Desenvolvimento de um Questionário Quantitativo de Freqüência Alimentar (QQFA) para gestantes usuárias de Unidades Básicas de Saúde de Ribeirão Preto, São Paulo, Brasil

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

Extending SUSY reach at the CERN large hadron collider using b-tagging

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

Antagonism of clonidine injected intracerebroventricularly in different models of salt intake

Clonidine, an alpha 2-adrenergic agonist, injected into the brain inhibits salt intake of animals treated by the diuretic model of sodium depletion. In the present study, we address the question of whether central injection of clonidine also inhibits salt intake in animals deprived of water or in the need-free state. Saline or clonidine (30 nmol) was injected into the anterior third ventricle of 24-h sodium-depleted (furosemide + removal of ambient sodium), of 24-h water-deprived and of normovolemic (need-free state) adult male rats, Clonidine injected intracerebroventricularly (icv) inhibited the 1.5% NaCl intake for 120 min by 50 to 90% in every model tested. Therefore, different models of salt intake are inhibited by icv injection of clonidine, Idazoxan, an alpha 2-adrenergic antagonist, injected icy at a dose of 160 nmol, inhibited the effect of clonidine only in the furosemide + removal of ambient sodium model of salt intake. This indicates that the antagonism of this effect by idazoxan is dependent on the body fluid/sodium status of the animal.

Predicting structural models for silicon clusters

This article introduces an efficient method to generate structural models for medium-sized silicon clusters. Geometrical information obtained from previous investigations of small clusters is initially sorted and then introduced into our predictor algorithm in order to generate structural models for large clusters. The method predicts geometries whose binding energies are close (95%) to the corresponding value for the ground-state with very low computational cost. These predictions can be used as a very good initial guess for any global optimization algorithm. As a test case, information from clusters up to 14 atoms was used to predict good models for silicon clusters up to 20 atoms. We believe that the new algorithm may enhance the performance of most optimization methods whenever some previous information is available. (C) 2003 Wiley Periodicals, Inc.

Curvas de lactação em rebanhos da raça holandesa no estado de Minas Gerais. Escolha do modelo de melhor ajuste

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Maximal lactate steady state in running mice: Effect of exercise training

1. Maximal lactate steady state (MLSS) corresponds to the highest blood lactate concentration (MLSSc) and workload (MLSSw) that can be maintained over time without continual blood lactate accumulation and is considered an important marker of endurance exercise capacity. The present study was undertaken to determine MLSSw and MLSSc in running mice. In addition, we provide an exercise training protocol for mice based on MLSSw.2. Maximal lactate steady state was determined by blood sampling during multiple sessions of constant-load exercise varying from 9 to 21 m/min in adult male C57BL/6J mice. The constant-load test lasted at least 21 min. The blood lactate concentration was analysed at rest and then at 7 min intervals during exercise.3. The MLSSw was found to be 15.1 +/- 0.7 m/min and corresponded to 60 +/- 2% of maximal speed achieved during the incremental exercise testing. Intra- and interobserver variability of MLSSc showed reproducible findings. Exercise training was performed at MLSSw over a period of 8 weeks for 1 h/day and 5 days/week. Exercise training led to resting bradycardia (21%) and increased running performance (28%). of interest, the MLSSw of trained mice was significantly higher than that in sedentary littermates (19.0 +/- 0.5 vs 14.2 +/- 0.5 m/min; P = 0.05), whereas MLSSc remained unchanged (3.0 mmol/L).4. Altogether, we provide a valid and reliable protocol to improve endurance exercise capacity in mice performed at highest workload with predominant aerobic metabolism based on MLSS assessment.

Analysis of the Stayability in Milk Buffaloes using Survival Models

In this study the trait Stayability (SA) was evaluated according to the year of cull after first calvin, i.e., SA 1 to 6 for 1 to 6 years from first calving in lactating females from bubaline milk herds spread in nine farms located in São Paulo state. Informations were used regarding 1027 lactating Murrah breed buffaloes. The statistical analyses were made using LIFEREG (SAS, 1999) procedure. The SA was evaluated using the fixed effects: farm production, birth year, calving season (Season 1- April to September and Season 2 October - March) and class of milk yield at 270 days. The age at first calving (AFC) was considered as a random effect. The mean observed for total milk yield was 1458.75Kg. Calving Season 2 encloses 65.6% of births. The means of cull age, in months, and the percentage of SA were, respectively: 10.69 e 69% (SA1), 19.30 e 63% (SA2), 26.4 e 54% (SA3), 33.15 e 42% (SA4), 38.53 e 36% (SA5) e 42.65 e 26% (SA6). It is verified that most of culls happens after the first lactation, among the sixth and eleventh month after first calving. It was observed that the factors: farm production, birth year and class of milk yield at 270 days affected significantly all SAs. Factors like calving season and the age at first calving (AFC) were only significant for SAL Being significant the factor AFC in level of 1% and factor time in 10%. For other SAs these factors were not statistically significant.

Prediction and spatial variability of soil dynamic properties in sugar cane fields of São Paulo State Brazil

The objective of this work was to model and diagnose the spatial variability of soil load support capacity (SLSC) in sugar cane crop fields, as well as to evaluate the management impact on São Paulo State soil structure. The investigated variables were: pressure preconsolidation (sigma(p)), apparent cohesion () and internal friction angle (). The conclusions from the results were that the models and spatial dependence maps constitute important tools in the prediction and location of the mechanical internal strength of soils cultivated with sugar cane. They will help future soil management decisions so that soil structure sustainability will not be compromised.

Test systems and mathematical models for transmission network expansion planning

The data of four networks that can be used in carrying out comparative studies with methods for transmission network expansion planning are given. These networks are of various types and different levels of complexity. The main mathematical formulations used in transmission expansion studies-transportation models, hybrid models, DC power flow models, and disjunctive models are also summarised and compared. The main algorithm families are reviewed-both analytical, combinatorial and heuristic approaches. Optimal solutions are not yet known for some of the four networks when more accurate models (e.g. The DC model) are used to represent the power flow equations-the state of the art with regard to this is also summarised. This should serve as a challenge to authors searching for new, more efficient methods.

Hybrid reflections in InGaP/GaAs(001) by synchrotron radiation multiple diffraction

Hybrid reflections (HRs) involving substrate and layer planes (SL type) [Morelhao et al., Appl. Phys. Len. 73 (15), 2194 (1998)] observed in Chemical Beam Epitaxy (CBE) grown InGaP/GaAs(001) structures were used as a three-dimensional probe to analyze structural properties of epitaxial layers. A set of (002) rocking curves (omega-scan) measured for each 15 degrees in the azimuthal plane was arranged in a pole diagram in phi for two samples with different layer thicknesses (#A -58 nm and #B - 370 nm) and this allowed us to infer the azimuthal epilayer homogeneity in both samples. Also, it was shown the occurrence of (1 (1) over bar3) HR detected even in the thinner layer sample. Mappings of the HR diffraction condition (omega:phi) allowed to observe the crystal truncation rod through the elongation of HR shape along the substrate secondary reflection streak which can indicate in-plane match of layer/substrate lattice parameters. (C) 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Bayesian L-optimal exact design of experiments for biological kinetic models

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

Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

As a new modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In this study, the SVR models had been introduced and developed to predict body and carcass-related characteristics of 2 strains of broiler chicken. To evaluate the prediction ability of SVR models, we compared their performance with that of neural network (NN) models. Evaluation of the prediction accuracy of models was based on the R-2, MS error, and bias. The variables of interest as model output were BW, empty BW, carcass, breast, drumstick, thigh, and wing weight in 2 strains of Ross and Cobb chickens based on intake dietary nutrients, including ME (kcal/bird per week), CP, TSAA, and Lys, all as grams per bird per week. A data set composed of 64 measurements taken from each strain were used for this analysis, where 44 data lines were used for model training, whereas the remaining 20 lines were used to test the created models. The results of this study revealed that it is possible to satisfactorily estimate the BW and carcass parts of the broiler chickens via their dietary nutrient intake. Through statistical criteria used to evaluate the performance of the SVR and NN models, the overall results demonstrate that the discussed models can be effective for accurate prediction of the body and carcass-related characteristics investigated here. However, the SVR method achieved better accuracy and generalization than the NN method. This indicates that the new data mining technique (SVR model) can be used as an alternative modeling tool for NN models. However, further reevaluation of this algorithm in the future is suggested.

Precipitação pluviométrica mensal no estado do Rio de Janeiro: sazonalidade e tendência

The objective of this work was to carry a descriptive analysis in the monthly precipitation of rainfall stations from Rio de Janeiro State, Brazil, using data of position and dispersion and graphical analyses, and to verify the presence of seasonality and trend in these data, with a study about the application of models of time series. The descriptive statistics was to characterize the general behavior of the series in three stations selected which present consistent historical series. The methodology of analysis of variance in randomized blocks and the determination of models of multiple linear regression, considering years and months as predictors variables, disclosed the presence of seasonality, what allowed to infer on the occurrence of repetitive natural phenomena throughout the time and absence of trend in the data. It was applied the methodology of multiple linear regression to removal the seasonality of these time series. The original data had been deducted from the estimates made by the adjusted model and the analysis of variance in randomized blocks for the residues of regression was preceded again. With the results obtained it was possible to conclude that the monthly rainfall present seasonality and they don't present trend, the analysis of multiple regression was efficient in the removal of the seasonality, and the rainfall can be studied by means of time series.

Challenges of modeling depth-integrated marine primary productivity over multiple decades: A case study at BATS and HOT

The performance of 36 models (22 ocean color models and 14 biogeochemical ocean circulation models (BOGCMs)) that estimate depth-integrated marine net primary productivity (NPP) was assessed by comparing their output to in situ (14)C data at the Bermuda Atlantic Time series Study (BATS) and the Hawaii Ocean Time series (HOT) over nearly two decades. Specifically, skill was assessed based on the models' ability to estimate the observed mean, variability, and trends of NPP. At both sites, more than 90% of the models underestimated mean NPP, with the average bias of the BOGCMs being nearly twice that of the ocean color models. However, the difference in overall skill between the best BOGCM and the best ocean color model at each site was not significant. Between 1989 and 2007, in situ NPP at BATS and HOT increased by an average of nearly 2% per year and was positively correlated to the North Pacific Gyre Oscillation index. The majority of ocean color models produced in situ NPP trends that were closer to the observed trends when chlorophyll-alpha was derived from high-performance liquid chromatography (HPLC), rather than fluorometric or SeaWiFS data. However, this was a function of time such that average trend magnitude was more accurately estimated over longer time periods. Among BOGCMs, only two individual models successfully produced an increasing NPP trend (one model at each site). We caution against the use of models to assess multiannual changes in NPP over short time periods. Ocean color model estimates of NPP trends could improve if more high quality HPLC chlorophyll-alpha time series were available.

Presence of excited electronic state in CaWO4 crystals provoked by a tetrahedral distortion: An experimental and theoretical investigation

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)