All hyper: Recent advances in the area of chiral hypervalent iodine reagents, notably by the groups of Kita, Ishihara, and Fujita, enable some oxidative transformations to be conducted in an enantioselective manner and with asymmetric inductions in the range of 86–95 % ee (see scheme, Mes=mesityl). This contribution highlights representative reactions, reagent/catalyst design, and mechanistic aspects of the reported transformations.

No abstract

The sordarin family of compounds, characterized by a unique tetracyclic diterpene core including a norbornene system, inhibits protein synthesis in fungi by stabilizing the ribosome/EF2 complex. This mode of action is in contrast to typical antifungals, which target the cell membrane. This unusual bioactivity makes sordarin a promising candidate for the development of new fungicidal agents, and provided the motivation for extensive research. Three total syntheses (by the Kato, Mander and Narasaka groups), modifications of the glycosyl unit, and changes to the diterpene core (Cuevas and Ciufolini models) will also be discussed in this review.

Electrocardiogram (ECG) signal feature extraction is important in diagnosing cardiovascular diseases. This paper presents a new method for nonlinear feature extraction of ECG signals by combining principal component analysis (PCA) and kernel independent component analysis (KICA). The proposed method first uses PCA to decrease the dimensions of the ECG signal training set and then employs KICA to cal-B Hongqiang Li Circuits Syst Signal Process culate the feature space for extracting the nonlinear features. Support vector machine (SVM) is utilized to determine the nonlinear features of the ECG signal classification. Genetic algorithm is also used to optimize the SVM parameters. The proposed method is advantageous because it does not require a huge amount of sampling data, and this technique is better than traditional strategies to select optimal features in the multi-domain feature space. Computer simulations reveal that the proposed method yields more satisfactory classification results on the MIT-BIH arrhythmia database, reaching an overall accuracy of 97.78 %.