Correct disulfide bond formation is essential for the activity and stability of numerous proteins of essential biological functions such as secreted signaling proteins and cell surface *** protein disulfide bonds typi...
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Correct disulfide bond formation is essential for the activity and stability of numerous proteins of essential biological functions such as secreted signaling proteins and cell surface *** protein disulfide bonds typically requires a large amount of highly purified proteins,and much relies on arduous sample preparation and manual data *** we report a robust and sensitive method for identifying disulfide bonds in *** the new method,we successfully mapped all 74 disulfide bonds from a mixture often purified proteins and hundreds of disulfide bonds from an *** periplasmic fraction,a *** mitochondrial fraction,and human whole-cell *** biological experiments verified nine out of nine randomly selected,newly identified disulfide bonds in *** proteins.
Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic *** this paper,a novel support vector machine with mixture of kernels(SVM-MK)for autom...
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Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic *** this paper,a novel support vector machine with mixture of kernels(SVM-MK)for automatic image annotation is *** one hand,the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as *** the other hand,SVM-MK is constructed to shoot for better annotating *** results on Corel dataset show that the proposed image feature representation method as well as automatic image annotation classifier,SVM-MK,can achieve higher annotating accuracy than SVM with any single kernel and mi-SVM for semantic image annotation.
Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use th...
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Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request.
Cloud computing has brought new opportunities for the development of traditional industries, but also appeared some new security risks. This paper analyzes the main data security problems in cloud computing field, and...
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Cloud computing has brought new opportunities for the development of traditional industries, but also appeared some new security risks. This paper analyzes the main data security problems in cloud computing field, and on this basis, to solve these security problems, puts forward the corresponding control strategy of data security.
In recent years, Wireless sensor network (WSNs) has become the main technology to construct underground network for intelligent mining system. Due to the complex roadway topology, there needs an optimized energy conse...
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In recent years, Wireless sensor network (WSNs) has become the main technology to construct underground network for intelligent mining system. Due to the complex roadway topology, there needs an optimized energy conservation routing protocol. We proposed EERP, an energy-efficient routing protocol for for WSN-based intelligent mining system, which can prolonged lifetime of a network and reduce energy consumption through construct a dominator chain by region partition. Simulation results show that EERP has 8.2 times stable working time than LEACH. When region length is 100m, EERP still can maintain 99% energy after 3000rd transmission.
Workflow is commonly used in e-commerce system as modeling method. In this paper, we proposed W2ME, a process-oriented meta model for OTO-oriented e-commerce. W2ME bases on the traditional activity network model and e...
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ISBN:
(纸本)9781479929528
Workflow is commonly used in e-commerce system as modeling method. In this paper, we proposed W2ME, a process-oriented meta model for OTO-oriented e-commerce. W2ME bases on the traditional activity network model and extends it. Based on the principle of minimum privileges in the authorization problem, W2ME propose the W2MEA algorithm which effectively reduces the complexity for authority management. Simulation results show that W2ME can save 20% CPU occupancy time and has 95th percentile average success rate.
The application of extended Kalman filter algorithm to ultrasonic positioning systems has difficulty in meeting the requirements of precision positioning because the algorithm produces a new calculation error when the...
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The application of extended Kalman filter algorithm to ultrasonic positioning systems has difficulty in meeting the requirements of precision positioning because the algorithm produces a new calculation error when the system is linearized. Modal optimization of the extended Kalman filter algorithm is thus investigated. The received ultrasonic signal is first decomposed by empirical mode decomposition, the intrinsic mode functions that best represent the original signal are then selected to restructure the waveform, and the transition time is finally corrected. Meanwhile, the ultrasonic wave velocity can be corrected. Traditional ultrasonic positioning can also be improved by combining with a radio-frequency module. It is experimentally shown that the proposed method limits positioning error to within ±5 cm and within ±1 cm after multiple recursions.
The training algorithm of classical twin support vector regression (TSVR) can be attributed to the solution of a pair of quadratic programming problems (QPPs) with inequality constraints in the dual ***,this solution ...
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The training algorithm of classical twin support vector regression (TSVR) can be attributed to the solution of a pair of quadratic programming problems (QPPs) with inequality constraints in the dual ***,this solution is affected by time and memory constraints when dealing with large *** this paper,we present a least squares version for TSVR in the primal space,termed primal least squares TSVR (PLSTSVR).By introducing the least squares method,the inequality constraints of TSVR are transformed into equality ***,we attempt to directly solve the two QPPs with equality constraints in the primal space instead of the dual space;thus,we need only to solve two systems of linear equations instead of two *** results on artificial and benchmark datasets show that PLSTSVR has comparable accuracy to TSVR but with considerably less computational *** further investigate its validity in predicting the opening price of stock.
Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theor...
Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. Some algorithms for MFA of unweighted complex networks have been proposed in the past a few years, including the sandbox (SB) algorithm recently employed by our group. In this paper, a modified SB algorithm (we call it SBw algorithm) is proposed for MFA of weighted networks. First, we use the SBw algorithm to study the multifractal property of two families of weighted fractal networks (WFNs): "Sierpinski" WFNs and "Cantor dust" WFNs. We also discuss how the fractal dimension and generalized fractal dimensions change with the edge-weights of the WFN. From the comparison between the theoretical and numerical fractal dimensions of these networks, we can find that the proposed SBw algorithm is efficient and feasible for MFA of weighted networks. Then, we apply the SBw algorithm to study multifractal properties of some real weighted networks - collaboration networks. It is found that the multifractality exists in these weighted networks, and is affected by their edge-weights.
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