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The anticancer traditional Chinese medicine (TCM), plumbagin (PLN), was isolated from Plumbago Zeylanica. Reaction of plumbagin with Cu(II) salt, afforded [Cu(PLN)(2)].2H(2)O (1). With 2,2'-bipyridine (bipy) as a co-ligand, PLN reacts with Cu(II) to give [Cu(PLN)(bipy)(H(2)O)](2)(NO(3))(2).4H(2)O (2). 1 and 2 were characterized by elemental analysis, IR, ESI-MS spectra. Their crystal structures were determined by single crystal X-ray diffraction methods. The in vitro cytotoxicity of PLN, 1 and 2 against seven human tumour cell lines was assayed. The metal-based compounds exhibit enhanced cytotoxicity vs. that of free PLN, suggesting that these compounds display synergy in the combination of metal ions with PLN. The binding properties of PLN, 1 and 2 to DNA were investigated through UV-vis, fluorescence, CD spectra, and gel mobility shift assay, which indicated that 1 and 2 were non-covalent binding and mainly intercalated the neighboring base pairs of DNA. PLN, 1 and 2 exhibit inhibition activity to topoisomerase I (TOPO I), but 1 and 2 were more effective than PLN.

Three new matrine-type alkaloids, (+)-5α-hydroxyoxysophocarpine (1), (-)-12β-hydroxyoxysophocarpine (2), and (+)-5α-hydroxylemannine (3), along with 14 known analogues, (-)-sophocarpine (4), (-)-5α-hydroxysophocarpine (5), (-)-9α-hydroxysophocarpine (6), (+)-12α-hydroxysophocarpine (7), (-)-12β-hydroxysophocarpine (8), (+)-oxysophocarpine (9), (+)-matrine (10), (+)-sophoranol (11), (+)-9α-hydroxymatrine (12), (-)-14β-hydroxymatrine (13), (+)-oxymatrine (14), (+)-5α-hydroxyoxymatrine (15), (-)-14β-hydroxyoxymatrine (16), and (+)-sophoramine (17), were isolated from the rhizomes of Sophora tonkinensis. Their structures were elucidated via spectrometric data analyses, and the absolute configurations were established by single-crystal X-ray diffraction and ECD data. Alkaloids 2, 6, 11, and 13 exhibited antiviral activity against the Coxsackie virus B3 (CVB3), with IC50 values of 26.62-252.18 μM, and alkaloids 7, 8, and 17 inhibited influenza virus A/Hanfang/359/95 (H3N2) replication with IC50 values of 63.07-242.46 μM.

Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, in stead of a single method, traders need to use various forecasting techniques to gain multiple signals and more information about the future of the markets. In this paper, ten different techniques of data mining are discussed and applied to predict price movement of Hang Seng index of Hong Kong stock market. The approaches include Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), K-nearest neighbor classification, Naïve Bayes based on kernel estimation, Logit model, Tree based classification, neural network, Bayesian classification with Gaussian process, Support vector machine (SVM) and Least squares support vector machine (LS-SVM). Experimental results show that the SVM and LS-SVM generate superior predictive performances among the other models. Specifically, SVM is better than LS-SVM for in-sample prediction but LS-SVM is, in turn, better than the SVM for the out-of-sample forecasts in term of hit rate and error rate criteria.