Os contributos do texto literário para o ensino-aprendizagem de PLE: elaboração de uma proposta de didatização de um conto de Sophia de Mello Breyner Andresen

Dissertação de mestrado em Português Língua Não Materna (PLNM) – Português Língua Estrangeira (PLE) e Língua Segunda (PL2)

O ensino da flauta de bisel em grupo como inovação pedagógica: Uma investigação-ação no 2.º Ciclo do Ensino Básico

Tese de Doutoramento em Estudos da Criança (Especialidade em Educação Musical)

Reasons and benefits associated with ISO 9001 - Certification for sugar and ethanol companies

With increasing business competitiveness, companies have sought to adapt their processes and / or products to worldwide established quality standards in order to achieve a greater share of consumers having as favorable aspect the quality assurance of the products and/or services provided. It was observed that companies of different sizes have different challenges regarding the certification however, the degree of difficulty is the same for all of them. The objective of this paper is to verify the reasons for the implementation of ISO 9001, the obstacles encountered during the implementation, the benefits arising from the use of the quality management system and the degree of difficulty to implement this standard. This work was developed based on a survey involving companies certified with ISO 9001:2008 from the productive sector of sugar, ethanol and derivatives of sugarcane, located in all Brazilian states. It was observed that companies of different sizes have different challenges regarding the certification however the degree of difficulty is the same for all of them. Thus, we believe that expected results represent a very important contribution to examining the reasons, benefits and difficulties of the ISO 9001 to both, the companies and certification bodies, and to researchers.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.