Electron driven reactions in sulphur containing biological prototypes

The interaction of ionising radiation with living tissues may direct or indirectly generate several secondary species with relevant genotoxic potential. Due to recent findings that electrons with energies below the ionisation threshold can effectively damage DNA, radiation-induced damage to biological systems has increasingly come under scrutiny. The exact physico-chemical processes that occur in the first stages of electron induced damage remain to be explained. However, it is also known that free electrons have a short lifetime in the physiological medium. Hence, electron transfer processes studies represent an alternative approach through which the role of "bound" electrons as a source of damage to biological tissues can be further explored. The thesis work consists of studying dissociative electron attachment (DEA) and electron transfer to taurine and thiaproline. DEA measurements were executed in Siedlce University with Prof. Janina Kopyra under COST action MP1002 (Nanoscale insights in ion beam cancer therapy). The electron transfer experiments were conducted in a crossed atom(potassium)-molecule beam arrangement. In these studies the anionic fragmentation patterns were obtained. The results of both mechanisms are shown to be significantly different, unveiling that the damaging potential of secondary electrons can be underestimated. In addition, sulphur atoms appear to strongly influence the dissociation process, demonstrating that certain reactions can be controlled by substitution of sulphur at specific molecular sites.

Experimental study of proton induced nuclear reactions in 6,7 Li

This work presents the results of the experimental study of proton induced nuclear reactions in lithium, namely the 7Li(p,α) 4He, 6Li(p,α) 3He and 7Li(p,p)7Li reactions. The amount of 7Li and 6Li identified as primordial and observed in very old stars of the Milky Way galactic halo strongly deviates from the predictions of primordial nucleosynthesis and stellar evolution models which depend, among other factors, on the cross sections of reactions like 7Li(p,α) 4He and 6Li(p,α) 3He. These discrepancies have triggered a large amount of research in the fields of stellar evolution, cosmology, pre-galactic evolution and low energy nuclear reactions. Focusing on nuclear reactions, this work has measured the 7Li(p,α) 4He and 6Li(p,α) 3He reactions cross sections (expressed in terms of the astrophysical S -factor) with higher accuracy, and the electron screening effects in these reactions for different environments (insulators and metallic targets). The 7Li(p,α) 4He angular distributions were also measured. These measurementstook place in two laboratory facilities, in the framework of the LUNA (Laboratory for Undergroud Nuclear Astrophysics) international collaboration, namely the Laboratorio ´ de Feixe de Ioes ˜ in ITN (Instituto Tecnologico ´ e Nuclear) Sacavem, ´ Portugal, and the Dynamitron-TandemLaboratorium in Ruhr-Universitat¨ Bochum, Germany. The ITN target chamber was modified to measure these nuclear reactions, with the design and construction of new components, the addition of one turbomolecular pump and a cold finger. The 7Li(p,α) 4He and 6Li(p,α) 3He reactions were measured concurrently with seven and four targets, respectively. These targets were produced in order to obtain adequate and stable lithium depth profiles. In metallic environments, the measured electron screening potential energies are much higher than the predictions of atomic-physics models. The Debye screening model applied to the metallic conduction electrons is able to explain these high values. It is a simple model, but also very robust. Concerning primordial nucleosynthesis and stellar evolution models, these results are very important as they show that laboratory measurements are well controlled, and the model inputs from these cross sections are therefore correct. In this work the 7Li(p,p)7Li differential cross section was also measured, which is useful to describe the 7Li(p,α) 4He entrance channel.

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.

Electron driven reactions in complexes embedded in superfluid helium droplets

A thesis submitted to the University of Innsbruck for the doctor degree in Natural Sciences, Physics and New University of Lisbon for the doctor degree in Physics, Atomic and Molecular Physics

Kinetics studies of the superoxide-mediated electron transfer reactions between rubredoxin-type proteins and superoxide reductases

J Biol Inorg Chem (2006) 11: 433–444 DOI 10.1007/s00775-006-0090-0

Children emotional reactions toward advertising and brands: A drawing experiment

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

Study of nuclear reactions relevant for astrophysics by Micro-AMS

Dissertação para obtenção do Grau de Doutor em Física

Fossil fuel dependence and interfuel substitution in the electricity sector of Western European countries between 1990-2011

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

The role of humor in television advertisement: A study regarding Portuguese consumers’ emotions and reactions

The purpose of this study is to explore the humorous side of television advertisement and its impact on Portuguese consumers’ hearts, minds and wallets. Both qualitative (through in-depth interviews) and quantitative (through an on-line survey and subsequent statistical data analysis) methods were used, guaranteeing a more consistent, strong and valid research. Twenty-five interviews with randomly chosen consumers were conducted face-to-face and three interviews via e-mail with marketers and television advertisers were performed in order to explore profoundly the subject. Moreover, 360 people have answered the on-line survey. Through the analysis of the data collected humor perception was found to be positively correlated with persuasion and intention to purchase the product; intention to share the advert; message comprehension; product liking and development of positive feelings towards the brand and brand credibility variables. The main implication of these findings relies on the fact that humor in advertising is able to boost its effectiveness.

Photochemical synthesis and functional transformations of bicyclic vinyl aziridines

Aziridines, a class of organic compounds containing a three membered heterocycle with a nitrogen atom, are extremely valuable molecules in organic and medicinal chemistry. They are frequently used as versatile precursors in the synthesis of natural products, and many biologically active molecules possess the aziridine moiety. The reactivity of aziridines has been studied, for example, in ring-opening reactions with thiols. However, not much interest seems to be given to reactions of aziridines in aqueous media, despite the numberless advantages of using water as solvent in organic chemistry. The nucleophilic ring-opening reaction of aziridines in aqueous media was here explored. Following the Kaplan aziridine synthetic methodology, in which pyridinium salts undergo a photochemical transformation to give bicyclic vinyl aziridines, new aziridines were synthetized. Their nucleophilic ring-opening reaction in water under physiological conditions was investigated and a range of sulphur, nitrogen, carbon and oxygen nucleophiles tested. Thiols, anilines and azide proved to be good nucleophiles to react with the aziridines, giving the ring-opening product in moderate to good yields. The best results were obtained with thiols, more specifically with cysteine-derived nucleophiles. Preliminary results show that these bicyclic vinyl aziridines can modify calcitonin, a peptide containing two cysteine amino acids residues, grating them the potential to be used in bioconjugation as ligands to cysteine-containing proteins, or even as enzyme inhibitors of, for example, cysteine proteases. Additionally, exploratory investigations suggest that the separation of both enantiomers of the bicyclic vinyl aziridine can be performed by taking advantage of an enzymatic methodology for the resolution of racemic secondary alcohols. Both enantiomers would be highly valuable as precursors in the synthesis of enantiomerically pure molecules, as no other method is currently reported for their separation.