VictorJuiz/thesis huggingface dataset:
AP-1 transcription factor is a barrier to nuclear reprogramming to pluripotency [electronic resource]

Mechanistic insight into nuclear reprogramming from one cell state to another is of fundamental and clinical importance. Here we use the heterokaryon cell-fusion model of nuclear reprogramming to capture the dynamic architecture of chromatin accessibility and gene expression. At the onset of reprogramming, we detect a transient, genome-wide increase in accessible sites enriched for the AP-1 transcription factor motif assayed by ATAC-seq. Inhibition of AP-1 by expression of a dominant-negative results in an increase in OCT4 expression in heterokaryons. Moreover, in human iPSC reprogramming, dominant negative AP-1 can replace exogenous OCT4 in the reprogramming cocktail. We identify c-Jun as the inhibitory AP-1 family member and show that its repressive activity is mediated through interaction with NURD complex component MBD3 in a phosphorylation dependent manner. Our findings reveal that AP-1, which is induced at the onset of reprogramming and traditionally thought to be an activator, creates a JUN-MBD3 repressor complex that inhibits nuclear reprogramming to pluripotency through direct targeting of an OCT4 distal regulatory element. These findings reveal an unexpected role for Jun as a repressive epigenetic gatekeeper of reprogramming to pluripotency.

Keywords: Genetics.
Advisor: Blau Helen M. primary advisor. Thesis advisor
Kundaje Anshul
Sage Julien advisor. Thesis advisor
Snyder Michael
Application of enantioselective dihalogenation to the total synthesis of terpene natural products

Halogenated natural products are ubiquitous and in many cases display promising biomedical potential. Due to the scarcity of methods for the stereoselective incorporation of halides into organic substrates and the difficulty of isolation from nature, access to most members for anticancer screening is severely limited. We were able to leverage an advancement made in our laboratory, which allows for chemo-, regio-, and enantioselective synthesis of vicinal dihalide products, toward the stereoselective total synthesis of several polyhalogenated natural products. One of these compounds, halomon, was tested on the Bassik laboratory target identification screen, which identified an interesting pathway that serves to export the drug from the cancer cell. We have further shown that enriched dihalides can act as novel forms of chiral, removable auxiliaries that enable synthesis of stereocenters that are difficult to access with traditional methods.

Keywords: Chemistry.
Advisor: Burns Noah
Du Bois Justin 
Kanan Matthew William
Applications of chemical biology and mass spectrometry for the analysis of the O-GlcNAcome and adeno-associated viruses

Proteins are involved in nearly all cellular processes and functions. Though there are some 20000 protein-coding genes in the human genome, the functional space of these proteins is expanded dramatically by post-translational modifications (PTMs). Therefore, understanding the functional significance of any given protein in a biological context necessitates the identification and characterization of the protein PTMs. Glycosylation is one of the most ubiquitous forms of protein PTM, and advances in bioanalytical and chemical technologies have begun to enable the study of glycoconjugates more thoroughly. O-GlcNAcylation has garnered significant attention due to its involvement in co-regulating protein phosphorylation and for its myriad functions in cellular signaling, gene transcription, translation, and development. In Chapter 1 of this dissertation, I present an overview of O-GlcNAcylation, detailing the various roles it plays in biology and the high level of regulation for this PTM. I discuss enzymatic substrate specificity related to O-GlcNAcylation, and I briefly introduce the interplay of this modification with various other PTMs. In Chapter 2, I present a brief overview of different technologies available for the study of the O-GlcNAcome. Additionally, I present preliminary data using many of these methods for the application of understanding the dynamics of protein O-GlcNAcylation during neural development. Chapter 3 of this dissertation focuses on the effects of perturbing normal O-GlcNAc cycling during stem cell differentiation. I discuss chemical inhibition of the enzymes controlling the addition and removal of the modification and introduce a potential workflow for achieving end-stage neuronal cells faster from pluripotent human embryonic stem cells. In Chapter 4, I describe more broadly a variety of orthogonal comparisons between two leading production mechanisms for adeno-associated virus (AAV) gene therapeutics used in the clinic. Using a combination of analytical approaches, including MS-based deep proteomic profiling, two-dimensional isoelectric focusing in combination with enzymatic modification removal, transmission electron microscopy, comparative capsid structural modeling by cryo-EM, human cytokine profiling in response to vector transduction, comparative functional transduction assessments in murine liver and skeletal muscle tissues in vivo, primary human fetal brain slices ex vivo, and a variety of primary and immortalized cell types in vitro, I detail how the therapeutics are chemically, structurally, and biologically distinct.

Keywords: Chemistry.
Advisor: Bertozzi Carolyn R.
Elias Joshua
Pitteri Sharon
Applications of operations research in finance and healthcare [electronic resource]

This thesis covers several applications of Operations Research in the domains of finance and healthcare. There are three chapters, each covering a different application. Chapter 1 applies techniques from deep learning to estimate mortgage risk. The near-elimination of feature engineering is substituted by models with several thousand parameters that require large amounts of training data. The predictive performance of these models strongly exceeds that of baseline models, especially for predicting prepayments. This increased accuracy, however, comes at the cost of a more opaque model which is harder for a human to interpret than simpler models like logistic regression, which are currently the industry standard. The work in Chapter 2 explores the design of policies for biometric authentication. It first develops a model for the joint distribution of similarity scores associated with different fingers and irises. In the second step, this model is harnessed to design near-optimal multi-stage policies that would be used for authentication, and are robust to gaming, can be computed in real-time and are personalized for optimal performance. The work shows that a reduction of several orders of magnitude in the error rates is achievable by solely changing the authentication policies -- and leaving the hardware unchanged. Chapter 3 is motivated by a humanitarian cause geared towards helping developing countries -- to reduce mortality in children by identifying effective interventions at the planning stage. It takes a descriptive health model (called LiST), which estimates mortality of children given coverage of interventions, and embeds that into an optimization engine in order to minimize mortality under a fixed budget. In doing so, it allows LiST to be used in a prescriptive framework, where policymakers can identify the optimal intervention set at a fixed budget as well as recognize the trade-off of mortality reduction and budget allocation. We find that a greedy strategy offers near-optimal performance with ease of implementation. The findings also highlight the critical role that optimization plays in mortality reduction.

Keywords: Management Science and Engineering.
Advisor: Giesecke Kay primary advisor. Thesis advisor
Wein Lawrence primary advisor. Thesis advisor
Ye Yinyu advisor. Thesis advisor
Applications of optimization to medical imaging

In this work, we present several applications of optimization to medical imaging. These applications include image fusion, estimation of the attenuation coefficient with Optical Coherence Tomography, and image quantitation with Magnetic Resonance Imaging. Additionally, we present the Adaptive Bounding Algorithm, a new optimization algorithm based on subgradient descent.

Keywords: Electrical Engineering.
Advisor: Pauly John (John M.) 
Hargreaves Brian Andrew 
Vasanawala Shreyas
Articulations of the ineffable : narratives, engagement, and historical anthropology with the Muwekma tribe of the San Francisco Bay Area

This dissertation integrates Indigenous and western theoretical perspectives to refine thought about the individual in the world, which helps us to approach rupture and growth in American Indian communities without taking recourse to traditional western colonial assumptions. This project has been developed through close collaboration with the Muwekma Tribe of the San Francisco Bay Area, descendants of the historically, federally recognized East Bay Ohlone, Verona Band of Alameda County. Although many of the families of the Muwekma Tribe have continued to live in their homeland for generations, their neglected federal status and their physical integration with various waves of settler communities has seemingly hidden their existence, as well as their ways of being. This project has drawn on transcontinental Indigenous theories, as well as Eurasian theoretical perspectives to develop a decolonial narrative articulating Muwekma ways of being in relationship to ancestral narratives, objects, bodies and places. Despite academic assertions of cultural extinction and dilution starting in the 1920s, the Muwekma families have continued traditions and ways of approaching homeland across three consecutive, violent, colonial regimes in central California. Through the interpretation of a series of interviews, historical ethnographic notes, archaeological praxis, and remains, this research illustrates how the Muwekma families weave western practices of research into their way of being autochthonous in their homeland. This dissertation provides an understanding of the significance of the tribal community's ancestral places, whether they be significant places of ancestor's post-European invasion, or ancestors from the deep past. This dissertation also proposes that to conduct a respectful archaeology, which asserts the contemporaneity of interlocating community's perspectives, is tantamount to a becoming, and requires a level of engagement beyond intellectual assent and objectifying the agents in one's study.

Keywords: Anthropology.
Advisor: Voss Barbara L.
Bauer Andrew M. 
Ebron Paulla A.
Hastorf Christine Ann
Lightfoot Kent G.
Artificial intelligence in healthcare and medicine [electronic resource] : enhancing the expert

Due to a convergence of large open-source datasets, significant improvements in the parallelization capabilities of hardware (notably, multi-thousand core graphics processing units), and renewed academic interest in decades-old neural network algorithms, primary subfields of AI have flourished in the past 5 years. Natural Language Processing, Computer Vision, and robotics have attained impressive performance across many key AI tasks. In this thesis we focus not on the development of new AI algorithms, but on their application to a series of important problems in healthcare and medicine. Machine learning and AI will impact industries that generate significant amounts of data, by extracting insights that humans cannot. Healthcare and medicine are examples of such industries and thus are excellent use cases for AI deployment. Our story is divided into 4 sections - neuroscience, psychiatry, drug screening, and dermatology - all linked by the common thread of using AI to \textit{enhance the expert}, either in-clinic or in the analysis of data. This underlying motif is the connection to a paradigm in AI development popularized as the \textit{virtual cycle of AI}: build a product that has front-facing user value, generate data with it, use that data to train AI algorithms that improve your product, acquire more data, etc.

Keywords: Electrical Engineering.
Advisor: Boyd Stephen P. primary advisor. Thesis advisor
Thrun Sebastian
Duchi John advisor. Thesis advisor
Artificial intelligence methods for molecular property prediction

This dissertation covers work discussed in the following papers: "Spatial Graph Convolutions for Drug Discovery" describes new deep neural network architectures for modeling drug-receptor interactions. We argue that the future of predicting the interactions between a drug and its prospective target demands more than simply applying deep learning algorithms from other domains, like vision and natural language, to molecules. "Machine Learning Harnesses Molecular Dynamics to Discover New Opioid Chemotypes" describes an algorithm that leverages protein motion to enrich the search for active molecules. We then applied the method to find a new chemical scaffold that we experimentally verified is an agonist for the μ Opioid Receptor. "Kinetic Machine Learning Unravels Ligand-Directed Conformational Change of Opioid Receptor" describes differential pathways of deactivation and differential conformational states sampled by the μ Opioid Receptor in response to different opioid ligands.

Advisor: Pande Vijay 
Dror Ron
Kobilka Brian K. 
Shamloo Mehrdad
Ascorbic Acid as an Epigenetic Therapy for Osteosarcoma

Osteosarcoma is an aggressive primary tumor of the bone, often presenting itself in childhood and adolescence. Treatments in the last 30 years have not improved considerably due to the genetic complexity and genomic instability of osteosarcoma. Therefore, it is necessary to consider epigenetic modifications that may contribute to osteosarcomagenesis. One set of epigenetic modifications are regulated by Ten-eleven translocation (TET) family enzymes, which oxidize 5-methylcytosines (5mC) to 5-hydroxymethylcytosines (5hmC). Current models propose that 5hmc is an intermediate base in the DNA demethylation process that operates during important reprogramming phases of mammalian development. We show that loss of TET affects osteoblast maturation by depleting 5hmc levels in the genome and can prime the epigenetic landscape of osteoblasts towards oncogenesis. Consequently, increasing TET activity in osteosarcoma patient-derived xenografts by adding its co-factor, ascorbic acid (AsA) rescues this decrease in 5hmc and abrogates cell proliferation. By investigating levels of global gene expression before and after AsA treatment, gains in 5hmc were observed in specific subsets of genes associated with apoptosis and cell cycle dynamics, which seem to mitigate the cancerous phenotype. Such results contribute to a better understanding of epigenetic regulation of osteosarcoma, and furthermore implicate AsA as a potential epigenetic therapy for osteosarcoma.

Keywords:  Department of Biology
Osteosarcoma
Ascorbic Acid
Vitamin C
Epigenetics
Methylation
Osteogenesis
Apoptosis
Cell Cycle
Stanford Medicine Department of Orthopedic Surgery
Stanford University School of Humanities and Sciences
Stanford University Department of Biology
Thesis
Advisor: Bhutani Nidhi
Mudgett Mary Beth
Aspects of three-dimensional gravity and two-dimensional conformal field theory

In this thesis, we study aspects of three-dimensional gravity in Anti de Sitter (AdS) space by studying the holographically dual two-dimensional conformal field theory (CFT). We begin by describing general constraints on the elliptic genus of a two-dimensional supersymmetric conformal field theory which has a gravity dual with large radius in Planck units. We discuss the distinction between theories with supergravity duals and those whose duals have strings at the scale set by the AdS curvature, using symmetric orbifolds as a case study. We then move on to study extremal theories, conjectured to be dual to "pure" three-dimensional gravity. We first provide an example of an extremal chiral N=2 superconformal field theory at c=24. We then consider extremal CFTs at large central charge, and consider the quantum corrections to the classical spectrum. Our conjecture passes various consistency checks, especially when generalized to include theories with supersymmetry. Finally, we examine a specific top-down construction of AdS3/CFT2 from string theory, called the D1/D5 system. We examine the low-lying quarter BPS spectrum of the K3 symmetric orbifold CFT at various points in moduli space, and look at a more refined count than the ordinary elliptic genus. We do a decomposition of the spectra into N=4 characters, and show that at large N the character decomposition satisfies an unusual property, in which the degeneracy only depends on a certain linear combination of left- and right-moving quantum numbers, suggesting deeper symmetry structure.

Keywords: Physics.
Advisor: Kachru Shamit
Hartnoll Sean
Silverstein Eva
The ASPEN platform : tools for mixed signal electronics, digital signal processing, and biomedical electronics

This thesis describes the motivation for, development and testing of the ASPEN Platform. ASPEN, short for Advanced Signal Processing ENvironment is a suite of hardware and software tools to enhance and facilitate prototyping and teaching of mixed signal electronics, digital signal processing, and the design of biomedical instrumentation.

Keywords: Electrical Engineering.
Advisor: Kovacs Gregory T. A. 
Giovangrandi Laurent
Rivas-Davila Juan
Assessment framework for additive manufacturing in the AEC industry

For the foreseeable future, building components will be produced with additive (AM) and conventional manufacturing (CM) methods. Since the value of AM is currently not transparent to architects, engineers, and contractors, they cannot make well-informed decisions about the application of AM for buildings. Case studies of two metallic building components, a curtain wall frame and a bracket, carried out with a global building façade contractor demonstrated that AM is technologically feasible for building components and lowers environmental impact by up to 87% but increases cost by a factor of 5 at least. Based on the case studies, this research formalized a 19-step framework for the rapid and consistent assessment of the applicability (A) and schedule (S), environmental (E), and cost (C) implications of producing building components with AM vs. CM. The validation of the formalized semi-automated framework showed a 97% reduction of the effort required for the ASEC analyses and improved the consistency of the A, S, and C results across different assessors. The framework also improved the consistency of the E results in total by a factor of 4, but some E results remained inconsistent. An uncertainty analysis of these inconsistencies identified the uncertainty sources, quantified their impact, and proposed mitigations. A fuller automation of the framework could reduce the inconsistencies of the E analysis and further reduce the effort for ASEC assessment of AM vs. CM for buildings.

Advisor: Fischer Martin
Bazjanac Vladimir 
Billington Sarah L. (Sarah Longstreth)
Lepech Michael
Asymptotics of Gaussian processes and Markov chains

In this thesis, we present several results on the asymptotic behavior of Gaussian processes and Markov chains. In the first part, focused on Gaussian processes, we prove a central limit theorem for the sum of i.i.d. high-dimensional random vectors. Surprisingly, not much is known about the optimal dependence of the convergence rate on the dimension of the vectors. Our main contribution is to prove a convergence rate in quadratic transportation distance that is close to optimal in both the dimension and the number of summands. We next prove a result (based on joint work with Jian Ding and Ronen Eldan) about general Gaussian processes: we show that if the maximum of a Gaussian process is strongly concentrated around its expectation (called "superconcentration"), then with high probability the process has many near-maximal values with low pairwise correlations (called "multiple peaks"). Such phenomena naturally arise in the analysis of disordered systems in statistical physics, where the Gaussian process values correspond to energy levels. Our result adds to an overall picture of the behavior of superconcentrated Gaussian processes described by Chatterjee. The second part of the thesis contains results concerning asymptotic behavior of Markov chains. For random walk on a graph, we prove a sharpening of a relationship established by Ding, Lee, and Peres between the cover time and the Gaussian free field. In particular, our estimate implies that in families of graphs (of size growing to infinity) where the hitting time is asymptotically much smaller than the cover time, the cover time is exponentially concentrated around its expectation, and this expectation has a simple asymptotic formula in terms of the Gaussian free field. We also analyze the mixing time of a Markov chain, known as the product replacement walk, on n-tuples of elements of some finite group. One step of the walk involves randomly choosing two of the elements a and b and multiplying a by either b or the inverse of b, with equal probability. The product replacement walk has been extensively studied in the context of random generation of group elements and is part of a larger class of Markov chains that includes random walks on matrix groups over finite fields and certain interacting particle system models. Based on joint work with Yuval Peres and Ryokichi Tanaka, we prove that the product replacement walk exhibits a cutoff phenomenon as n goes to infinity: the chain rapidly transitions from being unmixed to mixed after around 3/2 n log(n) steps.

Keywords: Mathematics.
Advisor: Dembo Amir 
Peres Y. (Yuval)
Chatterjee Sourav
At Arm's Length: Controlling State-Funded Militants in Civil War

Despite a $500 million investment, most Syrian militants receiving U.S. sponsorship did not attack ISIL. This failure is alarming; as civil wars begin to include more militant groups, states are increasingly interested in influencing their behavior in order to advance national interests. It is also surprising the degree to which militants are able to shirk given the power disparity between states and militants. This thesis asks why strong states struggle to control the weak militant groups they fund in Syria. Most answers rely on principal-agent theory alone, but I incorporate more recent literature on competition and alliances within insurgencies. I also employ a mixed-method analysis of 350 attacks conducted by Sunni militant groups with U.S. or Saudi sponsorship from 2014 to 2016.

Militant group adherence to state goals is highly mediated by participation in alliances. The extent to which Syrian militant groups in alliances obey their sponsors is influenced by the allocation of power within alliances and alliance members’ preferences. Curiously, militant groups are more inclined to follow state sponsor objectives when they ally with other militants who have different aims. Unlike alliances composed entirely of extremists, militant groups can credibly threaten to defect from alliances with groups that have dissimilar goals. In response, these alliances choose a narrow range of targets that conform to the preferences of all members and their state sponsors. This suggests that to avoid further failures, state sponsorship programs must place more consideration on alliances within insurgencies.

Keywords: Center for International Security and Cooperation
Stanford University
Syria
Militant Groups
Insurgents
foreign financing
Thesis
Advisor: Crenshaw Martha
Trinkunas Harold Antanas