Mitochondrial ATP-sensitive K(+) channels as redox signals to liver mitochondria in response to hypertriglyceridemia

We have recently demonstrated that hypertriglyceridemic (HTG) mice present both elevated body metabolic rates and mild mitochondrial uncoupling in the liver owing to stimulated activity of the ATP-sensitive potassium channel (mitoK(ATP)). Because lipid excess normally leads to cell redox imbalance, we examined the hepatic oxidative status in this model. Cell redox imbalance was evidenced by increased total levels of carbonylated proteins, malondialdehydes, and GSSG/GSH ratios in HTG livers compared to wild type. In addition, the activities of the extramitochondrial enzymes NADPH oxidase and xanthine oxidase were elevated in HTG livers. In contrast, Mn-superoxide dismutase activity and content, a mitochondrial matrix marker, were significantly decreased in HTG livers. isolated HTG liver mitochondria presented lower rates of H(2)O(2) production, which were reversed by mitoK(ATP) antagonists. In vivo antioxidant treatment with N-acetylcysteine decreased both mitoKATP activity and metabolic rates in HTG mice. These data indicate that high levels of triglycerides increase reactive oxygen generation by extramitochondrial enzymes that promote MitoK(ATP) activation. The mild uncoupling mediated by mitoK(ATP) increases metabolic rates and protects mitochondria against oxidative damage. Therefore, a biological role for mitoK(ATP) is a redox sensor is shown here for the first time in an in vivo model of systemic and cellular lipid excess, (C) 2009 Elsevier Inc. All rights reserved.

Metabotropic glutamate receptors transduce signals for neurite outgrowth after binding of the prion protein to laminin gamma 1 chain

The prion protein (PrP(C)) is highly expressed in the nervous system, and its abnormal conformer is associated with prion diseases. PrP(C) is anchored to cell membranes by glycosylphosphatidylinositol, and transmembrane proteins are likely required for PrP(C)-mediated intracellular signaling. Binding of laminin (Ln) to PrP(C) modulates neuronal plasticity and memory. We addressed signaling pathways triggered by PrP(C)-Ln interaction in order to identify transmembrane proteins involved in the transduction of PrP(C)-Ln signals. The Ln gamma 1-chain peptide, which contains the Ln binding site for PrP(C), induced neuritogenesis through activation of phospholipase C (PLC), Ca(2+) mobilization from intracellular stores, and protein kinase C and extracellular signal-regulated kinase (ERK1/2) activation in primary cultures of neurons from wild-type, but not PrP(C)-null mice. Phage display, coimmunoprecipitation, and colocalization experiments showed that group I metabotropic glutamate receptors (mGluR1/5) associate with PrP(C). Expression of either mGluR1 or mGluR5 in HEK293 cells reconstituted the signaling pathways mediated by PrP(C)-Ln gamma 1 peptide interaction. Specific inhibitors of these receptors impaired PrP(C)-Ln gamma 1 peptide-induced signaling and neuritogenesis. These data show that group I mGluRs are involved in the transduction of cellular signals triggered by PrP(C)-Ln, and they support the notion that PrP(C) participates in the assembly of multiprotein complexes with physiological functions on neurons.-Beraldo, F. H., Arantes, C. P., Santos, T. G., Machado, C. F., Roffe, M., Hajj, G. N., Lee, K. S., Magalhaes, A. C., Caetano, F. A., Mancini, G. L., Lopes, M. H., Americo, T. A., Magdesian, M. H., Ferguson, S. S. G., Linden, R., Prado, M. A. M., Martins, V. R. Metabotropic glutamate receptors trans-duce signals for neurite outgrowth after binding of the prion protein to laminin gamma 1 chain. FASEB J. 25, 265-279 (2011). www.fasebj.org

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

A Unified Load Generator for Geographically Distributed Generation ofNetwork Traffic

During the last decade, the Internet usage has been growing at an enormous rate which has beenaccompanied by the developments of network applications (e.g., video conference, audio/videostreaming, E-learning, E-Commerce and real-time applications) and allows several types ofinformation including data, voice, picture and media streaming. While end-users are demandingvery high quality of service (QoS) from their service providers, network undergoes a complex trafficwhich leads the transmission bottlenecks. Considerable effort has been made to study thecharacteristics and the behavior of the Internet. Simulation modeling of computer networkcongestion is a profitable and effective technique which fulfills the requirements to evaluate theperformance and QoS of networks. To simulate a single congested link, simulation is run with asingle load generator while for a larger simulation with complex traffic, where the nodes are spreadacross different geographical locations generating distributed artificial loads is indispensable. Onesolution is to elaborate a load generation system based on master/slave architecture.

A Contour-based Separation of VerticallyAttached Traffic Signs

This report presents an algorithm for locating the cut points for and separatingvertically attached traffic signs in Sweden. This algorithm provides severaladvanced digital image processing features: binary image which representsvisual object and its complex rectangle background with number one and zerorespectively, improved cross correlation which shows the similarity of 2Dobjects and filters traffic sign candidates, simplified shape decompositionwhich smoothes contour of visual object iteratively in order to reduce whitenoises, flipping point detection which locates black noises candidates, chasmfilling algorithm which eliminates black noises, determines the final cut pointsand separates originally attached traffic signs into individual ones. At each step,the mediate results as well as the efficiency in practice would be presented toshow the advantages and disadvantages of the developed algorithm. Thisreport concentrates on contour-based recognition of Swedish traffic signs. Thegeneral shapes cover upward triangle, downward triangle, circle, rectangle andoctagon. At last, a demonstration program would be presented to show howthe algorithm works in real-time environment.

Real-Time Recognition System for Traffic Signs

The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. Inreal time environment, vehicles move at high speed on roads. For the vehicle intelligent system itbecomes essential to detect, process and recognize the traffic sign which is coming in front ofvehicle with high relative velocity, at the right time, so that the driver would be able to pro-actsimultaneously on instructions given in the Traffic Sign. The system assists drivers about trafficsigns they did not recognize before passing them. With the Traffic Sign Recognition system, thevehicle becomes aware of the traffic environment and reacts according to the situation.The objective of the project is to develop a system which can recognize the traffic signs in real time.The three target parameters are the system’s response time in real-time video streaming, the trafficsign recognition speed in still images and the recognition accuracy. The system consists of threeprocesses; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. Thedetection process uses physical properties of traffic signs based on a priori knowledge to detect roadsigns. It generates the road sign image as the input to the recognition process. The recognitionprocess is implemented using the Pattern Matching algorithm. The system was first tested onstationary images where it showed on average 97% accuracy with the average processing time of0.15 seconds for traffic sign recognition. This procedure was then applied to the real time videostreaming. Finally the tracking of traffic signs was developed using Blob tracking which showed theaverage recognition accuracy to 95% in real time and improved the system’s average response timeto 0.04 seconds. This project has been implemented in C-language using the Open Computer VisionLibrary.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.