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Description
Coccidioidomycosis, also known as Valley Fever, is a disease caused by the dimorphic soil-dwelling fungus, Coccidioides sp. Coccidioidomycosis is difficult to diagnose because symptoms are similar to community-acquired pneumonia. Current diagnostic tests rely on antibody responses, but immune responses can be delayed and aberrant, resulting in false negative diagnoses. Unlike serology, detection of coccidioidal proteins or other fungal components in blood could distinguish valley fever from other pulmonary infections and provide a definitive diagnosis. Using mass spectrometry (LC-MS/MS) we examined the plasma peptidome from patients with serologically confirmed coccidioidomycosis. Mass spectra were searched using the protein database from the Coccidioides species, generated and annotated by the Broad Institute. 15 of 20 patients with serologically confirmed coccidioidomycosis demonstrated the presence of a peptide in plasma, "PGLDSKSLACTFSQV" (PGLD). The peptide is derived from an open reading frame from a "conserved hypothetical protein" annotated with 2 exons, and to date, found only in the C. posadasii strain Silviera RMSCC 3488 genomic sequence. In this thesis work, cDNA sequence analysis from polyadenylated RNA confirms the peptide sequence and genomic location of the peptide, but does not indicate that the intron in the gene prediction of C. posadasii strain Silviera RMSCC 3488 is present. A monoclonal antibody generated against the peptide bound to a 16kDa protein in T27K coccidioidal lysate. Detecting components of the fungus plasma could be a useful diagnostic tool, especially when serology does not provide a definitive diagnosis.
ContributorsDuffy, Stacy Leigh (Author) / Lake, Douglas (Thesis advisor) / Magee, Dewey Mitch (Committee member) / Antwi, Kwasi (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Modified and artificial water sources can be used as a management tool for game and non-game wildlife species. State, federal, and private agencies allocate significant resources to install and maintain artificial water sources (AWS) annually. Capture mark recapture methods were used to sample small mammal communities in the vicinity of five AWS and five paired control sites (treatments) in the surrounding Sonoran desert from October 2011 to May 2012. I measured plant species richness, density, and percent cover in the spring of 2012. A Multi-response Permutation Procedure was used to identify differences in small mammal community abundance, biomass, and species richness by season and treatment. I used Principle Component Analysis to reduce 11 habitat characteristics to five habitat factors. I related rodent occurrence to habitat characteristics using multiple and logistic regression. A total of 370 individual mammals representing three genera and eight species of rodents were captured across 4800 trap nights. Desert pocket mouse (Chaetodipus penicillatus) was the most common species in both seasons and treatments. Whereas rodent community abundance, biomass, and richness were similar between seasons, community variables of AWS were greater than CS. Rodent diversity was similar between treatments. Desert pocket mouse abundance and biomass were twice as high at AWS when compared to controls. Biomass of white-throated woodrat (Neotoma albigula) was five times greater at AWS. Habitat characteristics were similar between treatments. Neither presence of water nor distance to water explained substantial habitat variation. Occurrence of rodent species was associated with habitat characteristics. Desert rodent communities are adapted for arid environments (i.e. Heteromyids) and are not dependent on "free water". Higher abundances of desert pocket mouse at AWS were most likely related to increased disturbance and debris and not the presence of water. The results of this study and previous studies suggest that more investigation is needed and that short term studies may not be able to detect interactions (if any) between AWS and desert small mammal communities.
ContributorsSwitalski, Aaron (Author) / Bateman, Heather L (Thesis advisor) / Miller, William (Committee member) / Alford, Eddie (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
One of the main challenges in planetary robotics is to traverse the shortest path through a set of waypoints. The shortest distance between any two waypoints is a direct linear traversal. Often times, there are physical restrictions that prevent a rover form traversing straight to a waypoint. Thus, knowledge of the terrain is needed prior to traversal. The Digital Terrain Model (DTM) provides information about the terrain along with waypoints for the rover to traverse. However, traversing a set of waypoints linearly is burdensome, as the rovers would constantly need to modify their orientation as they successively approach waypoints. Although there are various solutions to this problem, this research paper proposes the smooth traversability of the rover using splines as a quick and easy implementation to traverse a set of waypoints. In addition, a rover was used to compare the smoothness of the linear traversal along with the spline interpolations. The data collected illustrated that spline traversals had a less rate of change in the velocity over time, indicating that the rover performed smoother than with linear paths.
ContributorsKamasamudram, Anurag (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Continuous Delivery, as one of the youngest and most popular member of agile model family, has become a popular concept and method in software development industry recently. Instead of the traditional software development method, which requirements and solutions must be fixed before starting software developing, it promotes adaptive planning, evolutionary development and delivery, and encourages rapid and flexible response to change. However, several problems prevent Continuous Delivery to be introduced into education world. Taking into the consideration of the barriers, we propose a new Cloud based Continuous Delivery Software Developing System. This system is designed to fully utilize the whole life circle of software developing according to Continuous Delivery concepts in a virtualized environment in Vlab platform.
ContributorsDeng, Yuli (Author) / Huang, Dijiang (Thesis advisor) / Davulcu, Hasan (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Objectives: Although childhood obesity has received growing attention, parents still fail to recognize overweight and obesity in their children. Accurate identification of overweight or obesity in their child is associated with the parent's responsiveness to interventions aimed at preventing weight-related health issues. Recent research shows that a child's age and gender are associated with parental misperception of their child's weight status, but little is known about the interaction of these factors across various age groups. This study examined the association between a wide range of parent, child, and household factors and the accuracy of parental perception of their child's body weight status compared to parent-measured body weight status. Methods: Data were collected from a random-digit-dial telephone survey of 1708 households located in five low-income New Jersey cities with large minority populations. A subset of 548 children whose parents completed the survey and returned a worksheet of parent-measured heights and weights were the focus of the analysis. Bivariate and multivariate analyses were performed to determine the factors significantly associated with parental perception of their child's body weight status. Results: Based on parent-measure heights and weights, 36% of the children were overweight or obese (OWOB). Only 21% of OWOB children were perceived by their parents as OWOB. Child gender, child body mass index (BMI) and parent BMI were significant independent predictors of parents' accuracy at perceiving their child's body weight status. Conclusion: Boys, OWOB children, and children of OWOB parents had significantly greater odds of parental underestimation of their body weight status. Parents had better recognition of OWOB in their daughters, especially older daughters, than in their sons, suggesting parental gender bias in identifying OWOB in children. Further research is needed regarding parental gender bias and its implications in OWOB identification in children.
ContributorsBader, Wendy (Author) / Ohri-Vachaspati, Punam (Thesis advisor) / Lloyd, Kristen (Committee member) / Crespo, Noe (Committee member) / Arizona State University (Publisher)
Created2013
Description
Objectives Through a cross-sectional observational study, this thesis evaluates the relationship between food insecurity and weight status, eating behaviors, the home food environment, meal planning and preparation, and perceived stress as it relates to predominantly Hispanic/Latino parents in Phoenix, Arizona. The purpose of this study was to address gaps in the literature by examining differences in "healthy" and "unhealthy" eating behaviors, foods available in the home, how time and low energy impact meal preparation, and the level of stress between food security groups. Methods Parents, 18 years or older, were recruited during two pre-scheduled health fairs, from English as a second language classes, or from the Women, Infants, and Children's clinic at a local community center, Golden Gate Community Center, in Phoenix, Arizona. An interview, electronic, or paper survey were offered in either Spanish or English to collect data on the variables described above. In addition to the survey, height and weight were collected for all participants to determine BMI and weight status. One hundred and sixty participants were recruited. Multivariate linear and logistic regression models, adjusting for weight status, education, race/ethnicity, income level, and years residing in the U.S., were used to assess the relationship between food security status and weight status, eating behaviors, the home food environment, meal planning and preparation, and perceived stress. Results Results concluded that food insecurity was more prevalent among parents reporting lower income levels compared to higher income levels (p=0.017). In adjusted models, higher perceived cost of fruits (p=0.004) and higher perceived level of stress (p=0.001) were associated with food insecurity. Given that the sample population was predominately women, a post-hoc analysis was completed on women only. In addition to the two significant results noted in the adjusted analyses, the women-only analysis revealed that food insecure mothers reported lower amounts of vegetables served with meals (p=0.019) and higher use of fast-food when tired or running late (p=0.043), compared to food secure mothers. Conclusion Additional studies are needed to further assess differences in stress levels between food insecure parents and food insecure parents, with special consideration for directionality and its relationship to weight status.
ContributorsVillanova, Christina (Author) / Bruening, Meg (Thesis advisor) / Ohri-Vachaspati, Punam (Committee member) / Vega-Lopez, Sonia (Committee member) / Arizona State University (Publisher)
Created2014
Description
As the complexity of robotic systems and applications grows rapidly, development of high-performance, easy to use, and fully integrated development environments for those systems is inevitable. Model-Based Design (MBD) of dynamic systems using engineering software such as Simulink® from MathWorks®, SciCos from Metalau team and SystemModeler® from Wolfram® is quite popular nowadays. They provide tools for modeling, simulation, verification and in some cases automatic code generation for desktop applications, embedded systems and robots. For real-world implementation of models on the actual hardware, those models should be converted into compilable machine code either manually or automatically. Due to the complexity of robotic systems, manual code translation from model to code is not a feasible optimal solution so we need to move towards automated code generation for such systems. MathWorks® offers code generation facilities called Coder® products for this purpose. However in order to fully exploit the power of model-based design and code generation tools for robotic applications, we need to enhance those software systems by adding and modifying toolboxes, files and other artifacts as well as developing guidelines and procedures. In this thesis, an effort has been made to propose a guideline as well as a Simulink® library, StateFlow® interface API and a C/C++ interface API to complete this toolchain for NAO humanoid robots. Thus the model of the hierarchical control architecture can be easily and properly converted to code and built for implementation.
ContributorsRaji Kermani, Ramtin (Author) / Fainekos, Georgios (Thesis advisor) / Lee, Yann-Hang (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2013