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Communication limitation at the edge is widely recognized as a major bottleneck for federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to enhance service coverage and spectrum efficiency at the edge, which could facilitate large-scale and efficient machine learning (ML) model aggregation. However, FL over multi-hop wireless networks has rarely been investigated. In this paper, we optimize FL over wireless mesh networks by taking into account the heterogeneity in communication and computing resources at mesh routers and clients. We present a framework that each intermediate router performs in-network model aggregation before sending the data to the next hop, so as to reduce the outgoing data traffic and hence aggregate more models under limited communication resources. To accelerate model training, we formulate our optimization problem by jointly considering model aggregation, routing, and spectrum allocation. Although the problem is a non-convex mixed-integer nonlinear programming, we transform it into a mixed-integer linear programming (MILP), and develop a coarse-grained fixing procedure to solve it efficiently. Simulation results demonstrate the effectiveness of the solution approach, and the superiority of the in-network aggregation scheme over the counterpart without in-network aggregation. [ABSTRACT FROM AUTHOR]

Vibrio harveyi causes vibriosis in nearly 70% of grouper (Epinephelus sp.), seriously limiting grouper culture. As well as directly inhibiting pathogens, the gut microbiota plays critical roles in immune homeostasis and provides essential health benefits to its host. However, there is still little information about the variations in the immune response to V. harveyi infection and the gut microbiota of grouper. To understand the virulence mechanism of V. harveyi in the pearl gentian grouper, we investigated the variations in the pathological changes, immune responses, and gut bacterial communities of pearl gentian grouper after exposure to differently virulent V. harveyi strains. Obvious histopathological changes were detected in heart, kidney, and liver. In particular, nodules appeared and huge numbers of V. harveyi cells colonized the liver at 12 h postinfection (hpi) with highly virulent V. harveyi. Although no V. harveyi was detected in the gut, the infection simultaneously induced a gut-liver immune response. In particular, the expression of 8 genes associated with cellular immune processes, including genes encoding inflammatory cytokines and receptors, and pattern recognition proteins, was markedly induced by V. harveyi infection, especially with the highly virulent V. harveyi strain. V. harveyi infection also induced significant changes in gut bacterial community, in which Vibrio and Photobacterium increased but Bradyrhizobium , Lactobacillus , Blautia , and Faecalibaculum decreased in the group infected with the highly virulent strain, with accounting for 82.01% dissimilarity. Correspondingly, four bacterial functions related to bacterial pathogenesis were increased by infection with highly virulent V. harveyi , whereas functions involving metabolism and genetic information processing were reduced. These findings indicate that V. harveyi colonizes the liver and induces a gut-liver immune response that substantially disrupts the composition of and interspecies interactions in the bacterial community in fish gut, thereby altering the gut-microbiota-mediated functions and inducing fish death. [ABSTRACT FROM AUTHOR]

Colanic acid (CA) is a group I extracellular polysaccharide (EPS) that contributes to resistance against adverse environments in many members of the Enterobacteriaceae. In the present study, a genetic locus EPSC putatively involved in CA biosynthesis was identified in Vibrio alginolyticus ZJ-51, which undergoes colony morphology variation between translucent/smooth (ZJ-T) and opaque/rugose (ZJ-O). EPSC in ZJ-T carries 21 ORFs and resembles the CA cluster of Escherichia coli K-12. The deletion of EPSC led to decreased EPS and biofilm formation in both genetic backgrounds but no alternation of lipopolysaccharide. The loss of this locus also changed the colony morphology of ZJ-O on the 2216E plate and reduced the motility of ZJ-T. Compared with ZJ-T, ZJ-O lacks a 10-kb fragment (epsT) in EPSC containing homologs of wecA, wzx and wzy that are essential for O-antigen synthesis. However, the deletion or overexpression of epsT resulted in no change of colony morphology, biofilm formation or EPS production. This study reported at the first time a genetic locus EPSC that may be involved in colanic acid synthesis in V. alginolyticus ZJ-51, and found that it was related to EPS biosynthesis, biofilm formation, colony morphology and motility, which may shed light on the environmental adaptation of the vibrios. [ABSTRACT FROM AUTHOR]

With the rapid development of various new types of services, autonomous driving has received extensive attention. Due to the dense traffic flow, the limited battery life and computing power of the vehicles, intelligent vehicles are unable to support some computationally intensive and urgent tasks. Autonomous driving imposes strict requirements on the response time of the task. Due to the strong computing power and proximity to the terminal of mobile edge computing (MEC) and the arrival of 5G, the task can be unloaded to MEC, and data can be exchanged in milliseconds, which can reduce the task execution time. However, the resources of the MEC server are still very limited. Therefore we proposed a scheduling algorithm that takes into account the special task of the autopilot. Tasks will select the appropriate edge cloud execution and schedule the execution sequence on the edge cloud by the scheduling algorithm. At the same time, we take the mobility of high-speed vehicles into consideration. The position of the vehicle can be obtained by the prediction algorithm, and the task results are returned to the vehicle by means of other edge clouds. The experimental results show that with the increase of the task amount, the algorithm can effectively schedule more tasks to be completed within the specified time, and in different time slots; it can also predict the location of the vehicle and return the result to the vehicle. [ABSTRACT FROM AUTHOR]