-the-fly design of adaptive k-space sampling patterns. Informed by new insights into k-space sampling, new methods for constrained reconstruction in DCE-MRI and MRI near metallic implants are introduced and evaluated in clinical imaging. A new k-space sampling trajectory is introduced in a clinical DCE-MRI protocol. This approach enables both conventional unconstrained reconstruction at lower temporal resolution or compressed sensing reconstruction at a retrospectively adjustable higher temporal resolution, providing robustness to breath hold loss or rapid contrast enhancement. Initial assessment of the technique, used in imaging over 3000 patients at Stanford, demonstrates its benefit. A similar approach is proposed to enable rapid imaging of patients with metallic implants by exploiting dependencies between excited slices. The reconstruction of on-resonance and off-resonance signals is formulated as robust principal component analysis, which exploits new sources of redundancy not describable in existing compressed sensing methods. Clinical evaluation in a small pilot study shows the feasibility of imaging near metal three-fold faster than the standard technique.
Data-driven methodologies for efficient materials calculations [electronic resource]
Keywords: Chemistry.

As the world of electronic simulations progresses from a world where we can perform tens to thousands to millions of calculations, the way we perform science must evolve in order to most efficiently use the increasingly vast amounts of data. This thesis aims to contribute to laying the groundwork for the necessary paradigm shift. One clear aspect that must tackled in such a shift is the implementation of additional automation, automating tasks ranging from the set-up and performance of calculations to the analysis of the ensuing results. This thesis consists of three projects tackling this problem, merging databases of electronic structure calculation with machine learning and model fitting techniques. These projects are i) A procedure to algorithmically determine optimal convergence parameters, for use in varied large-scale studies, ii) Frameworks for utilizing benchmark and reference data to create post-calculation schemes which are cheap, robust, and universal, and iii) Development and benchmarking of improvements to the Drag method for transition state determination, as well as first attempts at using data to accelerate more expensive NEB calculations.
Data-driven operations and incentives in healthcare [electronic resource]
Keywords: Electrical Engineering.

The United States has the highest per-capita healthcare spending in the world, surpassing annual national expenditures of $2.5 trillion a year. Yet, there are serious concerns about the quality of care, including medical errors, as well as the underuse and overuse of healthcare resources. This thesis focuses on improving patient outcomes through (1) optimizing hospital operations using machine learning and statistical decision-making tools based on increasingly available data, and (2) the design of healthcare policy that better aligns provider incentives with the goal of high-quality and cost-effective care. Chapter 1 proposes new algorithms for personalized medical decision-making that can efficiently leverage high-dimensional patient data. Chapter 2 empirically assesses pitfalls of current pay-for-performance healthcare policies. Chapter 3 studies the game-theoretic design of pay-for-performance policies that optimize hospitals' financial incentives in the presence of institutional constraints.
De novo mutations in autism implicate the synaptic elimination network
Keywords: O'Connell, Chloe
Wall, Dennis P.
Autism
synaptic elimination
Bioengineering
School of Engineering
Honors
Pediatrics
Biomedical Data Science
statistics
Thesis

Autism has been shown to have a major genetic risk component; the architecture of documented autism in families has been over and again shown to be passed down for generations. While inherited risk plays an important role in the autistic nature of children, de novo (germline) mutations have also been implicated in autism risk. Here we find that autism de novo variants verified and published in the literature are Bonferroni-significantly enriched in a gene set implicated in synaptic elimination. Additionally, several of the genes in this synaptic elimination set that were enriched in protein-protein interactions (CACNA1C, SHANK2, SYNGAP1, NLGN3, NRXN1, and PTEN) have been previously confirmed as genes that confer risk for the disorder. We then validated that the synaptic elimination gene set is implicated in autism by doing a case-control analysis using the iHART dataset, which currently contains 2308 whole genomes. The results demonstrate that autism-associated de novos are linked to proper synaptic pruning and density, hinting at the etiology of autism and suggesting pathophysiology for downstream correction and treatment.
Death on a pedestal [electronic resource] : political martyrdom in late Qing China
Keywords: East Asian Languages and Cultures.

This dissertation examines political martyrdom as a symptom of spiritual crisis in China's transformation toward a modern secular society at the turn of the twentieth century. The late Qing period witnessed an unprecedented impulse for self-destruction in assassination activities and civil rights movements, which generated symbolic expressions, intellectual discourses, and popular imaginations. Focusing on these accounts of how martyrdom was interpreted and performed, I argue that the excessive gesture of self-destruction, while serving various political causes, has a unique agenda of reviving the tradition of spiritual cultivation missing from emergent models of secular politics. Tracing late Qing political actors' anxiety regarding the teleological imaginaries of popular sovereignty, I investigate how they drew inspirations from transnational religio-philosophical sources to justify voluntary death, enlarging the scope of life beyond biological existence. The spiritual visions underlying political martyrdom point to alternative ways of constructing subjectivity, self-other relations, and community in Chinese political culture—a subject inherent but overlooked in the global wave of fin-de-siècle disenchantment with the secular order. Ultimately, this dissertation locates the register of Chinese spirituality beyond both established religious traditions and political ideological commitments. It also intervenes in the ongoing study of secularism by offering a new light to define what is secular in modern Chinese society.
Decision Making under uncertainty in groundwater management
Keywords:  Department of Geological Sciences
groundwater
uncertainty
Thesis

Managing water resources is one of the most pressing environmental chal-
lenge of the coming decades. With a changing climate, growing popula-
tion and change of land use, the stress on water resources has dramati-
cally increased (De Marsily & Abarca-del Rio, 2016). Whereas policies are
emerging on the use of surface water, groundwater is still lacking sustain-
able exploitation strategies. Designing optimal policies for groundwater
extraction is challenging. This is especially due to the uncertainties on the
climate, subsurface and human behavior. This study is focusing on the
importance of data in a decision-making framework to solve groundwater
management problems. Two approaches of groundwater management are
considered. In a first time, the formalism of Markov Decision Processes is
adopted. The problem of a single decision-maker trying to optimize the
groundwater extraction under an uncertain recharge is modeled and solved
using a reinforcement learning algorithm to produce optimal groundwater
usage policies. A Q-Learning algorithm successfully calculate the optimal
policy in that case. In a second time, the focus is directed on human be-
havior and in particular multiple groundwater user interactions. A Game
Theoretical framework is adopted and a two-players is successfully created.
The study is focusing on the sensitivity analysis of the Nash Equilibrium,
in particular on the different hydrogeological parameters that influence it.
A Value of Information problem is tackled, showing the importance of ac-
curate geological data in such a framework.
Demystifying disproportionality [electronic resource] : loose coupling in the special education identification process for English learners
Keywords: Education.

The disproportionate representation of culturally and linguistically diverse students in special education has been identified as a problem by educational researchers and policymakers for decades. Though it is well-established that English learners (ELs) are disproportionately represented in special education, what is less clear is why such disproportionality exists. This study fills a gap in the literature by taking a close look at how ELs are identified as eligible or not for special education. Using participant observation research methods, I followed 16 ELs across two schools through the process of being identified as eligible (or not) for special education. The findings from the study indicate that the process of identifying ELs for special education eligibility took place in fragmented environments. This study suggests that if the civil rights of ELs is to be maintained, significant changes must be made to how practitioners relate to, understand, and are trained to implement policy.
Dens of tyranny and oppression [electronic resource] : the politics of imprisonment for debt in seventeenth-century London
Keywords: History.

This dissertation provides the first archival account of England's seventeenth-century prisons, uncovering their internal social structures, their place within wider society and their role in political conflict. As inmate populations swelled with debtors due to dramatic economic shifts and growing strain placed upon credit relations, prisons became hubs of social and political activity. In ever-closer quarters, prisoners lived, petitioned, rioted, participated in self-government and developed novel political arguments. Reacting against what they perceived as oppressive legal and prison systems, inmates proposed more equitable means of maintaining credit networks, and developed radical new models of social relations. This was particularly significant during the English Revolution, when imprisonment was increasingly equated with royal absolutism. Arguments against imprisonment for debt were first developed within inmate societies, but in the mid-seventeenth century entered into broader debates over authority and tyranny, intersecting with a radical political milieu that included John Lilburne and the Levellers during the English Revolution. These developments were contingent upon (and reactions against) both economic turbulence and experiences of the participatory processes that constituted the early modern state. Inmates were the casualties of new levels of economic instability and the increasing activity of an agonistic legal system, left at the mercy of gaolers whose offices operated at the intersection of self-interest and state power. Within this context, inmates developed novel critiques of legal systems (particularly imprisonment for debt) and the social order. Thus, this dissertation considers how direct engagements with the ligatures of the early modern state precipitated by rapid economic change and dislocation could produce friction and contention that was not simply a by-product of growing participation in governance and negotiation with authority, but a key experience in generating political dissent and opposition. In short, it explores how straining credit networks, swelling inmate populations and civil war reshaped early modern prisons and their social worlds, and the political resonances this had within and beyond the prison walls.