A cellulose liquid crystal motor: a steam engine of the second kind

The salient feature of liquid crystal elastomers and networks is strong coupling between orientational order and mechanical strain. Orientational order can be changed by a wide variety of stimuli, including the presence of moisture. Changes in the orientation of constituents give rise to stresses and strains, which result in changes in sample shape. We have utilized this effect to build soft cellulose-based motor driven by humidity. The motor consists of a circular loop of cellulose film, which passes over two wheels. When humid air is present near one of the wheels on one side of the film, with drier air elsewhere, rotation of the wheels results. As the wheels rotate, the humid film dries. The motor runs so long as the difference in humidity is maintained. Our cellulose liquid crystal motor thus extracts mechanical work from a difference in humidity.

Molecular imaging agents for detection of β-amyloid plaques in Alzheimer’s disease

The formation of amyloid structures is a neuropathological feature that characterizes several neurodegenerative disorders, such as Alzheimer´s and Parkinson´s disease. Up to now, the definitive diagnosis of these diseases can only be accomplished by immunostaining of post mortem brain tissues with dyes such Thioflavin T and congo red. Aiming at early in vivo diagnosis of Alzheimer´s disease (AD), several amyloid-avid radioprobes have been developed for b-amyloid imaging by positron emission tomography (PET) and single-photon emission computed tomography (SPECT). The aim of this paper is to present a perspective of the available amyloid imaging agents, special those that have been selected for clinical trials and are at the different stages of the US Food and Drugs Administration (FDA) approval.

Hexanuclear and undecanuclear iron(III) carboxylates as catalyst precursors for cyclohexane oxidation

Two multinuclear complexes [Fe-6(mu(3)-O)(2)(mu(4)-O-2)L-10(OAc)(2)(H2O)(2)]center dot 2.625Et(2)O center dot 2.375H(2)O (1) and [(Fe11Cl)-Cl-III-(mu(4)-O)(3)(mu(3)-O)(5)L-16(dmf)(2.5)(H2O)(0.5)]center dot Et2O center dot 1.25dmf center dot 3.8H(2)O (2), where HL = 3,4,5-trimethoxybenzoic acid and dmf = dimethylformamide, have been prepared from trinuclear iron(III) carboxylates via their structural rearrangement in dimethylformamide or diethyl ether-dimethylformamide 9:1, respectively, and slow vapor diffusion of diethyl ether into the reaction mixture. Both compounds have been characterized by X-ray diffraction, optical, Mossbauer spectroscopy, and magnetic measurements. Complex 1 possesses a hexanuclear ferric peroxido-dioxido {Fe-6(O-2)(O)(2)}(12+) core unit, which adopts a recliner conformation, while complex 2 contains an unprecedented {Fe11O8Cl}(16+) core, in which 9 ferric ions are six-coordinate and the remaining two are five-coordinate. Another structural feature of note of the undecanuclear core is the presence of a deformed cubane entity {Fe-4(mu(3)-O)(mu(4)-O)(3)}(4+). Both complexes act as catalyst precursors for the oxidation of cyclohexane to cyclohexanol and cyclohexanone with aqueous H2O2, in the presence of pyrazinecarboxylic acid. Remarkable TONs and TOFs (the latter mainly for 1) with concomitant quite good yields have been achieved under mild conditions. Moreover, 1 exhibits remarkably high activity in an exceptionally short reaction time (45 min), being unprecedented for any metal catalyzed alkane oxidation by H2O2. The catalytic reactions proceed via Fenton type chemistry.

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.

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.

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.

3D-2D image registration by nonlinear regression

We propose a 3D-2D image registration method that relates image features of 2D projection images to the transformation parameters of the 3D image by nonlinear regression. The method is compared with a conventional registration method based on iterative optimization. For evaluation, simulated X-ray images (DRRs) were generated from coronary artery tree models derived from 3D CTA scans. Registration of nine vessel trees was performed, and the alignment quality was measured by the mean target registration error (mTRE). The regression approach was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach.

Otimização do processo de projeto de uma ferramenta progressiva para a estampagem de componentes metálicos

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

As freguesias e as políticas educativas locais: as necessidades e as obrigações

Dissertação apresentada para obtenção do grau de Mestre em Ciências da Educação Área de especialização em Administração Escolar 2013

A loucura e o cinema: representações cinematográficas de personalidade limite

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

Modelo de data mining para detecção de tumores em exames de rastreio

Dissertação para obtenção do grau de Mestre em Engenharia Informática

Reabilitação de edifícios: enquadramento e estudo da reabilitação num grupo empresarial

Dissertação para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização em Edificações