VictorJuiz/thesis huggingface dataset:
Injectable Biomimetic Hydrogels Providing Prolonged Delivery of GLP-1 Analogues for Enhanced Diabetes Treatment
Keywords:  Department of Biology
Biology
Bioengineering
Type 2 diabetes
Diabetes
Materials science
Text
Thesis

In the US, there are over 130 million people living with Type 2 diabetes (T2D), who suffer from impaired regulation of hepatic glucose production, which eventually leads to β-cell failure. While insulin treatments are often recommended, they are burdensome to appropriately dose and can lead to dangerous hypoglycemic events. In contrast, glucagon- like peptide-1 receptor agonists (GLP-1 RAs), which mimic an incretin hormone secreted following carbohydrate intake, stimulate insulin and block glucagon secretion in a glucose- dependent manner, thereby eliminating the risk of hypoglycemia. These treatments lower endogenous glucose production and drive expansion of insulin-secreting β-cells to restore the native ability of patients to regulate glycemia. Unfortunately, current GLP-1 RAs must be injected either daily (Liraglutide) or weekly (Semaglutide), which results in significant patient burden and poor patient compliance. An ideal GLP-1 RA drug would provide continuous therapy for upwards of four months from a single administration during each primary care visit, which is the frequency which T2D patients visit their physician. In this work, we leverage an injectable hydrogel depot technology to develop a long-acting GLP- 1 RA drug. We show that a single injection of hydrogel-based therapies prolongs GLP-1 RA exposure and significantly improves management of blood glucose and weight in a rat model of T2D when compared with daily injections of a leading drug molecule over 42 days. Overall, we anticipate this work demonstrates the potential of a GLP-1 RA depot to dramatically reduce the frequency of therapeutic interventions, significantly increasing patient quality of life.
Integrated lithium niobate platform for classical and quantum nonlinear optics
Keywords: Humanities and Sciences.
Applied Physics.

On-chip integrated photonic devices constitute the backbone of the systems we use to interface with technology. Semiconductor devices enable the transmission of information from every modern smartphone, wearable device, home appliance, and car directly to our eyes. On the other hand, an increasing number of consumer electronics use photonic components to collect information necessary for their functions, such as face recognition in phones, health monitoring in wearables, and autonomous driving. Moreover, integrated photonics is common in specialized applications like data centers, the internet, pollution monitoring, and medical diagnostics. Developing new types of integrated light sources will enable unexplored applications and advances in the existing ones. In this dissertation, I will describe our recent advancements in developing chip-integrated nonlinear optical light sources using lithium niobate. Nonlinear optics studies photon interactions through the intrinsic properties of the materials in which they propagate. It provides a versatile alternative to existing semiconductor technologies for light-source engineering. We have designed and fabricated devices that generate second-harmonic and enable tunable optical parametric oscillation with high efficiency and at low power. Our platform allows light generation from UV to mid-infrared, with lithography controlling the design wavelength. It also opens the door to novel chip-integrated light sources such as comb generators and squeezers. By combining the electro-optic capabilities of lithium niobate with our nonlinear optics platform, we create an on-chip frequency-modulated optical parametric oscillator. It produces a flat-top frequency comb composed of hundreds of distinct frequencies, ideal for applications in spectroscopy and communication. Finally, we utilize lithium niobate to generate squeezed light within a complex photonic integrated circuit and employ it as a quantum-enhanced sensor.
Integrating features of clonal evolution in the modeling of disease risk and drug sensitivity in acute myeloid leukemia
Keywords: Medicine.

Accurately predicting the severity of a cancer patient's disease and identifying targetable drivers of malignant transformation are critical for guiding tolerable treatment regiments, selecting optimal therapeutic interventions, and ultimately improving a patient's quality of life. However, even within a specific type of cancer, the combinatorial and heterogeneous nature of how different genetic mutations cooperate to initiate and drive disease often limits our ability to make such predictions. With the emergence of large, clinically annotated, and genetically profiled cohorts of cancer patients, more accurate predictions of patient risk and drug treatment have been developed. Despite these advances, little is known about how nuances in the evolutionary history of mutational acquisition in cancer impact disease aggressiveness or sensitivity to drugs. Towards this goal, my dissertation focuses on the following areas: First, I summarize available datasets and computational approaches for predicting mutation-specific drug sensitivity. Second, I describe how the clonal abundance and temporal associations of specific mutations are associated with poor survival. Third, I show how the interplay of mutational burden and architecture of cancer evolution are correlated with aggressive disease. Finally, I demonstrate novel associations between the clonal prevalence of mutations and drug sensitivity
Integrating Human Dimensions into Fisheries Management: A Case Study of Whale Entanglement in California’s Dungeness Crab Fishery
Keywords: Earth Systems Program
Fishery management
Qualitative research
Whales
Dungeness crab
Social sciences
Ecosystem management
Sustainability
Text
Thesis

The past decade has seen a sharp increase in reports of whale entanglement along the U.S. West Coast, with California’s commercial Dungeness crab fishery implicated in many such incidents. Current management strategies for whale entanglement focus primarily on ecological risk and often fall short in assessing impacts on fishermen, which are felt across a range of social, economic, and cultural scales. Integrated risk assessments for fisheries management that consider both ecological and human dimensions are precluded by an absence of social science data. The following study addresses this gap by engaging with members of the Dungeness crab fleet across a range of ports, operation sizes, and experience levels. Based on semi-structured interviews, a conceptual framework is proposed that characterizes the broad range of human dimensions of the fishery and explores the integration of these dimensions into management of whale entanglement risk. Using coupled inductive and deductive analytic approaches, interview transcripts were qualitatively coded to facilitate pattern recognition within and across the data. Participants recounted a diverse set of impacts stemming from entanglement-mitigation policies, though many converged on the shared loss of livelihood and identity. The incident and impact of these losses can be alleviated by fisheries management encompassing human dimensions. To this end, there is a need within existing management programs to reduce bias towards ecological information, improve fishery-specific social science research, and expand stakeholder engagement programs. The conceptual frameworks advanced by this study are widely generalizable and can shed light on the practical steps needed to achieve social, economic, and ecological sustainability across diverse human-environmental systems.
Integrating structure- and ligand-based modeling in drug discovery
Keywords: Engineering.

Most drugs are small molecules that act by binding to proteins and thereby influencing their function. Computational approaches for modeling interactions between small molecule ligands and proteins promise to make drug discovery more efficient and enable the design of unprecedented therapeutics. Existing approaches can be categorized into two groups based on the kind of information they use about the target protein: the 3D structure of the target or experimental measurements of the activities of other small molecules at the target. I present a series of novel methods that synergistically integrate these two sorts of information, providing better predictions than is possible with either alone. I also describe how we used structural modeling in combination with experimental measurements to understand molecular mechanisms determining the safety profiles of opioid drugs
Integration and measurement for building synthetic cells
Keywords: Engineering.
Bioengineering.

Integration--combining the many cell-essential molecular functions such that they work together--is the key challenge is building a functioning synthetic cell. In order to integrate, we must be able to measure multiple genetic circuits and molecular components working together, in the same cell, at the same time. We have prototyped a platform for integration, using cryo-electron microscopy to visualize individual synthetic cells operating integrated molecular modules, quantifying the synthetic cells and molecules within them, and characterizing their performance.
Interdisciplinary approaches to wildfire risk management
Keywords: Engineering.
Management Science and Engineering.

Wildfires pose a significant threat to the state of California. While climate change is contributing to increased fire risk, human activities, including ignitions, alterations to natural landscapes, and development patterns, also play a substantial role in shaping the risk. Moreover, the management actions in response to this risk have societal impacts themselves. This dissertation first frames the challenge of wildfire risk management by considering the tradeoffs between the cost of taking action, the reduction in wildfire damages, and welfare losses. It explores how misaligned incentives and the uneven distribution of costs and benefits lead individual decision-makers to undertake suboptimal mitigation measures. This dissertation then delves deeper into two specific management strategies: the efforts of investor-owned utilities to reduce powerline ignition risk and the actions of individuals in protecting their homes and safety. Using a variety of data collection techniques and methods from economics, risk analysis, and data science, this research provides insights into the costs, drivers, and effectiveness of these strategies. The findings highlight the need for aligning policies and incentives to minimize the impact of both wildfires and mitigation measures and ultimately seek to inform research and decision-making towards effective wildfire management.
Internalization of anti-GD2 antibodies as a key component of the antibody-induced cell death mechanism in pediatric neuroblastoma
Keywords:  Department of Biology
Neuroblastoma
GD2
Gangliosides
Monoclonal antibodies
dinutuximab
Cell death
internalization
AP2M1
AP2S1
clathrin-mediated endocytosis
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Thesis

Neuroblastoma is one of the most common solid cancers diagnosed in young children. Despite advancements in modern therapies, many high-risk patients experience recurrence and eventually die of their disease. Currently, front-line treatment therapy includes treatment with monoclonal antibodies recognizing the disialoganglioside GD2, a type of cell surface lipid. Neuroblastoma is characterized by the presence of GD2 on cancer cells, but recent data has shown that GD2 levels can be heterogeneous. In addition, other gangliosides such as GD3 and GM2 are present at varying levels and may serve as potential targets. It has been shown in the literature that anti-GD2 antibodies are able to cause cell death in an immune independent way that differs from classical antibody-induced cell death pathways such as antibody-dependent cell-mediated cytotoxicity and antibody-dependent cellular phagocytosis, though no consensus regarding the specific mechanism has been reached. This research aims to identify key genes in this cell death pathway and their effects on neuroblastoma and ganglioside levels. A CRISPR screen for genes that protect neuroblastoma cells from death due to anti-ganglioside antibodies yielded the candidate genes AP2M1 and AP2S1. The genes were knocked out to create cell lines for cell killing assays and assessed for protection against the anti-GD2 antibody dinutuximab. The levels of GD2 and other gangliosides in cell lines were monitored via flow cytometry. Cell-killing assays using dinutuximab showed decreased cell death in AP2M1 and AP2S1 knockout lines. Since AP2M1 and AP2S1 are genes involved in clathrin-mediated endocytosis, antibody internalization was monitored via time-lapse fluorescence microscopy for disruptions in the knockout cell lines. Internalization assays suggested that antibody internalization was necessary but not sufficient for neuroblastoma cell death. Furthermore, increased cell death and internalization was achieved in wildtype neuroblastoma cells when anti-GD2 antibodies were crosslinked. Altogether, these results provide more insight into antibody-ganglioside interactions in neuroblastoma and the internal cellular events that occur after antibody binding. There is great therapeutic potential in targeting internalization, offering a novel treatment applicable not only to pediatric neuroblastoma but also potentially to other types of cancers with similar ganglioside expression levels.
Interpenetration and kinetic mix in weakly collisional, fully-ionized plasma jets
Keywords: Engineering.
Mechanical Engineering.

Laser-driven "inverted corona" fusion targets have attracted interest as a low-convergence neutron source and as a platform for studying kinetic physics in inertially confined plasmas. These targets consist of a fuel layer lined along the interior surface of a hollow or gas-filled plastic hohlraum. The plasma streams generated in vacuum targets are initially nearly collisionless as they converge, leading to wide interaction length scales and long interaction time scales as the jets interpenetrate. With the inclusion of a low-density gas fill, ejected particles from the shell can pass far into the gas before colliding, leading to significant mixing across the gas-shell interface. Such interactions are difficult to accurately model using standard hydrodynamic simulations, which assume high collisionality. In this work we model the system using a kinetic-ion, fluid-electron hybrid particle-in-cell (PIC) approach. We also demonstrate the potential of the hybrid-PIC approach for full-scale modeling and experimental design. The entire system is modeled in one integrated simulation, including laser energy deposition, hydrodynamic/kinetic evolution of the plasma, and fusion dynamics. Simulations demonstrate significant kinetic effects (interpenetration, beam-beam fusion, and weakly collisional electrostatic shocks) that are mediated by collisional processes and can be tuned by changing the initial fill pressure of the gas. These effects are detectable through neutron diagnostics. Using two-dimensional simulations we also investigate compression symmetry, hotspot velocity, and directional differences in neutron spectra, as well as make comparison with x-ray emission images. Predictions of neutron yield scaling vs. gas pressure show excellent agreement with experimental data recorded at the OMEGA laser facility, suggesting that one-dimensional kinetic mechanisms may be sufficient to capture the mix process
Interpretation Makes All the Difference: How Content Moderators Construct Organizational Identity and Navigate Ambiguity
Keywords: Computing platforms
Labor
Organizational behavior > Study and teaching
Text
Thesis

Platforms are principal spaces for the public: to gather, share information, form online communities, and engage in self-expression. As such, social media platforms hold a massive influence on sociality and contemporary culture. Critical to platforms’ vision as a public space for all users are the commercial content moderators who enforce detailed policies to review, tag, and remove potentially harmful content from the platform. Through an analysis of interviews with TikTok content moderators and public materials, I examine how moderators navigate ambiguity in their roles from the lens of identity work. I argue that moderators construct organizational identities to cope with the limited agency and high pressures of their roles. While company culture provides moderators new incentives to conform to organizational guidelines and norms around decision-making, moderators’ organizational identities inevitably “collapse” due to burnout or disillusionment with the role or company. I discuss the implications of this burnout and disincentivizing moderator agency over policy changes.
The intersection of childhood health environments and educational opportunity
Keywords: Education.

Standardized test scores are the most common measure by which the United States evaluates its public school system. These test scores are also one of the primary mechanisms by which opportunity is distributed to students throughout the country. These practices continue despite the fact that researchers and policymakers know that social conditions have a large influence on test scores. This dissertation is comprised of three papers that use population-level data to examine the role of specific environmental health factors in explaining variation in national test scores. The first paper examines the role of the opioid crisis in disrupting third-grade achievement scores across U.S. counties. The second paper builds upon the first paper by investigating associations between the opioid crisis and learning rates for different groups of students. The third paper contributes a unique measure of health access by calculating the physician-to-child-population ratio for every school district in the country. This paper describes the distribution of physicians trained in pediatric care, describes the groups of students who are served by that distribution, and ultimately describes the link between the distribution and early childhood academic achievement. Together, these three papers suggest the importance of exploring non-educational factors when seeking to explain national variation in test scores.
Investigating drivers of small cell lung cancer progression and metastasis
Keywords: Medicine.

Most cancer patients die from the development of metastatic disease; however, the molecular mechanisms underlying metastatic progression remain poorly understood. Small cell lung cancer (SCLC) is a particularly metastatic disease in which tumor heterogeneity has hindered identification of many tumor drivers. Heterogeneity in SCLC is in part moderated by the Notch signaling pathway, and we found that modulating expression of the atypical Notch ligand DLL3 changes the balance of neuroendocrine and non-neuroendocrine cells within tumors. Furthermore, the transcription factor NFIB is a known driver of tumor growth and metastasis in SCLC, but the requirement for NFIB activity for tumor development has not been investigated in vivo. We developed conditional Nfib knockout mouse models of SCLC and found that NFIB contributes to tumor progression, but loss of NFIB does not prevent tumors from metastasizing using alternative mechanisms. We further identified the pioneer factor FOXA1 as a candidate NFIB-independent driver of SCLC. Our work shows the importance of identifying other drivers of SCLC progression and metastasis to fully understand the plasticity and heterogeneity of SCLC tumors
INVESTIGATING FLUID FLOW BEHAVIOR IN ENHANCED GEOTHERMAL SYSTEM (EGS) WELLS: NUMERICAL AND DATA-DRIVEN APPROACHES
Keywords: Enhanced Geothermal Systems
Chloride ion
Downhole tool
Geothermal engineering
Text
Thesis

Enhanced Geothermal Systems (EGS) wells utilize artificial fractures created by stimulation to produce hot fluids. Understanding the fluid flow behavior within EGS wellbores is crucial for developing tools that can effectively measure the effectiveness of the fracture conduit in energy extraction over the well's lifespan.
This research contributed to a broader project, which combines analytical methods, numerical simulations, laboratory experiments, and field trials to create and utilize a chloride-based downhole tool. The tool is designed to identify feed zones and measure flow rates in Enhanced Geothermal Systems (EGS) wells through chloride concentration readings along the wellbore. Specifically, this research emphasized numerical simulation and data-driven analysis. The study utilized data and well configurations from the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) field, with the aim of applying the findings to other EGS sites.
Computational fluid dynamics techniques were employed to simulate fluid flow behavior within wellbores across various EGS configurations. Stochastic modeling and machine learning techniques were also utilized to estimate potential measurement errors and build a feed zone flow rate predictor. The results highlight several important phenomena, including blind spots in the mixing zone, downward volume distribution, and fluid flow path alterations caused by periodic turbulence. These findings emphasize the need to consider these factors when designing effective measurement tools. Moreover, simulations involving different tool positions suggest that chemical-based measurements are accurate primarily within the feed zone jet, potentially requiring correction if measured outside the jet. This indicates the importance of incorporating a centralizer in the wireline tool design.
Overall, this research provided insights into fluid flow behavior in EGS wells, reducing uncertainty in EGS well development. The study also proposed a practical tool design for measuring feed zone effectiveness that can be implemented in other EGS sites.
Investigating Gender Bias in Artificial Intelligence: The case of GPT
Keywords: Public Policy Program
artificial intelligence
bias
algorithmic gender bias
OpenAI's GPT
gender bias
gender bias in artificial intelligence
Stanford University
Humanities and Sciences
Public Policy Program
Text
Thesis

In the past decade, there has been rapid growth in the field of artificial intelligence (AI), with 2022 alone witnessing an investment of $91.9 billion in the private sector. This technological explosion, fueled by the advent of big data, computational power, and innovative methodologies, has not only amplified the efficiency, safety, and quality of work but also heralded an era of unprecedented innovation and economic progress. At the same time, the application of AI to complex problems in real-world situations raises many ethical concerns for policy makers and business leaders, including the presence of bias in AI systems and their consequent perpetuation thereof. This paper explores whether one of the most popular AI algorithms, OpenAI’s GPT, exhibits gender bias. The research compares GPT’s characterization of the typical gender of certain occupations with Bureau of Labor Statistics data on the actual gender composition of those occupations. The analysis for this paper showed that GPT did exhibit gender bias in 38 of the 50 occupations studied. This finding suggests that both researchers and policy officials might wish to give further attention to gender bias in AI systems and consider steps to mitigate that bias.