Nonparametric entropy-based tests of independence between stochastic processes

This paper develops nonparametric tests of independence between two stationary stochastic processes. The testing strategy boils down to gauging the closeness between the joint and the product of the marginal stationary densities. For that purpose, I take advantage of a generalized entropic measure so as to build a class of nonparametric tests of independence. Asymptotic normality and local power are derived using the functional delta method for kernels, whereas finite sample properties are investigated through Monte Carlo simulations.

Project Pró-Natal: population-based study of perinatal and infant mortality in Natal, Northeast Brazil

RAMOS, Ana Maria de Oliveira et al. Project Pró-Natal: population-based study of perinatal and infant mortality in Natal, Northeast Brazil. Pediatric and Developmental Pathology, v.3, n.1, p.29-35, 2000

Sonic hedgehog signaling regulates mode of cell division of early cerebral cortex progenitors and increases

The morphogen Sonic Hedgehog (SHH) plays a critical role in the development of different tissues. In the central nervous system, SHH is well known to contribute to the patterning of the spinal cord and separation of the brain hemispheres. In addition, it has recently been shown that SHH signaling also contributes to the patterning of the telencephalon and establishment of adult neurogenic niches. In this work, we investigated whether SHH signaling influences the behavior of neural progenitors isolated from the dorsal telencephalon, which generate excitatory neurons and macroglial cells in vitro. We observed that SHH increases proliferation of cortical progenitors and generation of astrocytes, whereas blocking SHH signaling with cyclopamine has opposite effects. In both cases, generation of neurons did not seem to be affected. However, cell survival was broadly affected by blockade of SHH signaling. SHH effects were related to three different cell phenomena: mode of cell division, cell cycle length and cell growth. Together, our data in vitro demonstrate that SHH signaling controls cell behaviors that are important for proliferation of cerebral cortex progenitors, as well as differentiation and survival of neurons and astroglial cells.

Genomics-based design of defined growth media for the plant pathogen Xylella fastidiosa

Based on the genetic analysis of the phytopathogen Xylella fastidiosa genome, five media with defined composition were developed and the growth abilities of this fastidious prokaryote were evaluated in liquid media and on solid plates. All media had a common salt composition and included the same amounts of glucose and vitamins but differed in their amino acid content. XDM1 medium contained amino acids threonine, serine, glycine, alanine, aspartic acid and glutamic acid, for which complete degradation pathways occur in X fastidiosa; XDM2 included serine and methionine, amino acids for which biosynthetic enzymes are absent, plus asparagine and glutamine, which are abundant in the xylem sap; XDM3 had the same composition as XDM2 but with asparagine replaced by aspartic acid due to the presence of complete degradation pathway for aspartic acid; XDM4 was a minimal medium with glutamine as a sole nitrogen source; XDM5 had the same composition as XDM4, plus methionine. The liquid and solidified XDM2 and XDM3 media were the most effective for the growth of X. fastidiosa. This work opens the opportunity for the in silico design of bacterial defined media once their genome is sequenced. (C) 2002 Federation of European Microbiological Societies. Published by Elsevier B.V. B.V. All rights reserved.

Neural network based estimation of torque in induction motors for real-time applications

Induction motors are largely used in several industry sectors. The selection of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this article is to use artificial neural networks for torque estimation with the purpose of best selecting the induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since proposed approach estimates the torque behavior from the transient to the steady state, one of its main contributions is the potential to also be implemented in control schemes for real-time applications. Simulation results are also presented to validate the proposed approach.

Time-series-based maximization of distributed wind power generation integration

Energy policies and technological progress in the development of wind turbines have made wind power the fastest growing renewable power source worldwide. The inherent variability of this resource requires special attention when analyzing the impacts of high penetration on the distribution network. A time-series steady-state analysis is proposed that assesses technical issues such as energy export, losses, and short-circuit levels. A multiobjective programming approach based on the nondominated sorting genetic algorithm (NSGA) is applied in order to find configurations that maximize the integration of distributed wind power generation (DWPG) while satisfying voltage and thermal limits. The approach has been applied to a medium voltage distribution network considering hourly demand and wind profiles for part of the U.K. The Pareto optimal solutions obtained highlight the drawbacks of using a single demand and generation scenario, and indicate the importance of appropriate substation voltage settings for maximizing the connection of MPG.

Differential-Evolution-Based Optimization of the Dynamic Response for Parallel Operation of Inverters With No Controller Interconnection

In this paper, the use of differential evolution ( DE), a global search technique inspired by evolutionary theory, to find the parameters that are required to achieve optimum dynamic response of parallel operation of inverters with no interconnection among the controllers is proposed. Basically, in order to reach such a goal, the system is modeled in a certain way that the slopes of P-omega and Q-V curves are the parameters to be tuned. Such parameters, when properly tuned, result in system's eigenvalues located in positions that assure the system's stability and oscillation-free dynamic response with minimum settling time. This paper describes the modeling approach and provides an overview of the motivation for the optimization and a description of the DE technique. Simulation and experimental results are also presented, and they show the viability of the proposed method.

In silico network topology-based prediction of gene essentiality

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.

The size of excavators within a polymorphic termite species governs tunnel topology

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

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

rDNA-based characterization of a new binucleate Rhizoctonia spp. causing root rot on kale in Brazil

In this paper we present the first report of the occurrence of a binucleate Rhizoctonia spp. causing hypocotyl and root rot in kale in Brazil. Rhizoctonia spp. were isolated from kale (Brassica oleracea var. acephala) with symptoms of hypocotyl and root rot. The isolates, characterized as binucleate Rhizoctonia spp., did not show an anastomosis reaction with any of the binucleate Rhizoctonia spp. testers used. The pathogenicity of the isolates was tested under greenhouse conditions; all isolates were pathogenic and showed different symptom severities on kale. The ITS-5.8S rDNA sequences of kale isolates and 50 testers (25 binucleate Rhizoctonia spp. and 25 Rhizoctonia solani) were compared in order to characterize the genetic identity of Rhizoctonia spp. infecting kale. The kale isolates showed genetic identities ranging from 99.3 to 99.8% and were phylogenetically closely related to CAG 7 (AF354084), with identities of 98.5 and 98.7%. It is suggested that the binucleate Rhizoctonia spp. causing hypocotyl and root rot on kale Brazil comprises a new AG not yet described.

POPULATION-BASED INCIDENCE OF IDDM IN THE STATE OF SAO-PAULO, BRAZIL

OBJECTIVE- To study the incidence of IDDM among children, infants to 14 yr of age, in the state of São Paulo, Brazil, 1987-1991.RESEARCH DESIGN AND METHODS - A prospective population-based register was established, using physician reports of newly diagnosed IDDM patients < 15 yr of age as the primary source of case identification and school surveys as the main secondary source. Data were collected according to the methods recommended by the Diabetes Epidemiology Research International group.RESULTS - Case ascertainment was estimated at 95.0, 92.8, and 98.8% complete for each of the three cities studied. The average annual IDDM incidence was 7.6/100,000 inhabitants (95% confidence interval, 5.6-9.7). We found a higher incidence rate in girls than boys.CONCLUSIONS - the incidence of childhood IDDM in a tropical region in South America (São Paulo, Brazil) is in the middle incidence range observed in developed countries throughout the world. Increased incidence of IDDM in girls compared with boys will be tested by the ongoing Brazilian incidence study being developed in 18 other centers across the country.

A cost optimization-based design of precast concrete floors using genetic algorithms

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Urea-Based Synthesis of Zinc Oxide Nanostructures at Low Temperature

The preparation of nanometer-sized structures of zinc oxide (ZnO) from zinc acetate and urea as raw materials was performed using conventional water bath heating and a microwave hydrothermal (MH) method in an aqueous solution. The oxide formation is controlled by decomposition of the added urea in the sealed autoclave. The influence of urea and the synthesis method on the final product formation are discussed. Broadband photoluminescence (PL) behavior in visible-range spectra was observed with a maximum peak centered in the green region which was attributed to different defects and the structural changes involved with ZnO crystals which were produced during the nucleation process.