Two Plant Viral Suppressors of Silencing Require the Ethylene-Inducible Host Transcription Factor RAV2 to Block RNA Silencing

RNA silencing is a highly conserved pathway in the network of interconnected defense responses that are activated during viral infection. As a counter-defense, many plant viruses encode proteins that block silencing, often also interfering with endogenous small RNA pathways. However, the mechanism of action of viral suppressors is not well understood and the role of host factors in the process is just beginning to emerge. Here we report that the ethylene-inducible transcription factor RAV2 is required for suppression of RNA silencing by two unrelated plant viral proteins, potyvirus HC-Pro and carmovirus P38. Using a hairpin transgene silencing system, we find that both viral suppressors require RAV2 to block the activity of primary siRNAs, whereas suppression of transitive silencing is RAV2-independent. RAV2 is also required for many HC-Pro-mediated morphological anomalies in transgenic plants, but not for the associated defects in the microRNA pathway. Whole genome tiling microarray experiments demonstrate that expression of genes known to be required for silencing is unchanged in HC-Pro plants, whereas a striking number of genes involved in other biotic and abiotic stress responses are induced, many in a RAV2-dependent manner. Among the genes that require RAV2 for induction by HC-Pro are FRY1 and CML38, genes implicated as endogenous suppressors of silencing. These findings raise the intriguing possibility that HC-Pro-suppression of silencing is not caused by decreased expression of genes that are required for silencing, but instead, by induction of stress and defense responses, some components of which interfere with antiviral silencing. Furthermore, the observation that two unrelated viral suppressors require the activity of the same factor to block silencing suggests that RAV2 represents a control point that can be readily subverted by viruses to block antiviral silencing

Multivariate Statistical Process Control: Process Monitoring Methods and Applications

  Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas.   Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers

Two-dimensional Bayesian monitoring method for nonlinear multimode processes

Nonlinear and multimode are two common behaviors in modern industrial processes, monitoring research studies have been carried out separately for these two natures in recent years. This paper proposes a two-dimensional Bayesian method for monitoring processes with both nonlinear and multimode characteristics. In this method, the concept of linear subspace is introduced, which can efficiently decompose the nonlinear process into several different linear subspaces. For construction of the linear subspace, a two-step variable selection strategy is proposed. A Bayesian inference and combination strategy is then introduced for result combination of different linear subspaces. Besides, through the direction of the operation mode, an additional Bayesian combination step is performed. As a result, a two-dimensional Bayesian monitoring approach is formulated. Feasibility and efficiency of the method are evaluated by the Tennessee Eastman (TE) process case study. (C) 2011 Elsevier Ltd. All rights reserved

Mixture probabilistic PCR model for soft sensing of multimode processes

Principal component regression (PCR) has been widely used for soft sensor modeling and quality prediction in last several decades, which is still very popular for both academy researches and industry applications. However, most PCR models are determined by the projection method, which may lack probabilistic interpretation for the process data. In fact, due to the inevitable process noise, most process data are inherently random variables. Several probabilistic PCA methods have already been proposed in the past years. Compared to the deterministic modeling method, the probabilistic model is more appropriate to characterize the behavior of the random variables in the process. This paper first presents a probabilistic derivation of the PCR model (PPCR) and then extends it to the mixture form (MPPCR). For quality prediction of processes with multiple operation modes, a mixture probabilistic soft sensor is developed based on the MPPCR model. Simultaneously, the information of the operation mode can also be located by the proposed soft sensor. To evaluate the performance of the MPPCR model, a numerical example and a benchmark simulation case study of the Tennessee Eastman process are provided. Different methods have been compared with the proposed model, including the global, local, and multi-local PCR models. As a result, the proposed MPPCR model performs the best among these methods. (c) 2010 Elsevier B.V. All rights reserved

Batch process monitoring based on support vector data description method

Process monitoring can be considered as a one-class classification problem, the aim of which is to differentiate the normal data samples from the faulty ones. This paper introduces an efficient one-class classification method for batch process monitoring, which is called support vector data description (SVDD). Different from the traditional data description method such as principal component analysis (PCA) and partial least squares (PLS), SVDD has no Gaussian assumption of the process data, and is also effective for nonlinear process modeling. Furthermore, SVDD only incorporates a quadratic optimization step, which makes it easy for practical implementation. Based on the basic SVDD batch process monitoring approach, the method is further extended to multiphase and multimode batch processes. Two case studies are provided to evaluate the monitoring performance of the proposed methods. (C) 2011 Elsevier Ltd. All rights reserved

Improved two-dimensional dynamic batch process monitoring with support vector data description

For dynamic batch process monitoring, a two-dimensional dynamic modeling framework has recently been formulated, which is based on a two-dimensional autoregressive model and the principal component analysis (PCA) method. Different from traditional dynamic batch process monitoring, the two-dimensional method can monitor both within batch and batch-to-batch dynamic information of the process data. However, this PCA-related method has two main restrictions, which may render poor monitoring performance in practice. First, it is under the assumption that the distribution of the process data is Gaussian. Second, the correlations between different process variables are assumed to be linear with each other. Unfortunately, both of these two assumptions are difficult to satisfy in batch processes. In this paper, support vector data description (SVDD) is incorporated into the two-dimensional modeling framework, which has no Gaussian limitation of the data, and can also model the nonlinear relationship between process variables. For dynamic batch process monitoring, a distance based statistic is proposed. Based on results of a simulation case study, the monitoring performance has been improved. © 2011 IFAC

Nonlinear quality prediction for multiphase batch processes

Typically, a multiphase batch process comprises several steady phases and transition periods. In steady phases, the data characteristics remain similar during the phase and have a significant repeatability from batch to batch; thus most data nonlinearities can be removed through the batch normalization step. In contrast, in each transition period, process observations vary with time and from batch to batch, so nonlinearities in the data may not be eliminated through batch normalization. To improve quality prediction performance, an efficient nonlinear modeling methodrelevance vector machine (RVM) was introduced. RVMs were formulated for each transition period of the batch process, and for combining the results of different process phases. For process analysis, a phase contribution index and a variable contribution index are defined. Furthermore, detailed performance analyses on the prediction uncertainty and variation were also provided. The effectiveness of the proposed method is confirmed by an industrial example. (C) 2011 American Institute of Chemical Engineers AIChE J, 58: 17781787, 201

Incorporating Setting Information for Maintenance-Free Quality Modeling of Batch Processes

Typically, the operation condition of the batch process is changed frequently, following different recipes or manufacturing various production grades. For quality prediction purpose, the prediction model should also be updated or rebuilt, which leads to a significant model maintenance effort, especially for those processes which have various phases. To reduce such effort, a maintenance-free method is proposed in this article, which incorporates the setting information of the batch process for modeling. The whole process variations are separated into two parts: setting information related and other quality related variations. By constructing a relationship between setting variables and other process variables, the data variations explained by the setting information can be efficiently removed. Then, a robust regression model connecting process variables to the quality variable is developed in different phases of the batch process. The feasibility and effectiveness of the proposed method is evaluated through an industrial injection molding process. (C) 2012 American Institute of Chemical Engineers AIChE J, 59: 772-779, 201