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.

Monitoring chlorophyll-a with remote sensing techniques in the Tagus Estuary

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Gestão e Sistemas Ambientais

Sensitivity correction of images obtained with the prototype Clear-PEM in pre-clinical environment

Dissertation presented at Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa to obtain a Master Degree in Biomedical Engineering

Welfare analysis of regulating mobile termination rates in the UK with an application to the Orange/T-Mobile merger

We present a calibrated model of the UK mobile telephony market with four mobile networks; calls to and from the fixed network; network-based price discrimination; and call externalities. Our results show that reducing mobile termination rates broadly in line with the recent European Commission Recommendation to either pure long-run incremental cost ; reciprocal termination charges with fixed networks; or Bill & Keep (i.e. zero termination rates), increases social welfare, consumer surplus and networks profits. Depending on the strength of call externalities, social welfare may increase by as much as £ 990 million to £ 4.5 billion per year, with Bill & Keep leading to the highest increase in welfare. We also apply the model to estimate the welfare effects of the 2010 merger between Orange and T-Mobile under different scenarios concerning MTRs, and predict that consumer surplus decreases strongly.

Evolution of Teeth and Quadrate in Non-avian Theropoda (Dinosauria: Saurischia), with the Description of Torvosaurus Remains from Portugal

Theropods form a highly successful and morphologically diversified group of dinosaurs that gave rise to birds. They include most, if not all, carnivorous dinosaurs, yet many theropod clades were secondarily adapted to piscivory, omnivory and herbivory, and theropods show a large array of skull and dentition morphologies. This work aims to investigate aspects of the evolution of theropod dinosaurs by analyzing in detail both the anatomy and ontogeny of teeth and quadrates in non-avian theropods, and by studying embryonic and adult material of a new species of theropod. A standardized list of terms and notations for each anatomical entity of the tooth, quadrate, and maxilla is here proposed with the goal of facilitating descriptions of these important cranial and dental elements. The distribution of thirty dental characters among 113 theropod taxa is investigated, and a list of diagnostic dental characters is proposed. As an example, four isolated theropod teeth from the Lourinhã Formation (Kimmeridgian‒Tithonian) of Portugal are described and identified based on a cladistic analysis performed on a data matrix of 141 dentition-based characters coded in 60 taxa. Two shed teeth are referred to an abelisaurid, providing the first record of Abelisauridae in the Jurassic of Laurasia and the one of the oldest records of this clade in the world, suggesting a possible radiation of Abelisauridae in Europe well before the Upper Cretaceous. The consensus tree resulting from this phylogenetic analysis, the most extensive on theropod teeth, indicates that theropod teeth provide reliable data for identification at approximately family level, and this method will help identifying theropod teeth with more confidence. A detailed description of the dentition of Megalosauridae is also provided, and a discriminant analysis performed on a dataset of numerical data collected on the teeth of 62 theropod taxa reveals that megalosaurid teeth are hardly distinguishable from other theropod clades with ziphodont dentition. This study highlights the importance of detailing anatomical descriptions and providing additional morphometric data on teeth with the purpose of helping to identify isolated theropod teeth. In order to evaluate the phylogenetic potential and investigate the evolutionary transformations of the quadrate, a phylogenetic morphometric analysis as well as a cladistic analysis using 98 discrete quadrate related characters were conducted. The quadrate morphology by its own provides a wealth of data with strong phylogenetic signal, and the phylogenetic morphometric analysis reveals two main morphotypes of the mandibular articulation of the quadrate linked to function. As an example, six isolated quadrates from the Kem Kem beds (Cenomanian) of Morocco are determined to be from juvenile and adult individuals of Spinosaurinae based on phylogenetic, morphometric, and phylogenetic morphometric analyses. Morphofunctional analysis of the spinosaurid mandibular articulation has shown that the posterior parts of the two mandibular rami displaced laterally when the jaw was depressed due to a mediolaterally oriented intercondylar sulcus of the quadrate. Such lateral movement of the mandibular ramus was possible due to a movable mandibular symphysis in spinosaurids, allowing the pharynx to be widened. A new species of theropod from the Lourinhã Formation of Portugal, Torvosaurus gurneyi, is erected based on a right maxilla and an incomplete caudal centrum. This taxon supports the mechanism of vicariance that occurred in the Iberian Meseta during the Late Jurassic when the proto-Atlantic was already well formed. A theropod clutch containing several crushed eggs and embryonic material is also assigned to this new species of Torvosaurus. Investigation on the maxilla ontogeny in basal tetanurans reveals that crown denticles, elongation of the anterior ramus, and fusion of interdental plates appear at a posthatchling stage. On the other hand, maxillary pneumaticity is already present at an embryonic stage in non-avian theropods.

Cerebral vasculopathy in children with sickle cell disease A study of genetic modulators of the disease

Sickle cell disease (SCD) is a genetic disorder with recessive transmission, caused by the mutation HBB:c.20A>T. It originates hemoglobin S that forms polymers inside the erythrocyte, upon deoxygenation, deforming it and ultimately leading to premature hemolysis. The disease presents with high heterogeneity of clinical manifestations, the most devastating of which, ischemic stroke, occurs in 11% of patients until 20 years of age. In this study, we tried to identify genetic modifiers of risk and episodes of stroke by studying 66 children with SCD, grouped according to the degree of cerebral vasculopathy (Stroke, Risk and Control). Association studies were performed between the three phenotypic groups and hematological and biochemical parameters of patients, as well as with 23 polymorphic regions in genes related to vascular cell adhesion (VCAM-1, THBS-1 and CD36), vascular tonus (NOS3 and ET-1) and inflammation (TNF-α and HMOX-1). Relevant data was collected from patient’s medical records. Known genetic modulators of SCD (beta-globin cluster haplotype and HBA and BCL11A genotypes) and putative genetic modifiers of cerebral vasculopathy were characterized. Differences in their distribution among groups were assessed. VCAM-1 rs1409419 allele C and NOS3 rs207044 allele C were associated to stroke events, while VCAM-1 rs1409419 allele T was found to be protective. Alleles 4a and 4b of NOS3 27 bp VNTR appeared to be respectively associated to stroke risk and protection. HMOX-1 longer STRs seemed to predispose to stroke. Higher hemoglobin F levels were found in Control group, as a result of Senegal haplotype or of BCL11A rs11886868 allele T, and higher lactate dehydrogenase levels, marker of hemolysis, were found in Risk group. Molecular mechanisms underlying the modifier functions of the relevant genetic variants are discussed.