Hepatitis C virus (HCV) RNA replication occurs in the cytosol of infected cells within a specialised membranous compartment. How the viral non-structural (NS) proteins are associated and organised within these structu...
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Hepatitis C virus (HCV) RNA replication occurs in the cytosol of infected cells within a specialised membranous compartment. How the viral non-structural (NS) proteins are associated and organised within these structures remains poorly defined. We employed a super-resolution microscopy approach to visualise NS3 and NS5A in HCV infected cells. Using single molecule localisation microscopy, both NS proteins were resolved as clusters of localisations smaller than the diffraction-limited volume observed by wide-field. Analysis of the protein clusters identified a significant difference in size between the NS proteins. We also observed a reduction in NS5A cluster size following inhibition of RNA replication using daclatasvir, a phenotype which was maintained in the presence of the Y93H resistance associated substitution and not observed for NS3 clusters. These results provide insight into the NS protein organisation within hepatitis C virus RNA replication complexes and the mode of action of NS5A inhibitors.
In order to avoid increasing the workload of correlation function calculation for ambient noise tomography from intensive observation stations, a clustering method based on improved dbscan for ambient noise observatio...
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In order to avoid increasing the workload of correlation function calculation for ambient noise tomography from intensive observation stations, a clustering method based on improved dbscan for ambient noise observation stations algorithm is proposed to improve data processing efficiency. According to the ambient noise tomography principle, the main influencing factors of Green's function retrieving are analyzed. Combined with the actual situation of ambient noise observation station arrangement, the selection method of main parameters in cluster algorithm is given. 155 seismic observatory stations in the North America are clustered to improve data processing efficiency. The results show that the overall efficiency of correlation function calculation and superposition is increased by 15.1%, the total time of extraction and screening of dispersion curve is reduced by 18.7%, and the average time of ambient noise tomography data processing is reduced by 12.6%compared with that before clustering, while the quality of ambient noise tomography is guaranteed by clustering processing of intensive ambient noise observation stations.
In the telecommunications industry, there is a widespread problem of data imbalance. This problem seriously affects the prediction results, making it impossible for telecommunications operators to accurately find pote...
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In the telecommunications industry, there is a widespread problem of data imbalance. This problem seriously affects the prediction results, making it impossible for telecommunications operators to accurately find potential lost customers, causing a lot of *** at the problem of economic loss caused by the imbalance of telecommunications customer data that affects model prediction performance, this paper proposes two hybrid algorithms DB-QCS(dbscan Quadrilateral centroid SMOTE) and KM-QCS(K-Means Quadrilateral centroid SMOTE) to solve the above problems, The hybrid algorithm mainly solves the problem of further increasing the marginalization of the sample distribution and introducing noise when the SMOTE algorithm synthesizes new samples. The main idea is to first use the under-sampling method to delete outliers or edge samples in most classes of samples, thereby reducing the number of synthesized new samples to solve the problem of introducing excessive noise. Then, the problem of marginalization of the sample distribution is solved by limiting the synthesis area of the new sample during oversampling, and finally the sampled data set is used for classification training. A large number of experiments on 5 unbalanced telecom customer data sets show that the hybrid algorithm achieves higher F-measure, G-mean and AUC values compared with the SMOTE algorithm.
With the rapid development and wide popularization of information technology,a large amount of data also *** is very important to use data mining tools to screen valuable information from complex *** one of the widely...
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With the rapid development and wide popularization of information technology,a large amount of data also *** is very important to use data mining tools to screen valuable information from complex *** one of the widely used density clustering algorithms,densitybased spatial clustering of application with noisy(dbscan) algorithm is an important data mining *** can find the multi-dimensional relationship between data elements from the data set,complete the clustering of arbitrary shape and noisy data sets when the number of cluster classes is unknown,and support spatial ***,based on the example of judging the correctness of the relationship between the user's meter and the substation transformer,and supported by the clustering technology of dbscan,this paper finally verifies that the density-based clustering method has a good classification effect on the data with high complexity.
In the telecommunications industry, there is a widespread problem of data imbalance. This problem seriously affects the prediction results, making it impossible for telecommunications operators to accurately find pote...
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ISBN:
(纸本)9781450385053
In the telecommunications industry, there is a widespread problem of data imbalance. This problem seriously affects the prediction results, making it impossible for telecommunications operators to accurately find potential lost customers, causing a lot of losses. Aiming at the problem of economic loss caused by the imbalance of telecommunications customer data that affects model prediction performance, this paper proposes two hybrid algorithms DB-QCS (dbscan Quadrilateral centroid SMOTE) and KM-QCS (K-Means Quadrilateral centroid SMOTE) to solve the above problems, The hybrid algorithm mainly solves the problem of further increasing the marginalization of the sample distribution and introducing noise when the SMOTE algorithm synthesizes new samples. The main idea is to first use the under-sampling method to delete outliers or edge samples in most classes of samples, thereby reducing the number of synthesized new samples to solve the problem of introducing excessive noise. Then, the problem of marginalization of the sample distribution is solved by limiting the synthesis area of the new sample during oversampling, and finally the sampled data set is used for classification training. A large number of experiments on 5 unbalanced telecom customer data sets show that the hybrid algorithm achieves higher F-measure, G-mean and AUC values compared with the SMOTE algorithm.
With the rapid development of society and information technology,data information is expanding at an alarming rate,and mankind has entered the era of big *** data spawned new changes in government management and publi...
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With the rapid development of society and information technology,data information is expanding at an alarming rate,and mankind has entered the era of big *** data spawned new changes in government management and public *** introduction of large data to government management has become an effective way to realize the modernization of the national governance system and the ability of governance,as well as the inevitable requirement for the realization of the modernization of *** paper first introduces the basic technical foundation of AIS protocol,the characteristics and application value of cloud computing technology,and finally uses distributed computing storage technology.
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