VictorJuiz/thesis huggingface dataset:
A novel high-throughput screening system for directed evolution of robust optical reporters of electrical activity in the brain
Keywords: Bioengineering.

Dynamic patterns of neuronal activity within intact neuronal circuits underly the astounding complexity of the brain. To decipher this code, we need tools that can monitor neuronal electrical activity with sufficient resolution in space and time, in awake behaving animals. Genetically encoded voltage indicators (GEVIs) offer great potential in achieving this goal but require significant improvements before they can be routinely deployed in vivo. A major limitation to developing better voltage sensors has been a lack of high-throughput screening methods that can reproducibly induce a fast change in transmembrane potential of a cell. In my thesis work, I present the development of a new electroporation-based screening approach, and mutant library construction and expression methods that enabled functional testing of GEVI variants at unprecedented throughput. I further describe using the screening system and a mechanism-guided approach to improve ASAP-family voltage indicators by directed evolution. One of engineered variants, ASAP3, exhibits up to 51% fluorescence change in the physiological range, millisecond response kinetics, efficient membrane localization and full activity under two-photon illumination. Finally, I show imaging ASAP3 signals in the awake mammalian brain using volumetric kHz-rate random-access multiphoton microscopy. The improved responsivity of ASAP3 to voltage allowed imaging of oscillations and spikes in single cells in single trials, revealing correlations between neuronal activity and animal behavior.
Novel materials and nanofabrication methods for the Dielectric Laser Accelerator (DLA)
Keywords: Electrical Engineering.

Dielectric laser accelerators (DLA), laser-driven photonic structures whose evanescent fields can synchronously accelerate charged particles, have recently demonstrated acceleration gradients far exceeding those possible with conventional radio frequency (RF) sources, opening the possibility of shrinking both the size and cost of linear accelerators by orders of magnitude. The acceleration gradient of the DLA is ultimately limited by the laser-induced damage threshold (LIDT) of the constituent materials. A material with high LIDT which can be shaped precisely into sub-wavelength nano-structures is an ideal candidate for DLA. My contributions can be roughly categorized into two sections: improvements to single-stage DLA and proof-of-concept demonstrations of essential elements to realize a multi-stage implementation of DLA. My improvements to single-stage acceleration are as follows: I report the first successful DLA demonstration using novel materials with high LIDT. I present the nanofabrication methods that I have developed to shape these novel metal oxide materials (Al2O3 and Ga2O3) precisely into sub-wavelength nanostructures. I demonstrate the first successful metal oxide-based laser-driven electron acceleration in the sub-relativistic regime, with devices made of both Al2O3 and Ga2O3. I propose and simulate the performance of metal oxide-based DLA for the relativistic regime, which can achieve higher acceleration gradients when compared to current SiO2 DLA structures. Finally, I show that thin-layer coatings of these metal oxide materials act as a field reduction layer, which improves accelerator performance. My contributions to future multi-stage DLA are essential elements for a practical on-chip accelerator. I have fabricated Ga2O3-core/SiO2-clad waveguide on Si substrate and Al2O3-core/SiO2-clad waveguide on Si substrate for laser power delivery, which is crucial for future on-chip integrated DLA. I, along with Professor Peter Hommelhoff's Group, present work on realizing sustained acceleration. These include a chirped DLA grating to counteract dephasing effects, a laser-driven electron lens, and the first demonstration of a two-stage DLA. I show co-propagation-based DLA, including a ring resonator-based DLA and total internal reflection-based DLA. These results constitute an important step towards enabling an "Accelerator On A Chip.".
Novel methods and applications for kinetic plasma simulation
Keywords: Physics.

Understanding the behavior of plasma is important for a broad range of applications, such as understanding the production of energetic particles in astrophysics, developing predictive models for space weather, and harnessing the potential of nuclear fusion power. Due to limitations such as noise from numerical collisions and the large number of simulation particles required to capture the development of nonthermal tails in the particle distribution, multiscale plasma simulations are extremely challenging. In this thesis the simplex-in-cell algorithm is presented, which holds promise for overcoming these difficulties by interpreting the simulation particles as the vertices of a mesh that traces the evolution of the distribution function in phase space. This enables a discretization using deformable phase space volume elements rather than fixed-shape clouds of charge. Using test problems including Landau damping and the Weibel instability it is shown how this new view retains fine-scale structure in the distribution function and can drastically reduce the number of simulation particles required to reach a given noise level. Magnetic reconnection is a promising candidate mechanism for accelerating the nonthermal particles associated with explosive phenomena in astrophysics. Laboratory experiments with high-power lasers can play an important role in the study of the detailed microphysics of reconnection and the dominant particle acceleration mechanisms. In this thesis the results of particle-in-cell simulations used to explore particle acceleration in conditions relevant for current and future laser-driven reconnection experiments are presented. These simulations indicate that laser-driven plasmas offer a promising platform for studying particle acceleration from reconnection, with the potential to reach multi-plasmoid regimes of strong astrophysical interest. These results provide new insight into the physics of reconnection and particle acceleration and are now helping to guide experimental campaigns.
Novel pharmacological modulators of mitochondrial function in cell physiology and pathology
Keywords: Chemical and Systems Biology.

Within a single cell, thousands of mitochondria form a beautiful dynamic network. Beyond their canonical adenosine triphosphate (ATP) generating role, mitochondria more recently gained recognition as the hub of numerous signaling pathways that mediate cell life and death. Mitochondria are ubiquitous organelles that play a pivotal role in many organs. It is not therefore not surprising that dysfunctional mitochondria, and the excessive reactive oxygen species (ROS) they produce, have been implicated as the culprits of a variety of disorders spanning multiple body systems. This dissertation describes the development of novel pharmacological tools that target mitochondrial function in cell physiology and pathology. Chapters 1 and 2 introduce mitochondria as attractive therapeutic targets for cardiovascular and neurodegenerative diseases, respectively. Each chapter includes a review of relevant clinical trials and outcomes of therapeutics that either generally target ROS versus ones that specifically target mitochondrial dysfunction. In addition, they highlight the advantages and the challenges of therapeutic avenues involving mitochondria. Chapter 3 summarizes the use of peptides as specific protein-protein interaction (PPI) inhibitors for ameliorating the ischemia-reperfusion (IR) damage associated with myocardial infarction. These novel pharmacological tools target protein kinase C delta ( PKC), which translocates to the mitochondria in response to stress induced by IR and phosphorylates numerous mitochondrial substrates. These PPI peptide inhibitors each specifically inhibit the phosphorylation of one protein substrate. Chapters 4 and 5 focus on the characterization of activators and inhibitors of mitochondrial fission and fusion, the core processes of mitochondrial dynamics. Mitochondrial dynamics govern mitochondrial shape and size, which are tightly linked to function. Dysregulation of these processes is a hallmark of neurodegeneration, in particular Huntington's disease and Charcot Marie Tooth Type IIa, both of which serve as the backdrop model for these newly-identified peptide modulators.
Nuclear Propaganda in Select Regional Newspapers in Japan: A Snapshot Analysis of 1986 and 2010
Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
Japan
Nuclear Energy
STS
Media and Industry
Thesis

Given the devastation of suffering two nuclear attacks in its history, how has Japan become so comfortable and invested at a social level with nuclear energy? How has this manifested at a local level, outside of the urban center of such a centralized nation? Historically, the media and private energy industry have a long history of interconnectivity and, throughout the latter half of the 20th century, efforts were made to leverage nuclear energy as a nation-building socio-technological tool. Working within the framework of scholarship in Science, Technology and Society (STS) studies and Japanese scholarship, this thesis investigates pro-nuclear advertisements in regional newspapers in Japan in order to understand how public opinion towards nuclear energy was influenced at a local level. By accessing a unique data set of regional newspapers in 1986 and 2010, the qualitative analysis explores how advertising strategy and local characteristics shifted and evolved over the course of the years. This kind of investigation can shed light on the historical strategies used by the private energy industry in local regions, so that these tactics can be recognized in the future as Japan’s energy policy continues to evolve.
Numerical experiments in thermal convection with and without mean shear
Keywords: Mechanical Engineering.

The specific problem of thermal convection with and without mean shear is studied. By leveraging advances in flow visualization, numerical algorithms, and large-scale computing, both laboratory and numerical experiments are just beginning to outline a picture of turbulent convection in wide fluid layers and clarify the effects of buoyancy and related analogies on shear-flow turbulence in many industrial and environmental fluid flows. Herein, mean shear is established by mechanical means and density fluctuations induced by heating a horizontal fluid layer sandwiched between hot and cold walls. Even without mean shear, vigorous convection currents develop under the influence of such heating by the direct action of buoyant body forces. It is of some importance to predict how heat transport by both big and small eddies is affected by the action of buoyancy and mean shear in wall-bounded channel flows as well as in other more complex configurations. One of the most striking features of unstable buoyant shear layers is the appearance of unsteady longitudinal vortices, which are characterized by an organized helical circulation of individual buoyant thermals stirred into alignment by mean shear near the walls. These are in marked contrast to the polygonal convection cells and heat transport associated with Rayleigh-Benard convection without strong mean shear. This report provides a unique physical picture of wall turbulence in these flows and discusses the significance of the present results for predicting and modeling subgrid-scale phenomena.
Numerical simulations of turbulent reacting flows with finite-rate chemistry and combustion-model adaptation [electronic resource]
Keywords: Mechanical Engineering.

The utilization of finite-rate chemistry with realistic chemical kinetics in large-eddy simulations (LES) of turbulent reacting flows has attracted increasing interest in recent years. It enables further improvement in the prediction of emissions in the combustion process and helps eliminate the restriction often imposed by regime-specific combustion models. However, the cost of such approaches remains a great concern despite the continuing growth of computational capacity, as they are often orders-of-magnitude more expensive than the regime-specific flamelet-type models. In this study, the framework of Pareto-efficient combustion (PEC) modeling is developed, which allows the adaptive utilization of multiple combustion models under consideration of cost-fidelity characteristics for efficient simulations of complex turbulent flames. This adaptive approach provides a general combustion modeling capability with an adjustable level of fidelity and complexity. Based on the user-specific inputs on quantities of interest, desired simulation accuracy and computational cost, the PEC framework makes adaptive model assignments restricting the application of sub-models within their intended use. For complex problems, the proposed framework allows the usage of detailed description of the combustion chemistry in limited regions where it is necessary, while avoiding the concomitant overhead in regions where flamelet-type models are deemed sufficient. Efficient computational strategies are developed to accelerate large-scale parallel simulations with stiff finite-rate chemistry, including time stepping methods and load rebalancing techniques. The combined approach is applied to LES of turbulent flames, demonstrating its capability in improving the predictability and cost-effectiveness of turbulent combustion simulations.
Oil and Gas Development and Risk of Preterm Birth: Evidence from San Joaquin Valley, California
Keywords: Epidemiology and Clinical Research
Health Research and Policy
School of Medicine
preterm birth
environmental epidemiology
perinatal health
Thesis

Background. Prior studies report an association between adverse birth outcomes, including preterm birth, and proximity to oil and gas wells. Most attention has focused on unconventional natural gas developments, with little attention to conventional oil and gas extraction. Both conventional and unconventional oil and gas development have high levels of air pollution emissions.
Objective. Our objective was to determine whether maternal residence proximity to conventional oil and gas wells in active development increases the risk of preterm birth. Methods. We obtained health data from birth certificates linked to hospital discharge data from the Office of Statewide Health Planning and Development. Data on oil and gas wells came from the California Department of Conservation. After exclusion, the births dataset had 27,913 preterm cases and 197,461 term controls, with a total of 225,374 births, and the wells dataset included 11,972 oil and gas wells in active development. For each birth, we computed an exposure index by taking the sum of the inverse distance to all wells in active development within a 15 km radius of the maternal residence. We defined four preterm birth categories based on gestational length (in weeks): 20-23, 24-27, 28-31, and 32-36. We estimate crude and adjusted odds ratios for preterm birth using logistic regression. To test the robustness of our results, we used different radii and formulas to generate the exposure index and determined whether this changed the findings. To assess the effect of distance to wells, we generated an indicator for maternal residences with at least one well within a radius of 1 km, 3 km, 5 5 km, 7 km, or 10 km.
Results. Exposure to oil and gas wells in active development was associated with a small but significant increase in the risk of preterm birth. The risk was highest for the 32 to 36-week preterm birth category, with exposure throughout gestation associated with increased risk for this group. Several sensitivity analyses testing the assumptions of our exposure assessment method found similar results. In addition, the significant effects persisted for births that had at least one well within 1 km, 3 km, or 10 km for early preterm birth, and within 5 km or 7 km for late preterm birth.
Conclusions. Exposure to oil and gas wells in active development was associated with higher risk of preterm birth. More work is needed to infer whether a causal link exists between the exposure and adverse birth outcomes.
On Payday, and on Principle: Mick Mulvaney and the Bounds of Reasonable Debate in Consumer Financial Protection
Keywords:  Public Policy Program
Stanford University
Humanities and Sciences
Public Policy Program
CFPB
payday lending
Mick Mulvaney
consumer finance
Thesis

In the first half of 2018, amid a flurry of deregulatory action across financial markets, Consumer Financial Protection Bureau (CFPB) Acting Director Mick Mulvaney announced the reconsideration of newly adopted restrictions on payday loans alongside the sudden end of both lawsuits against and investigations into some of America’s largest payday lenders. The following paper evaluates the motivation, economic rationale, and context of Mulvaney’s actions. Payday loans are high-interest loans usually collateralized by a forward-dated check due on the borrower’s next payday. Academic sentiment concerning these loans is divided, with some positing that the product can be narrowly welfare promoting for the financially desperate, others maintaining that payday loans are simply a predatory debt trap, and both sides acknowledging a paucity of alternatives for those in financial despair. However, as I will argue, Mulvaney’s actions as Acting Director- especially those related to the enforcement of existing law pertaining to payday loans- fall outside the scope of what might be defended by those on either side of debate concerning the economics and policy choices surrounding payday lending. Instead, his actions make sense almost exclusively when viewed through the lens of Mulvaney’s stated distain for CFPB, his opposition to federal intervention in financial markets, and a desire to defend the payday industry at large. As I will discuss, this exists both aside from and in direct opposition to conservative ideals regarding regulation.
On String-Similarity Estimation Problems
Keywords:  Department of Computer Science
Stanford School of Engineering
Department of Computer Science
Algorithm
Edit Distance
Approximation Algorithm
Embedding
Thesis

In this thesis, we study two widely used measures of string similarity: edit distance and dynamic time warping (DTW). We investigate alignment algorithms, metric embeddings, fast (approximation) algorithms, and communication complexity.
Results on Edit Distance: Edit distance is a fundamental distance metric between strings with applications throughout computer science. While the problem of estimating edit distance has been studied extensively, the equally important question of producing an alignment (i.e., the sequence of edits) has received far less attention. Somewhat surprisingly, we show that any algorithm to estimate edit distance can be used in a black-box fashion to produce an approximate alignment of strings, with modest loss in approximation factor and small loss in run time. Plugging in a previous result, we obtain an alignment that is a $(\log n)^{O(1/\varepsilon^2)}$ approximation in time $\tilde{O}(n^{1 + \varepsilon})$.
Closely related to the study of approximation algorithms is the study of metric embeddings. We present three results which together demonstrate the effectiveness of min-hash techniques in designing edit distance embeddings: (1) An embedding from Ulam distance (edit distance over permutations) to Hamming space that matches the best known distortion of $O(\log n)$ and also implicitly encodes sequences of edits; (2) Given an upper bound $K$ on edit distance, we show that embeddings of edit distance into Hamming space with distortion $f(n)$ can be modified in a black-box fashion to give distortion $O(f(\operatorname{poly}(K)))$ for a class of periodic-free strings; (3) A randomized dimension-reduction map with contraction $c$ and asymptotically optimal expected distortion $O(c)$, improving on a previous result.
Results on Dynamic Time Warping: In the second half of the thesis, we turn our attention to dynamic time warping (DTW), a close cousin of edit distance whose primary applications are as a similarity measure between time series.
We present the first strongly subquadratic algorithm for DTW in the low-distance regime. Our algorithm runs in time $O(n \cdot \dtw(x, y))$ for $x$ and $y$ of length $n$ over an arbitrary metric. Building on this, we present an approximation algorithm for $\dtw$ over an arbitrary $2$-hierarchically well-separated tree metric, providing a $\alpha$-approximation in time $\tilde{O}(n^2/\alpha)$. Additionally, we obtain the first approximation algorithm for edit distance to work over an arbitrary metric space, providing an $\alpha$-approximation in time $\tilde{O}(n^2 / \alpha)$, with high probability.
Finally, we provide near tight bounds on the one-way communication complexity of DTW -- an upper bound of $\tilde{O}(n / \alpha)$ bits for efficiently computing an $\alpha$-approximation of DTW between strings of length $n$, and a lower bound of $\Omega(n / \alpha)$ bits for the same problem.
On the efficient analysis and sampling of mutant free energy landscapes
Keywords: Chemistry.

Molecular dynamics (MD) simulations are a computational technique capable of providing detailed atomic level understanding of molecular processes. They do this by numerically solving the microscopic interactions that govern these processes. Akin to a "computational microscope", MD simulations are used to predict and understand these protein free-energy landscapes. Given simulations from related mutant proteins, MD simulations could even predict the atomic level effects of mutations. These mutations might be oncogenic thereby increasing cell proliferation or they might abrogate an inhibitors' binding affinity or they might be completely benign. A structural and quantitative model for these mutations would be invaluable in both understanding the system and potentially designing personal therapeutics. However, MD simulations are very computationally expensive to converge and it is not immediately obvious how to compare multiple mutant simulations to one another in a statistically significant and efficient manner. In this thesis, I describe methods for analyzing and sampling mutant free energy landscapes. The first few chapters are dedicated towards building Markov state models (MSMs) for multiple protein kinases using milliseconds of aggregate simulation data. Currently, these are some of the largest kinase simulation datasets ever recorded. The latter chapters present several methodological advances on how to more efficiently predict mutational effects using a combination of enhanced sampling simulations and existing MSMs. This thesis attempts to merge the fields of Machine learning, enhanced sampling, and Markov state modeling for the efficient analysis and sampling of protein mutation landscapes.