In this paper, we propose two methods to enhance the synchronizability of a class of complex networks which do not hold the positive correlation between betweenness centrality （BC） and degree of a node, and observe ...In this paper, we propose two methods to enhance the synchronizability of a class of complex networks which do not hold the positive correlation between betweenness centrality （BC） and degree of a node, and observe other topology characteristics of the network affected by the methods. Numerical simulations show that both methods can effectively enhance the synchronizability of this kind of networks. Furthermore, we show that the maximal BC of all edges is an important factor to affect the network synchronizability, although it is not the unique factor.展开更多
AIM:To characterise the viral kinetics of enterovirus 71 (EV71).METHODS:In this study,human rhabdomyosarcoma (RD) cells were infected with EV71 at different multiplicity of infection (MOI).After infection,the cytopath...AIM:To characterise the viral kinetics of enterovirus 71 (EV71).METHODS:In this study,human rhabdomyosarcoma (RD) cells were infected with EV71 at different multiplicity of infection (MOI).After infection,the cytopathic effect (CPE) was monitored and recorded using a phase contrast microscope associated with a CCD camera at different time points post viral infection (0,6,12,24 h post infection).Cell growth and viability were measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay in both EV71 infected and mock infected cells at each time point.EV71 replication kinet-ics in RD cells was determined by measuring the total intracellular viral RNA with real-time reverse-transcription polymerase chain reaction (qRT-PCR).Also,the intracellular and extracellular virion RNA was isolated and quantified at different time points to analyze the viral package and secretion.The expression of viral protein was determined by analyze the levels of viral structure protein VP1 with Western blotting.RESULTS:EV71 infection induced a significant CPE as early as 6 h post infection (p.i.) in both RD cells infected with high ratio of virus (MOI 10) and low ratio of virus (MOI 1).In EV71 infected cells,the cell growth was inhibited and the number of viable cells was rapidly decreased in the later phase of infection.EV71 virions were uncoated immediately after entry.The intracellular viral RNA began to increase at as early as 3 h p.i.and the exponential increase was found between 3 h to 6 h p.i.in both infected groups.For viral structure protein synthesis,results from western-blot showed that intracellular viral protein VP1 could not be detected until 6 h p.i.in the cells infected at either MOI 1 or MOI 10;and reached the peak at 9 h p.i.in the cells infected with EV71 at both MOI 1 and MOI 10.Simultaneously,the viral package and secretion were also actively processed as the virus underwent rapid replication.The viral package kinetics was comparable for both MOI 1 and MOI 10 infected groups.It was observed that at 3 h p.i,the intracellular virions obviously decreased,thereafter,the intracellular virions began to increase and enter into the exponential phase until 12 h p.i.The total amounts of intracellular virons were decreased from 12 to 24 h p.i.Consistent with this result,the increase of virus secretion occurred during 6 to 12 h p.i.CONCLUSION:The viral kinetics of EV71 were established by analyzing viral replication,package and secretion in RD cells.展开更多
Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timesc...Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets con- taining millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape （geometric clustering） and kinetics （kinetic clustering）. In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, ag- glomerative algorithms （single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage）, center-based algorithms （K-Means, K-Medoids, K-Centers, and APM）, density-based algorithms （neighbor-based, DBSCAN, density-peaks, and Robust-DB）, and spectral-based algorithms （PCCA and PCCA＋）. In particular, differences between geomet- ric and kinetic clustering metrics will be discussed along with the performances of diflhrent clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algo- rithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.展开更多
基金The project supported by National Natural Science Foundation of China under Grant Nos.70431002 and 60674045
文摘In this paper, we propose two methods to enhance the synchronizability of a class of complex networks which do not hold the positive correlation between betweenness centrality （BC） and degree of a node, and observe other topology characteristics of the network affected by the methods. Numerical simulations show that both methods can effectively enhance the synchronizability of this kind of networks. Furthermore, we show that the maximal BC of all edges is an important factor to affect the network synchronizability, although it is not the unique factor.
基金Supported by Research Grant Council (RGC,CUHK4428/06M)a commissioned grant of the Research Fund for Control of Infectious Diseases (CU-09-02-02)Food and Health Bureau,the Government of Hong Kong Special Administration Region (HKSAR)
文摘AIM:To characterise the viral kinetics of enterovirus 71 (EV71).METHODS:In this study,human rhabdomyosarcoma (RD) cells were infected with EV71 at different multiplicity of infection (MOI).After infection,the cytopathic effect (CPE) was monitored and recorded using a phase contrast microscope associated with a CCD camera at different time points post viral infection (0,6,12,24 h post infection).Cell growth and viability were measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay in both EV71 infected and mock infected cells at each time point.EV71 replication kinet-ics in RD cells was determined by measuring the total intracellular viral RNA with real-time reverse-transcription polymerase chain reaction (qRT-PCR).Also,the intracellular and extracellular virion RNA was isolated and quantified at different time points to analyze the viral package and secretion.The expression of viral protein was determined by analyze the levels of viral structure protein VP1 with Western blotting.RESULTS:EV71 infection induced a significant CPE as early as 6 h post infection (p.i.) in both RD cells infected with high ratio of virus (MOI 10) and low ratio of virus (MOI 1).In EV71 infected cells,the cell growth was inhibited and the number of viable cells was rapidly decreased in the later phase of infection.EV71 virions were uncoated immediately after entry.The intracellular viral RNA began to increase at as early as 3 h p.i.and the exponential increase was found between 3 h to 6 h p.i.in both infected groups.For viral structure protein synthesis,results from western-blot showed that intracellular viral protein VP1 could not be detected until 6 h p.i.in the cells infected at either MOI 1 or MOI 10;and reached the peak at 9 h p.i.in the cells infected with EV71 at both MOI 1 and MOI 10.Simultaneously,the viral package and secretion were also actively processed as the virus underwent rapid replication.The viral package kinetics was comparable for both MOI 1 and MOI 10 infected groups.It was observed that at 3 h p.i,the intracellular virions obviously decreased,thereafter,the intracellular virions began to increase and enter into the exponential phase until 12 h p.i.The total amounts of intracellular virons were decreased from 12 to 24 h p.i.Consistent with this result,the increase of virus secretion occurred during 6 to 12 h p.i.CONCLUSION:The viral kinetics of EV71 were established by analyzing viral replication,package and secretion in RD cells.
基金supported by Shenzhen Science and Technology Innovation Committee(JCYJ20170413173837121)the Hong Kong Research Grant Council(HKUST C6009-15G,14203915,16302214,16304215,16318816,and AoE/P-705/16)+2 种基金King Abdullah University of Science and Technology(KAUST) Office of Sponsored Research(OSR)(OSR-2016-CRG5-3007)Guangzhou Science Technology and Innovation Commission(201704030116)Innovation and Technology Commission(ITCPD/17-9and ITC-CNERC14SC01)
文摘Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets con- taining millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape （geometric clustering） and kinetics （kinetic clustering）. In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, ag- glomerative algorithms （single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage）, center-based algorithms （K-Means, K-Medoids, K-Centers, and APM）, density-based algorithms （neighbor-based, DBSCAN, density-peaks, and Robust-DB）, and spectral-based algorithms （PCCA and PCCA＋）. In particular, differences between geomet- ric and kinetic clustering metrics will be discussed along with the performances of diflhrent clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algo- rithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.
