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- Genre: Doctoral Dissertation
Description
As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
Though it is a widespread adaptation in humans and many other animals, parental care comes in a variety of forms and its subtle physiological costs, benefits, and tradeoffs related to offspring are often unknown. Thus, I studied the hydric, respiratory, thermal, and fitness dynamics of maternal egg-brooding behavior in Children's pythons (Antaresia childreni). I demonstrated that tight coiling detrimentally creates a hypoxic developmental environment that is alleviated by periodic postural adjustments. Alternatively, maternal postural adjustments detrimentally elevate rates of egg water loss relative to tight coiling. Despite ventilating postural adjustments, the developmental environment becomes increasingly hypoxic near the end of incubation, which reduces embryonic metabolism. I further demonstrated that brooding-induced hypoxia detrimentally affects offspring size, performance, locomotion, and behavior. Thus, parental care in A. childreni comes at a cost to offspring due to intra-offspring tradeoffs (i.e., those that reflect competing offspring needs, such as water balance and respiration). Next, I showed that, despite being unable to intrinsically produce body heat, A. childreni adjust egg-brooding behavior in response to shifts in nest temperature, which enhances egg temperature (e.g., reduced tight coiling during nest warming facilitated beneficial heat transfer to eggs). Last, I demonstrated that A. childreni adaptively adjust their egg-brooding behaviors due to an interaction between nest temperature and humidity. Specifically, females' behavioral response to nest warming was eliminated during low nest humidity. In combination with other studies, these results show that female pythons sense environmental temperature and humidity and utilize this information at multiple time points (i.e., during gravidity [egg bearing], at oviposition [egg laying], and during egg brooding) to enhance the developmental environment of their offspring. This research demonstrates that maternal behaviors that are simple and subtle, yet easily quantifiable, can balance several critical developmental variables (i.e., thermoregulation, water balance, and respiration).
ContributorsStahlschmidt, Zachary R (Author) / DeNardo, Dale F (Thesis advisor) / Harrison, Jon (Committee member) / McGraw, Kevin (Committee member) / Rutowski, Ronald (Committee member) / Walsberg, Glenn (Committee member) / Arizona State University (Publisher)
Created2011
Description
Division of labor, whereby different group members perform different functions, is a fundamental attribute of sociality. It appears across social systems, from simple cooperative groups to complex eusocial colonies. A core challenge in sociobiology is to explain how patterns of collective organization are generated. Theoretical models propose that division of labor self-organizes, or emerges, from interactions among group members and the environment; division of labor is also predicted to scale positively with group size. I empirically investigated the emergence and scaling of division of labor in evolutionarily incipient groups of sweat bees and in eusocial colonies of harvester ants. To test whether division of labor is an emergent property of group living during early social evolution, I created de novo communal groups of the normally solitary sweat bee Lasioglossum (Ctenonomia) NDA-1. A division of labor repeatedly arose between nest excavation and guarding tasks; results were consistent with hypothesized effects of spatial organization and intrinsic behavioral variability. Moreover, an experimental increase in group size spontaneously promoted higher task specialization and division of labor. Next, I examined the influence of colony size on division of labor in larger, more integrated colonies of the harvester ant Pogonomyrmex californicus. Division of labor scaled positively with colony size in two contexts: during early colony ontogeny, as colonies grew from tens to hundreds of workers, and among same-aged colonies that varied naturally in size. However, manipulation of colony size did not elicit a short-term response, suggesting that the scaling of division of labor in P. californicus colonies is a product of functional integration and underlying developmental processes, rather than a purely emergent epiphenomenon. This research provides novel insights into the organization of work in insect societies, and raises broader questions about the role of size in sociobiology.
ContributorsHolbrook, Carter Tate (Author) / Fewell, Jennifer H (Thesis advisor) / Gadau, Jürgen (Committee member) / Harrison, Jon F. (Committee member) / Hölldobler, Berthold (Committee member) / Johnson, Robert A. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Service based software (SBS) systems are software systems consisting of services based on the service oriented architecture (SOA). Each service in SBS systems provides partial functionalities and collaborates with other services as workflows to provide the functionalities required by the systems. These services may be developed and/or owned by different entities and physically distributed across the Internet. Compared with traditional software system components which are usually specifically designed for the target systems and bound tightly, the interfaces of services and their communication protocols are standardized, which allow SBS systems to support late binding, provide better interoperability, better flexibility in dynamic business logics, and higher fault tolerance. The development process of SBS systems can be divided to three major phases: 1) SBS specification, 2) service discovery and matching, and 3) service composition and workflow execution. This dissertation focuses on the second phase, and presents a privacy preserving service discovery and ranking approach for multiple user QoS requirements. This approach helps service providers to register services and service users to search services through public, but untrusted service directories with the protection of their privacy against the service directories. The service directories can match the registered services with service requests, but do not learn any information about them. Our approach also enforces access control on services during the matching process, which prevents unauthorized users from discovering services. After the service directories match a set of services that satisfy the service users' functionality requirements, the service discovery approach presented in this dissertation further considers service users' QoS requirements in two steps. First, this approach optimizes services' QoS by making tradeoff among various QoS aspects with users' QoS requirements and preferences. Second, this approach ranks services based on how well they satisfy users' QoS requirements to help service users select the most suitable service to develop their SBSs.
ContributorsYin, Yin (Author) / Yau, Stephen S. (Thesis advisor) / Candan, Kasim (Committee member) / Dasgupta, Partha (Committee member) / Santanam, Raghu (Committee member) / Arizona State University (Publisher)
Created2011
Biological and immunological characterization of plant-produced HIV-1 Gag/dgp41 virus-like particles
Description
Anti-retroviral drugs and AIDS prevention programs have helped to decrease the rate of new HIV-1 infections in some communities, however, a prophylactic vaccine is still needed to control the epidemic world-wide. Despite over two decades of research, a vaccine against HIV-1 remains elusive, although recent clinical trials have shown promising results. Recent successes have focused on highly conserved, mucosally-targeted antigens within HIV-1 such as the membrane proximal external region (MPER) of the envelope protein, gp41. MPER has been shown to play critical roles in the viral mucosal transmission, though this peptide is not immunogenic on its own. Gag is a structural protein configuring the enveloped virus particles, and has been suggested to constitute a target of the cellular immunity potentially controlling the viral load. It was hypothesized that HIV-1 enveloped virus-like particles (VLPs) consisting of Gag and a deconstructed form of gp41 comprising the MPER, transmembrane, and cytoplasmic domains (dgp41) could be expressed in plants. Plant-optimized HIV-1 genes were constructed and expressed in Nicotiana benthamiana by stable transformation, or transiently using a tobacco mosaic virus-based expression system or a combination of both. Results of biophysical, biochemical and electron microscopy characterization demonstrated that plant cells could support not only the formation of HIV-1 Gag VLPs, but also the accumulation of VLPs that incorporated dgp41. These particles were purified and utilized in mice immunization experiments. Prime-boost strategies combining systemic and mucosal priming with systemic boosting using two different vaccine candidates (VLPs and CTB-MPR - a fusion of MPER and the B-subunit of cholera toxin) were administered to BALB/c mice. Serum antibody responses against both the Gag and gp41 antigens could be elicited in mice systemically primed with VLPs and these responses could be recalled following systemic boosting with VLPs. In addition, mucosal priming with VLPs allowed for a robust boosting response against Gag and gp41 when boosted with either candidate. Functional assays of these antibodies are in progress to test the antibodies' effectiveness in neutralizing and preventing mucosal transmission of HIV-1. This immunogenicity of plant-based Gag/dgp41 VLPs represents an important milestone on the road towards a broadly-efficacious and inexpensive subunit vaccine against HIV-1.
ContributorsKessans, Sarah (Author) / Mor, Tsafrir S (Thesis advisor) / Matoba, Nobuyuki (Committee member) / Mason, Hugh (Committee member) / Hogue, Brenda (Committee member) / Fromme, Petra (Committee member) / Arizona State University (Publisher)
Created2011
Description
The cyanobacterium Synechocystis sp. PCC 6803 performs oxygenic photosynthesis. Light energy conversion in photosynthesis takes place in photosystem I (PSI) and photosystem II (PSII) that contain chlorophyll, which absorbs light energy that is utilized as a driving force for photosynthesis. However, excess light energy may lead to formation of reactive oxygen species that cause damage to photosynthetic complexes, which subsequently need repair or replacement. To gain insight in the degradation/biogenesis dynamics of the photosystems, the lifetimes of photosynthetic proteins and chlorophyll were determined by a combined stable-isotope (15N) and mass spectrometry method. The lifetimes of PSII and PSI proteins ranged from 1-33 and 30-75 hours, respectively. Interestingly, chlorophyll had longer lifetimes than the chlorophyll-binding proteins in these photosystems. Therefore, photosynthetic proteins turn over and are replaced independently from each other, and chlorophyll is recycled from the damaged chlorophyll-binding proteins. In Synechocystis, there are five small Cab-like proteins (SCPs: ScpA-E) that share chlorophyll a/b-binding motifs with LHC proteins in plants. SCPs appear to transiently bind chlorophyll and to regulate chlorophyll biosynthesis. In this study, the association of ScpB, ScpC, and ScpD with damaged and repaired PSII was demonstrated. Moreover, in a mutant lacking SCPs, most PSII protein lifetimes were unaffected but the lifetime of chlorophyll was decreased, and one of the nascent PSII complexes was missing. SCPs appear to bind PSII chlorophyll while PSII is repaired, and SCPs stabilize nascent PSII complexes. Furthermore, aminolevulinic acid biosynthesis, an early step of chlorophyll biosynthesis, was impaired in the absence of SCPs, so that the amount of chlorophyll in the cells was reduced. Finally, a deletion mutation was introduced into the sll1906 gene, encoding a member of the putative bacteriochlorophyll delivery (BCD) protein family. The Sll1906 sequence contains possible chlorophyll-binding sites, and its homolog in purple bacteria functions in proper assembly of light-harvesting complexes. However, the sll1906 deletion did not affect chlorophyll degradation/biosynthesis and photosystem assembly. Other (parallel) pathways may exist that may fully compensate for the lack of Sll1906. This study has highlighted the dynamics of photosynthetic complexes in their biogenesis and turnover and the coordination between synthesis of chlorophyll and photosynthetic proteins.
ContributorsYao, Cheng I Daniel (Author) / Vermaas, Wim (Thesis advisor) / Fromme, Petra (Committee member) / Roberson, Robert (Committee member) / Webber, Andrew (Committee member) / Arizona State University (Publisher)
Created2011
Description
There is increasing evidence that ovarian status influcences behavioral phenotype in workers of the honey bee Apis mellifera. Honey bee workers demonstrate a complex division of labor. Young workers perform in-hive tasks (e.g. brood care), while older bees perform outside tasks (e.g. foraging for food). This age correlated division of labor is known as temporal polyethism. Foragers demonstrate further division of labor with some bees biasing collection towards protein (pollen) and others towards carbohydrates (nectar). The Reproductive Ground-plan Hypothesis proposes that the ovary plays a regulatory role in foraging division of labor. European honey bee workers that have been selectively bred to store larger amounts of pollen (High strain) also have a higher number of ovarioles per ovary than workers from strains bred to store less pollen (Low strain). High strain bees also initiate foraging earlier than Low strain bees. The relationship between ovariole number and foraging behavior is also observed in wild-type Apis mellifera and Apis cerana: pollen-biased foragers have more ovarioles than nectar-biased foragers. In my first study, I investigated the pre-foraging behavioral patterns of the High and Low strain bees. I found that High strain bees progress through the temporal polyethism at a faster rate than Low strain bees. To ensure that the observed relationship between the ovary and foraging bias is not due to associated separate genes for ovary size and foraging behavior, I investigated foraging behavior of African-European backcross bees. The backcross breeding program was designed to break potential gene associations. The results from this study demonstrated the relationship between the ovary and foraging behavior, supporting the proposed causal linkage between reproductive development and behavioral phenotype. The final study was designed to elucidate a regulatory mechanism that links ovariole number with sucrose sensitivity, and loading decisions. I measured ovariole number, sucrose sensitivity and sucrose solution load size using a rate-controlled sucrose delivery system. I found an interaction effect between ovariole number and sucrose sensitivity for sucrose solution load size. This suggests that the ovary impacts carbohydrate collection through modulation of sucrose sensitivity. Because nectar and pollen collection are not independent, this would also impact protein collection.
ContributorsSiegel, Adam J (Author) / Page, Jr., Robert E (Thesis advisor) / Hamilton, Andrew L. (Committee member) / Brent, Colin S (Committee member) / Amdam, Gro V (Committee member) / McGraw, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the rapid growth of mobile computing and sensor technology, it is now possible to access data from a variety of sources. A big challenge lies in linking sensor based data with social and cognitive variables in humans in real world context. This dissertation explores the relationship between creativity in teamwork, and team members' movement and face-to-face interaction strength in the wild. Using sociometric badges (wearable sensors), electronic Experience Sampling Methods (ESM), the KEYS team creativity assessment instrument, and qualitative methods, three research studies were conducted in academic and industry R&D; labs. Sociometric badges captured movement of team members and face-to-face interaction between team members. KEYS scale was implemented using ESM for self-rated creativity and expert-coded creativity assessment. Activities (movement and face-to-face interaction) and creativity of one five member and two seven member teams were tracked for twenty five days, eleven days, and fifteen days respectively. Day wise values of movement and face-to-face interaction for participants were mean split categorized as creative and non-creative using self- rated creativity measure and expert-coded creativity measure. Paired-samples t-tests [t(36) = 3.132, p < 0.005; t(23) = 6.49 , p < 0.001] confirmed that average daily movement energy during creative days (M = 1.31, SD = 0.04; M = 1.37, SD = 0.07) was significantly greater than the average daily movement of non-creative days (M = 1.29, SD = 0.03; M = 1.24, SD = 0.09). The eta squared statistic (0.21; 0.36) indicated a large effect size. A paired-samples t-test also confirmed that face-to-face interaction tie strength of team members during creative days (M = 2.69, SD = 4.01) is significantly greater [t(41) = 2.36, p < 0.01] than the average face-to-face interaction tie strength of team members for non-creative days (M = 0.9, SD = 2.1). The eta squared statistic (0.11) indicated a large effect size. The combined approach of principal component analysis (PCA) and linear discriminant analysis (LDA) conducted on movement and face-to-face interaction data predicted creativity with 87.5% and 91% accuracy respectively. This work advances creativity research and provides a foundation for sensor based real-time creativity support tools for teams.
ContributorsTripathi, Priyamvada (Author) / Burleson, Winslow (Thesis advisor) / Liu, Huan (Committee member) / VanLehn, Kurt (Committee member) / Pentland, Alex (Committee member) / Arizona State University (Publisher)
Created2011