Browsing by Author "Hsiao, Po-Wen"

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Celecoxib induces p53-PUMA pathway for apoptosis in human colorectal cancer cells
(2008-10-22)
Liu, Huei-Fang
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Hsiao, Po-Wen
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Chao, Jui-I
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生物科技學系
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Department of Biological Science and Technology
Celecoxib, a clinical non-steroidal anti-inflammatory drug, displays anticarcinogenic and chemopreventive activities in human colorectal cancers, although the mechanisms of apoptosis by celecoxib are poorly understood. The existence of functional p53 but not securin in colorectal cancer cells was higher on the induction of cytotoxicity than the p53-mutational colorectal cancer cells following celecoxib treatment. The p53-wild type HCT116 cells were more susceptible to increase similar to 25% cell death than the p53-null HCT116 cells after treatment with 100 mu M celecoxib for 24 h.Transfection with a small interfering RNA of p53 reduced the celecoxib-induced cytotoxicity in the RKO (p53-wild type) colorectal cancer cells. Celecoxib (80-100 mu M for 24 h) significantly increased total p53 proteins and the phosphorylated p53 proteins at serine-15,-20,-46, and -392 in RKO cells. However, the phospho-p53 (serine-15, 20, and -392) proteins were presented on the nuclei of cells but the phospho-p53 (serine-46) protein was located on the cytoplasma of apoptotic cells following treatment with celecoxib. Interestingly, the p53 up-regulated modulator of apoptosis (PUMA) protein, which located on the mitochondria, was induced by celecoxib in the p53-functional colorectal cancer cells but not in the p53-mutational cells. Together, this study provides the first time that celecoxib induces the various phosphorylated sites of p53 and activates p53-PUMA pathway, which potentiates the apoptosis induction in human colorectal cancer cells. Crown Copyright (c) 2008 Published by Elsevier Ireland Ltd. All rights reserved.
Exploring redundancy of HRTFs for fast training DNN-based HRTF personalization
(2018-01-01)
Chen, Tzu-Yu
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Hsiao, Po-Wen
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Chi, Tai-Shih
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電機工程學系
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Department of Electrical and Computer Engineering
A deep neural network (DNN) is constructed to predict the magnitude responses of the head-related transfer functions (HRTFs) of users for a specific direction and a specific ear. Using the CIPIC HRTF database (including 25 azimuth angles and 50 elevation angles for both ears), we trained 2500 DNNs to predict magnitude responses of all HRTFs of a user. To reduce training time, we propose to use the final weights of the trained DNN of a nearby direction as the initial weights of the current DNN under training since magnitude responses of the HRTFs are smoothly changing across nearby directions. Analysis of variance (ANOVA) was performed to show that the proposed training scheme produces equivalent magnitude responses of HRTFs as the standard training scheme with random initial weights in terms of the log-spectral distortion (LSD) measure. Meanwhile, the proposed training scheme can dramatically reduce training time by more than 95%.