Can we determine a high resolution protein structure quickly, say, in a week？ I will show this is possible by the current technologies together with new computational tools discussed in this article. We have three po...Can we determine a high resolution protein structure quickly, say, in a week？ I will show this is possible by the current technologies together with new computational tools discussed in this article. We have three potential paths to explore： X-ray crystallography. While this method has produced the most protein structures in the PDB （Protein Data Bank）, the nasty trial-and-error crystallization step remains to be an inhibitive obstacle.NMR （Nuclear Magnetic Resonance） spectroscopy. While the NMR experiments are relatively easy to do, the interpretation of the NMR data for structure calculation takes several months on average.In silico protein structure prediction. Can we actually predict high resolution structures consistently？ If the predicted models remain to be labeled as “predicted”, and these structures still need to be experimentally verified by the wet lab methods, then this method at best can serve only as a screening tool. I investigate the question of “quick protein structure determination” from a computer scientist point of view and actually answer the more relevant question “what can a computer scientist effectively contribute to this goal”.展开更多
Modal parameter identification is a core issue in health monitoring and damage detection for hydraulic structures. For a roof overflow hydropower station with a bulb tubular unit under ambient excitation, a complex un...Modal parameter identification is a core issue in health monitoring and damage detection for hydraulic structures. For a roof overflow hydropower station with a bulb tubular unit under ambient excitation, a complex unit-powerhouse-dam coupling vibration system increases the difficulties of modal parameter identification. In this study, in view of the difficulties of modal order determination and the noise jamming caused by ambient excitation, along with false mode identification and elimination problems, the ensemble empirical mode decomposition （EEMD） method was used to decrease noise, the singular entropy increment spectrum was used to determine system order, and multiple criteria were used to eliminate false modes. The eigensystem realization algorithm （ERA） and stochastic subspace identification （SSI） method were then used to identify modal parameters. The results show that the relative errors of frequencies in the first four modes were within 10% for the ERA method, while those of SSI were over 10% in the second and third modes. Therefore, the ERA method is more appropriate for identifying the structural modal parameters for this particular powerhouse layout.展开更多
基金supported by the National High Tech Research and Development 863 Program under Grant No.2008AA02Z313 from China’s Ministry of Science and TechnologyCanada’s NSERC under Grant No. OGP0046506Canada Research Chair Program,an NSERC Collaborative Grant,Ontario’s Premier’s Discovery Award
文摘Can we determine a high resolution protein structure quickly, say, in a week？ I will show this is possible by the current technologies together with new computational tools discussed in this article. We have three potential paths to explore： X-ray crystallography. While this method has produced the most protein structures in the PDB （Protein Data Bank）, the nasty trial-and-error crystallization step remains to be an inhibitive obstacle.NMR （Nuclear Magnetic Resonance） spectroscopy. While the NMR experiments are relatively easy to do, the interpretation of the NMR data for structure calculation takes several months on average.In silico protein structure prediction. Can we actually predict high resolution structures consistently？ If the predicted models remain to be labeled as “predicted”, and these structures still need to be experimentally verified by the wet lab methods, then this method at best can serve only as a screening tool. I investigate the question of “quick protein structure determination” from a computer scientist point of view and actually answer the more relevant question “what can a computer scientist effectively contribute to this goal”.
基金supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.51321065)the National Natural Science Foundation of China(Grants No.51379140,51209158,and 51379177)
文摘Modal parameter identification is a core issue in health monitoring and damage detection for hydraulic structures. For a roof overflow hydropower station with a bulb tubular unit under ambient excitation, a complex unit-powerhouse-dam coupling vibration system increases the difficulties of modal parameter identification. In this study, in view of the difficulties of modal order determination and the noise jamming caused by ambient excitation, along with false mode identification and elimination problems, the ensemble empirical mode decomposition （EEMD） method was used to decrease noise, the singular entropy increment spectrum was used to determine system order, and multiple criteria were used to eliminate false modes. The eigensystem realization algorithm （ERA） and stochastic subspace identification （SSI） method were then used to identify modal parameters. The results show that the relative errors of frequencies in the first four modes were within 10% for the ERA method, while those of SSI were over 10% in the second and third modes. Therefore, the ERA method is more appropriate for identifying the structural modal parameters for this particular powerhouse layout.
