Efeito fisiológico da cafeína em estudos neurológicos com base na ponderação em susceptibilidade ferromagnética (SWI)

Introdução – O presente estudo avaliou o efeito da cafeína no valor da razão contraste ruído (CNR) em imagens SWI. Objetivos – Avaliar o efeito da cafeína qualitativamente e quantificado pelo cálculo do valor CNR em imagens de magnitude e MIP para as estruturas: veia cerebral interna, seio sagital superior, tórcula e artéria cerebral média. Metodologia – A população do estudo incluiu 24 voluntários saudáveis que estiveram pelo menos 24h privados da ingestão de cafeína. Adquiriram-se imagens SWI antes e após a ingestão de 100ml de café. Os voluntários foram subdivididos em quatro grupos de seis indivíduos/grupo e avaliados separadamente após decorrido um intervalo de tempo diferente para cada grupo (15, 25, 30 ou 45min pós-cafeína). Utilizou-se um scanner Siemens Avanto 1,5 T com bobine standard de crânio e os parâmetros: T2* GRE 3D de alta resolução no plano axial, TR=49; TE=40; FA=15; FOV=187x230; matriz=221x320. O processamento de imagem foi efetuado no software OsiriX® e a análise estatística no GraphPadPrism®. Resultados e Discussão – As alterações de sinal e diferenças de contraste predominaram nas estruturas venosas e não foram significantes na substância branca, LCR e artéria cerebral média. Os valores CNR pré-cafeína diferiram significativamente do pós-cafeína nas imagens de magnitude e MIP na veia cerebral interna e nas imagens de magnitude do seio sagital superior e da tórcula (p<0,0001). Não se verificaram diferenças significativas entre os grupos avaliados nos diferentes tempos pós-cafeína. Conclusões – Especulamos que a cafeína possa vir a ser usada como agente de contraste nas imagens SWI barato, eficaz e de fácil administração.

Dominant set approach to ECG biometrics

Electrocardiographic (ECG) signals are emerging as a recent trend in the field of biometrics. In this paper, we propose a novel ECG biometric system that combines clustering and classification methodologies. Our approach is based on dominant-set clustering, and provides a framework for outlier removal and template selection. It enhances the typical workflows, by making them better suited to new ECG acquisition paradigms that use fingers or hand palms, which lead to signals with lower signal to noise ratio, and more prone to noise artifacts. Preliminary results show the potential of the approach, helping to further validate the highly usable setups and ECG signals as a complementary biometric modality.

O património local como recurso para o desenvolvimento de competências essenciais da história no 1.º ciclo

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e do 2.º Ciclo do Ensino Básico

Durability performance of self-compacting concrete (SCC) with binary and ternary mixes of fly ash and limestone filler

The basic objective of this work is to evaluate the durability of self-compacting concrete (SCC) produced in binary and ternary mixes using fly ash (FA) and limestone filler (LF) as partial replacement of cement. The main characteristics that set SCC apart from conventional concrete (fundamentally its fresh state behaviour) essentially depend on the greater or lesser content of various constituents, namely: greater mortar volume (more ultrafine material in the form of cement and mineral additions); proper control of the maximum size of the coarse aggregate; use of admixtures such as superplasticizers. Significant amounts of mineral additions are thus incorporated to partially replace cement, in order to improve the workability of the concrete. These mineral additions necessarily affect the concrete’s microstructure and its durability. Therefore, notwithstanding the many well-documented and acknowledged advantages of SCC, a better understanding its behaviour is still required, in particular when its composition includes significant amounts of mineral additions. An ambitious working plan was devised: first, the SCC’s microstructure was studied and characterized and afterwards the main transport and degradation mechanisms of the SCC produced were studied and characterized by means of SEM image analysis, chloride migration, electrical resistivity, and carbonation tests. It was then possible to draw conclusions about the SCC’s durability. The properties studied are strongly affected by the type and content of the additions. Also, the use of ternary mixes proved to be extremely favourable, confirming the expected beneficial effect of the synergy between LF and FA. © 2015 RILEM.

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.

Hyperspectral signal subspace estimation

Given an hyperspectral image, the determination of the number of endmembers and the subspace where they live without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper introduces a new minimum mean squared error based approach to infer the signal subspace in hyperspectral imagery. The method, termed hyperspectral signal identification by minimum error (HySime), is eigendecomposition based and it does not depend on any tuning parameters. It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.