THE EFFECT OF CORTICO-THALAMIC CONNECTIONS ON THE DIVERSITY OF CORTICAL ACTIVATIONS AS MODELED BY THE ISING MODEL

The influence of the thalamus on the diversity of cortical activations is investigated in terms of the Ising model with respect to progressive levels of cortico-thalamic connectivity. The results show that better diversity is achieved at lower modulation levels, being higher than those obtained with counterpart network models.

Bayesian inference for a skew-normal IRT model under the centred parameterization

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is interest in studying latent variables. These latent variables are directly considered in the Item Response Models (IRM) and they are usually called latent traits. A usual assumption for parameter estimation of the IRM, considering one group of examinees, is to assume that the latent traits are random variables which follow a standard normal distribution. However, many works suggest that this assumption does not apply in many cases. Furthermore, when this assumption does not hold, the parameter estimates tend to be biased and misleading inference can be obtained. Therefore, it is important to model the distribution of the latent traits properly. In this paper we present an alternative latent traits modeling based on the so-called skew-normal distribution; see Genton (2004). We used the centred parameterization, which was proposed by Azzalini (1985). This approach ensures the model identifiability as pointed out by Azevedo et al. (2009b). Also, a Metropolis Hastings within Gibbs sampling (MHWGS) algorithm was built for parameter estimation by using an augmented data approach. A simulation study was performed in order to assess the parameter recovery in the proposed model and the estimation method, and the effect of the asymmetry level of the latent traits distribution on the parameter estimation. Also, a comparison of our approach with other estimation methods (which consider the assumption of symmetric normality for the latent traits distribution) was considered. The results indicated that our proposed algorithm recovers properly all parameters. Specifically, the greater the asymmetry level, the better the performance of our approach compared with other approaches, mainly in the presence of small sample sizes (number of examinees). Furthermore, we analyzed a real data set which presents indication of asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of strong negative asymmetry of the latent traits distribution. (C) 2010 Elsevier B.V. All rights reserved.

Predicting random effects with an expanded finite population mixed model

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Recently, Stanek and Singer [Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 119-130] developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed models and superpopulation models. Their setup, however, does not allow for unequally sized clusters. To overcome this drawback, we consider an expanded finite population mixed model based on a larger set of random variables that span a higher dimensional space than those typically applied to such problems. We show that BLUPs for linear combinations of the realized cluster means derived under such a model have considerably smaller mean squared error (MSE) than those obtained from mixed models, superpopulation models, and finite population mixed models. We motivate our general approach by an example developed for two-stage cluster sampling and show that it faithfully captures the stochastic aspects of sampling in the problem. We also consider simulation studies to illustrate the increased accuracy of the BLUP obtained under the expanded finite population mixed model. (C) 2007 Elsevier B.V. All rights reserved.

Metabolomic assessment with CE-MS of the nutraceutical effect of Cystoseira spp extracts in an animal model

There is a need of scientific evidence of claimed nutraceutical effects, but also there is a social movement towards the use of natural products and among them algae are seen as rich resources. Within this scenario, the development of methodology for rapid and reliable assessment of markers of efficiency and security of these extracts is necessary. The rat treated with streptozotocin has been proposed as the most appropriate model of systemic oxidative stress for studying antioxidant therapies. Cystoseira is a brown alga containing fucoxanthin and other carothenes whose pressure-assisted extracts were assayed to discover a possible beneficial effect on complications related to diabetes evolution in an acute but short-term model. Urine was selected as the sample and CE-TOF-MS as the analytical technique to obtain the fingerprints in a non-target metabolomic approach. Multivariate data analysis revealed a good clustering of the groups and permitted the putative assignment of compounds statistically significant in the classification. Interestingly a group of compounds associated to lysine glycation and cleavage from proteins was found to be increased in diabetic animals receiving vehicle as compared to control animals receiving vehicle (N6, N6, N6-trimethyl-L-lysine, N-methylnicotinamide, galactosylhydroxylysine, L-carnitine, N6-acetyl-N6-hydroxylysine, fructose-lysine, pipecolic acid, urocanic acid, amino-isobutanoate, formylisoglutamine. Fructoselysine significantly decreased after the treatment changing from a 24% increase to a 19% decrease. CE-MS fingerprinting of urine has provided a group of compounds different to those detected with other techniques and therefore proves the necessity of a cross-platform analysis to obtain a broad view of biological samples.

A Cost Model for the Effect of Setup Time Reduction in Stainless Steel Strip Production

Setup time reduction facilitate the flexibility needed for just-in-time production. An integrated steel mill with meltshop, continuous caster and hot rolling mill is often operated as decoupled processes. Setup time reduction provides the flexibility needed to reduce buffering, shorten lead times and create an integrated process flow. The interdependency of setup times, process flexibility and integration were analysed through system dynamics simulation. The results showed significant reductions of energy consumption and tied capital. It was concluded that setup time reduction in the hot strip mill can aid process integration and hence improve production economy while reducing environmental impact.

Effect Of Different Hydrological Model Structures On The Assimilation Of Distributed Uncertain Observations

The reliable evaluation of the flood forecasting is a crucial problem for assessing flood risk and consequent damages. Different hydrological models (distributed, semi-distributed or lumped) have been proposed in order to deal with this issue. The choice of the proper model structure has been investigated by many authors and it is one of the main sources of uncertainty for a correct evaluation of the outflow hydrograph. In addition, the recent increasing of data availability makes possible to update hydrological models as response of real-time observations. For these reasons, the aim of this work it is to evaluate the effect of different structure of a semi-distributed hydrological model in the assimilation of distributed uncertain discharge observations. The study was applied to the Bacchiglione catchment, located in Italy. The first methodological step was to divide the basin in different sub-basins according to topographic characteristics. Secondly, two different structures of the semi-distributed hydrological model were implemented in order to estimate the outflow hydrograph. Then, synthetic observations of uncertain value of discharge were generated, as a function of the observed and simulated value of flow at the basin outlet, and assimilated in the semi-distributed models using a Kalman Filter. Finally, different spatial patterns of sensors location were assumed to update the model state as response of the uncertain discharge observations. The results of this work pointed out that, overall, the assimilation of uncertain observations can improve the hydrologic model performance. In particular, it was found that the model structure is an important factor, of difficult characterization, since can induce different forecasts in terms of outflow discharge. This study is partly supported by the FP7 EU Project WeSenseIt.

Correcting the fixed-effect estimator for endogenous switching

In this paper, we propose a two-step estimator for panel data models in which a binary covariate is endogenous. In the first stage, a random-effects probit model is estimated, having the endogenous variable as the left-hand side variable. Correction terms are then constructed and included in the main regression.

Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Random regression models to estimate genetic parameters for test-day milk yield in Brazilian Murrah buffaloes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

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

Modeling of average growth curve in Santa Ines sheep using random regression models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Genetic parameters for test-day yield of milk, fat and protein in buffaloes estimated by random regression models

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

EFFECT of TYPES of NOZZLES on SPRAY DEPOSITION on TWO SOYBEAN VARIETIES IN STAGE V3

The effect of application with different nozzle types and volume rates on spray deposition in the V3 stage of two soybean cultivars was evaluated. The experiments were conducted in the Facultad de Ciencias Agronomicas of the UNESP-Botucatu/SP. The nozzles evaluated were an air induced flat fan nozzle (Al 11015 at 150 L ha(-1), Al 11002 at 200 and 250 L ha(-1)), a twin flat fan nozzle (TJ 60 11002 at 150, 200 and 250 L ha(-1)), and a cone nozzle (TX 6 at 150 L ha(-1), TX 8 at 150 L ha(-1) and TX 10 at 250 L ha(-1)). To evaluate spray deposition on the plants, a tracer (Brilliant Blue FD&C-1) was added. The experimental design was random blocks with four replications. Deposition on plants was determined by absorbancy reading in 630 nm wavelength. The data were adjusted to a calibration curve and transformed into deposited spray volume in mL. The relationship deposition per unit of dry matter was adjusted to a regression curve (Gompertz model). In cultivar CD 208, the highest deposit was for the larger volumes and for the treatment TX 8 200 L ha(-1). The most uniform treatments were all the nozzles with the volume 150 L ha(-1) and the TJ60 nozzle for 200 1, ha(-1). In cultivar CD 216, the greatest spray depositions were achieved with the treatments Al at 200 and 250 L ha(-1) and TJ 60 at 250 L ha(-1), and the most uniform treatments were the TX 6 and TJ60 nozzles for the volume150 L ha(-1).

An Anisotropic Model of Damage and Unilateral Effect for Brittle Materials

This work deals with the initial applications and formulation of an aniscitropic plastic-damage constitutive model proposed for non-linear analysis of reinforced concrete structures submitted to a loading with change of the sign. The original constitutive model is based on the fundamental hypothesis of energy equivalence between real and continuous medium following the concepts of the Continuum Damage Mechanics. The concrete is assumed as an initial elastic isotropic medium presenting anisotropy, permanent strains and bimodularity (distinct elastic responses whether traction or compression stress states prevail) induced by damage evolution. In order to take into account the bimodularity, two damage tensors governing the rigidity in tension or compression regimes are introduced. Then, some conditions are introduced in the original version of the model in order to simulate the damage unilateral effect. The three-dimensional version of the proposed model is analyzed in order to validate its formulation when compared to micromechanical theory. The one-dimensional version of the model is applied in the analyses of a reinforced concrete beam submitted to a loading with change of the sign. Despite the parametric identification problems, the initial applications show the good performance of the model.

Rat model of depending prostaglandin renal state: Effect of ketoprofen

Introduction .The renal prostaglandins (PGs), vasodilators, preserve kidney function during increased activity of the renin-angiotensin system or renal sympathetic nerves (renal PG-dependent state [RPGD]). Ketoprofen (Ket) inhibits cyclooxygenase and, therefore, the synthesis of PGs. The aim of this study was to determine, in the rat, the action of Ket in the renal histology and function in a RPGD state (stress of anesthesia and hemorrhage). Material and Methods . Twenty male Wistar rats, anesthetized with sodium pentobarbital, were randomly divided into two groups: G1-control ( n = 10) and G2-Ket ( n = 10) submitted to arterial hemorrhage of 30% of volemia (estimated as 6% of body weight) three times (10% each 10 min), 65 min after anesthesia. G2 animals received Ket, 1.5 mg. kg -1 , venously, 5 min after anesthesia and 60 min before the first hemorrhage moment (first moment of the study [M1]). Medium arterial pressure (MAP), rectal temperature (T), and heart rate were monitored. G1 and G2 received para-aminohippurate sodium (PAH) and iothalamate sodium (IOT) solutions during the entire experimental time in order to determine clearance of PAH (effective renal plasma flow [ERPF]) and clearance of IOT (glomerular filtration rate [GFR]) without urine collection (determination of blood concentrations of PAH and IOT through the high-performance liquid chromatography), filtration fraction (FF), and renal vascular resistance (RVR). The animals were sacrificed in M3, 30 min after the third hemorrhage (M2) moment, and the kidneys and blood collected during the hemorrhage periods were utilized for histological study and determinations of hematocrit (Ht), serum creatinine (S Cr ), ERPF, GFR, FF, and RVR, respectively. Results . There were significant reductions of MAP, T, and Ht and a significant increase of S Cr . During the experiment, ERPF and GFR did not change, but ERPF was always higher in G1 than in G2. Ket did not alter FF, which increased in G1 over the duration of experiment. The Ket group had significantly higher RVR than the control group. The histology verified that both G1 and G2 were similar for tubular dilation and necrosis, but they were significantly different for tubular degeneration: G1 > G2. Conclusion . The changes observed in kidney histology probably were determined by hemorrhage and hypotension. Ket inhibited the synthesis of PGs and diminished tubular degeneration.