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
Throughout the Southwest, complex geology and physiography concomitant with climatic variability contribute to diverse stream hydrogeomorphologies. Many riparian plant species store their seeds in soil seed banks, and germinate in response to moisture pulses, but the climatic controls of this response are poorly understood. To better understand the ecological implications of a changing climate on riparian plant communities, I investigated seed bank responses to seasonal temperature patterns and to stream hydrogeomorphic type. I asked the following questions: Are there distinct suites of warm and cool temperature germinating species associated with Southwestern streams; how do they differ between riparian and terrestrial zones, and between ephemeral and perennial streams? How does alpha diversity of the soil seed bank differ between streams with ephemeral, intermittent, and perennial flow, and between montane and basin streams? Do streams with greater elevational change have higher riparian zone seed bank beta-diversity? Does nestedness or turnover contribute more to within stream beta-diversity?
I collected soil samples from the riparian and terrestrial zones of 21 sites, placing them in growth chambers at one of two temperature regimes, and monitoring emergence of seedlings for 12 weeks. Results showed an approximately equal number of warm and cool specialists in both riparian and terrestrials zones; generalists also were abundant, particularly in the riparian zone. The number of temperature specialists and generalists in the riparian zones did not differ significantly between perennial headwater and ephemeral stream types. In montane streams, alpha diversity of the soil seed bank was highest for ephemeral reaches; in basin streams the intermittent and perennial reaches had higher diversity. Spatial turnover was primarily responsible for within stream beta-diversity—reaches had different species assemblages. The large portion of temperature specialists found in riparian seed banks indicates that even with available moisture riparian zone plant community composition will likely be impacted by changing temperatures. However, the presence of so many temperature generalists in the riparian zones suggests that some component of the seed bank is adapted to variable conditions and might offer resilience in a changing climate. Study results confirm the importance of conserving multiple hydrogeomorphic reach types because they support unique species assemblages.
I collected soil samples from the riparian and terrestrial zones of 21 sites, placing them in growth chambers at one of two temperature regimes, and monitoring emergence of seedlings for 12 weeks. Results showed an approximately equal number of warm and cool specialists in both riparian and terrestrials zones; generalists also were abundant, particularly in the riparian zone. The number of temperature specialists and generalists in the riparian zones did not differ significantly between perennial headwater and ephemeral stream types. In montane streams, alpha diversity of the soil seed bank was highest for ephemeral reaches; in basin streams the intermittent and perennial reaches had higher diversity. Spatial turnover was primarily responsible for within stream beta-diversity—reaches had different species assemblages. The large portion of temperature specialists found in riparian seed banks indicates that even with available moisture riparian zone plant community composition will likely be impacted by changing temperatures. However, the presence of so many temperature generalists in the riparian zones suggests that some component of the seed bank is adapted to variable conditions and might offer resilience in a changing climate. Study results confirm the importance of conserving multiple hydrogeomorphic reach types because they support unique species assemblages.
ContributorsSetaro, Danika (Author) / Stromberg, Juliet (Thesis advisor) / Franklin, Janet (Committee member) / Makings, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2016
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
Bouteloua eriopoda (Torr.) Torr., also known as black grama, is a perennial bunchgrass native to arid and semiarid ecosystems in the southwestern region of North America. As a result of anthropogenic climate change, this region is predicted to increase in aridity and experience more frequent extreme drought and extreme wet years. This change in precipitation will no doubt affect black grama; however, few studies have investigated how the specific structural components of this grass will respond. The purpose of this study was to examine the effects of years since start of treatment and annual precipitation amount on tiller and stolon densities, and to test for interaction between the two predictor variables. Additionally, the effects of annual precipitation on ramets and axillary buds were investigated. By using 36 experimental plots that have been receiving drought, irrigated, or control treatments since 2007, tiller density was the most responsive component to both annual precipitation amount and years since start of treatment. Years since start of treatment and annual precipitation amount also had a statistically significant interaction, meaning the effect of precipitation amount on tiller density differs depending on how many years have passed since treatments began. Stolon density was the second-most responsive component; the predictor variables were found to have no statistically significant interaction, meaning their effects on stolon density are independent of one another. Ramet density, ramets per stolon, and axillary bud metabolic activity and density were found to be independent of annual precipitation amount for 2021. The results indicate that multiple-year extreme wet and multiple-year extreme dry conditions in the Southwest will both likely reduce tiller and stolon densities in black grama patches. Prolonged drought conditions reduced tiller and stolon production in black grama because of negative legacies from previous years. Reduced production during prolonged wet conditions could be due to increased competition between adjacent plants.
ContributorsSutter, Bryce Madison (Author) / Sala, Osvaldo E (Thesis advisor) / Makings, Elizabeth (Committee member) / Wojciechowski, Martin F (Committee member) / Arizona State University (Publisher)
Created2022