Browsing by Author "Yang, TS"

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Expression, characterization, and purification of recombinant porcine lactoferrin in Pichia pastoris
(2002-06-01)
Wang, SH
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Yang, TS
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Lin, SM
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Tsai, MS
;
Wu, SC
Recombinant porcine lactoferrin (rPLF) was synthesized in Pichia pastoris using a constitutive promoter from the glyceraldehyde-3-phosphate dehydrogenase gene. Strains expressing rPLF with its own signal sequence or with that from the yeast alpha-mating factor (alpha-MF) were able to produce and secrete rPLF, but levels were consistently higher using alpha-MF constructs. In contrast, P. pastoris strains that expressed rPLF without a signal sequence produced the protein in an insoluble intracellular form. Increasing the initial pH of shake-flask culture medium from 6.0 to 7.0 or adding ferric ions to the medium (to 100 muM) resulted irk significant improvements in expression of rPLF from P. pastoris. Expression levels (approximately 12 mg/L) were much higher than those observed from Saccharomyces cerevisiae strains (1-2 mg/L). P pastoris-secreted rPLF was isolated and purified via a one-step simple procedure using a heparin column. The molecular size (78 kDa), isoelectric point (8.8-9.0), N-terminal amino acid sequence, and iron-binding capability of rPLF were each similar to that of native milk PLF. (C) 2002 Elsevier Science (USA).
On the spatial entropy and patterns of two-dimensional cellular neural networks
(2002-01-01)
Lin, SS
;
Yang, TS
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應用數學系
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Department of Applied Mathematics
This work investigates binary pattern formations of two-dimensional standard cellular neural networks (CNN) as well as the complexity of the binary patterns. The complexity is measured by the exponential growth rate in which the patterns grow as the size of the lattice increases, i.e. spatial entropy. We propose an algorithm to generate the patterns in the finite lattice for general two-dimensional CNN. For the simplest two-dimensional template, the parameter space is split up into finitely many regions which give rise to different binary patterns. Qualitatively, the global patterns are classified for each region. Quantitatively, the upper bound of the spatial entropy is estimated by computing the number of patterns in the finite lattice, and the lower bound is given by observing a maximal set of patterns of a suitable size which can be adjacent to each other.
Spatial entropy of one-dimensional cellular neural network
(2000-09-01)
Lin, SS
;
Yang, TS
;
應用數學系
;
Department of Applied Mathematics
This work investigates the global mosaic pattern and spatial entropy for one-dimensional cellular neural network (CNN). A novel method is developed to partition the parameter space into finitely many regions. The CNNs, with parameters in each region, have the same global pattern. An algorithm is also presented to evaluate the spatial entropy.