Multi-omics Approach Reveals Influenza-A Virus Target Genes Associated Genomic,Clinical and Immunological Characteristics in Cancers

Objective To examine the precise function of influenza A virus target genes(IATGs)in malignancy.Methods Using multi-omics data from the TCGA and TCPA datasets,33 tumor types were evaluated for IATGs.IATG expression in cancer cells was analyzed using transcriptome analysis.Copy number variation(CNV)was assessed using GISTICS 2.0.Spearman’s analysis was used to correlate mRNA expression with methylation levels.GSEA was used for the enrichment analysis.Pearson’s correlation analysis was used to examine the association between IATG mRNA expression and IC50.The ImmuCellAI algorithm was used to calculate the infiltration scores of 24 immune cell types.Results In 13 solid tumors,IATG mRNA levels were atypically expressed.Except for UCS,UVM,KICH,PCPG,THCA,CHOL,LAMI,and MESO,most cancers contained somatic IATG mutations.The main types of CNVs in IATGs are heterozygous amplifications and deletions.In most tumors,IATG mRNA expression is adversely associated with methylation.RT-PCR demonstrated that EGFR,ANXA5,CACNA1C,CD209,UVRAG were upregulated and CLEC4M was downregulated in KIRC cell lines,consistent with the TCGA and GTEx data.Conclusion Genomic changes and clinical characteristics of IATGs were identified,which may offer fresh perspectives linking the influenza A virus to cancer.

An R2R3-type transcription factor gene AtMYB59 regulates root growth and cell cycle progression in Arabidopsis

MYB proteins play important roles in eukaryotic organisms. In plants, the R1R2R3-type MYB proteins function in cell cycle control. However, whether the R2R3-type MYB protein is also involved in the cell division process remains unknown. Here, we report that an R2R3-type transcription factor gene, AtMYB59, is involved in the regulation of cell cycle progression and root growth. The AtMYB59 protein is localized in the nuclei of onion epidermal cells and has transactivation activity. Expression of AtMYB59 in yeast cells suppresses cell proliferation, and the transfor- mants have more nuclei and higher anenpioid DNA content with longer cells. Mutation in the conserved domain of AtMYB59 abolishes its effects on yeast cell growth. In synchronized Arabidopsis cell suspensions, the AtMYB59 gene is specifically expressed in the S phase during cell cycle progression. Expression and promoter-GUS analysis reveals that the AtMYB59 gene is abundantly expressed in roots. Transgenic plants overexpressing AtMYB59 have shorter roots compared with wild-type plants （Arabidopsis accession Col-0）, and around half of the mitotic cells in root tips are at metaphase. Conversely, the null mutant myb59-1 has longer roots and fewer mitotic cells at metaphase than Col, suggesting that AtMYB59 may inhibit root growth by extending the metaphase of mitotic cells. AtMYB59 regulates many downstream genes, including the CYCB1;1 gene, probably through binding to MYB-responsive elements. These results support a role forAtMYB59 in cell cycle regulation and plant root growth.

Joint Multi-Domain Channel Estimation Based on Sparse Bayesian Learning for OTFS System

Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.

DQL-Based Intelligent Scheduling Algorithm for Automatic Driving in Massive MIMO V2I Scenarios

Connected and autonomous vehicle(CAV)vehicle to infrastructure(V2I)scenarios have more stringent requirements on the communication rate,delay,and reliability of the Internet of vehicles(Io V).New radio vehicle to everything(NR-V2X)adopts link adaptation(LA)to improve the efficiency and reliability of road safety information transmission.In order to solve the problem that the existing LA scheduling algorithms cannot adapt to the Doppler shift and complex fast time-varying channel in V2I scenario,resulting in low reliability of information transmission,this paper proposes a deep Q-learning(DQL)-based massive multiple-input multiple-output(MIMO)LA scheduling algorithm for autonomous driving V2I scenario.The algorithm combines deep neural network(DNN)with Q-learning(QL)algorithm,which is used for joint scheduling of modulation and coding scheme(MCS)and space division multiplexing(SDM).The system simulation results show that the algorithm proposed in this paper can fully adapt to the different channel environment in the V2I scenario,and select the optimal MCS and SDM for the transmission of road safety information,thereby the accuracy of road safety information transmission is improved,collision accidents can be avoided,and bring a good autonomous driving experience.

Modified Omega-K algorithm for processing helicopter-borne frequency modulated continuous waveform rotating synthetic aperture radar data

With appropriate geometry configuration, helicopter- borne rotating synthetic aperture radar （ROSAR） can break through the limitations of monostatic synthetic aperture radar （SAR） on forward-looking imaging. With this capability, ROSAR has extensive potential applications, such as self-navigation and self-landing. Moreover, it has many advantages if combined with the frequency modulated continuous wave （FMCW） technology. A novel geometric configuration and an imaging algorithm for helicopter-borne FMCW-ROSAR are proposed. Firstly, by per- forming the equivalent phase center principle, the separated trans- mitting and receiving antenna system is equalized to the case of system configuration with antenna for both transmitting and receiving signals. Based on this, the accurate two-dimensional spectrum is obtained and the Doppler frequency shift effect in- duced by the continuous motion of the platform during the long pulse duration is compensated. Next, the impacts of the velocity approximation error on the imaging algorithm are analyzed in de- tail, and the system parameters selection and resolution analysis are presented. The well-focused SAR image is then obtained by using the improved Omega-K algorithm incorporating the accurate compensation method for the velocity approximation error. FJnally, correctness of the analysis and effectiveness of the proposed al- gorithm are demonstrated through simulation results.

基于R-CNN的智能电表数值识别方法（英文）

针对传统抄表方式存在的识别精度低、识别效果差的问题,提出基于区域卷积神经网络(Region-based Convolutional Neural Network,R-CNN)的智能电表数值识别方法。该方法首先使用摄像头采集电表数字码盘的图像信息,然后对采集到的图像进行预处理,最后通过R-CNN网络对预处理后的图像进行自动识别。试验结果表明:所提方法利用R-CNN网络可以同时自动识别表盘位置信息和表盘读数信息,很大程度上减少了人工识表的压力,提高了表计识别精度。

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.

Joint Channel Estimation and Decoding Design for 5G-Enabled V2V Channel

This paper addresses the problem of channel estimation in 5G-enabled vehicular-to-vehicular(V2V) channels with high-mobility environments and non-stationary feature. Considering orthogonal frequency division multiplexing(OFDM) system, we perform extended Kalman filter(EKF) for channel estimation in conjunction with Iterative Detector & Decoder(IDD) at the receiver to improve the estimation accuracy. The EKF is proposed for jointly estimating the channel frequency response and the time-varying time correlation coefficients. And the IDD structure is adopted to reduce the estimation errors in EKF. The simulation results show that, compared with traditional methods, the proposed method effectively promotes the system performance.

基于模糊谱聚类的电力系统客户分群算法

国网重庆市电力公司信息通信分公司(简称公司)负责重庆电力信息通信系统客户服务工作。随着服务质量的提升以及客户满意度要求的提高,客户服务工作应由被动受理客户业务咨询变为主动为客户提供业务服务。然而目前公司未开展客户分级管理,所有客户按统一的标准开展服务,不利于客服资源的有效配置。为了给客户提供更好的体验,实现精准服务,提出一种将谱聚类与模糊聚类相结合的算法(简称模糊谱聚类算法),基于特征向量提取对电力公司信通客户进行分群,挖掘大量客户数据中的潜在信息。案例分析表明:和其他几种典型聚类算法的聚类指标相比,所提算法可以更有效地对电力公司信通客户进行聚类。

CSI Intelligent Feedback for Massive MIMO Systems in V2I Scenarios

With the rapid development of the Internet of vehicles(IoV),vehicle to everything(V2X)has strict requirements for ultra-reliable and low latency communications(URLLC),and massive multiinput multi-output(MIMO)channel state information(CSI)feedback can effectively support URLLC communication in 5G vehicle to infrastructure(V2I)scenarios.Existing research applies deep learning(DL)to CSI feedback,but most of its algorithms are based on low-speed outdoor or indoor environments and assume that the feedback link is perfect.However,the actual channel still has the influence of additive noise and nonlinear effects,especially in the high-speed V2I scene,the channel characteristics are more complex and time-varying.In response to the above problems,this paper proposes a CSI intelligent feedback network model for V2I scenarios,named residual mixnet(RM-Net).The network learns the channel characteristics in the V2I scenario at the vehicle user(User Equipment,UE),compresses the CSI and sends it to the channel;the roadside base station(Base Station,BS)receives the data and learns the compressed data characteristics,and then restore the original CSI.The system simulation results show that the RM-Net training speed is fast,requires fewer training samples,and its performance is significantly better than the existing DL-based CSI feedback algorithm.It can learn channel characteristics in high-speed mobile V2I scenarios and overcome the influence of additive noise.At the same time,the network still has good performance under high compression ratio and low signal-to-noise ratio(SNR).

Partitioned k-Exclusion Real-Time Locking Protocol Motivated by Multicore Multi-GPU Systems

Graphic processing units （GPUs） have been widely recognized as cost-efficient co-processors with acceptable size, weight, and power consumption. However, adopting GPUs in real-time systems is still challenging, due to the lack in framework for real-time analysis. In order to guarantee real-time requirements while maintaining system utilization ~in modern heterogeneous systems, such as multicore multi-GPU systems, a novel suspension-based k-exclusion real-time locking protocol and the associated suspension-aware schedulability analysis are proposed. The proposed protocol provides a synchronization framework that enables multiple GPUs to be efficiently integrated in multicore real-time systems. Comparative evaluations show that the proposed methods improve upon the existing work in terms of schedulability.

SET-MRTS:An Empirical Experiment Tool for Real-Time Scheduling and Synchronization

In the real-time scheduling theory,schedulability and synchronization analyses are used to evaluate scheduling algorithms and real-time locking protocols,respectively,and the empirical synthesis experiment is one of the major methods to compare the performance of such analyses.However,since many sophisticated techniques have been adopted to improve the analytical accuracy,the implementation of such analyses and experiments is often time-consuming.This paper proposes a schedulability experiment toolkit for multiprocessor real-time systems(SET-MRTS),which provides a framework with infrastructures to implement the schedulability and synchronization analyses and the deployment of empirical synthesis experiments.Besides,with well-designed peripheral components for the input and output,experiments can be conducted easily and flexibly on SET-MRTS.This demonstration further proves the effectiveness of SET-MRTS in both functionality and availability.

A Top-down Method of Extraction Entity Relationship Triples and Obtaining Annotated Data

The extraction of entity relationship triples is very important to build a knowledge graph(KG),meanwhile,various entity relationship extraction algorithms are mostly based on data-driven,especially for the current popular deep learning algorithms.Therefore,obtaining a large number of accurate triples is the key to build a good KG as well as train a good entity relationship extraction algorithm.Because of business requirements,this KG’s application field is determined and the experts’opinions also must be satisfied.Considering these factors we adopt the top-down method which refers to determining the data schema firstly,then filling the specific data according to the schema.The design of data schema is the top-level design of KG,and determining the data schema according to the characteristics of KG is equivalent to determining the scope of data’s collection and the mode of data’s organization.This method is generally suitable for the construction of domain KG.This article proposes a fast and efficient method to extract the topdown type KG’s triples in social media with the help of structured data in the information box on the right side of the related encyclopedia webpage.At the same time,based on the obtained triples,a data labeling method is proposed to obtain sufficiently high-quality training data,using in various Natural Language Processing(NLP)information extraction algorithms’training.

Long-term effects of oral tea polyphenols and Lactobacillus brevis M8 on biochemical parameters, digestive enzymes, and cytokines expression in broilers

This study investigates the long-term effects of oral tea polyphenols （TPs） and Lactobacillus brevis M8 （LB） on biochemical parameters, digestive enzymes, and cytokines expression in broilers. In experiment 1,240 broiler chickens were selected to investigate the effects of 0.06 g/kg body weight （BW） TP and 1.0 ml/kg BW LB on broilers; in experiment 2, 180 broiler chickens were assigned randomly to three groups to investigate the effects of different dosages of TP （0.03, 0.06, and 0.09 g/kg BW） combined with 1.0 ml/kg BW LB on broilers; in experiment 3, 180 broiler chickens were assigned randomly to three groups to investigate the effects of different dosages of LB （0.5, 1.0, and 1.5 ml/kg BW） combined with 0.06 g/kg BW TP on broilers. The results showed that TP and LB affected serum biochemical parameters, and TP reduced serum cholesterol （CHO） and low-density lipoprotein cholesterol （LDL-C） abundances in a dosage-dependent manner （P〈0.05） on Day 84. Meanwhile, broilers fed a diet supplemented with TP or LB had a lower intestinal lipase activity on Day 84 compared with the control group （P〈0.05）. Middle and high dosages of TP increased pancreatic lipase and proventriculus pepsin activities （P〈0.05）. Also middle and high dosages of LB significantly enhanced pancreatic lipase activity （P〈0.05）, while high LB supplementation inhibited intestinal trypsase （P〈0.05） on Day 84. Furthermore, both TP and LB reduced intestinal cytokine expression and nuclear factor-K B （NF-KB） mRNA level on Days 56 and 84. In conclusion, long-term treatment of TP and LB improved lipid metabolism and digestive enzymes activities, and affected intestinal inflammatory status, which may be associated with the NF-KB signal.

Whole-exome mutational landscape of metastasis in patient-derived hepatocellular carcinoma cells

In order to explore the genomic basis for liver cancer metastasis,whole-exome sequencing(WES)was performed on patient-derived hepatocellular carcinoma(HCC)cell lines with differential metastatic potentials and analyzed their clonal evolution relationships.An evolutionary tree based on genomic single nucleotide polymorphism(SNP)was constructed in MegaX software.The WES data showed that the average percentage of heterogeneous mutations in each HCC cell lines was 16.55%(range,15.38%e18.17%).C:G>T:A and T:A>C:G somatic transitions were the two most frequent substitutions.In these metastatic HCC cell lines,non-silent gene mutations were found in 21.88%of known driver genes and 10 classical signaling pathways.The protein interaction network was constructed by STRING,and hub genes were found in the shared trunk mutation genes and the heterogeneous branch mutations respectively.In cBioPortal database,some of the selected hub genes were found to be associated with poor overall survival(OS)of HCC patients.Among the mutated HCC driver genes,a novel KEAP1 mutation with a homozygous frameshift truncation at the c-terminal Nrf2 binding region was detected and verified in MHCC97-H and HCC97LM3 cells.In conclusion,WES data demonstrate that HCC cell lines from tumor biopsy specimens of the same patient have obtained different metastatic potentials through repeated selection in rodents in vivo,and they do indeed have a genetic relationship at the genomic level.

Adverse effects of chemoradiotherapy on invasion and metastasis of tumor cells

The phenomenon of enhanced invasion and metastasis of residual tumor cells has been observed in an increasing number of patients receiving chemoradiotherapy recently,and tumor metastasis will undoubtedly limit patient prognosis.However,the key mechanism by which chemoradiotherapy affects the invasion and metastasis of tumor cells remains unclear.Studies have shown that chemoradiotherapy may directly act on tumor cells and alter the tumor microenvironment,or induce cell apoptosis and autophagy to promote tumor cell survival and metastasis.In this review,we summarize the potential mechanisms by which chemoradiotherapy may affect the biological behavior of tumor cells and open up new avenues for reducing tumor recurrence and metastasis after treatment.These insights will improve the efficacy of chemoradiotherapy.

Obstacles and opportunities in the prevention and treatment of HBV-related hepatocellular carcinoma

Despite the tremendous progresses toward our understanding of the mechanisms of how liver cancer was developed,the therapeutic outcomes of liver cancer in the clinic have very limited improvement within the past three decades or so.In addition,both the incidence and mortality of liver cancer worldwide are not dropping,but increasing steadily,in the last decade.Thus,it is time for us to rethink what has been wrong and how could we do better in the upcoming years,in order to achieve our goal of improving the therapeutic outcomes of patients with liver cancer in the clinic,and at the meantime,effectively reducing the incidence of liver cancer by blocking malignant transformation of hepatocytes from chronic viral infection.This is also one of the main reasons why we try to organize this special issue on primary liver cancer in the journal of Genes&Diseases.In this perspective,I will summarize the major obstacles confronted with in the prevention and management of patients with chronic hepatitis B infection and subsequent development of liver cirrhosis and liver cancer.Next,I will delineate the pitfalls and underlying mechanisms of why the current anti-viral strategies and therapeutic agents are not as effective as one expected in terms of successful reduction or prevention chronic hepatitis B infection associated liver cirrhosis and liver cancer.I will then provide my personal perspectives on potential approaches and strategies for effective prevention and management of hepatitis B-related liver cancer.

Electroless copper plating using dimethylamine borane as reductant

Electroless copper plating was studied using dimethylamine borane （DMAB） as reductant and 1,5,8,12- tetraazadodecane as additive and triethanolamine （TEA） as buffer. The effects of pH, temperature and concentrations of reactants and additives on the anodic oxidation of DMAB and the cathodic reduction of copper ion were investigated. Experimental results indicate that high pH values （10-12.5） promote the oxidation of DMAB, and suppress the reduction of the copper ion, while high bath temperatures （55-70℃） accelerate both anodic oxidation and cathodic reduction. Increase of the Cu2＋and DMAB concentrations can improve the deposition rate of copper plating. Results for a dual-chelating-agent system indicate that 1,5,8,12-tetraazadodecane plays an important role in chelation, while the main effect of TEA is adsorption on copper surfaces to inhibit DMAB oxidation and to promote deposition.

Improved Task and Resource Partitioning Under the Resource-Oriented Partitioned Scheduling

Coordinated partitioning and resource sharing have attracted considerable research interest in the field of real-time multiprocessor systems.However,finding an optimal partition is widely known as NP-hard,even for independent tasks.A recently proposed resource-oriented partitioned(ROP)fixed-priority scheduling that partitions tasks and shared resources respectively has been shown to achieve a non-trivial speedup factor guarantee,which promotes the research of coordinated scheduling to a new level.Despite the theoretical elegance,the schedulability performance of ROP scheduling is restricted by the heuristic partitioning methods used in the original study.In this paper,we address the partitioning problem for tasks and shared resources under the ROP scheduling.A unified schedulability analysis framework for the ROP scheduling is proposed in the first place.A sophisticated partitioning approach based on integer linear programming(ILP)is then proposed based on the unified analysis.Empirical results show that the proposed methods improve the schedulability of ROP scheduling significantly,and the runtime complexity for searching a solution is reduced prominently compared with other ILP-based approaches as well.

The metabolic switch of cancer From the 2016 Sino-US Symposium on Cancer Metabolism, Chongqing, P.R. China, October 10e11, 2016

Although remarkable progress has been made in oncology research,cancer is still a leading cause of death worldwide.It is well recognized that cancer is a genetic disease,yet metabolic alterations or reprogramming are the major phenotypes associated with the(epi-)genetic modifications of cancer cells.Thus,understanding the metabolic changes of tumor cells will facilitate the diagnosis of cancer,alleviate drug resistance and provide novel druggable targets that can lead to cures for cancer.The first Sino-US Symposium on Cancer Metabolism was held in Chongqing on October 10th and 11th,with the theme of“cancer metabolism and precision cancer therapy”.The symposium brought about a dozen keynote speakers each from the US and China's Mainland,as well as one hundred delegates with an interest in cancer metabolism.This short article will briefly summarize the advances reported during this meeting.