Enhancing physical activity coaching through personalized motivational strategies and self-adaptive goal-setting: development of self-adaptive processes in a monitoring and coaching smartphone application

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Application of NMR RDC’s, relaxation and self-diffusion for the study of dynamic processes of small molecules in solution

Spin-lattice Relaxation, self-Diffusion coefficients and Residual Dipolar Couplings (RDC’s) are the basis of well established Nuclear Magnetic Resonance techniques for the physicochemical study of small molecules (typically organic compounds and natural products with MW < 1000 Da), as they proved to be a powerful and complementary source of information about structural dynamic processes in solution. The work developed in this thesis consists in the application of the earlier-mentioned NMR techniques to explore, analyze and systematize patterns of the molecular dynamic behavior of selected small molecules in particular experimental conditions. Two systems were chosen to investigate molecular dynamic behavior by these techniques: the dynamics of ion-pair formation and ion interaction in ionic liquids (IL) and the dynamics of molecular reorientation when molecules are placed in oriented phases (alignment media). The application of NMR spin-lattice relaxation and self-diffusion measurements was applied to study the rotational and translational molecular dynamics of the IL: 1-butyl-3-methylimidazolium tetrafluoroborate [BMIM][BF4]. The study of the cation-anion dynamics in neat and IL-water mixtures was systematically investigated by a combination of multinuclear NMR relaxation techniques with diffusion data (using by H1, C13 and F19 NMR spectroscopy). Spin-lattice relaxation time (T1), self-diffusion coefficients and nuclear Overhauser effect experiments were combined to determine the conditions that favor the formation of long lived [BMIM][BF4] ion-pairs in water. For this purpose and using the self-diffusion coefficients of cation and anion as a probe, different IL-water compositions were screened (from neat IL to infinite dilution) to find the conditions where both cation and anion present equal diffusion coefficients (8% water fraction at 25 ºC). This condition as well as the neat IL and the infinite dilution were then further studied by 13C NMR relaxation in order to determine correlation times (c) for the molecular reorientational motion using a mathematical iterative procedure and experimental data obtained in a temperature range between 273 and 353 K. The behavior of self-diffusion and relaxation data obtained in our experiments point at the combining parameters of molar fraction 8 % and temperature 298 K as the most favorable condition for the formation of long lived ion-pairs. When molecules are subjected to soft anisotropic motion by being placed in some special media, Residual Dipolar Couplings (RDCs), can be measured, because of the partial alignment induced by this media. RDCs are emerging as a powerful routine tool employed in conformational analysis, as it complements and even outperforms the approaches based on the classical NMR NOE or J3 couplings. In this work, three different alignment media have been characterized and evaluated in terms of integrity using 2H and 1H 1D-NMR spectroscopy, namely the stretched and compressed gel PMMA, and the lyotropic liquid crystals CpCl/n-hexanol/brine and cromolyn/water. The influence that different media and degrees of alignment have on the dynamic properties of several molecules was explored. Different sized sugars were used and their self-diffusion was determined as well as conformation features using RDCs. The results obtained indicate that no influence is felt by the small molecules diffusion and conformational features studied within the alignment degree range studied, which was the 3, 5 and 6 % CpCl/n-hexanol/brine for diffusion, and 5 and 7.5 % CpCl/n-hexanol/brine for conformation. It was also possible to determine that the small molecules diffusion verified in the alignment media presented close values to the ones observed in water, reinforcing the idea of no conditioning of molecular properties in such media.

Democratic dialogue and collective learning in the context of self-managed teams

This paper provides a longitudinal, empirical view of the multifaceted and reciprocal processes of organizational learning in a context of self-managed teams. Organizational learning is seen as a social construction between people and actions in a work setting. The notion of learning as situated (Brown & Duguid 1989, Lave& Wenger 1991, Gherardi & al. 1998, Easterby-Smith & Araujo 1999, Abma 2003) opens up the possibility for placing the focus of research on learning in the community rather than in individual learning processes. Further, in studying processes in their social context, we cannot avoid taking power relations into consideration (Contu & Willmott 2003). The study is based on an action research with a methodology close to the ‘democratic dialogue’ presented by Gustavsen (2001). This gives a ground for research into how the learning discourse developed in the case study organization over a period of 5 years, during which time the company abandoned a middle management level of hierarchy and the teams had to figure out how to work as self-managed units. This paper discusses the (re)construction of power relations and its role in organizational learning. Power relations are discussed both in vertical and horizontal work relations. A special emphasis is placed on the dialectic between managerial aims and the space for reflection on the side of employees. I argue that learning is crucial in the search for the limits for empowerment and that these limits are negotiated both in actions and speech. This study unfolds a purpose-oriented learning process, constructing an open dialogue, and describes a favourable context for creative, knowledge building communities.

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.

Natural and amyloid self-assembly of S100 proteins: structural basis of functional diversity

The S100 proteins are 10-12 kDa EF-hand proteins that act as central regulators in a multitude of cellular processes including cell survival, proliferation, differentiation and motility. Consequently, many S100 proteins are implicated and display marked changes in their expression levels in many types of cancer, neurodegenerative disorders, inflammatory and autoimmune diseases. The structure and function of S100 proteins are modulated by metal ions via Ca2+ binding through EF-hand motifs and binding of Zn2+ and Cu2+ at additional sites, usually at the homodimer interfaces. Ca2+ binding modulates S100 conformational opening and thus promotes and affects the interaction with p53, the receptor for advanced glycation endproducts and Toll-like receptor 4, among many others. Structural plasticity also occurs at the quaternary level, where several S100 proteins self-assemble into multiple oligomeric states, many being functionally relevant. Recently, we have found that the S100A8/A9 proteins are involved in amyloidogenic processes in corpora amylacea of prostate cancer patients, and undergo metal-mediated amyloid oligomerization and fibrillation in vitro. Here we review the unique chemical and structural properties of S100 proteins that underlie the conformational changes resulting in their oligomerization upon metal ion binding and ultimately in functional control. The possibility that S100 proteins have intrinsic amyloid-forming capacity is also addressed, as well as the hypothesis that amyloid self-assemblies may, under particular physiological conditions, affect the S100 functions within the cellular milieu.

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.

Development of thermoresponsive (polysulfone/polyacrylonitrile) - based membranes for bioseparation processes

Mestrado integrado em Engenharia Química e Bioquímica

Development of ion jelly thin films for electrochemical devices

Dissertação para obtenção do Grau de Doutor em Química Sustentável

Assessing self-organization and emergence in Evolvable Assembly Systems (EAS)

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

A solution for estimates in software development projects

The Corporate world is becoming more and more competitive. This leads organisations to adapt to this reality, by adopting more efficient processes, which result in a decrease in cost as well as an increase of product quality. One of these processes consists in making proposals to clients, which necessarily include a cost estimation of the project. This estimation is the main focus of this project. In particular, one of the goals is to evaluate which estimation models fit the Altran Portugal software factory the most, the organization where the fieldwork of this thesis will be carried out. There is no broad agreement about which is the type of estimation model more suitable to be used in software projects. Concerning contexts where there is plenty of objective information available to be used as input to an estimation model, model-based methods usually yield better results than the expert judgment. However, what happens more frequently is not having this volume and quality of information, which has a negative impact in the model-based methods performance, favouring the usage of expert judgement. In practice, most organisations use expert judgment, making themselves dependent on the expert. A common problem found is that the performance of the expert’s estimation depends on his previous experience with identical projects. This means that when new types of projects arrive, the estimation will have an unpredictable accuracy. Moreover, different experts will make different estimates, based on their individual experience. As a result, the company will not directly attain a continuous growing knowledge about how the estimate should be carried. Estimation models depend on the input information collected from previous projects, the size of the project database and the resources available. Altran currently does not store the input information from previous projects in a systematic way. It has a small project database and a team of experts. Our work is targeted to companies that operate in similar contexts. We start by gathering information from the organisation in order to identify which estimation approaches can be applied considering the organization’s context. A gap analysis is used to understand what type of information the company would have to collect so that other approaches would become available. Based on our assessment, in our opinion, expert judgment is the most adequate approach for Altran Portugal, in the current context. We analysed past development and evolution projects from Altran Portugal and assessed their estimates. This resulted in the identification of common estimation deviations, errors, and patterns, which lead to the proposal of metrics to help estimators produce estimates leveraging past projects quantitative and qualitative information in a convenient way. This dissertation aims to contribute to more realistic estimates, by identifying shortcomings in the current estimation process and supporting the self-improvement of the process, by gathering as much relevant information as possible from each finished project.

Self-initiation expatriation: A focus on Italian and Portuguese students

The purpose of this work project is to analyze the phenomenon of self-initiated expatriation (SIE) through its link to the Protean Career and Career Capital theories, focusing in particular on Italian and Portuguese students attending a Master in the business area. The main research questions are to understand the reasons driving the intention to expatriate, after the conclusion of the academic path, using three main categories (Adventure Motivation, Work Characteristic Motivation and instrumental Motivation) and the intention to repatriate. A sample of Italian and Portuguese students was obtained. Italians show a higher intention to expatriate relative to Portuguese; nevertheless, no other significant differences were found among the two populations, because of the similar cultural background and economic situation. Additionally, several heterogeneities were observed considering other clusters defined by Gender, Teaching Language of the Master and Past International Experiences, across the two nationalities. Furthermore, possible future researches and practical implications were discussed.

Engineering bio-based polymers using alternative solvents and processes

The work presented in this thesis explores novel routes for the processing of bio-based polymers, developing a sustainable approach based on the use of alternative solvents such as supercritical carbon dioxide (scCO2), ionic liquids (ILs) and deep eutectic solvents (DES). The feasibility to produce polymeric foams via supercritical fluid (SCF) foaming, combined with these solvents was assessed, in order to replace conventional foaming techniques that use toxic and harmful solvents. A polymer processing methodology is presented, based on SCF foaming and using scCO2 as a foaming agent. The SCF foaming of different starch based polymeric blends was performed, namely starch/poly(lactic acid) (SPLA) and starch/poly(ε-caprolactone) (SPCL). The foaming process is based on the fact that CO2 molecules can dissolve in the polymer, changing their mechanical properties and after suitable depressurization, are able to create a foamed (porous) material. In these polymer blends, CO2 presents limited solubility and in order to enhance the foaming effect, two different imidazolium based ILs (IBILs) were combined with this process, by doping the blends with IL. The use of ILs proved useful and improved the foaming effect in these starch-based polymer blends. Infrared spectroscopy (FTIR-ATR) proved the existence of interactions between the polymer blend SPLA and ILs, which in turn diminish the forces that hold the polymeric structure. This is directly related with the ability of ILs to dissolve more CO2. This is also clear from the sorption experiments results, where the obtained apparent sorption coefficients in presence of IL are higher compared to the ones of the blend SPLA without IL. The doping of SPCL with ILs was also performed. The foaming of the blend was achieved and resulted in porous materials with conductivity values close to the ones of pure ILs. This can open doors to applications as self-supported conductive materials. A different type of solvents were also used in the previously presented processing method. If different applications of the bio-based polymers are envisaged, replacing ILs must be considered, especially due to the poor sustainability of some ILs and the fact that there is not a well-established toxicity profile. In this work natural DES – NADES – were the solvents of choice. They present some advantages relatively to ILs since they are easy to produce, cheaper, biodegradable and often biocompatible, mainly due to the fact that they are composed of primary metabolites such as sugars, carboxylic acids and amino-acids. NADES were prepared and their physicochemical properties were assessed, namely the thermal behavior, conductivity, density, viscosity and polarity. With this study, it became clear that these properties can vary with the composition of NADES, as well as with their initial water content. The use of NADES in the SCF foaming of SPCL, acting as foaming agent, was also performed and proved successful. The SPCL structure obtained after SCF foaming presented enhanced characteristics (such as porosity) when compared with the ones obtained using ILs as foaming enhancers. DES constituted by therapeutic compounds (THEDES) were also prepared. The combination of choline chloride-mandelic acid, and menthol-ibuprofen, resulted in THEDES with thermal behavior very distinct from the one of their components. The foaming of SPCL with THEDES was successful, and the impregnation of THEDES in SPCL matrices via SCF foaming was successful, and a controlled release system was obtained in the case of menthol-ibuprofen THEDES.