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Structural and functional analysis of denitrification genes in Pseudomonas stutzeri A1501

Science China(Life Sciences) 2005年第6期48卷 585-592页

作者： YAN Yongliang1,2, YANG Jian2, CHEN Lihong2, YANG Fan2, DONG Jie2, XUE Ying2, XU Xingye2, ZHU Yafang2, YAO Zhijian3, LIN Min4, WANG Yiping5 & JIN Qi1,2 1. College of Biological Sciences, China Agricultural University, Beijing 100094, China 2. State Key Laboratory for Moleclular virology and Genetic Engineering, Beijing 100176, China 3. National center for Human Genome Research, Beijing 100176, China 4. Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China 5. College of Life Sciences, Peking University, Beijing 100871, China College of Biological Sciences China Agricultural University Beijing 100094 China State Key Laboratory for Moleclular virology and Genetic Engineering Beijing 100176 China State Key Laboratory for Moleclular virology and Genetic Engineering Beijing 100176 China National center for Human Genome Research Beijing 100176 China Biotechnology Research Institute Chinese Academy of Agricultural Sciences Beijing 100081 China College of Life Sciences Peking University Beijing 100871 China

Four gene clusters associated with denitrification were identified in the genome of A1501 strain, nar, nir, nor and nos, including 40 genes totally, which encode proteins for sub-stance transportation, gene regulation and reductases. The three gene clusters, nir, nor and nos are adjacent on chromosome and are far from nar gene cluster. Compared with other denitrifying bacteria, the 40 denitrification genes in A1501 strain compose a complete denitrification catalysis system. In A1501 strain, this system has the following characteristics: (i) only one copy of narK gene is found in nar gene cluster; (ii) a narM gene is present between narK and narG; (iii) two genes, dnarE and orfl are identified at downstream of narX and narL genes, of which dnrE per-haps is a transcriptional factor belonging to FNR family; (iv) there are 16 nir genes in A1501, the most in the known denitrifying bacteria; (v) it is for the first time that norR gene has been found in A1501 and also in Pseudomonas; (vi) nos gene cluster is relatively conservative, with a com-pletely identical composition and arrangement of genome to the reference bacteria strain.

关键词： Pseudomonas stutzeri, denitrification, nitrate reductase, nitrite reductase, nitric oxide reductase, nitrous oxide reductase.

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Estimation of Potato Biomass and Yield Based on Machine Learning from Hyperspectral Remote Sensing Data

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Journal of Agricultural Science and Technology(B) 2020年第4期10卷 195-213页

作者： Changchun Li Chunyan Ma Haojie Pei Haikuan Feng Jinjin Shi Yilin Wang Weinan Chen Yacong Li Xiaowei Feng Yonglei Shi School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuoHenan 454000China National Engineering Research center for Information Technology in Agriculture Beijing 10000China

The estimation of potato biomass and yield can optimize the planting pattern and tap the production *** on partial least square(PLSR),multiple linear regression(MLR),support vector machine(SVM),random forest(RF),BP neural network and other machine learning algorithms,the biomass estimation model of potato in different growth stages is constructed by using single variables such as original spectrum,first-order differential spectrum,combined spectrum index and vegetation index(vi)and their coupled combination *** accuracy of the models is compared and analyzed,and the best modeling method of biomass in different growth stages is *** on the optimized modeling method,the biomass of each growth stage is estimated,and the yield estimation model of different growth stages is constructed based on the estimation results and the linear regression analysis method,and the accuracy of the model is *** results showed that in tuber formation stage,starch accumulation stage and maturity stage,the biomass estimation accuracy based on combination variable was the highest,the best modeling method was MLR and SVM,in tuber growth stage,the best modeling method was MLR,the effect of yield estimation is *** provides a reference for the algorithm selection of crop biomass and yield models based on machine learning.

关键词： Biomass yield potato combination spectral index vegetation index combination variables machine learning

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