Il mercato del lavoro nell'ambito delle scienze motorie e sportive: una ricerca europea

The analysis of the employment and the labour force in the sport sector has implications that have received little attention in the public debate and in the scientific investigation. Therefore, the EU funded project named «A European Sector Skills Alliance for Sport » (ESSA-Sport) offered the opportunity to implement quality research and consultations, to identify the realities, trends and challenges facing the sector, and to undertake the first real analysis of the sport labour market in Europe. The methodology adopted was based on two main axes: the analysis of the micro-data of the Labour Force Survey (LFS) released from Eurostat for the years 2011-2016; a secondary anal-ysis of national sources, databases and research papers combined with con-sultations with national experts of the 28-EU countries. Results show that employment in the sport sector represents today, in Europe, an important element of the Member States’ economy. The sector is very dynamic: in the period 2011-2016 the sport employment in the 28 EU Member States shows an average annual rate of 2.2 %. In the future, the sectors of education, health care, etc. are likely to grow, and all these sectors are clearly linked with the sport sector that will see a further area of growth.L’analisi dell’occupazione e della forza lavoro nel settore delle scienze mo-torie e sportive sviluppa delle implicazioni che hanno ricevuto scarsa atten-zione sia nel dibattito pubblico che nella ricerca scientifica. A tal proposito, il progetto europeo denominato «A European Sector Skills Alliance for Sport» (ESSA-Sport)» ha offerto l’opportunità di condurre una ricerca internazionale e una serie di consultazioni finalizzate ad identificare le realtà, i trend e le sfide che il settore affronta, e a sviluppare la prima analisi del mer-cato del lavoro in Europa. La metodologia di studio si è fondata su due assi principali: un’analisi dei micro-dati disponibili tramite la Labour Force Survey (LFS) rilasciata da EUROSTAT tra gli anni 2011 e 2016; un’analisi secondaria di risorse nazionali, database e report di ricerca a cui si è aggiunta una consultazione con ricercatori esperti di ognuno dei 28 Paesi Membri della Unione Europea. I risultati hanno dimostrato come l’occupazione nel settore delle scienze motorie e sportive rappresenti in Europa, oggi, un importante elemento per l’economia degli Stati Membri. Il settore è fortemente dinamico: nel periodo 2011-2016 l’occupazione ha riportato un tasso di crescita annuo pari al 2.2%. In aggiunta, si sono evinti trend di crescita anche per gli anni futuri in rapporto allo sviluppo di settori connessi con le scienze motorie e sportive quali quelli della salute, dell'educazione, della cura.&nbsp

Transatlantic connections in colonial and post-colonial Haiti: archaeometric evidence for taches noires glazed tableware imported from Albissola, Italy to Fort Liberté, Haiti.

This paper presents the first archaeometrical data on colonial glazed wares (taches noires) imported in Haiti (Fort Liberté). The analysis evidenced the exclusive presence of Italian taches noires products, dated before 1820 and related to the colonial era. The presence of English wares next to colonial materials demonstrated continuity in the use of landscape after the Independence and the establishment of international trade relationships between the state of Haiti and the British Empire. Results are an important step forward in the understanding of production and movement of the Taches noires ware, which were exported globally between the eighteenth and nineteenth centurie

Synthetic Protein-to-DNA Input Exchange for Protease Activity Detection Using CRISPR-Cas12a

We present a novel activity-based detection strategy for matrix metalloproteinase 2 (MMP2), a critical cancer protease biomarker, leveraging a mechanism responsive to the proteolytic activity of MMP2 and its integration with CRISPR-Cas12a-assisted signal amplification. We designed a chemical translator comprising two functional units─a peptide and a peptide nucleic acid (PNA), fused together. The peptide presents the substrate of MMP2, while the PNA serves as a nucleic acid output for subsequent processing. This chemical translator was immobilized on micrometer magnetic beads as a physical support for an activity-based assay. We incorporated into our design a single-stranded DNA partially hybridized with the PNA sequence and bearing a region complementary to the RNA guide of CRISPR-Cas12a. The target-induced nuclease activity of Cas12a results in the degradation of FRET-labeled DNA reporters and amplified fluorescence signal, enabling the detection of MMP2 in the low picomolar range, showing a limit of detection of 72 pg/mL. This study provides new design principles for a broader applicability of CRISPR-Cas-based biosensing

The LHCb ultra-fast simulation option, Lamarr: design and validation

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. An evolution of technologies and techniques to produce simulated samples is mandatory to meet the upcoming needs of analysis to interpret signal versus background and measure efficiencies. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two-order-of-magnitude speed-up of the simulation phase.Comment: Under review in EPJ Web of Conferences (CHEP 2023

Fifth European Dirofilaria and Angiostrongylus Days (FiEDAD) 2016

Peer reviewe

Preoperative Predictive Factors of Successful Weight Loss and Glycaemic Control 1 Year After Gastric Bypass for Morbid Obesity

BACKGROUND: Gastric bypass (GBP) is one of the most effective surgical procedures to treat morbid obesity and the related comorbidities. This study aimed at identifying preoperative predictors of successful weight loss and type 2 diabetes mellitus (T2DM) remission 1 year after GBP. METHODS: Prospective longitudinal study of 771 patients who underwent GBP was performed at four Italian centres between November 2011 and May 2013 with 1-year follow-up. Preoperative anthropometric, metabolic and social parameters, the surgical technique and the previous failed bariatric procedures were analyzed. Weight, the body mass index (BMI), the percentage of excess weight lost (% EWL), the percentage of excess BMI lost (% BMIL) and glycated haemoglobin (HbA1c) were recorded at follow-up. RESULTS: Univariate and multivariate analysis showed that BMI <50 kg/m2 (p\u2009=\u20090.006) and dyslipidaemia (p\u2009=\u20090.05) were predictive factors of successful weight loss. Multivariate analysis of surgical technique showed significant weight loss in patients with a small gastric pouch (p\u2009<\u20090.001); the lengths of alimentary and biliary loops showed no statistical significance. All diabetic patients had a significant reduction of HbA1c (p\u2009<\u20090.001) after surgery. BMI\u2009 65\u200950 kg/m2 (p\u2009=\u20090.02) and low level of preoperative HbA1c (p\u2009<\u20090.01) were independent risk factors of T2DM remission after surgery. CONCLUSIONS: This study provides a useful tool for making more accurate predictions of best results in terms of weight loss and metabolic improvement

Genetic alterations analysis in prognostic stratified groups identified TP53 and ARID1A as poor clinical performance markers in intrahepatic cholangiocarcinoma

The incidence and mortality rates of intrahepatic cholangiocarcinoma have been rising worldwide. Few patients present an early-stage disease that is amenable to curative surgery and after resection, high recurrence rates persist. To identify new independent marker related to aggressive behaviour, two prognostic groups of patient were selected and divided according to prognostic performance. All patients alive at 36 months were included in good prognostic performers, while all patients died due to disease within 36 months in poor prognostic performers. Using high-coverage target sequencing we analysed principal genetic alterations in two groups and compared results to clinical data. In the 33 cases included in poor prognosis group, TP53 was most mutated gene (p\u2009=\u20090.011) and exclusively present in these cases. Similarly, ARID1A was exclusive of this group (p\u2009=\u20090.024). TP53 and ARID1A are mutually exclusive in this study. Statistical analysis showed mutations in TP53 and ARID1A genes and amplification of MET gene as independent predictors of poor prognosis (TP53, p\u2009=\u20090.0031, ARID1A, p\u2009=\u20090.0007, MET, p\u2009=\u20090.0003 in Cox analysis). LOH in PTEN was also identified as marker of disease recurrence (p\u2009=\u20090.04) in univariate analysis. This work improves our understanding of aggressiveness related to this tumour type and has identified novel prognostic markers of clinical outcome

The LHCb ultra-fast simulation option, Lamarr design and validation

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. The evolution of technologies and techniques for simulation production is then mandatory to meet the upcoming needs for the analysis of most of the data collected by the LHCb experiment. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two order of magnitude speed-up of the simulation phase