Glucose plays a key role as an energy source in most mammals, but its importance in fish appears to be limited that so far seemed to belong to diabetic humans only. Several laboratories worldwide have made important efforts in order to better understand this strange phenotype observed in fish. However, the mechanism of carbohydrate/glucose metabolism is astonishingly complex. Why basal glycaemia is different between fish and mammals and how carbohydrate metabolism is different amongst organisms is largely uncharted territory. The utilization of comparative systems biology with model vertebrates to explore fish metabolism has become an essential approach to unravelling hidden in vivo mechanisms. In this study, we first built a database containing 791, 593, 523, 666 and 698 carbohydrate/glucose metabolic genes from the genomes of Danio rerio, Xenopus tropicalis, Gallus gallus, Mus musculus and Homo sapiens, respectively, and most of these genes in our database are predicted to encode specific enzymes that play roles in defined reactions; over 57% of these genes are related to human type 2 diabetes. Then, we systematically compared these genes and found that more than 70% of the carbohydrate/glucose metabolic genes are conserved in the five species. Interestingly, there are 4 zebrafish-specific genes (si:ch211-167b20.8, CABZ01043017.1, socs9 and eif4e1c) and 1 human-specific gene (CALML6) that may alter glucose utilization in their corresponding species. Interestingly, these 5 genes are all carbohydrate regulation factors, but the enzymes themselves are involved in insulin regulation pathways. Lastly, in order to facilitate the use of our data sets, we constructed a glucose metabolism database platform ( http://101.200.43.1:10000/ ). This study provides the first systematic genomic insights into carbohydrate/glucose metabolism. After exhaustive analysis, we found that most metabolic genes are conserved in vertebrates. This work may resolve some of the complexities of carbohydrate/glucose metabolic heterogeneity amongst different vertebrates and may provide a reference for the treatment of diabetes and for applications in the aquaculture industry.

Caching content on the edge of a network can effectively localize traffic, reduce network latency and improve network throughput. In this paper, we propose an a posteriori caching mechanism rather than the mainstream apriority theory, in which the content placement strategy is determined based on the identical distribution of content popularity and user preference before the theoretic analysis of the placement gain is validated. We primarily investigate the optimal caching strategy subjected to the constraint of storage capacity in a heterogeneous network, where a macro base station (MBS), small base stations (SBSs) and user terminals (UTs) are integrated for proactive content storage in the physical layer. To maximize the local hit rate while reducing the transmission delay, we first analyze the optimal strategy by converting content placement into a 0-1 knapsack problem and address the optimization problem using the method of Lagrangian multipliers. We then determine the request probability for each user by exploiting the context information of the user's request history, user similarity and social ties to achieve reasonably well-optimized caching performance. The caching policy is further optimized into a low-complexity heuristic algorithm with the knowledge of request probability and the optimal copy volumes. Finally, the simulation results show that the proposed cooperative caching algorithm improves the performance metric in terms of hit rate and transmission delay under different benchmarks.

This paper studies the capacity of edge caching systems. Capacity is analyzed from the perspctive of node mobility, caching, content popularity, etc., neglecting the influence of edge node cooperation on system performance. However, cooperation among edge nodes has been shown to substantially improve system performance at the expense of a cooperation cost. Therefore, we attempt to maximize the capacity of mobile edge computing (MEC) subject to a budget constraint on the cooperation cost. We first transform the capacity maximization problem into a transmission distance minimization problem; then, we explore the average transmission distance of each source-to-destination pair under the assumption that the locations of the edge nodes follow a Poisson point process (PPP). We find that both the average transmission distance and the cooperation cost are associated with a key parameter, i.e., the number of content copies. We use the Lagrangian multiplier method to calculate the optimal copy number and propose a file allocation algorithm to store these copies. Finally, we analyze the influence of various parameters on the system capacity. The numerical results verify the efficiency of our solution compared with classic works.

Task offloading can meet users’ demands for the latency and energy consumption by offloading tasks from resource-constrained IoT devices to relatively resource-rich edge servers. Traditional task offloading usually makes use of fixed base stations or servers as edge servers. This would lead to limited range of services and increased costs due to large-scale deployment of edge servers. Therefore, deploying unmanned aerial vehicles (UAVs) as mobile edge servers for task offloading in complex terrains (e.g., forest, desert, etc.) is a worthwhile research problem. To this end, this paper proposes a UAV-assisted task offloading mechanism. The mechanism aims to minimize the weighted sum of latency and energy consumption through jointly optimizing resource allocation, offloading decision, and UAV trajectory. We first transform the non-convex optimization problem into convex optimization subproblems to obtain the optimal resource allocation. Second, we use an improved particle swarm optimization algorithm to find the optimal offloading decision. Finally, we present the deep determination policy gradient algorithm to optimize the UAV trajectory which is a kind of deep reinforcement learning algorithm. Through simulation experiments, we show that the proposed mechanism can efficiently reduce the weighted sum of latency and energy consumption.