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Key message: Jasmonate-induced accumulation of anti-herbivore compounds mediates rice resistance to the leaf folder Cnaphalocrocis medinalis. The rice leaf folder (LF), Cnaphalocrocis medinalis, is one of the most destructive insect pests in the paddy field. LF larvae induces leaf folding and scrapes the upper epidermis and mesophyll tissues reducing photosynthesis and yield in rice. Identifying plant defense pathways and genes involved in LF resistance is essential to understand better this plant–insect interaction and develop new control strategies for this pest. Jasmonate (JA) signaling controls a plethora of plant defenses against herbivores. Using RNA-seq time series analysis, we characterized changes in the transcriptome of wild-type (WT) leaves in response to LF damage and measured the dynamics of accumulation of JA phytohormone pools in time-course experiments. Genes related to JA signaling and responses, known to mediate resistance responses to herbivores, were induced by LF and were accompanied by an increment in the levels of JA pools in damaged leaves. The accumulation of defense compounds such as phenolamides and trypsin proteinase inhibitor (TPI) also increased after LF infestation in WT but not in JA mutant plants impaired in JA biosynthesis (aoc-2) and signaling (myc2-5). Consistent with all these responses, we found that LF larvae performed better in the JA mutant backgrounds than in the WT plants. Our results show that JA signaling regulates LF-induced accumulation of TPI and phenolamides and that these compounds are likely an essential part of the defense arsenal of rice plants against this insect pest. [ABSTRACT FROM AUTHOR]

An online social network can be used for the diffusion of malicious information like derogatory rumors, disinformation, hate speech, revenge pornography, etc. This motivates the study of influence minimization that aim to prevent the spread of malicious information. Unlike previous influence minimization work, this study considers the influence minimization in relation to a particular group of social network users, called targeted influence minimization. Thus, the objective is to protect a set of users, called target nodes, from malicious information originating from another set of users, called active nodes. This study also addresses two fundamental, but largely ignored, issues in different influence minimization problems: (i) the impact of a budget on the solution; (ii) robust sampling. To this end, two scenarios are investigated, namely unconstrained and constrained budget. Given an unconstrained budget, we provide an optimal solution; Given a constrained budget, we show the problem is NP-hard and develop a greedy algorithm with an (1 − 1 e) -approximation. More importantly, in order to solve the influence minimization problem in large, real-world social networks, we propose a robust sampling-based solution with a desirable theoretic bound. Extensive experiments using real social network datasets offer insight into the effectiveness and efficiency of the proposed solutions. [ABSTRACT FROM AUTHOR]