Sesquiterpenoids from the Resina Commiphora Promoting the Apoptotic Activity of PC-3 Prostate Cancer Cells

Four new germacrane-type sesquiterpenes commiphoranes M1-M4 ( 1 - 4 ) together with eighteen sesquiterpenes were isolated from the Resina Commiphora. The structures and relative configurations of new substances were determined by using spectroscopic methods. Biological activity investigation revealed that nine compounds including 7 , 9 , 14 , 16 , (+)- 17 , (−)- 17 , 18 , 19 , and 20 could induce the apoptosis of prostate cancer originated PC-3 cells, through classic apoptosis signaling pathway, even using flow cytometry showed that the compound (+)- 17 caused apoptosis of PC-3 cells more than 40 %, suggesting their potential therapeutic application in the development of novel drugs against prostate cancer.     

鼎湖山南亚热带常绿阔叶林植物功能性状变异与不同垂直层次个体生长的关联

建立植物功能性状与群落动态之间的关联是功能生态学的核心问题之一。本文基于鼎湖山1.44 ha塔吊样地的两次调查数据, 通过采集样地内所有4,142株个体的6种植物功能性状, 对比分析了个体水平植物功能性状和物种水平功能性状均值对不同垂直层次(灌木层、亚冠层和林冠层)个体生长的影响差异。首先, 分析了不同垂直层次下各植物功能性状的变化趋势; 其次, 计算了不同垂直层次下各植物功能性状的种内和种间变异水平; 最后, 运用结构方程模型探讨了植物功能性状、光竞争以及地下竞争对不同垂直层次树木生长的影响。结果表明: (1)不同垂直层次下的植物功能性状表现出明显的分异, 由灌木层至林冠层, 叶面积、比叶面积和能量供求关系指数显著降低, 而叶片厚度和叶片干物质含量显著升高; (2)不同垂直层次下植物功能性状的种间变异均大于种内变异, 且林冠层的种内功能性状变异均大于灌木层和亚冠层; (3)基于个体水平植物功能性状的结构方程模型较物种水平功能性状均值对生长具有更高的解释程度, 且个体水平植物功能性状的引入更有利于提高对灌木层个体生长的预测能力; (4)光竞争和地下竞争主要通过影响功能性状间接影响植物生长。由灌木层至林冠层, 同种间的相互作用逐渐减弱, 异种间的相互作用逐渐增强。综上, 将个体水平植物功能性状纳入分析有助于更好地理解群落的结构和动态。     

Extracting Drug-Drug Interaction from the Biomedical Literature Using a Stacked Generalization-Based Approach

Drug-drug interaction (DDI) detection is particularly important for patient safety. However, the amount of biomedical literature regarding drug interactions is increasing rapidly. Therefore, there is a need to develop an effective approach for the automatic extraction of DDI information from the biomedical literature. In this paper, we present a Stacked Generalization-based approach for automatic DDI extraction. The approach combines the feature-based, graph and tree kernels and, therefore, reduces the risk of missing important features. In addition, it introduces some domain knowledge based features (the keyword, semantic type, and DrugBank features) into the feature-based kernel, which contribute to the performance improvement. More specifically, the approach applies Stacked generalization to automatically learn the weights from the training data and assign them to three individual kernels to achieve a much better performance than each individual kernel. The experimental results show that our approach can achieve a better performance of 69.24% in F-score compared with other systems in the DDI Extraction 2011 challenge task.