Electrochemically time-dependent oxidative coupling/coupling-cyclization reaction between heterocycles: tunable synthesis of polycyclic indole derivatives with fluorescence properties

Science China Chemistry - A mild and practical protocol for selectively time-dependent dehydrogenative C-C coupling, as well as tandem coupling-cyclization reaction between indoles or/and other...     

范德瓦尔斯分子p—C6H4FCH3...Ar外部振动的共振双光子电离光谱

利用单色共振双光子电离光谱技术研究了ｐ－Ｃ６Ｈ４ＦＣＨ３与Ａｒ形成的范德瓦尔斯分子ｐ－Ｃ６Ｈ４ＦＣｈ３．．．Ａｒ电子太跃迁Ｏ带附近的光谱，观察到了许多谱带。分析表明，这些谱带，除来自于甲基ＣＨ３内转动跃迁外，都可以归属为Ａｒ相对于ｐ－Ｃ６Ｈ４ＦＣＨ３的振动跃迁。在用三维谐湃郛波函数一组合作为基和内德－琼斯作用的基础上，借助量子力学方法计算了ｐ－Ｃ６Ｈ４ＦＣＨ３．．．Ａｒ分子中范德瓦斯振动的能级，计     

A hybrid Bayesian network learning method for constructing gene networks

A Bayesian network (BN) is a knowledge representation formalism that has proven to be a promising tool for analyzing gene expression data. Several problems still restrict its successful applications. Typical gene expression databases contain measurements for thousands of genes and no more than several hundred samples, but most existing BNs learning algorithms do not scale more than a few hundred variables. Current methods result in poor quality BNs when applied in such high-dimensional datasets. We propose a hybrid constraint-based scored-searching method that is effective for learning gene networks from DNA microarray data. In the first phase of this method, a novel algorithm is used to generate a skeleton BN based on dependency analysis. Then the resulting BN structure is searched by a scoring metric combined with the knowledge learned from the first phase. Computational tests have shown that the proposed method achieves more accurate results than state-of-the-art methods. This method can also be scaled beyond datasets with several hundreds of variables.