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The new mineral marchettiite (IMA2017-066) is the natural equivalent of ammonium hydrogen urate. It has a simple molecular formula C5H7N5O3 and can be alternatively written as (NH4)C5H3N4O3. Marchettiite was found in a cleft at Mount Cervandone, Devero Valley, Piedmont, Italy, where it occurs as aggregates of opaque pale pink to white, platy prismatic crystals. This mineral has a white streak, dull and opaque lustre, it is not fluorescent and has a hardness of 2–2.5 (Mohs' scale). The tenacity is brittle and crystals have a good cleavage parallel to {001}. The calculated density is 1.69 g/cm3. Marchettiite is biaxial (–) with 2V of 47.24°; the optical properties of marchettiite were determined by periodic-DFT methods providing the following values: α = 1.372, β = 1.681 and γ = 1.768. No twinning was observed. Electron microprobe analyses gave the following chemical formula: C4.99H6.97N4.91O3.00. Although the small crystal size did not allow refinement of structural data by single-crystal diffraction, we were able to refine the structure by powder micro X-ray diffraction. Marchettiite has space group P $\bar{1}$ and the following unit-cell parameters: a = 3.6533(2) Å, b = 10.2046(7) Å, c = 10.5837(7) Å, α = 113.809(5)°, β = 91.313(8)°, γ = 92.44(1)° and V = 360.312 Å3. The strongest lines in the powder diffraction pattern [ d in Å (I)(hkl)] are: 9.784(50)(001); 8.663(80)(01 $\bar{1}$); 5.659(100)(011); 3.443(100)(10 $\bar{1}$); 3.241(70)(003) and 3.158(100)(1 $\bar{1}\bar{1})$. Marchettiite is named after Gianfranco Marchetti, the mineral collector who found this mineral. [ABSTRACT FROM AUTHOR]

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]

[Display omitted] • New empirical estimates are provided for monthly R-factors over Italy for 1981–2010. • R-factor is currently larger in autumn and summer, lower in spring and winter. • 29 EURO-CORDEX projections for 2021–2050 and 2051–2080 under three RCPs are used. • Future anomalies are critical in autumn (whole Italy), and in summer (North). • There is a large variability associated with the use of an ensemble of projections. The estimation of rainfall erosivity in the Revised Universal Soil Loss Equation (RUSLE) requires long series of sub-hourly rainfall observations. Due to the endemic unavailability of this information, at least with the required degree of detail and coverage, empirical models are frequently used for the estimation of rainfall erosivity basing on easily retrievable rainfall values at the monthly or annual scale. In this paper, an empirical model is calibrated for Italy to bridge the annual to monthly scale gap by means of 10-year series of monthly rainfall observations at 171 Italian rain gauges, and then applied using the ERA5-Land gridded rainfall dataset to obtain maps of monthly and seasonal rainfall erosivity covering the reference period 1981–2010. Successively, 29 EURO-CORDEX bias-corrected rainfall projections for two future horizons (2021–2050 and 2051–2080 under RCP 2.6, 4.5 and 8.5) are used to obtain expected anomalies of seasonal rainfall erosivity over Italy with respect to the reference period. Statistical and visual analysis of results shows that positive percentage anomalies are expected in large portions of the country, especially under RCP 2.6 but with significant increases expected also under RCPs 4.5 and 8.5, especially in autumn and, secondarily, in summer. This intra-annual rainfall erosivity anomaly pattern entails the existence of increasing trends in the erosion hazard that cannot be captured by empirical models only relying on annual rainfall. The position of those results in the related literature is deeply discussed, providing significant insights on uncertainty sources and possible future research developments. [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]