Matching Items (68)
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Description
The Cape Floral Region (CFR) in southwestern South Africa is one of the most diverse in the world, with >9,000 plant species, 70% of which are endemic, in an area of only ~90,000 km2. Many have suggested that the CFR's heterogeneous environment, with respect to landscape gradients, vegetation, rainfall, elevation,

The Cape Floral Region (CFR) in southwestern South Africa is one of the most diverse in the world, with >9,000 plant species, 70% of which are endemic, in an area of only ~90,000 km2. Many have suggested that the CFR's heterogeneous environment, with respect to landscape gradients, vegetation, rainfall, elevation, and soil fertility, is responsible for the origin and maintenance of this biodiversity. While studies have struggled to link species diversity with these features, no study has attempted to associate patterns of gene flow with environmental data to determine how CFR biodiversity evolves on different scales. Here, a molecular population genetic data is presented for a widespread CFR plant, Leucadendron salignum, across 51 locations with 5-kb of chloroplast (cpDNA) and 6-kb of unlinked nuclear (nuDNA) DNA sequences in a dataset of 305 individuals. In the cpDNA dataset, significant genetic structure was found to vary on temporal and spatial scales, separating Western and Eastern Capes - the latter of which appears to be recently derived from the former - with the highest diversity in the heart of the CFR in a central region. A second study applied a statistical model using vegetation and soil composition and found fine-scale genetic divergence is better explained by this landscape resistance model than a geographic distance model. Finally, a third analysis contrasted cpDNA and nuDNA datasets, and revealed very little geographic structure in the latter, suggesting that seed and pollen dispersal can have different evolutionary genetic histories of gene flow on even small CFR scales. These three studies together caution that different genomic markers need to be considered when modeling the geographic and temporal origin of CFR groups. From a greater perspective, the results here are consistent with the hypothesis that landscape heterogeneity is one driving influence in limiting gene flow across the CFR that can lead to species diversity on fine-scales. Nonetheless, while this pattern may be true of the widespread L. salignum, the extension of this approach is now warranted for other CFR species with varying ranges and dispersal mechanisms to determine how universal these patterns of landscape genetic diversity are.
ContributorsTassone, Erica (Author) / Verrelli, Brian C (Thesis advisor) / Dowling, Thomas (Committee member) / Cartwright, Reed (Committee member) / Rosenberg, Michael S. (Committee member) / Wojciechowski, Martin (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Woody plant encroachment is a worldwide phenomenon linked to water availability in semiarid systems. Nevertheless, the implications of woody plant encroachment on the hydrologic cycle are poorly understood, especially at the catchment scale. This study takes place in a pair of small semiarid rangeland undergoing the encroachment of Prosopis velutina

Woody plant encroachment is a worldwide phenomenon linked to water availability in semiarid systems. Nevertheless, the implications of woody plant encroachment on the hydrologic cycle are poorly understood, especially at the catchment scale. This study takes place in a pair of small semiarid rangeland undergoing the encroachment of Prosopis velutina Woot., or velvet mesquite tree. The similarly-sized basins are in close proximity, leading to equivalent meteorological and soil conditions. One basin was treated for mesquite in 1974, while the other represents the encroachment process. A sensor network was installed to measure ecohydrological states and fluxes, including precipitation, runoff, soil moisture and evapotranspiration. Observations from June 1, 2011 through September 30, 2012 are presented to describe the seasonality and spatial variability of ecohydrological conditions during the North American Monsoon (NAM). Runoff observations are linked to historical changes in runoff production in each watershed. Observations indicate that the mesquite-treated basin generates more runoff pulses and greater runoff volume for small rainfall events, while the mesquite-encroached basin generates more runoff volume for large rainfall events. A distributed hydrologic model is applied to both basins to investigate the runoff threshold processes experienced during the NAM. Vegetation in the two basins is classified into grass, mesquite, or bare soil using high-resolution imagery. Model predictions are used to investigate the vegetation controls on soil moisture, evapotranspiration, and runoff generation. The distributed model shows that grass and mesquite sites retain the highest levels of soil moisture. The model also captures the runoff generation differences between the two watersheds that have been observed over the past decade. Generally, grass sites in the mesquite-treated basin have less plant interception and evapotranspiration, leading to higher soil moisture that supports greater runoff for small rainfall events. For large rainfall events, the mesquite-encroached basin produces greater runoff due to its higher fraction of bare soil. The results of this study show that a distributed hydrologic model can be used to explain runoff threshold processes linked to woody plant encroachment at the catchment-scale and provides useful interpretations for rangeland management in semiarid areas.
ContributorsPierini, Nicole A (Author) / Vivoni, Enrique R (Thesis advisor) / Wang, Zhi-Hua (Committee member) / Mays, Larry W. (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are

Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
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Description
This dissertation investigates the condition of skeletal muscle insulin resistance using bioinformatics and computational biology approaches. Drawing from several studies and numerous data sources, I have attempted to uncover molecular mechanisms at multiple levels. From the detailed atomistic simulations of a single protein, to datamining approaches applied at the systems

This dissertation investigates the condition of skeletal muscle insulin resistance using bioinformatics and computational biology approaches. Drawing from several studies and numerous data sources, I have attempted to uncover molecular mechanisms at multiple levels. From the detailed atomistic simulations of a single protein, to datamining approaches applied at the systems biology level, I provide new targets to explore for the research community. Furthermore I present a new online web resource that unifies various bioinformatics databases to enable discovery of relevant features in 3D protein structures.
ContributorsMielke, Clinton (Author) / Mandarino, Lawrence (Committee member) / LaBaer, Joshua (Committee member) / Magee, D. Mitchell (Committee member) / Dinu, Valentin (Committee member) / Willis, Wayne (Committee member) / Arizona State University (Publisher)
Created2013
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Description
The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary

The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary to obtain an answer and 2) in the underlying structure of the data, which does not conform to classical normal theory statistical assumptions and includes clustering and unobserved latent constructs. Until recently, the methods and tools necessary to effectively address the complexity of biomedical data were not ordinarily available. The utility of four methods--High Performance Computing, Monte Carlo Simulations, Multi-Level Modeling and Structural Equation Modeling--designed to help make sense of complex biomedical data are presented here.
ContributorsBrown, Justin Reed (Author) / Dinu, Valentin (Thesis advisor) / Johnson, William (Committee member) / Petitti, Diana (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Critical care environments are complex in nature. Fluctuating team dynamics and the plethora of technology and equipment create unforeseen demands on clinicians. Such environments become chaotic very quickly due to the chronic exposure to unpredictable clusters of events. In order to cope with this complexity, clinicians tend to develop ad-hoc

Critical care environments are complex in nature. Fluctuating team dynamics and the plethora of technology and equipment create unforeseen demands on clinicians. Such environments become chaotic very quickly due to the chronic exposure to unpredictable clusters of events. In order to cope with this complexity, clinicians tend to develop ad-hoc adaptations to function in an effective manner. It is these adaptations or "deviations" from expected behaviors that provide insight into the processes that shape the overall behavior of the complex system. The research described in this manuscript examines the cognitive basis of clinicians' adaptive mechanisms and presents a methodology for studying the same. Examining interactions in complex systems is difficult due to the disassociation between the nature of the environment and the tools available to analyze underlying processes. In this work, the use of a mixed methodology framework to study trauma critical care, a complex environment, is presented. The hybrid framework supplements existing methods of data collection (qualitative observations) with quantitative methods (use of electronic tags) to capture activities in the complex system. Quantitative models of activities (using Hidden Markov Modeling) and theoretical models of deviations were developed to support this mixed methodology framework. The quantitative activity models developed were tested with a set of fifteen simulated activities that represent workflow in trauma care. A mean recognition rate of 87.5% was obtained in automatically recognizing activities. Theoretical models, on the other hand, were developed using field observations of 30 trauma cases. The analysis of the classification schema (with substantial inter-rater reliability) and 161 deviations identified shows that expertise and role played by the clinician in the trauma team influences the nature of deviations made (p<0.01). The results shows that while expert clinicians deviate to innovate, deviations of novices often result in errors. Experts' flexibility and adaptiveness allow their deviations to generate innovative ideas, in particular when dynamic adjustments are required in complex situations. The findings suggest that while adherence to protocols and standards is important for novice practitioners to reduce medical errors and ensure patient safety, there is strong need for training novices in coping with complex situations as well.
ContributorsVankipuram, Mithra (Author) / Greenes, Robert A (Thesis advisor) / Patel, Vimla L. (Thesis advisor) / Petitti, Diana B. (Committee member) / Dinu, Valentin (Committee member) / Smith, Marshall L. (Committee member) / Arizona State University (Publisher)
Created2012
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Description
This work involved the analysis of a public health system, and the design, development and deployment of enterprise informatics architecture, and sustainable community methods to address problems with the current public health system. Specifically, assessment of the Nationally Notifiable Disease Surveillance System (NNDSS) was instrumental in forming the design of

This work involved the analysis of a public health system, and the design, development and deployment of enterprise informatics architecture, and sustainable community methods to address problems with the current public health system. Specifically, assessment of the Nationally Notifiable Disease Surveillance System (NNDSS) was instrumental in forming the design of the current implementation at the Southern Nevada Health District (SNHD). The result of the system deployment at SNHD was considered as a basis for projecting the practical application and benefits of an enterprise architecture. This approach has resulted in a sustainable platform to enhance the practice of public health by improving the quality and timeliness of data, effectiveness of an investigation, and reporting across the continuum.
ContributorsKriseman, Jeffrey Michael (Author) / Dinu, Valentin (Thesis advisor) / Greenes, Robert (Committee member) / Johnson, William (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides in a multiplexed fashion. There are computational and statistical challenges to

Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides in a multiplexed fashion. There are computational and statistical challenges to the analysis of immunosignaturing data. The overall aim of my dissertation is to develop novel computational and statistical methods for immunosignaturing data to access its potential for diagnostics and drug discovery. Firstly, I discovered that a classification algorithm Naive Bayes which leverages the biological independence of the probes on our array in such a way as to gather more information outperforms other classification algorithms due to speed and accuracy. Secondly, using this classifier, I then tested the specificity and sensitivity of immunosignaturing platform for its ability to resolve four different diseases (pancreatic cancer, pancreatitis, type 2 diabetes and panIN) that target the same organ (pancreas). These diseases were separated with >90% specificity from controls and from each other. Thirdly, I observed that the immunosignature of type 2 diabetes and cardiovascular complications are unique, consistent, and reproducible and can be separated by 100% accuracy from controls. But when these two complications arise in the same person, the resultant immunosignature is quite different in that of individuals with only one disease. I developed a method to trace back from informative random peptides in disease signatures to the potential antigen(s). Hence, I built a decipher system to trace random peptides in type 1 diabetes immunosignature to known antigens. Immunosignaturing, unlike the ELISA, has the ability to not only detect the presence of response but also absence of response during a disease. I observed, not only higher but also lower peptides intensities can be mapped to antigens in type 1 diabetes. To study immunosignaturing potential for population diagnostics, I studied effect of age, gender and geographical location on immunosignaturing data. For its potential to be a health monitoring technology, I proposed a single metric Coefficient of Variation that has shown potential to change significantly when a person enters a disease state.
ContributorsKukreja, Muskan (Author) / Johnston, Stephen Albert (Thesis advisor) / Stafford, Phillip (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Triops (Branchiopoda: Notostraca) and Streptocephalus (Branchiopoda: Anostraca) are two crustaceans which cohabitate in ephemeral freshwater pools. They both lay desiccation resistant eggs that disperse passively to new hydrologically isolated environments. The extent of genetic distance among regions and populations is of perennial interest in animals that live in such isolated

Triops (Branchiopoda: Notostraca) and Streptocephalus (Branchiopoda: Anostraca) are two crustaceans which cohabitate in ephemeral freshwater pools. They both lay desiccation resistant eggs that disperse passively to new hydrologically isolated environments. The extent of genetic distance among regions and populations is of perennial interest in animals that live in such isolated habitats. Populations in six natural ephemeral pool habitats located in two different regions of the Sonoran Desert and a transition area between the Sonoran and Chihuahuan Deserts were sampled. Sequences from Genbank were used for reference points in the determination of species as well as to further identify regional genetic distance within species. This study estimated the amount of within and between genetic distance of individuals from each region and population through the use of a neutral marker, cytochrome oxidase I (COI). We concluded that, although the method of passive dispersal may differ between the two genera, the differences do not results in different patterns of genetic distances between regions and populations. Furthermore, we only found the putative species, Triops longicaudatus "short", with enough distinct speciation. Although Triops longicaudatus "long" and Triops newberryi may be in the early stages of speciation, this study does not find enough support to conclude that they have separated.
ContributorsMurphy Jr., Patrick Joseph (Author) / Rutowski, Ronald (Thesis director) / Cartwright, Reed (Committee member) / Lessios, Nikos (Committee member) / School of Life Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
The evolution of blindness in cave animals has been heavily studied; however, little research has been done on the interaction of migration and drift on the development of blindness in these populations. In this study, a model is used to compare the effect that genetic drift has on the fixation

The evolution of blindness in cave animals has been heavily studied; however, little research has been done on the interaction of migration and drift on the development of blindness in these populations. In this study, a model is used to compare the effect that genetic drift has on the fixation of a blindness allele for varying amounts of migration and selection. For populations where the initial frequency is quite low, genetic drift plays a much larger role in the fixation of blindness than populations where the initial frequency is high. In populations where the initial frequency is high, genetic drift plays almost no role in fixation. Our results suggest that migration plays a greater role in the fate of the blindness allele than selection.
ContributorsMerry, Alexandra Leigh (Author) / Cartwright, Reed (Thesis director) / Rosenberg, Michael (Committee member) / Schwartz, Rachel (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2014-05