Tecnologias da informação e comunicação na formação continuada de professores: estudode caso em escolas do Brasil e Portugal com recursos e aplicação do Google+

Tese de Doutoramento em Ciências da Educação (área de especialização em Tecnologia Educativa)

Gamification e game-based learning: uma abordagem lúdica à aprendizagem

Relatório de estágio de mestrado em Ensino de Informática

O ensino em grupo de instrumentos musicais: um estudo de caso múltiplo em Portugal e no Brasil

Tese de Doutoramento em Estudos da Criança (área de especialização em Educação Musical)

Cross-language influences on new second language vocabulary acquisition: the effect of learning method

Dissertação de mestrado integrado em Psicologia

Influence of glucose concentration on the structure and quantity of biofilms formed by Candida parapsilosis

Candida parapsilosis is nowadays an emerging opportunistic pathogen and its increasing incidence is part related to the capacity to produce biofilm. In addition, one of the most important C. parapsilosis pathogenic risk factors includes the organisms\textquoteright selective growth capabilities in hyper alimentation solutions. Thus, in this study, we investigated the role of glucose in C. parapsilosis biofilm modulation, by studying biofilm formation, matrix composition and structure. Moreover, the expression of biofilm-related genes (BCR1, FKS1 and OLE1) were analyzed in the presence of different glucose percentages. The results demonstrated the importance of glucose in the modulation of C. parapsilosis biofilm. The concentration of glucose had direct implications on the C. parapsilosis transition of yeast cells to pseudohyphae. Additionally, it was demonstrated that biofilm related genes BCR1, FKS1 and OLE1 are involved in biofilm modulation by glucose. The mechanism by which glucose enhances biofilm formation is not fully understood, however with this study we were able to demonstrate that C. parapsilosis respond to stress conditions caused by elevated levels of glucose by up-regulating genes related to biofilm formation (BCR1, FKS1 and OLE1).

Adult learning and education policies in Germany, Portugal and Sweden: An analysis of national reports to CONFINTEA VI

The chapter presents a theoretical proposal of three analytical models of Adult Learning and Education (ALE) policies. Some analytical categories and the corresponding dimensions are organised according to the ALE rationale which is typical of each social policy model. Historical, cultural and educational features are mentioned in connexion with the different policy models and its interpretative capacity to making sense of policies and practices implemented in Germany, Portugal and Sweden. !e analysis includes the states of the art and the official representations of ALE produced by the respective national authorities through national reports which were presented to CONFINTEA VI (2009).

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Learning Robotics for youngsters - the RoboParty experience

Series: "Advances in intelligent systems and computing , ISSN 2194-5357, vol. 417"

Da modelação matemática à simulação computacional: a experimentação matemática no ensino

Tese de Doutoramento em Ciências (área de especialização em Matemática).

Da modelação matemática à simulação computacional: a experimentação matemática no ensino

Tese de Doutoramento em Ciências (área de especialização em Matemática).

Um modelo para a gestão do conhecimento organizacional no contexto dos serviços partilhados com recurso à utilização do e-Learning

Tese de Doutoramento em Tecnologias e Sistemas de Informação

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Peptaibolin analogues by incorporation of α,α-dialkylglycines: synthesis and study of their membrane permeating ability

Analogues of Peptaibolin, a peptaibol with antibiotic activity, incorporating α,α-dialkylglycines (Deg, Dpg, and Ac6c) at selected positions were synthesised by MW-SPPS and fully characterized. A control analogue incorporating L-alanine was also prepared. The native peptide and the analogues were studied by fluorescence spectroscopy for their membrane permeating activity. Small unilamellar vesicles (SUVs) of egg phosphatidylcholine/ cholesterol (70:30) containing an encapsulated fluorescence probe (6-carboxyfluorescein) were used as membrane models. The assays of carboxyfluorescein release from SUVs upon peptide addition showed that Peptaibolin-Dpg and Peptaibolin-Ac6c are the most active peptides. These results indicate that the structure of the α,α-dialkylglycines is crucial for the membrane permeating ability of these Peptaibolin analogues.

Relatório de atividade profissional

Dissertação de mestrado em Ciências – Formação Contínua de Professores (área de especialização em Biologia e Geologia)