Anthocyanins have high antioxidant activities, and engineering of anthocyanin biosynthesis in staple crops, such as rice （Oryza sativa L.）, could provide health-promoting foods for improving human health. However, e...Anthocyanins have high antioxidant activities, and engineering of anthocyanin biosynthesis in staple crops, such as rice （Oryza sativa L.）, could provide health-promoting foods for improving human health. However, engineering metabolic pathways for biofortification remains difficult, and previous attempts to engineer anthocyanin production in rice endosperm failed because of the sophisticated genetic regulatory network of its biosynthetic pathway. In this study, we developed a high-efficiency vector system for transgene stacking and used it to engineer anthocyanin biosynthesis in rice endosperm. We made a construct containing eight anthocyanin-related genes （two regulatory genes from maize and six structural genes from Coleus） driven by the endosperm-specific promoters,plus a selectable marker and a gene for marker excision. Transformation of rice with this construct generated a novel biofortified germplasm ＂Purple Endosperm Rice＂ （called ＂Zijingmi＂ in Chinese）, which has high anthocyanin contents and antioxidant activity in the endosperm. This anthocyanin production results from expression of the transgenes and the resulting activation （or enhancement） of expression of 13 endogenous anthocyanin biosynthesis genes that are silenced or expressed at low levels in wild-type rice endosperm. This study provides an efficient, versatile toolkit for transgene stacking and demonstrates its use for successful engineering of a sophisticated biological pathway, suggesting the potential utility of this toolkit for synthetic biology and improvement of agronomic traits in plants.展开更多
Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are g...Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.展开更多
Mg-Zn-Y alloys with long-period stacking ordered structures were prepared by an ingot casting method. The corrosion performance of Mg-Zn-Y alloys was studied by combining gas-collecting test, immersion test and electr...Mg-Zn-Y alloys with long-period stacking ordered structures were prepared by an ingot casting method. The corrosion performance of Mg-Zn-Y alloys was studied by combining gas-collecting test, immersion test and electrochemical measurements in order to determine the corrosion rate and mechanism of the alloys. The results showed that the volume fraction of Mg（12）YZn phase increased and the shape of the Mg（12）YZn phase changed from discontinuous to continuous net-like with increasing Zn and Y content. The corrosion rate of the alloys greatly depended on the distribution and volume fraction of the Mg（12）YZn phase. Corrosion products appeared at the junction of Mg phase and Mg（12）YZn phase, indicating that the Mg（12）YZn phase accelerated galvanic corrosion of Mg matrix. Mg（97）Zn1Y2 alloy shows the lowest corrosion rate due to the continuous distribution of Mg（12）YZn phase.展开更多
Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble ...Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.展开更多
基金This work was supported by grants from National Natural Science Foundation of China （31000698）, the Ministry of Agriculture of China （2016ZX08010001 2016ZX08009002+1 种基金 2014ZX08010001）, and Guangdong Province Public Interest Research and Capacity Building Special Fund （2015B020201002 2016A020210084）.
文摘Anthocyanins have high antioxidant activities, and engineering of anthocyanin biosynthesis in staple crops, such as rice （Oryza sativa L.）, could provide health-promoting foods for improving human health. However, engineering metabolic pathways for biofortification remains difficult, and previous attempts to engineer anthocyanin production in rice endosperm failed because of the sophisticated genetic regulatory network of its biosynthetic pathway. In this study, we developed a high-efficiency vector system for transgene stacking and used it to engineer anthocyanin biosynthesis in rice endosperm. We made a construct containing eight anthocyanin-related genes （two regulatory genes from maize and six structural genes from Coleus） driven by the endosperm-specific promoters,plus a selectable marker and a gene for marker excision. Transformation of rice with this construct generated a novel biofortified germplasm ＂Purple Endosperm Rice＂ （called ＂Zijingmi＂ in Chinese）, which has high anthocyanin contents and antioxidant activity in the endosperm. This anthocyanin production results from expression of the transgenes and the resulting activation （or enhancement） of expression of 13 endogenous anthocyanin biosynthesis genes that are silenced or expressed at low levels in wild-type rice endosperm. This study provides an efficient, versatile toolkit for transgene stacking and demonstrates its use for successful engineering of a sophisticated biological pathway, suggesting the potential utility of this toolkit for synthetic biology and improvement of agronomic traits in plants.
基金funded by the National Natural Science Foundation of China(Grant No.41941019)the State Key Laboratory of Hydroscience and Engineering(Grant No.2019-KY-03)。
文摘Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.
基金support of the National Natural Science Foundation of China (No.50571073)the Ph.D. Programs Foundation of Ministry of Education of China (No. 20111402110004)the Natural Science Foundation of Shanxi Province, China (No.2009011028-3)
文摘Mg-Zn-Y alloys with long-period stacking ordered structures were prepared by an ingot casting method. The corrosion performance of Mg-Zn-Y alloys was studied by combining gas-collecting test, immersion test and electrochemical measurements in order to determine the corrosion rate and mechanism of the alloys. The results showed that the volume fraction of Mg（12）YZn phase increased and the shape of the Mg（12）YZn phase changed from discontinuous to continuous net-like with increasing Zn and Y content. The corrosion rate of the alloys greatly depended on the distribution and volume fraction of the Mg（12）YZn phase. Corrosion products appeared at the junction of Mg phase and Mg（12）YZn phase, indicating that the Mg（12）YZn phase accelerated galvanic corrosion of Mg matrix. Mg（97）Zn1Y2 alloy shows the lowest corrosion rate due to the continuous distribution of Mg（12）YZn phase.
基金We acknowledge the funding support from Australia Research Council(Grant Nos.DP200100549 and IH180100010).
文摘Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.
