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Copper oxide was used to modify zeolite NaY, NaZSM-5, NaA and mesoporous siliceous SBA-15, in order to strengthen their ability to remove volatile nitrosamines. Selective adsorption, temperature programmed surface reaction (TPSR) and FTIR along with TG-MS methods were employed to study the impact of copper modification on the ability of zeolite to adsorb and catalytically degrade nitrosamines. Due to the special interaction between the -N-N=O group of nitrosamines and the copper in the zeolite, nitrosamines can be very easily trapped by the copper modified zeolite, and this precedes or is the first step that leads to the catalytic reaction. Moreover, modification of copper oxide enabled the zeolite to degrade nitrosamines such as N-nitrosopyrrolidine (NPYR) at lower temperatures. It is practical to use the copper modified zeolites to treat mainstream smoke or add them to tobacco, reducing further the levels of nitrosamines in cigarette smoke compared with using the zeolite alone. y Preliminary communication: see ref. 42.NJC

DNA-binding proteins play pivotal roles in alternative splicing, RNA editing, methylating and many other biological functions for both eukaryotic and prokaryotic proteomes. Predicting the functions of these proteins from primary amino acids sequences is becoming one of the major challenges in functional annotations of genomes. Traditional prediction methods often devote themselves to extracting physiochemical features from sequences but ignoring motif information and location information between motifs. Meanwhile, the small scale of data volumes and large noises in training data result in lower accuracy and reliability of predictions. In this paper, we propose a deep learning based method to identify DNA-binding proteins from primary sequences alone. It utilizes two stages of convolutional neutral network to detect the function domains of protein sequences, and the long short-term memory neural network to identify their long term dependencies, an binary cross entropy to evaluate the quality of the neural networks. When the proposed method is tested with a realistic DNA binding protein dataset, it achieves a prediction accuracy of 94.2% at the Matthew’s correlation coefficient of 0.961. Compared with the LibSVM on the arabidopsis and yeast datasets via independent tests, the accuracy raises by 9% and 4% respectively. Comparative experiments using different feature extraction methods show that our model performs similar accuracy with the best of others, but its values of sensitivity, specificity and AUC increase by 27.83%, 1.31% and 16.21% respectively. Those results suggest that our method is a promising tool for identifying DNA-binding proteins.