Oscillatory and anti-oscillatory motifs in genetic regulatory networks

Recently, self-sustained oscillatory genetic regulatory networks （GRNs） have attracted significant attention in the biological field. Given a GRN, it is important to anticipate whether the network could generate oscillation with proper parameters, and what the key ingredients for the oscillation are. In this paper the ranges of some function-related parameters which are favorable to sustained oscillations are considered. In particular, some oscillatory motifs appearing with high-frequency in most of the oscillatory GRNs are observed. Moreover, there are some anti-oscillatory motifs which have a strong oscillation repressing effect. Some conclusions analyzing these motif effects and constructing oscillatory GRNs are provided.

Anti-windup compensation design for a class of distributed time-delayed cellular neural networks

Both time-delays and anti-windup(AW)problems are conventional problems in system design,which are scarcely discussed in cellular neural networks(CNNs).This paper discusses stabilization for a class of distributed time-delayed CNNs with input saturation.Based on the Lyapunov theory and the Schur complement principle,a bilinear matrix inequality(BMI)criterion is designed to stabilize the system with input saturation.By matrix congruent transformation,the BMI control criterion can be changed into linear matrix inequality(LMI)criterion,then it can be easily solved by the computer.It is a one-step AW strategy that the feedback compensator and the AW compensator can be determined simultaneously.The attraction domain and its optimization are also discussed.The structure of CNNs with both constant timedelays and distribute time-delays is more general.This method is simple and systematic,allowing dealing with a large class of such systems whose excitation satisfies the Lipschitz condition.The simulation results verify the effectiveness and feasibility of the proposed method.

Prediction of Anti-Inflammatory Activity of a Series of Pyrimidine Derivatives, by Multiple Linear Regression and Artificial Neural Networks

Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μD, the energy of the highest occupied molecular orbital EHOMO, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R2=91.28%, R2aj=89.11%, RMCE = 0.2831, R2ext=86.50% and R2=98.22%, R2aj=97.75%, RMCE = 0.1131, R2ext=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives.

Anti-synchronization Between Coupled Networks with Two Active Forms

这份报纸与非线性的信号的连接和互连网络行动学习在二个联合网络之间的反同步和它的控制。如果反同步不在二个如此的网络之间存在，适应控制器被设计到反同步他们。拓扑的结构被考虑的不同节点动力学和 nonidentical 和为在二个网络之间的反同步的有用标准被给。数字例子被举显示出我们的导出的结果的效率。

Existence and Exponential Stability of the Anti-Periodic Solutions for a Class of Impulsive CohenGrossberg Neural Networks with Mixed Delays

In this paper,we study the anti-periodic solutions for a class of impulsive Cohen-Grossberg neural networks with mixed delays.By using analysis techniques,some sufficient conditions are obtained which guarantee the existence and global exponential stability of the anti-periodic solutions.The criteria extend and improve some earlier results.Moreover,we give an examples to illustrate our main results.

FORCE RIPPLE SUPPRESSION TECHNOLOGY FOR LINEAR MOTORS BASED ON BACK PROPAGATION NEURAL NETWORK

Various force disturbances influence the thrust force of linear motors when a linear motor （LM） is running. Among all of force disturbances, the force ripple is the dominant while a linear motor runs in low speed. In order to suppress the force ripple, back propagation（BP） neural network is proposed to learn the function of the force ripple of linear motors, and the acquisition method of training samples is proposed based on a disturbance observer. An off-line BP neural network is used mainly because of its high running efficiency and the real-time requirement of the servo control system of a linear motor. By using the function, the force ripple is on-line compensated according to the position of the LM. The experimental results show that the force ripple is effectively suppressed by the compensation of the BP neural network.

China's landscape in oncology drug research:perspectives from research collaboration networks

Objective： Better understanding of China＇s landscape in oncology drug research is of great significance for discovering anti-cancer drugs in future. This article differs from previous studies by focusing on Chinese oncology drug research communities in co-publication networks at the institutional level. Moreover, this research aims to explore structures and behaviors of relevant research units by thematic community analysis and to address policy recommendations. Methods： This research used social network analysis to define an institutions network and to identify a community network which is characterized by thematic content. Results： A total of 675 sample articles from 2008 through 2012 were retrieved from the Science Citation Index Expanded （SCIE） database of Web of Science, and top institutions and institutional pairs are highlighted for further discussion. Meanwhile, this study revealed that institutions based in the Chinese mainland are located in a relatively central position, Taiwan＇s institutions are closely assembled on the side, and Hong Kong＇s units located in the middle of the Chinese mainland＇s and Taiwan＇s. Spatial division and institutional hierarchy are still critical barriers to research collaboration in the field of anti-cancer drugs in China. In addition, the communities focusing on hot research areas show the higher nodal degree, whereas communities giving more attention to rare research subjects are relatively marginalized to the periphery of network. Conclusions= This paper offers policy recommendations to accelerate cross-regional cooperation, such as through developing information technology and increasing investment. The brokers should focus more on outreach to other institutions. Finally, participation in topics of common interest is conducive to improved efficiency in research and development （R＆D） resource allocation.

Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

Curcumin,the medically active component from Curcuma longa(Turmeric),is widely used to treat inflammatory diseases.Protein interaction network(PIN) analysis was used to predict its mechanisms of molecular action.Targets of curcumin were obtained based on ChE MBL and STITCH databases.Protein–protein interactions(PPIs) were extracted from the String database.The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology(GO) enrichment analysis based on molecular complex detection(MCODE).A PIN of curcumin with 482 nodes and 1688 interactions was constructed,which has scale-free,small world and modular properties.Based on analysis of these function modules,the mechanism of curcumin is proposed.Two modules were found to be intimately associated with inflammation.With function modules analysis,the anti-inflammatory effects of curcumin were related to SMAD,ERG and mediation by the TLR family.TLR9 may be a potential target of curcumin to treat inflammation.

Towards a Unified Recurrent Neural Network Theory： The Uniformly Pseudo-Projection-Anti-Monotone Net

In the past decades, various neural network models have been developed for modeling the behavior of human brain or performing problem-solving through simulating the behavior of human brain. The recurrent neural networks are the type of neural networks to model or simulate associative memory behavior of human being. A recurrent neural network （RNN） can be generally formalized as a dynamic system associated with two fundamental operators： one is the nonlinear activation operator deduced from the input-output properties of the involved neurons, and the other is the synaptic connections （a matrix） among the neurons. Through carefully examining properties of various activation functions used, we introduce a novel type of monotone operators, the uniformly pseudo-projectionanti-monotone （UPPAM） operators, to unify the various RNN models appeared in the literature. We develop a unified encoding and stability theory for the UPPAM network model when the time is discrete. The established model and theory not only unify but also jointly generalize the most known results of RNNs. The approach has lunched a visible step towards establishment of a unified mathematical theory of recurrent neural networks.

Modeling and analysis of anti-worm in P2P networks

Anti-worm is an effective way to fight against malicious worm and has been followed closely by malicious worm researchers recently. However, active and passive confronting technologies in peer-to-peer （P2P） networks have not been studied in depth. This paper introduces both of them to fight against malicious worm in P2P networks. To study their effectiveness in P2P networks, this paper takes the topology degree in P2P networks into consideration and puts forward a four-state propagation model for active anti-worm and a five-state propagation model for passive anti-worm respectively. Both of the models are simplified in the case that size of a P2P network is large enough. The simulation results have not only validated the effectiveness of our propagation models but also evaluated the excellent performance of both active anti-worm and passive anti-worm.

Epidemic spreading on scale-free networks with diversity of node anti-attack abilities

In this article, a modified susceptible-infected-removed （SIR） model is proposed to study the influence of diversity of node anti-attack abilities on the threshold of propagation in scale-free networks. In particular, a vulnerability function related to node degree is introduced into the model to describe the diversity of a node anti-attack ability. Analytical results are derived using the mean-field theory and it is observed that the diversity of anti-attack of nodes in scale-free networks can increase effectively the threshold of epidemic propagation. The simulation results agree with the analytical results. The results show that the vulnerability functions can help adopt appropriate immunization strategies.

Cloud Computing-Based Forensic Analysis for Collaborative Network Security Management System

Internet security problems remain a major challenge with many security concerns such as Internet worms, spam, and phishing attacks. Botnets, well-organized distributed network attacks, consist of a large number of bots that generate huge volumes of spam or launch Distributed Denial of Service （DDoS） attacks on victim hosts. New emerging botnet attacks degrade the status of Internet security further. To address these problems, a practical collaborative network security management system is proposed with an effective collaborative Unified Threat Management （UTM） and traffic probers. A distributed security overlay network with a centralized security center leverages a peer-to-peer communication protocol used in the UTMs collaborative module and connects them virtually to exchange network events and security rules. Security functions for the UTM are retrofitted to share security rules. In this paper, we propose a design and implementation of a cloud-based security center for network security forensic analysis. We propose using cloud storage to keep collected traffic data and then processing it with cloud computing platforms to find the malicious attacks. As a practical example, phishing attack forensic analysis is presented and the required computing and storage resources are evaluated based on real trace data. The cloud- based security center can instruct each collaborative UTM and prober to collect events and raw traffic, send them back for deep analysis, and generate new security rules. These new security rules are enforced by collaborative UTM and the feedback events of such rules are returned to the security center. By this type of close-loop control, the collaborative network security management system can identify and address new distributed attacks more quickly and effectively.

城市防疫医疗救援网络的抗毁性与鲁棒性

为研究城市防疫医疗救援系统空间布局的合理性和防灾韧性,采用复杂网络技术构建了某城市防疫医疗救援网络模型,对其网络拓扑结构和网络特征基本参数进行分析,研究不同攻击模型下以边和点连通度为度量指标的结构抗毁性能力;通过设定不同的网络冗余与网络负荷,研究网络针对确定性攻击与随机性攻击的鲁棒性特征。研究结果可为城市防疫医疗救援系统的空间优化布局和核心场所强化建设提供理论支撑。

人工神经网络在治疗药物监测中的应用

人工神经网络(ANN)是对生物神经网络的一种模拟,通过相互连接而构成的自适应非线性动态网络系统,ANN的优势在于优化容易、建模简便、结果准确。该文根据文献资料对ANN在免疫抑制剂、抗菌药物、抗癫痫药物等治疗药物监测中的应用进展进行阐述,阐明ANN模型的优缺点以及未来的发展方向,希望为未来的研究者提供有价值的参考信息。ANN用于治疗药物监测有着巨大的潜在前景,有希望成为实现患者个体化用药的有效手段。

基于模糊神经网络的电网消防预警算法

针对传统基于阈值判别方法的电网火灾预警系统预测精度低、抗干扰能力弱的问题,提出了一种基于模糊神经网络的电网消防预警算法。该算法利用神经网络学习大规模电网数据,使用模糊逻辑推理算法来提升预测结果的推理能力,并通过结合神经网络对大规模数据的学习能力和模糊逻辑算法的推理能力来分析电网线路参数,从而提升电网消防预警系统的精度和抗干扰能力。实验与仿真结果表明,所提出方法能显著提升电网火灾的预警精度,且使用模糊逻辑推理可以得到更符合实际情况的电网火灾预警结果。

基于网络药理学探讨抗癌精方治疗胃癌的作用机制

目的:基于网络药理学探讨抗癌精方治疗胃癌药效物质基础及作用机制,为临床中药治疗胃癌提供生物信息学依据。方法:在中药系统药理数据库和分析平台(TCMSP)检索抗癌精方药物有效成分,结合UniProt数据库得到有效成分药物靶点,利用GeneCards、OMIM、TTD等数据库获得胃癌相关疾病靶点,使用Cytoscape 3.9.1软件构建“疾病-成分-靶点”网络,String数据库及Cytoscape软件构建PPI网络。通过UALCAN数据库分析核心基因的转录水平,Kaplan-Meier plotter数据库分析核心基因表达与患者生存之间的关系。通过DAVID数据库进行GO功能和KEGG通路富集分析。结果:抗癌精方活性成分236个,PPI网络筛选出16个关键靶点;MAPK3、MAPK1、RELA、AKT1、TP53、FOS、MAPK14、RXRA、MAPK8、EGFR在胃癌组织中异常表达(P<0.05),且均与胃癌患者预后呈相关性(P<0.05);GO功能分析主要富集于细胞分裂、细胞增殖凋亡等调控,KEGG通路富集分析主要富集于癌症通路、MAPK信号通路、Relaxin信号通路、TNF信号通路、T细胞受体信号通路、Prolactin信号通路、PI3K-Akt信号通路等。结论:抗癌精方具有多成分、多靶点、多途径的协同作用特点,主要通过槲皮素、山奈酚、β-谷甾醇、消旋卡文定碱等活性成分,作用于MAPK3、MAPK1、RELA、AKT1、TP53、FOS、MAPK14、RXRA、MAPK8、EGFR靶点,调控MAPK、Relaxin、TNF、T细胞受体、Prolactin、PI3K-Akt信号通路等发挥作用。

城市防灾避难场所网络结构特征与鲁棒性研究

良好的防灾避难疏散体系是城市灾后应急救灾能力的根本保障,为探讨城市防灾避难场所系统空间布局和应急疏散能力的合理性,采用复杂网络技术构建了某城市固定避难场所网络模型,对其网络拓扑结构、网络特征基本参数、社团划分进行了分析,并研究了不同攻击模型下以边和点连通度为度量指标的结构抗毁性能力;根据网络中失效位置的不同,探讨网络内节点失效和网络内节点连通受阻导致的合作中断鲁棒性特征,研究结果可为城市固定避难场所的空间优化布局和核心场所强化建设提供理论支撑。

广域网环境中舰船通信网络信息安全传输方法

为满足舰船在广域网环境的安全通信需求,提出广域网环境中舰船通信网络信息安全传输方法。基于云计算技术构建具备信息调度的舰船通信网络信息安全传输模型,并针对舰船通信广域网环境存在的来自不同方向的通信干扰源、信号衰减和噪声干扰,结合噪声消除算法进行舰船通信网络信息传输抗干扰设计,降低外界干扰影响;针对舰船通信广域网环境中实时信息在传输过程中面临的攻击、窃听、截取等多种安全威胁,采用双混沌互反馈加密方法完成舰船通信网络传输信息加密,保障信息在广域网环境下安全传输。实验结果表明,该方法可显著提升舰船通信速率,在不同干扰类型下均表现出良好的抗干扰性能;加密后信息可成功掩盖原始数据分布特征,保障传输信息安全性。

草苁蓉抗衰老作用的网络药理学研究

通过网络药理学和分子对接探讨草苁蓉抗衰老的活性成分、关键靶点及潜在分子机制,为草苁蓉抗衰老的开发提供依据.使用TCMSP和HERB数据库获得草苁蓉的活性成分和靶点;通过OMIM、GeneCards、DisGeNET数据库获取与衰老相关靶点;运用Venny 2.1.0平台获得交集靶点;采用STRING平台和Cytoscape 3.9.1软件制作PPI网络;运用DAVID数据库和微生信在线平台对交集靶点进行GO和KEGG分析;运用Openbabel、Autodock、PyMOL等软件,PLIP等平台进行分子对接验证和可视化处理.从草苁蓉中筛选得到活性成分30个,成分靶点436个,与衰老相关靶点1814个;成分-疾病交集靶点共215个.GO和KEGG分析发现,草苁蓉的抗衰老作用涉及了对细胞凋亡的负反馈、对药物的反应等多个生物学过程,交集靶点主要富集在癌症、PI3K-AKT信号传导、MAPK信号传导等20条核心通路上;分子对接结果表明,草苁蓉中的齐墩果酸、3-表齐墩果酸和草苁蓉纳拉苷与关键靶点GAPDH、AKT1、IL6具有良好的结合潜能.草苁蓉通过齐墩果酸、3-表齐墩果酸和草苁蓉纳拉苷等成分,作用于GAPDH、AKT1、IL6等关键靶点,参与PI3K-Akt信号通路、MAPK信号通路、HIF-1信号通路等多种途径,发挥RNA聚合酶Ⅱ转录因子活性,细胞凋亡的负反馈,对药物的反应等作用,从而发挥抗衰老的作用.