A fog computing based radio access network (F-RAN) is presented in this article as a promising paradigm for the fifth generation (5G) wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantages of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on fronthaul and avoid large-scale radio signal processing in the centralized baseband unit pool. This article comprehensively presents the system architecture and key techniques of F-RANs.In particular, key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed. Open issues in terms of edge caching, software-defined networking, and network function virtualization, are also identified. SUBMIT TO IEEE NETWORK, VOL. X, NO. Y, MON. 2015 2 I. INTRODUCTIONCompared to the fourth generation (4G) wireless communication system, the fifth generation (5G) wireless communication system should achieve system capacity growth by a factor of at least 1000, and the energy efficiency (EE) growth by a factor of at least 10 [1]. To achieve these goals, the cloud radio access network (C-RAN) has been proposed as a combination of emerging technologies from both the wireless and the information technology industries by incorporating cloud computing into radio access networks (RANs) [2]. C-RANs have come with their own challenges in the constrained fronthaul and centralized baseband unit (BBU) pool. A prerequisite requirement for the centralized processing in the BBU pool is an inter-connection fronthaul with high bandwidth and low latency. Unfortunately, the practical fronthaul is often capacity and time-delay constrained, which has a significant decrease on spectral efficiency (SE) and EE gains.To overcome the disadvantages of C-RANs with the fronthaul constraints, heterogeneous cloud radio access networks (H-CRANs) have been proposed in [3]. The user and control planes are decoupled in such networks, where high power nodes (HPNs) are mainly used to provide seamless coverage and execute the functions of control plane, while remote radio heads (RRHs) are deployed to provide high speed data rate for the packet traffic transmission in the user plane. HPNs are connected to the BBU pool via the backhaul links for interference coordination. Unfortunately, H-CRANs are still challenging in practice. First, since the location based social applications become more and more popular, the traffic data over the fronthaul between RRHs and the centralized BBU pool surges a lot of redundant information, which worsens the fronthaul constraints. Besides, H-CRANs do not take full advantage of processing and storage capabilities in edge devices, such as RRHs and "smart" user equipments (UEs), which is a promising approach to successfully alleviate the burden of the fronthaul and BBU pool. Moreover, operators need to deploy a huge number of fixed RRHs and HPNs in H-CRANs...
Taking full advantages of both heterogeneous networks (HetNets) and cloud access radio access networks (C-RANs), heterogeneous cloud radio access networks (H-CRANs) are presented to enhance both the spectral and energy efficiencies, where remote radio heads (RRHs) are mainly used to provide high data rates for users with high quality of service (QoS) requirements, while the high power node (HPN) is deployed to guarantee the seamless coverage and serve users with low QoS requirements. To mitigate the inter-tier interference and improve EE performances in H-CRANs, characterizing user association with RRH/HPN is considered in this paper, and the traditional soft fractional frequency reuse (S-FFR) is enhanced. Based on the RRH/HPN association constraint and the enhanced S-FFR, an energy-efficient optimization problem with the resource assignment and power allocation for the orthogonal frequency division multiple access (OFDMA) based H-CRANs is formulated as a non-convex objective function. To deal with the non-convexity, an equivalent convex feasibility problem is reformulated, and closedform expressions for the energy-efficient resource allocation solution to jointly allocate the resource block and transmit power are derived by the Lagrange dual decomposition method. Simulation results confirm that the H-CRAN architecture and the corresponding resource allocation solution can enhance the energy efficiency significantly.
Long non-coding RNAs (lncRNAs) can serve as blood-based biomarkers for cancer detection. To identify novel lncRNA biomarkers for gastric cancer (GC), we conducted, for the first time, genome-wide lncRNA screening analysis in two sets of samples: five paired preoperative and postoperative day 14 plasma samples from GC patients, and tissue samples from tumor and adjacent normal tissues. Candidate tumor-related lncRNAs were then quantitated and evaluated in three independent phases comprising 321 participants. The expression levels of lncRNAs were also measured in GC cell lines and the corresponding culture medium. Biomarker panels, lncRNA-based Index I and carcinoembryonic antigen (CEA)-based Index II, were constructed using logistic regression, and their diagnostic performance compared. Fagan's nomogram was plotted to facilitate clinical application. As a result, we identified five novel plasma lncRNAs (TINCR, CCAT2, AOC4P, BANCR and LINC00857), which, when combined in the lncRNA-based Index I, outperformed the CEA-based Index II (P < 0.001) and could distinguish GC patients from healthy controls with an area under the receiver-operating curve (AUC) of 0.91 (95% confidence interval (CI): 0.88-0.95). The lncRNA-based index decreased significantly by postoperative day 14 (P = 0.016), indicating its ability to monitor tumor dynamics. High values of the lncRNA-based index were correlated with tumor size (P = 0.036), depth of invasion (P = 0.025), lymphatic metastasis (P = 0.012) and more advanced tumor stages (P = 0.003). The lncRNA-based index was also able to discriminate GC patients from precancerous individuals and patients with gastrointestinal stromal tumor with AUC values of 0.82 (95% CI: 0.71-0.92) and 0.80 (95% CI: 0.68-0.91), respectively. Taken together, our findings demonstrate that this panel of five plasma lncRNAs could serve as a set of novel diagnostic biomarkers for GC detection.
Background Indole-3-acetic acid (IAA) is produced by microorganisms and plants via either tryptophan-dependent or tryptophan-independent pathways. Herein, we investigated the optimisation of IAA production by Streptomyces fradiae NKZ-259 and its formulation as a plant growth promoter to improve economic and agricultural development. Results The maximum IAA yield achieved using optimal conditions was 82.363 μg/mL in the presence of 2 g/L tryptophan after 6 days of incubation. Thin-layer chromatography (TLC) and high-performance liquid chromatography (HPLC) analysis of putative IAA revealed an RF value of 0.69 and a retention time of 11.842 min, comparable with the IAA standard. Regarding product formulation, kaolin-based powder achieved a suspension rate of 73.74% and a wetting time of 80 s. This carrier exhibited good shelf life stability for NKZ-259, and the cell population did not decrease obviously over 4 months of storage at 4 °C. In vivo analysis of plant growth promotion showed that tomato seedlings treated with kaolin powder containing NKZ-259 cells displayed a significant increase in root and shoot length of 7.97 cm and 32.77 cm, respectively, and an increase in fresh weight and dry weight of 6.72 g and 1.34 g. Compared to controls, plant growth parameters were increased almost it two-fold. Conclusion Optimising the culture conditions resulted in an almost four-fold increase in IAA secretion by NKZ-259 cells. The results clearly demonstrate that S. fradiae NKZ-259 holds great potential for plant growth promotion and IAA production. Furthermore, kaolin-based powder is an effective carrier for NKZ-259 cells and may be useful for commercial applications. Electronic supplementary material The online version of this article (10.1186/s12866-019-1528-1) contains supplementary material, which is available to authorized users.
IMPORTANCE The gastric cancer (GC)-associated long noncoding RNA1 (lncRNA-GC1) plays an important role in gastric carcinogenesis. However, exosomal lncRNA-GC1 and its potential role in GC are poorly understood. OBJECTIVE To evaluate the diagnostic value of circulating exosomal lncRNA-GC1 for early detection and monitoring progression of GC. DESIGN, SETTING, AND PARTICIPANTS We performed a multiphase investigation of circulating exosomal lncRNA-GC1 for early detection of GC involving consecutive patients with GC (n = 522), patients with gastric precancerous lesions (n = 85), and healthy donor individuals (HDs; n = 219) from December 2016 to February 2019 at Chinese People's Liberation Army General Hospital, China. LncRNA-GC1 was measured by reverse transcription-polymerase chain reaction by independent researchers who had no access to patients' information. Receiver operating characteristic curves were used to calculate diagnostic efficiency in comparison between lncRNA-GC1 and 3 traditional biomarkers (carcinoembryonic antigen [CEA], cancer antigen 72-4 [CA72-4], and CA19-9). MAIN OUTCOMES AND MEASURES Assessment of diagnostic efficiency on the basis of area under curve (AUC), specificity, and sensitivity. RESULTS Of the 826 patients included in the study, 508 were men (61.5%), and the median age of all patients was 60 years (range, 28-82 years). In the test phase, lncRNA-GC1 achieved better diagnostic performance than the standard biomarkers CEA, CA72-4, and CA19-9 (AUC = 0.9033) for distinguishing between the patients with GC and HDs. Additionally, exosomal lncRNA-GC1 levels were significantly higher in culture media from GC cells compared with those of normal gastric epithelial cells (t = 5.310; P = .002). In the verification phase, lncRNA-GC1 retained its diagnostic efficiency in discriminating patients with GC from those with gastric precancerous lesions as well from HDs. Moreover, lncRNA-GC1 exhibited a higher AUC compared with those of CEA, CA72-4, and CA19-9 for early detection of GC with sufficient specificity and sensitivity, especially for patients with GC with negative standard biomarkers. Moreover, the levels of circulating exosomal lncRNA-GC1 were significantly associated with GC from early to advanced stages (HD vs stage I, t = 20.98; P < .001; stage I vs stage II, t = 2.787; P = .006; stage II vs stage III, t = 4.471; P < .001; stage III vs stage IV, t = 1.023; P = .30), independent of pathological grading and Lauren classification (pathological grading: HD vs G1, t = 21.09; P < .001; G1 vs G2, t = 0.3718; P = .71; G2 vs G3, t = 0.3598; P = .72; Lauren classification: t = 24.81; P <.001). In the supplemental phase, the levels of circulating exosomal lncRNA-GC1 were consistent with those in GC tissues and cells and were higher compared with those in normal tissues and cells. Furthermore, the levels of circulating lncRNA-GC1 were unchanged after exosomes were treated with RNase and remained constant after prolonged exposure to room temperature or after repeated freezing and thawing (t = 1.443; P = ....
BackgroundCirculating tumor DNA (ctDNA) has offered a minimally invasive approach for detection and measurement of gastric cancer (GC). However, its diagnostic and prognostic value in gastric cancer still remains unclear.ResultsA total of 16 studies comprising 1193 GC patients met our inclusion criteria. The pooled sensitivity and specificity were 0.62 (95% confidence intervals (CI) 0.59−0.65) and 0.95 (95% CI 0.93–0.96), respectively. The AUSROC (area under SROC) curve was 0.94 (95% CI 0.89–0.98). The results showed that the presence of certain ctDNA markers was associated with larger tumor size (OR: 0.26, 95% CI 0.11–0.61, p = 0.002), TNM stage (I + II/III + IV, OR: 0.11, 95% CI 0.07−0.17, p = 0.000), as well as H. pylori infection. (H.p negative/H.p positive, OR: 0.57, 95% CI 0.36–0.91, p = 0.018). Moreover, there was also a significant association between the presence of ctDNA and worse overall survival (HR 1.77, 95% CI 1.38−2.28, p < 0.001), as well as disease-free survival (HR 4.36, 95% CI 3.08−6.16, p < 0.001).Materials and MethodsPubmed, Embase, Cochrane Library and Web of Science databases were searched for relating literature published up until November 30, 2016. Diagnostic accuracy variables were pooled by the Meta-Disc software. Engauge Digitizer and Stata software were applied for prognostic data extraction and analysis.ConclusionsOur meta-analysis indicates the detection of certain ctDNA targets is significantly associated with poor prognosis of GC patients, with high specificity and relatively moderate sensitivity.
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