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ContributorsChan, Robbie (Performer) / McCarrel, Kyla (Performer) / Sadownik, Stephanie (Performer) / ASU Library. Music Library (Contributor)
Created2018-04-18
ContributorsMayo, Joshua (Contributor) / Betts, Sarah (Performer) / Cericola, Marco (Performer) / Gonzalez, Nikolas (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-28
Description
Whenever a text is transmitted, or communicated by any means, variations may occur because editors, copyists, and performers are often not careful enough with the source itself. As a result, a flawed text may come to be accepted in good faith through repetition, and may often be preferred over the authentic version because familiarity with the flawed copy has been established. This is certainly the case with regard to Manuel M. Ponce's guitar editions. An inexact edition of a musical work is detrimental to several key components of its performance: musical interpretation, aesthetics, and the original musical concept of the composer. These phenomena may be seen in the case of Manuel Ponce's Suite in D Major for guitar. The single published edition by Peer International Corporation in 1967 with the revision and fingering of Manuel López Ramos contains many copying mistakes and intentional, but unauthorized, changes to the original composition. For the present project, the present writer was able to obtain a little-known copy of the original manuscript of this work, and to document these discrepancies in order to produce a new performance edition that is more closely based on Ponce's original work.
ContributorsReyes Paz, Ricardo (Author) / Koonce, Frank (Thesis advisor) / Solis, Theodore (Committee member) / Rotaru, Catalin (Committee member) / Arizona State University (Publisher)
Created2013
ContributorsDaval, Charles (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-26
Description
Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides in a multiplexed fashion. There are computational and statistical challenges to the analysis of immunosignaturing data. The overall aim of my dissertation is to develop novel computational and statistical methods for immunosignaturing data to access its potential for diagnostics and drug discovery. Firstly, I discovered that a classification algorithm Naive Bayes which leverages the biological independence of the probes on our array in such a way as to gather more information outperforms other classification algorithms due to speed and accuracy. Secondly, using this classifier, I then tested the specificity and sensitivity of immunosignaturing platform for its ability to resolve four different diseases (pancreatic cancer, pancreatitis, type 2 diabetes and panIN) that target the same organ (pancreas). These diseases were separated with >90% specificity from controls and from each other. Thirdly, I observed that the immunosignature of type 2 diabetes and cardiovascular complications are unique, consistent, and reproducible and can be separated by 100% accuracy from controls. But when these two complications arise in the same person, the resultant immunosignature is quite different in that of individuals with only one disease. I developed a method to trace back from informative random peptides in disease signatures to the potential antigen(s). Hence, I built a decipher system to trace random peptides in type 1 diabetes immunosignature to known antigens. Immunosignaturing, unlike the ELISA, has the ability to not only detect the presence of response but also absence of response during a disease. I observed, not only higher but also lower peptides intensities can be mapped to antigens in type 1 diabetes. To study immunosignaturing potential for population diagnostics, I studied effect of age, gender and geographical location on immunosignaturing data. For its potential to be a health monitoring technology, I proposed a single metric Coefficient of Variation that has shown potential to change significantly when a person enters a disease state.
ContributorsKukreja, Muskan (Author) / Johnston, Stephen Albert (Thesis advisor) / Stafford, Phillip (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2012
ContributorsMayo, Joshua (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-29
ContributorsDominguez, Ramon (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-15
ContributorsWhite, Bill (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-03
ContributorsSanchez, Armand (Performer) / Nordstrom, Nathan (Performer) / Roubison, Ryan (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-13