Pricing farm-level agricultural insurance: a Bayesian approach

This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.

Bayesian ratemaking procedure of crop insurance contracts with skewed distribution

Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

Shared structural determinants for the calcium-independent liposome membrane permeabilization and sarcolemma depolarization in Bothropstoxin-I, a LYS49-PLA(2) from the venom of Bothrops jararacussu

The structural determinants of myotoxicity of bothropstoxin-I (BthTX-I), a Lys49 phospholipase A(2) from Bothrops jararacussu venom, were studied by measuring the resting membrane potential in the mouse phrenic nerve-diaphragm preparation. This method proved to be around 100-fold more sensitive than the creatine kinase release assay, and was used to evaluate a total of 31 site-directed BthTX-I alanine scanning mutants. Mutants that reduced the resting membrane potential were located in a surface patch defined by residues in the C-terminal loop (residues 115-129), positions 37-39 in the membrane interfacial recognition surface (Y46 and K54), and residue K93. These results expand the known structural determinants of the biological activity as evaluated by previous creatine kinase release experiments. Furthermore, a strong correlation is observed between the structural determinants of sarcolemma depolarization and calcium-independent disruption of liposome membranes, suggesting that a common mechanism of action underlies the permeabilization of the biological and model membranes. (C) 2009 Elsevier Ltd. All rights reserved.

HUMAN CHOLESTERYL ESTER TRANSFER PROTEIN EXPRESSION ENHANCES THE MOUSE SURVIVAL RATE IN AN EXPERIMENTAL SYSTEMIC INFLAMMATION MODEL: A NOVEL ROLE FOR CETP

Mice expressing human cholesteryl ester transfer protein (huCETP) are more resistant to Escherichia coli bacterial wall LIPS because death rates 5 days after intraperitoneal inoculation of LIPS were higher in wild-type than in huCETP(+/-) mice, whereas all huCETP(+/+) mice remained alive. After LIPS inoculation, plasma concentrations of TNF-alpha and IL-6 increased less in huCETP(+/+) than in wild-type mice. LPS in vitro elicited lower TNF-alpha production by CETP expressing than by wild-type macrophages. In addition, TNF-alpha production by RAW 264.7 murine macrophages increased on incubation with LPS but decreased in a dose-dependent manner when human CETP was added to the medium. Human CETP in vitro enhanced the LIPS binding to plasma high-density lipoprotein/low-density lipoprotein. The liver uptake of intravenous infused C-14-LPS from Salmonella typhimurium was greater in huCETP(+/+) than in wild-type mice. Present data indicate for the first time that CETP is an endogenous component involved in the first line of defense against an exacerbated production of proinflammatory mediators.

A Dynamic Model of Hand-and-Foot Syndrome in Patients Receiving Capecitabine

For the purpose of developing a longitudinal model to predict hand-and-foot syndrome (HFS) dynamics in patients receiving capecitabine, data from two large phase III studies were used. Of 595 patients in the capecitabine arms, 400 patients were randomly selected to build the model, and the other 195 were assigned for model validation. A score for risk of developing HFS was modeled using the proportional odds model, a sigmoidal maximum effect model driven by capecitabine accumulation as estimated through a kinetic-pharmacodynamic model and a Markov process. The lower the calculated creatinine clearance value at inclusion, the higher was the risk of HFS. Model validation was performed by visual and statistical predictive checks. The predictive dynamic model of HFS in patients receiving capecitabine allows the prediction of toxicity risk based on cumulative capecitabine dose and previous HFS grade. This dose-toxicity model will be useful in developing Bayesian individual treatment adaptations and may be of use in the clinic.

A Bayesian approach to fuzzy hypotheses testing for the estimation of optimal age for vaccination against measles

Fuzzy Bayesian tests were performed to evaluate whether the mother`s seroprevalence and children`s seroconversion to measles vaccine could be considered as ""high"" or ""low"". The results of the tests were aggregated into a fuzzy rule-based model structure, which would allow an expert to influence the model results. The linguistic model was developed considering four input variables. As the model output, we obtain the recommended age-specific vaccine coverage. The inputs of the fuzzy rules are fuzzy sets and the outputs are constant functions, performing the simplest Takagi-Sugeno-Kang model. This fuzzy approach is compared to a classical one, where the classical Bayes test was performed. Although the fuzzy and classical performances were similar, the fuzzy approach was more detailed and revealed important differences. In addition to taking into account subjective information in the form of fuzzy hypotheses it can be intuitively grasped by the decision maker. Finally, we show that the Bayesian test of fuzzy hypotheses is an interesting approach from the theoretical point of view, in the sense that it combines two complementary areas of investigation, normally seen as competitive. (C) 2007 IMACS. Published by Elsevier B.V. All rights reserved.

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

Genetic Analysis of Age-at-Onset for Cardiovascular Risk Factors in a Brazilian Family Study

Background/Aims: Statistical analysis of age-at-onset involving family data is particularly complicated because there is a correlation pattern that needs to be modeled and also because there are measurements that are censored. In this paper, our main purpose was to evaluate the effect of genetic and shared family environmental factors on age-at-onset of three cardiovascular risk factors: hypertension, diabetes and high cholesterol. Methods: The mixed-effects Cox model proposed by Pankratz et al. [2005] was used to analyze the data from 81 families, involving 1,675 individuals from the village of Baependi, in the state of Minas Gerais, Brazil. Results: The analyses performed showed that the polygenic effect plays a greater role than the shared family environmental effect in explaining the variability of the age-at-onset of hypertension, diabetes and high cholesterol. The model which simultaneously evaluated both effects indicated that there are individuals which may have risk of hypertension due to polygenic effects 130% higher than the overall average risk for the entire sample. For diabetes and high cholesterol the risks of some individuals were 115 and 45%, respectively, higher than the overall average risk for the entire population. Conclusions: Results showed evidence of significant polygenic effects indicating that age-at-onset is a useful trait for gene mapping of the common complex diseases analyzed. In addition, we found that the polygenic random component might absorb the effects of some covariates usually considered in the risk evaluation, such as gender, age and BMI. Copyright (C) 2008 S. Karger AG, Basel

Role of cytokines in mediating mechanical hypernociception in a model of delayed-type hypersensitivity in mice

In the present study, we used the electronic version of the von Frey test to investigate the role of cytokines (TNF-alpha and IL-1 beta) and chemokines (KC/CXCL-1) in the genesis of mechanical hypernociception during antigen-induced inflammation in mice. The nociceptive test consisted of evoking a hindpaw flexion reflex with a hand-held force transducer (electronic anesthesiometer) adapted with a 0.5 mm(2) polypropylene tip. The intraplantar administration of methylated bovine serum albumin (mBSA) in previously immunized (IM), but not in sham-immunized (SI) mice, induced mechanical hypernociception in a dose-dependant manner. Hypernociception induced by antigen was reduced in animals pretreated with IL-lra and reparixin (a non-competitive allosteric inhibitor of CXCR2), and in TNF receptor type 1 deficient (TNFR1-/-) mice. Consistently, antigen challenge induced a time-dependent release of TNF-alpha, IL-1 beta and KC/CXCL-1 in IM, but not in SI, mice. Consistently, antigen challenge induced a time-dependent release of TNF-alpha, IL-1 beta and KC/CXCL-1 in IM, but not in SI, mice. The increase in TNF-alpha levels preceded the increase in IL-1 beta and KC/CXCL1. Antigen-induced release of IL-1 beta and KC/CXCL1 was reduced in TNFR1-/- mice, and TNF-alpha induced hypernociception was inhibited by IL-lra and reparixin. Hypernociception induced by IL-1 beta in immunized mice was inhibited by indomethacin, whereas KC/CXCL1-induced hypernociception was inhibited by indomethacin and guanethidine, Antigen-induced hypernociception was reduced by indomethacin and guanethidine and abolished by the two drugs combined. Together, these results suggest that inflammation associated with an adaptive immune response induces hypernociception that is mediated by an initial release of TNF-alpha, which triggers that subsequent release of IL-1 beta and KC/CXCL1. The latter cytokines in turn stimulate the release of the direct-acting final mediator, prostanoids and sympathetic amines. (C) 2008 European Federation of Chapters of the International Association for the Study of Pain. Published by Elsevier Ltd. All rights reserved.

Detection of Auditory Cortex Activity by fMRI Using a Dependent Component Analysis

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.

Bayesian analysis and constraints on kinematic models from union SNIa

The kinematic expansion history of the universe is investigated by using the 307 supernovae type Ia from the Union Compilation set. Three simple model parameterizations for the deceleration parameter ( constant, linear and abrupt transition) and two different models that are explicitly parametrized by the cosmic jerk parameter ( constant and variable) are considered. Likelihood and Bayesian analyses are employed to find best fit parameters and compare models among themselves and with the flat Lambda CDM model. Analytical expressions and estimates for the deceleration and cosmic jerk parameters today (q(0) and j(0)) and for the transition redshift (z(t)) between a past phase of cosmic deceleration to a current phase of acceleration are given. All models characterize an accelerated expansion for the universe today and largely indicate that it was decelerating in the past, having a transition redshift around 0.5. The cosmic jerk is not strongly constrained by the present supernovae data. For the most realistic kinematic models the 1 sigma confidence limits imply the following ranges of values: q(0) is an element of [-0.96, -0.46], j(0) is an element of [-3.2,-0.3] and z(t) is an element of [0.36, 0.84], which are compatible with the Lambda CDM predictions, q(0) = -0.57 +/- 0.04, j(0) = -1 and z(t) = 0.71 +/- 0.08. We find that even very simple kinematic models are equally good to describe the data compared to the concordance Lambda CDM model, and that the current observations are not powerful enough to discriminate among all of them.

Effects of exercise training on autonomic dysfunction management in an experimental model of menopause and myocardial infarction

Objective: The aim of this study was to investigate the effects of exercise training on cardiovascular autonomic dysfunction in ovariectomized rats submitted to myocardial infarction. Methods: Female Wistar rats were divided into the following ovariectomized groups: sedentary ovariectomized (SO), trained ovariectomized (TO), sedentary ovariectomized infarcted (SOI), and trained ovariectomized infarcted (TOI). Trained groups were submitted to an exercise training protocol on a treadmill (8 wk). Arterial baroreflex sensitivity was evaluated by heart rate responses to arterial pressure changes, and cardiopulmonary baroreflex sensitivity was tested by bradycardic and hypotension responses to serotonin injection. Vagal and sympathetic effects were calculated by pharmacological blockade. Results: Arterial pressure was reduced in the TO in comparison with the SO group and increased in the TOI in relation to the SOI group. Exercise training improved the baroreflex sensitivity in both the TO and TOI groups. The TOI group displayed improvement in cardiopulmonary reflex sensitivity compared with the SOI group at the 16 mu g/kg serotonin dose. Exercise training enhanced the vagal effect in both the TO (45%) and TOI (46%) animals compared with the SO and SOI animals and reduced the sympathetic effect in the TOI (38%) in comparison with the SOI animals. Significant correlations were obtained between bradycardic baroreflex responses and vagal (r = -0.7, P < 0.005) and sympathetic (r = 0.7, P < 0.001) effects. Conclusions: These results indicate that exercise training in ovariectomized rats submitted to myocardial infarction improves resting hemodynamic status and reflex control of the circulation, which may be due to an increase in the vagal component. This suggests a homeostatic role for exercise training in reducing the autonomic impairment of myocardial infarction in postmenopausal women.

Regression Models with Heteroscedasticity using Bayesian Approach

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.

Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They Should be able to recognize human beings and each other, and to engage in social, interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction. behavior control and learning from environment. Learning processes described oil Science of Behavior Analysis may lead to the development of promising methods and Structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation. are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction. (C) 2009 Elsevier Inc. All rights reserved.