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Second-generation biofuel feedstocks are currently grown in land-based systems that use valuable resources like water, electricity and fertilizer. This study investigates the potential of near-shore marine (ocean) seawater filtration as a source of planktonic biomass for biofuel production. Mixed marine organisms in the size range of 20µm to 500µm were isolated from the University of California, Santa Barbara (UCSB) seawater filtration system during weekly backwash events between the months of April and August, 2011. The quantity of organic material produced was determined by sample combustion and calculation of ash-free dry weights. Qualitative investigation required density gradient separation with the heavy liquid sodium metatungstate followed by direct transesterification and gas chromatography with mass spectrometry (GC-MS) of the fatty acid methyl esters (FAME) produced. A maximum of 0.083g/L of dried organic material was produced in a single backwash event and a study average of 0.036g/L was calculated. This equates to an average weekly value of 7,674.75g of dried organic material produced from the filtration of approximately 24,417,792 liters of seawater. Temporal variations were limited. Organic quantities decreased over the course of the study. Bio-fouling effects from mussel overgrowth inexplicably increased production values when compared to un-fouled seawater supply lines. FAMEs (biodiesel) averaged 0.004% of the dried organic material with 0.36ml of biodiesel produced per week, on average. C16:0 and C22:6n3 fatty acids comprised the majority of the fatty acids in the samples. Saturated fatty acids made up 30.71% to 44.09% and unsaturated forms comprised 55.90% to 66.32% of the total chemical composition. Both quantities and qualities of organics and FAMEs were unrealistic for use as biodiesel but sample size limitations, system design, geographic and temporal factors may have impacted study results.
ContributorsPierre, Christophe (Author) / Olson, Larry (Thesis advisor) / Sommerfeld, Milton (Committee member) / Brown, Albert (Committee member) / Arizona State University (Publisher)
Created2011
Description
In 2010, a monthly sampling regimen was established to examine ecological differences in Saguaro Lake and Lake Pleasant, two Central Arizona reservoirs. Lake Pleasant is relatively deep and clear, while Saguaro Lake is relatively shallow and turbid. Preliminary results indicated that phytoplankton biomass was greater by an order of magnitude in Saguaro Lake, and that community structure differed. The purpose of this investigation was to determine why the reservoirs are different, and focused on physical characteristics of the water column, nutrient concentration, community structure of phytoplankton and zooplankton, and trophic cascades induced by fish populations. I formulated the following hypotheses: 1) Top-down control varies between the two reservoirs. The presence of piscivore fish in Lake Pleasant results in high grazer and low primary producer biomass through trophic cascades. Conversely, Saguaro Lake is controlled from the bottom-up. This hypothesis was tested through monthly analysis of zooplankton and phytoplankton communities in each reservoir. Analyses of the nutritional value of phytoplankton and DNA based molecular prey preference of zooplankton provided insight on trophic interactions between phytoplankton and zooplankton. Data from the Arizona Game and Fish Department (AZGFD) provided information on the fish communities of the two reservoirs. 2) Nutrient loads differ for each reservoir. Greater nutrient concentrations yield greater primary producer biomass; I hypothesize that Saguaro Lake is more eutrophic, while Lake Pleasant is more oligotrophic. Lake Pleasant had a larger zooplankton abundance and biomass, a larger piscivore fish community, and smaller phytoplankton abundance compared to Saguaro Lake. Thus, I conclude that Lake Pleasant was controlled top-down by the large piscivore fish population and Saguaro Lake was controlled from the bottom-up by the nutrient load in the reservoir. Hypothesis 2 stated that Saguaro Lake contains more nutrients than Lake Pleasant. However, Lake Pleasant had higher concentrations of dissolved nitrogen and phosphorus than Saguaro Lake. Additionally, an extended period of low dissolved N:P ratios in Saguaro Lake indicated N limitation, favoring dominance of N-fixing filamentous cyanobacteria in the phytoplankton community in that reservoir.
ContributorsSawyer, Tyler R (Author) / Neuer, Susanne (Thesis advisor) / Childers, Daniel L. (Committee member) / Sommerfeld, Milton (Committee member) / Arizona State University (Publisher)
Created2011
Description
Rabies disease remains enzootic among raccoons, skunks, foxes and bats in the United States. It is of primary concern for public-health agencies to control spatial spread of rabies in wildlife and its potential spillover infection of domestic animals and humans. Rabies is invariably fatal in wildlife if untreated, with a non-negligible incubation period. Understanding how this latency affects spatial spread of rabies in wildlife is the concern of chapter 2 and 3. Chapter 1 deals with the background of mathematical models for rabies and lists main objectives. In chapter 2, a reaction-diffusion susceptible-exposed-infected (SEI) model and a delayed diffusive susceptible-infected (SI) model are constructed to describe the same epidemic process -- rabies spread in foxes. For the delayed diffusive model a non-local infection term with delay is resulted from modeling the dispersal during incubation stage. Comparison is made regarding minimum traveling wave speeds of the two models, which are verified using numerical experiments. In chapter 3, starting with two Kermack and McKendrick's models where infectivity, death rate and diffusion rate of infected individuals can depend on the age of infection, the asymptotic speed of spread $c^\ast$ for the cumulated force of infection can be analyzed. For the special case of fixed incubation period, the asymptotic speed of spread is governed by the same integral equation for both models. Although explicit solutions for $c^\ast$ are difficult to obtain, assuming that diffusion coefficient of incubating animals is small, $c^\ast$ can be estimated in terms of model parameter values. Chapter 4 considers the implementation of realistic landscape in simulation of rabies spread in skunks and bats in northeast Texas. The Finite Element Method (FEM) is adopted because the irregular shapes of realistic landscape naturally lead to unstructured grids in the spatial domain. This implementation leads to a more accurate description of skunk rabies cases distributions.
ContributorsLiu, Hao (Author) / Kuang, Yang (Thesis advisor) / Jackiewicz, Zdzislaw (Committee member) / Lanchier, Nicolas (Committee member) / Smith, Hal (Committee member) / Thieme, Horst (Committee member) / Arizona State University (Publisher)
Created2013
Description
Bacteriophage (phage) are viruses that infect bacteria. Typical laboratory experiments show that in a chemostat containing phage and susceptible bacteria species, a mutant bacteria species will evolve. This mutant species is usually resistant to the phage infection and less competitive compared to the susceptible bacteria species. In some experiments, both susceptible and resistant bacteria species, as well as phage, can coexist at an equilibrium for hundreds of hours. The current research is inspired by these observations, and the goal is to establish a mathematical model and explore sufficient and necessary conditions for the coexistence. In this dissertation a model with infinite distributed delay terms based on some existing work is established. A rigorous analysis of the well-posedness of this model is provided, and it is proved that the susceptible bacteria persist. To study the persistence of phage species, a "Phage Reproduction Number" (PRN) is defined. The mathematical analysis shows phage persist if PRN > 1 and vanish if PRN < 1. A sufficient condition and a necessary condition for persistence of resistant bacteria are given. The persistence of the phage is essential for the persistence of resistant bacteria. Also, the resistant bacteria persist if its fitness is the same as the susceptible bacteria and if PRN > 1. A special case of the general model leads to a system of ordinary differential equations, for which numerical simulation results are presented.
ContributorsHan, Zhun (Author) / Smith, Hal (Thesis advisor) / Armbruster, Dieter (Committee member) / Kawski, Matthias (Committee member) / Kuang, Yang (Committee member) / Thieme, Horst (Committee member) / Arizona State University (Publisher)
Created2012
Description
In vertebrate outer retina, changes in the membrane potential of horizontal cells affect the calcium influx and glutamate release of cone photoreceptors via a negative feedback. This feedback has a number of important physiological consequences. One is called background-induced flicker enhancement (BIFE) in which the onset of dim background enhances the center flicker response of horizontal cells. The underlying mechanism for the feedback is still unclear but competing hypotheses have been proposed. One is the GABA hypothesis, which states that the feedback is mediated by gamma-aminobutyric acid (GABA), an inhibitory neurotransmitter released from horizontal cells. Another is the ephaptic hypothesis, which contends that the feedback is non-GABAergic and is achieved through the modulation of electrical potential in the intersynaptic cleft between cones and horizontal cells. In this study, a continuum spine model of the cone-horizontal cell synaptic circuitry is formulated. This model, a partial differential equation system, incorporates both the GABA and ephaptic feedback mechanisms. Simulation results, in comparison with experiments, indicate that the ephaptic mechanism is necessary in order for the model to capture the major spatial and temporal dynamics of the BIFE effect. In addition, simulations indicate that the GABA mechanism may play some minor modulation role.
ContributorsChang, Shaojie (Author) / Baer, Steven M. (Thesis advisor) / Gardner, Carl L (Thesis advisor) / Crook, Sharon M (Committee member) / Kuang, Yang (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this research we consider stochastic models of Glioblastoma Multiforme brain tumors. We first look at a model by K. Swanson et al., which describes the dynamics as random diffusion plus deterministic logistic growth. We introduce a stochastic component in the logistic growth in the form of a random growth rate defined by a Poisson process. We show that this stochastic logistic growth model leads to a more accurate evaluation of the tumor growth compared its deterministic counterpart. We also discuss future plans to incorporate individual patient geometry, extend the model to three dimensions and to incorporate effects of different treatments into our model, in collaboration with a local hospital.
ContributorsManning, Michael Clare (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Gardner, Carl (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Letters and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2013-12
Description
Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm3 to 62 mm3, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.
ContributorsRutter, Erica (Author) / Stepien, Tracy (Author) / Anderies, Barrett (Author) / Plasencia, Jonathan (Author) / Woolf, Eric C. (Author) / Scheck, Adrienne C. (Author) / Turner, Gregory H. (Author) / Liu, Qingwei (Author) / Frakes, David (Author) / Kodibagkar, Vikram (Author) / Kuang, Yang (Author) / Preul, Mark C. (Author) / Kostelich, Eric (Author) / College of Liberal Arts and Sciences (Contributor)
Created2017-05-31
Description
Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT, is an alternative to continuous ADT that improves the patient’s quality of life while testosterone levels recover between cycles. In this paper,we explore the response of intermittent ADT to metastasized prostate cancer by employing a previously clinical data validated mathematical model to new clinical data from patients undergoing Abiraterone therapy. This cell quota model, a system of ordinary differential equations constructed using Droop’s nutrient limiting theory, assumes the tumor comprises of castration-sensitive (CS) and castration-resistant (CR)cancer sub-populations. The two sub-populations rely on varying levels of intracellular androgen for growth, death and transformation. Due to the complexity of the model,we carry out sensitivity analyses to study the effect of certain parameters on their outputs, and to increase the identifiability of each patient’s unique parameter set. The model’s forecasting results show consistent accuracy for patients with sufficient data,which means the model could give useful information in practice, especially to decide whether an additional round of treatment would be effective.
ContributorsBennett, Justin Klark (Author) / Kuang, Yang (Thesis director) / Kostelich, Eric (Committee member) / Phan, Tin (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Phytoplankton comprise the base of the marine food web, and, along with heterotrophic protists, they are key players in the biological pump that transports carbon from the surface to the deep ocean. In the world's subtropical oligotrophic gyres, plankton communities exhibit strong seasonality. Winter storms vent deep water into the euphotic zone, triggering a surge in primary productivity in the form of a spring phytoplankton bloom. Although the hydrographic trends of this "boom and bust" cycle have been well studied for decades, community composition and its seasonal and annual variability remains an integral subject of research. It is hypothesized here that proportions of different phytoplankton and protistan taxa vary dramatically between seasons and years, and that picoplankton represent an important component of this community and contributor to carbon in the surface ocean. Monthly samples from the Bermuda Atlantic Time-series Study (BATS) site were analyzed by epifluorescence microscopy, which permits classification by morphology, size, and trophic type. Epifluorescence counts were supplemented with flow cytometric quantification of Synechococcus, Prochlorococcus, and autotrophic pico- and nanoeukaryotes. Results from this study indicate Synechococcus and Prochlorococcus, prymnesiophytes, and hetero- and mixotrophic nano- and dinoflagellates were the major players in the BATS region plankton community. Ciliates, cryptophytes, diatoms, unidentified phototrophs, and other taxa represented rarer groups. Both flow cytometry and epifluorescence microscopy revealed Synechococcus to be most prevalent during the spring bloom. Prymnesiophytes likewise displayed distinct seasonality, with the highest concentrations again being noted during the bloom. Heterotrophic nano- and dinoflagellates, however, were most common in fall and winter. Mixotrophic dinoflagellates, while less abundant than their heterotrophic counterparts, displayed similar seasonality. A key finding of this study was the interannual variability revealed between the two years. While most taxa were more abundant in the first year, prymnesiophytes experienced much greater abundance in the second year bloom. Analyses of integrated carbon revealed further stark contrasts between the two years, both in terms of total carbon and the contributions of different groups. Total integrated carbon varied widely in the first study year but displayed less fluctuation after June 2009, and values were noticeably reduced in the second year.
ContributorsHansen, Amy (Author) / Neuer, Susanne (Thesis advisor) / Krajmalnik-Brown, Rosa (Committee member) / Sommerfeld, Milton (Committee member) / Arizona State University (Publisher)
Created2010
Description
Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as “economic epidemiology” or “epidemiological economics,” the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.
ContributorsPerrings, Charles (Author) / Castillo-Chavez, Carlos (Author) / Chowell-Puente, Gerardo (Author) / Daszak, Peter (Author) / Fenichel, Eli P. (Author) / Finnoff, David (Author) / Horan, Richard D. (Author) / Kilpatrick, A. Marm (Author) / Kinzig, Ann (Author) / Kuminoff, Nicolai (Author) / Levin, Simon (Author) / Morin, Benjamin (Author) / Smith, Katherine F. (Author) / Springborn, Michael (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / W.P. Carey School of Business (Contributor) / Economics (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor)
Created2015-12-01