Signal transduction-related responses to phytohormones and environmental challenges in sugarcane

Background: Sugarcane is an increasingly economically and environmentally important C4 grass, used for the production of sugar and bioethanol, a low-carbon emission fuel. Sugarcane originated from crosses of Saccharum species and is noted for its unique capacity to accumulate high amounts of sucrose in its stems. Environmental stresses limit enormously sugarcane productivity worldwide. To investigate transcriptome changes in response to environmental inputs that alter yield we used cDNA microarrays to profile expression of 1,545 genes in plants submitted to drought, phosphate starvation, herbivory and N-2-fixing endophytic bacteria. We also investigated the response to phytohormones (abscisic acid and methyl jasmonate). The arrayed elements correspond mostly to genes involved in signal transduction, hormone biosynthesis, transcription factors, novel genes and genes corresponding to unknown proteins.Results: Adopting an outliers searching method 179 genes with strikingly different expression levels were identified as differentially expressed in at least one of the treatments analysed. Self Organizing Maps were used to cluster the expression profiles of 695 genes that showed a highly correlated expression pattern among replicates. The expression data for 22 genes was evaluated for 36 experimental data points by quantitative RT-PCR indicating a validation rate of 80.5% using three biological experimental replicates. The SUCAST Database was created that provides public access to the data described in this work, linked to tissue expression profiling and the SUCAST gene category and sequence analysis. The SUCAST database also includes a categorization of the sugarcane kinome based on a phylogenetic grouping that included 182 undefined kinases.Conclusion: An extensive study on the sugarcane transcriptome was performed. Sugarcane genes responsive to phytohormones and to challenges sugarcane commonly deals with in the field were identified. Additionally, the protein kinases were annotated based on a phylogenetic approach. The experimental design and statistical analysis applied proved robust to unravel genes associated with a diverse array of conditions attributing novel functions to previously unknown or undefined genes. The data consolidated in the SUCAST database resource can guide further studies and be useful for the development of improved sugarcane varieties.

Estudo avaliativo de um algoritmo genético auto-organizável e multiobjetivo utilizando aprendizado de máquina para aplicações de telecomunicações

This paper presents an evaluative study about the effects of using a machine learning technique on the main features of a self-organizing and multiobjective genetic algorithm (GA). A typical GA can be seen as a search technique which is usually applied in problems involving no polynomial complexity. Originally, these algorithms were designed to create methods that seek acceptable solutions to problems where the global optimum is inaccessible or difficult to obtain. At first, the GAs considered only one evaluation function and a single objective optimization. Today, however, implementations that consider several optimization objectives simultaneously (multiobjective algorithms) are common, besides allowing the change of many components of the algorithm dynamically (self-organizing algorithms). At the same time, they are also common combinations of GAs with machine learning techniques to improve some of its characteristics of performance and use. In this work, a GA with a machine learning technique was analyzed and applied in a antenna design. We used a variant of bicubic interpolation technique, called 2D Spline, as machine learning technique to estimate the behavior of a dynamic fitness function, based on the knowledge obtained from a set of laboratory experiments. This fitness function is also called evaluation function and, it is responsible for determining the fitness degree of a candidate solution (individual), in relation to others in the same population. The algorithm can be applied in many areas, including in the field of telecommunications, as projects of antennas and frequency selective surfaces. In this particular work, the presented algorithm was developed to optimize the design of a microstrip antenna, usually used in wireless communication systems for application in Ultra-Wideband (UWB). The algorithm allowed the optimization of two variables of geometry antenna - the length (Ls) and width (Ws) a slit in the ground plane with respect to three objectives: radiated signal bandwidth, return loss and central frequency deviation. These two dimensions (Ws and Ls) are used as variables in three different interpolation functions, one Spline for each optimization objective, to compose a multiobjective and aggregate fitness function. The final result proposed by the algorithm was compared with the simulation program result and the measured result of a physical prototype of the antenna built in the laboratory. In the present study, the algorithm was analyzed with respect to their success degree in relation to four important characteristics of a self-organizing multiobjective GA: performance, flexibility, scalability and accuracy. At the end of the study, it was observed a time increase in algorithm execution in comparison to a common GA, due to the time required for the machine learning process. On the plus side, we notice a sensitive gain with respect to flexibility and accuracy of results, and a prosperous path that indicates directions to the algorithm to allow the optimization problems with "η" variables

Physical research in dynamized systems

The concept of dynamized medicines is commonly used in homeopathy. As a medical practice, homeopathy is based on clinical observations and experimental assays. In spite of the lack of a credible scientific explanation for its action mechanisms, clinical evidence has been collected since 200 years ago. This paper discusses the most important physical models related to dynamized systems, as well as some perspectives and consequences in studying such systems. While early theories dealt with structural changes in single solvent molecules, modern theories consider collective effects and self-organizing properties. The concept of information was recently introduced to model the capability of a dynamized system to interact with a biological organism, without a molecular agent being needed. The features and limitations of theoretical approaches, as well as experimental results are discussed. (C) 2002 Published by Elsevier B.V. Ltd.

Estatuto biológico do processamento de informação mental

Raciocinando no contexto do programa neomecanicista para a Biologia, estudamos a natureza do processamento de informação no sistema vivo em geral, e no cérebro humano em particular, onde uma aplicação do modelo da Auto-Organização nos conduz à hipótese do Supercódigo. Este seria um programa mental, molecularmente codificado, responsável pelas competências inatas, como a competência lingüística. Fazemos também uma comparação entre nossa hipótese e a da Linguagem do Pensamento, proposta por Jerry Fodor.

Modelos explicativos em saúde coletiva: abordagem biopsicossocial e auto-organização

A complexidade do processo saúde-doença tem ensejado a proposição de uma diversidade de modelos explicativos. Fazemos uma breve revisão dessas propostas, confrontando três perspectivas: o modelo oriundo da Medicina do século XIX, a lógica da História Natural da Doença e o debate epidemiológico no contexto da Medicina Social latino-americana. Tomando-se como referência teórica a ideia de causalidade circular presente na teoria da auto-organização, propomos que os fatores causais privilegiados em cada um dos modelos explicativos acima não seriam conflitantes. Uma noção-chave para se pensar o processo de autoorganização biopsicossocial é o efeito baldwiniano, que descreve uma relação dialética ou coevolutiva entre processos naturais e socioculturais.

A proposta de reabilitação psicossocial de Saraceno: um modelo de auto-organização?

Discute-se a teoria da Reabilitação Psicossocial, proposta por Benedetto Saraceno, tomando como referencial a teoria de Sistemas Auto-Organizados, elaborada, entre outros, por Michel Debrun. Observa-se que a proposta de Saraceno satisfaz diversos aspectos do processo de auto-organização, porém não chega a se constituir plenamente como tal. A partir dessa reflexão, pode-se entender melhor algumas das dificuldades da prática de reabilitação na área de Saúde Mental.

Perceptual Information Integration: Hypothetical Role of Astrocytes

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

Information and mechanical models of intelligence: What can we learn from cognitive science?

The impact of new advanced technology on issues that concern meaningful information and its relation to studies of intelligence constitutes the main topic of the present paper. The advantages, disadvantages and implications of the synthetic methodology developed by cognitive scientists, according to which mechanical models of the mind, such as computer simulations or self-organizing robots, may provide good explanatory tools to investigate cognition, are discussed. A difficulty with this methodology is pointed out, namely the use of meaningless information to explain intelligent behavior that incorporates meaningful information. In this context, it is inquired what are the contributions of cognitive science to contemporary studies of intelligent behavior and how technology may play a role in the analysis of the relationships established by organisms in their natural and social environments. © John Benjamins Publishing Company.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Formação de professores e avaliação formativa: análise de um projeto de interação universidade-escola

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Mineração de dados baseada em inteligência computacional: uma aplicação à determinação da tipologia de curvas de cargas

As concessionárias de energia, para garantir que sua rede seja confiável, necessitam realizar um procedimento para estudo e análise baseado em funções de entrega de energia nos pontos de consumo. Este estudo, geralmente chamado de planejamento de sistemas de distribuição de energia elétrica, é essencial para garantir que variações na demanda de energia não afetem o desempenho do sistema, que deverá se manter operando de maneira técnica e economicamente viável. Nestes estudos, geralmente são analisados, demanda, tipologia de curva de carga, fator de carga e outros aspectos das cargas existentes. Considerando então a importância da determinação das tipologias de curvas de cargas para as concessionárias de energia em seu processo de planejamento, a Companhia de Eletricidade do Amapá (CEA) realizou uma campanha de medidas de curvas de carga de transformadores de distribuição para obtenção das tipologias de curvas de carga que caracterizam seus consumidores. Neste trabalho apresentam-se os resultados satisfatórios obtidos a partir da utilização de Mineração de Dados baseada em Inteligência Computacional (Mapas Auto-Organizáveis de Kohonen) para seleção das curvas típicas e determinação das tipologias de curvas de carga de consumidores residenciais e industriais da cidade de Macapá, localizada no estado do Amapá. O mapa auto-organizável de Kohonen é um tipo de Rede Neural Artificial que combina operações de projeção e agrupamento, permitindo a realização de análise exploratória de dados, com o objetivo de produzir descrições sumarizadas de grandes conjuntos de dados.

Sociedade, estado e organizações não governamentais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sesquiterpene Lactones with Anti-Hepatitis C Virus Activity Using Molecular Descriptors

Hepatitis C is a worldwide public health problem. The available therapies are limited by their partial effectiveness and with meaningful side-effects. Sesquiterpene lactones (SLs) are a group of natural products with a wide variety of chemical structures and biological activities associated. There are few studies about the influence of the molecular structure of SLs for the anti-hepatitis C virus activity. In the present work, SLs are investigated in a subgenomic RNA replicon assay system and were analyzed using multiple linear regression along with self-organizing maps with DRAGON descriptors in order to identify the structural requirements for their biological activity and to predict the inhibitory potency of SLs. Characteristics such as stereochemistry and electronic effects demonstrated to be important for their anti-HCV activity, and the SOM produced a clear separation betwenn active and inactive compounds. Therefore, it is possible to use this map as a filter for virtual screening to predict the anti-HCV activity of SLs.

Signal transduction-related responses to phytohormones and environmental challenges in sugarcane

Abstract Background Sugarcane is an increasingly economically and environmentally important C4 grass, used for the production of sugar and bioethanol, a low-carbon emission fuel. Sugarcane originated from crosses of Saccharum species and is noted for its unique capacity to accumulate high amounts of sucrose in its stems. Environmental stresses limit enormously sugarcane productivity worldwide. To investigate transcriptome changes in response to environmental inputs that alter yield we used cDNA microarrays to profile expression of 1,545 genes in plants submitted to drought, phosphate starvation, herbivory and N2-fixing endophytic bacteria. We also investigated the response to phytohormones (abscisic acid and methyl jasmonate). The arrayed elements correspond mostly to genes involved in signal transduction, hormone biosynthesis, transcription factors, novel genes and genes corresponding to unknown proteins. Results Adopting an outliers searching method 179 genes with strikingly different expression levels were identified as differentially expressed in at least one of the treatments analysed. Self Organizing Maps were used to cluster the expression profiles of 695 genes that showed a highly correlated expression pattern among replicates. The expression data for 22 genes was evaluated for 36 experimental data points by quantitative RT-PCR indicating a validation rate of 80.5% using three biological experimental replicates. The SUCAST Database was created that provides public access to the data described in this work, linked to tissue expression profiling and the SUCAST gene category and sequence analysis. The SUCAST database also includes a categorization of the sugarcane kinome based on a phylogenetic grouping that included 182 undefined kinases. Conclusion An extensive study on the sugarcane transcriptome was performed. Sugarcane genes responsive to phytohormones and to challenges sugarcane commonly deals with in the field were identified. Additionally, the protein kinases were annotated based on a phylogenetic approach. The experimental design and statistical analysis applied proved robust to unravel genes associated with a diverse array of conditions attributing novel functions to previously unknown or undefined genes. The data consolidated in the SUCAST database resource can guide further studies and be useful for the development of improved sugarcane varieties.

Order induced charge carrier mobility enhancement in columnar liquid crystal diodes

Discotic molecules comprising a rigid aromatic core and flexible side chains have been promisingly applied in OLEDs as self-organizing organic semiconductors. Due to their potentially high charge carrier mobility along the columns, device performance can be readily improved by proper alignment of columns throughout the bulk. In the present work, the charge mobility was increased by 5 orders of magnitude due to homeotropic columnar ordering induced by the boundary interfaces during thermal annealing in the mesophase. State-of-the-art diodes were fabricated using spin-coated films whose homeotropic alignment with formation of hexagonal germs was observed by polarizing optical microscopy. The photophysical properties showed drastic changes at the mesophase-isotropic transition, which is supported by the gain of order observed by X-ray diffraction. The electrical properties were investigated by modeling the current−voltage characteristics by a space-charge-limited current transport with a field dependent mobility.