Allergic asthma exhaled breath metabolome: a challenge for comprehensive two-dimensional gas chromatography

Allergic asthma represents an important public health issue, most common in the paediatric population, characterized by airway inflammation that may lead to changes in volatiles secreted via the lungs. Thus, exhaled breath has potential to be a matrix with relevant metabolomic information to characterize this disease. Progress in biochemistry, health sciences and related areas depends on instrumental advances, and a high throughput and sensitive equipment such as comprehensive two-dimensional gas chromatography–time of flight mass spectrometry (GC × GC–ToFMS) was considered. GC × GC–ToFMS application in the analysis of the exhaled breath of 32 children with allergic asthma, from which 10 had also allergic rhinitis, and 27 control children allowed the identification of several hundreds of compounds belonging to different chemical families. Multivariate analysis, using Partial Least Squares-Discriminant Analysis in tandem with Monte Carlo Cross Validation was performed to assess the predictive power and to help the interpretation of recovered compounds possibly linked to oxidative stress, inflammation processes or other cellular processes that may characterize asthma. The results suggest that the model is robust, considering the high classification rate, sensitivity, and specificity. A pattern of six compounds belonging to the alkanes characterized the asthmatic population: nonane, 2,2,4,6,6-pentamethylheptane, decane, 3,6-dimethyldecane, dodecane, and tetradecane. To explore future clinical applications, and considering the future role of molecular-based methodologies, a compound set was established to rapid access of information from exhaled breath, reducing the time of data processing, and thus, becoming more expedite method for the clinical purposes.

Genetic and environmental heterogeneity of residual variance of weight traits in Nellore beef cattle

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

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUÇÃO: A malaria é uma doença endêmica na região da Amazônia Brasileira, e a detecção de possíveis fatores de risco pode ser de grande interesse às autoridades em saúde pública. O objetivo deste artigo é investigar a associação entre variáveis ambientais e os registros anuais de malária na região amazônica usando métodos bayesianos espaço-temporais. MÉTODOS: Utilizaram-se modelos de regressão espaço-temporais de Poisson para analisar os dados anuais de contagem de casos de malária entre os anos de 1999 a 2008, considerando a presença de alguns fatores como a taxa de desflorestamento. em uma abordagem bayesiana, as inferências foram obtidas por métodos Monte Carlo em cadeias de Markov (MCMC) que simularam amostras para a distribuição conjunta a posteriori de interesse. A discriminação de diferentes modelos também foi discutida. RESULTADOS: O modelo aqui proposto sugeriu que a taxa de desflorestamento, o número de habitants por km² e o índice de desenvolvimento humano (IDH) são importantes para a predição de casos de malária. CONCLUSÕES: É possível concluir que o desenvolvimento humano, o crescimento populacional, o desflorestamento e as alterações ecológicas associadas a estes fatores estão associados ao aumento do risco de malária. Pode-se ainda concluir que o uso de modelos de regressão de Poisson que capturam o efeito temporal e espacial em um enfoque bayesiano é uma boa estratégia para modelar dados de contagem de malária.

Social organization of marine Tucuxi dolphins, Sotalia guianensis, in the Cananeia Estuary of southeastern Brazil

Social organization is an important component of the population biology of a species that influences gene flow, the spatial pattern and scale of movements, and the effects of predation or exploitation by humans. An important element of social structure in mammals is group fidelity, which can be quantified through association indices. To describe the social organization of marine tucuxi dolphins (Sotalia guianensis) found in the Cananeia estuary, southeastern Brazil, association indices were applied to photo-identification data to characterize the temporal stability of relationships among members of this population. Eighty-seven days of fieldwork were conducted from May 2000 to July 2003, resulting in direct observations of 374 distinct groups. A total of 138 dolphins were identified on 1-38 distinct field days. Lone dolphins were rarely seen, whereas groups were composed of up to 60 individuals (mean +/- 1 SD = 12.4 +/- 11.4 individuals per group). A total of 29,327 photographs were analyzed, of which 6,312 (21.5%) were considered useful for identifying individuals. Half-weight and simple ratio indices were used to investigate associations among S. guianensis as revealed by the entire data set, data from the core study site, and data from groups composed of <= 10 individuals. Monte Carlo methods indicated that only 3 (9.3%) of 32 association matrices differed significantly from expectations based on random association. Thus, our study suggests that stable associations are not characteristic of S. guianensis in the Cananeia estuary.

Modelos alternativos para detecção de locos de características quantitativas (QTL) de carcaça e crescimento nos cromossomos 4, 5 e 7 de suínos

O conhecimento do genoma pode auxiliar na identificação de regiões cromossômicas e, eventualmente, de genes que controlam características quantitativas (QTLs) de importância econômica. em um experimento com 1.129 suínos resultantes do cruzamento entre machos da raça Meishan e fêmeas Large White e Landrace, foram analisadas as características gordura intramuscular (GIM), em %, e ganho dos 25 aos 90 kg de peso vivo (GP), em g/dia, em 298 animais F1 e 831 F2, e espessura de toucinho (ET), em mm, em 324 F1 e 805 F2. Os animais das gerações F1 e F2 foram tipificados com 29 marcadores microsatélites. Estudou-se a ligação entre os cromossomos 4, 6 e 7 com GIM, ET e GP. Análises de QTL utilizando-se metodologia Bayesiana foram aplicadas mediante três modelos genéticos: modelo poligênico infinitesimal (MPI); modelo poligênico finito (MPF), considerando-se três locos; e MPF combinado com MPI. O número de QTLs, suas respectivas posições nos três cromossomos e o efeito fenotípico foram estimados simultaneamente. Os sumários dos parâmetros estimados foram baseados nas distribuições marginais a posteriori, obtidas por meio do uso da Cadeia de Markov, algoritmos de Monte Carlo (MCMC). Foi possível evidenciar dois QTLs relacionados a GIM nos cromossomos 4 e 6 e dois a ET nos cromossomos 4 e 7. Somente quando se ajustou o MPI, foram observados QTLs no cromossomo 4 para ET e GIM. Não foi possível detectar QTLs para a característica GP com a aplicação dessa metodologia, o que pode ter resultado do uso de marcadores não informativos ou da ausência de QTLs segregando nos cromossomos 4, 6 e 7 desta população. Foi evidenciada a vantagem de se analisar dados experimentais ajustando diferentes modelos genéticos; essas análises ilustram a utilidade e ampla aplicabilidade do método Bayesiano.

Agent-based model to rural-urban migration analysis

In this paper, we analyze the rural-urban migration phenomenon as it is usually observed in economies which are in the early stages of industrialization. The analysis is conducted by means of a statistical mechanics approach which builds a computational agent-based model. Agents are placed on a lattice and the connections among them are described via an Ising-like model. Simulations on this computational model show some emergent properties that are common in developing economies, such as a transitional dynamics characterized by continuous growth of urban population, followed by the equalization of expected wages between rural and urban sectors (Harris-Todaro equilibrium condition), urban concentration and increasing of per capita income. (c) 2005 Elsevier B.V. All rights reserved.

Interleukin-2 and interleukin-6 gene promoter polymorphisms, and early failure of dental implants

Single nucleotide polymorphisms in the promoter region of the human interleukin (IL)-2 (T-330G) and IL-6 (G-174C) genes have modified the transcriptional activity of these cytokines and are associated with several diseases. The aim of this study was to investigate the possible relationship between these single nucleotide polymorphisms and early implant failure. A sample of 74 nonsmokers was divided into 2 groups: test group comprising 34 patients (mean age 49.3 years) with ĝ‰¥1 implants that failed and control group consisting of 40 patients (mean age 43.8 years) with ĝ‰¥1 healthy implants. Genomic deoxyribonucleic acid from oral mucosa was amplified by polymerase chain reaction and analyzed by restriction fragment length polymorphism. Monte Carlo simulations (P < 0.05) were used to assess differences in allele and genotypes frequencies of the single nucleotide polymorphisms between the 2 groups. No significant differences were observed in the allele and genotypes distribution of both polymorphisms when the 2 groups were compared. The results indicate that polymorphisms in the IL-2 (T-330G) and IL-6 (G-174C) genes are not associated with early implant failure, suggesting that the presence of those single nucleotide polymorphisms does not constitute a genetic risk factor for implant loss in the studied population. Copyright © 2005 by Lippincott Williams & Wilkins.

Uma abordagem probabilística do número de reprodução básica em modelos epidemiológicos com aplicação na ferrugem do eucalipto

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparison of a genetic group and unknown paternity models for growth traits in Nellore cattle

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

Estrutura genômica de uma população de suínos base Landrace

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A avaliação da efetividade de um modelo de intervenção breve (método BASICS) para o uso de risco de álcool em estudantes do ensino médio

Pós-graduação em Saúde Coletiva - FMB

A New Birth-Interval Approach to Estimating Demographic Parameters of Humpback Whales

A demographic model is developed based on interbirth intervals and is applied to estimate the population growth rate of humpback whales (Megaptera novaeangliae) in the Gulf of Maine. Fecundity rates in this model are based on the probabilities of giving birth at time t after a previous birth and on the probabilities of giving birth first at age x. Maximum likelihood methods are used to estimate these probabilities using sighting data collected for individually identified whales. Female survival rates are estimated from these same sighting data using a modified Jolly–Seber method. The youngest age at first parturition is 5 yr, the estimated mean birth interval is 2.38 yr (SE = 0.10 yr), the estimated noncalf survival rate is 0.960 (SE = 0.008), and the estimated calf survival rate is 0.875 (SE = 0.047). The population growth rate (l) is estimated to be 1.065; its standard error is estimated as 0.012 using a Monte Carlo approach, which simulated sampling from a hypothetical population of whales. The simulation is also used to investigate the bias in estimating birth intervals by previous methods. The approach developed here is applicable to studies of other populations for which individual interbirth intervals can be measured.

Distribution and molecular characterization of hepatitis C virus (HCV) genotypes in patients with chronic infection from Pernambuco State, Brazil

Hepatitis C virus (HCV) is a public health problem throughout the world and 3% of the world population is infected with this virus. It is estimated that 3-4 millions individuals are being infected every year. It has been estimated that around 1.5% of Brazilian population is anti-HCV positive and the Northeast region showed the highest prevalence in Brazil. The aim of this study was to characterize HCV genotypes circulating in Pernambuco State (PE), Brazil, located in the Northeast region of the country. This study included 85 anti-HCV positive patients followed up between 2004 and 2011. For genotyping, a 380bp fragment of HCV RNA in the NS5B region was amplified by nested PCR. Phylogenetic analysis was conducted using Bayesian Markov chain Monte Carlo simulation (MCMC) using BEAST v.1.5.3. From 85 samples, 63 (74.1%) positive to NS5B fragment were successfully sequenced. Subtype 1b was the most prevalent in this population (42-66.7%), followed by 3a (16-25.4%), 1a (4-6.3%) and 2b (1-1.6%). Twelve (63.1%) and seven (36.9%) patients with HCV and schistosomiasis were infected with subtypes 1b and 3a, respectively. Brazil is a large country with many different population backgrounds; a large variation in the frequencies of HCV genotypes is predictable throughout its territory. This study reports HCV genotypes from Pernambuco State where subtype 1b was found to be the most prevalent. Phylogenetic analysis suggests the presence of the different HCV strains circulating within this population. (C) 2012 Elsevier B.V. All rights reserved.

A Bayesian analysis of the Conway-Maxwell-Poisson cure rate model

The purpose of this paper is to develop a Bayesian analysis for the right-censored survival data when immune or cured individuals may be present in the population from which the data is taken. In our approach the number of competing causes of the event of interest follows the Conway-Maxwell-Poisson distribution which generalizes the Poisson distribution. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the proposed model. Also, some discussions on the model selection and an illustration with a real data set are considered.

A multiple time scale survival model with a cure fraction

Many recent survival studies propose modeling data with a cure fraction, i.e., data in which part of the population is not susceptible to the event of interest. This event may occur more than once for the same individual (recurrent event). We then have a scenario of recurrent event data in the presence of a cure fraction, which may appear in various areas such as oncology, finance, industries, among others. This paper proposes a multiple time scale survival model to analyze recurrent events using a cure fraction. The objective is analyzing the efficiency of certain interventions so that the studied event will not happen again in terms of covariates and censoring. All estimates were obtained using a sampling-based approach, which allows information to be input beforehand with lower computational effort. Simulations were done based on a clinical scenario in order to observe some frequentist properties of the estimation procedure in the presence of small and moderate sample sizes. An application of a well-known set of real mammary tumor data is provided.