Based on the evaluation of 3,902 local floristic references published between 2005 and 2025, an update to the floristic knowledge map of Italy is presented. To measure the progress in floristic knowledge across Italy’s administrative regions, the ‘Index of Floristic Knowledge Advancement’ (IFA) is proposed here. This index categorizes regions based on the proportion of potential improvement that has actually been achieved. Res...

Jellyfish, often primarily perceived by the public as a marine nuisance, represent an increasingly valuable and sustainable source of diverse biomolecules with significant potential for the biomedical sciences, in addition to their crucial ecological role within marine ecosystems. This review explores the properties, extraction and applications of key jellyfish-derived compounds, including collagen, peptides, sulfated polysacc...

Combustion byproducts contained in cigarette smoke are considered the main responsible for the occurring of serious chronic diseases. Aerosols from electronic cigarettes contain substantially fewer of these dangerous byproducts, potentially reducing health risks. This study examined the potential of four different commercial nicotine-rich e-liquids to induce cytotoxicity and mitochondrial dysfunction and oxidative stress in hu...

El Niño Southern Oscillation (ENSO) represents the major driver of interannual climate variability at global scale. Observational and model-based studies have fostered a long-standing debate on the shape and intensity of the ENSO influence over the Euro-Mediterranean sector. Indeed, the detection of this signal is strongly affected by the large internal variability that characterizes the atmospheric circulation in the North At...

In this work we show how it is possible to derive a new set of nudging equations, a tool still used in many data assimilation problems, starting from statistical physics considerations and availing ourselves of stochastic parameterizations that take into account unresolved interactions. The fluctuations used are thought of as Gaussian white noise with zero mean. The derivation is based on the conditioned Langevin dynamics tech...

Purpose The quantitative analysis of contrast-enhanced Computed Tomography Angiography (CTA) is essential to assess aortic anatomy, identify pathologies, and perform preoperative planning in vascular surgery. To overcome the limitations given by manual and semi-automatic segmentation tools, we apply a deep learning-based pipeline to automatically segment the CTA scans of the aortic lumen, from the ascending aorta to the iliac...