The synthesis and antitumor activities of the novel water soluble camptothecin derivatives 7-[(4-methylpiperazino)methyl]-10,11-(methylenedioxy)-(20S)-campto thecin trifluoroacetate (6) and 7-[(4-methylpiperazino)methyl]-10,11-(ethylenedioxy)-(20S)-camptot hecin trifluoroacetate (7) are described. The solubilities of compounds 6 and 7 were measured to be 4.5 and 5.8 mg/mL, respectively, in pH 5 acetate buffer in contrast to < 0.003 mg/mL for camptothecin in the same buffer. In the purified topoisomerase I cleavable complex enzyme assay, compounds 6 and 7 demonstrated potent inhibition of topoisomerase I with IC50's of 300 and 416 nM, respectively, in comparison to 679 nM for camptothecin and 1028 nM for topotecan. In human tumor cell cytotoxicity assays, compounds 6 and 7 demonstrated potent antitumor activity against ovarian (SKOV3), ovarian with upregulated MDRp-glycoprotein (SKVLB), melanoma (LOX), breast (T47D), and colon (HT29) with IC50's ranging from 0.5 to 102 nM. Compounds 6 and 7 induced tumor regressions in the HT29 human colon tumor xenograft model and demonstrated similar rank order of potency compared to in vitro assay results.

Test cases are one of the most important assets in the testing process. This paper presents the testing ontology based SWEBOK and software quality model. The management and retrieval of test cases will play a vital role in test cases reuse. The keyword-based, as well as facet-based retrieval cannot meet user's flexible query requirement because of lack of semantic information. SWEBOK provides a broad agreement on the content of the software engineering discipline. At last this paper discusses the management and retrieval of test cases based on the semantic similarity of two test concepts in two ontologies according to difference sets of super concept, sub concept, extension, and intension.

We proposed a novel generative adversarial net called similarity constraint generative adversarial network (SCGAN), which is capable of learning the disentangled representation in a completely unsupervised manner. Inspired by the smoothness assumption and our assumption on the content and the representation of images, we design an effective similarity constraint. SCGAN can disentangle interpretable representations by adding this similarity constraint between conditions and synthetic images. In fact, similarity constraint works as a tutor to instruct generator network to comprehend the difference of representations based on conditions. SCGAN successfully distinguishes different representations on a number of datasets. Specifically, SCGAN captures digit type on MNIST, clothing type on Fashion-MNIST, lighting on SVHN, and object size on CIFAR10. On the CelebA dataset, SCGAN captures more semantic representations, e.g., poses, emotions, and hair styles. Experiments show that SCGAN is comparable with InfoGAN (another generative adversarial net disentangles interpretable representations on these datasets unsupervisedly) on disentangled representation learning. Code is available at https://github.com/gauss-clb/SCGAN. INDEX TERMS Generative adversarial nets, representation learning, unsupervised learning.

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