Ion-sensing properties of 1D vanadium pentoxide nanostructures

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estados ligados em um potencial delta duplo via transformada de Laplace

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Potenciais delta revisitados via transformada de Fourier

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Hipersensibilidade e necrose sistêmica em Nicotiana benthamiana transformada com o gene de resistência Sw-5 de tomateiro

O gene Sw-5 do tomateiro confere resistência a várias espécies de tospovírus e codifica uma proteína contendo domínios de ligação a nucleotídeos e repetições ricas em leucina. Tomateiros com Sw-5 exibem reações necróticas nas folhas inoculadas com tospovírus. Estas reações e a estrutura da proteína Sw-5 indicam que a resistência ocorre por meio do reconhecimento do patógeno e desencadeamento da resposta de hipersensibilidade. A capacidade de Sw-5 de conferir resistência a tospovírus em tabaco selvagem (Nicotiana benthamiana Domin.) foi avaliada em plantas transgênicas. Uma construção com a seqüência aberta de leitura de Sw-5 e sua região 3 não-traduzida sob controle do promotor 35S do CaMV foi utilizada para transformação de N. benthamiana via Agrobacterium tumefaciens. Plantas de progênies R1 foram inoculadas com um isolado de tospovírus e avaliadas quanto à ocorrência de reação de hipersensibilidade e resistência à infecção sistêmica. em uma progênie com segregação 3:1 (resistente:suscetível), foi selecionada uma planta homozigota e sua progênie avaliada quanto ao espectro da resistência a tospovírus. Plantas com o transgene exibiram resposta de hipersensibilidade 48 h após a inoculação, sendo resistentes à infecção sistêmica. O fenótipo da resistência foi dependente do isolado viral e um isolado de Tomato chlorotic spot virus (TCSV) causou necrose sistêmica em todas as plantas inoculadas, enquanto que isolados de Groundnut ringspot virus (GRSV) e um isolado relacionado a Chrysanthemum stem necrosis virus (CSNV) ficaram restritos ao sítio de infecção. Comparações do espectro da resistência obtido neste trabalho com aquele observado em outros membros da família Solanaceae indicam que as vias de transdução de sinais e as respostas de defesa ativadas por Sw-5 são conservadas dentro desta família e polimorfismos genéticos nas vias de transdução de sinais ou em componentes das respostas de defesa podem resultar em diferentes níveis de resistência.

Feedforward neural networks based on PPS-wavelet activation functions

Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. Neural networks and wavenets have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. In this paper, it is shown how feedforward neural networks can be built using a different type of activation function referred to as the PPS-wavelet. An algorithm is presented to generate a family of PPS-wavelets that can be used to efficiently construct feedforward networks for function approximation.

A combined wavelet-element free Galerkin method for numerical calculations of electromagnetic fields

A combined wavelet-element free Galerkin (EFG) method is proposed for solving electromagnetic EM) field problems. The bridging scales are used to preserve the consistency and linear independence properties of the entire bases. A detailed description of the development of the discrete model and its numerical implementations is given to facilitate the reader to. understand the proposed algorithm. A numerical example to validate the proposed method is also reported.

The N terminus of the human alpha(1D)-adrenergic receptor prevents cell surface expression

We previously reported that truncation of the N-terminal 79 amino acids of alpha(1D)-adrenoceptors (Delta(1-79)alpha(1D)-ARs) greatly increases binding site density. In this study, we determined whether this effect was associated with changes in alpha(1D)-AR subcellular localization. Confocal imaging of green fluorescent protein (GFP)-tagged receptors and sucrose density gradient fractionation suggested that full-length alpha(1D)-ARs were found primarily in intracellular compartments, whereas Delta(1-79)alpha(1D)-ARs were translocated to the plasma membrane. This resulted in a 3- to 4-fold increase in intrinsic activity for stimulation of inositol phosphate formation by norepinephrine. We determined whether this effect was transplantable by creating N-terminal chimeras of alpha(1)-ARs containing the body of one subtype and the N terminus of another (alpha(1A) NT-D, alpha(1B) NT-D, alpha(1D) NT-A, and alpha(1D)NT-B). When expressed in human embryonic kidney 293 cells, radioligand binding revealed that binding densities of alpha(1A)- or alpha(1B)-ARs containing the alpha(1D)-N terminus decreased by 86 to 93%, whereas substitution of alpha(1A)- or alpha(1B)-N termini increased alpha(1D)-AR binding site density by 2- to 3-fold. Confocal microscopy showed that GFP-tagged alpha(1D)NT-B-ARs were found only on the cell surface, whereas GFP-tagged alpha(1B)NT-D-ARs were completely intracellular. Radioligand binding and confocal imaging of GFP-tagged alpha(1D)- and Delta(1-79)alpha(1D)-ARs expressed in rat aortic smooth muscle cells produced similar results, suggesting these effects are generalizable to cell types that endogenously express alpha(1D)-ARs. These findings demonstrate that the N-terminal region of alpha(1D)-ARs contain a transplantable signal that is critical for regulating formation of functional bindings, through regulating cellular localization.

The Yang-Lee zeros of the 1D Blume-Capel model on connected and non-connected rings

We carry out a numerical and analytic analysis of the Yang-Lee zeros of the ID Blume-Capel model with periodic boundary conditions and its generalization on Feynman diagrams for which we include sums over all connected and nonconnected rings for a given number of spins. In both cases, for a specific range of the parameters, the zeros originally on the unit circle are shown to depart from it as we increase the temperature beyond some limit. The curve of zeros can bifurcate- and become two disjoint arcs as in the 2D case. We also show that in the thermodynamic limit the zeros of both Blume-Capel models on the static (connected ring) and on the dynamical (Feynman diagrams) lattice tend to overlap. In the special case of the 1D Ising model on Feynman diagrams we can prove for arbitrary number of spins that the Yang-Lee zeros must be on the unit circle. The proof is based on a property of the zeros of Legendre polynomials.

Rede neural não supervisionada baseada em transformada Karhunen-Loève para processamento, compressão e reconhecimento de imagens

The merit of the Karhunen-Loève transform is well known. Since its basis is the eigenvector set of the covariance matrix, a statistical, not functional, representation of the variance in pattern ensembles is generated. By using the Karhunen-Loève transform coefficients as a natural feature representation of a character image, the eigenvector set can be regarded as an feature extractor for a classifier.

Activation function study for wavelet network

The main purpose of this paper is to investigate theoretically and experimentally the use of family of Polynomial Powers of the Sigmoid (PPS) Function Networks applied in speech signal representation and function approximation. This paper carries out practical investigations in terms of approximation fitness (LSE), time consuming (CPU Time), computational complexity (FLOP) and representation power (Number of Activation Function) for different PPS activation functions. We expected that different activation functions can provide performance variations and further investigations will guide us towards a class of mappings associating the best activation function to solve a class of problems under certain criteria.

Common architecture for discrete wavelet transform analysis and synthesis with sequential and constant processing elements

This paper adresses the problem on processing biological data such as cardiac beats, audio and ultrasonic range, calculating wavelet coefficients in real time, with processor clock running at frequency of present ASIC's and FPGA. The Paralell Filter Architecture for DWT has been improved, calculating wavelet coefficients in real time with hardware reduced to 60%. The new architecture, which also processes IDWT, is implemented with the Radix-2 or the Booth-Wallace Constant multipliers. Including series memory register banks, one integrated circuit Signal Analyzer, ultrasonic range, is presented.

Discrete wavelet transform signal analyzer

This paper addresses the problem of processing biological data, such as cardiac beats in the audio and ultrasonic range, and on calculating wavelet coefficients in real time, with the processor clock running at a frequency of present application-specified integrated circuits and field programmable gate array. The parallel filter architecture for discrete wavelet transform (DWT) has been improved, calculating the wavelet coefficients in real time with hardware reduced up to 60%. The new architecture, which also processes inverse DWT, is implemented with the Radix-2 or the Booth-Wallace constant multipliers. One integrated circuit signal analyzer in the ultrasonic range, including series memory register banks, is presented. © 2007 IEEE.

Microcalcification enhancement and classification on mammograms using the wavelet transform

This paper presents a method to enhance microcalcifications and classify their borders by applying the wavelet transform. Decomposing an image and removing its low frequency sub-band the microcalcifications are enhanced. Analyzing the effects of perturbations on high frequency subband it's possible to classify its borders as smooth, rugged or undefined. Results show a false positive reduction of 69.27% using a region growing algorithm. © 2008 IEEE.

An effective wavelet method to detect and mitigate low-frequency multipath effects

To ensure high accuracy results from GPS relative positioning, the multipath effects have to be mitigated. Although the careful selection of antenna site and the use of especial antennas and receivers can minimize multipath, it cannot always be eliminated and frequently the residual multipath disturbance remains as the major error in GPS results. The high-frequency multipath from large delays can be attenuated by double difference (DD) denoising methods. But the low-frequency multipath from short delays is very difficult to be reduced or modeled. In this paper, it is proposed a method based on wavelet regression (WR), which can effectively detect and reduce the low-frequency multipath. The wavelet technique is firstly applied to decompose the DD residuals into the low-frequency bias and high-frequency noise components. The extracted bias components by WR are then directly applied to the DD observations to correct them from the trend. The remaining terms, largely characterized by the high-frequency measurement noise, are expected to give the best linear unbiased solutions from a least-squares (LS) adjustment. An experiment was carried out using objects placed close to the receiver antenna to cause, mainly, low-frequency multipath. The data were collected for two days to verify the multipath repeatability. The ground truth coordinates were computed with data collected in the absence of the reflector objects. The coordinates and ambiguity solution were compared with and without the multipath mitigation using WR. After mitigating the multipath, ambiguity resolution became more reliable and the coordinates were more accurate.

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.