Assessment of climate change statistical downscaling methods: Application and comparison of two statistical methods to a single site in Lisbon

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente

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

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.

Assessing the efficacy of the Mental Health Gap Action Programme (mhGAP) training for non-specialized health workers in Ethiopia

RESUMO: A OMS lançou em 2008, o Programa de Acção do Gap em Saúde Mental (mhGAP) para suprir a falta de cuidados, especialmente em países de rendimento baixo e médio, para as pessoas que sofrem de perturbações mentais, neurológicas e de uso de substâncias (MNS). Um componente crucial do mhGAP é representado pelo esforço no sentido da integração da saúde mental nos cuidados de saúde primários. Na Etiópia, o mhGAP foi monitorizado durante 3 anos, graças a um projeto de demonstração implementado em clínicas selecionadas em quatro regiões do país. A fase de demonstração de mhGAP na Etiópia traduziu-se principalmente na formação de profissionais de saúde não especializados, fornecendo-lhes orientação e supervisão apoiada para a utilização de medicamentos psicotrópicos essenciais e na coordenação com o Ministério Etíope Federal da Saúde, Hospital Amanuel de Saúde Mental e as Secretarias Regionais de Saúde ( RHBs ). O presente trabalho investigou a eficácia do pacote de formação mhGAP através de uma análise das pontuações dos participantes no pré- e pós-testes. A análise estatística mostrou - com uma exceção - que a melhoria dos formandos é estatisticamente significativa, o que sugere que os conhecimentos dos participantes é melhorada na fase de pós-teste. A eficácia do pacote de formação mhGAP para profissionais de saúde não especializados é uma evidência promissora de que os mesmos podem ser treinados com sucesso para realizar um pacote básico de intervenções para a prestação de cuidados e tratamento para pessoas com perturbações mentais, neurológicas e de uso de substâncias. Este trabalho destaca, também, várias limitações não apenas inerentes ao próprio projecto de investigação tais como o número limitado de respostas que foram analisadas e a falta de dados de uma das quatro regiões onde mhGAP foi testado na Etiópia. As principais limitações decorrem de facto da abordagem global limitar as intervenções de saúde mental ao programa de formação e supervisão dos trabalhadores de cuidados de saúde primários . Este processo só será bem sucedido se, juntamente com outras intervenções - que vão desde o desenvolvimento de currículos para o desenvolvimento de uma legislação de saúde mental -, fôr incluído numa estratégia mais abrangente para a reforma da saúde mental e desafiar o status quo.-----------ABSTRACT:In 2008, WHO launched the Mental Health Gap Action Programme (mhGAP) to address the lack of care, especially in low- and middle- income countries, for people living with mental, neurological and substance use (MNS) disorders. A crucial component of mhGAP is represented by the endeavor towards integration of mental health into primary health care. In Ethiopia, mhGAP has been piloted for 3 years thanks to a demonstration project implemented in selected clinics in 4 regions of the country. The demonstration phase of mhGAP in Ethiopia has mainly translated into training of non-specialized health workers, providing them with mentorship and supportive supervision, availing essential psychotropic medications and coordinating with the Ethiopian Federal Ministry of Health, Amanuel Mental Health Hospital and the Regional Health Bureaus (RHBs). The present paper investigated the efficacy of the mhGAP training package through an analysis of the participants’ scores at pre-test and post-test. The statistical analysis showed - with one exception - that the improvement of trainees is statistically significant, therefore suggesting that the knowledge of participants is improved in the post-test phase. The efficacy of the mhGAP training package on non-specialized health workers is promising evidence that non-specialized health-care providers can be successfully trained to deliver a basic package of interventions for providing care and treatment for people with mental, neurological and substance use disorders. However, this paper also highlights several limitations, which are not only inherent to the research itself, such as the limited number of scores that was analyzed, or the lack of data from one of the four regions where mhGAP has been piloted in Ethiopia; major limitations occur in fact in the overall approach of confining mental health interventions to training and supervising primary health care workers. This process will only be successful if coupled with other interventions – ranging from curricula development to development of a mental health legislation - and if it is included in a more comprehensive strategy to reform mental health and challenge the status quo.

Politicization of non-political events

Spatial analysis and social network analysis typically take into consideration social processes in specific contexts of geographical or network space. The research in political science increasingly strives to model heterogeneity and spatial dependence. To better understand and geographically model the relationship between “non-political” events, streaming data from social networks, and political climate was the primary objective of the current study. Geographic information systems (GIS) are useful tools in the organization and analysis of streaming data from social networks. In this study, geographical and statistical analysis were combined in order to define the temporal and spatial nature of the data eminating from the popular social network Twitter during the 2014 FIFA World Cup. The study spans the entire globe because Twitter’s geotagging function, the fundamental data that makes this study possible, is not limited to a geographic area. By examining the public reactions to an inherenlty non-political event, this study serves to illuminate broader questions about social behavior and spatial dependence. From a practical perspective, the analyses demonstrate how the discussion of political topics fluсtuate according to football matches. Tableau and Rapidminer, in addition to a set basic statistical methods, were applied to find patterns in the social behavior in space and time in different geographic regions. It was found some insight into the relationship between an ostensibly non-political event – the World Cup - and public opinion transmitted by social media. The methodology could serve as a prototype for future studies and guide policy makers in governmental and non-governmental organizations in gauging the public opinion in certain geographic locations.