Insight into the composition and differentiation of endophytic microbial communities in kernels via 368 maize transcriptomes.

INTRODUCTION: Kernels are important reproductive organs in maize, yet there is a lack of systematic investigation on the differences in the composition of endophytic microorganisms in plants from a population perspective. OBJECTIVES: We aimed to elucidate the composition of endophytic microorganisms in developing maize kernels, emphasizing differences among various inbred lines. METHODS: The transcriptomic data of 368 maize inbred lines were used to explore the composition and diversity of endophytic microorganisms. RESULTS: The findings revealed a higher abundance of fungi than bacteria in developing maize kernels, followed by protozoa, while viruses were less abundant. There were significant differences in the composition and relative abundance of endophytic microorganisms among different maize lines. Diversity analysis revealed overall similarity in the community composition structure between tropical/subtropical (TST) and temperate (NSS) maize germplasm with apparent variations in community richness and abundance. The endophytic microorganisms network in the kernels from TST genotypes exhibited higher connectivity and stability compared to NSS kernels. Bacteria dominated the highly connected species in the networks, and different core species showed microbial phylum specificity. Some low-abundance species acted as core species, contributing to network stability. Beneficial bacteria were predominant in the core species of networks in TST kernels, while pathogenic bacteria were more abundant in the core species of networks in NSS kernels. CONCLUSION: Tropical maize germplasm may have advantages in resisting the invasion of pathogenic microorganisms, providing excellent genetic resources for disease-resistant breeding.

Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy.

Fritillaria has a long history in China, and it can be consumed as medicine and food. Owing to the high cost of Fritillaria cirrhosa, traders sometimes mix it with the cheaper Fritillaria thunbergii powder to make profit. Herein, we proposed a laser-induced breakdown spectroscopy (LIBS) technique to test the adulteration present in the sample of Fritillaria cirrhosa powder. Experimental samples with different adulteration levels were prepared, and their LIBS spectra were obtained. Partial least squares regression (PLSR) was adopted as the quantitative analysis model to compare the effects of four data standardization methods, namely, mean centring, normalization by total area, standard normal variable, and normalization by the maximum, on the performance of the PLSR model. Principal component analysis and least absolute shrinkage and selection operator (LASSO) were utilized for feature extraction and feature selection, and the performance of the PLSR model was determined based on its quantitative analysis. Subsequently, the optimal number of features was determined. The residuals were corrected using support vector regression (SVR). The mean absolute error and root mean square error of prediction obtained from the quantitative analysis results of the combined LASSO-PLSR-SVR model for the test set data were 5.0396% and 7.2491%, respectively, and the coefficient of determination R2 was 0.9983. The results showed that the LIBS technique can be adopted to test adulteration in the sample of Fritillaria cirrhosa powder and has potential applications in drug quality control.

Establishment and Validation of a New Analysis Strategy for the Study of Plant Endophytic Microorganisms.

Amplicon sequencing of bacterial or fungal marker sequences is currently the main method for the study of endophytic microorganisms in plants. However, it cannot obtain all types of microorganisms, including bacteria, fungi, protozoa, etc., in samples, nor compare the relative content between endophytic microorganisms and plants and between different types of endophytes. Therefore, it is necessary to develop a better analysis strategy for endophytic microorganism investigation. In this study, a new analysis strategy was developed to obtain endophytic microbiome information from plant transcriptome data. Results showed that the new strategy can obtain the composition of microbial communities and the relative content between plants and endophytic microorganisms, and between different types of endophytic microorganisms from the plant transcriptome data. Compared with the amplicon sequencing method, more endophytic microorganisms and relative content information can be obtained with the new strategy, which can greatly broaden the research scope and save the experimental cost. Furthermore, the advantages and effectiveness of the new strategy were verified with different analysis of the microbial composition, correlation analysis, inoculant content test, and repeatability test.

Self-adjusting inverse design method for nanophotonic devices.

Nanophotonic devices, which consist of multiple cell structures of the same size, are easy to manufacture. To avoid the optical proximity effect in the ultraviolet lithography process, the cell structures must be maintained at a distance from one another. In the inverse design process, the distance is maintained by limiting the optimized range of the location. However, this implementation can weaken the performance of the devices designed during transmission. To solve this problem, a self-adjusting inverse design method based on the adjoint variable method is developed. By introducing artificial potential field method, the location of one cell structure is modified only when the distances between this cell structure and other cell structures are smaller than a threshold. In this case, the range of the location can be expanded, and thus the performance of the designed devices can be improved. A wavelength demultiplexer with a channel spacing of 1.6 nm is designed to verify the performance of the proposed method. The experiment reveals that the transmission of the designed devices can be improved by 20%, and the self-adjusting inverse design process is 100 times faster than the inverse-design process based on the genetic algorithm.

Acceleration of inverse design process on adaptive mesh.

In inverse design, the design and background areas can be represented by different spatial resolutions; thus, adaptive meshes are more efficient than structured meshes. In this study, a second-order interpolation scheme is introduced to realize an inverse design process on an adaptive mesh. Experiment results show that the proposed scheme yields a 1.79-fold acceleration over that achieved using a structured mesh, aiding design time reduction or design area expansion. As the design area can be divided into multiple areas with different spatial resolutions, in future work, adaptive meshes can be combined with machine learning algorithms to further improve the inverse-design-process efficiency.

Discrimination of hazardous bacteria with combination laser-induced breakdown spectroscopy and statistical methods.

Real-time biohazard detectors must be developed to facilitate the rapid implementation of appropriate protective measures against foodborne pathogens. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for the real-time detection of hazardous bacteria (HB) in the field. However, distinguishing among various HBs that exhibit similar C, N, O, H, or trace metal atomic emissions complicates HB detection by LIBS. This paper proposes the use of LIBS and chemometric tools to discriminate Staphylococcus aureus, Bacillus cereus, and Escherichia coli on slide substrates. Principal component analysis (PCA) and the genetic algorithm (GA) were used to select features and reduce the size of spectral data. Several models based on the artificial neural network (ANN) and the support vector machine (SVM) were built using the feature lines as input data. The proposed PCA-GA-ANN and PCA-GA-SVM discrimination approaches exhibited correct classification rates of 97.5% and 100%, respectively.