Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-...Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-running and large-scale ambitious forestation projects.However,there is still a lack of year-to-year evaluation on the effects of afforestation on carbon sequestration.Satellite remote sensing provides continuous observations of vegetation dynamics and land use and land cover change,is becoming a practical tool to evaluate the changes of vegetation productivity driven by afforestation.Here,a spatially-explicit analysis was conducted by combining Moderate Resolution Imaging Spectroradiometer(MODIS)land cover and three up-to-date remote sensing gross primary productivity(GPP)datasets of China.The results showed that the generated afforestation maps have similar spatial distribution with the national forest inventory data at the provincial level.The accumulative areas of afforestation were 3.02×10^(5)km^(2)in China from 2002 to 2018,it was mainly distributed in Southwest(SW),South(Sou),Southeast(SE)and Northeast(NE)of China.Among them,SW possesses the largest afforestation sub-region,with an area of 9.38×10^(4)km^(2),accounting for 31.06%of the total.There were divergent trends of affores-tation area among different sub-regions.The southern sub-regions showed increasing trends,while the northern sub-regions showed decreasing trends.In keeping with these,the center of annual afforestation moved to the south after 2009.The southern sub-regions were the majority of the cumula-tive GPP,accounting for nearly 70%of the total.The GPP of new afforestation showed an increasing trend from 2002 to 2018,and the increasing rate was higher than existing forests.After afforestation,the GPP change of afforestation was higher than adjacent non-forest over the same period.Our work provides new evidence that afforestation of China has enhanced the carbon assimilation and will deepen our understanding of dynamics of carbon sequestration driven by afforestation.展开更多
Recently,Luolai Group released its Q12025 quarterly report.As a leading Chinese home textile enterprise listed on the Shenzhen Stock Exchange in 2009,the company covers the research,design,production,and sales of home...Recently,Luolai Group released its Q12025 quarterly report.As a leading Chinese home textile enterprise listed on the Shenzhen Stock Exchange in 2009,the company covers the research,design,production,and sales of home textile products,and has multiple brands covering different consumer markets.It has expanded its online and offline comprehensive multichannel sales system and is committed to creating a win-win home furnishings and textile industry ecosystem.展开更多
Assessing the sensitivities of ecosystem functions to climatic factors is essential to understanding the response of ecosystems to environmental change.Temperate plantation forests contribute to global greening and cl...Assessing the sensitivities of ecosystem functions to climatic factors is essential to understanding the response of ecosystems to environmental change.Temperate plantation forests contribute to global greening and climate change mitigation,yet little is known as to the sensitivity of gross primary production(GPP)and evapotranspiration(ET)of these forests to heat and drought stress.Based on near-continuous,eddy-covariance and hydrometeorological data from a young temperate plantation forest in Beijing,China(2012-2019),we used a slidingwindow-fitting technique to assess the seasonal and interannual variation in ecosystem sensitivity(i.e.,calculated slopes,S_(GPP-Ta),S_(ET-Ta),S_(GPP-EF),and S_(ET-EF))in GPP and ET to anomalies in air temperature(T_(a))and evaporative fraction(EF).The EF was used here as an indicator of drought.Seasonally,daily SGPP-Ta,SET-Ta,and SGPP-EF were greatest in summer,reaching maxima of 1.120.56 g C··m^(-2)·d^(-1)·℃^(-1),1.360.56 g H_(2)O·m^(-2)·d^(-1)·℃^(-1),and 0.370.35 g C·m^(-2)·d^(-1),respectively.Evapotranspiration was constrained by drought,especially during the spring-to-summer period,SET-EF reaching0.510.34 g H_(2)O·m^(-2)·d^(-1).Variables EF,T_(a),soil water content(SWC),vapor pressure deficit(VPD),and precipitation(PPT)were the main controls of sensitivity,with SGPP-Ta and SET-Ta increasing with Ta,VPD,and PPT(<50 mm·d^(-1))during both spring and autumn.Increased drought stress during summer caused the positive response in GPP and ET to decrease with atmospheric warming.Variable SET-EF intensified(i.e.,became more negative)with decreasing EF and increasing Ta.Interannually,annual S_(GPP-Ta)and S_(ET-Ta)were positive,S_(GPP-EF)near-neutral,and S_(ET-EF)negative.Interannual variability in S_(GPP-Ta),S_(ET-Ta),S_(ET-EF),and S_(GPP-EF)was largely due to variations in bulk surface conductance.Our study suggests that the dynamics associated with the sensitivity of ecosystems to changes in climatic factors need to be considered in the management of plantation forests under future global climate change.展开更多
The evolution of fretting wear behavior of zirconium alloy cladding tubes mated with dimples under the gross slip regime(GSR)was investigated.The findings revealed that the primary wear mechanisms under GSR were delam...The evolution of fretting wear behavior of zirconium alloy cladding tubes mated with dimples under the gross slip regime(GSR)was investigated.The findings revealed that the primary wear mechanisms under GSR were delamination,surface fatigue wear and abrasive wear,and the fretting damage rate mainly depends on delamination.The cross-sectional microstructure of the worn area could be divided into the third-body layer,tribologically transformed structure layer,and general deformation layer,with their formation mechanisms analyzed.Furthermore,the mechanism of wear-induced grain refinement was identified as dynamic recrystallization(DRX),including both continuous DRX and discontinuous DRX.Additionally,the processes of fretting wear and DRX were discussed.展开更多
Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GP...Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GPP in terrestrial ecosystems is essential for evaluating terrestrial carbon cycle processes.Machine learning(ML)models provide significant technical support in this domain.Presently,there is a deficiency of high-precision and robust GPP prediction variables and models.Challenges such as unclear contributions of predictive variables,extended model training durations,and limited robustness must be addressed.Solar-induced chlorophyll fluorescence(SIF),optimized multilayer perceptron neural networks,and ensemble learning models show the potential to overcome these challenges.This study aimed to develop an optimized multilayer perceptron neural network model and an ensemble learning model,while objectively assessing the capacity of SIF to predict GPP.Identifying robust models capable of enhancing the accuracy of GPP predictions was the ultimate goal.This study utilized continuous observations of SIF and meteorological data collected from 2020 to 2021 at a designated research observation station within the Populus plantation ecosystem of the Huanghuaihai agricultural protective forest system in Henan Province,China.By optimizing and evaluating the predictive accuracy and robustness of the models across different temporal scales(half-hourly and daily scales),a multi-layer perceptron(MLP)neural network optimization model based on the back propagation(BP)neural network(BPNN)algorithm(BP/MLP)and MLP and random forest(RF)integration(MLP-RF)ensemble models were constructed,utilizing SIF as the primary predictive variable for GPP.Both the BP/MLP(half-hourly scale model R^(2)=0.885,daily scale model R^(2)=0.921)and the MLP-RF(half-hourly scale model R^(2)=0.845,daily scale model R^(2)=0.914)models showed superior accuracy compared to the BPNN(half-hourly scale model R^(2)=0.841,daily scale model R^(2)=0.918)and the traditional RF(half-hourly scale model R^(2)=0.798,daily scale model R^(2)=0.867)models,with the BP/MLP model consistently outperforming the MLP-RF model.The BP/MLP model,which was optimized through particle swarm optimization(PSO),significantly enhanced the robustness of GPP predictions on a half-hourly scale and daily scale.Considering both half-hourly scale and daily scale in the PSO-BP/MLP modeling,the four indicators,light-use efficiency(LUE),photosynthetically active radiation(PAR),absorbed photosynthetically active radiation(APAR),and the variation in SIF with NIRvP(fSIF(NIRvP)),exhibited the potential for enhancing the accuracy of GPP predictions.This study employed a series of model optimization techniques to develop a GPP prediction model with enhanced performance that objectively evaluated the contributions of the predictive variables.This approach provided an innovative and effective method for assessing the carbon cycle in terrestrial ecosystems.展开更多
Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes...Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical para-meter leaf area index(LAI),which are not completely synchronized in seasonality with GPP.In this study,we proposed chlorophyll content-based light use efficiency model(CC-LUE)to improve GPP estimates,as chlorophyll is the direct site of photosynthesis,and only the light absorbed by chlorophyll is used in the photosynthetic process.The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content(Chlcanopy)and FPAR.This method was calibrated and validated utiliz-ing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five dif-ferent climate zones.Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions.The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season,outperforming the three widely used LUE models(MODIS-GPP algorithm,Vegetation Photosynthesis Model(VPM),and the eddy covariance-light use efficiency model(EC-LUE)).Additionally,the CC-LUE model(RMSE=0.50 g C/(m^(2)·d))significantly improved the underestimation of GPP during the growing season in semi-arid region,re-markably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%,73.4%,and 37.5%,compared with MOD17(RMSE=2.03 g C/(m^(2)·d)),VPM(RMSE=1.88 g C/(m^(2)·d)),and EC-LUE(RMSE=0.80 g C/(m^(2)·d))model.The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems,thereby provid-ing the possibility of a more precise depiction of forest seasonal carbon uptake.展开更多
Objective To explore the effect of heat-reinforcing acupuncture combined with Bobath rehabilita on training for the gross motor func on measurement（GMFM） in children with cerebral palsy. Methods Sixty cases of cereb...Objective To explore the effect of heat-reinforcing acupuncture combined with Bobath rehabilita on training for the gross motor func on measurement（GMFM） in children with cerebral palsy. Methods Sixty cases of cerebral palsy were randomized into a heat-reinforcing acupuncture combined with rehabilitation training group（group A） and a rehabilitation group（group B）,30 cases in each group. Rehabilitation training was applied in group B and heat-reinforcing acupuncture was added in group A. Jiāji（夹脊 EX-B 2）,Jiānyú（肩髃 LI 15）,Qūchí（曲池 LI 11）,Hégi（合谷 LI 4）,Yánglíngquán（阳陵泉 GB 34）,Yīnlíngquán（阴陵泉 SP 9）,Xuánzhōng（悬钟 GB 39）,Zúsānli（足三里 ST 36）,Sānyīnjiāo（三阴交 SP 6）,Chéngshān（承山 BL 57）,Tài chōng（太冲 LR 3）,Tàixī（太溪 KI 3） and Shénmén（神门 HT 4） were chosen in group A and the heat-reinforcing acupuncture was applied once a day. For scalp acupuncture,Biihuì（百会 GV 20）,Sìshéncōng（四神聪 EX-HN 1）,Zhìsānzhēn（智三针）,Nǎosānzhēn（脑三针）,Nièsānzhēn（颞三针） and motor area（运动区） were punctured without any manipulation,once every other day,3 months as a treatment course and 2 courses were needed. GMFM were selected to assess the children with cerebral palsy before treatment,a er 3 months treatment and a er 6 months treatment. Results The total eff ec ve rate in group A was 70.00%（21/30）,which is superior to that of 60.00%（24/30） in group B（P0.05）. In group A,the GMFM scores of decubitus position and turn-over of body,creeping and kneeling,sitting,standing,walking,running and jumping after6 months treatment were significantly improved compared with that before treatment and after 3 months treatment（all P0.05）. In group B,above indices a er 6 months treatment were significantly improved compared with those before treatment,the standing score after 6 months treatment was significantly improved compared with that a er 3 months treatment（P0.05）; the GMFM scores of creeping,kneeling,sitting,standing,walking,running and jumping after 6 months treatment in group A were more significantly improved than that in group B（all P0.05）. Conclusion The heat-reinforcing acupuncture combined with Bobath rehabilita on training has a be er eff ect than rehabilitation training on the GMFM scores of creeping and kneeling,si ng,standing,walking,running and jumping of children with cerebral palsy. It can be shown that the combination of heat-reinforcing acupuncture and Bobath rehabilita on training can improve the gross motor func on of children with cerebral palsy.展开更多
基金funded by the National Key Research and Development Program of China(Grant No.2020YFA0608103)the National Science Foundation of China(Grant Nos.42265012 and 31770765).
文摘Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-running and large-scale ambitious forestation projects.However,there is still a lack of year-to-year evaluation on the effects of afforestation on carbon sequestration.Satellite remote sensing provides continuous observations of vegetation dynamics and land use and land cover change,is becoming a practical tool to evaluate the changes of vegetation productivity driven by afforestation.Here,a spatially-explicit analysis was conducted by combining Moderate Resolution Imaging Spectroradiometer(MODIS)land cover and three up-to-date remote sensing gross primary productivity(GPP)datasets of China.The results showed that the generated afforestation maps have similar spatial distribution with the national forest inventory data at the provincial level.The accumulative areas of afforestation were 3.02×10^(5)km^(2)in China from 2002 to 2018,it was mainly distributed in Southwest(SW),South(Sou),Southeast(SE)and Northeast(NE)of China.Among them,SW possesses the largest afforestation sub-region,with an area of 9.38×10^(4)km^(2),accounting for 31.06%of the total.There were divergent trends of affores-tation area among different sub-regions.The southern sub-regions showed increasing trends,while the northern sub-regions showed decreasing trends.In keeping with these,the center of annual afforestation moved to the south after 2009.The southern sub-regions were the majority of the cumula-tive GPP,accounting for nearly 70%of the total.The GPP of new afforestation showed an increasing trend from 2002 to 2018,and the increasing rate was higher than existing forests.After afforestation,the GPP change of afforestation was higher than adjacent non-forest over the same period.Our work provides new evidence that afforestation of China has enhanced the carbon assimilation and will deepen our understanding of dynamics of carbon sequestration driven by afforestation.
文摘Recently,Luolai Group released its Q12025 quarterly report.As a leading Chinese home textile enterprise listed on the Shenzhen Stock Exchange in 2009,the company covers the research,design,production,and sales of home textile products,and has multiple brands covering different consumer markets.It has expanded its online and offline comprehensive multichannel sales system and is committed to creating a win-win home furnishings and textile industry ecosystem.
基金supported by the National Key Research and Development Program of China(No.2020YFA0608100)the National Natural Science Foundation of China(NSFC,No.32071842 and 32101588)。
文摘Assessing the sensitivities of ecosystem functions to climatic factors is essential to understanding the response of ecosystems to environmental change.Temperate plantation forests contribute to global greening and climate change mitigation,yet little is known as to the sensitivity of gross primary production(GPP)and evapotranspiration(ET)of these forests to heat and drought stress.Based on near-continuous,eddy-covariance and hydrometeorological data from a young temperate plantation forest in Beijing,China(2012-2019),we used a slidingwindow-fitting technique to assess the seasonal and interannual variation in ecosystem sensitivity(i.e.,calculated slopes,S_(GPP-Ta),S_(ET-Ta),S_(GPP-EF),and S_(ET-EF))in GPP and ET to anomalies in air temperature(T_(a))and evaporative fraction(EF).The EF was used here as an indicator of drought.Seasonally,daily SGPP-Ta,SET-Ta,and SGPP-EF were greatest in summer,reaching maxima of 1.120.56 g C··m^(-2)·d^(-1)·℃^(-1),1.360.56 g H_(2)O·m^(-2)·d^(-1)·℃^(-1),and 0.370.35 g C·m^(-2)·d^(-1),respectively.Evapotranspiration was constrained by drought,especially during the spring-to-summer period,SET-EF reaching0.510.34 g H_(2)O·m^(-2)·d^(-1).Variables EF,T_(a),soil water content(SWC),vapor pressure deficit(VPD),and precipitation(PPT)were the main controls of sensitivity,with SGPP-Ta and SET-Ta increasing with Ta,VPD,and PPT(<50 mm·d^(-1))during both spring and autumn.Increased drought stress during summer caused the positive response in GPP and ET to decrease with atmospheric warming.Variable SET-EF intensified(i.e.,became more negative)with decreasing EF and increasing Ta.Interannually,annual S_(GPP-Ta)and S_(ET-Ta)were positive,S_(GPP-EF)near-neutral,and S_(ET-EF)negative.Interannual variability in S_(GPP-Ta),S_(ET-Ta),S_(ET-EF),and S_(GPP-EF)was largely due to variations in bulk surface conductance.Our study suggests that the dynamics associated with the sensitivity of ecosystems to changes in climatic factors need to be considered in the management of plantation forests under future global climate change.
基金supported by the National Natural Science Foundation of China(No.52105221)the Postdoctoral Fellowship Program(Grade C)of China Postdoctoral Science Foundation(No.GZC20232749)the Youth Innovation Promotion Assessment CAS(No.2022187)and the IMR Innovation Fund(No.2024-PY13).
文摘The evolution of fretting wear behavior of zirconium alloy cladding tubes mated with dimples under the gross slip regime(GSR)was investigated.The findings revealed that the primary wear mechanisms under GSR were delamination,surface fatigue wear and abrasive wear,and the fretting damage rate mainly depends on delamination.The cross-sectional microstructure of the worn area could be divided into the third-body layer,tribologically transformed structure layer,and general deformation layer,with their formation mechanisms analyzed.Furthermore,the mechanism of wear-induced grain refinement was identified as dynamic recrystallization(DRX),including both continuous DRX and discontinuous DRX.Additionally,the processes of fretting wear and DRX were discussed.
基金supported by the National Key R&D Program of China(No.2023YFD2200400-01)the Fundamental Scientific Research Operation of Central-level Public Welfare Scientific Research Institutes(No.CAFYBB2023MA001).
文摘Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GPP in terrestrial ecosystems is essential for evaluating terrestrial carbon cycle processes.Machine learning(ML)models provide significant technical support in this domain.Presently,there is a deficiency of high-precision and robust GPP prediction variables and models.Challenges such as unclear contributions of predictive variables,extended model training durations,and limited robustness must be addressed.Solar-induced chlorophyll fluorescence(SIF),optimized multilayer perceptron neural networks,and ensemble learning models show the potential to overcome these challenges.This study aimed to develop an optimized multilayer perceptron neural network model and an ensemble learning model,while objectively assessing the capacity of SIF to predict GPP.Identifying robust models capable of enhancing the accuracy of GPP predictions was the ultimate goal.This study utilized continuous observations of SIF and meteorological data collected from 2020 to 2021 at a designated research observation station within the Populus plantation ecosystem of the Huanghuaihai agricultural protective forest system in Henan Province,China.By optimizing and evaluating the predictive accuracy and robustness of the models across different temporal scales(half-hourly and daily scales),a multi-layer perceptron(MLP)neural network optimization model based on the back propagation(BP)neural network(BPNN)algorithm(BP/MLP)and MLP and random forest(RF)integration(MLP-RF)ensemble models were constructed,utilizing SIF as the primary predictive variable for GPP.Both the BP/MLP(half-hourly scale model R^(2)=0.885,daily scale model R^(2)=0.921)and the MLP-RF(half-hourly scale model R^(2)=0.845,daily scale model R^(2)=0.914)models showed superior accuracy compared to the BPNN(half-hourly scale model R^(2)=0.841,daily scale model R^(2)=0.918)and the traditional RF(half-hourly scale model R^(2)=0.798,daily scale model R^(2)=0.867)models,with the BP/MLP model consistently outperforming the MLP-RF model.The BP/MLP model,which was optimized through particle swarm optimization(PSO),significantly enhanced the robustness of GPP predictions on a half-hourly scale and daily scale.Considering both half-hourly scale and daily scale in the PSO-BP/MLP modeling,the four indicators,light-use efficiency(LUE),photosynthetically active radiation(PAR),absorbed photosynthetically active radiation(APAR),and the variation in SIF with NIRvP(fSIF(NIRvP)),exhibited the potential for enhancing the accuracy of GPP predictions.This study employed a series of model optimization techniques to develop a GPP prediction model with enhanced performance that objectively evaluated the contributions of the predictive variables.This approach provided an innovative and effective method for assessing the carbon cycle in terrestrial ecosystems.
基金Under the auspices of the National Key Research and Development Program of China(No.2019YFA0606603)。
文摘Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical para-meter leaf area index(LAI),which are not completely synchronized in seasonality with GPP.In this study,we proposed chlorophyll content-based light use efficiency model(CC-LUE)to improve GPP estimates,as chlorophyll is the direct site of photosynthesis,and only the light absorbed by chlorophyll is used in the photosynthetic process.The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content(Chlcanopy)and FPAR.This method was calibrated and validated utiliz-ing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five dif-ferent climate zones.Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions.The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season,outperforming the three widely used LUE models(MODIS-GPP algorithm,Vegetation Photosynthesis Model(VPM),and the eddy covariance-light use efficiency model(EC-LUE)).Additionally,the CC-LUE model(RMSE=0.50 g C/(m^(2)·d))significantly improved the underestimation of GPP during the growing season in semi-arid region,re-markably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%,73.4%,and 37.5%,compared with MOD17(RMSE=2.03 g C/(m^(2)·d)),VPM(RMSE=1.88 g C/(m^(2)·d)),and EC-LUE(RMSE=0.80 g C/(m^(2)·d))model.The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems,thereby provid-ing the possibility of a more precise depiction of forest seasonal carbon uptake.
基金Supported by a project from Bureau of Public Health of Shanghai:2008 L 029 A
文摘Objective To explore the effect of heat-reinforcing acupuncture combined with Bobath rehabilita on training for the gross motor func on measurement（GMFM） in children with cerebral palsy. Methods Sixty cases of cerebral palsy were randomized into a heat-reinforcing acupuncture combined with rehabilitation training group（group A） and a rehabilitation group（group B）,30 cases in each group. Rehabilitation training was applied in group B and heat-reinforcing acupuncture was added in group A. Jiāji（夹脊 EX-B 2）,Jiānyú（肩髃 LI 15）,Qūchí（曲池 LI 11）,Hégi（合谷 LI 4）,Yánglíngquán（阳陵泉 GB 34）,Yīnlíngquán（阴陵泉 SP 9）,Xuánzhōng（悬钟 GB 39）,Zúsānli（足三里 ST 36）,Sānyīnjiāo（三阴交 SP 6）,Chéngshān（承山 BL 57）,Tài chōng（太冲 LR 3）,Tàixī（太溪 KI 3） and Shénmén（神门 HT 4） were chosen in group A and the heat-reinforcing acupuncture was applied once a day. For scalp acupuncture,Biihuì（百会 GV 20）,Sìshéncōng（四神聪 EX-HN 1）,Zhìsānzhēn（智三针）,Nǎosānzhēn（脑三针）,Nièsānzhēn（颞三针） and motor area（运动区） were punctured without any manipulation,once every other day,3 months as a treatment course and 2 courses were needed. GMFM were selected to assess the children with cerebral palsy before treatment,a er 3 months treatment and a er 6 months treatment. Results The total eff ec ve rate in group A was 70.00%（21/30）,which is superior to that of 60.00%（24/30） in group B（P0.05）. In group A,the GMFM scores of decubitus position and turn-over of body,creeping and kneeling,sitting,standing,walking,running and jumping after6 months treatment were significantly improved compared with that before treatment and after 3 months treatment（all P0.05）. In group B,above indices a er 6 months treatment were significantly improved compared with those before treatment,the standing score after 6 months treatment was significantly improved compared with that a er 3 months treatment（P0.05）; the GMFM scores of creeping,kneeling,sitting,standing,walking,running and jumping after 6 months treatment in group A were more significantly improved than that in group B（all P0.05）. Conclusion The heat-reinforcing acupuncture combined with Bobath rehabilita on training has a be er eff ect than rehabilitation training on the GMFM scores of creeping and kneeling,si ng,standing,walking,running and jumping of children with cerebral palsy. It can be shown that the combination of heat-reinforcing acupuncture and Bobath rehabilita on training can improve the gross motor func on of children with cerebral palsy.
基金Supported by The National Natural Science Foundation of China(10574057,10571074),Specialized Research Fund for the Doctoral Programof Higher Education(20050183010)and Research Fundfor Young Teachers(70Ey18)
