VictorJuiz/thesis huggingface dataset:
Reconstitution of B cell somatic hypermutation in yeast for directed evolution

Targeted mutagenesis is a powerful technique used by nature and scientists to generate biological diversity and evolve diverse biological properties of proteins such as enzymes and antibodies. In vivo targeted mutagenesis enables rapid and continuous evolution of desired phenotypes directly in a host organism. However, existing in vivo mutagenesis tools are limited by low mutation rates and arduous gene specific gRNA expression. We describe a new method for TaRgeted In vivo Diversification ENabled by T7 RNAP (TRIDENT). TRIDENT is a platform for targeted, continual, and inducible diversification at genes of interest in yeast at mutation rates up to one-million fold higher than natural genomic error rates. TRIDENT targets natural and engineered mutagenic deaminase enzymes to precise genetic loci by fusion to T7 RNA polymerase, resulting in mutation tracks of controllable size from tens to thousands of base pairs, dependent on the positions of a T7 promoter sequence. Mutation diversity is further increased by targeting of DNA repair factors to the sites of deaminase-driven mutation, enabling sustained mutation of all four DNA nucleotides at rates greater than one substitution mutations per thousand bp, a more than 10-fold improvement over recent state-of-the art in vivo mutagenesis systems. We demonstrate application of TRIDENT to entirely replace ex vivo mutagenesis by applying it to correct point mutations in enzymes, fluorescent proteins, and evolve a red-shifted fluorescent protein. TRIDENT is a powerful in vivo mutagenesis platform and will accelerate protein engineering.

Keywords: Bioengineering.
Advisor: Smolke Christina D. 
Cochran Jennifer R. 
Jarosz Daniel
Regulation & control of multicell behavior : interactive swarm control, collective migration, and synthetic aggregation

This thesis covers three separate projects investigating the mechanisms and applications of multicellular systems. The first project is a pioneering study in the application of swarms of the protist Euglena gracilis in developing interactive and educational biotechnology. I demonstrate we can control the taxis of these single-celled phototactic organisms with directional light stimuli. Furthermore, I discuss the utility of this phenomena in STEM education through the LudusScope - a do-it-yourself (DIY) platform that includes lessons in electronics, fabrication, programming and biology. I also report on the feedback from both students and teachers about the applicability of the platform in education. The second project discusses the role of the Epithelial Cell-Adhesion Molecule (EpCAM) on migration of multicelluar sheets of epithelial cells. I describe the phenotypic change in sheet migration due to over and under-expression of EpCAM, and report on several protein-protein interactions that are responsible for mediating this change in phenotype. In the last project I discuss the application of synthetic cell-cell adhesion proteins on pattern formation in multi-cell swarms of Escherichia coli. I describe the types of patterns formed by different kinds of cell adhesions, as well as the dynamics of this phenomena. Finally I discuss theoretical models of the phenomena explored through numerical simulations.

Keywords: Bioengineering.
Advisor: Riedel-Kruse Hans
Levenston Marc Elliot 
Wang Bo (Researcher in bioengineering)
Regulation and function of transcription factor dynamics during adipogenesis

A healthy metabolism requires proper fat tissue function. Thus, the generation of new fat cells through the process of adipogenesis is a critical component in the regulation of metabolic homeostasis and change. Prior work has established an extensive list of key transcriptional regulators of the adipogenic differentiation process including the important early regulator, CEBPB, and the master regulator, PPARG. However, the expression and activation dynamics of these transcriptional regulators along with the functional consequences of the dynamics remain poorly studied. A main challenge has been the lack of tools and techniques to study the dynamics in single cells in the context of adipogenesis. In this thesis, I aimed to overcome these challenges and address the important question of how the dynamics of key transcription factors regulate the process of adipogenesis. I present two works that introduce new fluorescent cell lines and live cell imaging pipelines that enable the quantitative analysis of transcription factor dynamics during adipogenesis. I first demonstrate that slow PPARG dynamics enable pre-adipocyte cells to regulate the degree of fat cell differentiation in response to different patterns of stimulus application. I show that this regulation depends on combination of fast and slow acting positive feedback loops centered on PPARG. This network architecture allows cells to discriminate between pulsatile and continuous stimulus patterns and may explain why loss of pulsatile glucocorticoid patterns in the body is so closely associated with obesity. I then used measurements of PPARG dynamics over the course of the cell cycle to identify a time range during adipogenesis when the decision to proliferate competes with the decision differentiate. Moreover, dynamic measurements enabled me to measure the point in time where cells committed to the differentiated state and I show that this commitment point corresponds to when differentiating cells permanently withdrew from the cell cycle. Taken together my work demonstrates how measuring the dynamics of key regulators of adipogenesis suggested new models for how the number of adipocytes is controlled in fat tissue. Importantly, our findings have more general implications for how other tissues can control the number of terminally differentiated cells since both the response to pulsatile hormonal signals and cell cycle control are general features shared by cells throughout the body.

Keywords: Chemical and Systems Biology.
Advisor: Teruel Mary
Ferrell James Ellsworth 
O'Brien Lucy Erin
Sage Julien
Regulation of membrane trafficking controls dendritic patterning

The construction of a large dendritic arbor requires robust growth and the precise delivery of membrane and protein cargoes to specific subcellular regions of the developing dendrite. How the microtubule-based vesicular trafficking and sorting systems are regulated to distribute these dendritic development factors throughout the dendrite is not well understood. We identified the small GTPase RAB-10, a conserved recycling endosome protein, and the exocyst complex as critical regulators of dendrite morphogenesis and patterning in the C. elegans sensory neuron PVD. In rab-10 and exocyst mutants, PVD dendritic branches are reduced in the posterior region of the cell but are excessive in the distal anterior region of the cell. In these mutants, multiple dendrite receptors are missing from the plasma membrane and are instead trapped in late endosomes. We also demonstrate that the dendritic branch distribution within PVD depends on the balance between the molecular motors kinesin-1/UNC-116 and dynein, and we propose that RAB-10 regulates dendrite morphology by balancing endosomal traffic to appropriately distribute branching factors, including the transmembrane receptor DMA-1. After characterizing the role of RAB-10 as an essential regulator of dendritic growth and patterning, we performed a forward genetic suppressor screen and identified the chromatin remodeling factors SIN-3 and HDA-3, two members of a histone deacetylase complex (HDAC), as regulators of RAB-10-independent membrane trafficking. Loss of SIN-3 or HDA-3 robustly suppresses the dramatic dendritic branch phenotype caused by loss of RAB-10 and the exocyst. The loss of SIN-3 or HDA-3 cannot resolve all trafficking defects observed in the rab-10 mutant, as membrane proteins still accumulate in intracellular vesicles in the rab-10 sin-3 or rab-10 hda-3 double mutants. However, the inactivation of SIN-3 or HDA-3 greatly increases the population of membrane receptor, leading to the suppression of the rab-10 mutant branching phenotype. Additionally, the inactivation of SIN-3 or HDA-3 results in a decrease of late endosomes and a possible reduction in degradation of membrane proteins. This surprising result provides a link between the inactivation of HDACs and endolysosomal signaling and indicates the PVD neuron is able to activate alternative membrane trafficking pathways to compensate for the loss of RAB-10-based trafficking. We hypothesize that chromatin remodeling by HDACs controls the choice between alternate neuronal states that utilize distinct membrane trafficking programs to support elaborate dendritic growth.

Advisor: Shen Kang
Clandinin Thomas R. (Thomas Robert)
Goodman Miriam Beth 
Luo Liqun
Relationships between Memory Retrieval, Media Multitasking, and Attention: An EEG/Pupillometry Approach

When an individual retrieves a memory, trial-level fluctuations between attention lapses, and memory may interact with subject-level differences in constructs such as media multitasking and sustained attention. I will present data and reflections on my master’s research project that lend support to this theoretical framework. Concurrent EEG+pupillometry was recorded during an encoding/retrieval task with 80 young adults to examine how (a) multimodal trial-level indices of attention lapsing relate to goal coding and memory, and how (b) trait differences in self-reported media multitasking and task-based sustained attention may contribute to these interactions. Pre-trial lapses of attention as recorded by tonic changes in alpha and theta oscillatory power and pupil diameter predicted retrieval accuracy; decreases in pupil diameter and theta power and increases in alpha – assays of lapsing – predicted retrieval misses vs. hits. Subject-level increases in media multitasking were related to decreases in sustained attention and retrieval accuracy using continuous and extreme group approaches. These results suggest that trial-level interactions in attention-control-memory and subject-level differences in media multitasking and sustained attention may help explain how an individual remembers.

Keywords: Symbolic Systems Program
Stanford University
Thesis
Advisor: Wagner Anthony
Reliable machine learning via distributional robustness

As machine learning systems increasingly get applied in high-stake domains such as autonomous vehicles and medical diagnosis, it is imperative that they maintain good performance when deployed. Modeling assumptions rarely hold due to noisy inputs, shifts in environment, unmeasured confounders, and even adversarial attacks to the system. The standard machine learning paradigm that optimize average performance is brittle to even small amounts of noise, and exhibit poor performance on underrepresented minority groups. We study \emph{distributionally robust} learning procedures that explicitly protect against potential shifts in the data-generating distribution. Instead of doing well just on average, distributionally robust methods learn models that can do well on a range of scenarios that are different to the training distribution. In the first part of thesis, we show that robustness to small perturbations in the data allows better generalization by optimally trading between approximation and estimation error. We show that robust solutions provide asymptotically exact confidence intervals and finite-sample guarantees for stochastic optimization problems. In the second part of the thesis, we focus on notions of distributional robustness that correspond to uniform performance across different subpopulations. We build procedures that balance tail-performance alongside classical notions of average performance. To trade these multiple goals \emph{optimally}, we show fundamental trade-offs (lower bounds), and develop efficient procedures that achieve these limits (upper bounds). Then, we extend our formulation to study partial covariate shifts, where we are interested in marginal distributional shifts on a subset of the feature vector. We provide convex procedures for these robust formulations, and characterize their non-asymptotic convergence properties. In the final part of the thesis, we develop and analyze distributionally robust approaches using Wasserstein distances, which allows models to generalize to distributions that have different support than the training distribution. We show that for smooth neural networks, our robust procedure guarantees performance under imperceptible adversarial perturbations. Extending such notions to protect against distributions defined on learned feature spaces, we show these models can also improve performance across unseen domains.

Keywords: Management Science and Engineering.
Advisor: Duchi John 
Glynn Peter W. 
Blanchet Jose H. 
Van Roy Benjamin
Remembering Okinawa: The Himeyuri Story and Construction of Wartime Memory in Japan

The Battle of Okinawa is remembered as the bloodiest battle of the Pacific War. It involved the mobilization of locals on an unprecedented scale, leading to countless civilian casualties inflicted by both forces. Himeyuri Corps was a nursing unit during the battle that was comprised of mobilized female students in Okinawa. Since the early years of the postwar, the story about the unit has become central to how the wartime is remembered in Japan. A series of media productions popularized the story, making the monument commemorating deceased members of the unit, the Himeyuri Cenotaph, a popular tourist destination since the 1950s. However, it was not until the 1980s the survivors publicly discussed their experience through founding the Himeyuri Peace Museum. This thesis will address the following questions: how did the story become a cultural icon? What was the survivors’ role in establishing and maintaining the story? This thesis will first discuss the popularity of the story and its reflection of Japanese territoriality. It will then look into the opening process of the museum alongside its current narrative.

Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
Japan
Okinawa
History
20th century
Battle of Okinawa
Museums
Memory
Territoriality
Peace
Thesis
Advisor: Uchida Jun
Representation theory, randomness, and quantum information science

Often, understanding physical systems or mathematical structures requires analyzing the generic behaviors instead of specific examples. In such cases, a common approach is identifying the degrees of freedom that are supposed to be generic and replacing them with entirely random values. This line of thinking has been fruitful in many areas of physics, such as nuclear physics, quantum chaos, quantum field theory, and quantum gravity. Within quantum information science, which is the primary focus of this dissertation, multiple areas including quantum entanglement, the quantum capacity of channels and quantum Shannon theory, the construction of quantum codes, and benchmarking quantum systems have heavily benefited from randomization techniques. Most of this manuscript is devoted to random constructions and relevant mathematical formalisms, such as representation theory and group theory, and their applications in quantum information science and quantum gravity. In particular, we will study the following topics: (1) Applications of representation theory and randomization in quantum error correction, including approximate versions of the Eastin-Knill theorem and the reference frame quantum error correction. (2) Schur-Weyl duality for the stabilizer formalism, with applications in quantum property testing and de Finetti theorems (3) Randomized constructions and information theoretic methods for holographic duality, including the holographic entropy cone and random tensor networks.

Keywords: Physics.
Advisor: Hayden Patrick (Patrick M.)
Hartnoll Sean
Shenker Stephen Hart
The response of electronic nematic order to dynamical strain : a study of the iron based superconductors

Over the last decade, there has been increasing evidence that electronic nematic states, which break discrete rotational symmetry in crystalline solids, and their associated critical points are important constituents in the phase diagrams of several widely studied families of materials. Electronic nematic states are evident in the iron-based superconductors, and there are mounting indications of their presence in the cuprate and heavy fermion superconductors. This work discusses new techniques to investigate signatures of electronic nematicity, and more broadly addresses how dynamical mechanical deformation can be used to control and study materials. This thesis begins with the development and characterization of an improved technique for measuring the resistance response of materials to strain, using AC strain at frequencies between 10mHz and 10kHz. By using strain of the same symmetry as the electronic-nematic order parameter in Ba(Fe0.975Co0.025)2As2, this thesis demonstrate a novel and effective way to measure the dynamical nematic susceptibility, which has not been studied previously, and highlight potential future use cases in disordered media or high magnetic fields. Next, using dynamical strain of a different symmetry class than the nematic and Neel order parameters I show new anomalies in the elastoresistivity and elastocaloric effect of Ba(Fe0.975Co0.025)2As2, which extends the well-known Fisher-Langer theory of critical phenomena. Lastly, I study how the resistance response to an external mechanical perturbation is influenced by and encodes information about the thermal activated relaxation of domain walls in response to external stresses.

Keywords: Physics.
Advisor: Fisher Ian R. (Ian Randal) 
Kivelson Steven 
Lee Young Hee
Retail to E-tail: Understanding how ecommerce has reshaped the retail industry

This thesis further explores and analyzes the changing landscape of the retail industry with the advent and growth of ecommerce. First, I provide background information of traditional brick and mortar retail, which I argue was largely defined by its cost structures associated with dependence on physical space. I begin my transition to understanding etail by following historical patterns of how retail changed alongside technological advancements. However, the internet and its capabilities quickly proved to be revolutionary and the traditional brick and mortar operation confined by traditional cost structures and physical spaces could not adapt adequately. Analysis of the evolving new consumer and rebalance of power dynamics reveals how the industry has begun to transform. This work concludes with three case studies to illustrate the complexities of this transformation and operations in the new e-tail heavy industry.

Keywords:  Department of Communication
Stanford Communication Department
Stanford University
Retail Industry
Retail
Ecommerce
Etail
Amazon.com
Amazon Effect
Retail Apocalypse
Thesis
Advisor: Hamilton James
Rethinking single-cell RNA-Seq analysis

Since the Human Genome Project was completed in 2003, scientists have developed technologies for measuring the RNA content of a single cell. In the last decade, the number of individual cells profiled per study has grown exponentially to over 1,000,000 cells. In this thesis, I will discuss some of the computational and statistical challenges associated with the analysis of such large single-cell datasets. After introducing background information, the thesis covers three main works. The first work introduces a novel, interpretable framework with the biologist end user in mind. The framework also addresses the clustering subjectivity issue by justifying its results based on a rigorous definition of cell type. This allows us to cluster using feature selection to uncover multiple levels of biologically meaningful populations in the data. The second work considers a novel approach for representing single-cell RNA-Seq data. We argue that gene or transcript expression vectors, while intuitive, are not the most optimal way for representing single cell genomic profiles. Rather than counting the number of reads that comes from each transcript, which requires resolving the ambiguity associated with read multimapping, we decide to count the number of reads that comes from each transcript set. We show that these new representations are both more computationally efficient to obtain and more information-rich. The third and perhaps most interesting work first observes a post-selection inference problem in standard single-cell computational pipelines. Standard pipelines perform differential analysis after clustering on the same dataset, and this reusing of the same dataset generates artificially low p-values and hence false discoveries. We introduce a valid post-clustering differential analysis framework which corrects for this problem. In summary, we discuss multiple works for drawing key insights from single-cell RNA-Seq data: a clustering method that emphasizes interpretability of results, a representation of single cells that retains more information from read data, and a framework for correcting the selection bias from standard analysis pipelines.

Keywords: Electrical Engineering.
Advisor: Tse David 
Nishimura Dwight George 
Zou James
Revealing the small scale structure of dark matter with gravitational lensing

The standard model for dark matter, the Cold Dark Matter model, which accurately predicts the formation and evolution of large scale structure in the universe, appears to be in disagreement with observations of structure on much smaller scales. It is, as of yet, unclear if this is due to astrophysical processes causing a reduction in the efficiency of star formation in low mass galaxies, or due to the suppression of low mass structure formation altogether caused by exciting new dark matter microphysics. Gravitational lensing offers a unique and luminosity-independent means of distinguishing between these possibilities. In this work, I present tools to simulate realistic ALMA observations of strong gravitational lenses, and to model the small scale distribution of dark matter using interferometric observations of strong lenses. We applied this technique to observations of the gravitational lens SDP 81, and identified a low mass perturbing object, and placed new constraints on the abundance of low mass dark matter halos. I also present several techniques to automate the inference of the smooth distribution of matter in these objects, which allow inference to proceed 1.6 billion times faster, and which strongly reduce the need for human supervision of the modeling process. With these techniques in hand, we are poised to extract important information about the elusive dark matter.

Keywords: Physics.
Advisor: Blandford Roger D.
Abel Tom G.
Wechsler Risa H. (Risa Heyrman)