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| 1 | Correction to:Development of a Novel Reverse Transcription Loop-Mediated Isothermal Amplification Method for Rapid Detection of SARS-CoV-2显示文摘In the original version of this article,the legend to Figure 1 was incorrect.The corrected legend is given below.Fig.1 A Location of the primers in SARS-CoV-2 genome.B Sequence comparison among seven human coronaviruses(SARS-CoV-2,SARS-CoV,MERS-CoV,HCoVOC43,HCoV-HKU-1,HCoV-NL63 and HCoV-229E).C Cross-reactivity test of the novel SARS-CoV-2 RTLAMP assay to other common respiratory viruses.Tested common respiratory viruses include HCoV-HKU-1,HCoV-NL63,HCoV-OC43,HCoV-229E,influenza A,B,and C viruses,parainfluenza viruses type 1-3,enterovirus,respiratory syncytial virus A and B groups,human rhinovirus,human metapneumovirus,adenovirus and bocavirus. | Renfei Lu Xiuming Wu Zhenzhou Wan Yingxue Li Lulu Zuo Jianru Qin Xia Jin Chiyu Zhang | 2020 | Virologica Sinica2020，35，4： | 8 |
| 2 | Development of a Novel Reverse Transcription Loop-Mediated Isothermal Amplification Method for Rapid Detection of SARS-CoV-2显示文摘Dear Editor,Since early December 2019, a large outbreak of pneumonia caused by a novel coronavirus(COVID-19) had emerged in Wuhan, China(Wu et al. 2020 a, b;Zhou et al. 2020;Zhu et al. 2020;Jiang and Shi 2020). Similar to severe acute respiratory syndrome coronavirus(SARS-CoV) and Middle East respiratory syndrome coronavirus(MERS-CoV),the new coronavirus also belongs to Betacoronavirus。 | Renfei Lu Xiuming Wu Zhenzhou Wan Yingxue Li Lulu Zuo Jianru Qin Xia Jin Chiyu Zhang | 2020 | Virologica Sinica2020，35，3： | 7 |
| 3 | Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections显示文摘Cyclist trajectory prediction is of great significance for both active collision avoidance and path planning of intelligent vehicles.This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios.This method is based on dynamic Bayesian network(DBN)and long short-term memory(LSTM).The motion intention of cyclists is hard to predict owing to potential large uncertainties.The DBN is used to infer the distribution of cyclists’intentions at intersections to improve the prediction time.The LSTM with encoder-decoder is used to predict the cyclists’trajectories to improve the accuracy of prediction.Therefore,the DBN and LSTM are adopted to guarantee prediction accuracy and improve the prediction time.The experiment results are presented to show the effectiveness of the predict strategies. | Hongbo GAO Hang SU Yingfeng CAI Renfei WU Zhengyuan HAO Yongneng XU Wei WU Jianqing WANG Zhijun LI Zhen KAN | 2021 | Science China(Information Sciences)2021，64，7： | 4 |
| 4 | Vehicle emissions of primary air pollutants from 2009 to 2019 and projection for the 14th Five-Year Plan period in Beijing, China显示文摘Over the past decade,the emission standards and fuel standards in Beijing have been upgraded twice,and the vehicle structure has been improved by accelerating the elimination of 2.95 million old vehicles.Through the formulation and implementation of these policies,the emissions of carbon monoxide(CO),volatile organic compounds(VOCs),nitrogen oxides(NO_(x)),and fine particulate matter(PM_(2.5))in 2019 were 147.9,25.3,43.4,and 0.91 kton in Beijing,respectively.The emission factor method was adopted to better understand the emissions characteristics of primary air pollutants from combustion engine vehicles and to improve pollution control.In combination with the air quality improvement goals and the status of social and economic development during the 14th Five-Year Plan period in Beijing,different vehicle pollution control scenarios were established,and emissions reductions were projected.The results show that the emissions of four air pollutants(CO,VOCs,NO_(x),and PM_(2.5))fromvehicles in Beijing decreased by an average of 68% in 2019,compared to their levels in 2009.The contribution of NOx emissions from diesel vehicles increased from 35% in 2009 to 56% in 2019,which indicated that clean and energy-saving diesel vehicle fleets should be further improved.Electric vehicle adoption could be an important measure to reduce pollutant emissions.With the further upgrading of vehicle structure and the adoption of electric vehicles,it is expected that the total emissions of the four vehicle pollutants can be reduced by 20%-41% by the end of the 14th Five-Year Plan period. | Tongran Wu Yangyang Cui Aiping Lian Ye Tian Renfei Li Xinyu Liu Jing Yan Yifeng Xue Huan Liu Bobo Wu | 2023 | Journal of Environmental Sciences2023，，2： | 1 |
| 5 | Regulating the Electron Localization of Metallic Bismuth for Boosting CO_(2)Electroreduction显示文摘Electrochemical reduction of CO_(2)to formate is economically attractive but improving the reaction selectivity and activity remains challenging.Herein,we introduce boron(B)atoms to modify the local electronic structure of bismuth with positive valence sites for boosting conversion of CO_(2)into formate with high activity and selectivity in a wide potential window.By combining experimental and computational investigations,our study indicates that B dopant differentiates the proton participations of rate-determining steps in CO_(2)reduction and in the competing hydrogen evolution.By comparing the experimental observations with the density functional theory,the dominant mechanistic pathway of B promoted formate generation and the B concentration modulated effects on the catalytic property of Bi are unravelled.This comprehensive study offers deep mechanistic insights into the reaction pathway at an atomic and molecular level and provides an effective strategy for the rational design of highly active and selective electrocatalysts for efficient CO_(2)conversion. | Dan Wu Renfei Feng Chenyu Xu Peng-Fei Sui Jiujun Zhang Xian-Zhu Fu Jing-Li Luo | 2022 | Nano-Micro Letters2022，14，2： | 0 |
| 6 | Ecogeographical variation of 12 morphological traits within Pinus tabulaeformis: the effects of environmental factors and demographic histories显示文摘Aims More data are needed about how genetic variation(GV)and envi-ronmental factors influence phenotypic variation within the natural populations of long-lived species with broad geographic distribu-tions.To fill this gap,we examined the correlations among envi-ronmental factors and phenotypic variation within and among 13 natural populations of Pinus tabulaeformis consisting of four demo-graphically distinct groups within the entire distributional range.Methods Using the Akaike’s information Criterion(AiC)model,we measured 12 morphological traits and constructed alternative candidate models for the relationships between each morphological trait and key climatic variables and genetic groups.We then compared the AiC weight for each candidate model to identify the best approximating model for ecogeographical variation of P.tabulaeformis.The partitioning of vari-ance was assessed subsequently by evaluating the independent vari-ables of the selected best models using partial redundancy analysis.Important Findings Significant phenotypic variation of the morphological traits was observed both within individual populations and among populations.Variation partition analyses showed that most of the phenotypic variation was co-determined by both GV and climatic factors.GV accounted for the largest proportion of reproductive trait variation,whereas local key climatic factors(i.e.actual evapotranspiration,AET)accounted for the largest proportion of phenotypic variation in the remaining investigated traits.Our results indicate that both genetic divergence and key environmental factors affect the phenotypic variation observed among populations of this species,and that reproductive and vegetative traits adaptively respond differently with respect to local environmental conditions.This partitioning of factors can inform those making predictions about phenotypic variation in response to future changes in climatic conditions(particularly those affecting AET). | Mingfei Ji Jianming Deng Buqing Yao Renfei Chen Zhexuan Fan Jiawei Guan Xiaowei Li Fan Wu Karl J.Niklas | 2017 | Journal of Plant Ecology2017，10，2： | 0 |
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