[Objective] The aim was to isolate the triazophos-degrading strain and study its degradation characteristics. [Method] A triazophos-degrading bacterium strain C-Y106 was isolated from sludge in an aeration tank of tri...[Objective] The aim was to isolate the triazophos-degrading strain and study its degradation characteristics. [Method] A triazophos-degrading bacterium strain C-Y106 was isolated from sludge in an aeration tank of triazophos manufacture. Then the strain C-Y106 was identified according to the morphology,physiological and biochemical characteristics,and 16S rRNA sequence analysis. The effect of medium with different nutrients on triazophos-degrading rate by C-Y106 was studied. [Result] The strain C-Y106 was identified as Bacillus subtilis. The strain C-Y106 could grow in the mineral salt medium with 40 mg/L of triazophos as the sole sources of carbon,Nitrogen and Phosphorus. The triazophos-degrading rate was the highest as 76.8% in the mineral salt medium with 40 mg/L of triazophos as the sole source of Phosphorus,after being incubated at 31 ℃,pH 8.0 and 150 r/min for 60 h. [Conclusion] The research had provided theoretical basis for the identification and purification of enzymes for triazophos degradation.展开更多
Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the ...Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.展开更多
文摘[Objective] The aim was to isolate the triazophos-degrading strain and study its degradation characteristics. [Method] A triazophos-degrading bacterium strain C-Y106 was isolated from sludge in an aeration tank of triazophos manufacture. Then the strain C-Y106 was identified according to the morphology,physiological and biochemical characteristics,and 16S rRNA sequence analysis. The effect of medium with different nutrients on triazophos-degrading rate by C-Y106 was studied. [Result] The strain C-Y106 was identified as Bacillus subtilis. The strain C-Y106 could grow in the mineral salt medium with 40 mg/L of triazophos as the sole sources of carbon,Nitrogen and Phosphorus. The triazophos-degrading rate was the highest as 76.8% in the mineral salt medium with 40 mg/L of triazophos as the sole source of Phosphorus,after being incubated at 31 ℃,pH 8.0 and 150 r/min for 60 h. [Conclusion] The research had provided theoretical basis for the identification and purification of enzymes for triazophos degradation.
基金Supported by the National Natural Science Foundation of China(61139002)~~
文摘Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.
