Pomegranate (Punica granatum L.) from Motya and its deepest oriental roots

Pomegranate remains and representations found in the Phoenician site of Motya in Western Sicily give the cue for a summary study of this plant and its fortune in the Near East and the Mediterranean. Fruits offered in wells, a terracotta relief depicting a pomegranate held by a goddess found in the Sacred Area of the Kothon at Motya, and, especially, a pottery vase in the shape of a pomegranate retrieved inside the Temple of Astarte in the same compound, witness the symbolic transcultural role of this fruit and of the pomegranate tree in ancient Mediterranean, from its farthest oriental origins to modern art and religio

COVID-19 prognosis estimation from CAT scan radiomics: comparison of different machine learning approaches for predicting patients survival and ICU Admission

Since the start of 2020 Sars-COVID19 has given rise to a world-wide pandemic. In an attempt to slow down the spreading of this disease various prevention and diagnostic methods have been developed. In this thesis the attention has been put on Machine Learning to predict prognosis based on data originating from radiological images. Radiomics has been used to extract information from images segmented using a software from the hospital which provided both the clinical data and images. The usefulness of different families of variables has then been evaluated through their performance in the methods used, i.e. Lasso regularized regression and Random Forest. The first chapter is introductory in nature, the second will contain a theoretical overview of the necessary concepts that will be needed throughout this whole work. The focus will be then shifted on methods and instruments used in the development of this thesis. The third chapter will report the results and finally some conclusions will be derived from the previously presented results. It will be concluded that the segmentation and feature extraction step is of pivotal importance in driving the performance of the predictions. In fact, in this thesis, it seems that the information from the images achieves the same predictive power that can be derived from the clinical data. This can be interpreted in three ways: first it can be taken as a symptom of the fact that even the more complex Sars-COVID19 cases can be segmented automatically, or semi-automatically by untrained personnel, leading to results competing with other methodologies. Secondly it can be taken to show that the performance of clinical variables can be reached by radiomic features alone in a semi-automatic pipeline, which could aid in reducing the workload imposed on medical professionals in case of pandemic. Finally it can be taken as proof that the method implemented has room to improve by more carefully investing in the segmentation phas

3d modelling of archaeological small finds by a low-cost range camera. Methodology and first results

The production of reliable documentation of small finds is a crucial process during archaeological excavations. Range cameras can be a valid alternative to traditional illustration methods: they are veritable 3D scanners able to easily collect the 3D geometry (shape and dimensions in metric units) of an object/scene practically in real-time. This work investigates precisely the potentialities of a promising low-cost range camera, the Structure SensorTM by Occipital, for rapid modelling archaeological objects. The accuracy assessment was thus performed by comparing the 3D model of a Cipriot-Phoenician globular jug captured by this device with the 3D model of the same object obtained through photogrammetry. In general, the performed analysis shows that Structure Sensor is capable to acquire the 3D geometry of a small object with an accuracy comparable at millimeter level to that obtainable with the photogrammetric method, even though the finer details are not always correctly modelled. The texture reconstruction is instead less accurate. In the end, it can be concluded that the range camera used for this work, due to its low-cost and flexibility, is a suitable tool for the rapid documentation of archaeological small finds, especially when not expert users are involved

Investigating plant micro‐remains embedded in dental calculus of the Phoenician inhabitants of Motya (Sicily, Italy)

Plant records reveal remarkable evidence about past environments and human cultures. Exploiting dental calculus analysis and using a combined approach of microscopy and gas chromatography mass spectrometry, our research outlines dietary ecology and phytomedicinal practices of the ancient community of Motya (Sicily, eight to sixth century BC), one of the most important Phoenician settlements in the Mediterranean basin. Micro‐remains suggest use or consumption of Triticeae cereals, and animal‐derived sources (e.g., milk and aquatic birds). Markers of grape (or wine), herbs, and rhizomes, endemic of Mediterranean latitudes and the East, provide insight into the subsistence of this colony, in terms of foodstuffs and phytotherapeutic products. The application of resins and wood of Gymnosperms for social and cultural purposes is hypothesized through the identification of Pinaceae secondary metabolites and pollen grains. The information hidden in dental calculus discloses the strong human‐plant interaction in Motya’s Phoenician community, in terms of cultural traditions and land use

Micro-Raman spectroscopy and complementary techniques applied for the study of copper and iron wastes from Motya (Italy)

This work is the first archaeometric investigation on copper and iron wastes from the Phoenician site of Motya (Sicily, Italy), dating back to the 8th to the 4th century BC. The samples were analyzed through micro-Raman Spectroscopy (μ-RS), Optical Microscopy (OM), Scanning Electron Microscope-Energy-Dispersive X-ray Spectroscopy (SEM-EDS), High-Resolution Field Emission Scanning Electron Microscopy (HR-FESEM), and Electron Micro-Probe Analysis (EMPA). Micro-Raman techinique permitted to identify both primary phases, for example, calchopyrite, and secondary products such as cuprite and copper thrihydroxychlorides in the Cu-slags and goethite in the Fe-slags. SEM and HR-FESEM imaging showed the occurrence of inhomogeneous microstructures in the Cu- and Fe-slags due to elements segregation, solidification, and corrosion. EMPA data revealed that the archaeometallurgical wastes from Motya can be differentiated on the basis of their chemical compositions. These preliminary results showed different typologies of by-products, such as base metals speiss, copper slags from smelting sulfide ore with matte, and iron smelting and smithing slags, suggesting different stages of copper and iron productions

Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images

Featured Application The present study demonstrates that semi-automatic segmentation enables the identification of regions of interest affected by SARS-CoV-2 infection for the extraction of prognostic features from chest CT scans without suffering from the inter-operator variability typical of segmentation, hence offering a valuable and informative second opinion. Machine Learning methods allow identification of the prognostic features potentially reusable for the early detection and management of other similar diseases. (1) Background: Chest Computed Tomography (CT) has been proposed as a non-invasive method for confirming the diagnosis of SARS-CoV-2 patients using radiomic features (RFs) and baseline clinical data. The performance of Machine Learning (ML) methods using RFs derived from semi-automatically segmented lungs in chest CT images was investigated regarding the ability to predict the mortality of SARS-CoV-2 patients. (2) Methods: A total of 179 RFs extracted from 436 chest CT images of SARS-CoV-2 patients, and 8 clinical and 6 radiological variables, were used to train and evaluate three ML methods (Least Absolute Shrinkage and Selection Operator [LASSO] regularized regression, Random Forest Classifier [RFC], and the Fully connected Neural Network [FcNN]) for their ability to predict mortality using the Area Under the Curve (AUC) of Receiver Operator characteristic (ROC) Curves. These three groups of variables were used separately and together as input for constructing and comparing the final performance of ML models. (3) Results: All the ML models using only RFs achieved an informative level regarding predictive ability, outperforming radiological assessment, without however reaching the performance obtained with ML based on clinical variables. The LASSO regularized regression and the FcNN performed equally, both being superior to the RFC. (4) Conclusions: Radiomic features based on semi-automatically segmented CT images and ML approaches can aid in identifying patients with a high risk of mortality, allowing a fast, objective, and generalizable method for improving prognostic assessment by providing a second expert opinion that outperforms human evaluation

L’emergenza e i rapporti tra lo Stato e le Regioni

Forum sul rapporto tra Stato e Regioni al tempo del Coronaviru

Emergenza Covid e organi costituzionali

Forum sul potere politico di fronte all'emergenza Covi

Cohort comparison study of cardiac disease and atherosclerotic burden in type 2 diabetic adults using whole body cardiovascular magnetic resonance imaging

BACKGROUND: Whole body cardiovascular MR (WB CVMR) combines whole body angiography and cardiac MR assessment. It is accepted that there is a high disease burden in patients with diabetes, however the quantification of the whole body atheroma burden in both arterial and cardiac disease has not been previously reported. In this study we compare the quantified atheroma burden in those individuals with and without diabetes by clinical cardiovascular disease (CVD) status. METHODS: 158 participants underwent WB CVMR, and were categorised into one of four groups: (1) type 2 diabetes mellitus (T2DM) with CVD; (2) T2DM without CVD; (3) CVD without T2DM; (4) healthy controls. The arterial tree was subdivided into 31 segments and each scored according to the degree of stenosis. From this a standardised atheroma score (SAS) was calculated. Cardiac MR and late gadolinium enhancement images of the left ventricle were obtained for assessment of mass, volume and myocardial scar assessment. RESULTS: 148 participants completed the study protocol—61 % male, with mean age of 64 ± 8.2 years. SAS was highest in those with cardiovascular disease without diabetes [10.1 (0–39.5)], followed by those with T2DM and CVD [4 (0–41.1)], then those with T2DM only [3.23 (0–19.4)] with healthy controls having the lowest atheroma score [2.4 (0–19.4)]. Both groups with a prior history of CVD had a higher SAS and left ventricular mass than those without (p < 0.001 for both). However after accounting for known cardiovascular risk factors, only the SAS in the group with CVD without T2DM remained significantly elevated. 6 % of the T2DM group had evidence of silent myocardial infarct, with this subcohort having a higher SAS than the remainder of the T2DM group [7.7 (4–19) vs. 2.8 (0–17), p = 0.024]. CONCLUSIONS: Global atheroma burden was significantly higher in those with known cardiovascular disease and without diabetes but not in those with diabetes and cardiovascular disease suggesting that cardiovascular events may occur at a lower atheroma burden in diabetes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12933-015-0284-2) contains supplementary material, which is available to authorized users