Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacoki...Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.展开更多
A high-resolution 2ooo-year methane record has been constructed from an ice core recovered at 7200 m a.s.1, on the Dasuopu Glacier in the central Himalayas. This sub-tropical methane record reveals an increasing trend...A high-resolution 2ooo-year methane record has been constructed from an ice core recovered at 7200 m a.s.1, on the Dasuopu Glacier in the central Himalayas. This sub-tropical methane record reveals an increasing trend in the concentration of methane during the industrial era that is similar to observations from polar regions. However, we also observed the differences in the atmospheric methane mixing ratio between this monsoon record and those from polar regions during pre-industrial times. In the time interval o N 1850 A.D., the average methane concentration in the Dasuopu ice core was 782±40 ppbv and the maximum temporal variation exceeded 200 ppbv. The difference gradient of methane concentration in Dasuopu ice core with Greenland and Antarctica cores are 66±40 ppbv and 107±40 ppbv, respectively. This suggests that the tropical latitudes might have acted as a major global methane source in preindustrial times. In addition, the temporal fluctuation of the pre-industrial methane records suggests that monsoon evolution incorporated with high methane emission from south Asia might be responsible for the relatively high methane concentration observed in the Dasuopu ice core around A.D. 800 and A.D. 1600. These results provide a rough understanding of the contribution of tropical methane source to the global methane budget and also the relationship betweenatmospheric methane and climate change.展开更多
基金Project(31200748)supported by the National Natural Science Foundation of China
文摘Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.
基金supported by the National Natural Science Foundation of China (40671044)the Ministry of Science and Technology of China (2005CB422004)
文摘A high-resolution 2ooo-year methane record has been constructed from an ice core recovered at 7200 m a.s.1, on the Dasuopu Glacier in the central Himalayas. This sub-tropical methane record reveals an increasing trend in the concentration of methane during the industrial era that is similar to observations from polar regions. However, we also observed the differences in the atmospheric methane mixing ratio between this monsoon record and those from polar regions during pre-industrial times. In the time interval o N 1850 A.D., the average methane concentration in the Dasuopu ice core was 782±40 ppbv and the maximum temporal variation exceeded 200 ppbv. The difference gradient of methane concentration in Dasuopu ice core with Greenland and Antarctica cores are 66±40 ppbv and 107±40 ppbv, respectively. This suggests that the tropical latitudes might have acted as a major global methane source in preindustrial times. In addition, the temporal fluctuation of the pre-industrial methane records suggests that monsoon evolution incorporated with high methane emission from south Asia might be responsible for the relatively high methane concentration observed in the Dasuopu ice core around A.D. 800 and A.D. 1600. These results provide a rough understanding of the contribution of tropical methane source to the global methane budget and also the relationship betweenatmospheric methane and climate change.
