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.

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.

Direct electrochemistry of the Desulfovibrio gigas aldehyde oxidoreductase

Eur. J. Biochem. 271, 1329–1338 (2004)

Fast-to-fed shift in glucose homeostasis: clues to an earlier detection of human prediabetic states

FCM: UC Bioquímica I - PhD Thesis

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

Fast start finance. Um compromisso internacional para combate às alterações climáticas

Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Ciência Política e Relações Internacionais na área de especialização em Globalização e Ambiente

Electrochemical characterization of Dps, a DNA-protecting protein

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Artefacts induced on c-type haem proteins by electrode surfaces

J Biol Inorg Chem (2011) 16:209–215 DOI 10.1007/s00775-010-0717-z

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

Fast screening for diagnostic of heart ischemic episodes

Dissertação para obtenção do Grau de Doutor em Química Sustentável

Green synthesis of 2-Oxazoline-based polymers with antimicrobial activity using scCO2

Using a green methodology, 17 different poly(2-oxazolines) were synthesized starting from four different oxazoline monomers. The polymerization reactions were conducted in supercritical carbon dioxide under a cationic ring-opening polymerization (CROP) mechanism using boron trifluoride diethyl etherate as the catalyst. The obtained living polymers were then end-capped with different types of amines, in order to confer them antimicrobial activity. For comparison, four polyoxazolines were end-capped with water, and by their hydrolysis the linear poly(ethyleneimine) (LPEI) was also produced. After functionalization the obtained polymers were isolated, purified and characterized by standard techniques (FT-IR, NMR, MALDI-TOF and GPC). The synthesized poly(2-oxazolines) revealed an unusual intrinsic blue photoluminescence. High concentration of carbonyl groups in the polymer backbone is appointed as a key structural factor for the presence of fluorescence and enlarges polyoxazolines’ potential applications. Microbiological assays were also performed in order to evaluate their antimicrobial profile against gram-positive Staphylococcus aureus NCTC8325-4 and gram-negative Escherichia coli AB1157 strains, two well known and difficult to control pathogens. The minimum inhibitory concentrations (MIC)s and killing rates of three synthesized polymers against both strains were determined. The end-capping with N,N-dimethyldodecylamine of living poly(2- methyl-2-oxazoline) and poly(bisoxazoline) led to materials with higher MIC values but fast killing rates (less than 5 minutes to achieve 100% killing for both bacterial species) than LPEI, a polymer which had a lower MIC value, but took a longer time to kill both E.coli and S.aureus cells. LPEI achieved 100% killing after 45 minutes in contact with E. coli and after 4 hours in contact with S.aureus. Such huge differences in the biocidal behavior of the different polymers can possibly underlie different mechanisms of action. In the future, studies to elucidate the obtained data will be performed to better understand the killing mechanisms of the polymers through the use of microbial cell biology techniques.

Diffusional kurtosis imaging using a fast heuristic constrained linear least squares algorithm: a plugin for OsiriX

Diffusion Kurtosis Imaging (DKI) is a fairly new magnetic resonance imag-ing (MRI) technique that tackles the non-gaussian motion of water in biological tissues by taking into account the restrictions imposed by tissue microstructure, which are not considered in Diffusion Tensor Imaging (DTI), where the water diffusion is considered purely gaussian. As a result DKI provides more accurate information on biological structures and is able to detect important abnormalities which are not visible in standard DTI analysis. This work regards the development of a tool for DKI computation to be implemented as an OsiriX plugin. Thus, as OsiriX runs under Mac OS X, the pro-gram is written in Objective-C and also makes use of Apple’s Cocoa framework. The whole program is developed in the Xcode integrated development environ-ment (IDE). The plugin implements a fast heuristic constrained linear least squares al-gorithm (CLLS-H) for estimating the diffusion and kurtosis tensors, and offers the user the possibility to choose which maps are to be generated for not only standard DTI quantities such as Mean Diffusion (MD), Radial Diffusion (RD), Axial Diffusion (AD) and Fractional Anisotropy (FA), but also DKI metrics, Mean Kurtosis (MK), Radial Kurtosis (RK) and Axial Kurtosis (AK).The plugin was subjected to both a qualitative and a semi-quantitative analysis which yielded convincing results. A more accurate validation pro-cess is still being developed, after which, and with some few minor adjust-ments the plugin shall become a valid option for DKI computation

Nanostructuring silicon probes via electrodeposition: characterization of electrode coatings for acute in vivo neural recordings

Understanding how the brain works will require tools capable of measuring neuron elec-trical activity at a network scale. However, considerable progress is still necessary to reliably increase the number of neurons that are recorded and identified simultaneously with existing mi-croelectrode arrays. This project aims to evaluate how different materials can modify the effi-ciency of signal transfer from the neural tissue to the electrode. Therefore, various coating materials (gold, PEDOT, tungsten oxide and carbon nano-tubes) are characterized in terms of their underlying electrochemical processes and recording ef-ficacy. Iridium electrodes (177-706 μm2) are coated using galvanostatic deposition under different charge densities. By performing electrochemical impedance spectroscopy in phosphate buffered saline it is determined that the impedance modulus at 1 kHz depends on the coating material and decreased up to a maximum of two orders of magnitude for PEDOT (from 1 MΩ to 25 kΩ). The electrodes are furthermore characterized by cyclic voltammetry showing that charge storage capacity is im-proved by one order of magnitude reaching a maximum of 84.1 mC/cm2 for the PEDOT: gold nanoparticles composite (38 times the capacity of the pristine). Neural recording of spontaneous activity within the cortex was performed in anesthetized rodents to evaluate electrode coating performance.