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Association of cytomegalovirus serostatus with ELOVL2 methylation: Implications for lipid metabolism, inflammation, DNA damage, and repair capacity in the MARK-AGE study population
No full textCytomegalovirus (CMV) infection has been linked to accelerated biological aging, potentially increasing the risk of cardiovascular disease. DNA methylation of the gene Elongation Of Very Long Chain Fatty Acids-Like 2 (ELOVL2) is a molecular biomarker for aging, and its gene product is involved in polyunsaturated fatty acid synthesis, which impacts immune and inflammatory responses. This study, conducted in the MARK-AGE population, aimed to investigate the relationship between CMV infection and ELOVL2 methylation in adults aged 35-75, as well as the influence of CMV IgG levels on lipid metabolism, inflammation, DNA damage, and DNA repair. Our data revealed a higher prevalence of ischemic heart disease, atrial fibrillation, hypertension, and diabetes in CMV-positive individuals. CMV IgG levels were positively associated with ELOVL2 methylation at specific CpG sites and with increased expression of DNA methyltransferase-1 (DNMT1). CMV IgG was linked to lipid imbalances, such as increased BMI, VLDL-cholesterol, triglycerides, and HDL1-cholesterol. Additionally, ELOVL2 methylation was associated with systemic inflammation markers, lipid parameters and altered T-cell subsets. A negative correlation was observed between CMV IgG levels and both baseline DNA integrity and repair capacity. These results suggest that CMV infection might promote cardiovascular disease through ELOVL2 hypermethylation, lipid dysregulation, inflammation, and DNA damage
Structural Adhesive Joints: State of the Art, Challenges, and Future Perspectives
Get PDFThe design of adhesive joints is crucial in industries like aerospace, automotive, and railways, where they offer a lightweight alternative to traditional methods such as welding and bolting. Adhesive joints distribute stress uniformly and enable the assembly of complex geometries. This review focuses on cutting-edge technologies, highlighting the role of nanomaterials in enhancing fatigue strength and chemical-thermal stability. It also explores the potential of additive manufacturing to create customized joint geometries and enable real-time monitoring through embedded sensors. The paper examines widely used adhesives, including epoxies and polyurethanes, as well as innovative joint designs, such as sinusoidal profiles and multi-material configurations, which improve stress distribution and structural integrity. Surface preparation techniques and advanced numerical tools, like cohesive zone modeling and artificial intelligence, are also discussed for their role in optimizing joint performance. The study identifies key challenges, including standardization of processes and integration of novel materials, and outlines strategies to enhance the performance of bonded joints in advanced industrial application
Technological sovereignty, development and adoption of digital technologies
No full textNegli ultimi anni, l'UE è rimasta indietro rispetto ad altri paesi nello sviluppo e nell'adozione di alcune tecnologie digitali avanzate, come l'Intelligenza Artificiale (IA) e la blockchain (Draghi Mario, 2024b). In questo contesto, l'UE ha evidenziato la necessità di rafforzare la propria sovranità tecnologica e digitale in aree strategiche (Bauer & Erixon, 2020). In Italia il ritardo nell'adozione di tecnologie digitali avanzate è più evidente, soprattutto nelle piccole e medie imprese (PMI).
La tesi mira ad analizzare se e in che misura le imprese italiane siano in grado di padroneggiare lo sviluppo e l'adozione delle tecnologie digitali e se una mancanza di autonomia nello sviluppo possa ostacolarne la diffusione. Nello specifico, questo lavoro si concentra sulla tecnologia blockchain e sulla sua applicazione per la tracciabilità delle filiere in Italia. Il primo capitolo discute il tema della sovranità tecnologica e digitale, poi il secondo capitolo si focalizza sullo sviluppo della tecnologia blockchain. Dato il basso livello di sviluppo della blockchain, i capitoli successivi si focalizzano sull’adozione di questa tecnologia nei settori “Made in Italy”. Nello specifico, l’analisi empirica investiga i fattori, a livello d’impresa e locale, che possono influenzare l'adozione della tecnologia blockchain nei settori alimentare e della moda. I risultati mostrano che solo un numero molto limitato di imprese ha adottato la tecnologia blockchain nei settori analizzati, nonostante i potenziali vantaggi per la tracciabilità della filiera. La dimensione d’impresa è uno dei principali fattori che ne spiegano l'adozione. Infine, l’ultimo capitolo fornisce l’analisi di un caso studio volta a supportare l’evidenza delle precedenti analisi quantitative. I risultati suggeriscono la necessità di progettare politiche industriali efficaci a livello regionale e nazionale per migliorare l'adozione della tecnologia blockchain, soprattutto nelle PMI.In recent years, the EU is lagging behind other countries in the development and adoption of some advanced digital technologies, such as Artificial Intelligence (AI) and blockchain (Draghi Mario, 2024b). In this context, the EU has highlighted the need to strengthen its technological and digital sovereignty in strategic areas (Bauer & Erixon, 2020). In Italy the delay in the adoption of advanced digital technologies is even more evident, especially in small and medium-sized enterprises (SMEs).
Given these premises, the thesis aims to analyse if and to what extent Italian companies are able to master the development and adoption of digital technologies and whether a lack of autonomy in development may hinder their diffusion. Specifically, this work focuses on blockchain technology and its application for supply chain traceability in Italy. The first chapter discusses the concept of technological and digital sovereignty, then the second chapter focuses on the development of blockchain technology. Given the low level of blockchain development in Italy, the subsequent chapters focus on the adoption of this technology in “Made in Italy” sectors. Specifically, the empirical analyses investigate the factors, at the company and local level, that may influence the adoption of blockchain technology in the food and fashion sectors. The results show that only a very limited number of companies have adopted blockchain technology in the sectors analysed, despite the potential benefits for the traceability of the supply chains. Moreover, the size of the company is one of the main factors explaining its adoption. Finally, the last chapter provides a case study analysis aiming to support the evidence of previous quantitative analyses. Findings suggest that there is a need to design effective industrial policies at regional and national level to enhance the adoption of blockchain technology, especially in SMEs
Digital Triplet Paradigm Based Brain Like Intelligence for Augmenting the Resilience of Intelligent Mechatronics, Towards Mitigating the Complexity of Cognitive Computing in the Oil and Gas Industry 5.0 Context
No full textL'integrazione dell’intelligenza artificiale (AI) e della trasformazione digitale ha accelerato l’evoluzione delle architetture Digital Triplet (D3), allineandosi ai principi umanocentrici di Industria 5.0. Incorporando le funzioni cognitive umane e l'intelligenza percettiva nei domini fisici e virtuali, il paradigma del digital triplet stabilisce una sinergia adattiva tra esseri umani e macchine. Questa ricerca amplia il concetto di digital twin intelligente, superando le metodologie tradizionali basate sui dati e integrando ragionamento, modellazione predittiva e computazione cognitiva, consentendo un processo decisionale adattivo e in tempo reale nei sistemi industriali.
Questa tesi analizza sistematicamente l'evoluzione dei digital twin, introducendo un framework gerarchico del digital triplet, che integra cognizione umana, volizione e intelligenza adattiva per migliorare le interazioni cyber-fisiche. Definendo i livelli di maturità, dominazione e volizione all'interno della gerarchia del digital triplet, la ricerca dimostra la sua capacità di potenziare le capacità percettive e cognitive nello spazio cibernetico. Tre studi di caso illustrano il ruolo del digital triplet nello sviluppo di siti brownfield, retrofit intelligenti e automazione industriale resiliente, con particolare attenzione alle valvole di controllo del flusso pneumaticamente attuate nell'industria petrolifera e del gas. Il framework proposto migliora la maturità digitale e l’intelligenza automatizzata, affrontando la complessità di un modello matematico multifisico innovativo per il sistema valvola-serbatoio, utilizzando metodologie guidate dall’IA e basate sui dati, e operando entro ordini cibernetici avanzati.
Il paradigma digital triplet favorisce l’intelligenza e la resilienza del sistema, integrando soft sensing, analisi predittiva e intelligenza prescrittiva. Colmando il divario tra osservazioni sperimentali e modelli data-driven, la ricerca avanza le capacità cognitive e percettive dei sistemi meccatronici intelligenti. Questa ricerca impiega regressori random forest basati su machine learning per costruire soft sensor per le valvole di controllo del flusso, migliorando la precisione predittiva e i meccanismi di controllo adattivo. La Optimized R2 Score Matrix evidenzia elevati punteggi di predittività (R2 ≈ 1.00) per le relazioni chiave tra le caratteristiche, come la portata d'acqua della valvola solenoide che prevede il flusso interpolato e lo spostamento della valvola che prevede la pressione sul diaframma. Tuttavia, le correlazioni moderate (R2 ≈ 0.6-0.9) evidenziano la necessità di ulteriori perfezionamenti nel feature engineering e nella modellazione multivariata.
Inoltre, modelli di deep learning come multilayer perceptron (MLP), long short-term memory (LSTM), spiking neural networks (SNNs) e liquid state machines (LSMs) sono integrati con i principi dell'apprendimento hebbiano e non-hebbiano per predire il comportamento delle valvole e potenziare le capacità cognitive basate sulla memoria. Le tecniche di ottimizzazione, come la mini-batch processing e l'inizializzazione Xavier, migliorano la robustezza predittiva della pressione di attuazione e dello spostamento, con un modello MLP ottimizzato che ha raggiunto un R2 di 0.9901 per la predizione della pressione pneumatica e dello spostamento. Inoltre, un modello ibrido SNN-LSTM con sei strati ha migliorato significativamente l'accuratezza della previsione del livello del serbatoio (R2 = 0.9777), dimostrando l'efficacia della combinazione tra dinamiche temporali basate sui picchi neuronali e architetture di memoria a lungo termine. Il modello MLP a tre strati con un livello di codifica Leaky Integrate-and-Fire (LIF) ha mostrato miglioramenti moderati nella predizione dello spostamento (R2 = 0.9425) e della pressione pneumatica (R2 = 0.8841), validando il ruolo del calcolo bio-ispirato nelle applicazioni di IA industriale.
Questa tesi evidenzia il potenziale trasformativo delle architetture digital triplet, unificando intelligenza artificiale, computazione cognitiva, apprendimento hebbiano e non-hebbiano, e principi neuromorfici. Sottolinea il ruolo strategico dei modelli digitali gerarchici, dei meccanismi di apprendimento ispirati al cervello e dell'analisi avanzata nel raggiungimento di capacità resilienti, percettive ed euristiche per applicazioni critiche nell’automazione industriale e nella meccatronica. Sfruttando IA bio-ispirata e intelligenza adattiva, questa ricerca pone le basi per la prossima generazione di digital twin cognitivi, in grado di auto-ottimizzarsi, prevedere e rispondere autonomamente, avanzando così il paradigma dell'automazione cognitiva nell'Industria 5.0 e oltre.The integration of artificial intelligence (AI) and digital transformation has accelerated the evolution of digital triplet (D3) architectures, aligning with the human-centric imperatives of Industry 5.0. By embedding human cognitive functions and perceptual intelligence into both physical and virtual domains, the digital triplet paradigm establishes an adaptive synergy between humans and machines. This research advances intelligent digital twins beyond traditional data-driven methodologies by incorporating reasoning, predictive modeling, and cognitive computing, enabling real-time adaptive decision-making in industrial systems.
This thesis systematically explores the evolution of digital twins, introducing a hierarchical digital triplet framework that integrates human cognition, volition, and adaptive intelligence to enhance cyber-physical interactions. By defining maturity, domination, and volition levels within the digital triplet hierarchy, this research demonstrates its capability to enhance perceptual and cognitive capacities in cyberspace. Three case studies illustrate its role in brownfield development, intelligent retrofitting, and resilient smart industrial automation, with a primary focus on pneumatically actuated flow control valves in the oil and gas industry. The proposed framework enhances digital maturity and automation intelligence, addressing the complexity of novel multiphysics mathematical model in valve-tank system through AI-driven, data-driven methodologies and within increased cybernetic orders.
The digital triplet paradigm fosters system intelligence and resilience by integrating soft sensing, predictive analytics, and prescriptive intelligence. By bridging the gap between experimental observations and data-driven models, it advances the cognitive and perceptual capabilities of intelligent mechatronic systems. This research employs machine learning-based random forest regressors to construct soft sensors for flow control valves, improving their predictive accuracy and adaptive control mechanisms. The Optimized R2 Score Matrix reveals high predictability scores (R2 ≈ 1.00) for critical feature relationships, such as solenoid valve water flow predicting interpolated flow rate and valve displacement predicting diaphragm pressure, while moderate predictability (R2 ≈ 0.6-0.9) highlights areas for further feature engineering and multivariate modeling.
Furthermore, deep learning models including multilayer perceptrons (MLP), long short-term memory (LSTM), spiking neural networks (SNNs), and liquid state machines (LSMs) are integrated with Hebbian and non-Hebbian learning principles to predict valve behaviors and enable memory-augmented cognitive capacities. Optimization techniques such as mini-batch processing and Xavier initialization enhance predictive robustness for actuation pressure and displacement, with the optimized MLP model achieving an R2 of 0.9901 for pneumatic pressure and displacement prediction. Additionally, a hybrid SNN-LSTM model with six layers significantly improved tank level prediction accuracy (R2 = 0.9777), highlighting the effectiveness of combining spiking-based temporal dynamics with long-term memory architectures. The three-layer MLP model with an additional Leaky Integrate-and-Fire (LIF) encoding layer demonstrated moderate performance improvements in displacement prediction (R2 = 0.9425) and pneumatic pressure prediction (R2 = 0.8841), validating the role of biologically inspired computation in industrial AI applications.
This thesis underscores the transformative potential of digital triplet architectures by merging AI, cognitive computing, Hebbian and non-Hebbian learning, and neuromorphic principles. It highlights the strategic role of hierarchical digital models, brain-inspired learning mechanisms, and advanced analytics in achieving resilient, perceptive, and heuristic capabilities for critical mechatronics and industrial automation applications. By leveraging bio-inspired AI and adaptive intelligence, this research lays the foundation for next-generation cognitive digital twins that self-optimize, predict, and respond autonomously advancing the cognitive automation paradigm in Industry 5.0 and beyond
Trace element concentration and toxicity in blackspotted smooth-hound sharks (Mustelus punctulatus) from the southern Adriatic Sea: Implications for consumer safety
No full textSharks are highly susceptible to heavy metals and metalloids bioaccumulation due to their high trophic position within marine ecosystems. However, heavy metal(oid)s concentration have been reported for few species, and their biological effects remain poorly understood. Here we report the concentration of 14 heavy metal(oid)s from kidney, liver, brain and muscle tissues of targeted blackspotted smooth-hound sharks from the Southern Adriatic Sea. Males exhibited significantly higher levels of silver and mercury than females, and kidney and liver tissues showed higher concentrations than muscle and brain tissues. No correlation was found between metal(oid) concentrations and body size and morphometric indices. The levels of mercury consistently exceeded regulatory maximum limit for by the EU suggesting danger to the consumer, which was further highlighted by the health risk assessment following the US EPA 2000 guideline
Micronutrient Deficiencies in Pediatric IBD: How Often, Why, and What to Do?
Get PDFInflammatory bowel disease (IBDs), including Crohn's disease (CD), and ulcerative colitis (UC) are complex diseases with a multifactorial etiology, associated with genetic, dietetic, and other environmental risk factors. Children with IBD are at increased risk for nutritional inadequacies, resulting from decreased oral intake, restrictive dietary patterns, malabsorption, enhanced nutrient loss, surgery, and medications. Follow-up of IBD children should routinely include evaluation of specific nutritional deficits and dietetic and/or supplementation strategies should be implemented in case deficiencies are detected. This narrative review focuses on the prevalence, risk factors, detection strategy, and management of micronutrient deficiencies in pediatric IBD
Towards Good Postulation of Fat Points, One Step at a Time
Get PDFWe give an overview on the landscape of polynomial interpolation theory. We will describe first the geometric approach, based on the base locus analysis of linear systems of hypersurfaces of with given degree and assigned multiplicity at a set of points. Secondly, we will consider the algebraic counterpart, with a discussion on the good postulation of fat point schemes of and their regularity index. In both cases we report on some complete, or partial, results and conjectures
Efficacy and Clinical Application of Physical Activity in Substance Use Disorder Rehabilitation: A Review on Mechanism and Benefits
Get PDFBackground: Substance Use Disorders (SUDs) are chronic conditions characterized by high relapse rates and significant psychological, physical, and social complications. Despite the availability of traditional pharmacological and psychotherapeutic interventions, many individuals struggle to maintain abstinence. Recently, physical activity (PA) has emerged as a promising complementary intervention. This review aims to examine the existing evidence on the effects of PA in individuals with SUDs, with a particular focus on neurobiological mechanisms. Methods: A narrative review was conducted on 30 September 2024, searching relevant keywords on PubMed, Web of Science, Google Scholar, and Scopus. Randomized clinical trials, cohort studies, reviews, and meta-analyses published between 1988 and 2024 were considered. Results: Fifty studies were included. Key themes included the role of PA in inducing neuroadaptation in individuals with SUDs, which is crucial for relapse prevention and impulse control, and the effects of PA depending on the type of PA and the specific SUD. Neurobiological modifications related to PA are of particular interest in the search for potential biomarkers. Additionally, studies explored the effects of PA on cravings, mental health, and quality of life. The review overall discusses the psychological changes induced by PA during SUD rehabilitation, identifies barriers to participation in PA programs, and suggests clinical and organizational strategies to enhance adherence. Conclusions: Physical activity is a promising adjunctive therapy for the management of Substance Use Disorders. Long-time longitudinal studies and meta-analyses are needed to sustain scientific evidence of efficacy. The success of PA programs moreover depends on overcoming barriers to adherence, including physical, psychological, and logistical challenges
Clinicians’ Perspectives and Methodological Application of Fluorescence in situ Hybridization (FISH) to Define Cytogenetic Risk in Multiple Myeloma: An Italian, Real-World, Survey-Based Report From the European Myeloma Network (EMN) Italy
Get PDFBackground: Fluorescence in situ hybridization (FISH) is the standard technique for the prognostic detection of cytogenetic abnormalities (CA) in multiple myeloma (MM). In Italy, the application of practical guidelines for FISH testing in clinical studies and the degree of standardization of laboratory techniques are largely unknown. Methods: We conducted a survey from April to July 2023 among 70 MM-treating centers associated with the European Myeloma Network Italy and geographically well distributed across Italy. We aimed to record laboratory and clinicians’ perspectives about FISH application in Italy, with a focus on 1q alterations. Results: FISH was widely accessible across the country, with 71% of centers performing it locally, while the remaining centers (predominantly those with <30 newly diagnosed MM cases/year) sent samples to external laboratories. Variability in laboratory techniques, such as CD138+ cell purification and CA detection thresholds, was observed among centers. The centers analyzed del(17p) (100%), t(4;14) (100%), t(14;16) (98%), 1q+ (96%, with 70% distinguishing between gain and amplification), t(11;14) (90%), del(1p32) (88%), del(13q) (68%), and hyperdiploidy (52%). FISH emerged as a crucial prognostic technique, since 94% of centers used the Revised International Staging System (R-ISS) at diagnosis, and 69% implemented the recent R2-ISS. Most centers performed FISH at diagnosis in all patients, while others did not routinely perform FISH in some categories of patients (e.g., aged >80 years). At relapse, 53% of centers routinely repeated FISH testing, 9% did not, while others repeated it selectively. Conclusions: This overview of FISH use in Italy provides a basis for future standardization efforts
The administrative judge's access to the Court of Justice: the obligation of the preliminary ruling and the impact on the Italian reform of justice
No full textThis paper analyses the role of the preliminary ruling procedure under Article 267 TFEU in the context of the Italian administrative justice system, with particular attention to its impact on judicial efficiency and ongoing justice reforms. Starting from the growing importance of the time factor in judicial proceedings, the study examines the obligation of last-instance courts to refer questions of EU law to the Court of Justice of the European Union and the tensions arising between the need for uniform interpretation of EU law and the reasonable duration of trials. The contribution highlights how an overly rigid application of the CILFIT criteria may lead to unnecessary delays and procedural abuses, undermining both the effectiveness of judicial protection and the autonomy of national judges. Through an analysis of recent case law of the Italian Council of State and the Court of Justice, the paper argues for a more flexible and purposive approach to preliminary references, limited to cases of genuine interpretative doubt. Such an approach would better reconcile judicial cooperation, procedural efficiency, and coherence of the EU legal order, while supporting the broader objectives of justice system reform and economic development