Poly(2vinylpyridine) films were deposited on Zn substrate by electropolymerization using a galvanostatic technique at 30 C in pH 5 aqueous solution containing methanol. Films were also formed by employing cyclic voltammetry and potentiostatic techniques; these were compared with those formed using galvanostatic electrolysis. The thickness of films formed by galvanostatic electrolysis increased in proportion to the amount of charge passed during electropolymerization but decreased with increasing current density because of increased hydrogen evolution. The FT-IR spectra and the color of the films suggested that the structure of poly(2-vinylpyridine) films changed from the nonbranched to the branched chain type at higher current densities. The anodic current density for Zn dissolution in 3% NaCl solution was significantly decreased by coating with poly(2-vinylpyridine) films. After electropolymerization at 50 A m À2 , the anodic reaction was most inhibited, showing excellent corrosion resistance. Although the electrochemical techniques employed had no influence on the thickness or the structure of films, the films formed by galvanostatic electrolysis contained the fewest cracks and gave the best corrosion resistance.
Poly(2 vinylpyridine) films were deposited on Zn substrate by electropolymerization using galvanostatic technique at 30°C in pH 5 aqueous solution containing methanol. Films were also formed by employing cyclic voltammetry and potentiostatic techniques; these were compared with those formed using galvanostatic electrolysis. The thickness of films formed by galvanostatic electrolysis increased in proportion to the amount of charge passed during electropolymerization but decreased with increasing current density because of increased hydrogen evolution. The FT IR spectra and the color of the films suggested that the structure of poly(2 vinylpyridine) films changed from the non branched to the branched chain type with the higher current densities. The anodic current density for Zn dissolution in 3NaCl solution was significantly decreased by coating with poly(2 vinylpyridine) films. After electropolymerization at 50 A・m -2 , the anodic reaction was most inhibited, showing excellent corrosion resistance. Although the electrochemical techniques employed for the film preparation had no influence on the thickness or the structure of films, the films formed by galvanostatic electrolysis contained the fewest cracks and gave the best corrosion resistance.
BackgroundIn recent years, protein-protein interaction (PPI) networks have been well recognized as important resources to elucidate various biological processes and cellular mechanisms. In this paper, we address the problem of predicting protein complexes from a PPI network. This problem has two difficulties. One is related to small complexes, which contains two or three components. It is relatively difficult to identify them due to their simpler internal structure, but unfortunately complexes of such sizes are dominant in major protein complex databases, such as CYC2008. Another difficulty is how to model overlaps between predicted complexes, that is, how to evaluate different predicted complexes sharing common proteins because CYC2008 and other databases include such protein complexes. Thus, it is critical how to model overlaps between predicted complexes to identify them simultaneously.ResultsIn this paper, we propose a sampling-based protein complex prediction method, RocSampler (Regularizing Overlapping Complexes), which exploits, as part of the whole scoring function, a regularization term for the overlaps of predicted complexes and that for the distribution of sizes of predicted complexes. We have implemented RocSampler in MATLAB and its executable file for Windows is available at the site, http://imi.kyushu-u.ac.jp/~om/software/RocSampler/.ConclusionsWe have applied RocSampler to five yeast PPI networks and shown that it is superior to other existing methods. This implies that the design of scoring functions including regularization terms is an effective approach for protein complex prediction.
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