Nitrogen assimilation in Citrus based on CitEST data mining

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Paternity study of an interspecific natural hybrid of the genus Eucalyptus L'Hér (Myrtaceae) based on cytogenetic data

Three species of the genus Eucalyptus (E. dunni, E. grandis, E. saligna) and interspecific hybrid were studied cytogenetically. The Eucalyptus species and the hybrid showed a symmetrical karyotype with 2n=22 chromosomes, with chromosome length ranging from 0.67 to 1.39 μm. Karyotypic analysis indicated a homogenous morphology and chromosome number for the species and the hybrid studied here. Based on the karyotype asymmetry data, together with the chromosome morphology results, the hybrid presented close similarity to E. saligna, suggesting that the latter is one of the parental species involved in the production of the hybrid.

The impact of adding SBAS data on GPS data processing in southeast of Brazil: Preliminary result

Nowadays, L1 SBAS signals can be used in a combined GPS+SBAS data processing. However, such situation restricts the studies over short baselines. Besides of increasing the satellite availability, SBAS satellites orbit configuration is different from that of GPS. In order to analyze how these characteristics can impact GPS positioning in the southeast area of Brazil, experiments involving GPS-only and combined GPS+SBAS data were performed. Solutions using single point and relative positioning were computed to show the impact over satellite geometry, positioning accuracy and short baseline ambiguity resolution. Results showed that the inclusion of SBAS satellites can improve the accuracy of positioning. Nevertheless, the bad quality of the data broadcasted by these satellites limits their usage. © Springer-Verlag Berlin Heidelberg 2012.

Análise da influência da contaminação do lubrificante por biodiesel em um equipamento Pin-on-disk

Pós-graduação em Engenharia Mecânica - FEIS

OntoSDM: an approach to improve quality on spatial data mining algorithms

The increase in new electronic devices had generated a considerable increase in obtaining spatial data information; hence these data are becoming more and more widely used. As well as for conventional data, spatial data need to be analyzed so interesting information can be retrieved from them. Therefore, data clustering techniques can be used to extract clusters of a set of spatial data. However, current approaches do not consider the implicit semantics that exist between a region and an object’s attributes. This paper presents an approach that enhances spatial data mining process, so they can use the semantic that exists within a region. A framework was developed, OntoSDM, which enables spatial data mining algorithms to communicate with ontologies in order to enhance the algorithm’s result. The experiments demonstrated a semantically improved result, generating more interesting clusters, therefore reducing manual analysis work of an expert.

Influence of data structure on the estimation of the additive genetic direct and maternal covariance for early growth traits in Nellore cattle

The objective of the present study was to investigate the effect of data structure on estimated genetic parameters and predicted breeding values of direct and maternal genetic effects for weaning weight (WW) and weight gain from birth to weaning (BWG), including or not the genetic covariance between direct and maternal effects. Records of 97,490 Nellore animals born between 1993 and 2006, from the Jacarezinho cattle raising farm, were used. Two different data sets were analyzed: DI_all, which included all available progenies of dams without their own performance; DII_all, which included DI_all + 20% of recorded progenies with maternal phenotypes. Two subsets were obtained from each data set (DI_all and DII_all): DI_1 and DII_1, which included only dams with three or fewer progenies; DI_5 and DII_5, which included only dams with five or more progenies. (Co)variance components and heritabilities were estimated by Bayesian inference through Gibbs sampling using univariate animal models. In general, for the population and traits studied, the proportion of dams with known phenotypic information and the number of progenies per dam influenced direct and maternal heritabilities, as well as the contribution of maternal permanent environmental variance to phenotypic variance. Only small differences were observed in the genetic and environmental parameters when the genetic covariance between direct and maternal effects was set to zero in the data sets studied. Thus, the inclusion or not of the genetic covariance between direct and maternal effects had little effect on the ranking of animals according to their breeding values for WW and BWG. Accurate estimation of genetic correlations between direct and maternal genetic effects depends on the data structure. Thus, this covariance should be set to zero in Nellore data sets in which the proportion of dams with phenotypic information is low, the number of progenies per dam is small, and pedigree relationships are poorly known. (c) 2012 Elsevier B.V. All rights reserved.

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

A low-power silicon-on-insulator PWM discriminator for biomedical applications

A CMOS/SOI circuit to decode PWM signals is presented as part of a body-implanted neurostimulator for visual prosthesis. Since encoded data is the sole input to the circuit, the decoding technique is based on a double-integration concept and does not require dc filtering. Nonoverlapping control phases are internally derived from the incoming pulses and a fast-settling comparator ensures good discrimination accuracy in the megahertz range. The circuit was integrated on a 2 mu m single-metal SOI fabrication process and has an effective area of 2mm(2) Typically, the measured resolution of encoding parameter a was better than 10% at 6MHz and V-DD=3.3V. Stand-by consumption is around 340 mu W. Pulses with frequencies up to 15MHz and alpha = 10% can be discriminated for V-DD spanning from 2.3V to 3.3V. Such an excellent immunity to V-DD deviations meets a design specification with respect to inherent coupling losses on transmitting data and power by means of a transcutaneous link.

THE EFFECT of ANIONIC SORPTION on THE METAKAOLINITE

This paper reports an investigation on the effect of thermal activation of kaolinite. It is well known that during calcinations (400-650 degrees C), kaolinite loses the OH lattice water and is transformed into metakaolinite or amorphous material. Arsenic is trace element that is toxic to animals including humans. The adsorption of arsenic on kaolinite was investigated at varying pH and thermal pretreatment. Calcination of sample is carried out at 650 degrees C for 3 h. The decomposition of kaolinite is recorded using methods of thermal analysis. The resultant product is identified by XRD. Laboratory experiments were conducted examining the effect of arsenic by thermally modified kaolinite. The Langmuir isotherm was used to describe arsenite and arsenate sorption by the calcined kaolinite. The equilibrium parameters used were based on experimental data obtained for the dynamic adsorption process of arsenic. Removal of arsenate using natural kaolinite was satisfactory, whereas arsenic was not removed by adsorption with thermally modified kaolinite. Moreover, the adsorption of arsenic by kaolinite and metakaolinite decreases with increasing pH.

Development of amorphous carbon protective coatings on poly(vinyl)chloride

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

An investigation on sliding wear behavior of PVD coatings

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Prevalence of Candida spp. associated with bacteria species on complete dentures

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

Limits on anomalous couplings from Higgs boson production at the Tevatron

We estimate the attainable limits on the coefficients of dimension-6 operators from the analysis of Higgs boson phenomenology, in the framework of a SUL(2) x U-Y(1) gauge-invariant effective Lagrangian. Our results, based on the data sample already collected by the collaborations at Fermilab Tevatron, show that the coefficients of Higgs-vector boson couplings can be determined with unprecedented accuracy. Assuming that the coefficients of all blind operators are of the same magnitude, we are also able to impose more restrictive bounds on the anomalous vector-boson triple couplings than the present limit from double gauge boson production at the Tevatron collider.

Influence of HVOF parameters on the corrosion and wear resistance of WC-Co coatings sprayed on AA7050 T7

Thermal spray WC-based coatings are widely used in the aircraft industry mainly for their resistance to wear, reworking and rebuilding operations and repair of worn components on landing gear, hydraulic cylinders, actuators, propeller hub assemblies, gas turbine engines, and so on. The aircraft industry is also trying to use thermal spray technology to replace electroplating coatings such as hard chromium. In the present work, WC-Co coatings were built up on an AA 7050 aluminum alloy using high velocity oxygen fuel (HVOF) technology and a liquid nitrogen cooling prototype system. The influence of the spray parameters (standard conditions, W19S, increasing the oxygen flux, W19H, and also increasing the carrier gas flux, W19F) on corrosion, friction, and abrasive wear resistance were also studied. The coatings were characterized using optical (OM) and scanning electron (SEM) microscopy, and X-ray diffraction (XRD). The friction and abrasive wear resistance of the coatings were studied using Rubber Wheel and Ball on Disk tests. The electrochemical studies were conducted using open-circuit potential (E(oc)) measurements and electrochemical impedance spectroscopy (EIS). Differences among coated samples were mainly related to the variation of the thermal spray parameters used during the spray process. No significant differences were observed in the wear resistance for the coatings studied, and all of them showed a wear rate around 10 times lower than that of the aluminum alloy. The results of mass loss and wear rate were interpreted considering different mechanisms. Comparing the different spray parameters, the oxygen flux (higher flame temperature) produced the sample which showed the highest corrosion resistance in aerated and unstirred 3.5% NaCl solution. Aluminum ions were detected on the surface almost immediately after the immersion of samples W19S and W19F in chloride solution, showing that the electrolyte reached the substrate and galvanic corrosion probably occurred. For sample W19H, aluminum ions were not detected even after 120 min of immersion in NaCl solution. (C) 2008 Elsevier B.V. All rights reserved.