An Overview of Algorithmic Music Composition in the Noteworks Software Platform

Presented at the 16th International Conference on Auditory Display (ICAD2010) on June 9-15, 2010 in Washington, DC.Noteworks is music composition software that re-imagines the way music is created, played, and shared. Users create musical compositions by building networks and interacting with them in real time. Noteworks reduces the learning curve for algorithmic-music composition, such that most individuals with a basic knowledge of computer interaction can create original compositions with limited instruction. Dynamic networks have the potential to play back for hours without repeating. This document will provide a brief summary overview of the GUI

Regulation of Liver Regeneration by Hepatocyte O-GlcNAcylation in Mice

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Background & Aims The liver has a unique capacity to regenerate after injury in a highly orchestrated and regulated manner. Here, we report that O-GlcNAcylation, an intracellular post-translational modification regulated by 2 enzymes, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), is a critical termination signal for liver regeneration following partial hepatectomy (PHX). Methods We studied liver regeneration after PHX on hepatocyte specific OGT and OGA knockout mice (OGT-KO and OGA-KO), which caused a significant decrease (OGT-KO) and increase (OGA-KO) in hepatic O-GlcNAcylation, respectively. Results OGA-KO mice had normal regeneration, but the OGT-KO mice exhibited substantial defects in termination of liver regeneration with increased liver injury, sustained cell proliferation resulting in significant hepatomegaly, hepatic dysplasia, and appearance of small nodules at 28 days after PHX. This was accompanied by a sustained increase in expression of cyclins along with significant induction in pro-inflammatory and pro-fibrotic gene expression in the OGT-KO livers. RNA-sequencing studies revealed inactivation of hepatocyte nuclear 4 alpha (HNF4α), the master regulator of hepatic differentiation and a known termination signal, in OGT-KO mice at 28 days after PHX, which was confirmed by both Western blot and immunohistochemistry analysis. Furthermore, a significant decrease in HNFα target genes was observed in OGT-KO mice, indicating a lack of hepatocyte differentiation following decreased hepatic O-GlcNAcylation. Immunoprecipitation experiments revealed HNF4α is O-GlcNAcylated in normal differentiated hepatocytes. Conclusions These studies show that O-GlcNAcylation plays a critical role in the termination of liver regeneration via regulation of HNF4α in hepatocytes

GROCS Collection for Noteworks 2007-2008

Collection of artifacts from the Noteworks GROCS project in 2008.We propose to design and implement a computer application that enables users to create sound experiences and musical compositions in a completely new way. In particular, our software will enable users to design dynamic temporal networks in which the nodes correspond to sound clips, and directed edges represent time and other relationships between nodes. Furthermore, we will embed functionality in the application so as to enable different instances of our software to interact with other musicians’ networks so as to create a truly interactive, collaborative music experience. We will also release our software to any interested parties so they can extend it as they see fit (and set up their own musical networks at home).GROCS: GRant Opportunities [collaborative spaces], a Digital Media Commons program to fund student research on the use of rich media in collaborative learning.http://deepblue.lib.umich.edu/bitstream/2027.42/62445/11/grocs_proposal_noteworks.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/62445/10/setup.AVIhttp://deepblue.lib.umich.edu/bitstream/2027.42/62445/9/NW Design Review 2.1.08.mp3http://deepblue.lib.umich.edu/bitstream/2027.42/62445/8/noteworks_screenshot.pnghttp://deepblue.lib.umich.edu/bitstream/2027.42/62445/7/noteworks_screencast.avihttp://deepblue.lib.umich.edu/bitstream/2027.42/62445/6/noteworks_melancholy.mp4http://deepblue.lib.umich.edu/bitstream/2027.42/62445/5/noteworks_logo.pnghttp://deepblue.lib.umich.edu/bitstream/2027.42/62445/4/noteworks.ziphttp://deepblue.lib.umich.edu/bitstream/2027.42/62445/3/Noteworks Demo April 5.movhttp://deepblue.lib.umich.edu/bitstream/2027.42/62445/2/michigan.AVIhttp://deepblue.lib.umich.edu/bitstream/2027.42/62445/1/bedtime.av

Combination of 3D skin surface texture features and 2D ABCD features for improved melanoma diagnosis

© 2015, International Federation for Medical and Biological Engineering. Two-dimensional asymmetry, border irregularity, colour variegation and diameter (ABCD) features are important indicators currently used for computer-assisted diagnosis of malignant melanoma (MM); however, they often prove to be insufficient to make a convincing diagnosis. Previous work has demonstrated that 3D skin surface normal features in the form of tilt and slant pattern disruptions are promising new features independent from the existing 2D ABCD features. This work investigates that whether improved lesion classification can be achieved by combining the 3D features with the 2D ABCD features. Experiments using a nonlinear support vector machine classifier show that many combinations of the 2D ABCD features and the 3D features can give substantially better classification accuracy than using (1) single features and (2) many combinations of the 2D ABCD features. The best 2D and 3D feature combination includes the overall 3D skin surface disruption, the asymmetry and all the three colour channel features. It gives an overall 87.8% successful classification, which is better than the best single feature with 78.0% and the best 2D feature combination with 83.1%. These demonstrate that (1) the 3D features have additive values to improve the existing lesion classification and (2) combining the 3D feature with all the 2D features does not lead to the best lesion classification. The two ABCD features not selected by the best 2D and 3D combination, namely (1) the border feature and (2) the diameter feature, were also studied in separate experiments. It found that inclusion of either feature in the 2D and 3D combination can successfully classify 3 out of 4 lesion groups. The only one group not accurately classified by either feature can be classified satisfactorily by the other. In both cases, they have shown better classification performances than those without the 3D feature in the combinations. This further demonstrates that (1) the 3D feature can be used to improve the existing 2D-based diagnosis and (2) including the 3D feature with subsets of the 2D features can be used in distinguishing different benign lesion classes from MM. It is envisaged that classification performance may be further improved if different 2D and 3D feature subsets demonstrated in this study are used in different stages to target different benign lesion classes in future studies