MicroRNAs (miRNAs) play important regulatory roles in plant development and stress responses. Tomato is an economically important vegetable crop in the world with publicly available genomic information database, but only a limited number of tomato miRNAs have been identified. In this study, two independent small RNA libraries from mock and Cucumber mosaic virus (CMV)-infected tomatoes were constructed, respectively, and sequenced with a high-throughput Illumina Solexa system. Based on sequence analysis and hairpin structure prediction, a total of 50 plant miRNAs and 273 potentially candidate miRNAs (PC-miRNAs) were firstly identified in tomato, with 12 plant miRNAs and 82 PC-miRNAs supported by both the 3p and 5p strands. Comparative analysis revealed that 79 miRNAs (including 15 new tomato miRNAs) and 40 PC-miRNAs were differentially expressed between the two libraries, and the expression patterns of some new tomato miRNAs and PC-miRNAs were further validated by qRT-PCR. Moreover, potential targets for some of the known and new tomato miRNAs were identified by the recently developed degradome sequencing approach, and target annotation indicated that they were involved in multiple biological processes, including transcriptional regulation and virus resistance. Gene ontology analysis of these target transcripts demonstrated that defense response- and photosynthesis-related genes were most affected in CMV-Fny-infected tomatoes. Because tomato is not only an important crop but also is a genetic model for basic biology research, our study contributes to the understanding of miRNAs in response to virus infection.

Viral infections generally cause disease symptoms by interfering with the microRNA (miRNA)-mediated regulation of gene expression of host plants. In tomato leaves, the accumulation levels of eleven miRNAs and ten target mRNAs were enhanced by different degrees upon Cucumber mosaic virus (CMV)-Fny and Tomato aspermy virus (TAV)-Bj infections. The ability of CMV-Fny to interfere with miRNA pathway was dramatically suppressed in the addition of the benign satellite (sat) RNA variant (satYn12), but was slightly affected when CMV-Fny was co-inoculated with the aggressive satRNA variant (satT1). In plants harboring the infection of CMV-FnyΔ2b (a CMV-Fny 2b-deletion mutant), the unaltered miRNAs and target mRNAs levels compared with mock inoculated plants indicated that 2b ORF was essential for perturbation of miRNA metabolism. When the amounts of viral open reading frames (ORFs) in these infections were quantified, we found satYn12 caused a higher reduction of CMV-Fny accumulation levels than satT1. These results indicate the complex mechanism by which satRNAs participate in CMV-tomato interaction, and suggest that the severity of disease symptoms positively correlates to some extent with the perturbation of miRNA pathway in tomato.

Cucumber mosaic virus (CMV) is widely spread worldwide, causing typical systemic mosaic and other symptoms in tomato (Solanum lycopersicum). Host responses to CMV and molecular mechanisms associated with the development of disease symptoms caused by this virus in tomato are largely unexplored. To investigate plant responses activated during this interaction, we used microarray analysis to monitor changes in host gene expression during disease development. Compared with genes from mock-inoculated control plants, seedlings to adults, 214 of the 3313 tomato genes represented on the array were differentially expressed in CMV-infected plants. Functional classification of CMV-responsive genes revealed that CMV activated typical basal defense responses in the host during the infection process, including induction of defense-related genes, production and scavenging of free oxygen radicals, and hormone synthesis. CMV infection also suppressed a subset of host genes involved in photosynthesis and metabolism. Our results indicate that a wide range of genes play an important role in regulation of the tomato susceptibility response to CMV.

MicroRNAs (miRNAs) are a newly identified class of non-coding small RNAs of about 21–24 nucleotides. They play important roles in multiple biological processes by degrading targeted mRNAs or repressing mRNA translation. To date, a total of 2,043 plant miRNAs are present in the miRNA Registry database (miRBase Release 14.0), and none for tobacco (Nicotiana tabacum). In this research, we used known plant miRNAs against both genomic survey sequence (GSS) and expressed sequence tags (EST) databases to search for potential miRNAs in tobacco. A total of 25 potential miRNAs were identified following a range of strict filtering criteria, and 33 potential targets of miRNAs were predicted by searching the tobacco Unigene database. Most of these miRNA targeting genes were predicted to encode transcription factors which play important roles in tobacco development. Additionally, real-time PCR assays were performed to profile the expression levels of 10 miRNAs after the infection of Cucumber mosaic virus (CMV) and Potato virus X (PVX). The results showed that symptom severity is correlated to the miRNA accumulation, and increased miR168 expression during virus infection is a common, plant- and virus-independent response.

This paper considers the position tracking problem of a magnetic levitation system in the presence of modelling errors due to uncertainties of physical parameters. The recently developed dynamic surface control is modified and applied to the system under study, to overcome the problem of "explosion of terms" associated with the backstepping design pro: procedure. Input-to-state stable property of the control system is analyzed, and experiment results are included to show the excellent position tracking performance.

Least-squares (LS) method is the most widely used method for the parameter estimation. However when applying directly the LS method to estimate parameters in presence of correlated noise, the LS method will be an asymptotically biased estimation method and be unable to give consistent estimates. There have been many studies to solve the problem of bias and one of them is bias-eliminated least-squares (BELS) method. In this paper, we examine the property of the BELS method and discuss the equivalence of BELS method and IV method under certain condition.

In this paper, a new control method for decentralized systems is proposed by using the concept of Nash equilibrium points of non co-operative game. It is supposed that the system stated in this article is composed of many subsystems including their own controllers, so each subsystem can be regarded as a player in the game. And, each subsystem and its controller are described by the Universal Learning Networks which have been proposed to provide a universal framework for the class of neural networks. Based on the above assumptions it is theoretically shown that if the criterion function for each subsystem can be defined individually, then the Nash equilibrium points can be calculated by the gradient learning algorithm. Simulation studies on a decentralized tank network control system show that the Nash equilibrium points can be obtained systematically and effectively by the proposed method.

We present a control design scheme for nonlinear systems based on a probability learning network (ProNet). ProNet is a learning network equipped with the capability to deal with stochastic signals. A plant and its controllers are described by using a set of related equations and form a unified learning network-ProNet where disturbances are considered as external inputs. In this way, controller design is transferred to ProNet learning. By including an effort to reduce variances of ProNet output in the criterion function for training, the trained ProNet has different sensitivities to signals of different frequencies. A ProNet control system is designed by taking this advantage to increase its robustness against disturbances. Computer simulations confirm the effectiveness of the ProNet control scheme.

This paper presents a control design scheme for nonlinear systems based on a probability learning network (ProNet) that has a capability dealing with stochastic signals. Plant and its controllers are described by using a set of related equations and form a unified learning network- ProNet where disturbances are considered as its external inputs. Then controller design is transfered to ProNet learning. By including an effort to reduce variance of ProNet output in training, the trained ProNet has different sensitivity to signals of different frequencies. By taking this advantage, a ProNet control system is designed to increase its robustness against disturbance.

The universal learning network (ULN) which is a superset of supervised learning networks has been already proposed. Parameters in ULN are trained in order to optimize a criterion function as conventional neural networks, and after training they are used as constant parameters. In this paper, a method to alter the parameters depending on the network flows is presented to enhance representation abilities of networks. In the proposed method, there exists two kinds of networks, the first one is a basic network which includes varying parameters and the other one is a network which calculates the optimal varying parameters depending on the network flows of the basic network. It is also proposed in this paper that any type of networks such as fuzzy inference networks, radial basis function networks and neural networks can be used for the basic and parameter calculation networks. From simulations where parameters in a neural network are altered by fuzzy inference networks, it is shown that the networks with the same number of varying parameters have higher representation abilities than the conventional networks

In this paper, a new method named orbital correction to adjust the parameters of the controller of nonlinear systems is presented. Control systems are described by the Universal Learning Network, in which the controller and the controlled object can be dealt with using the same network architecture. And training of the parameters of the controller is carried out by the gradient method. The basic idea of this method is to introduce a kind of a priori information on control performance into the learning process in order to enhance the learning ability. From the simulations of a nonlinear crane control system, it is shown that the proposed method can obtain the better controller than the conventional methods by selecting an appropriate time and volume of orbital correction. And the proposed method may be useful to overcome the local minimum problem always encountered in the gradient method.

A method for identifying nonlinear dynamic systems with noise is proposed by using probabilistic universal learning networks (PrULNs). PrULNs are extensions of universal learning networks (ULNs). ULNs form a superset of neural networks and were proposed to provide a universal framework for modeling and control of nonlinear large-scale complex systems. But the ULN does not provide any stochastic characteristics of the signals propagating through it. The PrULNs are equipped with machinery to calculate stochastic properties of signals and to train network parameters so that the signals behave with the pre-specified stochastic properties. On the other hand it is generally recognized that there exists an overfitting problem when identification of nonlinear dynamic systems with noise is done by neural networks. In this paper, it is shown from simulation results of identification of a nonlinear robot dynamics that PrULNs are useful for avoiding the overfitting.

In this paper, a new control method for decentralized systems is proposed by using the concept of Nash equilibrium points of game theory. It is supposed that the system stated in this article is composed of many subsystems including their controllers, so each subsystem can be recognized as each player in game. From the above assumption it is pointed out that the Nash equilibrium points can be calculated by the commonly-used back propagation algorithm if the criterion function of each subsystem is given.From tank-network simulations, it is shown that the controller obtained by the Nash equilibrium points can be used when the criterion function of each subsystem is independently given.

In this paper, Probabilistic Universal Learning Networks (PrULNs) are proposed, which are learning networks-with a capability of dealing with stochastic signals. PrULNs are extensions of Universal Learning Networks (ULNs). ULNs form a superset of neural networks and were proposed to provide a universal framework for modeling and control of nonlinear large-scale complex systems. A generalized learning algorithm has been devised for ULNs which can also be used in a unified manner for almost all kinds of learning networks. However, the ULNs can not deal with stochastic variables. Specific value of a stochastic signal can be propagated through a ULN, but the ULN does not provide any stochastic characteristics of the signals propagating through it. The PrULNs proposed here are equipped with machinery to calculate stochastic properties of signals and to train network parameters so that the signals behave with the pre-specified stochastic properties. The PrULNs will contribute to the solution of the following problems: (1) improving the generalization capability of the learning networks, (2) more sophisticated stochastic control than the conventional stochastic control, (3) designing problems for the complex systems such as chaotic systems. In this paper, PrULN is proposed and is applied to a nonlinear control system with noise.

This paper is a first step to develop a new control system which has better human like abilities than the conven tional control system, and proposes a new method of modelling of brain's function distribution. By comparison with the neuro network which is structured homogeniously, proposed model is based on the petri net which is composed of state and transition and in the petri net, it is introduced learning and self-organizing capabilities which result from modified back-propagation and Hebb like method. But, it is the fundamental different point from the neuro network that learning and self-organizing of the proposed method are carried out on the only network pass of the token transfer. From simulation results, it has been cleared that proposed model can realize brain like function distribution, and therefore, in order to obtain strong non-linear functions, proposed model is superior to the neuro network.

Most of real systems are continuous systems with nonlinearity. Usually, the systems are approximated by linear models, and are analyzed and designed using the well established linear system theory, because the analysis and the design of nonlinear systems are difficult, and the theories for them are not well established. However, there must be an approximation error due to the nonlinearity. In this paper, an approach to identification of nonlinear continuous time systems with measurement noise is proposed. In the approach the parameters of the linear approximate model are estimated from the sampled input-output data of the nonlinear systems by a low-pass filtering method, and the modeling error due to the nonlinearity is compensated by using neural network. Two types of neural network compensators are obtained based on two different ways of approximating the noise removed system output. In the training of the neural network, the teaching signals are provided by data smoothing method which enables on-line noise filtering and thus on-line training. The trained network compensats the modeling error effectively. An illustrative example is given to demonstrate the effectiveness of the proposed approach.

本研究应用实时荧光定量PCR技术，研究了黄瓜花叶病毒国际标准株系CMV-Fny、基因突变体CMV-FnyΔ2b、添加致弱卫星的CMV-Fny-satYn12、添加非致弱卫星的CMV-Fny-satT1以及番茄不孕病毒(TAV-Bj)侵染番茄后，寄主miRNA及其靶标mRNA表达量的特异性变化，结合CMV-Fny基因表达量的差异变化情况，探讨CMV致病决定子2b蛋白以及其寄生因子satRNAs干扰寄主miRNA沉默途径的作用，从病毒基因表达量和寄主miRNA及其靶标mRNA表达量方面来分析病毒侵染导致寄主症状产生的原因。研究结果显示，所检测的番茄叶片组织中的11条miRNAs和10条靶标mRNAs的表达量在病毒侵染后都有了不同程度的上升，卫星RNA改变了CMV-Fny基因表达量及其对寄主miRNAs的影响，缺失2b基因对CMV- Fny基因表达量及寄主miRNA调控途径有很大影响，显示出CMV 2b基因在致病过程中的重要性。这些结果为揭示卫星对CMV致病性影响的关系、植物miRNAs及其靶标mRNAs与病毒侵染的关系进而阐明病毒-寄主互作的分子机制提供了一些理论依据。

MicroRNAs (miRNAs)是一类广泛存在于动植物中高度保守的非编码的小分子RNA。本研究以Sanger miRbase 数据库（V15.0版本）中包括拟南芥、水稻、甘蔗、高粱等37个植物物种的2566条miRNA序列信息设计1450条探针，采用μParaflo? 技术原位合成miRNA 表达谱芯片。以桃叶型三叶半夏与其同源多倍体为材料，对多倍体半夏中的保守miRNA进行检测，并对两种半夏的microRNA差异表达进行分析。通过此方法，首次从三叶半夏中鉴定出1052条miRNA，它们分布于492个miRNA家族，占已知665个miRNA保守家族的73.98 %，同时，我们发现桃叶型三叶半夏与其同源多倍体之间有8条miRNA存在显著性差异表达。