Preface to Special Topic on Atmospheric Greenhouse Gas Measurement and Application in China

China initiated a national carbon trading market in December 2017.Commitments and actions to reduce greenhouse gas(GHG)emissions require consistent,reliable and timely information on GHG emissions.GHG monitoring and modeling studies provide GHG emission estimates to evaluate and guide progress towards emission reductions.GHG monitoring has mainly focused on global-scale background networks over the last few decades,while recent efforts have been made on regional and urban scales,such as projects in the Beijing-Tianjin-Hebei city cluster,in Paris,Washington-Baltimore.

The First High-quality Reference Genome of Sika Deer Provides Insights into High-tannin Adaptation

Sika deer are known to prefer oak leaves,which are rich in tannins and toxic to most mammals;however,the genetic mechanisms underlying their unique ability to adapt to living in the jungle are still unclear.In identifying the mechanism responsible for the tolerance of a highly toxic diet,we have made a major advancement by explaining the genome of sika deer.We generated the first high-quality,chromosome-level genome assembly of sika deer and measured the correlation between tannin intake and RNA expression in 15 tissues through 180 experiments.Comparative genome analyses showed that the UGT and CYP gene families are functionally involved in the adaptation of sika deer to high-tannin food,especially the expansion of the UGT family 2 subfamily B of UGT genes.The first chromosome-level assembly and genetic characterization of the tolerance to a highly toxic diet suggest that the sika deer genome may serve as an essential resource for understanding evolutionary events and tannin adaptation.Our study provides a paradigm of comparative expressive genomics that can be applied to the study of unique biological features in non-model animals.

A software defect prediction method with metric compensation based on feature selection and transfer learning

Cross-project software defect prediction solves the problem of insufficient training data for traditional defect prediction,and overcomes the challenge of applying models learned from multiple different source projects to target project.At the same time,two new problems emerge:(1)too many irrelevant and redundant features in the model training process will affect the training efficiency and thus decrease the prediction accuracy of the model;(2)the distribution of metric values will vary greatly from project to project due to the development environment and other factors,resulting in lower prediction accuracy when the model achieves cross-project prediction.In the proposed method,the Pearson feature selection method is introduced to address data redundancy,and the metric compensation based transfer learning technique is used to address the problem of large differences in data distribution between the source project and target project.In this paper,we propose a software defect prediction method with metric compensation based on feature selection and transfer learning.The experimental results show that the model constructed with this method achieves better results on area under the receiver operating characteristic curve(AUC)value and F1-measure metric.