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- All Subjects: Violin and piano music
ContributorsChang, Ruihong (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-29
Description
Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can potentially provide pre-symptomatic diagnosis for infectious diseases or detection of biological threats. Currently, traditional bioinformatics tools, such as data mining classification algorithms, are used to process the large amount of peptide microarray data. However, these methods generally require training data and do not adapt to changing immune conditions or additional patient information. This work proposes advanced processing techniques to improve the classification and identification of single and multiple underlying immune response states embedded in immunosignatures, making it possible to detect both known and previously unknown diseases or biothreat agents. Novel adaptive learning methodologies for un- supervised and semi-supervised clustering integrated with immunosignature feature extraction approaches are proposed. The techniques are based on extracting novel stochastic features from microarray binding intensities and use Dirichlet process Gaussian mixture models to adaptively cluster the immunosignatures in the feature space. This learning-while-clustering approach allows continuous discovery of antibody activity by adaptively detecting new disease states, with limited a priori disease or patient information. A beta process factor analysis model to determine underlying patient immune responses is also proposed to further improve the adaptive clustering performance by formatting new relationships between patients and antibody activity. In order to extend the clustering methods for diagnosing multiple states in a patient, the adaptive hierarchical Dirichlet process is integrated with modified beta process factor analysis latent feature modeling to identify relationships between patients and infectious agents. The use of Bayesian nonparametric adaptive learning techniques allows for further clustering if additional patient data is received. Significant improvements in feature identification and immune response clustering are demonstrated using samples from patients with different diseases.
ContributorsMalin, Anna (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Lacroix, Zoé (Committee member) / Arizona State University (Publisher)
Created2013
Description
US Senate is the venue of political debates where the federal bills are formed and voted. Senators show their support/opposition along the bills with their votes. This information makes it possible to extract the polarity of the senators. Similarly, blogosphere plays an increasingly important role as a forum for public debate. Authors display sentiment toward issues, organizations or people using a natural language.
In this research, given a mixed set of senators/blogs debating on a set of political issues from opposing camps, I use signed bipartite graphs for modeling debates, and I propose an algorithm for partitioning both the opinion holders (senators or blogs) and the issues (bills or topics) comprising the debate into binary opposing camps. Simultaneously, my algorithm scales the entities on a univariate scale. Using this scale, a researcher can identify moderate and extreme senators/blogs within each camp, and polarizing versus unifying issues. Through performance evaluations I show that my proposed algorithm provides an effective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In my experiments, I used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of my algorithm.
I also applied my algorithm on all the terms of the US Senate to the date for longitudinal analysis and developed a web based interactive user interface www.PartisanScale.com to visualize the analysis.
US politics is most often polarized with respect to the left/right alignment of the entities. However, certain issues do not reflect the polarization due to political parties, but observe a split correlating to the demographics of the senators, or simply receive consensus. I propose a hierarchical clustering algorithm that identifies groups of bills that share the same polarization characteristics. I developed a web based interactive user interface www.ControversyAnalysis.com to visualize the clusters while providing a synopsis through distribution charts, word clouds, and heat maps.
In this research, given a mixed set of senators/blogs debating on a set of political issues from opposing camps, I use signed bipartite graphs for modeling debates, and I propose an algorithm for partitioning both the opinion holders (senators or blogs) and the issues (bills or topics) comprising the debate into binary opposing camps. Simultaneously, my algorithm scales the entities on a univariate scale. Using this scale, a researcher can identify moderate and extreme senators/blogs within each camp, and polarizing versus unifying issues. Through performance evaluations I show that my proposed algorithm provides an effective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In my experiments, I used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of my algorithm.
I also applied my algorithm on all the terms of the US Senate to the date for longitudinal analysis and developed a web based interactive user interface www.PartisanScale.com to visualize the analysis.
US politics is most often polarized with respect to the left/right alignment of the entities. However, certain issues do not reflect the polarization due to political parties, but observe a split correlating to the demographics of the senators, or simply receive consensus. I propose a hierarchical clustering algorithm that identifies groups of bills that share the same polarization characteristics. I developed a web based interactive user interface www.ControversyAnalysis.com to visualize the clusters while providing a synopsis through distribution charts, word clouds, and heat maps.
ContributorsGokalp, Sedat (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Liu, Huan (Committee member) / Woodward, Mark (Committee member) / Arizona State University (Publisher)
Created2015
Description
Four Souvenirs for Violin and Piano was composed by Paul Schoenfeld (b.1947) in 1990 as a showpiece, spotlighting the virtuosity of both the violin and piano in equal measure. Each movement is a modern interpretation of a folk or popular genre, re- envisioned over intricate jazz harmonies and rhythms. The work was commissioned by violinist Lev Polyakin, who specifically requested some short pieces that could be performed in a local jazz establishment named Night Town in Cleveland, Ohio. The result is a work that is approximately fifteen minutes in length. Schoenfeld is a respected composer in the contemporary classical music community, whose Café Music (1986) for piano trio has recently become a staple of the standard chamber music repertoire. Many of his other works, however, remain in relative obscurity. It is the focus of this document to shed light on at least one other notable composition; Four Souvenirs for Violin and Piano. Among the topics to be discussed regarding this piece are a brief history behind the genesis of this composition, a structural summary of the entire work and each of its movements, and an appended practice guide based on interview and coaching sessions with the composer himself. With this project, I hope to provide a better understanding and appreciation of this work.
ContributorsJanczyk, Kristie Annette (Author) / Ryan, Russell (Thesis advisor) / Campbell, Andrew (Committee member) / Norton, Kay (Committee member) / Arizona State University (Publisher)
Created2015
Description
The greatest barrier to understanding how life interacts with its environment is the complexity in which biology operates. In this work, I present experimental designs, analysis methods, and visualization techniques to overcome the challenges of deciphering complex biological datasets. First, I examine an iron limitation transcriptome of Synechocystis sp. PCC 6803 using a new methodology. Until now, iron limitation in experiments of Synechocystis sp. PCC 6803 gene expression has been achieved through media chelation. Notably, chelation also reduces the bioavailability of other metals, whereas naturally occurring low iron settings likely result from a lack of iron influx and not as a result of chelation. The overall metabolic trends of previous studies are well-characterized but within those trends is significant variability in single gene expression responses. I compare previous transcriptomics analyses with our protocol that limits the addition of bioavailable iron to growth media to identify consistent gene expression signals resulting from iron limitation. Second, I describe a novel method of improving the reliability of centroid-linkage clustering results. The size and complexity of modern sequencing datasets often prohibit constructing distance matrices, which prevents the use of many common clustering algorithms. Centroid-linkage circumvents the need for a distance matrix, but has the adverse effect of producing input-order dependent results. In this chapter, I describe a method of cluster edge counting across iterated centroid-linkage results and reconstructing aggregate clusters from a ranked edge list without a distance matrix and input-order dependence. Finally, I introduce dendritic heat maps, a new figure type that visualizes heat map responses through expanding and contracting sequence clustering specificities. Heat maps are useful for comparing data across a range of possible states. However, data binning is sensitive to clustering cutoffs which are often arbitrarily introduced by researchers and can substantially change the heat map response of any single data point. With an understanding of how the architectural elements of dendrograms and heat maps affect data visualization, I have integrated their salient features to create a figure type aimed at viewing multiple levels of clustering cutoffs, allowing researchers to better understand the effects of environment on metabolism or phylogenetic lineages.
ContributorsKellom, Matthew (Author) / Raymond, Jason (Thesis advisor) / Anbar, Ariel (Committee member) / Elser, James (Committee member) / Shock, Everett (Committee member) / Walker, Sarah (Committee member) / Arizona State University (Publisher)
Created2017
ContributorsWhite, Aaron (Performer) / Kim, Olga (Performer) / Hammond, Marinne (Performer) / Shaner, Hayden (Performer) / Yoo, Katie (Performer) / Shoemake, Crista (Performer) / Gebe, Vladimir, 1987- (Performer) / Wills, Grace (Performer) / McKinch, Riley (Performer) / Freshmen Four (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-27
ContributorsRosenfeld, Albor (Performer) / Pagano, Caio, 1940- (Performer) / ASU Library. Music Library (Publisher)
Created2018-10-03
ContributorsCao, Yuchen (Performer) / Chen, Sicong (Performer) / Soberano, Chino (Performer) / Nam, Michelle (Performer) / Collins, Clarice (Performer) / Witt, Juliana (Performer) / Liu, Jingting (Performer) / Chen, Neilson (Performer) / Zhang, Aihua (Performer) / Jiang, Zhou (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-25