Vehicular networks are facing the challenges to support ubiquitous connections and high quality of service for numerous vehicles. To address these issues, mobile edge computing (MEC) is explored as a promising technology in vehicular networks by employing computing resources at the edge of vehicular wireless access networks. In this paper, we study the efficient task offloading schemes in vehicular edge computing networks. The vehicles perform the offloading time selection, communication, and computing resource allocations optimally, the mobility of vehicles and the maximum latency of tasks are considered. To minimize the system costs, including the costs of the required communication and computing resources, we first analyze the offloading schemes in the independent MEC servers scenario. The offloading tasks are processed by the MEC servers deployed at the access point (AP) independently. A mobility-aware task offloading scheme is proposed. Then, in the cooperative MEC servers scenario, the MEC servers can further offload the collected overloading tasks to the adjacent servers at the next AP on the vehicles' moving direction. A location-based offloading scheme is proposed. In both scenarios, the tradeoffs between the task completed latency and the required communication and computation resources are mainly considered. Numerical results show that our proposed schemes can reduce the system costs efficiently, while the latency constraints are satisfied.

Due to the flexible deployment and availability of line-of-sight (LoS) link, unmanned aerial vehicle (UAV) is able to assist the roadside unit (RSU) to provide timely computing resources to the covered vehicle users in the temporary congestion roads. In UAV-assisted vehicular edge computing networks (VECNs), caching necessary data in RSU/UAV reduces task execution delay and bandwidth cost significantly. However, for the constrained storage and computation capacities of UAV and the dynamic requests of users, the efficient caching data selection and replacement schemes are needed. In this paper, we propose a novel joint hybrid caching and replacement scheme in a scene that a single UAV assists RSU to cover a set of vehicle users with large number of iterative calculation tasks. In particular, both the content caching and service caching are considered for the RSU and UAV. To minimize the whole task completion delay of users, we joint optimize the hybrid caching data selection of UAV and the task offloading strategy of users, a deep Q-network (DQN)-based solution is proposed to improve the utility of UAV. Then, we design an optimal evaluation function, and propose a caching replacement scheme for both the RSU and UAV. For RSU and UAV can update its caching data separately, a double-DQN (DDQN)-based solution is proposed. Extensive simulation results show that the proposed algorithms have good convergence, and the designed schemes reduce the task completion delay efficiently.

Acid resistance is an intrinsic characteristic of intestinal bacteria in order to survive passage through the stomach. Adenosine triphosphate (ATP), the ubiquitous chemical used to power metabolic reactions, activate signaling cascades, and form precursors of nucleic acids, was also found to be associated with the survival of Escherichia coli (E. coli) in acidic environments. The metabolic pathway responsible for elevating the level of ATP inside these bacteria during acid adaptation has been unclear. E. coli uses several mechanisms of ATP production, including oxidative phosphorylation, glycolysis and the oxidation of organic compounds. To uncover which is primarily used during adaptation to acidic conditions, we broadly analyzed the levels of gene transcription of multiple E. coli metabolic pathway components. Our findings confirmed that the primary producers of ATP in E. coli undergoing mild acidic stress are the glycolytic enzymes Glk, PykF and Pgk, which are also essential for survival under markedly acidic conditions. By contrast, the transcription of genes related to oxidative phosphorylation was downregulated, despite it being the major producer of ATP in neutral pH environments.

Long non-coding RNA GAS5 and ZFAS1 are prognostic markers involved in translation targeted by miR-940 in prostate cancer Long non-coding RNA GAS5 and ZFAS1 are prognostic markers involved in translation targeted by miR-940 in prostate cancer

Vehicular edge computing (VEC) has emerged as a promising paradigm to ensure the real-time task processing caused by the emerging 5G or high level intelligent assisted driving applications. The computing tasks can be processed via the edge services deployed as the roadside units (RSUs) or moving vehicles. However, the high dynamic topology of the vehicular communication system and the time-varying available computing resources in RSUs make a challenge of the efficient task offloading of vehicles. In this paper, we consider an efficient task offloading scheme for VEC networks based on trajectory prediction, we focus on the serving handover between the adjacent RSUs. The moving vehicles can cooperate with RSUs or the surrounding vehicles for task processing. To reduce the latency of task transmission between vehicles, we present a cooperative vehicle selection method based on trajectory prediction. Then, we propose an efficient task offloading scheme based on deep reinforcement learning (DRL), while the dynamically available computing and communication resources are considered jointly. The simulation results show that the proposed task offloading scheme has great advantages in improving the utility of vehicles.

Aberrant activation of signaling pathways is frequently observed and reported to be associated with the progression and poor prognosis of prostate cancer (PCa). We aimed to identify key biological processes regulated by androgen receptor (AR) using gene co-expression network from single cell resolution. The bimodal index was used to evaluate whether two subpopulations exist among the single cells. Gene expression among single cells revealed averaging pitfalls and bimodality pattern. Weighted gene co-expression network analysis (WGCNA) was used to identify modules of highly correlated genes. Twenty-nine gene modules were identified and AR-regulated modules were screened by significantly overlapping reported androgen induced differentially expressed genes. The biological function “generation of precursor metabolites and energy” was significantly enriched by AR-regulated modules with bimodality, presenting differential androgen response among subpopulations. Integrating with public ChIP-seq data, two genes FECH, and CROT has AR binding sites. Public in vitro studies also show that androgen regulates FECH and CROT. After receiving androgen deprivation therapy, patients lowly express FECH and CROT. Further survival analysis indicates that FECH/CROT signature can predict PCa recurrence. We reveal the heterogeneous function of “generation of precursor metabolites and energy” upon androgen stimulation from the perspective of single cells. Inhibitors targeting this biological process will facilitate to prevent prostate cancer progression.