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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

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
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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

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
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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

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
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Description

Background: Extreme heat is a public health challenge. The scarcity of directly comparable studies on the association of heat with morbidity and mortality and the inconsistent identification of threshold temperatures for severe impacts hampers the development of comprehensive strategies aimed at reducing adverse heat-health events.

Objectives: This quantitative study was designed

Background: Extreme heat is a public health challenge. The scarcity of directly comparable studies on the association of heat with morbidity and mortality and the inconsistent identification of threshold temperatures for severe impacts hampers the development of comprehensive strategies aimed at reducing adverse heat-health events.

Objectives: This quantitative study was designed to link temperature with mortality and morbidity events in Maricopa County, Arizona, USA, with a focus on the summer season.

Methods: Using Poisson regression models that controlled for temporal confounders, we assessed daily temperature–health associations for a suite of mortality and morbidity events, diagnoses, and temperature metrics. Minimum risk temperatures, increasing risk temperatures, and excess risk temperatures were statistically identified to represent different “trigger points” at which heat-health intervention measures might be activated.

Results: We found significant and consistent associations of high environmental temperature with all-cause mortality, cardiovascular mortality, heat-related mortality, and mortality resulting from conditions that are consequences of heat and dehydration. Hospitalizations and emergency department visits due to heat-related conditions and conditions associated with consequences of heat and dehydration were also strongly associated with high temperatures, and there were several times more of those events than there were deaths. For each temperature metric, we observed large contrasts in trigger points (up to 22°C) across multiple health events and diagnoses.

Conclusion: Consideration of multiple health events and diagnoses together with a comprehensive approach to identifying threshold temperatures revealed large differences in trigger points for possible interventions related to heat. Providing an array of heat trigger points applicable for different end-users may improve the public health response to a problem that is projected to worsen in the coming decades.

ContributorsPettiti, Diana B. (Author) / Hondula, David M. (Author) / Yang, Shuo (Author) / Harlan, Sharon L. (Author) / Chowell, Gerardo (Author)
Created2016-02-01
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Description

Maricopa County, Arizona, anchor to the fastest growing megapolitan area in the United States, is located in a hot desert climate where extreme temperatures are associated with elevated risk of mortality. Continued urbanization in the region will impact atmospheric temperatures and, as a result, potentially affect human health. We aimed

Maricopa County, Arizona, anchor to the fastest growing megapolitan area in the United States, is located in a hot desert climate where extreme temperatures are associated with elevated risk of mortality. Continued urbanization in the region will impact atmospheric temperatures and, as a result, potentially affect human health. We aimed to quantify the number of excess deaths attributable to heat in Maricopa County based on three future urbanization and adaptation scenarios and multiple exposure variables.

Two scenarios (low and high growth projections) represent the maximum possible uncertainty range associated with urbanization in central Arizona, and a third represents the adaptation of high-albedo cool roof technology. Using a Poisson regression model, we related temperature to mortality using data spanning 1983–2007. Regional climate model simulations based on 2050-projected urbanization scenarios for Maricopa County generated distributions of temperature change, and from these predicted changes future excess heat-related mortality was estimated. Subject to urbanization scenario and exposure variable utilized, projections of heat-related mortality ranged from a decrease of 46 deaths per year (− 95%) to an increase of 339 deaths per year (+ 359%).

Projections based on minimum temperature showed the greatest increase for all expansion and adaptation scenarios and were substantially higher than those for daily mean temperature. Projections based on maximum temperature were largely associated with declining mortality. Low-growth and adaptation scenarios led to the smallest increase in predicted heat-related mortality based on mean temperature projections. Use of only one exposure variable to project future heat-related deaths may therefore be misrepresentative in terms of direction of change and magnitude of effects. Because urbanization-induced impacts can vary across the diurnal cycle, projections of heat-related health outcomes that do not consider place-based, time-varying urban heat island effects are neglecting essential elements for policy relevant decision-making.

ContributorsHondula, David M. (Author) / Georgescu, Matei (Author) / Balling, Jr., Robert C. (Author)
Created2014-04-28
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Description

Preventing heat-associated morbidity and mortality is a public health priority in Maricopa County, Arizona (United States). The objective of this project was to evaluate Maricopa County cooling centers and gain insight into their capacity to provide relief for the public during extreme heat events. During the summer of 2014, 53

Preventing heat-associated morbidity and mortality is a public health priority in Maricopa County, Arizona (United States). The objective of this project was to evaluate Maricopa County cooling centers and gain insight into their capacity to provide relief for the public during extreme heat events. During the summer of 2014, 53 cooling centers were evaluated to assess facility and visitor characteristics. Maricopa County staff collected data by directly observing daily operations and by surveying managers and visitors. The cooling centers in Maricopa County were often housed within community, senior, or religious centers, which offered various services for at least 1500 individuals daily. Many visitors were unemployed and/or homeless. Many learned about a cooling center by word of mouth or by having seen the cooling center’s location. The cooling centers provide a valuable service and reach some of the region’s most vulnerable populations. This project is among the first to systematically evaluate cooling centers from a public health perspective and provides helpful insight to community leaders who are implementing or improving their own network of cooling centers.

ContributorsBerisha, Vjollca (Author) / Hondula, David M. (Author) / Roach, Matthew (Author) / White, Jessica R. (Author) / McKinney, Benita (Author) / Bentz, Darcie (Author) / Mohamed, Ahmed (Author) / Uebelherr, Joshua (Author) / Goodin, Kate (Author)
Created2016-09-23
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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

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.
Created2015-12-01
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Description

Nutrient recycling by fish can be an important part of nutrient cycles in both freshwater and marine ecosystems. As a result, understanding the mechanisms that influence excretion elemental ratios of fish is of great importance to a complete understanding of aquatic nutrient cycles. As fish consume a wide range of

Nutrient recycling by fish can be an important part of nutrient cycles in both freshwater and marine ecosystems. As a result, understanding the mechanisms that influence excretion elemental ratios of fish is of great importance to a complete understanding of aquatic nutrient cycles. As fish consume a wide range of diets that differ in elemental composition, stoichiometric theory can inform predictions about dietary effects on excretion ratios.
We conducted a meta-analysis to test the effects of diet elemental composition on consumption and nutrient excretion by fish. We examined the relationship between consumption rate and diet N : P across all laboratory studies and calculated effect sizes for each excretion metric to test for significant effects.
Consumption rate of N, but not P, was significantly negatively affected by diet N : P. Effect sizes of diet elemental composition on consumption-specific excretion N, P and N : P in laboratory studies were all significantly different from 0, but effect size for raw excretion N : P was not significantly different from zero in laboratory or field surveys.
Our results highlight the importance of having a mechanistic understanding of the drivers of consumer excretion rates and ratios. We suggest that more research is needed on how consumption and assimilation efficiency vary with N : P and in natural ecosystems in order to further understand mechanistic processes in consumer-driven nutrient recycling.

ContributorsMoody, Eric (Author) / Corman, Jessica (Author) / Elser, James (Author) / Sabo, John (Author) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor)
Created2015-03-01
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Description
Preserving a system’s viability in the presence of diversity erosion is critical if the goal is to sustainably support biodiversity. Reduction in population heterogeneity, whether inter- or intraspecies, may increase population fragility, either decreasing its ability to adapt effectively to environmental changes or facilitating the survival and success of ordinarily

Preserving a system’s viability in the presence of diversity erosion is critical if the goal is to sustainably support biodiversity. Reduction in population heterogeneity, whether inter- or intraspecies, may increase population fragility, either decreasing its ability to adapt effectively to environmental changes or facilitating the survival and success of ordinarily rare phenotypes. The latter may result in over-representation of individuals who may participate in resource utilization patterns that can lead to over-exploitation, exhaustion, and, ultimately, collapse of both the resource and the population that depends on it. Here, we aim to identify regimes that can signal whether a consumer–resource system is capable of supporting viable degrees of heterogeneity. The framework used here is an expansion of a previously introduced consumer–resource type system of a population of individuals classified by their resource consumption. Application of the Reduction Theorem to the system enables us to evaluate the health of the system through tracking both the mean value of the parameter of resource (over)consumption, and the population variance, as both change over time. The article concludes with a discussion that highlights applicability of the proposed system to investigation of systems that are affected by particularly devastating overly adapted populations, namely cancerous cells. Potential intervention approaches for system management are discussed in the context of cancer therapies.
Created2015-02-01
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Description
Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study

Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study proposes and implements an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. We first proposed a conceptual model to formally represent the spatiotemporal variation of change data, which is essential knowledge to support various environmental and social studies, such as deforestation and urbanization studies. Then, a spatial ontology was created to encode these semantic spatiotemporal data in a machine-understandable format. Based on the knowledge defined in the ontology and related reasoning rules, a semantic platform was developed to support the semantic query and change trajectory reasoning of areas with LULCC. This semantic platform is innovative, as it integrates semantic and spatial reasoning into a coherent computational and operational software framework to support automated semantic analysis of time series data that can go beyond LULC datasets. In addition, this system scales well as the amount of data increases, validated by a number of experimental results. This work contributes significantly to both the geospatial Semantic Web and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making.
Created2016-10-25