Aprendizagem da cultura chinesa e aquisição de caracteres

Dissertação de mestrado em Estudos Interculturais Português / Chinês: Tradução, Formação e Comunicação Empresarial

O "ser português" e o "outro": Revisitar a história de Portugal no diálogo com a civilização chinesa - O caso Tomás Pereira

Tese de Doutoramento em Ciências da Cultura - Especialidade em Culturas do Extremo Oriente

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.

Radio mobility in the digital era: interactivity, participation and content share possibilities in Iberian broadcasters

In the context of the scientific research into radio, recent years have encouraged many theories about the meaning of a post-radio (Oliveira & Portela, 2011), thus enlisting several parameters regarding the inclusion of contemporary radio in the digital and online environments. This digital migration has led to the development of mobile applications for radio, broadening the communicative potential of audiences (Aguado, Feijoo & Martínez, 2013), as well as promoting convergence of interactive content among listeners-users. Aware of this opportunity, the main broadcasters in Spain and Portugal have broadened their radiophonic scope to the mobile platform, especially geared towards smartphones through the development of mobile applications, commonly known as apps (Cerezo, 2010). As a symbol of a culture in permanent changing, smartphones not only provide greater easiness in terms of access and interaction, but also afford larger opportunities for disseminating content among audiences, a phenomenon that some studies have labelled as user distributed content (Villi, 2012). This article presents an exploratory analysis of the current policies of the main Spanish and Portuguese radio broadcasters regarding mobile applications, evaluating the different levels of interaction and participation in these platforms. This observation led to the conclusion, among other findings, that the mobile platform represents a supplementary channel for traditional FM radio, rather than a new medium with its own language and expression.

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.

Development of advanced cell/tissue culture systems, based on enhanced polymeric scaffolds and sophisticated bioreactors, for tissue engineering applications

Programa Doutoral em Engenharia Biomédica