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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
Studies of ancient pathogens are moving beyond simple confirmatory analysis of diseased bone; bioarchaeologists and ancient geneticists are posing nuanced questions and utilizing novel methods capable of confronting the debates surrounding pathogen origins and evolution, and the relationships between humans and disease in the past. This dissertation examines two ancient human diseases through molecular and bioarchaeological lines of evidence, relying on techniques in paleogenetics and phylogenetics to detect, isolate, sequence and analyze ancient and modern pathogen DNA within an evolutionary framework. Specifically this research addresses outstanding issues regarding a) the evolution, origin and phylogenetic placement of the pathogen causing skeletal tuberculosis in New World prior to European contact, and b) the phylogeny and origins of the parasite causing the human leishmaniasis disease complex. An additional chapter presents a review of the major technological and theoretical advances in ancient pathogen genomics to frame the contributions of this work within a rapidly developing field. This overview emphasizes that understanding the evolution of human disease is critical to contextualizing relationships between humans and pathogens, and the epidemiological shifts observed both in the past and in the present era of (re)emerging infectious diseases. These questions continue to be at the forefront of not only pathogen research, but also
bioarchaeological and paleopathological scholarship.
bioarchaeological and paleopathological scholarship.
ContributorsHarkins, Kelly M (Author) / Buikstra, Jane E. (Thesis advisor) / Stone, Anne C (Thesis advisor) / Knudson, Kelly (Committee member) / Kumar, Sudhir (Committee member) / Krause, Johannes (Committee member) / Arizona State University (Publisher)
Created2014
Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
Description
Objectives: The objective of this research is to develop a better understanding of the ways in which Transition Analysis estimates differ from traditional estimates in terms of age-at-death point estimation and inter-observer error. Materials and methods: In order to achieve the objectives of the research, 71 adult individuals from an archaeological site in northern Sudan were subjected to Transition Analysis age estimation by the author, a beginner-level osteologist. These estimates were compared to previously produced traditional multifactorial age estimates for these individuals, as well as a small sample of Transition Analysis estimates produced by an intermediate-level investigator. Results: Transition Analysis estimates do not have a high correlation with traditional estimates of age at death, especially when those estimates fall within middle or old adult age ranges. The misalignment of beginner- and intermediate-level Transition Analysis age estimations calls into question intra-method as well as inter-method replicability of age estimations. Discussion: Although the poor overall correlation of Transition Analysis estimates and traditional estimates in this study might be blamed on the relatively low experience level of the analyst, the results cast doubt on the replicability of Transition Analysis estimations, echoing the Bethard's (2005) results on a known-age sample. The results also question the validity of refined age estimates produced for individuals previously estimated to be in the 50+ age range by traditional methods and suggest that Transition Analysis tends to produce younger estimates than its traditional counterparts. Key words: age estimation, Transition Analysis, human osteology, observer error
ContributorsPhillips, Megann M. (Author) / Baker, Brenda (Thesis director) / Norris, Annie Laurie (Committee member) / School of International Letters and Cultures (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This dissertation examines the interrelationships between stress, frailty, growth, mortality, and diet at the Qinifab School site, Sudan, using a combination of osteological, paleopathological, and biogeochemical methods. The skeletal sample, from the fourth cataract region of Nubia, is comprised of 100 individuals from a Late Meroitic to Christian period (~250-1400 CE) cemetery. Standard osteological methods were used to estimate age and sex, and measurements were taken to assess body dimensions. Preadults were aged by dental and skeletal development, producing two independent ages to categorize individuals as developmentally “normal” or “delayed.” Data were collected on nonspecific indicators of stress, including linear enamel hypoplasias (LEHs), porotic hyperostosis (PH), and cribra orbitalia (CO). In preadults, these were compared to World Health Organization (WHO) growth standards to identify individuals who experienced stunting or wasting. For all ages, evidence of stress was compared with age at death and growth/body size. Finally, stable carbon and nitrogen isotope analyses were conducted on bone collagen and carbonate samples from a representative sample of 60 individuals, of which 46 collagen samples and all carbonates had acceptable preservation.“Delayed” preadults generally showed reduced body size relative to “normal” individuals, they were more likely to be stunted, and their growth trajectories were less similar to WHO standards. However, childhood stress had little impact on adult body size. CO occurred at higher frequencies in preadults and individuals with mixed/active lesions died at younger ages. PH rarely developed before age 6 but was present in most individuals over that age. Individuals with earlier formed LEHs tended to experience more stress overall and die younger. Active/mixed CO was associated with stunting in preadults and reduced brachial index in adults.
A greater proportion of individuals in the Christian period were affected by CO compared to the Post-Meroitic. A temporal shift also occurred in diet between the Post-Meroitic and Christian periods based upon the δ13CCOLL and δ15NCOLL values. Lower δ15N and the greater difference in δ13CAP-COLL suggest a shift toward intensified agriculture and decreased use of animal products and a potential dietary etiology for the increase in CO.
ContributorsNorris, Annie Laurie (Author) / Baker, Brenda J (Thesis advisor) / Knudson, Kelly (Committee member) / Dupras, Tosha (Committee member) / Arizona State University (Publisher)
Created2021