Segment drying is a severe physiological disorder of citrus fruit,and vesicles become granulated or collapsed.Aside from the hypothesis that alteration of cell wall metabolism is the main factor of citrus granulation,...Segment drying is a severe physiological disorder of citrus fruit,and vesicles become granulated or collapsed.Aside from the hypothesis that alteration of cell wall metabolism is the main factor of citrus granulation,little is known about vesicle collapse.This study aimed to elucidate the changes in pectin metabolism during vesicle collapse in blood orange.Vesicle collapse was characterized by decreased nutrients and increased chelate-and sodium carbonate-soluble pectin and calcium content.The nanostructure of chelate-soluble pectin became complex and developed multi-branching upon collapse.The activity of pectin methylesterase increased,while that of polygalacturonase and pectate lyase decreased upon collapse.Genome-wide transcriptional analysis revealed an increasing pattern of genes encoding pectin methylesterase and other enzymes involved in pectin synthesis and demethylesterification upon collapse.Drying vesicles were characterized by increased abscisic acid content and relevant gene expression.In conclusion,we discovered alteration in pectin metabolism underlying citrus vesicle collapse,mainly promoting pectin demethylesterification,remodeling pectin structures,and further inhibiting pectin degradation,which was hypothesized to be a main factor for citrus collapse.This is the first study to disclose the potential intrinsic mechanism underlying vesicle collapse in orange fruit.展开更多
Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced mach...Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.展开更多
基金funded by the National Natural Science Foundation of China(No.32172262)the Fundamental Research Funds for the Central Universities(No.XDJK2021F008)Chongqing Graduate Student Research Innovation Project(No.CYS21118),China.
文摘Segment drying is a severe physiological disorder of citrus fruit,and vesicles become granulated or collapsed.Aside from the hypothesis that alteration of cell wall metabolism is the main factor of citrus granulation,little is known about vesicle collapse.This study aimed to elucidate the changes in pectin metabolism during vesicle collapse in blood orange.Vesicle collapse was characterized by decreased nutrients and increased chelate-and sodium carbonate-soluble pectin and calcium content.The nanostructure of chelate-soluble pectin became complex and developed multi-branching upon collapse.The activity of pectin methylesterase increased,while that of polygalacturonase and pectate lyase decreased upon collapse.Genome-wide transcriptional analysis revealed an increasing pattern of genes encoding pectin methylesterase and other enzymes involved in pectin synthesis and demethylesterification upon collapse.Drying vesicles were characterized by increased abscisic acid content and relevant gene expression.In conclusion,we discovered alteration in pectin metabolism underlying citrus vesicle collapse,mainly promoting pectin demethylesterification,remodeling pectin structures,and further inhibiting pectin degradation,which was hypothesized to be a main factor for citrus collapse.This is the first study to disclose the potential intrinsic mechanism underlying vesicle collapse in orange fruit.
基金supported by the National Natural Science Foundation of China Civil Aviation Joint Fund (U1833110)Research on the Dual Prevention Mechanism and Intelligent Management Technology f or Civil Aviation Safety Risks (YK23-03-05)。
文摘Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.