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Background: Rice (Oryza sativa L.), which is a staple food for more than half of the world's population, is frequently attacked by herbivorous insects, including the rice stem borer, Chilo suppressalis. C. suppressalis substantially reduces rice yields in temperate regions of Asia, but little is known about how rice plants defend themselves against this herbivore at molecular and biochemical level. Results: In the current study, we combined next-generation RNA sequencing and metabolomics techniques to investigate the changes in gene expression and in metabolic processes in rice plants that had been continuously fed by C. suppressalis larvae for different durations (0, 24, 48, 72, and 96 h). Furthermore, the data were validated using quantitative real-time PCR. There were 4,729 genes and 151 metabolites differently regulated when rice plants were damaged by C. suppressalis larvae. Further analyses showed that defense-related phytohormones, transcript factors, shikimate-mediated and terpenoid-related secondary metabolism were activated, whereas the growth-related counterparts were suppressed by C. suppressalis feeding. The activated defense was fueled by catabolism of energy storage compounds such as monosaccharides, which meanwhile resulted in the increased levels of metabolites that were involved in rice plant defense response. Comparable analyses showed a correspondence between transcript patterns and metabolite profiles. Conclusion: The current findings greatly enhance our understanding of the mechanisms of induced defense response in rice plants against C. suppressalis infestation at molecular and biochemical levels, and will provide clues for development of insect-resistant rice varieties. [ABSTRACT FROM AUTHOR]

As a typical cyber-physical system (CPS), ship system, with the continuous improvement of its informatization degree, CPS information security incidents occur frequently. Most of the existing key node identification methods for complex networks are aimed at power system networks, and the evaluation indicators based on these networks are difficult to apply to CPS networks outside the power system. Aiming at the shortcomings of the existing key node identification methods in CPS, this paper proposes an information network key node identification method based on entropy weight method and FMEA. The concept of effective influence range is added to the indicator characterization of failure mode and effects analysis (FMEA), and the entropy weight method is used to objectively weight the indicators in FMEA. Combined with the risk sequence number in the FMEA with strong subjectivity, the three evaluation indicators in the risk sequence number are used as the indicators of the decision matrix in the entropy weight method. Finally, the correctness and feasibility of the method are verified by the case of ship support mission information flow node network. The analysis results show that the comprehensive weighted ranking of the three indicators based on the entropy weight method is more reasonable than a single indicator.