VictorJuiz/thesis huggingface dataset:
Information and influence [electronic resource] : organizational influence on public policy through advisory committees

How do private organizations influence public policy, particularly through non-economic means? Scholarship in political science and corporate political activity has primarily examined the influence of private organizations via lobbying, campaign donations and the "revolving door." These studies are inherently limited by a perspective that views legislation as the outcome of the market for dollars and votes and by focusing on legislation almost exclusively. My dissertation addresses these limitations through a mixed-methods study combining interviews and a multi-year analysis of how organizations participate in federal advisory committees as a means of influencing regulation (rather than legislation). Advisory committees are a common tool federal agencies use to solicit feedback and gather expert advice from non-governmental sources. I find that agencies utilize these committees as part of an extensive information gathering network. As such, these committees, and those represented on them, have the ability to affect agency decision making by influencing the information that an agency receives. This effect I demonstrate through a statistical analysis of the relationship between representation on committees and the receipt of policy-related grants from related agencies. These findings demonstrate the importance of studying regulation, not legislation, as the "hand of government" that most frequently interacts with organizations and the ability of private organizations to influence regulation. In addition, my dissertation adds nuance to existing literature by showing a new pathway for corporate political activity, information sharing, that has long been the focus of organization theory in other contexts.

Keywords: Management Science and Engineering.
Advisor: Barley Stephen R. primary advisor. Thesis advisor
Hinds Pamela primary advisor. Thesis advisor
Byers Thomas (Thomas H.) Thesis advisor
Parigi Paolo
An information-theoretic study of problems in wireless relaying [electronic resource]

Wireless relaying (using additional radio devices to improve the communication between a source and a destination) is poised to emerge as a key component in heterogeneous networks, internet of things and device-to-device communication. As a result, it is crucial to construct relaying models that capture practical constraints, derive fundamental limits on the performance in these models, and construct schemes that approach these performance limits. This thesis will describe our work in four specific instances in this direction. The first two chapters focus on questions that arise while operating a network of multiple relays. In the first of these, we take a closer look at the popular relaying paradigm of ``compress-forward'', wherein relays simply attempt to describe their received signals to the destination. We show that this paradigm can be optimized to deliver significantly better performance than what is suggested in existing results. The improvement is based on the surprising notion that the relays should do a poor job of describing their observations. In the next chapter, we consider the problem of selecting a subset of relays that is small but nevertheless good in the sense that it supports high end-to-end rates. This is non-trivial due to the large search space and difficult-to-evaluate objective function. We exploit the structure of the network to develop an approximation algorithm based on simulated annealing. The algorithm performs exceptionally well in terms of both the runtime and the objective of the selected set of relays. The next two chapters focus on the single relay channel, and study the impact of practical constraints of relay such as half-duplexing and lack of complete channel state information. In the first, we consider the problem of operating a single relay that can at any time either act as a receiver or a transmitter, but not both (half-duplex). Studying this setup gives rise to highly interesting questions, such as when should the relay receive and when should it transmit, whether it can decide this as a function of dynamically available information or not, what this function should be, what information about the channel is available at the relay, and how should it process the received signal. We provide a comprehensive investigation into these questions. In the next chapter, we consider a relay which is assumed to be uninformed about the state of the channel. This captures the fact that it can be difficult for the relay to obtain timely information about the fading between source and destination. We address the inadequacy of existing schemes by developing a novel scheme "cooperative-bin-forward" that achieves the capacity. This scheme leverages the benefits of the two basic relaying paradigms of "decode-forward" and "compress-forward".

Keywords: Electrical Engineering.
Advisor: Özgür Ayfer primary advisor. Thesis advisor
El Gamal Abbas A. advisor. Thesis advisor
Weissman Tsachy advisor. Thesis advisor
inHabit

Entering college means having to make decisions for the first time about how you learn best. These skills are usually developed on one’s own through trial and error, if at all. inHabit in an interactive map that guides learners through a place-based self-reflection cycle: from exploring which physical spaces support which kinds of learning, to reflecting on their feelings once in the space. Students not only learn how they learn best in the context of physical space, but also gain feelings of ownership and agency over their work, which can intrinsically motivate them to learn and succeed in college.

Keywords: Learning Spaces
Metacognition
Space Design
Environmental Psychology
Thesis
Injectable Hydrogels Enhance Cellular Transplantations for Spinal Cord Injury Repair

Approximately 12,000 new spinal cord injuries (SCI) occur in the US each year, and less than 1% of SCI patients have full neurological recovery. Current treatments for SCI are limited to reducing inflammation and preventing further degradation. As such, developing a regenerative therapy for SCI would be transformational. The cellular transplantation of olfactory ensheathing glia (OEG) has been shown to be a potential solution, but developing an efficient cell-delivery and engraftment mechanism for transplantation remains a significant challenge. Previous studies have suggested that cell delivery within a carrier gel may increase cell viability, but a majority of these gels suffer from critical limitations that prevent their translation to clinical use. A recently developed family of hydrogels called Sheer-thinning Hydrogel for Injectable Encapsulation and Long-term Delivery (SHIELD) that are biocompatible, customizable, and reproducible may provide a more translatable and optimized solution to enhance transplantation for SCI repair. Here we assay in vitro the behavior of OEGs encapsulated in SHIELD and demonstrate that these hydrogels are capable of protecting and supporting the OEGs. Metabolic activity and cell death assays along with fluorescence immunocytochemistry show that the cells are healthier when encapsulated within SHIELD compared to cell-only controls. OEG delivery in vivo was also shown to be enhanced when encapsulated with SHIELD. These results suggest that encapsulating OEGs in the hydrogel prior to transplantation is an attractive strategy to increase the efficacy of cellular transplantations and may serve to significantly increase functional regeneration in SCI repair. In the future, SHIELD may increase the efficacy of a variety of cell-based therapies.

Keywords:  Department of Biology
Spinal cord injury (SCI)
graft
regeneration
Olfactory Ensheathing Glia (OEG)
hydrogel
encapsulation
cellular transplantation
Stanford University
Thesis
Advisor: Plant Giles
McConnell Susan
Innate immune decision-making [electronic resource] : insights from single-cell dynamic responses

The innate immune system is the body's ﬁrst response to infection and gateway to an inﬂammatory or adaptive immune response. Many of the receptor and signaling pathways involved in the innate immune system have been delineated in great detail, including those recognizing microorganism-speciﬁc molecular patterns and damage or stress signals released in response to infection. But relatively little is known about how speciﬁcity to diﬀerent pathogens is conferred within a class of microorganisms. This thesis is dedicated to achieving a better understanding of the innate immune system and its speciﬁc and self-limited response to pathogens, namely bacterial infection. Using a combination of microscopy and single-cell dynamics, computational modeling, and multiplex assays, we were able to gain insight into how the innate immune response is coordinated. We show that innate immune system function is more speciﬁc to pathogens than previously thought, that the response consists of variable and invariable dynamic aspects, and that the innate immune system can employ combinatorial synergies to amplify its response to dangerous microbes. These ﬁndings may provide clues to how many immunodeﬁciencies and autoimmune diseases develop and may inform the design of novel vaccine adjuvants and immunotherapies.

Keywords: Bioengineering.
Advisor: Covert Markus primary advisor. Thesis advisor
Huang Kerwyn Casey
Schneider David (David Samuel)
Innate immune mechanisms of osteoarthritis [electronic resource]

The pathogenesis of osteoarthritis (OA), the most common disease of the joints, remains unclear and there are currently no treatments that can prevent or slow disease progression. Although synovitis (i.e., inflammation of the membrane that lines synovial joints) and mildly elevated levels of white blood cells in synovial fluid are well-established features of osteoarthritis, the role of inflammation in osteoarthritis, as well as the precise cellular and molecular mechanisms involved, are unknown. The studies presented in this dissertation support our overarching hypothesis that chronic, low-grade inflammation and activation of the innate immune system drive OA pathogenesis. In Chapter 2, we demonstrate a key pathogenic role for mast cells in OA. Genetic deficiency of mast cells protects against OA, and engraftment of mast cells reverses this protection. Furthermore, transcriptomic and proteomic analyses of OA synovial tissues and fluids demonstrate evidence of mast cell activation in human disease. In vitro studies support a mechanistic role for tryptase as a mediator of mast cell-driven pathology in OA: tryptase promotes cartilage catabolism, induces chondrocyte apoptosis, and stimulates proliferation and proinflammatory responses of synovial fibroblasts. In Chapter 3, we demonstrate that the complement system, acting through its membrane attack complex-mediated arm, is pathogenic in OA. Genetic deficiency of complement components C5 and C6 or pharmacologic inhibition of complement with either CR2-fH, a fusion protein that inhibits activation of C3 and C5, or a monoclonal antibody to C5 attenuates joint damage in a mouse model of OA, whereas deficiency of the MAC-inhibitor CD59a exacerbates disease. Moreover, complement expression is dysregulated in human OA, and MAC, when present on the cell surface at sublytic concentrations, induces chondrocyte expression of proinflammatory mediators and degradative enzymes relevant to OA. Finally, in Chapter 4, we demonstrate that Carboxypeptidase B (CPB) protects against OA, and it does so in part by inhibiting complement activation. Genetic deficiency of CPB exacerbates OA in mice. Moreover, we show that levels of CPB strongly correlate with levels of MAC in OA synovial fluid, and CPB inhibits MAC formation and activity in vitro. Taken together, our findings emphasize the significance of innate immune responses in OA pathogenesis and support a thorough reexamination of the prevailing dogma that osteoarthritis is a non-inflammatory, 'wear and tear' arthritis. Furthermore, our increased understanding of the inflammatory underpinnings of OA suggests multiple avenues for the development of new, disease-modifying therapies.

Advisor: Robinson William (William Hewitt) primary advisor. Thesis advisor
Fathman C. Garrison
Miklos David (David B.) advisor. Thesis advisor
Wyss-Coray Anton advisor. Thesis advisor
Innovation in the global economy [electronic resource]

International migration and international trade are two major components of the current globalization process. Their impact on the global economy has been studied extensively. This dissertation explores how international migration and trade influence global innovative activities. In particular, I study the trade flow and migration of skilled workers from developing countries to the U.S., and how such flows affect the U.S. as well as the source countries. In the first chapter, I study the migration of science and engineering (S& E) workers from non-OECD (i.e., developing) countries to the US. In particular, I evaluate the impact of such brain drain on both the US and the source countries using a multi-country endogenous growth model. The proposed framework introduces and quantifies a "frontier growth effect" of skilled migration: migrants from developing countries create more frontier knowledge in the U.S., and the non-rivalrous knowledge diffuses to all countries. In particular, each source country is able to adopt technology invented by migrants from other countries, a previously ignored externality of skilled migration. I quantify the model by matching both micro and macro moments, and then consider counterfactuals wherein U.S. immigration policy changes. My results suggest that a policy -- which doubles the number of immigrants from every non-OECD country -- would boost U.S. productivity growth by 0.1 percentage point per year, and improve average welfare in the U.S. by 3.3%. Such a policy can also benefit the source countries because of the "frontier growth effect". Taking India as an example source country, I find that the same policy would lead to faster long-run growth and a 0.9% increase in average welfare in India. This welfare gain in India is largely the result of additional non-Indian migrants, indicating the significance of the previously overlooked externality. The second chapter is co-authored with Kaiji Gong. We analyze the impact of rising Chinese import competition on innovative activities in the U.S. We adopt a similar identification strategy as in Autor et al. (2013), whereby we use Compustat firm-level data and industry- level trade data to exploit cross-industry variation in exposure to import competition. We find that U.S. firms on average reduce R& D investment when faced with more import com- petition from China. The negative effect is driven by smaller firms and highly leveraged firms. When we weight the firms by their initial sizes, the average impact of import com- petition became insignificant. Our findings cannot fully be explained by a simple story of negative demand shock. Further empirical exploration is needed to identify the channels through which import competition affects firm's R& D investment.

Keywords: Economics.
Advisor: Klenow Peter J. primary advisor. Thesis advisor
Jones Charles I. (Charles Irving) advisor. Thesis advisor
Kurlat Pablo
Integrated decision support tool for nested, spatially-scaled urban recycled water infrastructure planning [electronic resource]

Globally, the main drivers for expansion of water reuse are: 1) Increasing water demand to support industrial and population growth; 2) Water scarcity and droughts, particularly in arid and semi-arid regions; and 3) Environmental protection and enhancement. Development of water reuse systems that can address these various drivers is complex. Here, we introduce an integrated decision-support software tool for planning of water reuse infrastructure using multiple weighted criteria. The main contributions of this research are: 1. A high level decision methodology that selects the combination of centralized and satellite resource (water/energy) recovery and reuse systems that best balances monetary and non-monetary criteria. A case study for the Palo Alto wastewater catchment is used to demonstrate the methodology. 2. Development and implementation of decision support software components: 1) a Locator for satellite treatment and wastewater scalping facilities, 2) a Calculator for water and organic mass balances, 3) a Calculator for recycled water and sewer costs and incentive, and 4) a pipeline Designer. The designer is used to identify near-minimum-cost pipeline routes for multiple users, and accounts for existing configurations, environmental or social constraints, and trade-offs in pipeline length, construction methods, and traffic congestion during construction. In addition, the designer optimizes pipe diameters, pipe pressure classes, pump station locations, and pumping energy and includes the theoretical additional capacity of each pipe, facilitating future expansion. To illustrate the utility of the designer, a case study was conducted for the City of Palo Alto, CA. 3. An Integrator that combines the above software components and adds capability for wastewater treatment process selection to enable nested, spatially-scaled, urban recycled water reuse system design. This decision support tool is applied to a case study of Golden, CO.

Keywords: Civil and Environmental Engineering.
Advisor: Criddle Craig primary advisor. Thesis advisor
Freyberg David L. primary advisor. Thesis advisor
Cath Tzahi Y. advisor. Thesis advisor
Fischer Martin
Integrating omics and histopathology profiles for precision medicine [electronic resource]

Precision medicine is an approach that accounts for individual differences to guide disease prevention and treatment. Previous research has identified associations between individual omics differences - such as genomic, transcriptomic, or proteomic variations - and a number of disease phenotypes. However, the molecular processes leading to many complex diseases remain obscure. In addition, the clinical value of integrating omics with pathology evaluation is yet to be established. To address this gap, this work aims to elucidate the molecular and histomorphology patterns underpinning clinical phenotypes of non-small cell lung cancer, ovarian cancer, and bronchopulmonary dysplasia. This dissertation has three parts: (1) the analysis of omics patterns in complex diseases, (2) the associations between quantitative histopathology image features and patient phenotypes, and (3) the integration of histopathology images and omics profiles to gather further biomedical insights. In the first part, I describe how omics information can guide precision medicine with two examples: understanding the biological processes involved in bronchopulmonary dysplasia through exome sequencing and predicting ovarian cancer patients' response to chemotherapy with tumor proteomics information. The second section demonstrates the utility of a fully automated bioinformatics pipeline for analyzing histopathology images and predicting the prognosis of non-small cell lung cancer patients, with the results validated in an independent cohort. In the last part, I describe the integration of omics profiles and histopathology findings to better understand the biological pathways underpinning different microscopic morphology in lung adenocarcinoma. I conclude that this integration yields better clinical predictions for precision medicine. These results suggest that the inter-individual differences in omics profiles and histopathology patterns not only provide useful information for clinical decision-making, but also reveal the molecular mechanisms driving the diverse tumor cell morphology. Through the integration of histopathology and omics studies, we can understand the biological processes associated with different disease phenotypes, identify the inter-individual differences in prognosis, and contribute to personalizing treatment plans for each patient. This approach will reduce the cost of healthcare and improve the quality of life in numerous patients.

Advisor: Altman Russ primary advisor. Thesis advisor
Snyder Michael
Ré Christopher advisor. Thesis advisor
Rijn Matt van de advisor. Thesis advisor
Intelligent urban traffic signal control using multiagent reinforcement learning [electronic resource]

Urban traffic management plays an important role in building smart and sustainable cities, from social, economical, and environmental points of view. However, modern traffic signal control systems have no been significantly improved over the last four decades despite extensive studies conducted in urban transportation research. One major reason is that most traditional signal control models rely heavily on statistical assumptions and heuristic criteria, which cannot support adaptive microscopic signal controls, while most proposed intelligent signal control methods are not robust and scalable enough for managing real-world large-scale urban traffic flows. This thesis proposes a systematic and realistic approach for implementing reinforcement learning (RL) to design adaptive, optimal, and scalable intelligent urban signal control systems. The direct adoption of existing RL models is impractical for large-scale signal control because of the extremely high computational complexity of learning and searching of control policies. To overcome this difficulty, this thesis proposes a practical multiagent RL framework that is scalable for large-scale traffic signal control, by applying various approximation methods. These methods are developed from three major perspectives: (1) integrating heuristic rules in traditional urban transportation studies to reduce the complexity of control policy searching; (2) applying state-of-the-art supervised learning techniques to achieve efficient feature extraction and Q-function approximation; and (3) implementing distributed control techniques to achieve effective communication and coordination in large-scale traffic networks. To evaluate the performance of this MARL framework, various numerical experiments are conducted in microscopic traffic simulations. The results demonstrate that proposed MARL methods outperform the major traditional and intelligent signal control methods, with a linear computational time complexity which is acceptable for real-world applications.

Keywords: Civil and Environmental Engineering.
Advisor: Leckie Jim
Lepech Michael advisor. Thesis advisor
Wang Jie (Researcher in adaptive computational learning)