Browsing by Author "Su, Wei-Shuo"

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Neuroimaging Spectrum at Pre-, Early, and Late Symptomatic Stages of SCA17 Mice
(1970-01-01)
Chen, Chiao-Chi
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Yao, Nai-Wei
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Lin, Chia-Wei
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Su, Wei-Shuo
;
Wu, Chin-Tien
Spinocerebellar ataxia (SCA) is a hereditary neurodegenerative disease. We have generated SCA17 transgenic mice bearing human TBP with 109 CAG repeats under the Purkinje cell-specific L7/pcp2 promoter. These mice recapitulate the patients' phenotypes and are suitable for the study of the SCA17 pathomechanism. Magnetic resonance imaging (MRI) and immunostainings were performed to identify the neuroimaging spectrum during disease progression. The results indicate that despite an overall normal appearance at birth, postnatal brain damage takes place rapidly in SCA17. Cerebellar atrophy, fourth-ventricle enlargement, and reduced cerebellar N-acetylaspartate levels were detected at the presymptomatic stage, when the mice were juvenile. The aberrations, which included reductions in body weight; cerebral size; striatal size; and the mean, radial, and axial diffusivities of the cerebellum, became more salient as the disease progressed to the old, late-symptomatic stage. Phosphorylated H2A histone family, member X (gamma H2AX) immunostaining revealed that the cerebellum underwent severe cell senescence in the old stage while the striatum appeared relatively unaffected by aging. Morphometric analysis indicated that the cerebellar atrophy occurred in all subregions with aging. The data establish that the SCA17 mouse brain appears normal at birth but becomes aberrant at the presymptomatic/juvenile stage. More widespread deficits add to the pathological spectrum at the old stage. The study provides information for the expression and expansion of L7/pcp2 promoter and implies the disease progression of SCA17 patients.
Palindromic quadratization and structure-preserving algorithm for palindromic matrix polynomials of even degree
(2011-08-01)
Huang, Tsung-Ming
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Lin, Wen-Wei
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Su, Wei-Shuo
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應用數學系
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Department of Applied Mathematics
In this paper, we propose a palindromic quadratization approach, transforming a palindromic matrix polynomial of even degree to a palindromic quadratic pencil. Based on the (S + S(-1))-transform and Patel's algorithm, the structure-preserving algorithm can then be applied to solve the corresponding palindromic quadratic eigenvalue problem. Numerical experiments show that the relative residuals for eigenpairs of palindromic polynomial eigenvalue problems computed by palindromic quadratized eigenvalue problems are better than those via palindromic linearized eigenvalue problems or polyeig in MATLAB.