Fauna de Culicidae da Serra da Cantareira, São Paulo, Brasil

OBJETIVO: Comparar a fauna de culicídeos nos ambientes de mata, ecótono e peridomicílio quanto ao número de espécies e de indivíduos, estimativas de diversidade, riqueza, heterogeneidade e similaridade. Determinou-se ainda as espécies dominantes e as relações entre dominância específica e fatores climáticos. MÉTODOS: Foram conduzidas no Parque Ecológico da Cantareira coletas quinzenais com armadilhas luminosas tipo CDC-CO2, dispostas em cinco ambientes ecologicamente diferentes, de fevereiro de 2001 a janeiro de 2002. As análises foram feitas utilizando o índice de Diversidade de Margalef e o de Menhinick. Para similaridade, foi utilizado o índice de Sorensen e, para dominância de espécies, o índice de Berger-Parker. A heterogeneidade foi estimada pelos índices de Simpson e de Shannon. A relação entre dominância específica e fatores climáticos foi estimada por correlação de Spearman. RESULTADOS: Foram coletados 2.219 culicídeos, distribuídos em 11 gêneros e 21 espécies. O ambiente mata apresentou maior riqueza (Mg=3,64) de espécies e o peridomicílio maior dominância (d=0,85). A temperatura mostrou a correlação mais elevada (Rs=0,747; p<0,0001) na relação entre dados climáticos e número de indivíduos capturados no Núcleo Pedra Grande. CONCLUSÕES: O fato do Parque Ecológico da Cantareira ser fragmento urbano de mata o diferencia de outros fragmentos inseridos em ambiente rural, o que pode alterar as relações ecológicas nos criadouros utilizados pelos mosquitos. A ausência de anofelinos do subgênero Kerteszia e também da espécie Culex quinquefasciatus, somado à presença de espécimes da Tribo Sabethini e da espécie Cx. (Mel.) vaxus, indica que o Parque Ecológico da Cantareira é fragmento de mata com características silvestres com interferência antrópica.

Educação para o Desenvolvimento Sustentável (EDS) no âmbito do Currículo Regional da Educação Básica da Região Autónoma dos Açores (CREB)

Mestrado, Educação Pré-Escolar e Ensino do 1.º Ciclo do Ensino Básico, 26 de Junho de 2013, Universidade dos Açores (Relatório de Estágio).

Conectividade em populações de lapas nas ilhas da Macaronésia (Atlântico-NE): uma abordagem multidisciplinar

Jornadas "Ciência nos Açores – que futuro? Tema Ciências Naturais e Ambiente", Ponta Delgada, 7-8 de Junho de 2013.

Pesquisa de péptidos em Macroalgas dos Açores com potencial atividade inibitória da Enzima Conversora da Angiotensina I (ECA): Extração, purificação, caracterização e avaliação da sua atividade anti-hipertensiva

Jornadas "Ciência nos Açores – que futuro? Tema Ciências Naturais e Ambiente", Ponta Delgada, 7-8 de Junho de 2013.

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.

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%.

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.