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Nucleotide-binding site (NBS) disease resistance genes play an important role in defending plants from a variety of pathogens and insect pests. Many R-genes have been identified in various plant species. However, little is known about the NBS-encoding genes in Brachypodium distachyon. In this study, using computational analysis of the B. distachyon genome, we identified 126 regular NBS-encoding genes and characterized them on the bases of structural diversity, conserved protein motifs, chromosomal locations, gene duplications, promoter region, and phylogenetic relationships. EST hits and full-length cDNA sequences (from Brachypodium database) of 126 R-like candidates supported their existence. Based on the occurrence of conserved protein motifs such as coiled-coil (CC), NBS, leucine-rich repeat (LRR), these regular NBS-LRR genes were classified into four subgroups: CC-NBS-LRR, NBS-LRR, CC-NBS, and X-NBS. Further expression analysis of the regular NBS-encoding genes in Brachypodium database revealed that these genes are expressed in a wide range of libraries, including those constructed from various developmental stages, tissue types, and drought challenged or nonchallenged tissue.

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Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm.

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