Estimating wave spectra from the motions of moored vessels: Experimental validation

The practicability of estimating directional wave spectra based on a vessel`s 1st order response has been recently addressed by several researchers. Different alternatives regarding statistical inference methods and possible drawbacks that could arise from their application have been extensively discussed, with an apparent preference for estimations based on Bayesian inference algorithms. Most of the results on this matter, however, rely exclusively on numerical simulations or at best on few and sparse full-scale measurements, comprising a questionable basis for validation purposes. This paper discusses several issues that have recently been debated regarding the advantages of Bayesian inference and different alternatives for its implementation. Among those are the definition of the best set of input motions, the number of parameters required for guaranteeing smoothness of the spectrum in frequency and direction and how to determine their optimum values. These subjects are addressed in the light of an extensive experimental campaign performed with a small-scale model of an FPSO platform (VLCC hull), which was conducted in an ocean basin in Brazil. Tests involved long and short crested seas with variable levels of directional spreading and also bimodal conditions. The calibration spectra measured in the tank by means of an array of wave probes configured the paradigm for estimations. Results showed that a wide range of sea conditions could be estimated with good precision, even those with somewhat low peak periods. Some possible drawbacks that have been pointed out in previous works concerning the viability of employing large vessels for such a task are then refuted. Also, it is shown that a second parameter for smoothing the spectrum in frequency may indeed increase the accuracy in some situations, although the criterion usually proposed for estimating the optimum values (ABIC) demands large computational effort and does not seem adequate for practical on-board systems, which require expeditious estimations. (C) 2009 Elsevier Ltd. All rights reserved.

Phylogeography of Chelonus insularis (Hymenoptera: Braconidae) and Campoletis sonorensis (Hymenoptera: Ichneumonidae), Two Primary Neotropical Parasitoids of the Fall Armyworm (Lepidoptera: Noctuidae)

In a previous study, we observed no spatial genetic structure in Mexican populations of the parasitoids Chelonus insularis Cresson (Hymenoptera: Braconidae) and Campoletis sonorensis Cameron (Hymenoptera: Ichneumonidae) by using microsatellite markers In the current study, we Investigated whether for these important parasitoids of the fall armyworm (Lepidoptera: Noctuidae) there is any genetic structure at a larger scale Insects of both species were collected across the American continent and their phylogeography was Investigated using both nuclear and mitochondria] markers Our results suggest an ancient north-south migration of C insularis, whereas no clear pattern] could be determined for C sonorensis. Nonetheless, the resulting topology indicated the existence of a cryptic taxon within this later species. a few Canadian specimens determined as C. sonorensis branch outside a clack composed of the Argentinean Chelonus grioti Blanchard, the Brazilian Chelonus flavicincta Ashmead, and the rest of the C sonorensis individuals The individuals revealing the cryptic taxon were collected from Thichoplusia in (Hubner) (Lepidoptera. Noctuidae) on tomato (Lycopersicon spp) and may represent a biotype that has adapted to the early season phenology of its host. Overall, the loosely defined spatial genetic structure previously shown at a local fine scale also was found at the larger scale, for both species Dispersal of these insects may be partly driven by wind as suggested by genetic similarities between Individuals coming from very distant locations.

The impact of El Nino Southern Oscillation phases on off-season maize yield for a subtropical region of Brazil

In recent years, maize has become one of the main alternative crops for the autumn winter growing season in the central-western and southeastern regions of Brazil. However, water deficits, sub-optimal temperatures and low solar radiation levels are common problems that are experienced during this growing season by local farmers. One methodology to assess the impact of variable weather conditions on crop production is the use of crop simulation models. The goal of this study was to evaluate the effect of climate variability on maize yield for a subtropical region of Brazil. Specific objectives for this study were (1) to analyse the effect of El Nino Southern Oscillation (ENSO) on precipitation and air temperature for four locations in the state of Sao Paulo and (2) to analyse the impact of ENSO on maize grown off-season for the same four locations using a crop simulation model. For each site, historical weather data were categorised as belonging to one of three phases of ENSO: El Nino (warm sea surface temperature anomalies in the Pacific), La Nina (cool sea surface temperature anomalies) or neutral, based on an index derived from observed sea surface temperature anomalies. During El Nino, there is a tendency for an increase in the rainfall amount during May for the four selected locations, and also during April, mainly in three of the locations, resulting in an increase in simulated maize yield planted between February 15 and March 15. In general, there was a decrease in the simulated yield for maize grown off-season during neutral years. This study showed how a crop model can be used to assess the impact of climate variability on the yield of maize grown off-season in a subtropical region of Brazil. The outcomes of this study can be very useful for both policy makers and local farmers for agricultural planning and decision making. Copyright (C) 2009 Royal Meteorological Society

Dynamics of Hepatitis D (delta) virus genotype 3 in the Amazon region of South America

Hepatitis delta virus (HDV) is widely distributed and associated with fulminant hepatitis epidemics in areas with high prevalence of HBV. Several studies performed in the 1980s showed data on HDV infection in South America, but there are no studies on the viral dynamics of this virus. The aim of this study was to conduct an evolutionary analysis of hepatitis delta genotype 3 (HDV/3) prevalent in South America: estimate its nucleotide substitution rate, determine the time of most recent ancestor (TMRCA) and characterize the epidemic history and evolutionary dynamics. Furthermore, we characterized the presence of HBV/HDV infection in seven samples collected from patients who died due to fulminant hepatitis from Amazon region in Colombia and included them in the evolutionary analysis. This is the first study reporting HBV and HDV sequences from the Amazon region of Colombia. Of the seven Colombian patients, five were positive for HBV-DNA and HDV-RNA. Of them, two samples were successfully sequenced for HBV (subgenotypes F3 and Fib) and the five samples HDV positive were classified as HDV/3. By using all HDV/3 available reference sequences with sampling dates (n = 36), we estimated the HDV/3 substitution rate in 1.07 x 10(-3) substitutions per site per year (s/s/y), which resulted in a time to the most recent common ancestor (TMRCA) of 85 years. Also, it was determined that HDV/3 spread exponentially from early 1950s to the 1970s in South America. This work discusses for the first time the viral dynamics for the HDV/3 circulating in South America. We suggest that the measures implemented to control HBV transmission resulted in the control of HDV/3 spreading in South America, especially after the important raise in this infection associated with a huge mortality during the 1950s up to the 1970s. The differences found among HDV/3 and the other HDV genotypes concerning its diversity raises the hypothesis of a different origin and/or a different transmission route. (C) 2011 Elsevier B.V. All rights reserved.

Using non-homogeneous Poisson models with multiple change-points to estimate the number of ozone exceedances in Mexico City

In this paper, we consider some non-homogeneous Poisson models to estimate the probability that an air quality standard is exceeded a given number of times in a time interval of interest. We assume that the number of exceedances occurs according to a non-homogeneous Poisson process (NHPP). This Poisson process has rate function lambda(t), t >= 0, which depends on some parameters that must be estimated. We take into account two cases of rate functions: the Weibull and the Goel-Okumoto. We consider models with and without change-points. When the presence of change-points is assumed, we may have the presence of either one, two or three change-points, depending of the data set. The parameters of the rate functions are estimated using a Gibbs sampling algorithm. Results are applied to ozone data provided by the Mexico City monitoring network. In a first instance, we assume that there are no change-points present. Depending on the adjustment of the model, we assume the presence of either one, two or three change-points. Copyright (C) 2009 John Wiley & Sons, Ltd.

Estimating the number of ozone peaks in Mexico City using a non-homogeneous Poisson model

In this paper, we consider the problem of estimating the number of times an air quality standard is exceeded in a given period of time. A non-homogeneous Poisson model is proposed to analyse this issue. The rate at which the Poisson events occur is given by a rate function lambda(t), t >= 0. This rate function also depends on some parameters that need to be estimated. Two forms of lambda(t), t >= 0 are considered. One of them is of the Weibull form and the other is of the exponentiated-Weibull form. The parameters estimation is made using a Bayesian formulation based on the Gibbs sampling algorithm. The assignation of the prior distributions for the parameters is made in two stages. In the first stage, non-informative prior distributions are considered. Using the information provided by the first stage, more informative prior distributions are used in the second one. The theoretical development is applied to data provided by the monitoring network of Mexico City. The rate function that best fit the data varies according to the region of the city and/or threshold that is considered. In some cases the best fit is the Weibull form and in other cases the best option is the exponentiated-Weibull. Copyright (C) 2007 John Wiley & Sons, Ltd.

Methods of Estimation in Multiple Linear Regression: Application to Clinical Data

Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.

Using stochastic volatility models to analyse weekly ozone averages in Mexico City

In this paper we make use of some stochastic volatility models to analyse the behaviour of a weekly ozone average measurements series. The models considered here have been used previously in problems related to financial time series. Two models are considered and their parameters are estimated using a Bayesian approach based on Markov chain Monte Carlo (MCMC) methods. Both models are applied to the data provided by the monitoring network of the Metropolitan Area of Mexico City. The selection of the best model for that specific data set is performed using the Deviance Information Criterion and the Conditional Predictive Ordinate method.

A Bayesian Long-term Survival Model Parametrized in the Cured Fraction

The main goal of this paper is to investigate a cure rate model that comprehends some well-known proposals found in the literature. In our work the number of competing causes of the event of interest follows the negative binomial distribution. The model is conveniently reparametrized through the cured fraction, which is then linked to covariates by means of the logistic link. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis in the proposed model. The procedure is illustrated with a numerical example.

Bayesian estimation of regression parameters in elliptical measurement error models

The main object of this paper is to discuss the Bayes estimation of the regression coefficients in the elliptically distributed simple regression model with measurement errors. The posterior distribution for the line parameters is obtained in a closed form, considering the following: the ratio of the error variances is known, informative prior distribution for the error variance, and non-informative prior distributions for the regression coefficients and for the incidental parameters. We proved that the posterior distribution of the regression coefficients has at most two real modes. Situations with a single mode are more likely than those with two modes, especially in large samples. The precision of the modal estimators is studied by deriving the Hessian matrix, which although complicated can be computed numerically. The posterior mean is estimated by using the Gibbs sampling algorithm and approximations by normal distributions. The results are applied to a real data set and connections with results in the literature are reported. (C) 2011 Elsevier B.V. All rights reserved.

Bayesian Analysis for the Generalized Lognormal Distribution Applied to Failure Time Analysis

There are several versions of the lognormal distribution in the statistical literature, one is based in the exponential transformation of generalized normal distribution (GN). This paper presents the Bayesian analysis for the generalized lognormal distribution (logGN) considering independent non-informative Jeffreys distributions for the parameters as well as the procedure for implementing the Gibbs sampler to obtain the posterior distributions of parameters. The results are used to analyze failure time models with right-censored and uncensored data. The proposed method is illustrated using actual failure time data of computers.

First cases of exclusive paternal care in stink bugs (Hemiptera: Pentatomidae)

We describe paternal care in two pentatomid bugs, Lopadusa (Lopadusa) augur Stål, 1860 and Edessa nigropunctata Berg, 1884. Field and laboratory observations showed that males remain with their eggs and early hatched nymphs, while females abandon the eggs after oviposition. Guarding males defensive behaviors towards their clutches were similar to those described for guarding females of pentatomids. Since there is no detailed information on the internal phylogeny of Pentatomidae, it is not possible to make a robust inference on whether paternal care in L. augur and E. nigropunctata has arisen independently or not. If the latter, the two new cases of paternal care we describe here represent the fifth event of independent evolution of this rare behavioral trait in Heteroptera.

Wet deposition of major ions in a rural area impacted by biomass burning emissions

This work concerns the influence of industrialized agriculture in the tropics on precipitation chemistry. A total of 264 rain events were sampled using a wet-only collector in central Sao Paulo State, Brazil, between January 2003 and July 2007. Electroneutrality balance calculations (considering H(+), K(+), Na(+), NH(4)(+), Ca(2)(+), Mg(2)(+), Cl(-), NO(3)(-), SO(4)(2-), F(-), PO(4)(3-), H(3)CCOO(-), HCOO(-), C(2)O(4)(2-) and HCO(3)(-)) showed that there was an excess of cations (similar to 15%), which was attributed to the presence of unmeasured organic anion species originating from biomass burning and biogenic emissions. On average, the three ions NH(4)(+), NO(3)(-) and H(+) were responsible for >55% of the total ion concentrations in the rainwater samples. Concentrations (except of H(+)) were significantly higher (t-test; P = 0.05), by between two to six-fold depending on species, during the winter sugar cane harvest period, due to the practice of pre-harvest burning of the crop. Principal component analysis showed that three components could explain 88% of the variance for measurements made throughout the year: PC1 (52%, biomass burning and soil dust resuspension); PC2 (26%, secondary aerosols); PC3 (10%, road transport emissions). Differences between harvest and non-harvest periods appeared to be mainly due to an increased relative importance of road transport/industrial emissions during the summer (non-harvest) period. The volume-weighted mean (VWM) concentrations of ammonium (23.4 mu mol L(-1)) and nitrate (17.5 mu mol L(-1)) in rainwater samples collected during the harvest period were similar to those found in rainwater from Sao Paulo city, which emphasizes the importance of including rural agro-industrial emissions in regional-scale atmospheric chemistry and transport models. Since there was evidence of a biomass burning source throughout the year, it appears that rainwater composition will continue to be affected by vegetation fires, even after sugar cane burning is phased out as envisaged by recent Sao Paulo State legislation. (C) 2011 Elsevier Ltd. All rights reserved.

Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models

We discuss the expectation propagation (EP) algorithm for approximate Bayesian inference using a factorizing posterior approximation. For neural network models, we use a central limit theorem argument to make EP tractable when the number of parameters is large. For two types of models, we show that EP can achieve optimal generalization performance when data are drawn from a simple distribution.

Seasonal patterns of viral and bacterial infections among children hospitalized with community-acquired pneumonia in a tropical region

Community-acquired pneumonia (CAP) is a common cause of morbidity among children. Evidence on seasonality, especially on the frequency of viral and bacterial causative agents is scarce; such information may be useful in an era of changing climate conditions worldwide. To analyze the frequency of distinct infections, meteorological indicators and seasons in children hospitalized for CAP in Salvador, Brazil, nasopharyngeal aspirate and blood were collected from 184 patients aged < 5 y over a 21-month period. Fourteen microbes were investigated and 144 (78%) cases had the aetiology established. Significant differences were found in air temperature between spring and summer (p = 0.02) or winter (p < 0.001), summer and fall (p = 0.007) or winter (p < 0.001), fall and winter (p = 0.002), and on precipitation between spring and fall (p = 0.01). Correlations were found between: overall viral infections and relative humidity (p = 0.006; r = 0.6) or precipitation (p = 0.03; r = 0.5), parainfluenza and precipitation (p = 0.02; r = -0.5), respiratory syncytial virus (RSV) and air temperature (p = 0.048; r = -0.4) or precipitation (p = 0.045; r = 0.4), adenovirus and precipitation (p = 0.02; r = 0.5), pneumococcus and air temperature (p = 0.04; r = -0.4), and Chlamydia trachomatis and relative humidity (p = 0.02; r = -0.5). The frequency of parainfluenza infection was highest during spring (32.1%; p = 0.005) and that of RSV infection was highest in the fall (36.4%; p < 0.001). Correlations at regular strength were found between several microbes and meteorological indicators. Parainfluenza and RSV presented marked seasonal patterns.