铝厂污水零排放研究

本文从水质方面分析了西南某铝厂污水零排放的可行性。为了实现污水零排放,对该铝厂东片区、氧化铝厂、热电厂三大污水处理系统进行改进。从现场试验监测及统计分析结果表明,改进后的处理系统处理的水质满足再生水水质指标要求,该铝厂污水零排放的实现创造了极大的经济和社会效益。     

Die Wirkung des Toluidinblaus und der Thrombokinase auf den Vorgang der Thrombininaktivierung

Summary The disappearance of thrombin—formed in the blood, or added to serum-follows a manomolecular reaction-type. Heparin increases the reaction-velocity of this thrombin-inactivating process.Our investigation established that toluidine blue or kinase, which, according to the literature, bind heparin, strongly reduce the speed of thrombin-inactivation too. Therefore the heparin-binding capacity of these substances is also manifested in the decrease of thrombin-inactivation.     

文学的存在方式现象学

文学的存在方式是指文学赖以存在的整体结构。通过对文学创作活动(事件)、文学接受活动(事件)的现象学描述,建构了文学的存在方式———文学存在的四维整体结构和文学存在之主体间的交流结构,后两者共同组建着文学的存在整体。     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.