The Biolog system(Biolog Inc. USA,BIS)with Microstation TM V3.5 software were used for rapid identification of several bacterial pathogens causing rice leaf streak and related plant-pathogenic bacteria. The results sh...The Biolog system(Biolog Inc. USA,BIS)with Microstation TM V3.5 software were used for rapid identification of several bacterial pathogens causing rice leaf streak and related plant-pathogenic bacteria. The results showed that 12 of 16 strains tested were correctly identified at species(pathovars) level, and 4 strains at genus level. Cluster analysis using Mlclust program(Biolog) and Proc cluster(SAS release 6.04)process showed that the Biolog catabolic profiling between strains of leersiae bacterial leaf streak(BLS) and ”rice short streak”(eg R1008) was similar, having higher phenotypic similarity with wheat-derived isolates(eg TAS),but differed from rice bacterial blight and rice BLS pathogens. Multivariate statistics were first used to analysis the Biolog data.The result indicated that cluster analysis and principal component(PC) analysis were very useful in testing for significent differences between communties,and that PC analysis was advantageous to find discriminating carbon sourses according to weighted factor loadings. Fig 1, Tab 2, Ref展开更多
文摘The Biolog system(Biolog Inc. USA,BIS)with Microstation TM V3.5 software were used for rapid identification of several bacterial pathogens causing rice leaf streak and related plant-pathogenic bacteria. The results showed that 12 of 16 strains tested were correctly identified at species(pathovars) level, and 4 strains at genus level. Cluster analysis using Mlclust program(Biolog) and Proc cluster(SAS release 6.04)process showed that the Biolog catabolic profiling between strains of leersiae bacterial leaf streak(BLS) and ”rice short streak”(eg R1008) was similar, having higher phenotypic similarity with wheat-derived isolates(eg TAS),but differed from rice bacterial blight and rice BLS pathogens. Multivariate statistics were first used to analysis the Biolog data.The result indicated that cluster analysis and principal component(PC) analysis were very useful in testing for significent differences between communties,and that PC analysis was advantageous to find discriminating carbon sourses according to weighted factor loadings. Fig 1, Tab 2, Ref
