A responsabilidade de traduzir o in-traduzível: Jacques Derrida e o desejo de [la] tradução

How should one consider the responsibility of the translator, who is located between the differences of two linguistic systems and in the middle of the various idioms constitute each of the languages involved in the translation? (P. Ottoni). What is the role of the translator in inter-acting with both his/her mother tongue and the idiom of the other? These two questions will be discussed in order to reflect on the responsibility of translating the un-translatable. Two hypotheses orient the paper: 1 - an idiom spoken idiomatically is known as the mother tongue and is not appropriated, so that accommodating the other in one's own language automatically considers his/her idiom (J. Derrida) and 2 - face-to-face with language and its idioms, the translator is trapped in a double (responsibility) bind; faced with something which cannot be translated, he/she is forced to perceive it in another way. In conclusion, how should one consider the responsibility of translating the un-translatable Jacques Derrida?

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.