ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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Description
Endocrine disruptors are chemicals that interact with the hormone system to negative effect. They ‘disrupt’ normal processes to cause diseases like vaginal cancer and obesity, reproductive issues like t-shaped uteri and infertility, and developmental abnormalities like spina bifida and cleft palate. These chemicals are ubiquitous in our daily lives, components in everything from toothpaste to microwave popcorn to plastic water bottles. My dissertation looks at the history, science, and regulation of these impactful substances in order to answer the question of how endocrine disruptors appeared, got interpreted by different groups, and what role science played in the process. My analysis reveals that endocrine disruptors followed a unique science policy trajectory in the US, rapidly going from their proposal in 1991 to their federal regulation in 1996, even amid intense and majority scientific disagreement over whether the substances existed at all. That trajectory resulted from the work of a small number of scientist-activists who constructed a concept and category as scientific, social, and regulatory. By playing actors from each sphere against each other and advancing a very specific scientific narrative that fit into a regulatory and social window of opportunity in the 1990s, those scientist-activists made endocrine disruptors a national issue that few could ignore. Those actions resulted in the Endocrine Disruptor Screening Program, a heavily-criticized and ineffective regulatory program. My dissertation tells a story of the past that informs the present. In 2018, the work of researchers, public media, and policymakers in the 1990s continues to play out, evident in the deep scientific division over endocrine disrupting effects and the inability of the European Union to settle on even a definition of endocrine disruptors for regulation purposes.
ContributorsAbboud, Alexis J (Author) / Maienschein, Jane A (Thesis advisor) / Crow, Michael M. (Committee member) / Hurlbut, J. Benjamin (Committee member) / Marchant, Gary E (Committee member) / Arizona State University (Publisher)
Created2018
Description
Cancer is a disease involving abnormal growth of cells. Its growth dynamics is perplexing. Mathematical modeling is a way to shed light on this progress and its medical treatments. This dissertation is to study cancer invasion in time and space using a mathematical approach. Chapter 1 presents a detailed review of literature on cancer modeling.
Chapter 2 focuses sorely on time where the escape of a generic cancer out of immune control is described by stochastic delayed differential equations (SDDEs). Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and stochasticity in the tumor proliferation rate are studied by including delay and noise in the model. Stability, persistence and extinction of the tumor are analyzed. The result shows that both time delay and noise can induce the transition from low tumor burden equilibrium to high tumor equilibrium. The aforementioned work has been published (Han et al., 2019b).
In Chapter 3, Glioblastoma multiforme (GBM) is studied using a partial differential equation (PDE) model. GBM is an aggressive brain cancer with a grim prognosis. A mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. A novel method is developed to approximate key characteristics of the wave profile, which can be compared with MRI data. Several test cases of MRI data of GBM patients are used to yield personalized parameterizations of the model. The aforementioned work has been published (Han et al., 2019a).
Chapter 4 presents an innovative way of forecasting spatial cancer invasion. Most mathematical models, including the ones described in previous chapters, are formulated based on strong assumptions, which are hard, if not impossible, to verify due to complexity of biological processes and lack of quality data. Instead, a nonparametric forecasting method using Gaussian processes is proposed. By exploiting the local nature of the spatio-temporal process, sparse (in terms of time) data is sufficient for forecasting. Desirable properties of Gaussian processes facilitate selection of the size of the local neighborhood and computationally efficient propagation of uncertainty. The method is tested on synthetic data and demonstrates promising results.
Chapter 2 focuses sorely on time where the escape of a generic cancer out of immune control is described by stochastic delayed differential equations (SDDEs). Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and stochasticity in the tumor proliferation rate are studied by including delay and noise in the model. Stability, persistence and extinction of the tumor are analyzed. The result shows that both time delay and noise can induce the transition from low tumor burden equilibrium to high tumor equilibrium. The aforementioned work has been published (Han et al., 2019b).
In Chapter 3, Glioblastoma multiforme (GBM) is studied using a partial differential equation (PDE) model. GBM is an aggressive brain cancer with a grim prognosis. A mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. A novel method is developed to approximate key characteristics of the wave profile, which can be compared with MRI data. Several test cases of MRI data of GBM patients are used to yield personalized parameterizations of the model. The aforementioned work has been published (Han et al., 2019a).
Chapter 4 presents an innovative way of forecasting spatial cancer invasion. Most mathematical models, including the ones described in previous chapters, are formulated based on strong assumptions, which are hard, if not impossible, to verify due to complexity of biological processes and lack of quality data. Instead, a nonparametric forecasting method using Gaussian processes is proposed. By exploiting the local nature of the spatio-temporal process, sparse (in terms of time) data is sufficient for forecasting. Desirable properties of Gaussian processes facilitate selection of the size of the local neighborhood and computationally efficient propagation of uncertainty. The method is tested on synthetic data and demonstrates promising results.
ContributorsHan, Lifeng (Author) / Kuang, Yang (Thesis advisor) / Fricks, John (Thesis advisor) / Kostelich, Eric (Committee member) / Baer, Steve (Committee member) / Gumel, Abba (Committee member) / Arizona State University (Publisher)
Created2020
Description
The analysis focuses on a two-population, three-dimensional model that attempts to accurately model the growth and diffusion of glioblastoma multiforme (GBM), a highly invasive brain cancer, throughout the brain. Analysis into the sensitivity of the model to
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.
ContributorsTrent, Austin Lee (Author) / Kostelich, Eric (Thesis advisor) / Gumel, Abba (Committee member) / Kuang, Yang (Committee member) / Arizona State University (Publisher)
Created2020
Description
This dissertation investigates how ideas of the right relationships among science, the public, and collective decision-making about science and technology come to be envisioned in constructions of public engagement. In particular, it explores how public engagement has come to be constructed in discourse around gene editing to better understand how it holds together with visions for good, democratic governance of those technologies and with what effects. Using a conceptual idiom of the co-production of science and the social order, I investigate the mutual formation of scientific expertise, responsibility, and democracy through constructions of public engagement. I begin by tracing dominant historical narratives of contemporary public engagement as a continuation of public understanding of science’s projects of social ordering for democratic society. I then analyze collections of prominent expert meetings, publications, discussions, and interventions about development, governance, and societal implications human heritable germline gene editing and gene drives that developed in tandem with commitments to public engagement around those technologies. Synthesizing the evidence from across gene editing discourse, I offer a constructive critique of constructions of public engagement as expressions and evidence of scientific responsibility as ultimately reasserting and reinforcing of scientific experts' authority in gene editing decision-making, despite intentions for public engagement to extend decision-making participation and power to publics. Such constructions of public engagement go unrecognized in gene editing discourse and thereby subtly reinforce broader visions of scientific expertise as essential to good governance by underwriting the legitimacy and authority of scientific experts to act on behalf of public interests. I further argue that the reinforcement of scientific expert authority in gene editing discourse through public engagement also centers scientific experts in a sociotechnical imaginary that I call “not for science alone.” This sociotechnical imaginary envisions scientific experts as guardians and guarantors of good, democratic governance. I then propose a possible alternatives to public engagement alone to improve gene editing governance by orienting discourse around notions of public accountability for potential shared benefits and collective harms of gene editing.
ContributorsRoss, Christian (Author) / Hurlbut, James B. (Thesis advisor) / Maienschein, Jane (Thesis advisor) / Collins, James P. (Committee member) / Crow, Michael M. (Committee member) / Sarewitz, Daniel R. (Committee member) / Arizona State University (Publisher)
Created2021