Regulation of inflammation in Japanese encephalitis

Uncontrolled inflammatory response of the central nervous system is a hallmark of severe Japanese encephalitis (JE). Although inflammation is necessary to mount an efficient immune response against virus infections, exacerbated inflammatory response is often detrimental. In this context, cells of the monocytic lineage appear to be important forces driving JE pathogenesis

SHP-2 Promotes the Maturation of Oligodendrocyte Precursor Cells Through Akt and ERK1/2 Signaling In Vitro

Background: Oligodendrocyte precursor cells (OPCs) differentiate into oligodendrocytes (OLs), which are responsible for myelination. Myelin is essential for saltatory nerve conduction in the vertebrate nervous system. However, the molecular mechanisms of maturation and myelination by oligodendrocytes remain elusive. Methods and Findings: In the present study, we showed that maturation of oligodendrocytes was attenuated by sodium orthovanadate (a comprehensive inhibitor of tyrosine phosphatases) and PTPi IV (a specific inhibitor of SHP-2). It is also found that SHP-2 was persistently expressed during maturation process of OPCs. Down-regulation of endogenous SHP-2 led to impairment of oligodendrocytes maturation and this effect was triiodo-L-thyronine (T3) dependent. Furthermore, overexpression of SHP-2 was shown to promote maturation of oligodendrocytes. Finally, it has been identified that SHP-2 was involved in activation of Akt and extracellular-regulated kinases 1 and 2 (ERK1/2) induced by T3 in oligodendrocytes

Classification of Static Security Status Using Multi-Class Support Vector Machines

This paper presents a Multi-class Support Vector Machine (SVM) based Pattern Recognition (PR) approach for static security assessment in power systems. The multi-class SVM classifier design is based on the calculation of a numeric index called the static security index. The proposed multi-class SVM based pattern recognition approach is tested on IEEE 57 Bus, 118 Bus and 300 Bus benchmark systems. The simulation results of the SVM classifier are compared to a Multilayer Perceptron (MLP) network and the Method of Least Squares (MLS). The SVM classifier was found to give high classification accuracy and a smaller misclassification rate compared to the other classifier techniques

Pattern directed inference system for fault classification and analysis

Pattern directed inference systems (PDISs) are programs similar to rule based systems that look for interesting or important situations occurring as patterns in their input or memory data. An artificial intelligence approach to pattern recognition involves the description of abstract concepts and the recognition of instances of these in signals. Each concept is represented by several hierarchical levels of abstraction, where knowledge appropriate to a particular level is used to identify components at a higher level concept. The use of pattern recognition to problem solving has been advocated to a variety of problems including diagnosis. Fault classification and diagnosis of thyristor power converter systems using PDIS is discussed in this paper and a case study described in detai

Model-based reasoning for power system fault diagnosis

First generation expert systems use judgemental knowledge of the human experts which use heuristic and empirical associations between observed data patterns and final or intermediate conclusions. Second generation diagnostic expert systems, also termed as model-based diagnostics, are based on the structure and functional (causal) behaviour of the physical system. Model-based diagnosis reasons from the structure and behaviour of the system to be analysed and constitutes what is called the deep knowledge of the system. Application of model-based reasoning to fault diagnosis of power systems is discussed in this paper and a case study described in detai

Fault Detection and Diagnosis of Power Converters Using Artificial Neural Networks

Fault detection and diagnosis in real-time are areas of research interest in knowledge-based expert systems. Rule-based and model-based approaches have been successfully applied to some domains, but are too slow to be effectively applied in a real-time environment. This paper explores the suitability of using artificial neural networks for fault detection and diagnosis of power converter systems. The paper describes a neural network design and simulation environment for real-time fault diagnosis of thyristor converters used in HVDC power transmission system

Fault Detection and Diagnosis of Power Systems using Artificial Neural Networks

Real time fault detection and diagnosis (FDD) is an important area of research interest in knowledge based expert systems. Neurocomputing is one of fastest growing areas of research in the fields of artificial intelligence and pattern recognition. The authors explore the suitability of pattern classification approach of neural networks for fault detection and diagnosis. The suitability of using neural networks as pattern classifiers for power system fault diagnosis is described in detail. A neural network design and simulation environment for real-time FDD is presented. An analysis of the learning, recall and generalization characteristic of the neural network diagnostic system is presented and discussed in detail