PurposeThe prognosis of pancreatic cancer ranks among the worst of all cancer types, which is primarily due to the fact that during the past decades little progress has been made in its diagnosis and treatment. Here, we set out to investigate the role of microRNA 138 (miR-138-5p) in the regulation of pancreatic cancer cell growth and to assess its role as putative therapeutic target.MethodsqRT-PCR was used to examine the expression of miR-138-5p in 8 pancreatic cancer cell lines and 18 primary human pancreatic cancer samples. A lentivirual vector containing miR-138-5p mimics (lv-miR-138-5p) was used to exogenously over-express miR-138-5p in the pancreatic cancer cells lines Capan-2 and PANC-1. The effect of this over-expression on cell proliferation was examined using an in vitro propidium iodide fluorescence assay. Capan-2 cells exogenously over-expressing miR-138-5p were transplanted into nude mice to examine its in vivo effect on tumor growth. A predicted target of miR-138-5p (FOXC1) was first validated using a luciferase assay and, subsequently, down-regulated by siRNA to assess its effect on pancreatic cancer cell growth.ResultsWe found that miR-138-5p was markedly down-regulated in both pancreatic cancer cell lines and primary human pancreatic cancer samples, compared to a human pancreas ductal epithelial (HPDE) cell line and normal pancreatic tissues, respectively (P < 0.05). In addition, we found that in the pancreatic cancer cells lines Capan-2 and PANC-1 lentiviral transfection of miR-138-5p mimicked up-regulation of the endogenous expression of miR-138-5p and, concomitantly, inhibited cancer cell proliferation (P < 0.05). The exogenous over-expression of miR-138-5p also led to a significant inhibition of tumor formation in vivo. Using a luciferase assay, we found that miR-138-5p directly targets FOXC1. In conformity with this notion, we found that FOXC1 was down-regulated upon miR-138-5p over-expression in pancreatic cancer cells. Finally, we found that silencing of FOXC1 by siRNA had an inhibitory effect on pancreatic cancer cell growth.ConclusionsOur data indicate that miR-138-5p may play an important role in regulating pancreatic cancer cell growth, possibly through targeting FOXC1. Over-expression of miR-138-5p may serve as a novel approach for the treatment of patients with pancreatic cancer.
Currently, the Internet of Things (IoT) solutions are playing an important role in numerous areas, especially in smart homes and buildings, health-care, vehicles, and energy. It will continue to expand in various fields in the future. However, some issues limit the further development of IoT technologies. First, the battery-powered feature increases the maintenance cost of replacing batteries for IoT devices. Second, existing Cloud-IoT frameworks are not able to cope with emerging delay-constrained applications in the IoT system due to its centralized mode of operation and the considerable communication delay. Existing studies neither satisfy the demand for the quick response in timeconstraint IoT applications nor fundamentally solving the problem of energy sustainability. Therefore, this paper studies the problem of energy sustainability and timeliness in IoT system. Based on Energy Harvesting Technologies (EHT), the Green and Sustainable Mobile Edge Computing (GS-MEC) framework is proposed to make IoT devices self-powered by utilizing the green energy in the IoT environment. In this framework, we formulate the problem of minimizing response time and packet losses of tasks under the limitation of energy queue stability to improve the timeliness and reliability of task processing. Additionally, the dynamic parallel computing offloading and energy management (DPCOEM) algorithm is designed to solve the problem based on the Lyapunov optimization technology. Finally, theoretical analysis demonstrates the effectiveness of the proposed algorithm, and the numerical result of simulation shows that the average performance of the proposed algorithm is an order of magnitude better than state-of-the-art algorithms.Index Terms-Mobile edge computing, internet of things (IoT), partial computation offloading, energy harvesting, resource allocation, lyapunov optimization. I. INTRODUCTIONI NTERNET of Things (IoT) solutions are playing an important role in numerous areas, especially in smart homes, smart buildings, healthcare, vehicles, and energy. These areas are defined as the sectors that are currently utilizing the IoT to the Manuscript
The smart city is increasingly gaining worldwide attention. It has the potential to improve the quality of life in convenience, at work, and in safety, among many others' utilizations. Nevertheless, some of the emerging applications in the smart city are computation-intensive and time-sensitive, such as real-time vision processing applications used for public safety and the virtual reality classroom application. Both of them are hard to handle due to the quick turnaround requirements of ultra-short time and large amounts of computation that are necessary. Fortunately, the abundant resource of the Internet of Vehicles (IoV) can help to address this issue and improve the development of the smart city. In this paper, we focus on the problem that how to schedule tasks for these computation-intensive and time-sensitive smart city applications with the assistance of IoV based on multi-server mobile edge computing. Task scheduling is a critical issue due to the limited computational power, storage, and energy of mobile devices. To handle tasks from the aforementioned applications in the shortest time, this paper introduces a cooperative strategy for IoV and formulates an optimization problem to minimize the completion time with a specified cost. Furthermore, we develop four evolving variants based on the alternating direction method of multipliers (ADMM) algorithm to solve the proposed problem: variable splitting ADMM, Gauss-Seidel ADMM, distributed Jacobi ADMM, and distributed improved Jacobi (DIJ)-ADMM algorithms. These algorithms incorporate an augmented Lagrangian function into the original objective function and divide the large problem into two sub-problems to iteratively solve each sub-problem. The theoretical analysis and simulation results show that the proposed algorithms have a better performance than the existing algorithms. In addition, the DIJ-ADMM algorithm demonstrates optimal performance, and it converges after approximately ten iterations and improves the task completion time and offloaded tasks by 89% and 40%, respectively. INDEX TERMS Task scheduling, smart city, mobile edge computing, Internet of Vehicle, alternating direction method of multipliers (ADMM) algorithm.
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