Gender differences in CF phenotype associated with low bone mineral density

Adolescents and adults with CF have lower bone mineral density (BMD) than normal, but its relationship with phenotype is not well understood. Point FEV1% predicted (FEV) and rate of change of FEV are biased estimates of disease severity, because progressively older subjects represent a selected survivor population, with females at greater risk of death than males. To investigate the relationship between BMD and phenotype we used an index (predicted age at death) derived from Bayesian estimates of slope and intercept of FEV, age at last measurement and survival status. Predictive equations for the index were derived from 97 subjects (78 survivors) from the RCH CF clinic, and applied to a group of 102 comparable subjects who had BMD measured, classified as having‘mild’ ()75th), ‘moderate’ (25– 75th), or ‘severe’ (-25th centile) phenotype. Total body (TB) and lumbar spine (LS) BMD z-scores (Z) were compared, adjustingfor gender effects, using 2-way ANOVA. Annual mean change in FEV segregated, as expected, according to phenotype, ‘severe’ (ns25), ‘moderate’ (ns51) and ‘mild’ (ns25) y3.01(y3.73 to y2.30)%, y0.85(y1.36 to y0.35)%, 2.70(1.92 to 3.46)%, respectively, with no gender difference. LS and TB BMDZ were different in each phenotype (P-s 0.002), LS BMDZ for ‘severe’, ‘moderate’ and ‘mild’ y1.63(CI: y2.07 to y 1.19), y0.86(CI: y1.17 to y0.55), y0.06(CI: y0.54 to 0.41). Males had lower LS BMDZ than females overall (y1.22 (CI: y1.54 to y0.91) vs. y0.48(CI: y 0.84 to y0.12) Ps0.002). In the ‘severe’ group, males had lower TB BMDZ and LS BMDZ (PF0.002). Low BMD is associated with ‘moderate’ and ‘severe’ phenotypes, with relative preservation in females in the ‘severe’ group. Female biology (reproductive fitness) might promote resistance to bone resorption at a critical level of BMD loss.

Padrões espaço-temporais da mortalidade por câncer de mama em mulheres no Estado do Espírito Santo - um estudo ecológico

O câncer de mama é a principal neoplasia maligna que acomete o sexo feminino no Brasil. O câncer de mama é hoje uma doença de extrema importância para a saúde pública nacional, motivando ampla discussão em torno das medidas que promova o seu diagnóstico precoce, a redução em sua morbidade e mortalidade. A presente pesquisa possui três objetivos, cujos resultados encontram-se organizados em artigos. O primeiro objetivo buscou analisar a completude dos dados do Sistema de Informação de Mortalidade sobre os óbitos por câncer de mama em mulheres no Espírito Santo, Sudeste e Brasil (1998 a 2007). Realizou-se um estudo descritivo analítico baseado em dados secundários, onde foi analisado o número absoluto e percentual de não preenchimento das variáveis nas declarações de óbitos. Adotou-se escore para avaliar os graus de não completude. Os resultados para as variáveis sexo e idade foram excelentes tanto para o Espírito Santo, Sudeste e Brasil. O preenchimento das variáveis raça/cor, grau de escolaridade e estado civil apresentam problemas no Espírito Santo. Enquanto no Sudeste e Brasil as variáveis raça/cor e escolaridade têm tendência decrescente para a não completude, no Espírito Santo a tendência se mantém estável. Para a variável estado civil, a não completude tem tendência crescente no Estado do Espírito Santo. O segundo objetivo foi analisar a evolução das taxas de mortalidade por câncer de mama, em mulheres no Espírito Santo no período de 1980 a 2007. Estudo de série temporal, cujos dados sobre óbitos foram obtidos do Sistema de Informação de Mortalidade e as estimativas populacionais segundo idade e anos-calendário, do Instituto Brasileiro Geografia e Estatística. Os coeficientes específicos 9 de mortalidade, segundo faixa etária, foram calculados anualmente. A análise de tendência foi realizada por meio da padronização das taxas de mortalidade pelo método direto, em que a população do senso IBGE-2000, foi considerada padrão. No período de estudo, ocorreram 2.736 óbitos por câncer de mama. O coeficiente de mortalidade neste período variou de 3,41 a 10,99 por 100.000 mulheres. Os resultados indicam que há tendência de mortalidade por câncer de mama ao longo da série (p=0,001 com crescimento de 75,42%). Todas as faixas etárias a partir de 30 anos apresentaram tendência de crescimento da mortalidade estatisticamente significante (p=0,001). Os percentuais de crescimento foram aumentando, segundo as idades mais avançadas, sendo 48,4% na faixa de 40 a 49 anos, chegando a 92,3%, na faixa de 80 anos e mais. O terceiro objetivo foi realizar a análise espacial dos óbitos em mulheres por câncer de mama no estado do Espírito Santo, nos anos de 2003 a 2007, com análise das correlações espaciais dessa mortalidade e componentes do município. O cenário foi o Estado do Espírito Santo, composto por 78 municípios. Para análise dos dados, utilizou-se a abordagem bayesiana (métodos EBest Global e EBest Local) para correção de taxas epidemiológicas. Calculou-se o índice I de Moran, para dependência espacial em nível global e a estatística Moran Local. As maiores taxas estão concentradas em 19 municípios pertencentes às Microrregiões: Metropolitana (Fundão, Vitória, Vila Velha, Viana, Cariacica e Guarapari), Metrópole Expandida Sul (Anchieta, Alfredo Chaves), Pólo Cachoeiro (Vargem Alta, Rio Novo do Sul, Mimoso do Sul, Cachoeiro de Itapemirim, Castelo, Jerônimo Monteiro, Bom Jesus do Norte, Apiacá e Muqui) e Caparaó (Alegre e São José do Calçado). Os resultados da Estimação Bayesiana (Índice de Moran) dos óbitos por câncer de mama em mulheres ocorridos no estado do Espírito Santo, segundo os dados brutos e 10 ajustados indicam a existência de correlação espacial significativa para o mapa Local (I = 0,573; p = 0,001) e Global (I = 0,118; p = 0,039). Os dados brutos não apresentam correlação espacial (I = 0,075; p = 0,142).

Fluorescence Microscopy Imaging Denoising with Log-Euclidean Priors and Photobleaching Compensation

Fluorescent protein microscopy imaging is nowadays one of the most important tools in biomedical research. However, the resulting images present a low signal to noise ratio and a time intensity decay due to the photobleaching effect. This phenomenon is a consequence of the decreasing on the radiation emission efficiency of the tagging protein. This occurs because the fluorophore permanently loses its ability to fluoresce, due to photochemical reactions induced by the incident light. The Poisson multiplicative noise that corrupts these images, in addition with its quality degradation due to photobleaching, make long time biological observation processes very difficult. In this paper a denoising algorithm for Poisson data, where the photobleaching effect is explicitly taken into account, is described. The algorithm is designed in a Bayesian framework where the data fidelity term models the Poisson noise generation process as well as the exponential intensity decay caused by the photobleaching. The prior term is conceived with Gibbs priors and log-Euclidean potential functions, suitable to cope with the positivity constrained nature of the parameters to be estimated. Monte Carlo tests with synthetic data are presented to characterize the performance of the algorithm. One example with real data is included to illustrate its application.

Elements of a methodology to assess the alignment of core-values in collaborative networks

Collaborative networks are typically formed by heterogeneous and autonomous entities, and thus it is natural that each member has its own set of core-values. Since these values somehow drive the behaviour of the involved entities, the ability to quickly identify partners with compatible or common core-values represents an important element for the success of collaborative networks. However, tools to assess or measure the level of alignment of core-values are lacking. Since the concept of 'alignment' in this context is still ill-defined and shows a multifaceted nature, three perspectives are discussed. The first one uses a causal maps approach in order to capture, structure, and represent the influence relationships among core-values. This representation provides the basis to measure the alignment in terms of the structural similarity and influence among value systems. The second perspective considers the compatibility and incompatibility among core-values in order to define the alignment level. Under this perspective we propose a fuzzy inference system to estimate the alignment level, since this approach allows dealing with variables that are vaguely defined, and whose inter-relationships are difficult to define. Another advantage provided by this method is the possibility to incorporate expert human judgment in the definition of the alignment level. The last perspective uses a belief Bayesian network method, and was selected in order to assess the alignment level based on members' past behaviour. An example of application is presented where the details of each method are discussed.

Convex Total Variation Denoising of Poisson Fluorescence Confocal Images With Anisotropic Filtering

Fluorescence confocal microscopy (FCM) is now one of the most important tools in biomedicine research. In fact, it makes it possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules over time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is proposed to remove the Poisson intensity dependent noise corrupting the FCM image sequences. The observations are organized in a 3-D tensor where each plane is one of the images acquired along the time of a cell nucleus using the fluorescence loss in photobleaching (FLIP) technique. The method removes simultaneously the noise by considering different spatial and temporal correlations. This is accomplished by using an anisotropic 3-D filter that may be separately tuned in space and in time dimensions. Tests using synthetic and real data are described and presented to illustrate the application of the algorithm. A comparison with several state-of-the-art algorithms is also presented.

Role of malnutrition and parasite infections in the spatial variation in children’s anaemia risk in Northern Angola

Anaemia has a significant impact on child development and mortality and is a severe public health problem in most countries in sub-Saharan Africa. Nutritional and infectious causes of anaemia are geographically variable and anaemia maps based on information on the major aetiologies of anaemia are important for identifying communities most in need and the relative contribution of major causes. We investigated the consistency between ecological and individual-level approaches to anaemia mapping, by building spatial anaemia models for children aged ≤15 years using different modeling approaches. We aimed to a) quantify the role of malnutrition, malaria, Schistosoma haematobium and soil-transmitted helminths (STH) for anaemia endemicity in children aged ≤15 years and b) develop a high resolution predictive risk map of anaemia for the municipality of Dande in Northern Angola. We used parasitological survey data on children aged ≤15 years to build Bayesian geostatistical models of malaria (PfPR≤15), S. haematobium, Ascaris lumbricoides and Trichuris trichiura and predict small-scale spatial variation in these infections. The predictions and their associated uncertainty were used as inputs for a model of anemia prevalence to predict small-scale spatial variation of anaemia. Stunting, PfPR≤15, and S. haematobium infections were significantly associated with anaemia risk. An estimated 12.5%, 15.6%, and 9.8%, of anaemia cases could be averted by treating malnutrition, malaria, S. haematobium, respectively. Spatial clusters of high risk of anaemia (>86%) were identified. Using an individual-level approach to anaemia mapping at a small spatial scale, we found that anaemia in children aged ≤15 years is highly heterogeneous and that malnutrition and parasitic infections are important contributors to the spatial variation in anemia risk. The results presented in this study can help inform the integration of the current provincial malaria control program with ancillary micronutrient supplementation and control of neglected tropical diseases, such as urogenital schistosomiasis and STH infection.

Role of malnutrition and parasite infections in the spatial variation in children’s anaemia risk in northern Angola

Anaemia is known to have an impact on child development and mortality and is a severe public health problem in most countries in sub-Saharan Africa. We investigated the consistency between ecological and individual-level approaches to anaemia mapping by building spatial anaemia models for children aged ≤15 years using different modelling approaches. We aimed to (i) quantify the role of malnutrition, malaria, Schistosoma haematobium and soil-transmitted helminths (STHs) in anaemia endemicity; and (ii) develop a high resolution predictive risk map of anaemia for the municipality of Dande in northern Angola. We used parasitological survey data for children aged ≤15 years to build Bayesian geostatistical models of malaria (PfPR≤15), S. haematobium, Ascaris lumbricoides and Trichuris trichiura and predict small-scale spatial variations in these infections. Malnutrition, PfPR≤15, and S. haematobium infections were significantly associated with anaemia risk. An estimated 12.5%, 15.6% and 9.8% of anaemia cases could be averted by treating malnutrition, malaria and S. haematobium, respectively. Spatial clusters of high risk of anaemia (>86%) were identified. Using an individual-level approach to anaemia mapping at a small spatial scale, we found that anaemia in children aged ≤15 years is highly heterogeneous and that malnutrition and parasitic infections are important contributors to the spatial variation in anaemia risk. The results presented in this study can help inform the integration of the current provincial malaria control programme with ancillary micronutrient supplementation and control of neglected tropical diseases such as urogenital schistosomiasis and STH infections.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

eTourismGUI - Adaptative Interface

Mestrado em Engenharia Informática. Área de Especialização em Tecnologias do Conhecimento e Decisão.

Sei whale (Balaenoptera borealis) ecology and management in the North Atlantic

Tese de Doutoramento em Ciências do Mar, especialidade em Ecologia Marinha.

Estudo morfológico e filogenético das subespécies Daucus carota ssp. Azoricus e Daucus carota ssp. Maritimus na ilha de S. Miguel

Dissertação de Mestrado, Biodiversidade e Biotecnologia Vegetal, 17 de Março de 2015, Universidade dos Açores.

Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization

Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Determining the number of clusters in categorical data

Cluster analysis for categorical data has been an active area of research. A well-known problem in this area is the determination of the number of clusters, which is unknown and must be inferred from the data. In order to estimate the number of clusters, one often resorts to information criteria, such as BIC (Bayesian information criterion), MML (minimum message length, proposed by Wallace and Boulton, 1968), and ICL (integrated classification likelihood). In this work, we adopt the approach developed by Figueiredo and Jain (2002) for clustering continuous data. They use an MML criterion to select the number of clusters and a variant of the EM algorithm to estimate the model parameters. This EM variant seamlessly integrates model estimation and selection in a single algorithm. For clustering categorical data, we assume a finite mixture of multinomial distributions and implement a new EM algorithm, following a previous version (Silvestre et al., 2008). Results obtained with synthetic datasets are encouraging. The main advantage of the proposed approach, when compared to the above referred criteria, is the speed of execution, which is especially relevant when dealing with large data sets.