Assistive locomotion strategies for active lower limb devices

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)

Criação de um sistema de apoio para o suporte avançado de vida

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)

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.

Qualitative risk assessment during polymer mortar test specimens preparation - methods comparison

Polymer binder modification with inorganic nanomaterials (NM) could be a potential and efficient solution to control matrix flammability of polymer concrete (PC) materials without sacrificing other important properties. Occupational exposures can occur all along the life cycle of a NM and “nanoproducts” from research through scale-up, product development, manufacturing, and end of life. The main objective of the present study is to analyse and compare different qualitative risk assessment methods during the production of polymer mortars (PM) with NM. The laboratory scale production process was divided in 3 main phases (pre-production, production and post-production), which allow testing the assessment methods in different situations. The risk assessment involved in the manufacturing process of PM was made by using the qualitative analyses based on: French Agency for Food, Environmental and Occupational Health & Safety method (ANSES); Control Banding Nanotool (CB Nanotool); Ecole Polytechnique Fédérale de Lausanne method (EPFL); Guidance working safely with nanomaterials and nanoproducts (GWSNN); Istituto Superiore per la Prevenzione e la Sicurezza del Lavoro, Italy method (ISPESL); Precautionary Matrix for Synthetic Nanomaterials (PMSN); and Stoffenmanager Nano. It was verified that the different methods applied also produce different final results. In phases 1 and 3 the risk assessment tends to be classified as medium-high risk, while for phase 2 the more common result is medium level. It is necessary to improve the use of qualitative methods by defining narrow criteria for the methods selection for each assessed situation, bearing in mind that the uncertainties are also a relevant factor when dealing with the risk related to nanotechnologies field.