Seven natural and two “semi-natural” reserves have so far been established. Since 1996, a small group of finless porpoises has been successfully reared in a facility at the Institute of Hydrobiology of the Chinese Academy of Sciences; three babies were born in captivity on July 5, 2005, June 2, 2007 and July 5, 2008. These are the first freshwater cetaceans ever born in captivity in the world. Several groups of these porpoises caught in the main stream of the Yangtze River,
or rescued, have been introduced into the Tian’e-Zhou Semi-natural Reserve since 1990. These efforts have proven that, Panobinostat inhibitor not only can these animals survive in the area, they are also to reproduce naturally and successfully. More than 30 calves had been born in the reserve since then, with one to three born each year. Taking deaths and transfers into account, there were approximately 30 individuals living in the reserve as of the end of 2007. Among eight mature females captured in April 2008, five were confirmed pregnant. This effort represents the first successful attempt at off-site protection of a cetacean species AZD9291 datasheet in the world, and establishes a solid base for conservation of the
Yangtze finless porpoise. A lesson must be drawn from the tragedy of Chinese River Dolphin (Lipotes vexillifer), which has already been declared likely extinct. Strong, effective and appropriate protective measures must be carried out quickly to prevent the Yangtze finless porpoise from becoming a second Chinese River Dolphin, and save the biodiversity of the Yangtze River as a whole.”
“This paper presents the new automated detection method for electrocardiogram (ECG) arrhythmias. The detection system is implemented with integration of complex valued feature extraction and classification parts. In feature extraction phase of selleck compound proposed method, the feature values for each arrhythmia are extracted using complex discrete wavelet transform (CWT).
The aim of using CWT is to compress data and to reduce training time of network without decreasing accuracy rate. Obtained complex valued features are used as input to the complex valued artificial neural network (CVANN) for classification of ECG arrhythmias. Ten types of the ECG arrhythmias used in this study were selected from MIT-BIH ECG Arrhythmias Database. Two different classification tasks were performed by the proposed method. In first classification task (CT-1), whether CWT-CVANN can distinguish ECG arrhythmia from normal sinus rhythm was examined one by one. For this purpose, nine classifiers were improved and executed in CT-1. Second classification task (CT-2) was to recognize ten different ECG arrhythmias by one complex valued classifier with ten outputs. Training and test sets were formed by mixing the arrhythmias in a certain order. Accuracy rates were obtained as 99.8% (averaged) and 99.