\uabPi\uf9 saggia di Edipo\ubb. Su alcune fonti di \uab 6dipus und die Sphinx\ubb di Hofmannsthal

Il saggio analizza la tragedia 6dipus und die Sphinx (1906) di Hugo von Hofmannsthal in relazione con le sue fonti, mostrando come la figura di Eleonora Duse, tanto venerata dal poeta austriaco da indurlo a plasmare su di lei il ruolo di Giocasta, abbia provocato sia l\u2019allontanamento dell\u2019opera dalla primitiva fonte francese di \u152dipe et le Sphinx (1903) di Jos\ue9phin P\ue9ladan sia il suo avvicinamento al mondo poetico dannunziano, di cui l\u2019attrice italiana era stata ispiratrice e interprete. Allo stesso tempo il saggio mostra come Hofmannsthal affidi alla dialettica interna ai personaggi della sua tragedia e in particolare a Giocasta il compito di ridimensionare e correggere il superomismo dannunziano, lasciando emergere, qui appena accennato, il mondo di delicate relazioni che caratterizzer\ue0 le commedie della maturit\ue0.This essay analyses Hofmannsthal\u2019s tragedy 6dipus und die Sphinx (1906) in relation to its sources, showing how Eleonora Duse, the Italian actress whom the Austrian poet so highly esteemed as to write for her the role of Giocasta, drove Hofmannsthal\u2019s tragedy away from its first source, P\ue9ladan\u2019s French tragedy \u152dipe et le Sphinx (1903), towards d\u2019Annunzio\u2019s poetic world. Furthermore, the essay shows how Hofmannsthal entrusts the character of Giocasta with the task of reshaping and correcting d\u2019Annunzio\u2019s cbermenschtum, thus giving a foretaste of the mutual and delicate relationships which will be typical of Hofmannsthal\u2019s later plays

PI is back! Switching Acquisition Functions in Bayesian Optimization

Bayesian Optimization (BO) is a powerful, sample-efficient technique to optimize expensive-to-evaluate functions. Each of the BO components, such as the surrogate model, the acquisition function (AF), or the initial design, is subject to a wide range of design choices. Selecting the right components for a given optimization task is a challenging task, which can have significant impact on the quality of the obtained results. In this work, we initiate the analysis of which AF to favor for which optimization scenarios. To this end, we benchmark SMAC3 using Expected Improvement (EI) and Probability of Improvement (PI) as acquisition functions on the 24 BBOB functions of the COCO environment. We compare their results with those of schedules switching between AFs. One schedule aims to use EI's explorative behavior in the early optimization steps, and then switches to PI for a better exploitation in the final steps. We also compare this to a random schedule and round-robin selection of EI and PI. We observe that dynamic schedules oftentimes outperform any single static one. Our results suggest that a schedule that allocates the first 25 % of the optimization budget to EI and the last 75 % to PI is a reliable default. However, we also observe considerable performance differences for the 24 functions, suggesting that a per-instance allocation, possibly learned on the fly, could offer significant improvement over the state-of-the-art BO designs.Comment: 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making System

The role of teicoplanin in the treatment of SARS-CoV-2 infection: a retrospective study in critically ill COVID-19 patients (Tei-COVID Study)

Teicoplanin has a potential antiviral activity expressed against SARS-CoV-2 and was suggested as a complementary option to treat COVID-19 patients. In this multicentric, retrospective, observational research the aim was to evaluate the impact of teicoplanin on the course of COVID-19 in critically ill patients

Expression analysis of miRNA hsa-let7b-5p in naso-oropharyngeal swabs of COVID-19 patients supports its role in regulating ACE2 and DPP4 receptors

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the novel coronavirus responsible for worldwide coronavirus disease (COVID-19). We previously observed that Angiotensin-converting enzyme 2 (ACE2) and Dipeptidyl peptidase-4 (DPP4) are significantly overexpressed in naso-oropharyngeal swabs (NPS) of COVID-19 patients, suggesting their putative functional role in the disease progression. ACE2 and DPP4 overexpression in COVID-19 patients may be associated to epigenetic mechanism, such as miRNA differential expression. We investigated if hsa-let7b-5p, reported to target both ACE2 and DPP4 transcripts, could be involved in the regulation of these genes. We verified that the inhibition and overexpression of hsa-let7b-5p matched to a modulation of both ACE2 and DPP4 levels. Then, we observed a statistically significant downregulation (FC = -1.5; p < 0.05) of hsa-let7b-5p in the same COVID-19 and control samples of our previous study. This is the first study that shows hsa-let7b-5p low expression in naso-oropharyngeal swabs of COVID-19 patients and demonstrates a functional role of this miR in regulating ACE2 and DPP4 levels. These data suggest the involvement of hsa-let7b-5p in the regulation of genes necessary for SARS-CoV-2 infections and its putative role as a therapeutic target for COVID-19

BNT162B2 mRNA COVID-19 vaccine in heart and lung transplanted young adults: is an alternative SARS-CoV-2 immune response surveillance needed?

[no abstract available

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

L’«Elettra» (1903) di Hugo von Hofmannsthal tra Sofocle e D’Annunzio

Hofmannsthal’s play «Elektra» aroused controversial reactions when it appeared in 1903: on the one hand, great enthusiasm, on the other hand irritation and hostility towards its undoubted “modernity”. This essay highlights some scarcely known aspects of the context in which the play came into being, focusing particularly on its relationship with the Sophoclean subtext and Hofmannsthal’s coeval and later poetical works, as well as with D’Annunzio’s drama «La città morta», performed in Vienna in 1902 and 1903 with Eleonora Duse in the main role

Towards the Usage of Advanced Composite Materials in the Optimization of Wind Turbine Blades.

Nowadays, fossil fuel consumption is the major contributor to the increasing concentration of greenhouse gases, which are the key cause of climate change. The main solution strategy is represented by renewable-energy sources, wind power among the others. However, existing wind turbines are made of composite material components that result to be very expensive and not easy to dispose of at the end of their life cycle. Therefore, substantial effort has to be put into the design phase of the new products, where numerical simulation and optimization techniques represent precious resources. This thesis presents advances in two main fields: (1) the mechanical analysis of natural fiber composite materials by means of experimental campaigns and numerical/analytical models, and (2) the development of modeling techniques for the Topology Optimization (TO) of mechanical components subjected to external loading conditions. The numerical and analytical models on natural fiber composites focus on the impact behavior of hemp/vinylester, flax/epoxy composite plates, together with hybrid stacking sequences where hemp and glass layers are alternated. The impact resistance of the laminates is investigated under low-velocity impact conditions, with loads in the thickness direction. In particular, analytical models available from the literature for carbon-fiber composites, are here adapted and tested on new material configurations. The main aims of these models are the prediction of a threshold impact load for the damage onset and extent and the approximation of the loading phase of the typical load–displacement curve. The TO of simple mechanical structures – ensembles of interconnected beams – exposed to static and dynamic external loads was addressed through surrogate modeling techniques. In this context, since the objective function evaluations rely on computationally expensive finite element simulations, optimization techniques based on the construction of cheap-to-evaluate approximation models are more convenient than population-based approaches, like for example evolution strategies, which need many more evaluations to reach a near-optimal solution. Novel approaches developed by the author of this manuscript are the Kriging-Assisted Level Set Method (KG-LSM) for TO, together with its hybrid version – the Hybrid Kriging-Assisted Level Set Method (HKG-LSM). Both these methods are based on Bayesian Optimization (BO), which is known to show poor performance of at relatively high dimensionalities, typically when dealing with more than 15 variables. To scale up BO for high-dimensional optimization problems, the Principal Component Analysis assisted Bayesian Optimization (PCA-BO) algorithm has been also developed. Several experimental and optimization studies have been performed, highlighting the potential of the presented techniques and the concrete possibility to benefit from their advantages in practical applications that are very relevant for the society