Unravelling the role of alpha 2,6 sialic acid on mouse dentritic cells functions

RESUMO: As células dendríticas (CDs) são fundamentais na imunomodulação e iniciação de respostas imunes adaptativas, enquanto os ácidos siálicos (Sias) são potenciais imunomoduladores. Estas células expressam níveis elevados da sialiltransferase ST6Gal-1, que transfere Sias para a posição terminal de oligossacáridos. De facto, a maturação de CDs está associada a uma diminuição da sialilação na sua superfície celular. Apesar de ter função biológica desconhecida, a forma solúvel, extracelular de ST6Gal-1 aumenta em cancros e inflamação. Ainda assim, esta foi recentemente identificada como moduladora da hematopoiese. Considerando o importante papel das CDs na iniciação de respostas anticancerígenas, uma ligação entre a sialilação extrínseca induzida por ST6Gal-1 extracelular e o seu papel na modulação de CDs deve ser identificada. Neste trabalho hipotetizou-se que a sialilação α2,6 extrínseca de CDs diminui o seu perfil de maturação mediante ativação por lipopolissacarídeo (LPS). O objetivo principal foi sialilar extrinsecamente em α2,6 CDs da medula óssea de murganhos, avaliando os seus perfis de maturação e de libertação de citocinas, após estimulação com LPS (por Citometria de Fluxo e ELISA, respetivamente). Ao contrário da hipótese, o perfil celular não foi modulado, usando várias abordagens. Por outro lado, a consequência da falta de α2,6 Sias na maturação de CDs foi avaliada analisando: 1) CDs da medula óssea de murganhos tratadas com sialidase, 2) CDs da medula óssea e 3) CDs das vias aéreas, ambas de murganhos deficientes em ST6Gal-1, comparando com a estirpe selvagem. Estes resultados sugerem que a perta total de α2,6 Sias se relaciona com o aumento da expressão do complexo de histocompatibilidade principal de classe II. Apesar de controverso, é provável existirem mecanismos inerentes à ativação por LPS, reduzindo a eficácia de ST6Gal-1 extracelular. Por outro lado, a modificação no perfil de CDs de murganhos deficientes em ST6Gal-1 poderá relacionar-se com uma predisposição para um estado inflamatório severo. Com isto, o trabalho desenvolvido abriu futuras linhas de investigação, nomeadamente explorar outros fatores envolvidos na (de)sialilação α2,6 de CDs, podendo ter impacto em imunoterapia com uso de CDs.--------------------------ABSTRACT: Dendritic cells (DCs) are vital for immunomodulation and the initiation of adaptive immune responses, whereas sialic acids (Sias) are potential immunomodulators. These cells express high levels of sialyltransferase ST6Gal-1, responsible for transferring Sias to the terminal position of oligosaccharide chains. Indeed, DCs’ maturation is associated with decreased cell surface sialylation. Although its biological significance is unknown, the soluble, extracellular form of ST6Gal-1 increases in cancers and inflammation. However, extracellular ST6Gal-1 was recently identified as modulator of hematopoiesis. Considering that DCs play a crucial role in the initiation of a productive anti-cancer immune response, a link between extrinsic sialylation by the extracellular ST6Gal-1 on DC function needs to be investigated. We hypothesize that extrinsic α2,6 sialylation of DCs diminishes their maturation features upon lipopolysaccharide (LPS) stimulation. The main goal was to extrinsically α2,6 sialylate mice bone marrow derived DCs (BMDCs) and to evaluate their maturation and cytokine profiles upon LPS stimulation (by Flow Cytometry and ELISA, respectively). Unlike the hypothesis, we observed that BMDCs’ profile is not modulated, even using several approaches. In contrast, the consequence of lacking cell surface α2,6 Sias in DC maturation was assessed by analysing: 1) sialidase treated BMDCs, 2) BMDCs from mice lacking ST6Gal-1 and 3) DCs from mice airways, comparing wild type with ST6Gal-1 knockout mice. These results suggest that overall lack in α2,6 Sias is related with increased expression of major histocompatibility class II (MHC-II). Although appearing to be controversial findings, other intracellular mechanisms might be occurring upon LPS-induced BMDC activation, probably reducing extracellular ST6Gal-1 effect. In opposite, the modification observed in DC profile of ST6Gal-1 knockout mice might be related to its predisposition to a more severe inflammatory status. With this, the developed work opened future lines of investigation, namely exploring other factors involved in α2,6 (de)sialylation of DC, which might have influence in immunotherapy using DCs.

Constraint solving over multi-valued logics - application to digital circuits

Due to usage conditions, hazardous environments or intentional causes, physical and virtual systems are subject to faults in their components, which may affect their overall behaviour. In a ‘black-box’ agent modelled by a set of propositional logic rules, in which just a subset of components is externally visible, such faults may only be recognised by examining some output function of the agent. A (fault-free) model of the agent’s system provides the expected output given some input. If the real output differs from that predicted output, then the system is faulty. However, some faults may only become apparent in the system output when appropriate inputs are given. A number of problems regarding both testing and diagnosis thus arise, such as testing a fault, testing the whole system, finding possible faults and differentiating them to locate the correct one. The corresponding optimisation problems of finding solutions that require minimum resources are also very relevant in industry, as is minimal diagnosis. In this dissertation we use a well established set of benchmark circuits to address such diagnostic related problems and propose and develop models with different logics that we formalise and generalise as much as possible. We also prove that all techniques generalise to agents and to multiple faults. The developed multi-valued logics extend the usual Boolean logic (suitable for faultfree models) by encoding values with some dependency (usually on faults). Such logics thus allow modelling an arbitrary number of diagnostic theories. Each problem is subsequently solved with CLP solvers that we implement and discuss, together with a new efficient search technique that we present. We compare our results with other approaches such as SAT (that require substantial duplication of circuits), showing the effectiveness of constraints over multi-valued logics, and also the adequacy of a general set constraint solver (with special inferences over set functions such as cardinality) on other problems. In addition, for an optimisation problem, we integrate local search with a constructive approach (branch-and-bound) using a variety of logics to improve an existing efficient tool based on SAT and ILP.

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.

Patterns of Associational Life in Western Europe, 1800-2000

Thesis submitted for assessment with a view to obtaining the degree of Doctor of Political and Social Science of the European University Institute

On the initial conditions in continuous-time fractional linear systems

Signal Processing, Vol. 83, nº 11

Gender and reproductive autonomy : are there second-class citizens in Europe?

EUROPEAN MASTER’S DEGREE IN HUMAN RIGHTS AND DEMOCRATISATION Academic Year 2007/2008

Reducing the number of membership functions in linguistic variables

Dissertation presented at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia in fulfilment of the requirements for the Masters degree in Mathematics and Applications, specialization in Actuarial Sciences, Statistics and Operations Research

Extremum estimators and stochastic optimization methods

Submitted in partial fulfillment for the Requirements for the Degree of PhD in Mathematics, in the Speciality of Statistics in the Faculdade de Ciências e Tecnologia

New spectroscopic and electrochemical insights on a class I superoxide reductase: evidence for an intramolecular electron-transfer pathway

Biochem. J. (2011) 438,485–494 doi:10.1042/BJ20110836

Nitrite reduction by xanthine oxidase family enzymes: a new class of nitrite reductases

J Biol Inorg Chem (2011) 16:443–460 DOI 10.1007/s00775-010-0741-z

The first crystal structure of class III superoxide reductase from Treponema pallidum

J Biol Inorg Chem (2006) 11: 548–558 DOI 10.1007/s00775-006-0104-y

The impact of the social class on the development of money knowledge

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Implementation of a sigma delta modulator for a class D audio power amplifier

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Design of a power output stage for a class D audio power amplifier based on an 1.5-bit ∑ ∆ M

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Protesting or justifying? A latent class model for contingent valuation with attitudinal data

This article develops a latent class model for estimating willingness-to-pay for public goods using simultaneously contingent valuation (CV) and attitudinal data capturing protest attitudes related to the lack of trust in public institutions providing those goods. A measure of the social cost associated with protest responses and the consequent loss in potential contributions for providing the public good is proposed. The presence of potential justification biases is further considered, that is, the possibility that for psychological reasons the response to the CV question affects the answers to the attitudinal questions. The results from our empirical application suggest that psychological factors should not be ignored in CV estimation for policy purposes, allowing for a correct identification of protest responses.