Nonexistence of global solutions for a class of complex vector fields on two-torus

The goal of this paper is study the global solvability of a class of complex vector fields of the special form L = partial derivative/partial derivative t + (a + ib)(x)partial derivative/partial derivative x, a, b epsilon C(infinity) (S(1) ; R), defined on two-torus T(2) congruent to R(2)/2 pi Z(2). The kernel of transpose operator L is described and the solvability near the characteristic set is also studied. (c) 2008 Elsevier Inc. All rights reserved.

Gevrey solvability near the characteristic set for a class of planar complex vector fields of infinite type

We study the Gevrey solvability of a class of complex vector fields, defined on Omega(epsilon) = (-epsilon, epsilon) x S(1), given by L = partial derivative/partial derivative t + (a(x) + ib(x))partial derivative/partial derivative x, b not equivalent to 0, near the characteristic set Sigma = {0} x S(1). We show that the interplay between the order of vanishing of the functions a and b at x = 0 plays a role in the Gevrey solvability. (C) 2008 Elsevier Inc. All rights reserved.

Interactive Vector Field Feature Identification

We introduce a flexible technique for interactive exploration of vector field data through classification derived from user-specified feature templates. Our method is founded on the observation that, while similar features within the vector field may be spatially disparate, they share similar neighborhood characteristics. Users generate feature-based visualizations by interactively highlighting well-accepted and domain specific representative feature points. Feature exploration begins with the computation of attributes that describe the neighborhood of each sample within the input vector field. Compilation of these attributes forms a representation of the vector field samples in the attribute space. We project the attribute points onto the canonical 2D plane to enable interactive exploration of the vector field using a painting interface. The projection encodes the similarities between vector field points within the distances computed between their associated attribute points. The proposed method is performed at interactive rates for enhanced user experience and is completely flexible as showcased by the simultaneous identification of diverse feature types.

Improved voice activity detection algorithm using wavelet and support vector machine

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETS1) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD. (C) 2009 Elsevier Ltd. All rights reserved.

Measuring time series predictability using support vector regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.

UNIT VECTOR FIELDS ON ANTIPODALLY PUNCTURED SPHERES: BIG INDEX, BIG VOLUME

We establish in this paper a lower bound for the volume of a unit vector field (v) over right arrow defined ou S(n) \ {+/-x}, n = 2,3. This lower bound is related to the sum of the absolute values of the indices of (v) over right arrow at x and -x.

Periodic geodesics and geometry of compact Lorentzian manifolds with a Killing vector field

We study the geometry and the periodic geodesics of a compact Lorentzian manifold that has a Killing vector field which is timelike somewhere. Using a compactness argument for subgroups of the isometry group, we prove the existence of one timelike non self-intersecting periodic geodesic. If the Killing vector field is nowhere vanishing, then there are at least two distinct periodic geodesics; as a special case, compact stationary manifolds have at least two periodic timelike geodesics. We also discuss some properties of the topology of such manifolds. In particular, we show that a compact manifold M admits a Lorentzian metric with a nowhere vanishing Killing vector field which is timelike somewhere if and only if M admits a smooth circle action without fixed points.

Trypanosoma cruzi MSH2: Functional analyses on different parasite strains provide evidences for a role on the oxidative stress response

Components of the DNA mismatch repair (MMR) pathway are major players in processes known to generate genetic diversity, such as mutagenesis and DNA recombination. Trypanosoma cruzi, the protozoan parasite that causes Chagas disease has a highly heterogeneous population, composed of a pool of strains with distinct characteristics. Studies with a number of molecular markers identified up to six groups in the T. cruzi population, which showed distinct levels of genetic variability. To investigate the molecular basis for such differences, we analyzed the T. cruzi MSH2 gene, which encodes a key component of MMR, and showed the existence of distinct isoforms of this protein. Here we compared cell survival rates after exposure to genotoxic agents and levels of oxidative stress-induced DNA in different parasite strains. Analyses of msh2 mutants in both T. cruzi and T. brucei were also used to investigate the role of Tcmsh2 in the response to various DNA damaging agents. The results suggest that the distinct MSH2 isoforms have differences in their activity. More importantly, they also indicate that, in addition to its role in MMR, TcMSH2 acts in the parasite response to oxidative stress through a novel mitochondrial function that may be conserved in T. brucei. (C) 2010 Elsevier B.V. All rights reserved.

Melatonin triggers PKA activation in the rodent malaria parasite Plasmodium chabaudi

Calcium (Ca2+) is a critical regulator of many aspects of the Plasmodium reproductive cycle. In particular, intra-erythrocyte Plasmodium parasites respond to circulating levels of the melatonin in a process mediated partly by intracellular Ca2+. Melatonin promotes the development and synchronicity of parasites, thereby enhancing their spread and worsening the clinical implications. The signalling mechanisms underlying the effects of melatonin are not fully established, although both Ca2+ and cyclic AMP (cAMP) have been implicated. Furthermore, it is not clear whether different strains of Plasmodium use the same, or divergent, signals to control their development. The aim of this study was to explore the signalling mechanisms engaged by melatonin in P. chabaudi, a virulent rodent parasite. Using parasites at the throphozoite stage acutely isolated from mice erythrocytes, we demonstrate that melatonin triggers cAMP production and protein kinase A (PKA) activation. Interestingly, the stimulation of cAMP/PKA signalling by melatonin was dependent on elevation of Ca2+ within the parasite, because buffering Ca2+ changes using the chelator BAPTA prevented cAMP production in response to melatonin. Incubation with melatonin evoked robust Ca2+ signals within the parasite, as did the application of a membrane-permeant analogue of cAMP. Our data suggest that P. chabaudi engages both Ca2+ and cAMP signalling systems when stimulated by melatonin. Furthermore, there is positive feedback between these messengers, because Ca2+ evokes cAMP elevation and vice versa. Melatonin more than doubled the observed extent of parasitemia, and the increase in cAMP concentration and PKA activation was essential for this effect. These data support the possibility to use melatonin antagonists or derivates in therapeutic approach.

Expression, purification and kinetic characterization of His-tagged glyceraldehyde-3-phosphate dehydrogenase from Trypanosoma cruzi

Trypanosomes are flagellated protozoa responsible for serious parasitic diseases that have been classified by the World Health Organization as tropical sicknesses of major importance. One important drug target receiving considerable attention is the enzyme glyceraldehyde-3-phosphate dehydrogenase from the protozoan parasite Trypanosoma cruzi, the causative agent of Chagas disease (T. cruzi Glyceraldehyde-3-phosphate dehydrogenase (TcGAPDH); EC 1.2.1.12). TcGAPDH is a key enzyme in the glycolytic pathway of T. cruzi and catalyzes the oxidative phosphorylation of D-glyceraldehyde-3-phosphate (G3P) to 1,3-bisphosphoglycerate (1,3-BPG) coupled to the reduction of oxidized nicotinamide adenine dinucleotide, (NAD(+)) to NADH, the reduced form. Herein, we describe the cloning of the T. cruzi gene for TcGAPDH into the pET-28a(+) vector, its expression as a tagged protein in Escherichia coli, purification and kinetic characterization. The His(6)-tagged TcGAPDH was purified by affinity chromatography. Enzyme activity assays for the recombinant His(6)-TcGAPDH were carried out spectrophotometrically to determine the kinetic parameters. The apparent Michaelis-Menten constant (K(M)(app)) determined for D-glyceraldehyde-3-phosphate and NAD(+) were 352 +/- 21 and 272 +/- 25 mu M, respectively, which were consistent with the values for the untagged enzyme reported in the literature. We have demonstrated by the use of Isothermal Titration Calorimetry (ITC) that this vector modification resulted in activity preserved for a higher period. We also report here the use of response surface methodology (RSM) to determine the region of optimal conditions for enzyme activity. A quadratic model was developed by RSM to describe the enzyme activity in terms of pH and temperature as independent variables. According to the RMS contour plots and variance analysis, the maximum enzyme activity was at 29.1 degrees C and pH 8.6. Above 37 degrees C, the enzyme activity starts to fall, which may be related to previous reports that the quaternary structure begins a process of disassembly. (C) 2010 Elsevier Inc. All rights reserved.

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.

Color Segmentation using LVQ-Learning Vector Quantization

This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in between 0.726sec to 0.844sec. The method was tested in different environmental conditions and LVQ showed its capacity to reasonably segment color despite remarkable illumination differences. The results showed high robustness.

Testing common nonlinear features in vector nonlinear autoregressive models

This paper studies a special class of vector smooth-transition autoregressive (VSTAR) models that contains common nonlinear features (CNFs), for which we proposed a triangular representation and developed a procedure of testing CNFs in a VSTAR model. We first test a unit root against a stable STAR process for each individual time series and then examine whether CNFs exist in the system by Lagrange Multiplier (LM) test if unit root is rejected in the first step. The LM test has standard Chi-squared asymptotic distribution. The critical values of our unit root tests and small-sample properties of the F form of our LM test are studied by Monte Carlo simulations. We illustrate how to test and model CNFs using the monthly growth of consumption and income data of United States (1985:1 to 2011:11).

Forecasting with Vector Nonlinear Time Series Models

This work concerns forecasting with vector nonlinear time series models when errorsare correlated. Point forecasts are numerically obtained using bootstrap methods andillustrated by two examples. Evaluation concentrates on studying forecast equality andencompassing. Nonlinear impulse responses are further considered and graphically sum-marized by highest density region. Finally, two macroeconomic data sets are used toillustrate our work. The forecasts from linear or nonlinear model could contribute usefulinformation absent in the forecasts form the other model.

Common Features in Vector Nonlinear Time Series Models

This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in these area. Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified. The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An Ftypetest for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail,and thereafter illustrated within two corresponding macroeconomic data sets.