Uma cidade portuária: a Horta entre 1880 e 1926. Sociedade e cultura com a política em fundo

Tese de Doutoramento, História (História Contemporânea), 29 de Novembro de 2013, Universidade dos Açores.

Dietary patterns and risk of oral cancer: a case-control study in São Paulo, Brazil

OBJECTIVE: To analyze the association between dietary patterns and oral cancer. METHODS: The study, part of a Latin American multicenter hospital-based case-control study, was conducted in São Paulo, Southeastern Brazil, between November 1998 and March 2002 and included 366 incident cases of oral cancer and 469 controls, frequency-matched with cases by sex and age. Dietary data were collected using a food frequency questionnaire. The risk associated with the intake of food groups defined a posteriori, through factor analysis (called factors), was assessed. The first factor, labeled "prudent," was characterized by the intake of vegetables, fruit, cheese, and poultry. The second factor, "traditional," consisted of the intake of rice, pasta, pulses, and meat. The third factor, "snacks," was characterized as the intake of bread, butter, salami, cheese, cakes, and desserts. The fourth, "monotonous," was inversely associated with the intake of fruit, vegetables and most other food items. Factor scores for each component retained were calculated for cases and controls. After categorization of factor scores into tertiles according to the distribution of controls, odds ratios and 95% confidence intervals were calculated using unconditional multiple logistic regression. RESULTS: "Traditional" factor showed an inverse association with cancer (OR=0.51; 95% CI: 0.32; 0.81, p-value for trend 0.14), whereas "monotonous" was positively associated with the outcome (OR=1.78; 95% CI: 1.78; 2.85, p-value for trend <0.001). CONCLUSIONS: The study data suggest that the traditional Brazilian diet, consisting of rice and beans plus moderate amounts of meat, may confer protection against oral cancer, independently of any other risk factors such as alcohol intake and smoking.

Deep-Sea ecosystem model of the Condor seamount

Dissertação de Mestrado, Estudos Integrados dos Oceanos, 25 de Julho 2013, Universidade dos Açores.

Geographical, temporal and environmental determinants of bryophyte species richness in the Macaronesian Islands

Copyright: © 2014 Aranda et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Potencial biotecnológico do mar dos Açores

O ambiente marinho constitui uma extraordinária reserva de compostos com características estruturais únicas, diferentes das encontradas nos Produtos Naturais de origem terrestre. Os compostos de origem marinha assumem assim um papel cada vez mais significativo na descoberta de novos medicamentos, de novas aplicações em cosmética, na produção de enzimas com características específicas ou como biomateriais para engenharia de tecidos. A investigação nesta área, no âmbito do Crescimento Azul, ganhou novo impulso com a Estratégia Nacional para o Mar e as diretivas do Horizonte 2020. A elevada biodiversidade do mar dos Açores e os ambientes e ecossistemas que o distinguem de outras regiões, nomeadamente a influência do vulcanismo ativo e residual, estão na base da investigação que tem vindo a ser feita na Universidade dos Açores, pelo Grupo de Biotecnologia do Centro de Investigação em Recursos Naturais (CIRN), em colaboração com o DCTD, DB e CIBIO-Açores.

Aplicação de regressão linear para correção de dados dietéticos

OBJETIVO: Estratégias metodológicas vêm sendo desenvolvidas para minimizar o efeito do erro de medida da dieta. O objetivo do estudo foi descrever a aplicação de uma estratégia para correção da informação dietética pelo erro de medida. MÉTODOS: Foram obtidos dados de consumo alimentar pela aplicação do Questionário de Freqüência Alimentar a 79 adolescentes do Município de São Paulo em 1999. Os dados dietéticos obtidos foram corrigidos por meio de regressão linear, após o ajuste pela energia usando-se o método dos resíduos. O método de referência utilizado foi o recordatório de 24 horas, aplicado em três momentos distintos. RESULTADOS: Os valores corrigidos aproximaram-se dos valores de referência. O fator de correção lambda foi de 0,89 para energia. Para os macronutrientes, os fatores foram de 0,41; 0,22 e 0,20, para carboidratos, lipídios e proteínas, respectivamente. CONCLUSÕES: As médias e desvios-padrão dos valores corrigidos denotam que houve uma correção do erro de medida. Apesar disso, debate-se o desempenho desses métodos, que são notoriamente imperfeitos quando seus pressupostos teóricos não são atendidos, o que é comum nos estudos da dieta que usam instrumentos de medida baseados no relato dos indivíduos.

An ultrasound based computer-aided diagnosis tool for steatosis detection

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. In this paper, a new computer-aided diagnosis (CAD) system for steatosis classification, in a local and global basis, is presented. Bayes factor is computed from objective ultrasound textural features extracted from the liver parenchyma. The goal is to develop a CAD screening tool, to help in the steatosis detection. Results showed an accuracy of 93.33%, with a sensitivity of 94.59% and specificity of 92.11%, using the Bayes classifier. The proposed CAD system is a suitable graphical display for steatosis classification.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Global and local detection of liver steatosis from ultrasound

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. Steatosis is usually a diffuse liver disease, since it is globally affected. However, steatosis can also be focal affecting only some foci difficult to discriminate. In both cases, steatosis is detected by laboratorial analysis and visual inspection of ultrasound images of the hepatic parenchyma. Liver biopsy is the most accurate diagnostic method but its invasive nature suggest the use of other non-invasive methods, while visual inspection of the ultrasound images is subjective and prone to error. In this paper a new Computer Aided Diagnosis (CAD) system for steatosis classification and analysis is presented, where the Bayes Factor, obatined from objective intensity and textural features extracted from US images of the liver, is computed in a local or global basis. The main goal is to provide the physician with an application to make it faster and accurate the diagnosis and quantification of steatosis, namely in a screening approach. The results showed an overall accuracy of 93.54% with a sensibility of 95.83% and 85.71% for normal and steatosis class, respectively. The proposed CAD system seemed suitable as a graphical display for steatosis classification and comparison with some of the most recent works in the literature is also presented.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Mapping multi-species habitat use for marine conservation planning

Tese de Doutoramento, Ciências do Mar (Biologia Marinha)

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

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.