Summary Necrotrophic fungus Rhizoctonia solani Kühn (R. solani) causes serious diseases in many crops worldwide, including rice and maize sheath blight (ShB). Crop resistance to the fungus is a quantitative trait and resistance mechanism remains largely unknown, severely hindering the progress on developing resistant varieties. In this study, we found that resistant variety YSBR1 has apparently stronger ability to suppress the expansion of R. solani than susceptible Lemont in both field and growth chamber conditions. Comparison of transcriptomic profiles shows that the photosynthetic system including chlorophyll biosynthesis is highly suppressed by R. solani in Lemont but weakly in YSBR1. YSBR1 shows higher chlorophyll content than that of Lemont, and inducing chlorophyll degradation by dark treatment significantly reduces its resistance. Furthermore, three rice mutants and one maize mutant that carry impaired chlorophyll biosynthesis all display enhanced susceptibility to R. solani. Overexpression of OsNYC3, a chlorophyll degradation gene apparently induced expression by R. solani infection, significantly enhanced ShB susceptibility in a high‐yield ShB‐susceptible variety ‘9522’. However, silencing its transcription apparently improves ShB resistance without compromising agronomic traits or yield in field tests. Interestingly, altering chlorophyll content does not affect rice resistance to blight and blast diseases, caused by biotrophic and hemi‐biotrophic pathogens, respectively. Our study reveals that chlorophyll plays an important role in ShB resistance and suppressing chlorophyll degradation induced by R. solani infection apparently improves rice ShB resistance. This discovery provides a novel target for developing resistant crop to necrotrophic fungus R. solani.

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With the rapidly development of mobile cloud computing (MCC), the Internet of Things (IoT), and artificial intelligence (AI), user equipment (UEs) are facing explosive growth. In order to effectively solve the problem that UEs may face with insufficient capacity when dealing with computationally intensive and delay sensitive applications, we take Mobile Edge Computing (MEC) of the IoT as the starting point and study the computation offloading strategy of UEs. First, we model the application generated by UEs as a directed acyclic graph (DAG) to achieve fine-grained task offloading scheduling, which makes the parallel processing of tasks possible and speeds up the execution efficiency. Then, we propose a multi-population cooperative elite algorithm (MCE-GA) based on the standard genetic algorithm, which can solve the offloading problem for tasks with dependency in MEC to minimize the execution delay and energy consumption of applications. Experimental results show that MCE-GA has better performance compared to the baseline algorithms. To be specific, the overhead reduction by MCE-GA can be up to 72.4%, 38.6%, and 19.3%, respectively, which proves the effectiveness and reliability of MCE-GA.

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