Efeito agudo do exercício físico na rigidez arterial

Este estudo pretendeu reproduzir uma tarefa da vida diária (marcha com transporte de peso nos membros superiores) realizada de forma rotineira e até aconselhada no contexto da prevenção e reabilitação de patologia cardiovascular, desta forma, teve como objectivo avaliar o efeito agudo de uma sessão de exercício aeróbio associado a exercício isométrico na rigidez arterial em indivíduos jovens. Foram recrutados 14 indivíduos do sexo masculino com média de idade de 30,3±3,6 anos. Foram realizadas duas sessões de exercício com uma semana de intervalo. Em ambas as sessões de exercício, os sujeitos caminharam num tapete rolante a uma velocidade de 5,0 km/h por um período de dez minutos, sendo que, numa das sessões, determinada aleatoriamente, carregaram o equivalente a 10% do seu peso corporal distribuído igualmente pelos dois membros superiores em dois garrafões de água. Avaliou-se em repouso e imediatamente após o exercício, a pressão arterial periférica, a frequência cardíaca e a rigidez arterial. A análise da rigidez arterial foi efectuada na artéria radial do membro superior direito com um sistema transdutor automático de pressão. Os parâmetros reportados neste estudo são a frequência cardíaca, a pressão arterial sistólica e diastólica, a pressão arterial média, a pressão central (aórtica) sistólica, a pressão de pulso central, a pressão de aumentação e o índice de aumentação a 75 bmp (Aix@75). Em repouso, não se verificaram diferenças significativas entre as duas sessões de exercício nos parâmetros avaliados. O principal resultado do presente estudo indica que a rigidez arterial aumenta de forma aguda com a realização de caminhada (exercício aeróbio) apenas quando esta é acompanhada do transporte nos membros superiores de uma carga externa (exercício isométrico) [Aix@75: -5.5(8.4) para -1.36(8.17)%, p<0.05]. Relativamente ao protocolo exclusivamente aeróbio não se verificaram diferenças significativas na rigidez arterial [Aix@75: -4.2(9.1) para -4.69(7.93)%, p>0.05]. Em conclusão, os resultados do presente estudo indicam que a rigidez arterial, em indivíduos jovens e aparentemente saudáveis, aumenta de forma significativa apenas quando o exercício aeróbio é acompanhado de exercício isométrico de membros superiores com transporte de carga adicional.

A similarity measure for music signals

One of the goals in the field of Music Information Retrieval is to obtain a measure of similarity between two musical recordings. Such a measure is at the core of automatic classification, query, and retrieval systems, which have become a necessity due to the ever increasing availability and size of musical databases. This paper proposes a method for calculating a similarity distance between two music signals. The method extracts a set of features from the audio recordings, models the features, and determines the distance between models. While further work is needed, preliminary results show that the proposed method has the potential to be used as a similarity measure for musical signals.

Concepção e instalação de tabelas multifunções para recintos desportivos cobertos

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Da tradução automática à tradução manual: estudo contrastivo da tradução automática e manual, através da tradução de dois artigos científicos

Trabalho de Projeto apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Tradução e Interpretação Especializadas, sob orientação do Mestre Alberto Couto.

An Interface to Retrieve Personal Memories Using an Iconic Visual Language

Relevant past events can be remembered when visualizing related pictures. The main difficulty is how to find these photos in a large personal collection. Query definition and image annotation are key issues to overcome this problem. The former is relevant due to the diversity of the clues provided by our memory when recovering a past moment and the later because images need to be annotated with information regarding those clues to be retrieved. Consequently, tools to recover past memories should deal carefully with these two tasks. This paper describes a user interface designed to explore pictures from personal memories. Users can query the media collection in several ways and for this reason an iconic visual language to define queries is proposed. Automatic and semi-automatic annotation is also performed using the image content and the audio information obtained when users show their images to others. The paper also presents the user interface evaluation based on tests with 58 participants.

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.

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%.

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.

Cyclotech - contribution to solve the Technetium shortage by using low energy medical cyclotrons

This paper presents work in progress, to develop an efficient and economic way to directly produce Technetium 99metastable (99mTc) using low-energy cyclotrons. Its importance is well established and relates with the increased global trouble in delivering 99mTc to Nuclear Medicine Departments relying on this radioisotope. Since the present delivery strategy has clearly demonstrated its intrinsic limits, our group decided to follow a distinct approach that uses the broad distribution of the low energy cyclotrons and the accessibility of Molybdenum 100 (100Mo) as the Target material. This is indeed an important issue to consider, since the system here presented, named CYCLOTECH, it is not based on the use of Highly Enriched (or even Low Enriched) Uranium 235 (235U), so entirely complying with the actual international trends and directives concerning the use of this potential highly critical material. The production technique is based on the nuclear reaction 100Mo (p,2n) 99mTc whose production yields have already been documented. Until this moment two Patent requests have already been submitted (the first at the INPI, in Portugal, and the second at the USPTO, in the USA); others are being prepared for submission on a near future. The object of the CYCLOTECH system is to present 99mTc to Nuclear Medicine radiopharmacists in a routine, reliable and efficient manner that, remaining always flexible, entirely blends with established protocols. To facilitate workflow and Radiation Protection measures, it has been developed a Target Station that can be installed on most of the existing PET cyclotrons and that will tolerate up to 400 μA of beam by allowing the beam to strike the Target material at an adequately oblique angle. The Target Station permits the remote and automatic loading and discharge of the Targets from a carriage of 10 Target bodies. On other hand, several methods of Target material deposition and Target substrates are presented. The object was to create a cost effective means of depositing and intermediate the target material thickness (25 - 100μm) with a minimum of loss on a substrate that is able to easily transport the heat associated with high beam currents. Finally, the separation techniques presented are a combination of both physical and column chemistry. The object was to extract and deliver 99mTc in the identical form now in use in radiopharmacies worldwide. In addition, the Target material is recovered and can be recycled.

Machine Learning Algorithms at Myocardial Perfusion Imaging - a Preliminary Study

Introduction: A major focus of data mining process - especially machine learning researches - is to automatically learn to recognize complex patterns and help to take the adequate decisions strictly based on the acquired data. Since imaging techniques like MPI – Myocardial Perfusion Imaging on Nuclear Cardiology, can implicate a huge part of the daily workflow and generate gigabytes of data, there could be advantages on Computerized Analysis of data over Human Analysis: shorter time, homogeneity and consistency, automatic recording of analysis results, relatively inexpensive, etc.Objectives: The aim of this study relates with the evaluation of the efficacy of this methodology on the evaluation of MPI Stress studies and the process of decision taking concerning the continuation – or not – of the evaluation of each patient. It has been pursued has an objective to automatically classify a patient test in one of three groups: “Positive”, “Negative” and “Indeterminate”. “Positive” would directly follow to the Rest test part of the exam, the “Negative” would be directly exempted from continuation and only the “Indeterminate” group would deserve the clinician analysis, so allowing economy of clinician’s effort, increasing workflow fluidity at the technologist’s level and probably sparing time to patients. Methods: WEKA v3.6.2 open source software was used to make a comparative analysis of three WEKA algorithms (“OneR”, “J48” and “Naïve Bayes”) - on a retrospective study using the comparison with correspondent clinical results as reference, signed by nuclear cardiologist experts - on “SPECT Heart Dataset”, available on University of California – Irvine, at the Machine Learning Repository. For evaluation purposes, criteria as “Precision”, “Incorrectly Classified Instances” and “Receiver Operating Characteristics (ROC) Areas” were considered. Results: The interpretation of the data suggests that the Naïve Bayes algorithm has the best performance among the three previously selected algorithms. Conclusions: It is believed - and apparently supported by the findings - that machine learning algorithms could significantly assist, at an intermediary level, on the analysis of scintigraphic data obtained on MPI, namely after Stress acquisition, so eventually increasing efficiency of the entire system and potentially easing both roles of Technologists and Nuclear Cardiologists. In the actual continuation of this study, it is planned to use more patient information and significantly increase the population under study, in order to allow improving system accuracy.

Plataforma de gestão de incidentes

Mestrado em Engenharia Electrotécnica e de Computadores. Área de Especialização de Telecomunicações.

Análise do transporte ferroviário de mercadorias associado à problemática da diferença de bitola entre linhas da rede ferroviária: estudo do caso prático: ligação do porto de Aveiro ao corredor internacional de mercadorias E-80

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil