BackgroundExploiting soil microorganisms in the rhizosphere of plants can significantly improve agricultural productivity; however, the mechanism by which microorganisms specifically affect agricultural productivity is poorly understood. To clarify this uncertainly, the rhizospheric microbial communities of super rice plants at various growth stages were analysed using 16S rRNA high-throughput gene sequencing; microbial communities were then related to soil properties and rice productivity.ResultsThe rhizospheric bacterial communities were characterized by the phyla Proteobacteria, Acidobacteria, Chloroflexi, and Verrucomicrobia during all stages of rice growth. Rice production differed by approximately 30% between high- and low-yield sites that had uniform fertilization regimes and climatic conditions, suggesting the key role of microbial communities. Mantel tests showed a strong correlation between soil conditions and rhizospheric bacterial communities, and microorganisms had different effects on crop yield. Among the four growing periods, the rhizospheric bacterial communities present during the heading stage showed a more significant correlation (p <  0.05) with crop yield, suggesting their potential in regulating crop production. The biological properties (i.e., microbes) reflected the situation of agricultural land better than the physicochemical characterics (i.e., nutrient elements), which provides theoretical support for agronomic production. Molecular ecological network (MEN) analysis suggested that differences in productivity were caused by the interaction between the soil characteristics and the bacterial communities.ConclusionsDuring the heading stage of rice cropping, the rhizospheric microbial community is vital for the resulting rice yield. According to network analysis, the cooperative relationship (i.e., positive interaction) between between microbes may contribute significantly to yield, and the biological properties (i.e., microbes) better reflected the real conditions of agricultural land than did the physicochemical characteristics (i.e., nutrient elements).Electronic supplementary materialThe online version of this article (10.1186/s12866-018-1174-z) contains supplementary material, which is available to authorized users.

Heterologous expression of biosynthetic pathways is an important way to research and discover microbial natural products. Bacillus subtilis is a suitable host for the heterologous production of natural products from bacilli and related Firmicutes. Existing technologies for heterologous expression of large biosynthetic gene clusters in B. subtilis are complicated. Herein, we present a simple and rapid strategy for direct cloning based heterologous expression of biosynthetic pathways in B. subtilis via Red/ET recombineering, using a 5.2 kb specific direct cloning vector carrying homologous sequences to the amyE gene in B. subtilis and CcdB counterselection marker. Using a two-step procedure, two large biosynthetic pathways for edeine (48.3 kb) and bacillomycin (37.2 kb) from Brevibacillus brevis X23 and B. amyloliquefaciens FZB42, respectively, were directly cloned and subsequently integrated into the chromosome of B. subtilis within one week. The gene cluster for bacillomycin was successfully expressed in the heterologous host, although edeine production was not detectable. Compared with similar technologies, this method offers a simpler and more feasible system for the discovery of natural products from bacilli and related genera.

BackgroundThere are two major challenges in automated heart sound analysis: segmentation and classification. An efficient segmentation is capable of providing valuable diagnostic information of patients. In addition, it is crucial for some feature-extraction based classification methods. Therefore, the segmentation of heart sound is of significant value.MethodsThis paper presents an automatic heart sound segmentation method that combines the time-domain analysis, frequency-domain analysis and time–frequency-domain analysis. Employing this method, the boundaries of heart sound components are first located, and the components are then recognized. Finally, the heart sounds are divided into several segments on the basis of the results of boundary localization and component identification.ResultsIn order to evaluate the performance of the proposed method, quantitative experiments are performed on an authoritative heart sound database. The experimental results show that the boundary localization has a sensitivity (Se) of 100%, a positive predictive value (PPV) of 99.3% and an accuracy (Acc) of 99.93%. Moreover, the Se, PPV and Acc of component identification reach 98.63, 99.86 and 98.49%, respectively.ConclusionThe proposed method shows reliable performance on the segmentation of heart sounds. Compared with previous works, this method can be applied to not only normal heart sounds, but also the sounds with S3, S4 and murmurs, thus greatly increasing the applied range.