The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) m...The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean Absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.展开更多
Using the latest reported homologous Chemokine receptors （PDB ID： 3ODU, 3OE0 and 3OE6） as templates, twenty models of angiotensin II （Ang II） type 1 （AT1） receptor （known as p30556） were generated by multiple...Using the latest reported homologous Chemokine receptors （PDB ID： 3ODU, 3OE0 and 3OE6） as templates, twenty models of angiotensin II （Ang II） type 1 （AT1） receptor （known as p30556） were generated by multiple templates homology modeling. According to the results of the initial validation of these twenty models, the model 0020 was finally chosen as the best one for further studies. Then, a 2 ns molecular dynamic （MD） simulation for model 0020 was conducted in normal saline （0.9%, w/F） under periodical boundary conditions, which was followed by docking studies of model 0020 with several existing AT1 receptor blockers （ARBs）. The docking results reveal that model 0020 possesses good affinities with these docked ARBs which are in accordance with both the IC50 inhibitor values and their curative effects. The results also show more potent interactions between the model 0020 and its ARBs than those of ever reported results, such as hydrogen bonds, hydrophobic interactions, and especially cation-n interactions and π-π interactions which have never been reported before. This may reveal that the structure of the model 0020 is quite close to its real crystal structure and the model 0020 may have the potential to be used for structure based drug design：展开更多
文摘The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean Absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.
基金Project(20876180)supported by the National Natural Science Foundation of China
文摘Using the latest reported homologous Chemokine receptors （PDB ID： 3ODU, 3OE0 and 3OE6） as templates, twenty models of angiotensin II （Ang II） type 1 （AT1） receptor （known as p30556） were generated by multiple templates homology modeling. According to the results of the initial validation of these twenty models, the model 0020 was finally chosen as the best one for further studies. Then, a 2 ns molecular dynamic （MD） simulation for model 0020 was conducted in normal saline （0.9%, w/F） under periodical boundary conditions, which was followed by docking studies of model 0020 with several existing AT1 receptor blockers （ARBs）. The docking results reveal that model 0020 possesses good affinities with these docked ARBs which are in accordance with both the IC50 inhibitor values and their curative effects. The results also show more potent interactions between the model 0020 and its ARBs than those of ever reported results, such as hydrogen bonds, hydrophobic interactions, and especially cation-n interactions and π-π interactions which have never been reported before. This may reveal that the structure of the model 0020 is quite close to its real crystal structure and the model 0020 may have the potential to be used for structure based drug design：
