Processos de autoformação: uma produção singular de si-próprio

Dissertação de mestrado em Ciências da Educação: área de Educação e Desenvolvimento

Dos percursos de autoformação aos processos de transformação: uma produção singular de sucesso educativo em meio rural

Dissertação de mestrado em Ciências da Educação: área de Educação e Desenvolvimento

Public private collaboration: Potentials for spatial data infrastructure development in developing countries - A caste study of Nigeria

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Identificação da actividade do sistema nervoso simpático e parassimpático em dados de pupilometria, utilizando a Singular Spectrum Analysis

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Structural change in the ruhr region: problems, potentials and developments

Due to the decline of the heavy industries in the Ruhr region, the area has to reinvent itself. The orientation towards service industries proves to be a difficult task for the district and its population. This paper examines the challenges, problems and potentials of the Ruhr region against the backdrop of its economical history out of a sociological perspective. Thereby the economical situation and its outcome towards the population stand in the foreground of the paper.

Rua das Portas de Santo Antão e a singular modernidade lisboeta (1890–1925): arquitectura e práticas urbanas

Via antiga da cidade, fazendo-a comunicar com o seu termo, a Rua das Portas de Santo Antão tornou-se, nos finais do século XIX e primeiras décadas do século XX, um espaço aurático da vida cosmopolita de Lisboa onde se instalaram importantes equipamentos como o Coliseu dos Recreios, o Ateneu Comercial, a Sede da Sociedade de Geografia ou o Teatro Politeama. “Traseiras” da Avenida da Liberdade, sem a modernidade e amplidão do seu desenho urbano, a velha Rua das Portas de Santo Antão foi, na verdade, o palco não exposto da Lisboa moderna.

O âmbito de aplicação material da legislação de proteção de dados pessoais e o tratamento de imagem por pessoa singular

Images have gained a never before seen importance. Technological changes have given the Information Society extraordinary means to capture, treat and transmit images, wheter your own or those of others, with or without a commercial purpose, with no boundaries of time or country, without “any kind of eraser”. From the several different ways natural persons may engage in image processing with no commercial purpose, the cases of sharing pictures through social networks and video surveillance assume particular relevance. Consequently there are growing legitimate concerns with the protection of one's image, since its processing may sometimes generate situations of privacy invasion or put at risk other fundamental rights. With this in mind, the present thesis arises from the question: what are the existent legal instruments in Portuguese Law that enable citizens to protect themselves from the abusive usage of their own pictures, whether because that image have been captured by a smartphone or some video surveillance camera, whether because it was massively shared through a blog or some social network? There is no question the one's right to not having his or her image used in an abusive way is protected by the Portuguese constitution, through the article 26th CRP, as well as personally right, under the article 79th of the Civil Code, and finally through criminal law, articles 192nd and 193rd of the Criminal Code. The question arises in the personal data protection context, considering that one's picture, given certain conditions, is personal data. Both the Directive 95/46/CE dated from 1995 as well as the LPD from 1998 are applicable to the processing of personal data, but both exclude situations of natural persons doing so in the pursuit of activities strictly personal or family-related. These laws demand complex procedures to natural persons, such as the preemptive formal authorisation request to the Data Protection National Commission. Failing to do so a natural person may result in the application of fines as high as €2.500,00 or even criminal charges. Consequently, the present thesis aims to study if the image processing with no commercial purposes by a natural person in the context of social networks or through video surveillance belongs to the domain of the existent personal data protection law. To that effect, it was made general considerations regarding the concept of video surveillance, what is its regimen, in a way that it may be distinguishable from Steve Mann's definition of sousveillance, and what are the associated obligations in order to better understand the concept's essence. The application of the existent laws on personal data protection to images processing by natural persons has been analysed taking into account the Directive 95/46/CE, the LPD and the General Regulation. From this analysis it is concluded that the regimen from 1995 to 1998 is out of touch with reality creating an absence of legal shielding in the personal data protection law, a flaw that doesn't exist because compensated by the right to image as a right to personality, that anyway reveals the inability of the Portuguese legislator to face the new technological challenges. It is urgent to legislate. A contrary interpretation will evidence the unconstitutionality of several rules on the LPD due to the obligations natural persons are bound to that violate the right to the freedom of speech and information, which would be inadequate and disproportionate. Considering the recently approved General Regulation and in the case it becomes the final version, the use for natural person of video surveillance of private spaces, Google Glass (in public and private places) and other similar gadgets used to recreational purposes, as well as social networks are subject to its regulation only if the images are shared without limits or existing commercial purposes. Video surveillance of public spaces in all situations is subject to General Regulation provisions.

Copper-containing nitrite reductase from Pseudomonas chlororaphis DSM 50135

Eur. J. Biochem. 271, 2361–2369 (2004)

The isolation and characterization of cytochrome c nitrite reductase subunits (NrfA and NrfH) from Desulfovibrio desulfuricans ATCC 27774

Eur. J. Biochem. 270, 3904–3915 (2003)

Electrochemical studies on small electron transfer proteins using membrane electrodes

Journal of Electroanalytical Chemistry 541 (2003) 153-162

On the fractional central differences and derivatives

Fractional central differences and derivatives are studied in this article. These are generalisations to real orders of the ordinary positive (even and odd) integer order differences and derivatives, and also coincide with the well known Riesz potentials. The coherence of these definitions is studied by applying the definitions to functions with Fourier transformable functions. Some properties of these derivatives are presented and particular cases studied.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.