Joint Optimal Energy-Efficient Cooperative Spectrum Sensing and Transmission in Cognitive Radio

In order to improve the energy efficiency(EE) in cognitive radio(CR), a joint optimal energy-efficient cooperative spectrum sensing(CSS) and transmission in multi-channel CR is proposed in this paper. EE is described as a tradeoff between the throughput and the entirely consumed power. A joint optimization problem is formulated to maximize EE by jointly optimizing local sensing time, number of cooperative sensing secondary users(SU), transmission bandwidth and power. A combined optimization algorithm of bi-level optimization, Polyblock optimization and Dinkelbach's optimization is proposed to solve the proposed non-convex optimization problem effectively. The simulation results show that, compared with throughput maximization model(TMM), the energy efficiency maximization model(EEMM) improves EE of the CR system and limits the excessive power consumption effectively.

m6A-TSHub:Unveiling the Context-specific m^(6)A Methylation and m^(6)A-affecting Mutations in 23 Human Tissues

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs(lncRNAs),N6-methyladenosine(m^(6)A)RNA methylation has been shown to participate in essential biological processes.Recent studies have revealed the distinct patterns of m^(6)A methylome across human tissues,and a major challenge remains in elucidating the tissue-specific presence and circuitry of m^(6)A methylation.We present here a comprehensive online platform,m^(6)A-TSHub,for unveiling the context-specific m^(6)A methylation and genetic mutations that potentially regulate m^(6)A epigenetic mark.m^(6)A-TSHub consists of four core components,including(1)m^(6)A-TSDB,a comprehensive database of 184,554 functionally annotated m^(6)A sites derived from 23 human tissues and 499,369 m^(6)A sites from 25 tumor conditions,respectively;(2)m^(6)A-TSFinder,a web server for high-accuracy prediction of m^(6)A methylation sites within a specific tissue from RNA sequences,which was constructed using multi-instance deep neural networks with gated attention;(3)m^(6)ATSVar,a web server for assessing the impact of genetic variants on tissue-specific m^(6)A RNA modifications;and(4)m^(6)A-CAVar,a database of 587,983 The Cancer Genome Atlas(TCGA)cancer mutations(derived from 27 cancer types)that were predicted to affect m^(6)A modifications in the primary tissue of cancers.The database should make a useful resource for studying the m^(6)A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue(or cancer type).m^(6)A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m^(6)ats.