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Background: Recent evidence highlights the epidemiological value of blood DNA methylation (DNAm) as surrogate biomarker for exposure to risk factors for non-communicable diseases (NCD). DNAm surrogate of exposures predicts diseases and longevity better than self-reported or measured exposures in many cases. Consequently, disease prediction models based on blood DNAm surrogates may outperform current state-of-the-art prediction models. This study aims to develop novel DNAm surrogates for cardiovascular diseases (CVD) risk factors and develop a composite biomarker predictive of CVD risk. We compared the prediction performance of our newly developed risk score with the state-of-the-art DNAm risk scores for cardiovascular diseases, the 'next-generation' epigenetic clock DNAmGrimAge, and the prediction model based on traditional risk factors SCORE2. Results: Using data from the EPIC Italy cohort, we derived novel DNAm surrogates for BMI, blood pressure, fasting glucose and insulin, cholesterol, triglycerides, and coagulation biomarkers. We validated them in four independent data sets from Europe and the USA. Further, we derived a DNAmCVDscore predictive of the time-to-CVD event as a combination of several DNAm surrogates. ROC curve analyses show that DNAmCVDscore outperforms previously developed DNAm scores for CVD risk and SCORE2 for short-term CVD risk. Interestingly, the performance of DNAmGrimAge and DNAmCVDscore was comparable (slightly lower for DNAmGrimAge, although the differences were not statistically significant). Conclusions: We described novel DNAm surrogates for CVD risk factors useful for future molecular epidemiology research, and we described a blood DNAm-based composite biomarker, DNAmCVDscore, predictive of short-term cardiovascular events. Our results highlight the usefulness of DNAm surrogate biomarkers of risk factors in epigenetic epidemiology to identify high-risk populations. In addition, we provide further evidence on the effectiveness of prediction models based on DNAm surrogates and discuss methodological aspects for further improvements. Finally, our results encourage testing this approach for other NCD diseases by training and developing DNAm surrogates for disease-specific risk factors and exposures. [ABSTRACT FROM AUTHOR]

Previous studies for cancer biomarker discovery based on pre-diagnostic blood DNA methylation (DNAm) profiles, either ignore the explicit modeling of the Time To Diagnosis (TTD), or provide inconsistent results. This lack of consistency is likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work, we aim to identify blood DNAm profiles associated with TTD, with the aim to improve the reliability of the results, as well as their biological meaningfulness. We argue that a global approach to estimate CpG sites effect profile should capture the complex (potentially non-linear) relationships interplaying between sites. To prove our concept, we develop a new Deep Learning-based approach assessing the relevance of individual CpG Islands (i.e., assigning a weight to each site) in determining TTD while modeling their combined effect in a survival analysis scenario. The algorithm combines a tailored sampling procedure with DNAm sites agglomeration, deep non-linear survival modeling and SHapley Additive exPlanations (SHAP) values estimation to aid robustness of the derived effects profile. The proposed approach deals with the common complexities arising from epidemiological studies, such as small sample size, noise, and low signal-to-noise ratio of blood-derived DNAm. We apply our approach to a prospective case-control study on breast cancer nested in the EPIC Italy cohort and we perform weighted gene-set enrichment analyses to demonstrate the biological meaningfulness of the obtained results. We compared the results of Deep Survival EWAS with those of a traditional EWAS approach, demonstrating that our method performs better than the standard approach in identifying biologically relevant pathways. Author summary: Blood-derived DNAm profiles could be exploited as new biomarkers for cancer risk stratification and possibly, early detection. This is of particular interest since blood is a convenient tissue to assay for constitutional methylation and its collection is non-invasive. Exploiting pre-diagnostic blood DNAm data opens the further opportunity to investigate the association of DNAm at baseline on cancer risk, modeling the relationship between sites' methylation and the Time to Diagnosis. Previous studies mostly provide inconsistent results likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work we argue that an approach to estimate single CpG sites' effect while modeling their combined effect on the survival outcome is needed, and we claim that such approach should capture the complex (potentially non-linear) relationships interplaying between sites. We prove this concept by developing a novel approach to analyze a prospective case-control study on breast cancer nested in the EPIC Italy cohort. A weighted gene set enrichment analysis confirms that our approach outperforms standard EWAS in identifying biologically meaningful pathways. [ABSTRACT FROM AUTHOR]

Stimulated by the recent advances in computational tools for the simulation of seismic wave propagation problems in realistic geological configurations, this paper presents a 3D physicsbased numerical analysis of near-source ground motion during the MW 6.0 2012 May 29 earthquake in the Po Plain, Northern Italy. To reproduce with sufficient accuracy some of the most peculiar features of the near-source strong-motion records and of the spatial variability of damage distribution, this study required a sequence of investigations, starting from the analysis of a wide set of near-source records, to the calibration of an improved kinematic seismic source model, up to the development of a 3D numerical model of the portion of the Po Plain interested by the earthquake. The latter includes the basin geometry, characterized by sediment thickness sharply varying from few tens of metres to some kilometres. The spatial resolution of the numerical model is suitable to propagate frequencies up to about 1.5 Hz. Numerical simulations were performed using the open-source high-performance code SPEED, based on the Discontinuous Galerkin Spectral Elements method. The 3D numerical model, coupled with the updated slip distribution along the rupturing fault, proved successful to reproduce with good agreement, measured through quantitative goodness-of-fit criteria, the most relevant features of the observed ground motion. These include: (i) the large fault normal velocity peaks at the near-source stations driven by updip directivity effects; (ii) the small-scale variability at short distance from the source, resulting in the out-of-phase motion at stations separated by only 3 km distance; (iii) the propagation of prominent trains of surface waves, especially in the Northern direction; (iv) the map of earthquake-induced ground uplift with maximum values of about 10 cm, in substantial agreement with geodetic measurements and (v) the two-lobed pattern of the peak ground velocity map, well correlated with the distribution of macroseismic intensity. [ABSTRACT FROM AUTHOR]

• Seismic fragility analysis of existing concrete dams. • gPCE-based predictive models for the dynamic behaviour of concrete dams. • Hierarchical Bayesian framework for uncertainty reduction in the fragility analysis. • Operational modal analysis as source of information in the Bayesian inference. • MCMC-based algorithm for inverse problem resolution in OMA. Concrete gravity dams are critical infrastructures for communities to meet the basic human needs as well as rising standards of living. Most of the existing concrete gravity dams in Italy were built before the introduction of seismic regulations. Although no concrete gravity dams have as yet suffered a catastrophic collapse during or after a seismic event, their preservation remains a key aspect for communities, also in view of that older dams may have deteriorated to a critical level. For these reasons, researchers and practitioners in dam engineering are working to improve seismic fragility, and ultimately seismic risk, assessment procedures. Since no case histories are available, numerical modelling plays an important role, even though many uncertainties can affect the models and then the estimation of the seismic fragility. This paper presents a robust hierarchical Bayesian framework for the calibration of dynamic parameters of dam numerical models based on ambient vibrations, which allows an analyst to reduce uncertainties in the seismic fragility derivation. A probabilistic predictive model of the dam modal behaviour based on the general Polynomial Chaos Expansion is adopted in order to reduce the computational burden and a numerical algorithm for the solution of the inverse problem based on Markov Chain Monte Carlo is also presented. The proposed approach is applied to an existing large concrete gravity dam in Italy, and the effect of epistemic uncertainty reduction is finally evaluated in terms of fragility curves. [ABSTRACT FROM AUTHOR]