Galactofuranose (Galf)-containing sugar chain contributes to the hyphal growth, conidiation and virulence of F. oxysporum f.sp. cucumerinum

The ascomycete fungus Fusarium oxysporum f.sp. cucumerinum causes vascular wilt diseases in cucumber. However, few genes related to morphogenesis and pathogenicity of this fungal pathogen have been functionally characterized. BLASTp searches of the Aspergillus fumigatus UgmA and galatofuranosyltransferases (Galf-transferases) sequences in the F. oxysporum genome identified two genes encoding putative UDP-galactopyranose mutase (UGM), ugmA and ugmB, and six genes encoding putative Galf-transferase homologs. In this study, the single and double mutants of the ugmA, ugmB and gfsB were obtained. The roles of UGMs and GfsB were investigated by analyzing the phenotypes of the mutants. Our results showed that deletion of the ugmA gene led to a reduced production of galactofuranose-containing sugar chains, reduced growth and impaired conidiation of F. oxysporum f.sp. cucumerinum. Most importantly, the ugmA deletion mutant lost the pathogenicity in cucumber plantlets. Although deletion of the ugmB gene did not cause any visible phenotype, deletion of both ugmA and ugmB genes caused more severe phenotypes as compared with the Delta ugmA, suggesting that UgmA and UgmB are redundant and they can both contribute to synthesis of UDP-Galf. Furthermore, the Delta gfsB exhibited an attenuated virulence although no other phenotype was observed. Our results demonstrate that the galactofuranose (Galf) synthesis contributes to the cell wall integrity, germination, hyphal growth, conidiation and virulence in Fusarium oxysporum f.sp. cucumerinum and an ideal target for the development of new anti-Fusarium agents

Comparative Genome Structure, Secondary Metabolite, and Effector Coding Capacity across Cochliobolus Pathogens

The genomes of five Cochliobolus heterostrophus strains, two Cochliobolus sativus strains, three additional Cochliobolus species (Cochliobolus victoriae, Cochliobolus carbonum, Cochliobolus miyabeanus), and closely related Setosphaeria turcica were sequenced at the Joint Genome Institute (JGI). The datasets were used to identify SNPs between strains and species, unique genomic regions, core secondary metabolism genes, and small secreted protein (SSP) candidate effector encoding genes with a view towards pinpointing structural elements and gene content associated with specificity of these closely related fungi to different cereal hosts. Whole-genome alignment shows that three to five percent of each genome differs between strains of the same species, while a quarter of each genome differs between species. On average, SNP counts among field isolates of the same C. heterostrophus species are more than 25x higher than those between inbred lines and 50x lower than SNPs between Cochliobolus species. The suites of nonribosomal peptide synthetase (NRPS), polyketide synthase (PKS), and SSP-encoding genes are astoundingly diverse among species but remarkably conserved among isolates of the same species, whether inbred or field strains, except for defining examples that map to unique genomic regions. Functional analysis of several strain-unique PKSs and NRPSs reveal a strong correlation with a role in virulence

Comparative genome structure, secondary metabolite, and effector coding capacity across Cochliobolus pathogens.

The genomes of five Cochliobolus heterostrophus strains, two Cochliobolus sativus strains, three additional Cochliobolus species (Cochliobolus victoriae, Cochliobolus carbonum, Cochliobolus miyabeanus), and closely related Setosphaeria turcica were sequenced at the Joint Genome Institute (JGI). The datasets were used to identify SNPs between strains and species, unique genomic regions, core secondary metabolism genes, and small secreted protein (SSP) candidate effector encoding genes with a view towards pinpointing structural elements and gene content associated with specificity of these closely related fungi to different cereal hosts. Whole-genome alignment shows that three to five percent of each genome differs between strains of the same species, while a quarter of each genome differs between species. On average, SNP counts among field isolates of the same C. heterostrophus species are more than 25× higher than those between inbred lines and 50× lower than SNPs between Cochliobolus species. The suites of nonribosomal peptide synthetase (NRPS), polyketide synthase (PKS), and SSP-encoding genes are astoundingly diverse among species but remarkably conserved among isolates of the same species, whether inbred or field strains, except for defining examples that map to unique genomic regions. Functional analysis of several strain-unique PKSs and NRPSs reveal a strong correlation with a role in virulence

Origin identification of Cornus officinalis based on PCA-SVM combined model.

Infrared spectroscopy can quickly and non-destructively extract analytical information from samples. It can be applied to the authenticity identification of various Chinese herbal medicines, the prediction of the mixing amount of defective products, and the analysis of the origin. In this paper, the spectral information of Cornus officinalis from 11 origins was used as the research object, and the origin identification model of Cornus officinalis based on mid-infrared spectroscopy was established. First, principal component analysis was used to extract the absorbance data of Cornus officinalis in the wavenumber range of 551~3998 cm-1. The extracted principal components contain more than 99.8% of the information of the original data. Second, the extracted principal component information was used as input, and the origin category was used as output, and the origin identification model was trained with the help of support vector machine. In this paper, this combined model is called PCA-SVM combined model. Finally, the generalization ability of the PCA-SVM model is evaluated through an external test set. The three indicators of Accuracy, F1-Score, and Kappa coefficient are used to compare this model with other commonly used classification models such as naive Bayes model, decision trees, linear discriminant analysis, radial basis function neural network and partial least square discriminant analysis. The results show that PCA-SVM model is superior to other commonly used models in accuracy, F1 score and Kappa coefficient. In addition, compared with the SVM model with full spectrum data, the PCA-SVM model not only reduces the redundant variables in the model, but also has higher accuracy. Using this model to identify the origin of Cornus officinalis, the accuracy rate is 84.8%

Application of combined model of stepwise regression analysis and artificial neural network in data calibration of miniature air quality detector

Abstract In this paper, six types of air pollutant concentrations are taken as the research object, and the data monitored by the micro air quality detector are calibrated by the national control point measurement data. We use correlation analysis to find out the main factors affecting air quality, and then build a stepwise regression model for six types of pollutants based on 8 months of data. Taking the stepwise regression fitting value and the data monitored by the miniature air quality detector as input variables, combined with the multilayer perceptron neural network, the SRA-MLP model was obtained to correct the pollutant data. We compared the stepwise regression model, the standard multilayer perceptron neural network and the SRA-MLP model by three indicators. Whether it is root mean square error, average absolute error or average relative error, SRA-MLP model is the best model. Using the SRA-MLP model to correct the data can increase the accuracy of the self-built point data by 42.5% to 86.5%. The SRA-MLP model has excellent prediction effects on both the training set and the test set, indicating that it has good generalization ability. This model plays a positive role in scientific arrangement and promotion of miniature air quality detectors. It can be applied not only to air quality monitoring, but also to the monitoring of other environmental indicators

Mesoproterozoic (ca. 1.3 Ga) A-Type Granites on the Northern Margin of the North China Craton: Response to Break-Up of the Columbia Supercontinent

Mesoproterozoic (ca. 1.3 Ga) magmatism in the North China Craton (NCC) was dominated by mafic intrusions (dolerite sills) with lesser amounts of granitic magmatism, but our lack of knowledge of this magmatism hinders our understanding of the evolution of the NCC during this period. This study investigated porphyritic granites from the Huade–Kangbao area on the northern margin of the NCC. Zircon dating indicates the porphyritic granites were intruded during the Mesoproterozoic between 1285.4 ± 2.6 and 1278.6 ± 6.1 Ma. The granites have high silica contents (SiO2 = 63.10–73.73 wt.%), exhibit alkali enrichment (total alkalis = 7.71–8.79 wt.%), are peraluminous, and can be classified as weakly peraluminous A2-type granites. The granites have negative Eu anomalies (δEu = 0.14–0.44), enrichments in large-ion lithophile elements (LILEs; e.g., K, Rb, Th, and U), and depletions in high-field-strength elements (HFSEs; e.g., Nb, Ta, and Ti). εHf(t) values range from –6.43 to +2.41, with tDM2 ages of 1905–2462 Ma, suggesting the magmas were derived by partial melting of ancient crustal material. The geochronological and geochemical data, and regional geological features, indicate the Mesoproterozoic porphyritic granites from the northern margin of the NCC formed in an intraplate tectonic setting during continental extension and rifting, which represents the response of the NCC to the break-up of the Columbia supercontinent