Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Geospatial assessment of pest-induced forest damage through the use of UAV-bases NIR imaging and Gi-technology

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

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.

Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Analysis of temporal and spatial variations of forest. A case of study in notheastern Armenia

The forest has a crucial ecological role and the continuous forest loss can cause colossal effects on the environment. As Armenia is one of the low forest covered countries in the world, this problem is more critical. Continuous forest disturbances mainly caused by illegal logging started from the early 1990s had a huge damage on the forest ecosystem by decreasing the forest productivity and making more areas vulnerable to erosion. Another aspect of the Armenian forest is the lack of continuous monitoring and absence of accurate estimation of the level of cuts in some years. In order to have insight about the forest and the disturbances in the long period of time we used Landsat TM/ETM + images. Google Earth Engine JavaScript API was used, which is an online tool enabling the access and analysis of a great amount of satellite imagery. To overcome the data availability problem caused by the gap in the Landsat series in 1988- 1998, extensive cloud cover in the study area and the missing scan lines, we used pixel based compositing for the temporal window of leaf on vegetation (June-late September). Subsequently, pixel based linear regression analyses were performed. Vegetation indices derived from the 10 biannual composites for the years 1984-2014 were used for trend analysis. In order to derive the disturbances only in forests, forest cover layer was aggregated and the original composites were masked. It has been found, that around 23% of forests were disturbed during the study period.

An overview of the higher level classification of Pucciniomycotina based on combined analyses of nuclear large and small subunit rDNA sequences

Mycologia, Vol. 98, nº6

Automatic sleep apnea detection and sleep classification using the ECG and the SpO2 signals

Dissertation for a Masters Degree in Computer and Electronic Engineering

Unsupervised automatic music genre classification

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Time-domain optimization of amplifiers based on distributed genetic algorithms

Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer Engineering

A framework for supporting knowledge representation – an ontological based approach

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Unsupervised classification of remote sensing images combining Self Organizing Maps and segmentation techniques

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Mental health care assessed based on structure, process and outcome : a retrospective cohort study

RESUMO: As doenças mentais são comuns, universais e associadas a uma significativa sobrecarga pessoal, familiar, social e económica. Os Serviços de Saúde Mental devem abordar de forma adequada as necessidades dos pacientes e familiares tanto ao nível clínico como também ao nível social. O presente estudo foi realizado num período de grande transformação nos sistemas de saúde primário e de saúde mental em Portugal, num Departamento de Psiquiatria desenvolvido com base nos princípios da OMS. Os objectivos incluem a caracterização: 1) das Unidades Funcionais do Departamento; 2) dos pacientes internados pela primeira vez no internamento de agudos; 3) da utilização dos serviços nas equipas comunitárias após a alta; e 4) da avaliação de alguns dos indicadores de qualidade do departamento, com recurso ao modelo de Donabedian sobre a articulação entre a Estrutura-Processo-Resultados. Metodologia: Foi escolhido um estudo de coorte retrospectivo. Todos os pacientes internados pela primeira vez entre 2008 e 2010 foram incluídos no estudo. Os seus processos clínicos e a base de dados do hospital onde são registados todos os contactos que estes tiveram com os profissionais de saúde mental foram revistos de forma a obter dados sociodemográficos e clínicos, durante o período do estudo e após a alta. Os instrumentos utilizados foram o WHO-ICMHC (Classificação Internacional de Cuidados de Saúde Mental), para caracterizar o Departamento, o AIESMP (Avaliação Inicial de Enfermagem em Saúde Mental e Psiquiatria) para recolha dos dados sociodemográficos, e o VSSS (Escala de Satisfação com os Serviços de Verona) de forma a avaliar a satisfação dos pacientes em relação aos cuidados recebidos. A análise estatística incluiu a análise descritiva, quantitativa e qualitativa dos dados. Resultados: As Unidades Funcionais do Departamento revelaram níveis elevados de articulação e consistência com as necessidades de cuidados psiquiátricos e reabilitação psicossocial dos pacientes. Os 543 pacientes admitidos pela primeira vez eram maioritariamente (56.9%) mulheres, caucasianas (81.2%), com diagnóstico de perturbações do humor (66.3%), internadas voluntariamente (59.7%), e uma idade média de 45.1 anos. Estas eram significativamente mais velhas, mais frequentemente empregadas, casadas/coabitar e tinham uma prevalência mais elevada de perturbações do humor, comparativamente aos homens. O internamento compulsivo era mais significativo nos homens (54.7%). A taxa de abandono no pós-alta (4.2%) e a taxa de reinternamentos (2.9%) na quinzena após a alta revelaram-se inferiores aos padrões na literatura internacional. De forma global, a satisfação dos pacientes com os cuidados de saúde mental foi positiva. Conclusões: Os cuidados prestados mostraram-se eficazes, adaptados e baseados nas necessidades e problemas específicos dos pacientes. A continuidade e a abrangência de cuidados foram difundidos e mantidos ao longo do processo de cuidados. Este Departamento pode ser considerado um exemplo de como proporcionar tratamento digno e eficiente, e uma referência para futuros serviços de psiquiatria.-------------- ABSTRACT: Mental health disorders are common, universal, and associated with heavy personal, family, social and economic burden. Mental health services should be aimed at adequately addressing patients’ and families’ needs at clinical and social level. The current study was carried out at a time of great transformation in the health and mental health systems in Portugal, in a Psychiatric Department developed taking in consideration the WHO principles. The objectives included characterizing: 1) the Psychiatric Department’s different units; 2) the patients admitted for the first time to the inpatient unit; 3) their use of community mental health services after discharge; and 4) assessing some of the department’s quality indicators, with resource to Donabedian’s Structure-Process-Outcome model. Methodology: A retrospective cohort design was chosen. All the firstly admitted patients in the period between 2008 and 2010 were included in the study. Their clinical records and the hospital’s database which registers all of the contacts the patients had with the mental health professionals during the study period, were reviewed to retrieve sociodemographic and clinical data and information on follow-up. The instruments used were the WHO International Classification of Mental Health Care (ICMHC) to characterize the department, the Initial Nurses’ Assessment in Mental Health and Psychiatry (AIESMP) for patients’ sociodemographic data, and the Verona Service Satisfaction Scale (VSSS) to assess patients’ satisfaction with care received. Statistical analysis included descriptive, quantitative and qualitative analysis of the data. Results: The Department’s Functional units revealed high levels of articulation, and were consistent with patients’ needs for psychiatric care and psychosocial rehabilitation. The 543 patients firstly admitted were mainly (56.9%) female, Caucasian (81.2%), diagnosed with mood disorders (66.3%), voluntarily admitted (59.7%), and with a mean age of 45.1 years. Female patients were significantly older, more frequently employed, married/cohabiting and had a higher prevalence of mood disorders when compared to males. Involuntary admission was more significant in males (54.7%). Dropout rates during follow-up (4.2%) and readmission rates (2.9%) in the fortnight following discharge were lower than standards in international literature. Overall patients’ satisfaction with mental health care was positive. Conclusions: The care delivered was effective, adapted and based on the patients’ specific needs and problems. Continuity and comprehensiveness of care was endorsed and maintained throughout the care process. This department may be considered an example of both humane and effective treatment, and a reference for future psychiatric care.

Development of novel active pharmaceutical ionic liquids and salts based on antibiotics and anti-fungal drugs

Ionic Liquids (ILs) are class of compounds, which have become popular since the mid-1990s. Despite the fact that ILs are defined by one physical property (melting point), many of the potential applications are now related to their biological properties. The use of a drug as a liquid can avoid some problems related to polymorphism which can influence a drug´s solubility and thus its dosages. Also, the arrangement of the anion or cation with a specific drug might be relevant in order to: a) change the correspondent biopharmaceutical drug classification system; b) for the drug formulation process and c) the change the Active Pharmaceutical Ingredients’ (APIs). The main goal of this Thesis is the synthesis and study of physicochemical and biological properties of ILs as APIs from beta-lactam antibiotics (ampicillin, penicillin G and amoxicillin) and from the anti-fungal Amphotericin B. All the APIs used here were neutralized in a buffer appropriate hydroxide cations. The cation hydroxide was obtained on Amberlite resin (in the OH form) in order to exchange halides. The biological studies of these new compounds were made using techniques like the micro dilution and colorimetric methods. Overall a total of 19 new ILs were synthesised (6 ILs based on ampicillin, 4 ILs, based on amoxicillin, 6 ILs based on penicillin G and 4 ILs based on amphotericin B) and characterized by spectroscopic and analytical methods in order to confirm their structure and purity. The study of the biological properties of the synthesised ILs showed that some have antimicrobial activity against bacteria and yeast cells, even in resistant bacteria. Also this work allowed to show that ILs based on ampicillin could be used as anti-tumour agents. This proves that with a careful selection of the organic cation, it is possible to provoke important physico-chemical and biological alteration in the properties of ILs-APIs with great impact, having in mind their applications.

UAV - based imagery processing using structure from motion and remote sensing technology

With the recent advances in technology and miniaturization of devices such as GPS or IMU, Unmanned Aerial Vehicles became a feasible platform for a Remote Sensing applications. The use of UAVs compared to the conventional aerial platforms provides a set of advantages such as higher spatial resolution of the derived products. UAV - based imagery obtained by a user grade cameras introduces a set of problems which have to be solved, e. g. rotational or angular differences or unknown or insufficiently precise IO and EO camera parameters. In this work, UAV - based imagery of RGB and CIR type was processed using two different workflows based on PhotoScan and VisualSfM software solutions resulting in the DSM and orthophoto products. Feature detection and matching parameters influence on the result quality as well as a processing time was examined and the optimal parameter setup was presented. Products of the both workflows were compared in terms of a quality and a spatial accuracy. Both workflows were compared by presenting the processing times and quality of the results. Finally, the obtained products were used in order to demonstrate vegetation classification. Contribution of the IHS transformations was examined with respect to the classification accuracy.