Matching Items (84)
Description
Nonlinear dispersive equations model nonlinear waves in a wide range of physical and mathematics contexts. They reinforce or dissipate effects of linear dispersion and nonlinear interactions, and thus, may be of a focusing or defocusing nature. The nonlinear Schrödinger equation or NLS is an example of such equations. It appears as a model in hydrodynamics, nonlinear optics, quantum condensates, heat pulses in solids and various other nonlinear instability phenomena. In mathematics, one of the interests is to look at the wave interaction: waves propagation with different speeds and/or different directions produces either small perturbations comparable with linear behavior, or creates solitary waves, or even leads to singular solutions. This dissertation studies the global behavior of finite energy solutions to the $d$-dimensional focusing NLS equation, $i partial _t u+Delta u+ |u|^{p-1}u=0, $ with initial data $u_0in H^1,; x in Rn$; the nonlinearity power $p$ and the dimension $d$ are chosen so that the scaling index $s=frac{d}{2}-frac{2}{p-1}$ is between 0 and 1, thus, the NLS is mass-supercritical $(s>0)$ and energy-subcritical $(s<1).$ For solutions with $ME[u_0]<1$ ($ME[u_0]$ stands for an invariant and conserved quantity in terms of the mass and energy of $u_0$), a sharp threshold for scattering and blowup is given. Namely, if the renormalized gradient $g_u$ of a solution $u$ to NLS is initially less than 1, i.e., $g_u(0)<1,$ then the solution exists globally in time and scatters in $H^1$ (approaches some linear Schr"odinger evolution as $ttopminfty$); if the renormalized gradient $g_u(0)>1,$ then the solution exhibits a blowup behavior, that is, either a finite time blowup occurs, or there is a divergence of $H^1$ norm in infinite time. This work generalizes the results for the 3d cubic NLS obtained in a series of papers by Holmer-Roudenko and Duyckaerts-Holmer-Roudenko with the key ingredients, the concentration compactness and localized variance, developed in the context of the energy-critical NLS and Nonlinear Wave equations by Kenig and Merle. One of the difficulties is fractional powers of nonlinearities which are overcome by considering Besov-Strichartz estimates and various fractional differentiation rules.
ContributorsGuevara, Cristi Darley (Author) / Roudenko, Svetlana (Thesis advisor) / Castillo_Chavez, Carlos (Committee member) / Jones, Donald (Committee member) / Mahalov, Alex (Committee member) / Suslov, Sergei (Committee member) / Arizona State University (Publisher)
Created2011
Description
In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.
ContributorsOran, Paul (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Ros, Alexandra (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
Pathogenic Gram-negative bacteria employ a variety of molecular mechanisms to combat host defenses. Two-component regulatory systems (TCR systems) are the most ubiquitous signal transduction systems which regulate many genes required for virulence and survival of bacteria. In this study, I analyzed different TCR systems in two clinically-relevant Gram-negative bacteria, i.e., oral pathogen Porphyromonas gingivalis and enterobacterial Escherichia coli. P. gingivalis is a major causative agent of periodontal disease as well as systemic illnesses, like cardiovascular disease. A microarray study found that the putative PorY-PorX TCR system controls the secretion and maturation of virulence factors, as well as loci involved in the PorSS secretion system, which secretes proteinases, i.e., gingipains, responsible for periodontal disease. Proteomic analysis (SILAC) was used to improve the microarray data, reverse-transcription PCR to verify the proteomic data, and primer extension assay to determine the promoter regions of specific PorX regulated loci. I was able to characterize multiple genetic loci regulated by this TCR system, many of which play an essential role in hemagglutination and host-cell adhesion, and likely contribute to virulence in this bacterium. Enteric Gram-negative bacteria must withstand many host defenses such as digestive enzymes, low pH, and antimicrobial peptides (AMPs). The CpxR-CpxA TCR system of E. coli has been extensively characterized and shown to be required for protection against AMPs. Most recently, this TCR system has been shown to up-regulate the rfe-rff operon which encodes genes involved in the production of enterobacterial common antigen (ECA), and confers protection against a variety of AMPs. In this study, I utilized primer extension and DNase I footprinting to determine how CpxR regulates the ECA operon. My findings suggest that CpxR modulates transcription by directly binding to the rfe promoter. Multiple genetic and biochemical approaches were used to demonstrate that specific TCR systems contribute to regulation of virulence factors and resistance to host defenses in P. gingivalis and E. coli, respectively. Understanding these genetic circuits provides insight into strategies for pathogenesis and resistance to host defenses in Gram negative bacterial pathogens. Finally, these data provide compelling potential molecular targets for therapeutics to treat P. gingivalis and E. coli infections.
ContributorsLeonetti, Cori (Author) / Shi, Yixin (Thesis advisor) / Stout, Valerie (Committee member) / Nickerson, Cheryl (Committee member) / Sandrin, Todd (Committee member) / Arizona State University (Publisher)
Created2013
Description
The study of bacterial resistance to antimicrobial peptides (AMPs) is a significant area of interest as these peptides have the potential to be developed into alternative drug therapies to combat microbial pathogens. AMPs represent a class of host-mediated factors that function to prevent microbial infection of their host and serve as a first line of defense. To date, over 1,000 AMPs of various natures have been predicted or experimentally characterized. Their potent bactericidal activities and broad-based target repertoire make them a promising next-generation pharmaceutical therapy to combat bacterial pathogens. It is important to understand the molecular mechanisms, both genetic and physiological, that bacteria employ to circumvent the bactericidal activities of AMPs. These understandings will allow researchers to overcome challenges posed with the development of new drug therapies; as well as identify, at a fundamental level, how bacteria are able to adapt and survive within varied host environments. Here, results are presented from the first reported large scale, systematic screen in which the Keio collection of ~4,000 Escherichia coli deletion mutants were challenged against physiologically significant AMPs to identify genes required for resistance. Less than 3% of the total number of genes on the E. coli chromosome was determined to contribute to bacterial resistance to at least one AMP analyzed in the screen. Further, the screen implicated a single cellular component (enterobacterial common antigen, ECA) and a single transporter system (twin-arginine transporter, Tat) as being required for resistance to each AMP class. Using antimicrobial resistance as a tool to identify novel genetic mechanisms, subsequent analyses were able to identify a two-component system, CpxR/CpxA, as a global regulator in bacterial resistance to AMPs. Multiple previously characterized CpxR/A members, as well as members found in this study, were identified in the screen. Notably, CpxR/A was found to transcriptionally regulate the gene cluster responsible for the biosynthesis of the ECA. Thus, a novel genetic mechanism was uncovered that directly correlates with a physiologically significant cellular component that appears to globally contribute to bacterial resistance to AMPs.
ContributorsWeatherspoon-Griffin, Natasha (Author) / Shi, Yixin (Thesis advisor) / Clark-Curtiss, Josephine (Committee member) / Misra, Rajeev (Committee member) / Nickerson, Cheryl (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2013
Description
The dynamics of urban water use are characterized by spatial and temporal variability that is influenced by associated factors at different scales. Thus it is important to capture the relationship between urban water use and its determinants in a spatio-temporal framework in order to enhance understanding and management of urban water demand. This dissertation aims to contribute to understanding the spatio-temporal relationships between single-family residential (SFR) water use and its determinants in a desert city. The dissertation has three distinct papers to support this goal. In the first paper, I demonstrate that aggregated scale data can be reliably used to study the relationship between SFR water use and its determinants without leading to significant ecological fallacy. The usability of aggregated scale data facilitates scientific inquiry about SFR water use with more available aggregated scale data. The second paper advances understanding of the relationship between SFR water use and its associated factors by accounting for the spatial and temporal dependence in a panel data setting. The third paper of this dissertation studies the historical contingency, spatial heterogeneity, and spatial connectivity in the relationship of SFR water use and its determinants by comparing three different regression models. This dissertation demonstrates the importance and necessity of incorporating spatio-temporal components, such as scale, dependence, and heterogeneity, into SFR water use research. Spatial statistical models should be used to understand the effects of associated factors on water use and test the effectiveness of certain management policies since spatial effects probably will significantly influence the estimates if only non-spatial statistical models are used. Urban water demand management should pay attention to the spatial heterogeneity in predicting the future water demand to achieve more accurate estimates, and spatial statistical models provide a promising method to do this job.
ContributorsOuyang, Yun (Author) / Wentz, Elizabeth (Thesis advisor) / Ruddell, Benjamin (Thesis advisor) / Harlan, Sharon (Committee member) / Janssen, Marcus (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis outlines the development of a vector retrieval technique, based on data assimilation, for a coherent Doppler LIDAR (Light Detection and Ranging). A detailed analysis of the Optimal Interpolation (OI) technique for vector retrieval is presented. Through several modifications to the OI technique, it is shown that the modified technique results in significant improvement in velocity retrieval accuracy. These modifications include changes to innovation covariance portioning, covariance binning, and analysis increment calculation. It is observed that the modified technique is able to make retrievals with better accuracy, preserves local information better, and compares well with tower measurements. In order to study the error of representativeness and vector retrieval error, a lidar simulator was constructed. Using the lidar simulator a thorough sensitivity analysis of the lidar measurement process and vector retrieval is carried out. The error of representativeness as a function of scales of motion and sensitivity of vector retrieval to look angle is quantified. Using the modified OI technique, study of nocturnal flow in Owens' Valley, CA was carried out to identify and understand uncharacteristic events on the night of March 27th 2006. Observations from 1030 UTC to 1230 UTC (0230 hr local time to 0430 hr local time) on March 27 2006 are presented. Lidar observations show complex and uncharacteristic flows such as sudden bursts of westerly cross-valley wind mixing with the dominant up-valley wind. Model results from Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) and other in-situ instrumentations are used to corroborate and complement these observations. The modified OI technique is used to identify uncharacteristic and extreme flow events at a wind development site. Estimates of turbulence and shear from this technique are compared to tower measurements. A formulation for equivalent wind speed in the presence of variations in wind speed and direction, combined with shear is developed and used to determine wind energy content in presence of turbulence.
ContributorsChoukulkar, Aditya (Author) / Calhoun, Ronald (Thesis advisor) / Mahalov, Alex (Committee member) / Kostelich, Eric (Committee member) / Huang, Huei-Ping (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2013
Description
Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier.
ContributorsBuddi, Sai (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Nelson, Randall (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
Ponderosa pine forests are a dominant land cover type in semiarid montane areas. Water supplies in major rivers of the southwestern United States depend on ponderosa pine forests since these ecosystems: (1) receive a significant amount of rainfall and snowfall, (2) intercept precipitation and transpire water, and (3) indirectly influence runoff by impacting the infiltration rate. However, the hydrologic patterns in these ecosystems with strong seasonality are poorly understood. In this study, we used a distributed hydrologic model evaluated against field observations to improve our understandings on spatial controls of hydrologic patterns, appropriate model resolution to simulate ponderosa pine ecosystems and hydrologic responses in the context of contrasting winter to summer transitions. Our modeling effort is focused on the hydrologic responses during the North American Monsoon (NAM), winter and spring periods. In Chapter 2, we utilized a distributed model explore the spatial controls on simulated soil moisture and temporal evolution of these spatial controls as a function of seasonal wetness. Our findings indicate that vegetation and topographic curvature are spatial controls. Vegetation controlled patterns during dry summer period switch to fine-scale terrain curvature controlled patterns during persistently wet NAM period. Thus, a climatic threshold involving rainfall and weather conditions during the NAM is identified when high rainfall amount (such as 146 mm rain in August, 1997) activates lateral flux of soil moisture and frequent cloudy cover (such as 42% cloud cover during daytime of August, 1997) lowers evapotranspiration. In Chapter 3, we investigate the impacts of model coarsening on simulated soil moisture patterns during the NAM. Results indicate that model aggregation quickly eradicates curvature features and its spatial control on hydrologic patterns. A threshold resolution of ~10% of the original terrain is identified through analyses of homogeneity indices, correlation coefficients and spatial errors beyond which the fidelity of simulated soil moisture is no longer reliable. Based on spatial error analyses, we detected that the concave areas (~28% of hillslope) are very sensitive to model coarsening and root mean square error (RMSE) is higher than residual soil moisture content (~0.07 m3/m3 soil moisture) for concave areas. Thus, concave areas need to be sampled for capturing appropriate hillslope response for this hillslope. In Chapter 4, we investigate the impacts of contrasting winter to summer transitions on hillslope hydrologic responses. We use a distributed hydrologic model to generate a consistent set of high-resolution hydrologic estimates. Our model is evaluated against the snow depth, soil moisture and runoff observations over two water years yielding reliable spatial distributions during the winter to summer transitions. We find that a wet winter followed by a dry summer promotes evapotranspiration losses (spatial averaged ~193 mm spring ET and ~ 600 mm summer ET) that dry the soil and disconnect lateral fluxes in the forested hillslope, leading to soil moisture patterns resembling vegetation patches. Conversely, a dry winter prior to a wet summer results in soil moisture increases due to high rainfall and low ET during the spring (spatially averaged 78 mm ET and 232 mm rainfall) and summer period (spatially averaged 147 mm ET and 247 mm rainfall) which promote lateral connectivity and soil moisture patterns with the signature of terrain curvature. An opposing temporal switch between infiltration and saturation excess runoff is also identified. These contrasting responses indicate that the inverse relation has significant consequences on hillslope water availability and its spatial distribution with implications on other ecohydrological processes including vegetation phenology, groundwater recharge and geomorphic development. Results from this work have implications on the design of hillslope experiments, the resolution of hillslope scale models, and the prediction of hydrologic conditions in ponderosa pine ecosystems. In addition, our findings can be used to select future hillslope sites for detailed ecohydrological investigations. Further, the proposed methodology can be useful for predicting responses to climate and land cover changes that are anticipated for the southwestern United States.
ContributorsMahmood, Taufique Hasan (Author) / Vivoni, Enrique R. (Thesis advisor) / Whipple, Kelin X. (Committee member) / Shock, Everett (Committee member) / Heimsath, Arjun M. (Committee member) / Ruddell, Benjamin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cancer claims hundreds of thousands of lives every year in US alone. Finding ways for early detection of cancer onset is crucial for better management and treatment of cancer. Thus, biomarkers especially protein biomarkers, being the functional units which reflect dynamic physiological changes, need to be discovered. Though important, there are only a few approved protein cancer biomarkers till date. To accelerate this process, fast, comprehensive and affordable assays are required which can be applied to large population studies. For this, these assays should be able to comprehensively characterize and explore the molecular diversity of nominally "single" proteins across populations. This information is usually unavailable with commonly used immunoassays such as ELISA (enzyme linked immunosorbent assay) which either ignore protein microheterogeneity, or are confounded by it. To this end, mass spectrometric immuno assays (MSIA) for three different human plasma proteins have been developed. These proteins viz. IGF-1, hemopexin and tetranectin have been found in reported literature to show correlations with many diseases along with several carcinomas. Developed assays were used to extract entire proteins from plasma samples and subsequently analyzed on mass spectrometric platforms. Matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) mass spectrometric techniques where used due to their availability and suitability for the analysis. This resulted in visibility of different structural forms of these proteins showing their structural micro-heterogeneity which is invisible to commonly used immunoassays. These assays are fast, comprehensive and can be applied in large sample studies to analyze proteins for biomarker discovery.
ContributorsRai, Samita (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Borges, Chad (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2012
Description
It is possible in a properly controlled environment, such as industrial metrology, to make significant headway into the non-industrial constraints on image-based position measurement using the techniques of image registration and achieve repeatable feature measurements on the order of 0.3% of a pixel, or about an order of magnitude improvement on conventional real-world performance. These measurements are then used as inputs for a model optimal, model agnostic, smoothing for calibration of a laser scribe and online tracking of velocimeter using video input. Using appropriate smooth interpolation to increase effective sample density can reduce uncertainty and improve estimates. Use of the proper negative offset of the template function has the result of creating a convolution with higher local curvature than either template of target function which allows improved center-finding. Using the Akaike Information Criterion with a smoothing spline function it is possible to perform a model-optimal smooth on scalar measurements without knowing the underlying model and to determine the function describing the uncertainty in that optimal smooth. An example of empiric derivation of the parameters for a rudimentary Kalman Filter from this is then provided, and tested. Using the techniques of Exploratory Data Analysis and the "Formulize" genetic algorithm tool to convert the spline models into more accessible analytic forms resulted in stable, properly generalized, KF with performance and simplicity that exceeds "textbook" implementations thereof. Validation of the measurement includes that, in analytic case, it led to arbitrary precision in measurement of feature; in reasonable test case using the methods proposed, a reasonable and consistent maximum error of around 0.3% the length of a pixel was achieved and in practice using pixels that were 700nm in size feature position was located to within ± 2 nm. Robust applicability is demonstrated by the measurement of indicator position for a King model 2-32-G-042 rotameter.
ContributorsMunroe, Michael R (Author) / Phelan, Patrick (Thesis advisor) / Kostelich, Eric (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2012