VictorJuiz/thesis huggingface dataset:
Molecular tools to modulate voltage-gated sodium channels
Keywords: Chemistry.

Voltage-gated sodium channels (NaVs) are responsible for sodium influx upon cellular depolarization and, as such, are fundamental to action potential initiation and propagation. There are nine voltage-sensing channel subtypes, NaV1.1--1.9, which are differentially distributed throughout the body. NaV dysfunction—whether in terms of channel activity, expression, or localization—is implicated in a host of diseases, including epilepsy, multiple sclerosis, and neuropathic pain. Efforts to fully elucidate the mechanisms of NaV-dependent pathogenesis are limited by a lack of rapid, efficient, and selective methods for the manipulation of NaV subpopulations (Chapter 1). Accordingly, we have developed tools for subtype-selective (Chapter 2) and location-specific (Chapters 3--4) channel inhibition. Toward the goal of subtype-selective channel inhibition, we designed a mutant NaV-electrophile pair for the covalent labelling of individual NaV subtypes. Building off work from Drs. William Parsons and Darren Finkelstein, we determined that a reactive saxitoxin (STX) derived probe, saxitoxin ethyl maleimide, selectively and irreversibly inhibits engineered NaVs expressing appropriately positioned cysteine point mutations. Labelling occurs rapidly and efficiently at low probe concentrations—i.e., up to 40% irreversible inhibition after 6-minute application of 500 nM toxin. Modification of STX ethyl maleimide with a biotin moiety or 'clickable' tetrazine tag should enable live cell imaging of target NaV subtypes. The efficacy of NaV labelling by such trifunctional probes has been demonstrated by patch clamp electrophysiology; efforts continue to translate these results to channel visualization with confocal microscopy and immunoblotting. Toward the goal of location-specific channel inhibition, one-photon (Chapter 3) and two-photon (Chapter 4) photocleavable groups were appended to saxitoxin. Derivatization of the photocage with carboxylate groups destabilized STX binding to the NaV. As such, inert photocaged STX compounds (STX PCs) could be applied to cells prior to selective, light-induced, focal release. Several rounds of optimization yielded STX PCs with large differences in potency pre- vs. post-uncaging—5.2 µM vs. 90 nM against rat hippocampal neurons for the optimal one-photon STX PC; 2.1 µM vs. 100 nM for the analogous two-photon STX PC—following exposure to only 5 ms of 130 mW UV light. These large differences in potency enable precise control of action potential firing in hippocampal neurons, with tandem laser--STX PC application acting as a molecular switch for electrical activity. Collaborations using these STX PCs to examine the role of NaVs in epilepsy are ongoing. The precise control of NaV function, whether through the modulation of a single channel subtype or a localized subpopulation, will help disentangle the complex biochemistry underlying electrogenesis and ectopic excitability, thereby aiding efforts to develop better treatments for electrical signaling disorders
Momentum Subspaces in Gapped Relativistic Quantum Field Theories as Codespaces for Quantum Error Correction
Keywords:  Department of Physics
Quantum error correction
Quantum field theory
Thesis

This thesis reports a study of the quantum error correction properties of quantum field theory through the investigation of codespaces comprising momentum states subject to the error of spatial erasure. A formulation of code quality that measures state distinguishability using expectation values of localized operators is proposed, after which it is evaluated in the context of gapped relativistic theories under the limit of a small erasure region. An analytic bound on the code quality is derived for free theory as a function of the size of erasure and momentum, while an estimate of the code quality is provided for general interacting theories, with substantial progress towards a precise bound. A discussion of relations with the information structure of quantum field theory and possible directions for further research is provided.
Monte Carlo planning and reinforcement learning for large scale sequential decision problems
Keywords: Aeronautics and Astronautics.

Autonomous agents have the potential to do tasks that would otherwise be too repetitive, difficult, or dangerous for humans. Solving many of these problems requires reasoning over sequences of decisions in order to reach a goal. Autonomous driving, inventory management, and medical diagnosis and treatment are all examples of important real-world sequential decision problems. Approximate solution methods such as reinforcement learning and Monte Carlo planning have achieved superhuman performance in some domains. In these methods, agents learn good actions to take in response to inputs. Problems with many widely varying inputs or possible actions remain challenging to efficiently solve without extensive problem-specific engineering. One of the key challenges in solving sequential decision problems is efficiently exploring the many different paths an agent may take. For most problems, it is infeasible to test every possible path. Many existing approaches explore paths using simple random sampling. Problems in which many different actions may be taken at each step often require more efficient exploration to be solved. Large, unstructured input spaces can also challenge conventional learning approaches. Agents must learn to recognize inputs that are functionally similar while simultaneously learning an effective decision strategy. As a result of these challenges, learning agents are often limited to solving tasks in virtual domains where very large amounts of trials can be conducted relatively safely and cheaply. When problems are solved using black-box models such as neural networks, the resulting decision making policy is impossible for a human to meaningfully interpret. This can also limit the use of learning agents to low-regret tasks such as image classification or video game playing. The work in this thesis addresses the challenges of learning in large-space sequential decision problems. The thesis first considers methods to improve scaling of deep reinforcement learning and Monte Carlo tree search methods. We present neural network architectures for the common case of exchangeable object inputs in deep reinforcement learning. The presented architecture accelerates learning by efficiently sharing learned representations among objects of the same type. The thesis then addresses methods to efficiently explore large action spaces in Monte Carlo tree search. We present two algorithms, PA-POMCPOW and BOMCP, that improve search by guiding exploration to actions with good expected performance or information gain. We then propose methods to improve the use of offline learned policies within online Monte Carlo planning through importance sampling and experience generalization. Finally, we study methods to interpret learned policies and expected search performance. Here, we present a method to represent high-dimensional policies with interpretable local surrogate trees. We also propose bounds on the error rates for Monte Carlo estimation that can be numerically calculated using empirical quantities
A morphology framework for understanding fungal-bacterial interactions in soil
Keywords: Biology.

Soils are physically, chemically, and morphologically complex at microscopic scales. Bacterial and fungal communities in soils, likewise, display extreme micro-heterogeneity. This dissertation explores deeper consideration of microscale soil context in studying microbial biogeography. I begin with the assumption that microbial communities are structured by their physical habitats -- microscale soil pores -- and that soil conditions that influence pore size, shape, and connectivity may be significant predictors of microbial communities and interactions at landscape scale. Further, that consideration of microbial traits related to the way they physically navigate soil pores (filamentous growth form, flagellate motility) may predict their individual and collective responses to changes in the soil environment. In my first chapter, I surveyed bacterial and fungal biomass and communities across a landscape with diverse soil characteristics and plant communities. I found that community structures of unicellular and filamentous fungi and bacteria were correlated with distinct soil and plant factors. These communities were sampled from pooled, sieved soils, as is common in microbial ecology. In my second chapter, I investigated the effect of sieving soil samples on patterns of bacterial and fungal biogeography, and the detection and strength of microbial interactions. Sampling microbial communities in individual aggregates revealed distinct distance-decay relationships between fungi and bacteria, and between different bacterial genera. Further, network analysis of soil aggregates revealed distinct positive associations between members of specific fungal genera and putative fungal-mutualist bacteria from genus Burkholderia compared to sieved soil. These bacterial-fungal interactions might be mediated by environmental context, especially degree and frequency of soil saturation. Specifically, flagellate soil bacteria may move along water films surrounding fungal hyphae, exploiting 'fungal highways' to bypass air-filled pores. In my third chapter, I tested the fungal highway mechanism by culturing diverse flagellate bacteria and filamentous fungi from sediment, co-culturing a selected pair in saturated and unsaturated sand, and measuring their distribution and biomass in relationship to each other. The bacterium benefited from exploitation of fungal highways in unsaturated -- but not saturated -- soil media. By investigating the impact of soil morphology on microbial biogeography and interactions, my dissertation shows the potential utility of soil pore-scale considerations in reducing the noise of a seemingly intractably complex system
Multimodal representations for vision, language, and embodied AI
Keywords: Electrical Engineering.

Recent years have seen incredible growth and advances in artificial intelligence research. Much of this progress has primarily been made on three fronts: computer vision, natural language processing, and robotics. For example, image recognition is widely considered the holy grail of computer vision, whereas language modeling and translation have been fundamental tasks in natural language processing. However, many practical applications and tasks require going beyond solving these domain-specific problems and instead require solving problems which involve all three of the domains together. An autonomous system not only needs to be able to recognize objects in an image, but also interpret natural language descriptions or commands and understand how they might relate to its perceived visual observations. Furthermore, a robot needs to utilize this information for decision-making and determining which physical actions to take in order to complete a task. In the first part of this dissertation, I present a method for learning how to relate natural language and 3D shapes such that the system can draw connections about words like "round" described in a text description with the geometric attributes of round in a 3D object. To relate the two modalities, we rely a cross-modal embedding space for multimodal reasoning and learn this space without fine-grained, attribute-level categorical annotations. By learning how to relate these two modalities, we can perform tasks such as text-to-shape retrieval and shape manipulation, and also enable new tasks such as text-to-shape generation. In the second part of this dissertation, we allow the agent to be embodied and explore a task which relies on all three domains (computer vision, natural language, and robotics): robot navigation by following natural language instructions. Rather than relying on a fixed dataset of images or 3D objects, the agent is now situated in a physical environment and captures its own visual observations of the space using an onboard camera. To draw connections between vision, language, and robot physical state, we propose a system that performs planning and control using a topological map. This fundamental abstraction allows the agent to relate parts of the language instruction with relevant spatial regions of the environment and to relate a stream of visual observations with physical movements and actions
Multiple Sclerosis Relapses After Vaccination: A Retrospective Claims Analysis
Keywords: multiple sclerosis
vaccination
relapses
real-world data
claims
epidemiology
Thesis

Introduction
Multiple sclerosis (MS) is an autoimmune, inflammatory condition of the central nervous system, characterized by subacute focal neurological episodes, referred to as MS relapses. As high efficacy disease modifying therapies (DMTs) are increasingly available, the goals of MS treatment have shifted toward a greater focus on health maintenance. Accordingly, individuals with MS are advised to undergo routine vaccinations following the same schedules as the general population. However, vaccination with live attenuated vaccines remains an exception to this general rule, due to concerns that live vaccines can exacerbate MS.
Methods
We conducted a retrospective cohort study of individuals in the real world, using administrative health claims and McNemar’s test, to examine relapse rates in 7,631 individuals with MS in the 60 days before and after several common vaccinations: influenza (flu); tetanus, diphtheria, pertussis (Tdap); human papillomavirus (HPV); live attenuated varicella zoster (VZV); and yellow fever.
Results
Our study found no statistically significant difference in the number of pre- and post-vaccination relapses for individuals who received flu (n=5,550), Tdap (n=2,745), and live VZV vaccines (n=377). There were not enough individuals who received HPV or yellow fever vaccines to perform statistical analysis.
Discussion
While our findings support current clinical practice when recommending routine non-live vaccinations, they are in contrast to prior studies suggesting increased relapse rates after vaccination with live attenuated vaccines. To our knowledge, ours is the first study to examine a cohort larger than 700 individuals, leveraging real-world data in a study of vaccine-associated MS relapses. While further analyses are warranted, our study suggests that live attenuated vaccines do not appreciably increase the risk of MS relapses.
Multi-probe cluster cosmology analyses with photometric surveys
Keywords: Physics.

The universe we live in is expanding faster and faster. This phenomenon called cosmic acceleration is one of the most puzzling cosmological discoveries in the past 25 years: even the least exotic explanation requires a new pervasive energy component in our universe (called dark energy). Despite the mysterious nature of dark energy, a model ($\Lambda$CDM) based on Einstein's general relativity, a cosmological constant (a specific form of dark energy), and slowly moving dark matter, seems to be able to describe a variety of observations from the high- to low-redshift universe. To understand the nature of dark energy and to test the $\Lambda$CDM paradigm, ambitious cosmological surveys, such as the Dark Energy Survey (DES), the Dark Energy Spectroscopic Instrument (DESI), the Rubin Observatory's Legacy Survey of Space and Time (LSST), and the Roman Space Telescope, aim to precisely and robustly measure cosmic structure and its evolution via various cosmological probes, such as weak gravitational lensing, galaxy clustering, and other techniques. Combining multiple cosmological probes (known as multi-probe analyses) provides precise and robust cosmological constraints. Galaxy clustering, weak gravitational lensing, and abundances of galaxy clusters each are sensitive to different aspects of cosmic structure formation and are affected by different astrophysical and observational uncertainties. Thus, their combination is expected to be more precise and robust than any of the probe alone. Among these probes, the abundances and spatial distribution of galaxy clusters, which are associated with the highest peaks in the matter density field, provide powerful probes of cosmic structure and its evolution. This thesis presents original research that improves our understandings of the universe by observations of galaxy clusters. In the three self-contained projects, I (1) develop and validate methods for combining cluster abundances and two-point correlation functions, (2) perform the first blind cosmology analysis on combining cluster abundances, weak gravitational lensing, and galaxy clustering using data taken in the first season (DES-Y1) of the Dark Energy Survey, and (3) quantify the connections between red galaxies and their host dark matter halos by modeling luminosity functions of galaxies in galaxy clusters. While these three projects have already advanced our understandings of the cosmos, they also serve as an example of how one can use millions of clusters expected to be detected with the upcoming surveys in 2020s to improve our knowledge of the universe. These opportunities are also discussed in this thesis
Multiracial Mandarin : educational equity and heritage language in racialized tension
Keywords: Education.

In this dissertation, I present findings from a two-year long ethnographic study of Mandarin world language in two public high schools: primarily, an urban, working-class, ethnically/racially diverse school, and secondarily, an affluent ethnoburban high school. I study how local discourses among school participants, at the primary field site, create educational equity as a cultural object, imbued with meanings of race. I then discuss the implications of this version of equity for Mandarin language education at the primary field site, as its teachers are motivated by goals and ideals of racial equity. I show how these goals and ideals exist in tension with the heritage language multilingualism of the program's Chinese- and Asian-diasporic students, which is further illuminated by data from the secondary field site
Multi-wavelength laser absorption spectroscopy for high-temperature reaction kinetics
Keywords: Mechanical Engineering.

This dissertation presents advanced measurement science that ultimately supports the design, development, and characterization of next-generation energy systems. Part I presents a mathematical framework and approach for measuring the composition of complex, reacting gaseous mixtures with multi-wavelength laser-absorption spectroscopy. Suites of laser systems were interfaced with shock tubes to measure the composition of mixtures during the pyrolysis of a single-component fuel (1-butene), alternative (carbon-neutral) jet fuel mixtures, and traditional jet fuels. These methods were established to help determine the time-dependent composition of complex high-temperature chemistry. With the framework of Part I in place, Part II presents work that increases the number of laser wavelengths used by a factor of 10, while reducing the hardware needed to acquire this extra information. The second part of this thesis adopts an emerging technology, known as dual-comb spectroscopy, for use in high-temperature reaction kinetic studies. This approach provides ultra-fast broadband absorption data used in a variety of conditions in the different chapters of Part II. Taken together, the two parts of this thesis provide a framework for characterizing complex and reacting high-temperature gas mixtures and introduce advanced tools that push the boundary of measurement capability
Muslims on campus : religion, secularity, and the protestant legacy
Keywords: Education.

This dissertation explores religion and secularity in higher education through the case of Muslim students at an elite, residential, and secular campus on the West Coast of the United States. Based on three years of qualitative research including participant observation, ethnographic fieldwork, and interviews, I argue that the case of Muslim students at West Coast University (WCU) compels us to reconsider the standard narratives of religion in higher education. These narratives propose either that the academy is wholly secular and religion has been shut out (Marsden 1994); or the opposite, that religion does, indeed, have a place on campus because higher education now exists in a "post-secular" age (Jacobsen & Jacobsen 2008; Schmaulzbauer & Mahoney 2018). The latter perspective also contends that religion on campus not only survives but also often thrives, continuing to contribute to diversity and pluralism (Patel 2020). In my dissertation, I show via the case of WCU Muslim students that the story is more complex. As Muslim students are immersed in what might be considered a secular campus, they encounter religion generally and engage Islam particularly through Protestant features, which arbitrate being Muslim on campus. I thus characterize their campus context as Protestant-secular and explore its influence in terms of pluralism, privatization, and personalization on campus. Whether or not they do so knowingly, these Muslims students interact with and within Protestant-secularity as they experience being Muslim on campus and as they think about Islam. Their case tells us which ideas of religion thrive in American higher education and the kind of discursive structures on campus that make this possible. It also shows us how, for some religious communities, the place of religion on campus remains fraught in subtle but significant ways
A mutagenesis screen for identifying essential plastid biogenesis genes in the human malaria parasite
Keywords: Biochemistry.

Endosymbiosis has driven major molecular and cellular innovations. Plasmodium spp. parasites that cause malaria contain an essential, non-photosynthetic plastid—the apicoplast—which originated from a secondary (eukaryote--eukaryote) endosymbiosis. To discover organellar pathways with evolutionary and biomedical significance, we performed a mutagenesis screen for essential genes required for apicoplast biogenesis in Plasmodium falciparum. Apicoplast(−) mutants were isolated using a chemical rescue that permits conditional disruption of the apicoplast and a new fluorescent reporter for organelle loss. Five candidate genes were validated (out of 12 identified), including a triosephosphate isomerase (TIM)-barrel protein that likely derived from a core metabolic enzyme but evolved a new activity. Our results demonstrate, to our knowledge, the first forward genetic screen to assign essential cellular functions to unannotated P. falciparum genes. A putative TIM-barrel enzyme and other newly identified apicoplast biogenesis proteins open opportunities to discover new mechanisms of organelle biogenesis, molecular evolution underlying eukaryotic diversity, and drug targets against multiple parasitic diseases