SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks

Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements

Baseline of Pollution of Heavy Metals and Physico-chemical Parameters in Surface Sediments from Quanzhou Bay, China, in 2006-2007

According to the monitoring results of the near-shore sediments of Quanzhou Bay in 2006-2007, we analyzed the near-shore depositional environmental quality of Quanzhou Bay and assessed it by the single-factor evaluation on the basis of corresponding standards of local marine functional areas. The results showed that the sediments from the inner part of Quanzhou Bay were polluted more seriously than that of the open part, which might be due to the increasing human activities in coastal areas. The main exceeding standard items are petroleum, Cu, Zn and Pb. In additions, the pollutions caused by sulfide and Cr are different in different regions. The contents of Hg and As are basically in according with the sedimentary quality standards of the corresponding marine functional areas. According the evaluating results, we also provided the corresponding measures to control the pollutions in sedimentary environment of Quanzhou Bay. (C) 2011 Published by Elsevier B.V. Selection and/or peer-review under responsibility of National University of Singapore

Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

Background Early recurrence is the major cause of poor prognosis in hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are deeply involved in HCC prognosis. In this study, we aimed to establish a prognostic lncRNA signature for HCC early recurrence. Methods The lncRNA expression profile and corresponding clinical data were retrieved from total 299 HCC patients in TCGA database. LncRNA candidates correlated to early recurrence were selected by differentially expressed gene (DEG), univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. A 25-lncRNA prognostic signature was constructed according to receiver operating characteristic curve (ROC). Kaplan-Meier and multivariate Cox regression analyses were used to evaluate the performance of this signature. ROC and nomogram were used to evaluate the integrated models based on this signature with other independent clinical risk factors. Gene set enrichment analysis (GSEA) was used to reveal enriched gene sets in the high-risk group. Tumor infiltrating lymphocytes (TILs) levels were analyzed with single sample Gene Set Enrichment Analysis (ssGSEA). Immune therapy response prediction was performed with TIDE and SubMap. Chemotherapeutic response prediction was conducted by using Genomics of Drug Sensitivity in Cancer (GDSC) pharmacogenomics database. Results Compared to low-risk group, patients in high-risk group showed reduced disease-free survival (DFS) in the training (p < 0.0001) and validation cohort (p = 0.0132). The 25-lncRNA signature, AFP, TNM and vascular invasion could serve as independent risk factors for HCC early recurrence. Among them, the 25-lncRNA signature had the best predictive performance, and combination of those four risk factors further improves the prognostic potential. Moreover, GSEA showed significant enrichment of “E2F TARGETS”, “G2M CHECKPOINT”, “MYC TARGETS V1” and “DNA REPAIR” pathways in the high-risk group. In addition, increased TILs were observed in the low-risk group compared to the high-risk group. The 25-lncRNA signature negatively associates with the levels of some types of antitumor immune cells. Immunotherapies and chemotherapies prediction revealed differential responses to PD-1 inhibitor and several chemotherapeutic drugs in the low- and high-risk group. Conclusions Our study proposed a 25-lncRNA prognostic signature for predicting HCC early recurrence, which may guide postoperative treatment and recurrence surveillance in HCC patients

Combining regenerative medicine strategies to provide durable reconstructive options: auricular cartilage tissue engineering

Recent advances in regenerative medicine place us in a unique position to improve the quality of engineered tissue. We use auricular cartilage as an exemplar to illustrate how the use of tissue-specific adult stem cells, assembly through additive manufacturing and improved understanding of postnatal tissue maturation will allow us to more accurately replicate native tissue anisotropy. This review highlights the limitations of autologous auricular reconstruction, including donor site morbidity, technical considerations and long-term complications. Current tissue-engineered auricular constructs implanted into immune-competent animal models have been observed to undergo inflammation, fibrosis, foreign body reaction, calcification and degradation. Combining biomimetic regenerative medicine strategies will allow us to improve tissue-engineered auricular cartilage with respect to biochemical composition and functionality, as well as microstructural organization and overall shape. Creating functional and durable tissue has the potential to shift the paradigm in reconstructive surgery by obviating the need for donor sites

Absolute stability of time-varying delay Lurie indirect control systems with unbounded coefficients

This paper investigates the absolute stability problem of time-varying delay Lurie indirect control systems with variable coefficients. A positive-definite Lyapunov-Krasovskii functional is constructed. Some novel sufficient conditions for absolute stability of Lurie systems with single nonlinearity are obtained by estimating the negative upper bound on its total time derivative. Furthermore, the results are generalised to multiple nonlinearities. The derived criteria are especially suitable for time-varying delay Lurie indirect control systems with unbounded coefficients. The effectiveness of the proposed results is illustrated using simulation examples

Support Vector Machine Based Mobility Prediction Scheme in Heterogeneous Wireless Networks

To improve the intelligence of the mobile-aware applications in the heterogeneous wireless networks (HetNets), it is essential to establish an advanced mechanism to anticipate the change of the user location in every subnet for HetNets. This paper proposes a multiclass support vector machine based mobility prediction (Multi-SVMMP) scheme to estimate the future location of mobile users according to the movement history information of each user in HetNets. In the location prediction process, the regular and random user movement patterns are treated differently, which can reflect the user movements more realistically than the existing movement models in HetNets. And different forms of multiclass support vector machines are embedded in the two mobility patterns according to the different characteristics of the two mobility patterns. Moreover, the introduction of target region (TR) cuts down the energy consumption efficiently without impacting the prediction accuracy. As reported in the simulations, our Multi-SVMMP can overcome the difficulties found in the traditional methods and obtain a higher prediction accuracy and user adaptability while reducing the cost of prediction resources