VictorJuiz/thesis huggingface dataset:
IgG Autoantibodies Induced by COVID-19 and Other Respiratory Disease
Keywords:  Department of Biology
Biology
Immunology
COVID-19
Text
Thesis

Coronavirus Disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), was first identified in December of 2019 and has since spread worldwide, leading to the current pandemic. Symptoms of COVID-19 usually include fever, cough, fatigue, respiratory difficulties, as well as loss of smell and taste. However, the disease is associated with a wide range of clinical manifestations, including hallmarks of autoimmunity and autoantibody production. My lab developed three different protein arrays to measure IgG autoantibodies associated with Connective Tissue Diseases (CTDs), Anti-Cytokine Antibodies (ACA), and anti-viral antibody responses in 147 hospitalized COVID-19 patients from three different centers. Autoantibodies in patients with other respiratory diseases, severe Influenza and Acute respiratory distress syndrome (ARDS), from two other centers were also measured. Overall, autoantibodies were identified in approximately 50% of COVID-19 patients, but in <15% of healthy controls. Anti-nuclear antibodies (ANA) were observed in ~25% of patients. In comparison, ANAs were observed in 33% and 22% of severe Influenza and ARDS patients, respectively. For COVID-19 patients, autoantibodies tracked with longitudinal development of IgG antibodies that recognized SARS-CoV-2 structural proteins such as S1, S2, M, N and a subset of non-structural proteins, but not proteins from influenza, seasonal coronaviruses or other pathogenic viruses. I conclude that SARS-CoV-2 causes development of new-onset IgG autoantibodies in a significant proportion of hospitalized COVID-19 patients and are positively correlated with immune responses to SARS-CoV-2 proteins. However, severe Influenza and ARDS induce fewer autoantibodies than COVID-19.
Imaging the response of mesoscopic devices and materials to magnetic fields
Keywords: Applied Physics.

Scanning SQUID Microscopy (SSM) is a powerful tool for measuring nanoscale superconducting heterostructures. In this thesis, I describe results from several projects motivated by the goal of developing foundational models and characterization methods for engineering superconducting heterostructures. First, I discuss an approach to studying the dynamics of vortices in type-II superconductors, which creates unwanted dissipation in superconducting circuits, using a combination of SSM and 2D London modeling (Chapter 2). Next, I cover three projects studying materials systems engineered with the goal of achieving topologically nontrivial quasiparticle states called Majorana modes, which can be used to build qubits for fault-tolerant topological computing. Chapters 3-4 discuss interfacial and thickness effects and domain wall superconductivity in hybrid superconducting-semiconducting-ferromagnetic systems, and Chapter 5 presents SSM imaging results of diamagnetism and vortex motion in superconductor-topological insulator-superconductor junctions. Understanding these effects will be key to controlling the behavior of these devices in future iterations. Finally, Chapter 6 considers a model experimental system that mimics disorder in a natural 2D superconductor using an array of proximity-coupled superconducting islands with engineered disorder. Experimental susceptibility imaging results combined with Josephson Junction modeling show that these arrays can be described as arrays of superconductor-normal metal-superconductor junctions
Improved light-field control and light-matter interaction for device applications via optically resonant nanostructures
Keywords: Materials Science and Engineering.

Starting from the 1970s, great efforts have been made to miniaturize bulky optical devices. This progress accelerated significantly over the last decade due to the emerging field of metasurfaces. These planar nanophotonic devices, made from judiciously engineered, subwavelength-thick optical nanoresonators, are capable of controlling the amplitude, phase, polarization, and spectral properties of light waves with subwavelength resolution, and therefore have the potential to replace a wide range of bulk optical elements with flat optics. Here, we go one step further to explore the emergent properties of optical nanoresonators beyond the concept in conventional optics. The radiative nature of optical resonances results in emerging collective modes in nanoresonator arrays beyond the description of the renowned chemical bonding model, leading to tremendous degrees of freedom for manipulating the light fields. Given the dimension of these flat optical elements becoming compatible with on-chip electronics, we now foresee an unprecedented opportunity to boost the performance and enable novel functionalities in integrated devices by bridging the gap between optics and electronics using metasurfaces. The carefully engineered local photonic environment created by optical nanoresonators can selectively interact with quantum states in a broad range of material systems, probing and extracting the quantum information as needed. In this thesis, I will specifically illustrate how to leverage the above-mentioned emergent properties of optically resonant nanostructures to achieve the improved control over the emission, propagation, and absorption of the light fields at the nanoscale. Eventually, it leads to three representative device applications: a transparent spectro-polarimetric silicon nanowire photodetector, a comprehensive metasurface optofluidic platform for dynamic control of light fields, and an efficient monolayer semiconductor free-space electro-optical modulator operating at room temperature. These works validate the crucial role that optical nanoresonators play in revolutionizing various optical and optoelectronic devices that may find use in real-world applications. The underlying physics behind these applications further reveals the opportunity to employ optical nanoresonators as a tool to probe the fundamental nature of complex physical systems
Improving cloud efficiency with online learning
Keywords: Electrical Engineering.

Cloud computing has emerged as the dominant platform for computing due to its ease of use and scalability advantages. To maintain reliable and high-performance services at a cost-effective manner, cloud providers need to efficiently administer a large number of management tasks. Traditional heuristic-based static management policies often fall short of adapting to the varying demands of diverse cloud workloads. In contrast, machine learning (ML) assisted policies have the ability to learn patterns of different workloads and use predictions to guide their management decisions. This dissertation focuses on how to effectively and safely use online learning techniques to improve management of cloud platforms. We first present SmartHarvest, a system that improves server CPU utilization by dynamically assigning the number of CPU cores to Virtual Machines (VMs) of different priorities. It uses an online cost-sensitive multi-class classification algorithm to predict the core demand of primary VMs and safely harvest the predicted unused cores to run a secondary best-effort VM. In order to incorporate online learning more easily to various types of server node management agents, we then introduce SOL, a Safe On-node Learning framework for developing and operating learning-based node agents. SOL simplifies agent implementation and enables developers to focus on the design of agent-specific components (e.g., power management or memory migration). SOL further ensures that agents implemented on top of it are robust to the range of abnormal or failure conditions that can occur in production, so they can be safely deployed alongside customer workloads in the cloud. We demonstrate the use of SOL by implementing three node agents that leverage online learning to improve management of CPU cores, node power, and memory placement
Improving contacts and defect characterization toward industry-ready 2D semiconductors
Keywords: Electrical Engineering.

For six decades, silicon technology has driven advances in computing; however, silicon is approaching its fundamental scaling limits. In contrast, two-dimensional (2D) semiconductors offer excellent electrical properties in sub-nanometer thin channels, and they are compatible with 3D heterogeneous integration. This could enable ultra-dense logic and memory stacked vertically on a chip, unlocking vast new possibilities in computing advances. While 2D materials show great promise, their parasitic contact resistance and defect densities are well-known limitations for their electrical performance. In this thesis, I will describe the problem of contact resistance to 2D semiconductors and uncover our advances in understanding the interaction between metal contacts and the 2D semiconductor MoS2. I will also present a statistical analysis on the effect of specific metal properties on contact resistance with MoS2. I will then show how we have utilized these findings to demonstrate record-low contact resistances to monolayer MoS2. Next, I will address MoS2 defects, how they affect transistor performance, and how to quantify their densities quickly and non-destructively using Raman spectroscopy, which enables 100x higher throughput for material optimization. Combined, these advances provide fundamental insights into 2D semiconductor optimization, while bringing them closer to industrial adoption
Improving TRN performance over incomplete and inaccurate map regions
Keywords: Mechanical Engineering.

The primary focus of this thesis is on improving the operation of terrain relative navigation (TRN) in areas with incomplete map information. TRN is a navigation algorithm for vehicles operating in GPS denied environments. It operates by correlating measurements of the terrain to a prior terrain map in order to localize the vehicle. To address self-similarity between different sections of the terrain, TRN uses a non-parametric sample based representation of the belief distribution of vehicle locations such as a particle filter. The basic TRN measurement update requires map information for every particle in order to perform the TRN correlation step. When a subset of the particles has projected measurements that fall in an unmapped area, these off-map particles create an incomplete map problem. This thesis addresses the incomplete map problem through a new measurement update for TRN. This is called the Subcloud measurement update as it performs different updates for the on-map and off-map subsets of the particle cloud. At the core of the Subcloud measurement update is a new measurement probability for the off-map particles which is derived from the distribution of terrain heights in the off-map area. When this distribution is correct and other standard TRN assumptions are met, the Subcloud measurement update provably produces the correct posterior distribution in the on-map area. This means TRN will converge correctly when the vehicle is on-map and will correctly identify when the vehicle is off-map. For more realistic situations where the true distribution of terrain heights is not known, this thesis develops bounds on the safe operation and a modification to the Subcloud measurement update which guarantees the Subcloud measurement update will remain within these safe bounds. Finally, working with the Monterey Bay Aquarium Research Institute (MBARI), the Subcloud measurement update is demonstrated in the field for autonomous return-to-site missions with incomplete maps near the ocean floor. This thesis also presents preliminary work on improving the operation of TRN in areas where the map is inaccurate. This contribution is a pre-filter for TRN measurements that works to prevent inaccurate map information from entering the primary TRN estimator. This pre-filter exploits the presence of a multibeam sonar to run a bank of secondary TRN filters which vote to identify a consensus estimate and reject information from inaccurate map regions. This framework is tested in post processing on data from the site of an underwater volcanic eruption
In Mail We Trust: Exploring the Effects of All-Mail Voting on Voter Turnout in the 2020 US Presidential Election
Keywords:  Fisher Family Honors Program in Democracy
Electoral Reforms
Voter Turnout
Vote-by-Mail
All-Mail Voting
2020 US Presidential Election
US Elections
Covid-19 Legislation
Text
Thesis

The highly polemicized 2020 US presidential election occurred within the logistically challenging contexts of the Covid-19 pandemic, prompting states to implement widely varying electoral reforms, including increased access to Vote-by-Mail (“VBM”) and adoption of All-Mail Voting (“AMV”). Despite polarized rhetoric around the increased use of mail ballots and varied degrees of VBM expansion, voter turnout universally increased across the US. Historic literature fails to explain the disparate effects of various electoral reforms on voter turnout; this paper thus seeks consensus through consideration of the 2020 election and contemporary literature. This paper begins with a synthesis of the current state of electoral reform legislation and theories on voter participation. It then synthesizes current and historic literature on the effects of various electoral reforms on voter turnout, turning ultimately to the effects of VBM and AMV on voter turnout in US elections. It then addresses historic use of VBM before analyzing the performance of VBM and AMV in the 2020 election. Lastly, it considers the effects of electoral reforms on demographic subgroups and contests commonly cited alternative explanations. Historic findings indicate that AMV increases voter turnout by 4%, while the 2020 election suggests rates closer to 6%. Increased access to VBM was found to have less significant, though nonetheless positive effects on voter turnout. Analysis of 2020 election data also demonstrates that partisan lean correlates with increased availability of VBM in the 2020 election, and that such availability, with the exception of states experiencing already high turnout rates, positively correlates with significant increases in voter turnout. Data from administration of the 2020 election in California also suggests that AMV spurs increased turnout among Black, Latino, Asian, and youth voters and accordingly decreases the turnout gap across demographic subgroups. Further research into the effects of AMV on the turnout of demographic subgroups is nonetheless required; additionally, further study on the intersectional effects of AMV and other electoral reforms is necessary to fully understand the mechanisms by which voter turnout changes.
In search of 'other' venuses : images of vulva-covering Venus at the margins of the ancient world and modern typological discourse
Keywords: Classics.

Praxiteles' Knidian Aphrodite was one of the most famous statues in the ancient Roman world. It was so famous that, 1500 years after its destruction, more than 500 images that replicate its distinctive vulva-covering gesture survive. In the past, antiquarians and art historians have collected, categorized, and cataloged metadata about these "replicas." Using this metadata, they have constructed a typology that has allowed them to reconstruct the Knidian Aphrodite's form. However, it has also established a material and visual hierarchy of images. Moreover, it has severed the contextual ties that bind surviving images to the diverse ancient contexts for which they were created. This dissertation investigates the ancient significances of these vulva-covering goddess (VCG) images and their modern discursive formations. It studies three groups of VCGs: alabaster cinerary urns made in Republican Volterra; bronze Provincial Roman coins from Knidos, Philadelphia, and Saitta in the Roman province of Asia; and bronze statuettes from Antarados and Marathus on the coast of the Roman province of Syria. This dissertation demonstrates that scholars have constructed each of these groups of ancient images as being at or beyond the margins of the modern typology used to describe and interpret "replicas" of Praxiteles' Knidian Aphrodite. It deconstructs this discourse. Then, it writes three counternarratives of the contextual meanings of VCG images for each of these three groups of images in the ancient times and places for which they were created. This project demonstrates that the images that comprise these three assemblages cannot be dismissed as poor-quality imitations of Praxiteles' Knidian Aphrodite. Instead, it shows that the patrons and makers of these images selectively appropriated elements of the form and reputation of the Knidian Aphrodite. They gradually created the tradition of simulacra of the Knidian Aphrodite that remains apparent to the present day. However, while these images share aspects of Praxiteles' statue's form, their significances are significantly altered. My investigation contributes three things to ancient studies. First, it offers three chapter-length case studies that articulate how the signification of form was a dynamic process that changed over time. Second, it analyzes the theories and methodologies by which modern historians of ancient art have collected, categorized, and cataloged VCG images. Third, this project concludes that we will only discover what "Other" VCG images exist with stories to be told if we discard the deeply problematic VCG typology
In the World of Disorder: Trafficking, Vagabonds, and State in Late Qing Jianghuai
Keywords:  Stanford Global Studies
Qing Dynasty (China)
Human trafficking
Organized crime
East Asian Studies
Text
Thesis

In recent years, historians of late imperial and modern China have paid increasing attention to the practices and logic of traffic in women in the contexts of wife-selling and transactional families. Trafficking in women as a large-scale and networked crime, however, has remained insufficiently researched. This paper examines crimes of trafficking in late Qing Jianghuai primarily based on materials including legal cases, governmental documents, periodicals, and literature. I argue that the development of crimes of trafficking in late Qing Jianghuai was a result of integrated factors, including the market for women, the growing group of vagabonds that emerged in social disorder, and the mobile local society characterized by the brokered labor market and transportation networks. The provincial state’s regulation of the crimes thus reveals the growing conflict between the unruly and mobile population who took advantage of the disorder and the local government that aimed to restore the order by controlling its people’s information and mobility. In Part I, I explore how crimes of trafficking developed by increasingly integrating with the flourishing crimes of smuggling and banditry that produced an enlarging population of vagabonds. In Part II, I discuss trafficking as a networked and professional crime from the perspective of family, community, and employment relations and the social groups of professional traffickers. Part III investigates the local state of Jianghuai’s efforts of eliminating the crimes by regulating the gender order and bureaucracy. By illustrating how regional dynamics created a distinctive image of crimes of trafficking in Jianghuai, this study serves as a building block for future studies that explore how the entanglements between trafficking as an organized crime, river travel, and local state-building reflected and influenced the socio-economic and political changes in late Qing China.
In vivo mapping of neural genes in neuroendocrine, neuronal, and glial cells during embryonic lung development
Keywords:  Department of Biology
Biology
Pulmonary
Neuroendocrine
Slithering
Ascl1
Text
Thesis

Small-cell lung cancer (SCLC) is the most deadly form of lung cancer. The disease primarily arises from pulmonary neuroendocrine cells (PNECs), a specialized epithelial cell subtype with numerous sensory and regulatory functions. During development, PNEC progenitors undergo slithering, a unique form of directed migration. Reactivation of slithering in adulthood may explain how normal PNECs transform into tumor cells. Currently, the molecular basis of slithering is unclear. Furthermore, expression of the earliest known PNEC marker, Ascl1, is not exclusive to the cell class during embryonic development. The timing and pattern of Ascl1 expression in these non-PNEC lineages have not been well characterized. Furthermore, subtypes of SCLC may arise from or be influenced by these non-PNEC Ascl1+ cells. Using an inducible-Cre mouse model, this study finds two distinct waves of Ascl1 expression in pulmonary development. Neuronal and glial cell precursors exhibit the first, transient wave; PNEC progenitors exhibit the second wave and sustain Ascl1 expression into adulthood. The same model was used to evaluate the slithering candidacy of several proneural genes identified via in silico analysis of single-cell RNA sequencing (scRNA-seq), with results verified by RNA hybridization and/or immunostaining. The proneural transcription factor Myt1 appears more consistent with a post-slithering role, as well as roundabout receptors Robo1,2 canonically known for their involvement in axon migration. scRNA-seq analysis also detected expression of the actin bundler Fscn1 in PNEC progenitors along with cells of the pulmonary stroma. These results provide the beginnings of a temporal framework for neuroendocrine, neuronal, and glial development that may aid in mapping expression timelines for other predicted lineage markers. More broadly, it is a first step towards understanding the signal pathways that underlie slithering, which may provide insight into developing targeted therapies for SCLC and other pulmonary diseases of neuroendocrine origin.
InAlN/GaN high electron mobility transistors for Venus surface exploration
Keywords: Electrical Engineering.

Historical and proposed missions to the surface of Venus have been severely limited in both scope and duration by the lack of robust microelectronics that can operate within Venus' extreme environment. Traditional silicon technology cannot survive on Venus without the use of expensive and bulky active cooling measures due to the scalding surface temperature of 465°C. To address this technological gap and enable extended duration robotic surface exploration on Venus, I propose the use of wide-bandgap gallium nitride (GaN) technology for uncooled microelectronics (e.g., sensors, micromechanical resonators, transistors). In this thesis, I present original research on the design and microfabrication of InAlN/GaN high electron mobility transistors (HEMTs), which leverage a two-dimensional electron gas (2DEG) conducting channel and can provide sensing, power, and telecommunications for a Venus lander or rover. I explore the use of several Schottky gate electrode materials that are attractive candidates for reliable high-temperature operation. The experimental results from electrically characterizing HEMTs utilizing these gate materials up to 600°C in air for durations of up to 6 days are presented. I then analyze Schottky barrier properties and make recommendations for engineering superior Schottky contacts. The electrical operation of HEMTs exposed to simulated Venus surface conditions (465°C, > 90 bar, supercritical CO2 ambient) for up to 10 days is presented. Finally, I present the first every demonstration of an uncooled GaN device successfully operating in situ simulated Venus surface conditions. The in situ experiment concluded after 5 days and 21 hours of device operation, which is a 70-fold greater duration than the 2 hour record operating length of silicon-based microelectronics used on the Venera 13 mission. As a whole, these contributing proof-of-concept experiments support the use of the InAlN/GaN HEMT platform and provide a road map for further maturing this technology for uncooled microelectronics