Fragmentos de um adeus: fado, tango e música popular brasileira em cena

Trabalho de Projecto submetido à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Artes Performativas - especialização em Teatro-Música.

Slaughterhouses fungal burden assessment: a contribution for the pursuit of a better assessment strategy

In slaughterhouses, the biological risk is present not only from the direct or indirect contact with animal matter, but also from the exposure to bioaerosols. Fungal contamination was already reported from the floors and walls of slaughterhouses. This study intends to assess fungal contamination by cultural and molecular methods in poultry, swine/bovine and large animal slaughterhouses. Air samples were collected through an impaction method, while surface samples were collected by the swabbing method and subjected to further macro- and micro-scopic observations. In addition, we collected air samples using the impinger method in order to perform real-time quantitative PCR (qPCR) amplification of genes from specific fungal species, namely A. flavus, A. fumigatus and A. ochraceus complexes. Poultry and swine/bovine slaughterhouses presented each two sampling sites that surpass the guideline of 150 CFU/m3. Scopulariopsis candida was the most frequently isolated (59.5%) in poultry slaughterhouse air; Cladosporium sp. (45.7%) in the swine/bovine slaughterhouse; and Penicillium sp. (80.8%) in the large animal slaughterhouse. Molecular tools successfully amplified DNA from the A. fumigatus complex in six sampling sites where the presence of this fungal species was not identified by conventional methods. This study besides suggesting the indicators that are representative of harmful fungal contamination, also indicates a strategy as a protocol to ensure a proper characterization of fungal occupational exposure.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.

In situ NIR spectroscopy monitoring of plasmid production processes effect of producing strain, medium composition and the cultivation strategy

BACKGROUNDWhile the pharmaceutical industry keeps an eye on plasmid DNA production for new generation gene therapies, real-time monitoring techniques for plasmid bioproduction are as yet unavailable. This work shows the possibility of in situ monitoring of plasmid production in Escherichia coli cultures using a near infrared (NIR) fiber optic probe. RESULTSPartial least squares (PLS) regression models based on the NIR spectra were developed for predicting bioprocess critical variables such as the concentrations of biomass, plasmid, carbon sources (glucose and glycerol) and acetate. In order to achieve robust models able to predict the performance of plasmid production processes, independently of the composition of the cultivation medium, cultivation strategy (batch versus fed-batch) and E. coli strain used, three strategies were adopted, using: (i) E. coliDH5 cultures conducted under different media compositions and culture strategies (batch and fed-batch); (ii) engineered E. coli strains, MG1655endArecApgi and MG1655endArecA, grown on the same medium and culture strategy; (iii) diverse E. coli strains, over batch and fed-batch cultivations and using different media compositions. PLS models showed high accuracy for predicting all variables in the three groups of cultures. CONCLUSIONNIR spectroscopy combined with PLS modeling provides a fast, inexpensive and contamination-free technique to accurately monitoring plasmid bioprocesses in real time, independently of the medium composition, cultivation strategy and the E. coli strain used.