Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Nonlinear set-membership estimation: a support vector machine approach

In this paper a support vector machine (SVM) approach for characterizing the feasible parameter set (FPS) in non-linear set-membership estimation problems is presented. It iteratively solves a regression problem from which an approximation of the boundary of the FPS can be determined. To guarantee convergence to the boundary the procedure includes a no-derivative line search and for an appropriate coverage of points on the FPS boundary it is suggested to start with a sequential box pavement procedure. The SVM approach is illustrated on a simple sine and exponential model with two parameters and an agro-forestry simulation model.

Bicorrelations and cross-bicorrelations as non-linearity tests and tools for exchange rate forecasting

This paper proposes and implements a new methodology for forecasting time series, based on bicorrelations and cross-bicorrelations. It is shown that the forecasting technique arises as a natural extension of, and as a complement to, existing univariate and multivariate non-linearity tests. The formulations are essentially modified autoregressive or vector autoregressive models respectively, which can be estimated using ordinary least squares. The techniques are applied to a set of high-frequency exchange rate returns, and their out-of-sample forecasting performance is compared to that of other time series models

Interaction among non-toxic phytoplankton, toxic phytoplankton and zooplankton: inferences from field observations

We explore the mutual dependencies and interactions among different groups of species of the plankton population, based on an analysis of the long-term field observations carried out by our group in the North–West coast of the Bay of Bengal. The plankton community is structured into three groups of species, namely, non-toxic phytoplankton (NTP), toxic phytoplankton (TPP) and zooplankton. To find the pair-wise dependencies among the three groups of plankton, Pearson and partial correlation coefficients are calculated. To explore the simultaneous interaction among all the three groups, a time series analysis is performed. Following an Expectation Maximization (E-M) algorithm, those data points which are missing due to irregularities in sampling are estimated, and with the completed data set a Vector Auto-Regressive (VAR) model is analyzed. The overall analysis demonstrates that toxin-producing phytoplankton play two distinct roles: the inhibition on consumption of toxic substances reduces the abundance of zooplankton, and the toxic materials released by TPP significantly compensate for the competitive disadvantages among phytoplankton species. Our study suggests that the presence of TPP might be a possible cause for the generation of a complex interaction among the large number of phytoplankton and zooplankton species that might be responsible for the prolonged coexistence of the plankton species in a fluctuating biomass.

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.

A semi-Markov multistate model for estimation of the mean quality-adjusted survival for non-progressive processes

We discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models. We generalize an earlier work, considering the sojourn times in health states are not identically distributed, for a given vector of covariates. Approaches based on semiparametric and parametric (exponential and Weibull distributions) methodologies are considered. A simulation study is conducted to evaluate the performance of the proposed estimator and the jackknife resampling method is used to estimate the variance of such estimator. An application to a real data set is also included.

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.

Bronquiolite viral aguda fatores prognóticos em lactentes hospitalizados previamente hígidos

A bronquiolite viral aguda (BVA) é uma doença respiratória que acomete crianças principalmente no primeiro ano de vida. O Vírus Sincicial Respiratório é responsável por aproximadamente 75% dos casos de bronquiolite viral aguda; entretanto, outros agentes também podem desencadear doença semelhante, como Adenovirus1, 7,3 e 21, Rinovírus, Parainfluenza, Influenza, Metapneumovirus e, menos freqüentemente, o Mycoplasma pneumoniae. A BVA é uma doença com padrão sazonal, de evolução benigna na maioria dos lactentes hígidos, entretanto 0,5% a 2% necessitam hospitalização, dos quais 15% necessitam cuidados intensivos, e destes apenas 3 a 8% desenvolvem falência ventilatória necessitando de ventilação mecânica. A mortalidade entre crianças previamente hígidas está em torno de 1% dos pacientes internados. O objetivo deste trabalho é identificar fatores de prognóstico na BVA e correlacionar com tempo de internação em lactentes previamente hígidos. Durante o inverno de 2002, foram acompanhados em estudo de coorte 219 pacientes menores de um ano de idade com diagnóstico clínico de bronquiolite viral aguda. Estes pacientes foram avaliados e classificados conforme escore clinico modificado (DE BOECK et al., 1997) na internação, no terceiro dia e no momento da alta hospitalar. O tempo de internação real foi registrado e foi estimado o tempo de internação ideal, conforme critérios de alta clínica definidos por Wainwright e cols. , em 2003, como não uso de oxigênio por mais de 10 horas, tiragem intercostal mínima ou ausente, sem uso de medicação parenteral e com capacidade de alimentação via oral. O escore clinico na internação foi 3,88±1, 81, o tempo médio de uso de oxigênio 5,3±3,83 dias. Estes pacientes apresentaram tempo de internação real de 7,02±3,89 dias e tempo de internação ideal de 5,92±3,83 dias (p<0,001). Considerando tempo de internação ideal como variável dependente em um modelo de regressão logística, observa-se que para cada ponto de aumento no escore clinico aumenta em 1,9 a chance de o paciente permanecer internado por mais de três dias. Conclui-se, então, que se pode predizer o tempo de internação de lactentes hígidos com BVA através do escore clínico, indicando seu uso na avaliação inicial destes pacientes.

The relationships of alternative energies with the technology sector and non-renewable energies

Este trabalho tem como objectivo compreender de que forma os investidores veem as energias renováveis: se as veem como parte do sector tecnológico, à espera de novos desenvolvimentos, ou como uma alternativa aos métodos existentes de produção de energia. Para responder a esta questão, foi desenvolvido um modelo de vectores autoregressivos com quatro variáveis de forma a se poder aplicar um Granger causality test e Impulse Response function. Os resultados sugerem que para o período de 2002-2007 à escala global ambas as hipóteses se confirmam, porém de 2009-2014 os resultados sugerem que os investidores não reconhecem as energias renováveis como um ramo do sector tecnológico, neste período. Para além disso, durante o período de 2009-2014, e quando comparados investidores Americanos com Europeus, os resultados sugerem que apenas o último identifica as energias renováveis como uma fonte viável para a produção energética.

Studies in non-Gaussian analysis

This thesis presents general methods in non-Gaussian analysis in infinite dimensional spaces. As main applications we study Poisson and compound Poisson spaces. Given a probability measure μ on a co-nuclear space, we develop an abstract theory based on the generalized Appell systems which are bi-orthogonal. We study its properties as well as the generated Gelfand triples. As an example we consider the important case of Poisson measures. The product and Wick calculus are developed on this context. We provide formulas for the change of the generalized Appell system under a transformation of the measure. The L² structure for the Poisson measure, compound Poisson and Gamma measures are elaborated. We exhibit the chaos decomposition using the Fock isomorphism. We obtain the representation of the creation, annihilation operators. We construct two types of differential geometry on the configuration space over a differentiable manifold. These two geometries are related through the Dirichlet forms for Poisson measures as well as for its perturbations. Finally, we construct the internal geometry on the compound configurations space. In particular, the intrinsic gradient, the divergence and the Laplace-Beltrami operator. As a result, we may define the Dirichlet forms which are associated to a diffusion process. Consequently, we obtain the representation of the Lie algebra of vector fields with compact support. All these results extends directly for the marked Poisson spaces.

Experimental infection of Newcastle disease virus in pigeons (Columba livia): Humoral antibody response, contact transmission and viral genome shedding

The aim of this study was to evaluate the humoral antibody response, the genome viral excretion and the contact transmission of pathogenic chicken origin Newcastle disease virus (NDV) from experimentally infected pigeons (Columba livia) to in-contact pigeon. The antibody response to infection was assessed by the hemagglutination inhibition (HI) test and the genome viral excretion was detected by RT-PCR. Viral strain induced high antibody levels, both in inoculated and in sentinel birds. The pathogenic viral strain for chickens was unable to produce clinical signs of the disease in experimentally infected pigeons, although it induced the Immoral antibody response and produced NDV genome shedding. NDV genome was detected intermittently throughout the experimental period, from 5 days post-infection (dpi) to 24 dpi. Therefore, viral genome shedding occurred for 20 days. The viral genome was detected in all birds, between I I and 13 dpi. Furthermore, the high infectivity of the virus was confirmed, as all non-inoculated sentinel pigeons showed antibody levels as high as those of inoculated birds. (C) 2007 Elsevier B.V. All rights reserved.

Position-dependent effective mass Dirac equations with PT-symmetric and non-PT-symmetric potentials

We present a new procedure to construct the one-dimensional non-Hermitian imaginary potential with a real energy spectrum in the context of the position-dependent effective mass Dirac equation with the vector-coupling scheme in 1 + 1 dimensions. In the first example, we consider a case for which the mass distribution combines linear and inversely linear forms, the Dirac problem with a PT-symmetric potential is mapped into the exactly solvable Schrodinger-like equation problem with the isotonic oscillator by using the local scaling of the wavefunction. In the second example, we take a mass distribution with smooth step shape, the Dirac problem with a non-PT-symmetric imaginary potential is mapped into the exactly solvable Schrodinger-like equation problem with the Rosen-Morse potential. The real relativistic energy levels and corresponding wavefunctions for the bound states are obtained in terms of the supersymmetric quantum mechanics approach and the function analysis method.

Generalized duality between local vector theories in D=2+1

The existence of an interpolating master action does not guarantee the same spectrum for the interpolated dual theories. In the specific case of a generalized self-dual (GSD) model defined as the addition of the Maxwell term to the self-dual model in D = 2 + 1, previous master actions have furnished a dual gauge theory which is either nonlocal or contains a ghost mode. Here we show that by reducing the Maxwell term to first order by means of an auxiliary field we are able to define a master action which interpolates between the GSD model and a couple of non-interacting Maxwell-Chern-Simons theories of opposite helicities. The presence of an auxiliary field explains the doubling of fields in the dual gauge theory. A generalized duality transformation is defined and both models can be interpreted as self-dual models. Furthermore, it is shown how to obtain the gauge invariant correlators of the non-interacting MCS theories from the correlators of the self-dual field in the GSD model and vice-versa. The derivation of the non-interacting MCS theories from the GSD model, as presented here, works in the opposite direction of the soldering approach.

Real spectra for the non-Hermitian Dirac equation in 1+1 dimensions with the most general coupling

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