Eggs and eggshells of Crocodylomorpha from the Upper Jurassic of Portugal

Crocodylomorph eggs are relatively poorly known in the fossil record when compared with skeletal remains, which are found all over the world, or when compared with dinosaur eggs. Herein are described crocodiloid eggshells from the Upper Jurassic Lourinhã Formation of Portugal, recovered from five sites: Cambelas (clutch), Casal da Rola, Peralta (eggshell fragments), and Paimogo North and South (three partial crushed eggs and eggshell fragments). The clutch of Cambelas, composed of 13 eggs, is the only sample not found in association with dinosaur eggshells. Morphological characters of the eggshells described herein, such as shell units and microstructure, are consistent with the crocodiloid morphotype. As such, this material is assigned to the oofamily Krokolithidae, making them the oldest known crocodylomorph eggs so far and the best record for eggs of non-crocodylian crocodylomorphs. Two new ootaxa are erected, Suchoolithus portucalensis oogen. et oosp. nov, for the clutch of Cambelas, and Krokolithes dinophilus, oosp. nov., for the remaining eggshells. The basic structure of crocodilian eggshells has remained stable since at least the Late Jurassic. Additionally, the findings suggest previously unknown biological associations with contemporary archosaurs, shedding light on the poorly understood egg morphology, reproduction strategies and paleobiology of crocodylomorphs during the Late Jurassic.

Isolation and characterisation of metallothionein from the clam Ruditapes decussatus

Aquatic Toxicology 63 (2003) 307-318

Purification and Preliminary Characterization of Tetraheme Cytochrome and Adenylylsulf te Reductase from the Peptidolytic Sulfate-Reducing Bacterium Desulfovibrio aminophilus DSM 12254

Bioinorganic Chemistry and Applications Volume 3 (2005), Issue 1-2, Pages 81-91

Recognising, measuring and disclosing intangible assets under IAS 38 and IFRIC 12: Evidence from the Portuguese listed companies

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

Mental health and neuroscience. A perspective from the magazine market

Mental health awareness has been rising worldwide, motivated by its social and economic costs. Despite the investment in research in neuroscience in the recent years, little is known about the underlying mechanisms in the brain that are correlated with psychiatric conditions. This project, through two feature articles suitable to be published in magazines, provides perspectives onto mental health research. First it presents an example where psychiatry joins forces with neuroscience and computer science in an interdisciplinary effort to improve the life of those affected by mental disorders. The second article gathers opinions which claim that mental health research priorities should be set by patients themselves, or even that people with lived experience of mental health issues should have an active role in that research. This project was planned and researched while I was an Erasmus student at Nottingham Trent University, in the United Kingdom.

Universal banks and corporate control: Evidence from the global syndicated loan market

We investigate the effects of bank control over borrower firms whether by representation on boards of directors or by the holding of shares through bank asset management divisions. Using a large sample of syndicated loans, we find that banks are more likely to act as lead arrangers in loans when they exert some control over the borrower firm. Bank-firm governance links are associated with higher loan spreads during the 2003-2006 credit boom, but lower spreads during the 2007-2008 financial crisis. Additionally, these links mitigate credit rationing effects during the crisis. The results are robust to several methods to correct for the endogeneity of the bank- firm governance link. Our evidence, consistent with intertemporal smoothing of loan rates, suggests there are costs and benefits from banks’ involvement in firm governance.

CEO overconfidence and leverage ratio: Evidence from the founder members of Mila

The study investigates the impact of the managerial overconfidence bias on the capital structure of a sample of 78 firms from Chile, Peru and Colombia, during the years 1996-2014. We infer that there is a positive relation between the leverage ratio and a) the overconfidence; b) the experience and c) the male gender of the executive. Overconfidence is measured according to the status of the CEO (entrepreneur or not-entrepreneur) and the hypotheses are tested through dynamic panel data model. The empirical results show a highly significant positive correlation between overconfidence and leverage ratio and between gender and leverage ratio while, in contrast, the relation between experience and leverage ratio is negative.

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.