In this paper, in order to fully investigate the great potential of increasingly powerful vehicles and their predictable mobility in improving dynamic network performance, we develop a V2V task offloading algorithm for SVCNs (Software-defined Vehicular Cooperative Networks). First of all, a three-layer SVCNs architecture is built with vehicle edge layer, SDN control layer and cloud layer from bottom to top, moreover, at SDN control layer, there are multiple SDN controllers responsible for data collection and resource scheduling, and a dual LSTM-based vehicle trajectory prediction model is also designed with the aim of achieving a spatiotemporal trajectory prediction process. We then formulate an optimization problem and further show our V2V task offloading algorithm design, which consists of a distributed V2V (dV2V) offloading sub-algorithm and a centralized V2V (cV2V) offloading sub-algorithm. Finally, we test our trajectory prediction model with TensorFlow, and simulate our V2V task offloading algorithm on NS3 platform, results show that not only our prediction model obtains the highest accuracy, but also our V2V task offloading design outperforms the comparison schemes in terms of average service time, task success ratio and algorithm convergence with various vehicle density and motion modes.

It is well‑established that steroid‑resistant nephrotic syndrome commonly involves podocytic injury, however, the underlying mechanism remains to be elucidated. Previous studies have demonstrated that glucocorticoids enhance podocytic recovery predominantly through the functional isoform, glucocorticoid receptor (GR)α. Our previous study demonstrated the reduced expression of GRα in podocytes from patients with steroid‑resistant nephrotic syndrome compared with those with steroid‑sensitive syndromes, which suggested that microRNAs (miRNAs) may be a potential therapeutic target in the modulation of steroid sensitivity. The present study screened miRNAs in murine injured podocytes by microarray and identified 10 miRNAs significantly upregulated, including miR‑30a, miR‑30d, miR‑100, miR181c, miR‑5099, miR‑3535, miR‑140‑3p, miR‑148‑3p and miR‑103‑3p. Bioinformatic target prediction indicated that the GR was a candidate target gene of miR‑30a. The data also indicated that miR‑30a negatively regulated GRα in normal and injured podocytes induced by puromycin aminonucleoside (PAN). In addition, the inhibition of miR‑30a prevented podocytic apoptosis induced by PAN. However, luciferase reporter assay data suggested an indirect effect on the transcriptional activity of GRα. The present study indicated that silencing of miR‑30a may improve steroid sensitivity in injured podocytes, although the mechanism cannot be explained by conventional means and remains to be elucidated.

It is difficult for TCP/IP to manage the mobile nodes in the dynamic environment of VANET (Vehicular Ad hoc Network), so combining urban VANET with a new network architecture—ICN (information-centric network)—this paper proposes a FIB (forwarding information base) selecting scheme based on link quality, and we name it as RSBLQ (Routing Selecting Based on Link Quality). The metrics of link quality in RSBLQ are mainly LET (link expiration time) and link available probability. To reduce network delay, cache redundancy, and network traffic, our RSBLQ has the following designs: (1) we modify the structure of PIT (pending interest table) by adding two domains: receive time and tolerance time; (2) we introduce the algorithm of LET to help with the content routing selection in FIB; (3) RSBLQ also gets the link available probability to be another metric for our algorithm. Simulation results show that in terms of average response time, the performance improvement of RSBLQ is about 10, 15–20, and 60% compared to CCVN, V-NDN, and TCP/IP, respectively; it approximately has 8 and 13.8% improvement than CCVN and V-NDN about cache hit ratio; and the network traffic also decreases a lot.