Caracterização molecular e diversidade genética de diferentes variedades de abacate por marcadores microssatélites

Pertencente à família Lauraceae, o abacateiro compreende três raças hortícolas: antilhana, guatemalense e mexicana. Os marcadores moleculares são uma ferramenta rápida e eficaz para estudos genômicos, uma vez que detectam o polimorfismo diretamente ao nível do DNA e não sofrem qualquer tipo de influência ambiental. Com base nesse polimorfismo, é possível fazer inferências sobre as relações entre o genótipo e o fenótipo dos indivíduos, o que, em última análise, permite aumentar a eficiência dos programas de melhoramento. Diante o exposto, o objetivo foi investigar a diversidade genética entre sete variedades de abacate a partir de 5 lócus de marcadores moleculares microssatélites (SSR). Nas amostras de abacateiros avaliadas, encontrou-se um total de 18 alelos, com uma média de 3,6 alelos por lócus. O dendrograma gerado a partir de análise de agrupamento UPGMA agrupou, separadamente do resto dos genótipos, a cultivar Geada da raça Antilhana, possivelmente por esta variedade ser uma raça pura, e o restante foi agrupado em dois grandes grupos das raças, a Guatemalense e a Mexicana. Os genótipos das sete variedades de abacate apresentam diversidade genética nos cinco lócus de marcadores moleculares microssatélites (SSR) avaliados, o que indica que são materiais promissores para utilização em futuros programas de melhoramento.

Caracterização de três genótipos de umezeiro (Prunus mume Sieb. et Zucc.) por marcadores RAPD

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Identificação e validação de marcadores microssatélites ligados ao gene Rpp5 de resistência à ferrugem-asiática-da-soja

O objetivo deste trabalho foi identificar novos marcadores microssatélites, ligados ao gene Rpp5 de resistência à ferrugem-da-soja, e validar os marcadores previamente mapeados, para que possam ser utilizados em programas de seleção assistida por marcadores moleculares (SAM). Para tanto, uma população F2 com 100 indivíduos, derivada do cruzamento entre a PI 200526 e a cultivar Coodetec 208, suscetível à ferrugem, foi artificialmente infectada e avaliada quanto à sua reação de resistência à ferrugem. Marcadores microssatélites foram testados nos genitores e em dois bulks contrastantes, para a identificação de marcadores ligados. Dois novos marcadores, potencialmente associados à resistência, foram testados em plantas individuais, e se constatou que eles estão ligados ao gene Rpp5 e estão presentes no grupo de ligação N da soja. A eficiência de seleção foi determinada em relação a todos os marcadores ligados ao gene Rpp5, e a combinação entre os marcadores Sat_275+Sat_280 foi de 100%.

Marcadores RAPD para detecção de resistência à ferrugem-asiática-da-soja

Os objetivos deste trabalho foram confirmar a herança da resistência da PI 459025 (Rpp4) à ferrugem-asiática-da-soja e identificar marcadores moleculares do tipo RAPD, ligados a este gene de resistência, em populações de soja. Pelo cruzamento dos genitores contrastantes PI 459025 x Coodetec 208 obteve-se uma população, cujas populações das gerações F2 e F2:3 foram artificialmente infectadas e avaliadas quanto à reação ao fungo Phakopsora pachyrhizi, pelo tipo de lesão (RB - resistente e TAN - suscetível). Com os resultados da avaliação fenotípica, dois bulks foram obtidos com DNA de plantas homozigóticas resistentes e suscetíveis, respectivamente, pela análise de bulks segregantes. de 600 iniciadores RAPD aleatórios, foram identificados três com fragmentos polimórficos entre os bulks e parentais contrastantes quanto à resistência. Pela análise do qui-quadrado, confirmaram-se: a herança monogênica, com dominância completa quanto à resistência ao patógeno, e a segregação 3:1 para a presença de banda dos três marcadores. Os três marcadores são ligados respectivamente a 5,1, 6,3 e 14,7 cM de distância do loco de resistência, em fase de repulsão no grupo de ligação G, o que foi confirmado pela utilização do marcador microssatélite Satt288. Estes marcadores são promissores na seleção assistida para resistência à ferrugem-asiática-da-soja.

AVALIAÇÃO DE PLANTAS MATRIZES DE ABACAXIZEIRO CULTIVAR SMOOTH CAYENNE UTILIZANDO MARCADORES RAPD E PADRÕES ISOENZIMÁTICOS

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comparação de marcadores de fase sólida para medir fluxo de matéria seca e matéria orgânica no duodeno

Two external markers (chromic oxide and ytterbium chloride) and two internal markers (indigestible neutral detergent fiber-NDF and indigestible acid detergent fiber-ADF) were compared in order to determine the flow of dry and organic matter in the duodenum. Three steers with ruminal and duodenal cannulas were fed with roughage: concentrate ratios of 80:20 60:40 and 40:60. As these ratios as well as animals and experimental periods did not show significant effects, only markers will be discussed the flow of the duodenal dry matter were 3816.8, 3269.3, 2739.2 and 2713.2g/day and of the organic matter were 3305.1, 2841.6, 2392.2 and 2351.3g/day estimated by chromic oxide, ytterbium chloride, indigestible NDF and indigestible ADF, respectively. The coefficients of ruminal digestion of the dry matter expressed as a percentage of the total digested were 38.8, 57.8, 80.2 and 81.9% and organic matter were 48.4, 65.3 84.8 and 85.7, when estimated by chromic oxide, ytterbium chloride, indigestible NDF and indigestible ADF, respectively. Ir was concluded that different markers lead to different estimation of duodenal flow and indigestible NDF and indigestible ADF are equivalent markers.

Comparação de marcadores para estimativas de produção fecal e de fluxo de digesta em bovinos

Foram realizados três experimentos procurando avaliar a utilização de dois marcadores internos (FDN e FDA indigestíveis), obtidos por meio da incubação in vitro e in situ, e de um marcador externo (óxido crômico) para estimativa da produção fecal e do fluxo da digesta duodenal em bovinos. Para análise dos dados, adotou-se o delineamento em blocos inteiramente ao acaso, sendo os tratamentos distribuídos em esquema fatorial, constituindo cinco marcadores e três volumosos. Os teores de FDN e FDA indigestíveis mostraram-se variáveis para cada volumosos, independentemente da metodologia utilizada (in vitro ou in situ), indicando que possivelmente a incubação por 144 horas não reproduz a fração indigestível total. As estimativas de produção fecal e de fluxo da digesta duodenal, obtidas por intermédio dos marcadores avaliados, apresentaram comportamento bastante diferenciado de acordo com cada volumoso. Os marcadores internos (FDN e FDA indigestíveis) podem ser utilizados como preditores dos parâmetros avaliados, desde que alguns cuidados sejam tomados na sua determinação.

Classificação de distúrbios na rede elétrica usando redes neurais e wavelets

Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbance s occurrences in the network. This work presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks

Análise e otimização de superfícies seletivas de Frequência utilizando redes neurais artificiais e algoritmos de otimização natural

The bidimensional periodic structures called frequency selective surfaces have been well investigated because of their filtering properties. Similar to the filters that work at the traditional radiofrequency band, such structures can behave as band-stop or pass-band filters, depending on the elements of the array (patch or aperture, respectively) and can be used for a variety of applications, such as: radomes, dichroic reflectors, waveguide filters, artificial magnetic conductors, microwave absorbers etc. To provide high-performance filtering properties at microwave bands, electromagnetic engineers have investigated various types of periodic structures: reconfigurable frequency selective screens, multilayered selective filters, as well as periodic arrays printed on anisotropic dielectric substrates and composed by fractal elements. In general, there is no closed form solution directly from a given desired frequency response to a corresponding device; thus, the analysis of its scattering characteristics requires the application of rigorous full-wave techniques. Besides that, due to the computational complexity of using a full-wave simulator to evaluate the frequency selective surface scattering variables, many electromagnetic engineers still use trial-and-error process until to achieve a given design criterion. As this procedure is very laborious and human dependent, optimization techniques are required to design practical periodic structures with desired filter specifications. Some authors have been employed neural networks and natural optimization algorithms, such as the genetic algorithms and the particle swarm optimization for the frequency selective surface design and optimization. This work has as objective the accomplishment of a rigorous study about the electromagnetic behavior of the periodic structures, enabling the design of efficient devices applied to microwave band. For this, artificial neural networks are used together with natural optimization techniques, allowing the accurate and efficient investigation of various types of frequency selective surfaces, in a simple and fast manner, becoming a powerful tool for the design and optimization of such structures

Uma Aplicação de Redes Neurais Auto-Organizaveis à Reconstrução Tridimensional de Superfícies

We propose a multi-resolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen s self-organizing map. Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multi-resolution, iterative scheme. Reconstruction was experimented with several point sets, induding different shapes and sizes. Results show generated meshes very dose to object final shapes. We include measures of performance and discuss robustness.

Contribuições à análise de robustez de sistemas de controle usando redes neurais

This work develops a robustness analysis with respect to the modeling errors, being applied to the strategies of indirect control using Artificial Neural Networks - ANN s, belong to the multilayer feedforward perceptron class with on-line training based on gradient method (backpropagation). The presented schemes are called Indirect Hybrid Control and Indirect Neural Control. They are presented two Robustness Theorems, being one for each proposed indirect control scheme, which allow the computation of the maximum steady-state control error that will occur due to the modeling error what is caused by the neural identifier, either for the closed loop configuration having a conventional controller - Indirect Hybrid Control, or for the closed loop configuration having a neural controller - Indirect Neural Control. Considering that the robustness analysis is restrict only to the steady-state plant behavior, this work also includes a stability analysis transcription that is suitable for multilayer perceptron class of ANN s trained with backpropagation algorithm, to assure the convergence and stability of the used neural systems. By other side, the boundness of the initial transient behavior is assured by the assumption that the plant is BIBO (Bounded Input, Bounded Output) stable. The Robustness Theorems were tested on the proposed indirect control strategies, while applied to regulation control of simulated examples using nonlinear plants, and its results are presented

Detecção e isolamento de falhas em sistemas dinâmicos baseados em redes neurais

This master dissertation presents the development of a fault detection and isolation system based in neural network. The system is composed of two parts: an identification subsystem and a classification subsystem. Both of the subsystems use neural network techniques with multilayer perceptron training algorithm. Two approaches for identifica-tion stage were analyzed. The fault classifier uses only residue signals from the identification subsystem. To validate the proposal we have done simulation and real experiments in a level system with two water reservoirs. Several faults were generated above this plant and the proposed fault detection system presented very acceptable behavior. In the end of this work we highlight the main difficulties found in real tests that do not exist when it works only with simulation environments

Algoritmos genéticos e processamento paralelo aplicados à definição e treinamento de redes neurais perceptron de múltiplas camadas

ln this work, it was deveIoped a parallel cooperative genetic algorithm with different evolution behaviors to train and to define architectures for MuItiIayer Perceptron neural networks. MuItiIayer Perceptron neural networks are very powerful tools and had their use extended vastIy due to their abiIity of providing great resuIts to a broad range of appIications. The combination of genetic algorithms and parallel processing can be very powerful when applied to the Iearning process of the neural network, as well as to the definition of its architecture since this procedure can be very slow, usually requiring a lot of computational time. AIso, research work combining and appIying evolutionary computation into the design of neural networks is very useful since most of the Iearning algorithms deveIoped to train neural networks only adjust their synaptic weights, not considering the design of the networks architecture. Furthermore, the use of cooperation in the genetic algorithm allows the interaction of different populations, avoiding local minima and helping in the search of a promising solution, acceIerating the evolutionary process. Finally, individuaIs and evolution behavior can be exclusive on each copy of the genetic algorithm running in each task enhancing the diversity of populations

Identificação remota de sistemas operacionais utilizando análise de processos aleatórios e redes neurais artificiais

A new method to perform TCP/IP fingerprinting is proposed. TCP/IP fingerprinting is the process of identify a remote machine through a TCP/IP based computer network. This method has many applications related to network security. Both intrusion and defence procedures may use this process to achieve their objectives. There are many known methods that perform this process in favorable conditions. However, nowadays there are many adversities that reduce the identification performance. This work aims the creation of a new OS fingerprinting tool that bypass these actual problems. The proposed method is based on the use of attractors reconstruction and neural networks to characterize and classify pseudo-random numbers generators

Sistema Híbrido de Inferência Baseado em Análise de Componentes Principais e Redes Neurais Artificiais Aplicado a Plantas de Processamento de Gás Natural

Nowadays, where the market competition requires products with better quality and a constant search for cost savings and a better use of raw materials, the research for more efficient control strategies becomes vital. In Natural Gas Processin Units (NGPUs), as in the most chemical processes, the quality control is accomplished through their products composition. However, the chemical composition analysis has a long measurement time, even when performed by instruments such as gas chromatographs. This fact hinders the development of control strategies to provide a better process yield. The natural gas processing is one of the most important activities in the petroleum industry. The main economic product of a NGPU is the liquefied petroleum gas (LPG). The LPG is ideally composed by propane and butane, however, in practice, its composition has some contaminants, such as ethane and pentane. In this work is proposed an inferential system using neural networks to estimate the ethane and pentane mole fractions in LPG and the propane mole fraction in residual gas. The goal is to provide the values of these estimated variables in every minute using a single multilayer neural network, making it possibly to apply inferential control techniques in order to monitor the LPG quality and to reduce the propane loss in the process. To develop this work a NGPU was simulated in HYSYS R software, composed by two distillation collumns: deethanizer and debutanizer. The inference is performed through the process variables of the PID controllers present in the instrumentation of these columns. To reduce the complexity of the inferential neural network is used the statistical technique of principal component analysis to decrease the number of network inputs, thus forming a hybrid inferential system. It is also proposed in this work a simple strategy to correct the inferential system in real-time, based on measurements of the chromatographs which may exist in process under study