One bioleaching bacterium, named as strain DXS, was isolated from acid mine drainages （AMDs） of Dongxiangshan Mine of Hami, Xinjiang Province, China. The strain DXS is gram-negative and rod-shaped with a size of （0...One bioleaching bacterium, named as strain DXS, was isolated from acid mine drainages （AMDs） of Dongxiangshan Mine of Hami, Xinjiang Province, China. The strain DXS is gram-negative and rod-shaped with a size of （0.40±0.05） μm x （1.3±0.5） μm. The optimal temperature and pH for growth are 30 ℃ and pH 2.0, respectively. It can grow autotrophically by using ferrous iron, elemental sulfur and NaS203 as sole energy sources. In the phylogenetic tree, strain DXS has similarity with Acidithiobacillus ferrooxidans type strain ATCC 23270 with 99.57% sequence similarity. The cloning and sequencing of Iro protein gene （iro） and tetrathionate hydrolase gene （tth） reveal that strain DXS is completely identical in iro gene sequence to A. ferrooxidans LY （DQ166841）, and almost identical in tth gene sequene to .4. ferrooxidans （AB259312） （only two nucleotides change）. The bioleaching experiments of marmatite and pyrite reveal that the leached zinc and iron concentrations reach 3.01 g/L and 2.75 g/L, respectively. The strain has a well potential application in industry bioleaching.展开更多
Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may ...Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may be more relevant to the class （defective or non-defective）, but others may be redundant or irrelevant. To fully measure the correlation between different features and the class, we present a feature selection approach based on a similarity measure （SM） for software defect prediction. First, the feature weights are updated according to the similarity of samples in different classes. Second, a feature ranking list is generated by sorting the feature weights in descending order, and all feature subsets are selected from the feature ranking list in sequence. Finally, all feature subsets are evaluated on a k-nearest neighbor （KNN） model and measured by an area under curve （AUC） metric for classification performance. The experiments are conducted on 11 National Aeronautics and Space Administration （NASA） datasets, and the results show that our approach performs better than or is comparable to the compared feature selection approaches in terms of classification performance.展开更多
基金Projects(50974140, 50674101) supported by the National Natural Science Foundation of ChinaProject(2010CB630902) supported by the National Basic Research Program of China
文摘One bioleaching bacterium, named as strain DXS, was isolated from acid mine drainages （AMDs） of Dongxiangshan Mine of Hami, Xinjiang Province, China. The strain DXS is gram-negative and rod-shaped with a size of （0.40±0.05） μm x （1.3±0.5） μm. The optimal temperature and pH for growth are 30 ℃ and pH 2.0, respectively. It can grow autotrophically by using ferrous iron, elemental sulfur and NaS203 as sole energy sources. In the phylogenetic tree, strain DXS has similarity with Acidithiobacillus ferrooxidans type strain ATCC 23270 with 99.57% sequence similarity. The cloning and sequencing of Iro protein gene （iro） and tetrathionate hydrolase gene （tth） reveal that strain DXS is completely identical in iro gene sequence to A. ferrooxidans LY （DQ166841）, and almost identical in tth gene sequene to .4. ferrooxidans （AB259312） （only two nucleotides change）. The bioleaching experiments of marmatite and pyrite reveal that the leached zinc and iron concentrations reach 3.01 g/L and 2.75 g/L, respectively. The strain has a well potential application in industry bioleaching.
基金Project supported by the National Natural Science Foundation of China （Nos. 61673384 and 61502497）, the Guangxi Key Laboratory of Trusted Software （No. kx201530）, the China Postdoctoral Science Foundation （No. 2015M581887）, and the Scientific Research Innovation Project for Graduate Students of Jiangsu Province, China （No. KYLX15 1443）
文摘Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may be more relevant to the class （defective or non-defective）, but others may be redundant or irrelevant. To fully measure the correlation between different features and the class, we present a feature selection approach based on a similarity measure （SM） for software defect prediction. First, the feature weights are updated according to the similarity of samples in different classes. Second, a feature ranking list is generated by sorting the feature weights in descending order, and all feature subsets are selected from the feature ranking list in sequence. Finally, all feature subsets are evaluated on a k-nearest neighbor （KNN） model and measured by an area under curve （AUC） metric for classification performance. The experiments are conducted on 11 National Aeronautics and Space Administration （NASA） datasets, and the results show that our approach performs better than or is comparable to the compared feature selection approaches in terms of classification performance.
