Valuing water resources in developing countries: A semiparametric approach to valuation models

Valuation of projects for the preservation of water resources provides important information to policy makers and funding institutions. Standard contingent valuation models rely on distributional assumptions to provide welfare measures. Deviations from assumed and actual distribution of benefits are important when designing policies in developing countries, where inequality is a concern. This article applies semiparametric methods to obtain estimates of the benefit from a project for the preservation of an important Brazilian river basin. These estimates lead to significant differences from those obtained using the standard parametric approach.

Framework for Skew-Probit Links in Binary Regression

We review several asymmetrical links for binary regression models and present a unified approach for two skew-probit links proposed in the literature. Moreover, under skew-probit link, conditions for the existence of the ML estimators and the posterior distribution under improper priors are established. The framework proposed here considers two sets of latent variables which are helpful to implement the Bayesian MCMC approach. A simulation study to criteria for models comparison is conducted and two applications are made. Using different Bayesian criteria we show that, for these data sets, the skew-probit links are better than alternative links proposed in the literature.

Bayesian Estimation of the Logistic Positive Exponent IRT Model

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered.

Transition in Yield and Azimuthal Shape Modification in Dihadron Correlations in Relativistic Heavy Ion Collisions

Hard-scattered parton probes produced in collisions of large nuclei indicate large partonic energy loss, possibly with collective produced-medium response to the lost energy. We present measurements of pi(0) trigger particles at transverse momenta p(T)(t) = 4-12 GeV/c and associated charged hadrons (p(T)(a) = 0.5-7 GeV/c) vs relative azimuthal angle Delta phi in Au + Au and p + p collisions at root s(NN) = 200 GeV. The Au + Au distribution at low p(T)(a), whose shape has been interpreted as a medium effect, is modified for p(T)(t) < 7 GeV/c. At higher p(T)(t), the data are consistent with unmodified or very weakly modified shapes, even for the lowest measured p(T)(a), which quantitatively challenges some medium response models. The associated yield of hadrons opposing the trigger particle in Au + Au relative to p + p (I(AA)) is suppressed at high p(T) (I(AA) approximate to 0.35-0.5), but less than for inclusive suppression (R(AA) approximate to 0.2).

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.

Optimization of the Enzymatic Interesterification of Milk Fat and Canola Oil Blends Using Immobilized Rhizopus oryzae Lipase by Response Surface Methodology

Blends of milk fat and canola oil (MF:CNO) were enzymatically interesterified (EIE) by Rhizopus oryzne lipase immobilized on polysiloxane-polyvinyl alcohol (SiO(2)-PVA) composite, in a solvent-free system. A central composite design (CCD) was used to optimize the reaction, considering the effects of different mass fractions of binary blends of MF:CNO (50:50, 65:35 and 80:20) and temperatures (45, 55 and 65 degrees C) on the composition and texture properties of the interesterified products, taking the interesterification degree (ID) and consistency (at 10 degrees C) as response variables. For the ID variable both mass fraction of milk fat in the blend and temperature were found to be significant, while for the consistency only mass fraction of milk fat was significant. Empiric models for ID and consistency were obtained that allowed establishing the best interesterification conditions: blend with 65 % of milk fat and 35 %, of canola oil, and temperature of 45 degrees C. Under these conditions, the ID was 19.77 %) and the consistency at 10 degrees C was 56 290 Pa. The potential of this eco-friendly process demonstrated that a product could be obtained with the desirable milk fat flavour and better spreadability under refrigerated conditions.

The young, tight, and low-mass binary TWA22AB: a new calibrator for evolutionary models? Orbit, spectral types, and temperature

Context. Tight binaries discovered in young, nearby associations are ideal targets for providing dynamical mass measurements to test the physics of evolutionary models at young ages and very low masses. Aims. We report the binarity of TWA22 for the first time. We aim at monitoring the orbit of this young and tight system to determine its total dynamical mass using an accurate distance determination. We also intend to characterize the physical properties (luminosity, effective temperature, and surface gravity) of each component based on near-infrared photometric and spectroscopic observations. Methods. We used the adaptive-optics assisted imager NACO to resolve the components, to monitor the complete orbit and to obtain the relative near-infrared photometry of TWA22 AB. The adaptive-optics assisted integral field spectrometer SINFONI was also used to obtain medium-resolution (R(lambda) = 1500-2000) spectra in JHK bands. Comparison with empirical and synthetic librairies were necessary for deriving the spectral type, the effective temperature, and the surface gravity for each component of the system. Results. Based on an accurate trigonometric distance (17.5 +/- 0.2 pc) determination, we infer a total dynamical mass of 220 +/- 21 M(Jup) for the system. From the complete set of spectra, we find an effective temperature T(eff) = 2900(-200)(+200) K for TWA22A and T(eff) = 2900(-100)(+200) for TWA22 B and surface gravities between 4.0 and 5.5 dex. From our photometry and an M6 +/- 1 spectral type for both components, we find luminosities of log(L/L(circle dot)) = -2.11 +/- 0.13 dex and log(L/L(circle dot)) = -2.30 +/- 0.16 dex for TWA22 A and B, respectively. By comparing these parameters with evolutionary models, we question the age and the multiplicity of this system. We also discuss a possible underestimation of the mass predicted by evolutionary models for young stars close to the substellar boundary.

Dose-response effects of systemic anandamide administration in mice sequentially submitted to the open field and elevated plus-maze tests

The endocannabinoid system is involved in the control of many physiological functions, including the control of emotional states. In rodents, previous exposure to an open field increases the anxiety-like behavior in the elevated plus-maze. Anxiolytic-like effects of pharmacological compounds that increase endocannabinoid levels have been well documented. However, these effects are more evident in animals with high anxiety levels. Several studies have described characteristic inverted U-shaped dose-response effects of drugs that modulate the endocannabinoid levels. However, there are no studies showing the effects of different doses of exogenous anandamide, an endocannabinoid, in animal models of anxiety. Thus, in the present study, we determined the dose-response effects of exogenous anandamide at doses of 0.01, 0.1, and 1.0 mg/kg in C57BL/6 mice (N = 10/group) sequentially submitted to the open field and elevated plus-maze. Anandamide was diluted in 0.9% saline, ethyl alcohol, Emulphor® (18:1:1) and administered ip (0.1 mL/10 g body weight); control animals received the same volume of anandamide vehicle. Anandamide at the dose of 0.1 mg/kg (but not of 0.01 or 1 mg/kg) increased (P < 0.05) the time spent and the distance covered in the central zone of the open field, as well as the exploration of the open arms of the elevated plus-maze. Thus, exogenous anandamide, like pharmacological compounds that increase endocannabinoid levels, promoted a characteristic inverted U-shaped dose-response effect in animal models of anxiety. Furthermore, anandamide (0.1 mg/kg) induced an anxiolytic-like effect in the elevated plus-maze (P < 0.05) after exposing the animals to the open field test.

First stars XI. Chemical composition of the extremely metal-poor dwarfs in the binary CS 22876-032

Context. Unevolved metal-poor stars constitute a fossil record of the early Galaxy, and can provide invaluable information on the properties of the first generations of stars. Binary systems also provide direct information on the stellar masses of their member stars. Aims. The purpose of this investigation is a detailed abundance study of the double-lined spectroscopic binary CS 22876-032, which comprises the two most metal-poor dwarfs known. Methods. We used high-resolution, high-S/N ratio spectra from the UVES spectrograph at the ESO VLT telescope. Long-term radial-velocity measurements and broad-band photometry allowed us to determine improved orbital elements and stellar parameters for both components. We used OSMARCS 1D models and the TURBOSPECTRUM spectral synthesis code to determine the abundances of Li, O, Na, Mg, Al, Si, Ca, Sc, Ti, Cr, Mn, Fe, Co and Ni. We also used the (COBOLD)-B-5 model atmosphere code to compute the 3D abundance corrections, notably for Li and O. Results. We find a metallicity of [Fe/H] similar to -3.6 for both stars, using 1D models with 3D corrections of similar to -0.1 dex from averaged 3D models. We determine the oxygen abundance from the near-UV OH bands; the 3D corrections are large, -1 and -1.5 dex for the secondary and primary respectively, and yield [O/Fe] similar to 0.8, close to the high-quality results obtained from the [OI] 630 nm line in metal-poor giants. Other [alpha/Fe] ratios are consistent with those measured in other dwarfs and giants with similar [Fe/H], although Ca and Si are somewhat low ([X/Fe] less than or similar to 0). Other element ratios follow those of other halo stars. The Li abundance of the primary star is consistent with the Spite plateau, but the secondary shows a lower abundance; 3D corrections are small. Conclusions. The Li abundance in the primary star supports the extension of the Spite Plateau value at the lowest metallicities, without any decrease. The low abundance in the secondary star could be explained by endogenic Li depletion, due to its cooler temperature. If this is not the case, another, yet unknown mechanism may be causing increased scatter in A( Li) at the lowest metallicities.

Respiratory allergy to Blomia tropicalis: Immune response in four syngeneic mouse strains and assessment of a low allergen-dose, short-term experimental model

Background: The dust mite Blomia tropicalis is an important source of aeroallergens in tropical areas. Although a mouse model for B. tropicalis extract (BtE)-induced asthma has been described, no study comparing different mouse strains in this asthma model has been reported. The relevance and reproducibility of experimental animal models of allergy depends on the genetic background of the animal, the molecular composition of the allergen and the experimental protocol. Objectives: This work had two objectives. The first was to study the anti-B. tropicalis allergic responses in different mouse strains using a short-term model of respiratory allergy to BtE. This study included the comparison of the allergic responses elicited by BtE with those elicited by ovalbumin in mice of the strain that responded better to BtE sensitization. The second objective was to investigate whether the best responder mouse strain could be used in an experimental model of allergy employing relatively low BtE doses. Methods: Groups of mice of four different syngeneic strains were sensitized subcutaneously with 100 mu g of BtE on days 0 and 7 and challenged four times intranasally, at days 8, 10, 12, and 14, with 10 mu g of BtE. A/J mice, that were the best responders to BtE sensitization, were used to compare the B. tropicalis-specific asthma experimental model with the conventional experimental model of ovalbumin (OVA)-specific asthma. A/J mice were also sensitized with a lower dose of BtE. Results: Mice of all strains had lung inflammatory-cell infiltration and increased levels of anti-BtE IgE antibodies, but these responses were significantly more intense in A/J mice than in CBA/J, BALB/c or C57BL/6J mice. Immunization of A/J mice with BtE induced a more intense airway eosinophil influx, higher levels of total IgE, similar airway hyperreactivity to methacholine but less intense mucous production, and lower levels of specific IgE, IgG1 and IgG2 antibodies than sensitization with OVA. Finally, immunization with a relatively low BtE dose (10 mu g per subcutaneous injection per mouse) was able to sensitize A/J mice, which were the best responders to high-dose BtE immunization, for the development of allergy-associated immune and lung inflammatory responses. Conclusions: The described short-term model of BtE-induced allergic lung disease is reproducible in different syngeneic mouse strains, and mice of the A/J strain was the most responsive to it. In addition, it was shown that OVA and BtE induce quantitatively different immune responses in A/J mice and that the experimental model can be set up with low amounts of BtE.

Hidden vorticity in binary Bose-Einstein condensates

We consider a binary Bose-Einstein condensate (BEC) described by a system of two-dimensional (2D) Gross-Pitaevskii equations with the harmonic-oscillator trapping potential. The intraspecies interactions are attractive, while the interaction between the species may have either sign. The same model applies to the copropagation of bimodal beams in photonic-crystal fibers. We consider a family of trapped hidden-vorticity (HV) modes in the form of bound states of two components with opposite vorticities S(1,2) = +/- 1, the total angular momentum being zero. A challenging problem is the stability of the HV modes. By means of a linear-stability analysis and direct simulations, stability domains are identified in a relevant parameter plane. In direct simulations, stable HV modes feature robustness against large perturbations, while unstable ones split into fragments whose number is identical to the azimuthal index of the fastest growing perturbation eigenmode. Conditions allowing for the creation of the HV modes in the experiment are discussed too. For comparison, a similar but simpler problem is studied in an analytical form, viz., the modulational instability of an HV state in a one-dimensional (1D) system with periodic boundary conditions (this system models a counterflow in a binary BEC mixture loaded into a toroidal trap or a bimodal optical beam coupled into a cylindrical shell). We demonstrate that the stabilization of the 1D HV modes is impossible, which stresses the significance of the stabilization of the HV modes in the 2D setting.

Approximate algorithms for credal networks with binary variables

This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief functions and possibilistic measures. All algorithms for approximate inference in this paper rely on exact inferences in credal networks based on polytrees with binary variables, as these inferences have polynomial complexity. We are inspired by approximate algorithms for Bayesian networks; thus the Loopy 2U algorithm resembles Loopy Belief Propagation, while the Iterated Partial Evaluation and Structured Variational 2U algorithms are, respectively, based on Localized Partial Evaluation and variational techniques. (C) 2007 Elsevier Inc. All rights reserved.

Physical Stability Assessment and Sensory Optimization of a Dairy-Free Emulsion Using Response Surface Methodology

Desserts made with soy cream, which are oil-in-water emulsions, are widely consumed by lactose-intolerant individuals in Brazil. In this regard, this study aimed at using response surface methodology (RSM) to optimize the sensory attributes of a soy-based emulsion over a range of pink guava juice (GJ: 22% to 32%) and soy protein (SP: 1% to 3%). WHC and backscattering were analyzed after 72 h of storage at 7 degrees C. Furthermore, a rating test was performed to determine the degree of liking of color, taste, creaminess, appearance, and overall acceptability. The data showed that the samples were stable against gravity and storage. The models developed by RSM adequately described the creaminess, taste, and appearance of the emulsions. The response surface of the desirability function was used successfully in the optimization of the sensory properties of dairy-free emulsions, suggesting that a product with 30.35% GJ and 3% SP was the best combination of these components. The optimized sample presented suitable sensory properties, in addition to being a source of dietary fiber, iron, copper, and ascorbic acid.

Processing optimization of probiotic yogurt containing glucose oxidase using response surface methodology

Exposure to oxygen may induce a lack of functionality of probiotic dairy foods because the anaerobic metabolism of probiotic bacteria compromises during storage the maintenance of their viability to provide benefits to consumer health. Glucose oxidase can constitute a potential alternative to increase the survival of probiotic bacteria in yogurt because it consumes the oxygen permeating to the inside of the pot during storage, thus making it possible to avoid the use of chemical additives. This research aimed to optimize the processing of probiotic yogurt supplemented with glucose oxidase using response surface methodology and to determine the levels of glucose and glucose oxidase that minimize the concentration of dissolved oxygen and maximize the Bifidobacterium longum count by the desirability function. Response surface methodology mathematical models adequately described the process, with adjusted determination coefficients of 83% for the oxygen and 94% for the B. longum. Linear and quadratic effects of the glucose oxidase were reported for the oxygen model, whereas for the B. longum count model an influence of the glucose oxidase at the linear level was observed followed by the quadratic influence of glucose and quadratic effect of glucose oxidase. The desirability function indicated that 62.32 ppm of glucose oxidase and 4.35 ppm of glucose was the best combination of these components for optimization of probiotic yogurt processing. An additional validation experiment was performed and results showed acceptable error between the predicted and experimental results.

Synergism on antioxidant activity between natural compounds optimized by response surface methodology

Despite the increase in the use of natural compounds in place of synthetic derivatives as antioxidants in food products, the extent of this substitution is limited by cost constraints. Thus, the objective of this study was to explore the synergism on the antioxidant activity of natural compounds, for further application in food products. Three hydrosoluble compounds (x(1) = caffeic acid, x(2) = carnosic acid, and x(3) = glutathione) and three liposoluble compounds (x(1) = quercetin, x(2) = rutin, and x(3) = genistein) were mixed according to a ""centroid simplex design"". The antioxidant activity of the mixtures was analyzed by the ferric reducing antioxidant power (FRAP) and oxygen radical absorbance capacity (ORAL) methodologies, and activity was also evaluated in an oxidized mixed micelle prepared with linoleic acid (LAOX). Cubic polynomial models with predictive capacity were obtained when the mixtures were submitted to the LAOX methodology ((y) over cap = 0.56 x(1) + 0.59 x(2) + 0.04 x(3) + 0.41 x(1)x(2) - 0.41 x(1)x(3) - 1.12 x(2)x(3) - 4.01 x(1)x(2)x(3)) for the hydrosoluble compounds, and to FRAP methodology ((y) over cap = 3.26 x(1) + 2.39 x(2) + 0.04 x(3) + 1.51 x(1)x(2) + 1.03 x(1)x(3) + 0.29 x(1)x(3) + 3.20 x(1)x(2)x(3)) for the liposoluble compounds. Optimization of the models suggested that a mixture containing 47% caffeic acid + 53% carnosic acid and a mixture containing 67% quercetin + 33% rutin were potential synergistic combinations for further evaluation using a food matrix.