Disease Analysis and Pathogen Identification of Black Spot Disease of Chinese Cabbage [Alternaria brassicae (Berk.) Sace.] in Bijie City

[Objective] Disease analysis and pathogen identification of black spot disease of Chinese cabbage [Alternaria brassicae（Berk.） Sace.] in Bijie City of Guizhou Province was carried out,so as to raise the corresponding prevention measures.[Method] Pathogen was isolated from diseased leaves using tissue surface separation method for cultivation,microscopic examination,spore size determination and pathogen identification.[Result] Microscopic examination showed that the disease was caused by Alternaria sp.,the size of its spores was（15.0-38.5） μm×（7.5-13.5） μm.Combining with the characteristics of host and pathogen,the pathogen caused black spot disease of Chinese cabbage was identified to be Alternaria brassicicola.[Conclusion] A.brassicicola was the pathogenic fungi of black spot disease of Chinese cabbage in Bijie City of Guizhou Province,and damage caused by the disease could be reduced through agricultural measures and chemical control.

Cloning and Sequencing Characteristics of rDNA ITS Region of the Pathogen Causing Black Spot Disease on Tumorous Stem Mustard

Taking tumorous stem mustard infected by black spot disease as the research material, the ribosomal 5.8S rDNA and its flanking ITS region were cloned, sequenced and aligned in the study. The results showed that the base sequence of pathogens collected from five different sites was almost identical with that of Alternaria brassicae, and the similarity degree reached 99.68%, without base difference greater than 3 bp. It had obvious differences with the sequences of A. brassicicola and A. japonica, and the base difference was greater than 3 bp with a lot of fragment deletions. It was preliminarily determined that the pathogen cau- sing black spot disease on tumorous stem mustard was A. brassicae.

Identification of differentially expressed proteins in poplar leaves in-duced by Marssonina brunnea f. sp. Multigermtubi

Black spot disease in poplar is a disease of the leaf caused by fungus. The major pathogen is Marssonina brunnea f. sp. multigermtubi. To date, little is known about the molecular mechanism of poplar （M. brunnea） interaction. In order to identify the proteins related to disease resistance and understand its molecular basis, the clone ＂NL895＂ （P. euramericana CL＂NL895＂）, which is highly resistant to M. brunnea f. sp. multigermtubi, was used in this study. We used two-dimensional gel electrophoresis （2-DE） and mass spectrometry （MS） to identify the proteins in poplar leaves that were differentially expressed in response to black spot disease pathogen, M. brunnea f. sp. multigermtubi. Proteins extracted from poplar leaves at 0, 12, 24, 48, and 72 h after pathogen-inoculation were separated by 2-DE, About 500 reproducible protein spots were detected, of which 40 protein spots displayed differential expression in levels and were subjected to Matrix assisted laser desorption/ionization time of flight mass spectrometry （MALDI-TOF MS） followed by database searching. According to the function, the identified proteins were sorted into five categories, that is, protein synthesis, metabolism, defense response and unclassified proteins.

Phyllosticta species associated with citrus diseases in China

Phyllosticta species associated with diseases of four commercial Citrus species grown in China are reported.Totally,496 Phyllosticta strains were isolated from mandarins(Citrus reticulata),pomeloes(C.maxima),oranges(C.sinensis)and lemons(C.limon)in the main citrus producing regions across China,and 74 strains were selected for phylogenetic analysis.Analyses inferred from the sequences of internal transcribed spacer region(ITS1,5.8S nrDNA and ITS2),partial translation elongation factor 1-alpha(TEF1)and partial actin gene(ACT),showed these representative Phyllosticta isolates clustered in four distinct clades corresponding to three known,and one undescribed species.The newly resolved taxon,Phyllosticta citrichinaensis was isolated from leaves and fruits of all four Citrus species and is introduced in this paper.This taxon caused minor damage,showing irregular spots or freckles.Phyllosticta citriasiana,associated with tan spot of pomeloes,was isolated only from pomeloes,and never from lemons,mandarins and oranges.Phyllosticta citricarpa,the citrus black spot pathogen,which is presently subjected to phytosanitary legislation in the EU and United States,was isolated from lemons,mandarins and oranges,but never from pomeloes.The isolates of P.citricarpa clustered in two subclades,one from mandarins,the other from oranges and lemons.P.capitalensis was isolated from all four Citrus species as an endophyte,causing false melanose,or together with P.citricarpa or P.citriasiana.Morphological,cultural and biochemical characters were consistent with the results of phylogenetic analysis.In addition,a specific primer pair Pca8/ITS4 was designed and selected,and its corresponding PCR procedure was developed for the detection of P.citriasiana in this study.

Rapid and non-destructive decay detection of Yali pears using hyperspectral imaging coupled with 2D correlation spectroscopy

The black spot disease caused by Alternaria alternata on Yali pears is a great concern as it compromises their edible quality and commercial value.To realize rapid and non-destructive classification of this disease,hyperspectral imaging(HSI)technology was combined with two-dimensional correlation spectroscopy(2DCOS)analysis.A total of 150 pear samples at different decay grades were prepared.After obtaining the HSI images,the whole sample was demarcated as the region of interest,and the spectral information was extracted.Seven preprocessing methods were applied and compared to build the classification models.Thereafter,using the inoculation day as an external perturbation,2DCOS was used to select the feature-related wavebands for black spot disease identification,and the result was compared to those obtained using competitive adaptive reweighting sampling and the successive projections algorithm.Results demonstrated that the simplified least squares support vector model based on 2DCOS-identified feature wavebands yielded the best performance with the identification accuracy,precision,sensitivity,and specificity of 97.30%,94.60%,96.16%,and 98.21%,respectively.Therefore,2DCOS can effectively interpret the feature-related wavebands,and its combination with HSI is an effective tool to predict black spot disease on Yali pears.