VictorJuiz/thesis huggingface dataset:
Inferring social structure from digital trace data : applications to organizations and markets

This thesis explores the use of large, unstructured, digital-trace data toward inferring aspects of latent social structure in one market context and one organizational context. It is structured as three separate papers (chapters), where, although starkly di erent in setting, topic, and methods, each exhibits this overarching theme in some way. In Chapter 1, the context is cultural product markets, where quality assessments are largely subjective; consumers are subject to rapid satiation upon repeated exposure to the same stimuli; and the forms and attributes of the products o ered are determined principally by cultural norms. Although organizational sociologists often test theories in these contexts as empirical settings, as is argued in that chapter, those theories are often impoverished by the coarseness of the data they typically work with. In Chapter 1's analysis, digital-trace data (in the form of crowd-sourced identifications of product attributes) is applied toward identifying implicit category spanning and characterizing the products to a richer extent than is typical in the literature. This ultimately allows more nuanced hypotheses of strategic positioning to be tested. In Chapter 2, a natural language processing model is developed for quantifying the extent to which an input text is "elaborate" vs "compact", i.e., for quantifying the text's linguistic code. The notion derives from a concept in sociolinguistics which asserts that the linguistic codes used between two speakers reveals the nature of the relationship between them, with regard to the subject matter at hand. Elaborated code is accessible to outsiders, and its use implies an assumption of a lack of shared contextual background, whereas compact code implies an assumption of insider status. The model combines multiple methods in an ensemble approach, and post training is validated using two distinct assessments. In Chapter 3 this model is applied on digital trace data of communication in an organizational setting, toward an investigation of gender-based returns to communication styles. The analysis revisits an empirical inconsistency in the literature, leveraging the trace data to explore consequences of speech patterns at scale. I  nd signals of distinct patterns of communication between men and women, which correlate with disparate outcomes in performance evaluations and hiring

Keywords: Business.
Advisor: Carroll Glenn
Goldberg Amir
Sorensen Jesper B.
Influence of Land Cover on Woody Plant Encroachment in the Northern Rocky Mountains: A Geospatial Analysis

Over the last 100 to 160 years, shrubs and trees have been proliferating across grasslands in the American West as part of a global phenomenon called woody plant encroachment (WPE). WPE has resulted from changing climatic conditions and the removal of natural processes that have historically inhibited the spread of woody plants and leads to decreased forage availability for livestock, uncertain or negative changes to ecosystem services, and worsening wildfires. In this study, LANDFIRE Existing Vegetation Type data and tree-rangeland classifications from the 1950’s and 2019 were used to calculate the amount of conifer cover change that occurred in a variety of vegetation types and elevation zones over approximately 60 years across a portion of the High Divide region in Idaho. This was done in an effort to determine if and to what degree WPE is influenced by biophysical conditions and existing vegetation type. This data was also used to investigate whether conifer cover growth tends to densify existing forest stands or spread to previously unforested areas and how strong this preference is over relatively long time scales. Existing vegetation type, and specifically the amount of pre-existing forest cover, was found to be strongly related to conifer cover change. Varying amounts of pre-existing forest cover across elevation zone classes created a confounding effect that prevented determination of the impact of elevation on WPE without additional analysis. Finally, the analysis indicated that conifer cover increase was more likely to densify existing forest stands than to result in colonization of unforested areas which has substantial implications for increased wildfire severity risk across U.S. rangelands.

Keywords: Woody plant encroachment
Conifer expansion
forest densification
High Divide
Northern Rocky Mountains
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Thesis
Advisor: Lambin Eric
Initial Transit Effects of New York City's Ride-Hailing Congestion Tax

In February 2019, New York City implemented a $2.75 tax on non-pooled ride-hailing trips, a $0.75 tax on pooled trips, and a $2.50 tax on taxi trips that pass through a designated congestion zone in Manhattan. This paper offers an initial empirical analysis of how this tax may have impacted ride-hailing, taxi, and subway usage. Using a log-linear difference-in-difference approach between trip counts within the congestion zone versus outside the congestion zone, I find that the congestion tax was associated with a statistically significant decline in relative ride-hailing and taxi usage. Declines were generally larger during the day than at night, and shared ride-hailing trips actually saw a relative increase within the congestion zone, indicating potential substitution between non-shared and shared trips. Meanwhile, the tax was not significantly associated with a relative rise in subway ridership in the congestion zone, suggesting that, on average, there was likely not a large substitution effect between these modes after the tax. Additional analysis of concurrent policy changes, such as New York City’s August 2018 ride-hailing license cap, will be required to more confidently separate the tax’s impact from other ongoing changes in the city.

Keywords: Public Policy Program
Congestion pricing
Subways
Taxicabs
Ride-Hailing
Stanford University
Humanities and Sciences
Public Policy Program
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Thesis
Advisor: Levi Margaret
Injectable Biomimetic Hydrogels Providing Prolonged Delivery of GLP-1 Analogues for Enhanced Diabetes Treatment

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.

Keywords:  Department of Biology
Biology
Bioengineering
Type 2 diabetes
Diabetes
Materials science
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Thesis
Advisor: Appel Eric
d'Aquino Andrea
Dixon Scott
Integrated lithium niobate platform for classical and quantum nonlinear optics

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.

Keywords: Humanities and Sciences.
Applied Physics.
Advisor: Safavi-Naeini Amir H.
Fejer Martin M. (Martin Michael)
Miller D. A. B.
Vuckovic Jelena
Integrating features of clonal evolution in the modeling of disease risk and drug sensitivity in acute myeloid leukemia

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

Keywords: Medicine.
Advisor: Majeti Ravindra
Attardi Laura
Gentles Andrew J.
Jaiswal Siddhartha
Integrating Human Dimensions into Fisheries Management: A Case Study of Whale Entanglement in California’s Dungeness Crab Fishery

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.

Keywords: Earth Systems Program
Fishery management
Qualitative research
Whales
Dungeness crab
Social sciences
Ecosystem management
Sustainability
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Thesis
Advisor: Crowder Larry
Early-Capistrán Michelle María
Integrating structure- and ligand-based modeling in drug discovery

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

Keywords: Engineering.
Advisor: Dror Ron
Chen James Kenneth
Ré Christopher
Integration and measurement for building synthetic cells

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.

Keywords: Engineering.
Bioengineering.
Advisor: Endy Drew
Chiu Wah
Salit Marc
Interdisciplinary approaches to wildfire risk management

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.

Keywords: Engineering.
Management Science and Engineering.
Advisor: Weyant John
Azevedo Ines
Sweeney James
Internalization of anti-GD2 antibodies as a key component of the antibody-induced cell death mechanism in pediatric neuroblastoma

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.

Keywords:  Department of Biology
Neuroblastoma
GD2
Gangliosides
Monoclonal antibodies
dinutuximab
Cell death
internalization
AP2M1
AP2S1
clathrin-mediated endocytosis
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Thesis
Advisor: Majzner Robbie
Dixon Scott
Long Sharon
Interpenetration and kinetic mix in weakly collisional, fully-ionized plasma jets

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

Keywords: Engineering.
Mechanical Engineering.
Advisor: Cappelli Mark A. (Mark Antony)
Glenzer S. H.
Meezan Nathan
Interpretation Makes All the Difference: How Content Moderators Construct Organizational Identity and Navigate Ambiguity

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.

Keywords: Computing platforms
Labor
Organizational behavior > Study and teaching
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Thesis
Advisor: Christin Angèle