Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 10 of 94
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
Reductive dechlorination by members of the bacterial genus Dehalococcoides is a common and cost-effective avenue for in situ bioremediation of sites contaminated with the chlorinated solvents, trichloroethene (TCE) and perchloroethene (PCE). The overarching goal of my research was to address some of the challenges associated with bioremediation timeframes by improving the rates of reductive dechlorination and the growth of Dehalococcoides in mixed communities. Biostimulation of contaminated sites or microcosms with electron donor fails to consistently promote dechlorination of PCE/TCE beyond cis-dichloroethene (cis-DCE), even when the presence of Dehalococcoides is confirmed. Supported by data from microcosm experiments, I showed that the stalling at cis-DCE is due a H2 competition in which components of the soil or sediment serve as electron acceptors for competing microorganisms. However, once competition was minimized by providing selective enrichment techniques, I illustrated how to obtain both fast rates and high-density Dehalococcoides using three distinct enrichment cultures. Having achieved a heightened awareness of the fierce competition for electron donor, I then identified bicarbonate (HCO3-) as a potential H2 sink for reductive dechlorination. HCO3- is the natural buffer in groundwater but also the electron acceptor for hydrogenotrophic methanogens and homoacetogens, two microbial groups commonly encountered with Dehalococcoides. By testing a range of concentrations in batch experiments, I showed that methanogens are favored at low HCO3 and homoacetogens at high HCO3-. The high HCO3- concentrations increased the H2 demand which negatively affected the rates and extent of dechlorination. By applying the gained knowledge on microbial community management, I ran the first successful continuous stirred-tank reactor (CSTR) at a 3-d hydraulic retention time for cultivation of dechlorinating cultures. I demonstrated that using carefully selected conditions in a CSTR, cultivation of Dehalococcoides at short retention times is feasible, resulting in robust cultures capable of fast dechlorination. Lastly, I provide a systematic insight into the effect of high ammonia on communities involved in dechlorination of chloroethenes. This work documents the potential use of landfill leachate as a substrate for dechlorination and an increased tolerance of Dehalococcoides to high ammonia concentrations (2 g L-1 NH4+-N) without loss of the ability to dechlorinate TCE to ethene.
ContributorsDelgado, Anca Georgiana (Author) / Krajmalnik-Brown, Rosa (Thesis advisor) / Cadillo-Quiroz, Hinsby (Committee member) / Halden, Rolf U. (Committee member) / Rittmann, Bruce E. (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Microbial mat communities that inhabit hot springs in Yellowstone National Park have been studied for their biodiversity, energetics and evolutionary history, yet little is know about how these communities cope with nutrient limitation. In the present study the changes in assimilatory gene expression levels for nitrogen (nrgA), phosphorus (phoA), and iron (yusV) were measured under various nutrient enrichment experiments. While results for nrgA and phoA were inconclusive, results for yusV showed an increase in expression with the addition of N and Fe. This is the first data that shows the impact of nutrients on siderophore uptake regulation in hot spring microbes.
ContributorsThorne, Michele (Author) / Elser, James J (Thesis advisor) / Touchman, Jeffrey (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2012
Description
Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand.
ContributorsBhattacharya, Indrani (Author) / Sen, Arunabha (Thesis advisor) / Kempf, Karl G. (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.
ContributorsLe, Tien D (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
The prevalence of antibiotic resistant bacterial pathogens has increased since the introduction of penicillin in the 1940s. Insufficient development of novel antibacterial agents is leaving us with a failing arsenal of therapies to combat these pathogenic organisms. We have identified a clay mineral mixture (designated CB) that exhibits in vitro antibacterial activity against a broad spectrum of bacterial pathogens, yet the antibacterial mechanism of action remains unknown. Antibacterial susceptibility testing of four different clay samples collected from the same source revealed that these natural clays had markedly different antibacterial activity. X-ray diffraction analyses of these minerals revealed minor mineralogical differences across the samples; however, ICP analyses demonstrated that the concentrations of many elements, Fe, Co, Cu, Ni, and Zn in particular, vary greatly across the four clay mixture leachates. Supplementation of a non-antibacterial leachate containing lower concentrations of Fe, Co, Ni, Cu, and Zn to final ion concentrations and a pH equivalent to that of the antibacterial leachate resulted in antibacterial activity against E. coli and MRSA, confirming the role of these ions in the in vitro antibacterial clay mixture leachates. The prevailing hypothesis is that metal ions participate in redox cycling and produce ROS, leading to oxidative damage to macromolecules and resulting in cellular death. However, E. coli cells showed no increase in DNA or protein oxidative lesions and a slight increase in lipid peroxidation following exposure to CB-L. Supplementation of CB-L with ROS scavengers eliminated oxidative damage in E. coli, but did not rescue the cells from killing, indicating that in vitro killing is due to direct metal toxicity and not to indirect oxidative damage. Finally, we ion-exchanged non-antibacterial clays with Fe, Co, Cu, and Zn and established antibacterial activity in these samples. Treatment of MRSA skin infections with both natural and ion-exchanged clays significantly decreased the bacterial load after 7 days of treatment. We conclude that 1) in vitro clay-mediated killing is due to toxicity associated directly with released metal ions and not to indirect oxidative damage and 2) that in vivo killing is due to the physical properties of the clays rather than metal ion toxicity.
ContributorsOtto, Caitin Carol (Author) / Haydel, Shelley (Thesis advisor) / Stout, Valerie (Committee member) / Roberson, Robby (Committee member) / Sandrin, Todd (Committee member) / Rege, Kaushal (Committee member) / Arizona State University (Publisher)
Created2014
Description
Most data cleaning systems aim to go from a given deterministic dirty database to another deterministic but clean database. Such an enterprise pre–supposes that it is in fact possible for the cleaning process to uniquely recover the clean versions of each dirty data tuple. This is not possible in many cases, where the most a cleaning system can do is to generate a (hopefully small) set of clean candidates for each dirty tuple. When the cleaning system is required to output a deterministic database, it is forced to pick one clean candidate (say the "most likely" candidate) per tuple. Such an approach can lead to loss of information. For example, consider a situation where there are three equally likely clean candidates of a dirty tuple. An appealing alternative that avoids such an information loss is to abandon the requirement that the output database be deterministic. In other words, even though the input (dirty) database is deterministic, I allow the reconstructed database to be probabilistic. Although such an approach does avoid the information loss, it also brings forth several challenges. For example, how many alternatives should be kept per tuple in the reconstructed database? Maintaining too many alternatives increases the size of the reconstructed database, and hence the query processing time. Second, while processing queries on the probabilistic database may well increase recall, how would they affect the precision of the query processing? In this thesis, I investigate these questions. My investigation is done in the context of a data cleaning system called BayesWipe that has the capability of producing multiple clean candidates per each dirty tuple, along with the probability that they are the correct cleaned version. I represent these alternatives as tuples in a tuple disjoint probabilistic database, and use the Mystiq system to process queries on it. This probabilistic reconstruction (called BayesWipe–PDB) is compared to a deterministic reconstruction (called BayesWipe–DET)—where the most likely clean candidate for each tuple is chosen, and the rest of the alternatives discarded.
ContributorsRihan, Preet Inder Singh (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
As the size and scope of valuable datasets has exploded across many industries and fields of research in recent years, an increasingly diverse audience has sought out effective tools for their large-scale data analytics needs. Over this period, machine learning researchers have also been very prolific in designing improved algorithms which are capable of finding the hidden structure within these datasets. As consumers of popular Big Data frameworks have sought to apply and benefit from these improved learning algorithms, the problems encountered with the frameworks have motivated a new generation of Big Data tools to address the shortcomings of the previous generation. One important example of this is the improved performance in the newer tools with the large class of machine learning algorithms which are highly iterative in nature. In this thesis project, I set about to implement a low-rank matrix completion algorithm (as an example of a highly iterative algorithm) within a popular Big Data framework, and to evaluate its performance processing the Netflix Prize dataset. I begin by describing several approaches which I attempted, but which did not perform adequately. These include an implementation of the Singular Value Thresholding (SVT) algorithm within the Apache Mahout framework, which runs on top of the Apache Hadoop MapReduce engine. I then describe an approach which uses the Divide-Factor-Combine (DFC) algorithmic framework to parallelize the state-of-the-art low-rank completion algorithm Orthogoal Rank-One Matrix Pursuit (OR1MP) within the Apache Spark engine. I describe the results of a series of tests running this implementation with the Netflix dataset on clusters of various sizes, with various degrees of parallelism. For these experiments, I utilized the Amazon Elastic Compute Cloud (EC2) web service. In the final analysis, I conclude that the Spark DFC + OR1MP implementation does indeed produce competitive results, in both accuracy and performance. In particular, the Spark implementation performs nearly as well as the MATLAB implementation of OR1MP without any parallelism, and improves performance to a significant degree as the parallelism increases. In addition, the experience demonstrates how Spark's flexible programming model makes it straightforward to implement this parallel and iterative machine learning algorithm.
ContributorsKrouse, Brian (Author) / Ye, Jieping (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2014
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