Lacuna voluntária na citação de um texto

Um passo de Claudiano, de larga circulação entre o século IV e o Renascimento, ecoa numa oitava de Luís de Camões. Esse makarismós de antiga tradição sofreu uma mudança substancial no curso da sua transmissão. Com efeito, os três versos originais do autor latino deram lugar a uma redação voluntariamente reduzida, por causa da censura religiosa. Foi só na época humanista, com a renovação dos estudos filológicos, que o texto lacunoso teve a dita de ser reconstituído na sua integridade. Em todo este processo (censura e reconstituição) participaram nomes ilustres que vão de Santo Agostinho a Petrarca

MESA REDONDA SOBRE EÇA DE QUEIROZ

Eça de Queiroz reúne as novidades principais, tanto linguísticas, como narratológicas, que caracterizam o romance europeu do século XIX

Sá de Miranda e o Ms. Denis

Sá de Miranda’s Poems were transmitted from five principal manuscripts, to which Ms Denis belongs. The name comes from its ancient owner, Mr Ferdinand Denis, who made the code available to Carolina Michaëlis de Vasconcelos, during long years, while preparing the edition of the Poesias de Francisco de Sá de Miranda, published in 1885. Chosen by the German philologist as the basic manuscript of her work, Ms Denis has only been described in the Introduction of Michaëlisde Vasconcelos’ critical edition. Before any further study analyzing the content and material organization of the code, it is interesting to follow the path of this manuscript after 1885, that is, after its last use.Na tradição manuscrita das Obras de Francisco de Sá de Miranda, o chamado Ms. Denis, designação decorrente do nome de seu antigo possuidor, M. Ferdinand Denis, ocupa um lugar de relevo. Carolina Michaëlis de Vasconcelos escolheu-o como base da sua edição das Poesias, publicada em 1885. Até hoje, a única descrição desse códice quinhentista deve-se precisamente à filóloga alemã. Antes de qualquer estudo que vise a feitura e o conteúdo do ms., torna-se essencial reconstruir as vicissitudes pelas quais o Ms. Denis passou a partir de 1885, data da sua última utilização

La presenza di Luigi Groto in Shakespeare e negli autori elisabettiani

Econdo una prassi consolidata nell’ambito della filologia inglese, ogni edizione di un’opera teatrale di Shakespeare è accompa­gnata da un apposito capitolo sulle fonti. Inti­tolato semplicemente Sources, questo capi­tolo riassume in forma ora stringata, ora più esauriente e distesa, le indicazioni che si sono venute accumulando nei commenti precedenti, nel corso di una stratificazione spesso secolare. Nel caso di Romeo and Juliet le edizioni di riferimento, prima fra tutte quella di Brian Gi..

«Sorgi Homer, vien Petrarca, esci Marone». I corrispondenti in versi di Luigi Groto

Il genere delle rime di corrispondenza, inteso come scambio epistolare in versi che contempla una proposta e una risposta, occupa una zona assolutamente marginale nella produzione lirica di Luigi Groto, e del resto non figura nella Prima parte delle Rime, la sola curata dall’autore per la stampa (1577, 1584). L’ultimo editore secentesco, Ambrogio Dei, che si prefisse lo scopo di riunire la totalità delle poesie attribuite al Cieco d’Adria, recuperò appena un paio di testi oggetto di scambio f..

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

BACKGROUND No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed. METHODS Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions. RESULTS Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models). CONCLUSIONS Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models

Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.

Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models' accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings