Da leitura à rescrita: o processo de tradução de “Nota al pie”, de Rodolfo Walsh

Orientadoras: Doutora Manuela Veloso, Mtre. Célia Sousa

Survey of trace elements (Al, As, Cd, Cr, Co, Hg, Mn, Ni, Pb, Se, and Si) in retail samples of flavoured and bottled waters

Concentrations of eleven trace elements (Al, As, Cd, Cr, Co, Hg, Mn, Ni, Pb, Se, and Si) were measured in 39 (natural and flavoured) water samples. Determinations were performed using graphite furnace electrothermetry for almost all elements (Al, As, Cd, Cr, Co, Mn, Ni, Pb, and Si). For Se determination hydride generation was used, and cold vapour generation for Hg. These techniques were coupled to atomic absorption spectrophotometry. The trace element content of still or sparkling natural waters changed from brand to brand. Significant differences between natural still and natural sparkling waters (p<0.001) were only apparent for Mn. The Mann–Whitney U-test was used to search for significant differences between flavoured and natural waters. The concentration of each element was compared with the presence of flavours, preservatives, acidifying agents, fruit juice and/or sweeteners, according to the labelled composition. It was shown that flavoured waters generally increase the trace element content. The addition of preservatives and acidifying regulators had a significant influence on Mn, Co, As and Si contents (p<0.05). Fruit juice can also be correlated to the increase of Co and As. Sweeteners did not provide any significant difference in Mn, Co, Se and Si content.

TURTLE: Systems and technologies for deep ocean long term presence

This paper describes the TURTLE project that aim to develop sub-systems with the capability of deep-sea long-term presence. Our motivation is to produce new robotic ascend and descend energy efficient technologies to be incorporated in robotic vehicles used by civil and military stakeholders for underwater operations. TURTLE contribute to the sustainable presence and operations in the sea bottom. Long term presence on sea bottom, increased awareness and operation capabilities in underwater sea and in particular on benthic deeps can only be achieved through the use of advanced technologies, leading to automation of operation, reducing operational costs and increasing efficiency of human activity.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.