Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quan...Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.展开更多
The time processes of photosynthetic induction responses to various irradiances in Korean pine (Pinus koraiensis) seedlings grown in open-light environments and in understory of forest were studied in an area near the...The time processes of photosynthetic induction responses to various irradiances in Korean pine (Pinus koraiensis) seedlings grown in open-light environments and in understory of forest were studied in an area near the Research Station of Changbai Mountain Forest Ecosystems, Jilin Province, China from July 15 to August 5, 1997. The results showed that at 200 靘olm-2s-1 photosynthetic photon flux density (PPFD) and 500 靘olm-2s-1 PPFD, the induction time for the photosynthetic rates of understory-grown seedlings to reach 50% and 90% steady-state net photosynthetic rates was longer than that of the open-grown seedlings. The induction responses of open-growth seedlings at 500 靘olm-2s-1 PPFD were slower than those at 200 靘olm-2s-1 PPFD, but it was the very reverse for understory-growth seedlings, which indicates that the photosynthetic induction times of Korean pine seedlings grown in the understory depended on the sunfleck intensity.展开更多
The organic carbon contents,carbon density and carbon storage of the soil in the Pinus koraiensis plantation ecosystem were investigated in Maoershan experimental forest farm,Shangzhi County,Heilongjiang,on the west s...The organic carbon contents,carbon density and carbon storage of the soil in the Pinus koraiensis plantation ecosystem were investigated in Maoershan experimental forest farm,Shangzhi County,Heilongjiang,on the west slope of the Zhangguangcai Mountains in northeastern China for providing data to evaluation of the carbon balance in forest ecosystem of northeastern China.These soil carbon indicators were measured in three forest types,pure P.koraiensis plantation,P.koraiensis and Betula platyphylla mixed forest,and the P.koraiensis and Quercus mongolica mixed forest.The soil carbon pool consisted of four compartments,namely L layer,F layer,H layer and B layer.With variance analysis,we found that both organic carbon content and carbon density of the soil were significantly affected by forest types,soil compartments and slope positions.The highest soil carbon density（278.63 Mg·ha^-1）.was observed in the mixed forest of P.koraiensis and Q.mongolica.The B layer had the highest carbon density（212.28 Mg·ha^-1） among all the soil compartments.In terms of slope position,the highest soil carbon density（394.18 Mg·ha^-1） presented in the low slope.Besides,soil carbon content and carbon density had a marked change with the organic matter content and vertical depth of the soil in each compartment.The results of this study implied that in the temperate humid region,the mixed ecosystem of regional Pinus koraiensis plantations and natural forest had relatively high carbon storage capability.展开更多
To avoid spatial aliasing problems in broad band high resolution seismic sections, I present a high density migration processing solution. I first analyze the spatial aliasing definition for stack and migration seismi...To avoid spatial aliasing problems in broad band high resolution seismic sections, I present a high density migration processing solution. I first analyze the spatial aliasing definition for stack and migration seismic sections and point out the differences between the two. We recognize that migration sections more often show spatial aliasing than stacked sections. Second, from wave propagation theory, I know that migration output is a new spatial sampling process and seismic prestack time migration can provide the high density sampling to prevent spatial aliasing on high resolution migration sections. Using a 2D seismic forward modeling analysis, I have found that seismic spatial aliasing noise can be eliminated by high density spatial sampling in prestack migration. In a 3D seismic data study for Daqing Oilfield in the Songliao Basin, I have also found that seismic sections obtained by high-density spatial sampling （10 ×10 m） in prestack migration have less spatial aliasing noise than those obtained by conventional low density spatial sampling （20 × 40 m） in prestack migration.展开更多
文摘Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.
文摘The time processes of photosynthetic induction responses to various irradiances in Korean pine (Pinus koraiensis) seedlings grown in open-light environments and in understory of forest were studied in an area near the Research Station of Changbai Mountain Forest Ecosystems, Jilin Province, China from July 15 to August 5, 1997. The results showed that at 200 靘olm-2s-1 photosynthetic photon flux density (PPFD) and 500 靘olm-2s-1 PPFD, the induction time for the photosynthetic rates of understory-grown seedlings to reach 50% and 90% steady-state net photosynthetic rates was longer than that of the open-grown seedlings. The induction responses of open-growth seedlings at 500 靘olm-2s-1 PPFD were slower than those at 200 靘olm-2s-1 PPFD, but it was the very reverse for understory-growth seedlings, which indicates that the photosynthetic induction times of Korean pine seedlings grown in the understory depended on the sunfleck intensity.
基金supported by National Technology Support Project (2008BAD95B10-6)
文摘The organic carbon contents,carbon density and carbon storage of the soil in the Pinus koraiensis plantation ecosystem were investigated in Maoershan experimental forest farm,Shangzhi County,Heilongjiang,on the west slope of the Zhangguangcai Mountains in northeastern China for providing data to evaluation of the carbon balance in forest ecosystem of northeastern China.These soil carbon indicators were measured in three forest types,pure P.koraiensis plantation,P.koraiensis and Betula platyphylla mixed forest,and the P.koraiensis and Quercus mongolica mixed forest.The soil carbon pool consisted of four compartments,namely L layer,F layer,H layer and B layer.With variance analysis,we found that both organic carbon content and carbon density of the soil were significantly affected by forest types,soil compartments and slope positions.The highest soil carbon density（278.63 Mg·ha^-1）.was observed in the mixed forest of P.koraiensis and Q.mongolica.The B layer had the highest carbon density（212.28 Mg·ha^-1） among all the soil compartments.In terms of slope position,the highest soil carbon density（394.18 Mg·ha^-1） presented in the low slope.Besides,soil carbon content and carbon density had a marked change with the organic matter content and vertical depth of the soil in each compartment.The results of this study implied that in the temperate humid region,the mixed ecosystem of regional Pinus koraiensis plantations and natural forest had relatively high carbon storage capability.
文摘To avoid spatial aliasing problems in broad band high resolution seismic sections, I present a high density migration processing solution. I first analyze the spatial aliasing definition for stack and migration seismic sections and point out the differences between the two. We recognize that migration sections more often show spatial aliasing than stacked sections. Second, from wave propagation theory, I know that migration output is a new spatial sampling process and seismic prestack time migration can provide the high density sampling to prevent spatial aliasing on high resolution migration sections. Using a 2D seismic forward modeling analysis, I have found that seismic spatial aliasing noise can be eliminated by high density spatial sampling in prestack migration. In a 3D seismic data study for Daqing Oilfield in the Songliao Basin, I have also found that seismic sections obtained by high-density spatial sampling （10 ×10 m） in prestack migration have less spatial aliasing noise than those obtained by conventional low density spatial sampling （20 × 40 m） in prestack migration.
