Browsing by Author "Wang, Tzu-Hao"

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A highly selective turn-on fluorescent chemosensor for copper(II) ion
(2010-12-18)
Wu, Shu-Pao
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Wang, Tzu-Hao
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Liu, Shi-Rong
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應用化學系
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Department of Applied Chemistry
A new pyrene derivative (1) containing a diaminomaleonitrile moiety exhibits high selectivity for Cu(2+) detection. Significant fluorescence enhancement was observed with chemosensor 1 in the presence of Cu(2+). However, the metal ions Ag(+), Ca(2+), Cd(2+), Co(2+), Fe(2+), Fe(3+), Hg(2+), Mg(2+), Mn(2+), Ni(2+), Pb(2+), and Zn(2+) produced only minor changes in fluorescence values for the system. The apparent association constant (K(a)) of Cu(2+) binding in chemosensor 1 was found to be 5.55x10(3) M(-1). The maximum fluorescence enhancement caused by Cu(2+) binding in chemosensor 1 was observed over the pH range 5-7.5. (C) 2010 Elsevier Ltd. All rights reserved.
A highly selective turn-on fluorescent chemosensor for copper(II) ion (vol 66, pg 9655, 2010)
(2011-04-01)
Wu, Shu-Pao
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Wang, Tzu-Hao
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Liu, Shi-Rong
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應用化學系
;
Department of Applied Chemistry
dbPTM: an information repository of protein post-translational modification
(2006-01-01)
Lee, Tzong-Yi
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Huang, Hsien-Da
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Hung, Jui-Hung
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Huang, Hsi-Yuan
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Yang, Yuh-Shyong
dbPTM is a database that compiles information on protein post-translational modifications (PTMs), such as the catalytic sites, solvent accessibility of amino acid residues, protein secondary and tertiary structures, protein domains and protein variations. The database includes all of the experimentally validated PTM sites from Swiss-Prot, PhosphoELM and O-GLYCBASE. Only a small fraction of SwissProt proteins are annotated with experimentally verified PTM. Although the Swiss-Prot provides rich information about the PTM, other structural properties and functional information of proteins are also essential for elucidating protein mechanisms. The dbPTM systematically identifies three major types of protein PTM (phosphorylation, glycosylation and sulfation) sites against Swiss-Prot proteins by refining our previously developed prediction tool, KinasePhos (http://kinasephos.mbc.nctu.edu.tw/). Solvent accessibility and secondary structure of residues are also computationally predicted and are mapped to the PTM sites. The resource is now freely available at http://dbPTM.mbc.nctu.edu.tw/.
Functional Analyses of Endometriosis-Related Polymorphisms in the Estrogen Synthesis and Metabolism-Related Genes
(2012-11-06)
Wang, Hsin-Shih
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Wu, Hsien-Ming
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Cheng, Bi-Hwa
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Yen, Chih-Feng
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Chang, Pi-Yueh
Endometriosis is determined by genetic factors, and the prevalence of genetic polymorphisms varies greatly depending on the ethnic group studied. The objective of this study was to investigate the relationship between single nucleotide polymorphisms (SNPs) of 9 genes involved in estrogen biosynthesis and metabolism and the risks of endometriosis. Three hundred patients with endometriosis and 337 non-endometriotic controls were recruited. Thirty four non-synonymous SNPs, which change amino acid residues, were analyzed using matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF MS). The functions of SNP-resulted amino acid changes were analyzed using multiple web-accessible databases and phosphorylation predicting algorithms. Among the 34 NCBI-listed SNPs, 22 did not exhibit polymorphism in this study of more than 600 Taiwanese Chinese women. However, homozygous and heterozygous mutants of 4 SNPs - rs6165 (genotype GG+GA, 307(Ala/Ala)+307(Ala/Thr)) of FSHR, rs 6166 (genotype GG+GA, 680(Ser/Asn)+680(Ser/Ser)) of FSHR, rs2066479 (genotype AA+AG, 289(Ser/Ser)+289(Ser/Gly)) of HSD17B3 and rs700519 (genotype TT+TC, 264(Cys/Cys)+264(Cys/Arg)) of CYP19, alone or in combination, were significantly associated with decreased risks of endometriosis. Bioinformatics results identified 307(Thr) of FSHR to be a site for O-linked glycosylation, 680(Ser) of FSHR a phosphorylated site by protein kinase B, and 289(Ser) of HSD17B3 a phosphorylated site by protein kinase B or ribosomal protein S6 kinase 1. Results of this study suggest that non-synonymous polymorphisms of FSHR, HSD17B3 and CYP19 genes may modulate the risk of endometriosis in Taiwanese Chinese women. Identification of the endometrosis-preferential non-synonymous SNPs and the conformational changes in those proteins may pave the way for the development of more disease-specific drugs.
Microstructural and thermal characterizations of light-emitting diode employing a low-temperature die-bonding material
(2016-08)
Wang, Tzu-Hao
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Lee, Hsuan
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Chen, Chih-Ming
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Chen, Ming-Guan
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Hu, Chi-Chang
A Sn/Bi bilayer was deposited on a hot air solder leveling (HASL)-treated metal-core printed circuit board (MCPCB) using electroplating as a low-temperature die-bonding material for light-emitting diode (LED). The eutectic feature of the Sn/Bi contact enabled the die-bonding process to accomplish through a liquid/solid reaction at 185 degrees C with a proper compression force. A high-temperature die-bonding structure composed of a Bi layer sandwiched by two intermetallic compounds (IMCs) formed after thermocompression. Employment of the Sn/Bi bilayer for low-temperature die-bonding prevented the LEDs from thermal stress problems, and the resulting high-temperature IMC/Bi/IMC die-bonding structure was capable of withstanding multiple bonding reactions and high temperature/current operation environment. Durability tests including mechanical, thermal, and optical performance were systematically performed and compared with other commercially available die-bonding materials (Ag paste and solder alloys). (C) 2016 Elsevier Ltd. All rights reserved.
Modeling neurogenesis impairment in down syndrome with induced pluripotent stem cells from Trisomy 21 amniotic fluid cells
(2013-02-15)
Lu, Huai-En
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Yang, Yao-Chen
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Chen, Sheng-Mei
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Su, Hong-Lin
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Huang, Pai-Cheng
Down syndrome (DS), or Trisomy 21 (121) syndrome, one of the most common chromosomal abnormalities, is caused by an extra duplication of chromosome 21. In studies of neuron development, experimental models based on human cells are considered to be the most desired and accurate for basic research. The generation of diseased induced pluripotetn stem (iPS) cell is a critical step in understanding the developmental stages of complex neuronal diseases. Here, we generated human DS iPS cell lines from second trimester amniotic fluid (AF) cells with 121 by co-expressing Yamanaka factors through lentiviral delivery and subsequently differentiated them into neuronal progenitor cells (NPCs) for further analyses. 121 AF-iPS cells were characterized for the expression of pluripotent markers and for their ability to differentiate into all three germ layers by forming embryoid bodies in vitro and teratomas in vivo. The 121 AF-iPS cells maintained their unique pattern of chromosomal karyotypes: three pairs of chromosome 21. The level of amyloid precursor protein was significantly increased in NPCs derived from 121 AF-iPS cells compared with NPCs from normal AF-iPS cells. The expression levels of miR-155 and miR-802 in 121 AF-iPS-NPCs were highly elevated in the presence of low expression of MeCP2. We observed that 121 iPS-NPCs generated fewer neurons compared with controls. 121 iPS-NPCs exhibit developmental defects during neurogenesis. Our findings suggest that 121 AF-iPS cells serve as a good source to further elucidate the impairment neurogenesis of DS and the onset of Alzheimer's disease. (C) 2012 Elsevier Inc. All rights reserved.
Selection of alkaline phosphatase-positive induced pluripotent stem cells from human amniotic fluid-derived cells by feeder-free system
(2011-08-01)
Lu, Huai-En
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Tsai, Ming-Song
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Yang, Yao-Chen
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Yuan, Chen-Ching
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Wang, Tzu-Hao
Generation of induced pluripotent stem (iPS) cells from somatic cells has been successfully achieved by ectopic expression of four transcription factors, Oct4, Sox2, Klf4 and c-Myc, also known as the Yamanaka factors. In practice, initial iPS colonies are picked based on their embryonic stem (ES) cell-like morphology, but often may go on to fail subsequent assays, such as the alkaline phosphate (AP) assay. In this study, we co-expressed through lenti-viral delivery the Yamanaka factors in amniotic fluid-derived (AF) cells. ES-like colonies were picked onto a traditional feeder layer and a high percentage AF-iPS with partial to no AP activity was found. Interestingly, we obtained an overwhelming majority of fully stained AP positive (AP+) AF-iPS colonies when colonies were first seeded on a feeder-free culture system, and then transferred to a feeder layer for expansion. Furthermore, colonies with no AP activity were not detected. This screening step decreased the variation seen between morphology and AP assay. We observed the AF-iPS colonies grown on the feeder layer with 28%AP+ colonies, 45% AP partially positive (AP+/-) colonies and 27% AP negative (AP-) colonies, while colonies screened by the feeder-free system were 84%AP+ colonies, 16% AP+/- colonies and no AP- colonies. The feeder-free screened AP+ AF-iPS colonies were also positive for pluripotent markers, OCT4, SOX2, NANOG,TRA-1-60,TRA-1-81,SSEA-3 and SSEA-4 as well as having differentiation abilities into three germ layers in vitro and in vivo. In this study, we report a simplistic, one-step method for selection of AP+ AF-iPS cells via feeder-free screening. (C) 2011 Elsevier Inc. All rights reserved.
Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis
(2012-04-01)
Huang, Mei-Ling
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Hung, Yung-Hsiang
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Lee, Wen-Ming
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Li, R. K.
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Wang, Tzu-Hao
Breast cancer is a common to females world-wide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.