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Greening is a subsidy provided by the Common Agricultural Policy (CAP), related to mowing and designed to protect environment. National or regional paying agencies (PP) monitor and verify compliance of farmers' declarations with CAP rules. In this work, an operational procedure is proposed aimed at supporting PPs in detecting, mapping and quantifying the number of times mowing occurred in a meadow field. In particular, 72,539 meadows fields within the Piemonte region (NW – Italy) were analysed with a time series of Sentinel-2 (S2) data. The procedure is based on the processing of filtered and regularized time series of NDVI maps. The Fast Fourier Transform (FFT) was applied at field level to decompose the local NDVI temporal profile. The frequency ($${\omega _{peak}})$$ ω p e a k ) corresponding to the maximum amplitude ($${A_{peak}})$$ A p e a k ) was therefore considered. $${A_{peak}}$$ A p e a k value was used to detect not-mowed meadows by thresholding based on the application of the non-parametric Kolmogorov-Smirnov test. Mowing counting were achieved with reference to $${\omega _{peak}}$$ ω p e a k and the correspondent map (called MCM) generated for the study area. MCM was, finally, tested against the validation set (285 fields). Results showed an Overall Accuracy (OA) > 87%, confirming the effectiveness of the proposed procedure in detecting, mapping and quantifying the number of times mowing occurred. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 pandemic reminded us how vaccination can be a divisive topic on which the public conversation is permeated by misleading claims, and thoughts tend to polarize, especially on online social networks. In this work, motivated by recent natural language processing techniques to systematically extract and quantify opinions from text messages, we present a differential framework for bivariate opinion formation dynamics that is coupled with a compartmental model for fake news dissemination. Thanks to a mean-field analysis we demonstrate that the resulting Fokker-Planck system permits to reproduce bimodal distributions of opinions as observed in polarization dynamics. The model is then applied to sentiment analysis data from social media platforms in Italy, in order to analyze the evolution of opinions about Covid-19 vaccination. We show through numerical simulations that the model is capable to describe correctly the formation of the bimodal opinion structure observed in the vaccine-hesitant dataset, which is witness of the known polarization effects that happen within closed online communities. [ABSTRACT FROM AUTHOR]

Automatic passenger counting (APC) systems in public transport are useful in collecting information that can help improve the efficiency of transport networks. Focusing on video-based passenger counting, the aim of this study was to evaluate and compare an existing APC system, claimed by its manufacturer to be highly accurate (98%), with a newly developed low-cost APC system operating under the same real-world conditions. For this comparison, a low-cost APC system using a Raspberry Pi with a camera and a YOLOv5 object detection algorithm was developed, and an in-field experiment was performed in collaboration with the public transport companies operating in the cities of Turin and Asti in Italy. The experiment shows that the low-cost system was able to achieve an accuracy of 72.27% and 74.59%, respectively, for boarding and alighting, while the tested commercial APC system had an accuracy, respectively, of 53.11% and 55.29%. These findings suggest that current APC systems might not meet expectations under real-world conditions, while low-cost systems could potentially perform at the same level of accuracy or even better than very expensive commercial systems. [ABSTRACT FROM AUTHOR]

In the article a new approach for measuring the spatial concentration of human population is presented and tested. The new procedure is based on the concept of concentration introduced by Gini and, at the same time, on its spatial extension (i.e., taking into account the concept of spatial autocorrelation, polarization). The proposed indicator, the Spatial Gini Index, is then computed by using two different kind of territorial partitioning methods: MaxMin (MM) and the Constant Step (CS) distance. In this framework an ad hoc extension of the Rey and Smith decomposition method is then introduced. We apply this new approach to the Italian and foreign population resident in almost 7,900 statistical units (Italian municipalities) in 2002, 2010 and 2018. All elaborations are based on a new ad hoc library developed and implemented in Python. [ABSTRACT FROM AUTHOR]

Simple Summary: Pest molecular characterization is an essential practice to support and complete the classical morphological taxonomy of insects, as well as to provide helpful information that may lead to finding alternative control strategies or detection methods. Molecular biology methods are of great importance, especially for the species whose identification requires highly qualified operators or the support of optical tools for microscopic organisms. This fact is even more amplified if the pest under study is an alien species that can quickly spread into a new area, as in the case of Toumeyella parvicornis (Cockerell) in Italy and Europe at large. This species demonstrates the need for molecular characterization, inspiring the present study. Toumeyella parvicornis (Cockerell) (Hemiptera, Coccidae) is becoming a potential main pest of stone pine plants (Pinus pinea L.), both in urban parks and pinewood forests in Europe. Its recent distribution is a source of concern in several regions of Italy and in France. The early detection of this pest plays a fundamental role to contain its geographical expansion, but its taxonomic identification is still based on laborious observations of the morphological traits. The identification is also complicated by the small size of the pest, which makes the observations possible only through a stereomicroscope. Molecular identification is beneficial for detection, but currently, there is only a single gene sequence available for this pest. This study fills this gap in knowledge by providing the sequences of five different genes (COI, 28S, elongation factor (EF-1α), wingless (wg), and histone H3 (HexA)), together with a phylogenetic analysis carried out among species belonging to Coccidae, one of the most important families of scale insects. The results provide new valuable information about T. parvicornis and may represent useful data for its detection and management practices. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa is a xylem‐limited plant pathogen infecting many crops globally and is the cause of the recent olive disease epidemic in Italy. One strategy proposed to mitigate losses is to replant susceptible crops with resistant varieties. Several genetic, biochemical and biophysical traits are associated to X. fastidiosa disease resistance. However, mechanisms underpinning resistance are poorly understood. We hypothesize that the susceptibility of olive cultivars to infection will correlate to xylem vessel diameters, with narrower vessels being resistant to air embolisms and having slower flow rates limiting pathogen spread. To test this, we scanned stems from four olive cultivars of varying susceptibility to X. fastidiosa using X‐ray computed tomography. Scans were processed by a bespoke methodology that segmented vessels, facilitating diameter measurements. Though significant differences were not found comparing stem‐average vessel section diameters among cultivars, they were found when comparing diameter distributions. Moreover, the measurements indicated that although vessel diameter distributions may play a role regarding the resistance of Leccino, it is unlikely they do for FS17. Considering Young–Laplace and Hagen–Poiseuille equations, we inferred differences in embolism susceptibility and hydraulic conductivity of the vasculature. Our results suggest susceptible cultivars, having a greater proportion of larger vessels, are more vulnerable to air embolisms. In addition, results suggest that under certain pressure conditions, functional vasculature in susceptible cultivars could be subject to greater stresses than in resistant cultivars. These results support investigation into xylem morphological screening to help inform olive replanting. Furthermore, our framework could test the relevance of xylem geometry to disease resistance in other crops. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to investigate the effectiveness of corticosteroid therapy for children suffering from Sydenham chorea (SC). Methods: The design of the study was observational, retrospective and conducted at the single center of the Rheumatology Unit of Policlinic Hospital of Milan, Italy, from May 1995 to May 2022. All data about the patients were collected from medical records. Results: From a total of 59 patients enrolled in the study (44 females and 15 males; median age 9.3 years, range 7.4–10.6 years), 49 were eligible for primary outcome analysis (10 patients were excluded due to incomplete data). Overall, 75% of patients received steroid therapy, while the remaining cases were treated with symptomatic drugs, including neuroleptics and antiseizure drugs. We found that the duration of chorea was significantly shorter in patients treated with corticosteroids in comparison to those receiving symptomatic treatment (median time: 31 vs. 41 days, p = 0.023). Additionally, patients with arthritis at the onset of the disease had a longer duration of chorea than those without arthritis (median time 90.5 vs. 39 days, p = 0.02). We also found that chorea recurred in 12% of the patients and seemed to be linked to a younger age at onset (p = 0.01). Conclusions: The study suggests that corticosteroid therapy can lead to a faster resolution of SC when compared to neuroleptics and antiseizure drugs treatment. [ABSTRACT FROM AUTHOR]

Mountain springs' recharging mechanisms have undergone significant changes due to climate change, as well as altered water consumption patterns and policies. Specifically, climate change has influenced the characteristics of spring discharges, evapotranspiration, snow-rainfall ratios, and snow seasonality, bringing new rainfall patterns associated with increased average air temperatures. Consequently, examining how groundwater storage mechanisms are changing in response to climate-driven has become crucial for understanding the future scenarios associated with water availability in such areas. Therefore, increasingly automated tools and simplified approaches need to be applied to continuously monitor hydrogeological variables affecting the spring recharge system. SOURCE (a semi-automatic tool for Spring mOnitoring data analysis and aqUifeR CharactErization) is an advanced semi-automatic Python tool that automates the hydrogeological characterization of the springs' aquifers. Its functionalities were tested through the analysis of the Promise and Alpe Perrot mountain springs and the related meteorological stations (La Thuile-Villaret and Champdepraz-Chevrère) located in the Aosta Valley region (NW Italy). Input data (flow rate, temperature, electrical conductivity, and rainfall) can be rapidly processed, providing graphical outputs, as well as values for the main hydrodynamic parameters (e.g. auto and cross-correlation coefficients) for an aquifer. Besides, to comprehend the relationship between changes in weather conditions and water availability in the Aosta Valley Region (Northwestern Italy), 7-year precipitation and discharge trends were considered. Despite the increasing trends in flow rate, considering the limited vulnerability values obtained for Alpe Perrot, it is possible to hypothesize a delayed impact of climatic changes on the spring system. The impact of climatic changes on Promise spring is supposed to be faster. However, the nature and dimensions of the aquifer are such as to be resilience to increasing water temperature. Being able to continuously monitor and define the effects induced on water reserves through simplified analysis approaches, such as those presented in this paper, is increasingly necessary, especially for local authorities. A single software package such as SO URC E that contains all the main methods of water spring analysis has the potential to significantly reduce any analysis times. [ABSTRACT FROM AUTHOR]

All-cause mortality is a very coarse grain, albeit very reliable, index to check the health implications of lifestyle determinants, systemic threats and socio-demographic factors. In this work, we adopt a statistical-mechanics approach to the analysis of temporal fluctuations of all-cause mortality, focusing on the correlation structure of this index across different regions of Italy. The correlation network among the 20 Italian regions was reconstructed using temperature oscillations and traveller flux (as a function of distance and region's attractiveness, based on GDP), allowing for a separation between infective and non-infective death causes. The proposed approach allows monitoring of emerging systemic threats in terms of anomalies of correlation network structure. [ABSTRACT FROM AUTHOR]

In this study, we propose a time‐dependent susceptible‐exposed‐infected‐recovered (SEIR) model for the analysis of the SARS‐CoV‐2 epidemic outbreak in three different countries, the United States, Italy, and Iceland using public data inherent the numbers of the epidemic wave. Since several types and grades of actions were adopted by the governments, including travel restrictions, social distancing, or limitation of movement, we want to investigate how these measures can affect the epidemic curve of the infectious population. The parameters of interest for the SEIR model were estimated employing a composite likelihood approach. Moreover, standard errors have been corrected for temporal dependence. The adoption of restrictive measures results in flatten epidemic curves, and the future evolution indicated a decrease in the number of cases. [ABSTRACT FROM AUTHOR]

CUORE Upgrade with Particle IDentification (CUPID) is a foreseen ton-scale array of Li2MoO4 (LMO) cryogenic calorimeters with double readout of heat and light signals. Its scientific goal is to fully explore the inverted hierarchy of neutrino masses in the search for neutrinoless double beta decay of 100Mo. Pile-up of standard double beta decay of the candidate isotope is a relevant background. We generate pile-up heat events via injection of Joule heater pulses with a programmable waveform generator in a small array of LMO crystals operated underground in the Laboratori Nazionali del Gran Sasso, Italy. This allows to label pile-up pulses and control both time difference and underlying amplitudes of individual heat pulses in the data. We present the performance of supervised learning classifiers on data and the attained pile-up rejection efficiency. [ABSTRACT FROM AUTHOR]

Geographic profiling, or locus operandi, is a scientific approach that has been applied to forensic investigations for several years. However, it has never been applied to a phenomenon as complex as the search for missing persons. This article presents three Italian case studies in which geographic profiling was used to narrow the areas to search for missing persons. The geographical data were mapped and processed on a geographic information system platform using computational geometry analysis. However, these disappearances occurred during the Italian lockdown period, imposed to address the COVID-19 outbreak, which made the searches slower and more complex than usual. [ABSTRACT FROM AUTHOR]

Rainfall is the main precursor of floods and landslide events that cause major damages every year worldwide. For an early warning system to be useful for risk mitigation and prompt interventions, the use of accurate rainfall forecasting models is crucial. With this aim, a blended model named PRAISE-MET (Prediction of Rainfall Amount Inside Storm Events with METeo) is proposed. The model is based on a coupling of a stochastic model and numerical weather prediction (NWP) model, and its characteristics are best for improving rainfall prediction at catchment scales, where the models generally used are often based on stochastic processing without using information from weather forecasts. PRAISE-MET can be easily adapted to provide input data in models for rainfall-runoff or landslide prediction, as shown for the case study illustrated here that occurred on 1 March 2006 in Montenero di Bisaccia (Central Italy). The application revealed how the use of the PRAISE-MET model would provide useful information on landslide triggering conditions in advance. The obtained results show that it is possible to gain some hours to effectively activate the protection procedures by setting the proper levels of exceedance probability for rainfall thresholds. [ABSTRACT FROM AUTHOR]

Summary: Pseudomonas savastanoi pv. savastanoi, the causal agent of olive knot disease, is a fluorescent Gram‐negative bacterium classified, according to the specific LOPAT profile, as Ib. However, during the 90s, a number of atypical non‐fluorescent levan‐positive strains of Pseudomonas savastanoi pv. savastanoi have been unexpectedly isolated from olive knots in Central Italy. Since its first report, several studies were conducted on this species variant, but its genome sequence has never been reported. The complete genome sequence and two additional plasmids of PVFi1, a representative strain, were here obtained using a hybrid sequencing approach with both Oxford Nanopore Technology and Illumina sequencing. A thorough genomic analysis unravelled several genetic features of this peculiar strain, showing a transposase insertion downstream a fragmented copy of the levansucrase gene. The same features were previously reported on levan‐negative Pseudomonas savastanoi pv. savastanoi strains. In addition, a second copy of the levansucrase gene fully equipped for a gene expression and comparable to the levan‐positive Pseudomonas savastanoi pv. glycinea, may explain the levan‐positive test. This result provides a solid genetic demonstration that the bacterial species Pseudomonas savastanoi contains either levan‐positive or levan‐negative strains, providing insights for an update of the related LOPAT classification. [ABSTRACT FROM AUTHOR]

The evaluation of the impact of intermittent water supply on water distribution systems is a complex task. Laboratory tests, in controlled conditions with virtually no constraints on instrument and device location, allow the analysis of the effects of single parameters on pressures and flows. Within the framework of a research consultancy commissioned by the World Bank, the test network at the Water Engineering Laboratory of the University of Perugia, Italy, was modified to analyse pipe filling and emptying phenomena. Herein we describe the characteristics of the set-up, the available instruments and data acquisition system, as well as the installed devices (e.g., air release and in- and off-line ball valves). The set-up was also designed to investigate the effects of the air flow on the overbilling of water meters. The results of a preliminary test allow the typical phases of the phenomenon to be analysed, including the filling of the pipe with water and the discharging of air, the arrival of the water front at the downstream end causing a pressure variation typical of water hammer, and the emptying process and the filling with air. The analysis allows the operational mechanism to be understood and remedial interventions designed and validated. [ABSTRACT FROM AUTHOR]

It is typically interpreted that more moisture in the atmosphere leads to more intense rains. This notion may be supported, for example, by taking a scatterplot between rain and column precipitable water. The present paper suggests, however, that the main consequence of intense rains with more moisture in the atmosphere is that there is a higher chance of occurrence rather than an increase in the expected magnitude. This tendency equally applies to any rains above 1 mm (6 h)−1 to a lesser extent. The result is derived from an analysis of 33 local rain gauge station data and a shared sounding over Friuli Venezia Giulia, northeast Italy. Significance Statement: Moisture is the source of clouds. Clouds, in turn, are source of rain. So we may expect that more moisture in the atmosphere causes more intense rains. We may further speculate that with more moisture in the atmosphere as a consequence of the global warning, we must face more catastrophic rain events and floods. However, this paper, by analyzing data over Friuli Venezia Giulia, northeast Italy, suggests otherwise: more moisture indeed increases frequencies of intense rains, but not their magnitudes as much. [ABSTRACT FROM AUTHOR]

This paper deals with the quantification of the different rates in epidemiological models from a function estimation framework, with the objective of identifying the desired unknowns without defining a priori basis functions for describing its behaviour. This approach is used to analyze data for the Covid-19 pandemic in Italy and Brazil. The forward problem is written in terms of the SIRD model, while the inverse problem is solved by combining the Levenberg–Marquardt method with Tikhonov regularization. A very good agreement was achieved between data and the calculated values. The resulting methodology is robust and very versatile, being easily applicable to other epidemiology models and data from other countries. [ABSTRACT FROM AUTHOR]

Lockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals. Here we study socioeconomic determinants of mobility disruption during both the lockdown and the recovery phases in Italy. For this purpose, we analyze a massive data set on Italian mobility from February to October 2020 and we combine it with detailed data on pre-existing local socioeconomic features of Italian administrative units. Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters. Notably, the former cluster features significantly higher income per capita and lower income inequality than the latter. This distinction persists once the lockdown is lifted. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels. These results are then detailed and confirmed with a quantile regression analysis. Our findings show that economic segregation has, thus, strengthened during the pandemic. [ABSTRACT FROM AUTHOR]

In recent years, the Asian gall wasp Dryocosmus kuriphilus has invaded chestnut trees and significantly affected the Portuguese chestnut production. Studies in other countries, such as Japan or Italy, have shown that the parasitoid Torymus sinensis can successfully achieve biological control of D. kuriphilus. Mathematical models help us to understand the dynamics of the interaction between the pest D. kuriphilus and its parasitoid T. sinensis and, consequently, they can help to implement measures that enhance crop pest management. In this work, the evolution of the density of D. kuriphilus and T. sinensis across time and space is studied through the numerical solution of models that include parameters based on observations made in Portugal. Simultaneous releases of the parasitoid are simulated at various locations and at different times. The results indicate that, in the case of a small and homogeneous orchard, biological control can be effective, but, in the case of extensive domains, the pest control is much more difficult to achieve. In order for biological control to be efficient, it is necessary to implement, in each chestnut-producing region, a collective strategy based on the annual monitoring of infestation levels. [ABSTRACT FROM AUTHOR]

The ongoing coronavirus disease 2019 (COVID-19) pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an outbreak with more than 10 million confirmed infections, and more than 500,000 reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic–stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time series is assessed by an extended version of the SIR [Susceptible–Infected–Recovered–Protected–Isolated (SIRCX)] model, whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX model. The estimated Basic reproduction numbers for Italy and Spain based on SIRCX model are found to be 4. 7 8 and 3. 2 5 , respectively. ISA model-based results reveal that the number of cases in Italy and Spain between 11 May, 2020–9 June, 2020 will be 10,982 (6383–15,582) and 13,731 (3395–29,013), respectively. Additionally, the expected number of daily cases on 9 July, 2020 for Italy and Spain is estimated to be 30 (0–183) and 92 (0–602), respectively. [ABSTRACT FROM AUTHOR]

Landslides represent a major geomorphological feature influencing the evolution of the southern Apennine slopes with earth flows being one of the most representative types of landslide. The development of earth flows in the southern Apennines is facilitated by the widespread occurrence of clayey lithologies. Earth flows can either produce marked scars along the slopes, when their activity is high, or give rise to bumpy areas that are frequently covered by vegetation, when their activity is low. In this latter case, although the risk is significantly reduced, the earth flow can still represent a problem for buildings and infrastructure. The Picerno earth flow in the Basilicata region is an example of a low-activity earth flow. This NW-SE–oriented landslide extends for a length of ~ 5.5 km, has an average width of ~ 680 m, and affects the village of Picerno. Our investigations mainly focused on the terminal portion of the landslide and revealed that some significant linear infrastructures (e.g., the Potenza–Naples railway) and important provincial and municipal roads are affected by the earth flow. In order to better define the amount of displacement characterizing the Picerno earth flow and gain insights into the lateral extension and the depth of the main detachment surface, we undertook a detailed geological and geomorphological survey. Interferometry synthetic aperture radar data provided by the processing of SAR images, with the aim of highlighting the regions of the landslide that are currently active, together with electrical resistivity tomography data which have been utilized to define the geometry of the landslide body. Our results provide useful suggestions for planning appropriate actions aimed at stabilizing the landslide body. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests. We developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergency-room with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response. We have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases. This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation (This tool is available at https://covid19-blood-ml.herokuapp.com/). [ABSTRACT FROM AUTHOR]

The aim of this study is to provide an evaluation of the impact of two largely used data assimilation techniques, namely three‐ and four‐dimensional variational data assimilation systems (3D‐Var and 4D‐Var), on the forecasting of heavy precipitation events using the Weather Research and Forecasting (WRF) model. For this purpose, two flash flood events in central Italy are analysed. The first occurred on September 14, 2012 during an Intensive Observation Period of the Hydrological cycle in the Mediterranean experiment (HyMeX) campaign, while the other occurred on May 3, 2018. Radial velocity and reflectivity acquired by C‐band weather radars at Mt. Midia (central Italy) and San Pietro Capofiume (northern Italy), as well as conventional observations (SYNOP and TEMP), are assimilated into the WRF model to simulate these damaging flash flood events. In order to evaluate the impact of the 3D‐Var and 4D‐Var assimilation systems on the estimation of short‐term quantitative precipitation forecasts, several experiments are carried out using conventional observations with and without radar data. Rainfall evaluation is performed by means of point‐by‐point and filtering methodologies. The results point to a positive impact of the 4D‐Var technique compared to results without assimilation and with 3D‐Var experiments. More specifically, the 4D‐Var system produces an increase of up to 22% in terms of the Fractions Skill Score compared to 3D‐Var for the first flash flood event, while an increase of about 5% is achieved for the second event. The use of a warm start initialization results in a considerable reduction in the spin‐up time and a significant improvement in the rainfall forecast, suggesting that the initial precipitation spin‐up problem still occurs when using 4D‐Var. [ABSTRACT FROM AUTHOR]

Our understanding of earthquake sources is limited by the availability and the quality of observations and the fidelity of our physical models. Uncertainties in our physical models will naturally bias our inferences of subsurface fault slip. These uncertainties will always persist to some level as we will never have a perfect knowledge of the Earth's interior. The choice of the forward physics is thus ambiguous, with the frequent need to fix the value of several parameters such as crustal properties or fault geometry. Here, we explore the impact of uncertainties related to the choice of both fault geometry and elastic structure, as applied to the 2016  M w 6.2 Amatrice earthquake, central Italy. This event, well instrumented and characterized by a relatively simple fault morphology, allows us to explore the role of uncertainty in basic fault parameters, such as fault dip and position. We show that introducing uncertainties in fault geometry in a static inversion reduces the sensitivity of inferred models to different geometric assumptions. Accounting for uncertainties thus helps infer more realistic and robust slip models. We also show that uncertainties in fault geometry and Earth's elastic structure significantly impact estimated source models, particularly if near-fault observations are available. [ABSTRACT FROM AUTHOR]

Otranto is an Italian city located on the Adriatic coast of the Salento peninsula, on the easternmost stretch of Italy. Always considered one of the pearls of the Mediterranean, Otranto exudes a timeless charm, with its countless historical, artistic and naturalistic beauties. "Otranto Treasures in 3D" is a video-documentary that tells the millennial charm of Otranto's, landscape, culture and nature through video footage and 3D reconstructions. The video allows a guided virtual tour of the wonders of Otranto, to discover this ancient town, to know and admire its churches and its splendid monuments and some ancient settlements and archaeological sites of remarkable relevance, such as the Grotta dei Cervi in Porto Badisco, and to dive into a crystal blue sea, a realm of enchanting habitats rich in biodiversity and spectacular backdrops. [ABSTRACT FROM AUTHOR]

Aftershock point cloud data provide direct evidence for the characteristics of underground faults. However, there has been a dearth of studies using state-of-the-art visual analytics methods to explore the data. In this paper, we present a novel interactive visual analysis approach for visualizing the aftershock point cloud. Our method employs a variety of interactive operations, rapid visual computing functions, flexible display modes, and various filtering approaches to present and explore the desired information for the fault geometry and aftershock dynamics. The case study conducted for the 2016 Central Italy earthquake sequence shows that the proposed approach can facilitate the discovery of the geometry of the four main fault segments and three secondary fault segments. It can also clearly reveal the spatiotemporal evolution of the aftershocks, helping to find the fluid-driven mechanism of this sequence. An open-source prototype system based on the approach is also developed and is freely available. [ABSTRACT FROM AUTHOR]

The free core nutation (FCN) is one of the Earth's rotational eigenmodes, which is caused by the retrograde motion of the liquid core relative to the mantle. The FCN period and Q-factor are determined by the elastic properties of the core/mantle boundary (CMB) and their electromagnetic interaction. In the celestial coordinate system, the FCN period is about 430 days; in the Earth-fixed reference frame this effect manifests itself in the form of the free core resonance (FCR) whose frequency falls in the diurnal tidal band. FCR observation requires highly accurate measurement of the amplitudes and phases of the near-diurnal tidal waves. In particular, the parameter estimates for minor waves K1, P1, PSI1, and PHI1 are critically important for evaluating the FCR effect, i.e., the period and decay of this resonant mode. The progress in the experimental study of FCR is mainly due to the accumulation of the data from superconducting gravimeters and VLBI; at the same time, also the data of the precision laser strainmeters were used. In this work, the FCR effect is studied based on the long-term strain precision records by two European stations: Baksan, Russia (laser interferometer–strainmeter with a measuring armlength of 75 m (Milyukov et al., 2005; 2007) and Gran Sasso, Italy (two perpendicular laser interferometer–strainmeters, BA and BC, each with a measuring armlength of 90 m (Amoruso and Crescentini, 2009)). [ABSTRACT FROM AUTHOR]

Cities of the 21st century are places where various actors interact, where physical systems, that are sometime geographically distant, are strictly dependent, where relational mechanisms become crucial, and where the boundaries between individual and collective, local and global, real and digital become more and more blurred. In this context, social media can be used as a digital lens to analyze the space and the territory of cities. In fact, they offer a great opportunity to individualize and understand the connections that might exist between different spheres. In this article, we use Twitter to analyze the language mix of the city and to detect language communities within the city neighborhoods. We then compare these “digital” communities, discovered through Twitter, with the “real” communities identified by the traditional census data. Milan, a city which is increasingly becoming an international melting pot, is chosen as a case study for this work. [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]

A new species of chigger mite, Lacertacarus sardiniensissp. nov., is described from a lizard Podarcis tiliguerta (Gmelin) collected in Sardinia. One species, Ericotrombidium caucasicum (Schluger, 1967), previously known from Southern Russia and Southern Ukraine, is recorded for the first time on the lizards Podarcis filfolensis (Bedriaga) and Podarcis siculus (Rafinesque-Schmaltz), and on Malta, Lipari Island and Alicudi Island (Aeolian Islands, Sicily, Italy). http://zoobank.org/urn:lsid:zoobank.org:pub:CD41E85B-1801-4FA3-BC42-28F5F1AC14C1 [ABSTRACT FROM AUTHOR]

A dataset collected during a measurement campaign in the middle of the Po Valley, Italy, is used to investigate the boundary-layer structure in stable conditions. Empirical formulations for temperature and wind profiles derived from Monin-Obukhov similarity theory are used as regression curves to fit radiosounding profiles in the lower half of the boundary-layer. The best fitting parameters of the regression are then compared to the surface turbulent fluxes as measured by a co-located sonic anemometer. This comparison shows significant discrepancies and supports earlier results showing that surface fluxes, in the limit of high stability, are not adequate scalings for mean profiles. The most evident differences are found for cases for which the bulk Richardson number turns out to be quite large. One of the practical consequences is that boundary-layer height diagnostic formulations that mainly rely on surface fluxes are in disagreement with those obtained by inspecting the thermodynamic profiles recorded during the radiosounding ascent. Moreover the incorrect scaling of similarity profiles in stable conditions leads to the erroneous diagnosis of 2-m air temperatures used in numerical weather prediction validation. [ABSTRACT FROM AUTHOR]

SUMMARY We analyse eight years of discontinuous strain records from two crossed 90-m long laser interferometers (extensometers) operating in the Gran Sasso underground observatory (Central Italy) to investigate the FCN parameters. At first, inverting realistic synthetic tidal parameters (amplitude and phase of eight diurnal tidal components) we show that the resolving power of strain tides is comparable to that of gravity tides if tidal parameters are inverted minimizing the misfit (as usually done). Both resolving powers improve if data are inverted minimizing the misfit, and this improvement is particularly notable for gravity tides. The analyses of strain records have been performed after correcting measured strain for local distortion of the regional strain field and ocean loading. Data are inverted minimizing the misfit. Parameter uncertainty assessment is performed both inverting synthetic tidal parameter sets, obtained from the real one by adding a random noise, and estimating the Bayesian marginal probabilities from an ensemble of parameter space samples. Even if we analyse data from a single station, our results for the period of the Free Core Nutation (about 429 sidereal days) are robust and comparable to those from gravity tides, obtained from the joint inversion of data from several stations. The quality factor is not well constrained because of the large uncertainty on the Ψ1 phase; however our results are consistent with recently published values (≈20 000). The agreement between observations and predictions looks better than in any previous work that makes use of strain tides. Our inversions provide also an independent estimate of Re( h1− 3 l1), which is the dominating term in the resonance strength expression, in agreement with IERS Conventions (2003). [ABSTRACT FROM AUTHOR]

We present a robust and reliable methodology to calibrate and test the Constant Elasticity of Variance (CEV) model. Precisely, the parameters of the model are estimated by maximum likelihood, and an efficient numerical method to maximize the likelihood function is developed. Furthermore, a consistent and effective goodness-of-fit test of the CEV model is obtained using the Rosenblatt probability transformation and the χ 2 analysis. The novel procedure is employed to investigate the performances of the model on the Italian market. This analysis reveals that the CEV model does not offer a correct description of equity prices. [ABSTRACT FROM AUTHOR]

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We present a method to estimate seismic velocity and density contrasts at a given interface in a 1-D layered model using PS-to- PP reflection amplitude ratios. The velocity structure above the reflector is constrained by traveltime modelling, and the amplitude ratios are determined using the same source–receiver pair for the measured PP and PS amplitudes (common-offset geometry). Thereby, source and receiver site effects are cancelled, and the remaining propagation effects are included in the ray theoretical forward modelling of theoretical PS-to- PP ratios. A minimization of the least-squares misfit between observed and modelled ratios provides the remaining elastic parameters below the reflector of interest ( P velocity, P-to- S velocity ratio, density). 1-D examples and a 2-D synthetic case study with a dipping reflector and a laterally varying overburden demonstrate the possibilities and limitations of the method. An application of the method to a 0.6 km deep reflector below the Campi Flegrei caldera, Italy, reveals a strong contrast with a P-velocity increase from less than 2 to 3.5 km s−1 and a decrease of the P-to- S velocity ratio from 3.6 to 1.75. The proposed PS-to- PP amplitude ratio analysis is applicable for wide-angle seismic reflection data, especially when strong elastic parameter contrasts are expected and when source amplitudes or site effects are poorly known. [ABSTRACT FROM AUTHOR]

The paper introduces a stochastic technique for forecasting rainfall in space-time domain: the PRAISEST Model (Prediction of Rainfall Amount Inside Storm Events: Space and Time). The model is based on the assumption that the rainfall height H accumulated on an interval Δt between the instants iΔt and (i+1) Δt and on a spatial cell of size ΔxΔy is correlated either with a variable Z, representing antecedent precipitation at the same point, either with a variable W, representing simultaneous rainfall at neighbour cells. The mathematical background is given by a joined probability density fH,W,Z (h,w, z) in which the variables have a mixed nature, that is a finite probability for null value and infinitesimal probabilities for the positive values. As study area, the Calabria region, in Southern Italy, has been selected. The region has been discretised by 10 km×10 km cell grid, according to the raingauge network density in this area. Storm events belonging to 1990-2004 period were analyzed to test performances of the PRAISEST model. [ABSTRACT FROM AUTHOR]

In this paper, we present a hierarchical spatial model for the analysis of geographical variation in mortality between the Italian provinces in the year 2001, according to gender, age class, and cause of death. When analysing counts data specific to geographical locations, classical empirical rates or standardised mortality ratios may produce estimates that show a very high level of overdispersion due to the effect of spatial autocorrelation among the observations, and due to the presence of heterogeneity among the population sizes. We adopt a Bayesian approach and a Markov chain Monte Carlo computation with the goal of making more consistent inferences about the quantities of interest. While considering information for the year 1991, we also take into account a temporal effect from the previous geographical pattern. Results have demonstrated the flexibility of our proposal in evaluating specific aspects of a counts spatial process, such as the clustering effect and the heterogeneity effect. [ABSTRACT FROM AUTHOR]

The aim of this article is to investigate the ground motion attenuation of the most industrialized and populated regions of Italy, evaluating the capability of different approaches to estimate site dependent models. The 5.2 local magnitude earthquake on November 24, 2004 shocked the areas of Northern Italy producing damage of about 215 million euros. The data set, including 243 earthquakes of local magnitude up to 5.2, has been collected in the period December 2002-October 2005 by 30 three-component seismic stations managed by Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Milano (INGV-MI). Empirical attenuation relationships have been estimated for horizontal peak ground velocity (PGHV), acceleration (PGHA), displacement (PGHD), and for response spectral acceleration (SA) for periods between 0.1 and 1.5 s. To estimate suitable attenuation models, in particular for sites characterized by thick sedimentary geological formations, a soil discrimination based on EU8 code can lead to wrong evaluations. On the contrary, a classification based on H/V spectral ratios of seismic ambient noise (NHV) allows the models to fit better real and predicted data and to reduce the uncertainties of the process. For each receiver, NHV have been strengthened by additional H/V spectral ratio of earthquake data (EHV), calculated considering different portions of the analysed signals. In order to validate the PGHA attenuation relationship for greater magnitudes, accelerometric records, relative to Central-Northern Italy strong motions occurring in the last 30 years, have been collected and superimposed to our attenuation curves. [ABSTRACT FROM AUTHOR]

In August 2000 and July 2001 two seismic sequences, characterized by mainshocks with Ml (local magnitude) respectively 5.1 and 4.8, occurred in the Monferrato region (Italy). The regional seismic network of North-western Italy (RSNI) recorded more than 250 foreshocks and aftershocks. The routine locations, obtained from the Hypoellipse code, show a seismic activity concentrated in a circular area, of a radius of about 15 km, located near Acqui Terme, and randomly distributed in depth. Location errors, due to an uneven azimuthal station distribution of the regional seismic network, prevent recognition of the geometry of the active zone. Waveform analysis revealed the presence of several multiplets. In order to discriminate and successively relocate them, an automatic cross-correlation procedure was applied. Normalized cross-correlation matrixes, for the RSNI stations which recorded almost 90% of considered events, on the basis of a signal to noise ratio analysis, were computed using only S wave time windows. The use of a relocation procedure, based on the double-difference method which incorporates ordinary absolute travel-time measurements and/or cross-correlation differential travel-times, allowed us to overcome the constraints of the uneven distribution of stations giving a quite different frame of seismicity. The improved locations showed that the seismic activity is mainly arranged along a NE-SW oriented volume, at a depth range of 8–20 km, involving the basement crystalline units. This orientation is confirmed by the analysis of the focal mechanisms: most focal solutions show a strike slip component with one of the nodal planes consistent with the main orientation of the seismic events. [ABSTRACT FROM AUTHOR]

A neural network system for P and S-picking and location of earthquakes in Northeastern Italy is described. It is applied to 7108 seismograms corresponding to 1147 earthquakes occurring in Northeastern Italy and surrounding area in the period 2000–2003. Its results are compared with two sets of manual picks and with the picks performed by the existing seismic alert system. The new system recognizes 89% and 67% of P and S arrival times, respectively, which allows locating 92% of the earthquakes. P and S-picks differ from the best available manual picks by 0.00 ± 0.07 s and 0.00 ± 0.18 s, respectively. The corresponding earthquake locations differ by −0.18± 0.77 km in longitude, 0.10± 0.62 km in latitude and 0.1± 2.0 km in depth. These results suggest its use for alert purposes and rapid production of preliminary bulletins. [ABSTRACT FROM AUTHOR]

The destructive 1980 Campania-Lucania earthquake was a complex event, consisting of at least three subevents. While the main features of the first (0 s) and of the last (40 s) subevents are widely accepted, the second subevent (20 s) is still controversial. Fault geometries are here determined from levelling data, using a global minimization technique. Even if uplifts of southernmost benchmarks (the most sensitive to the 20 s subevent) are small, new proper statistical analysis allows us to draw a few robust conclusions. Our results agree with previously accepted models as regards the 0 and 40 s subevents. As regards the 20 s subevent, previous models always assumed a northeast-dipping fault, while levelling data show that it could have been equivalently caused by a rupture dipping either southwest or northeast. The statistical significance of the two models is about the same, and strike and dip of the two faults are consistent with the two focal planes previously obtained from teleseismic data. Aftershock distribution seems to favour the southwest-dipping fault model. [ABSTRACT FROM AUTHOR]

We report the first results of the field operation of a novel, portable diode-laser spectrometer for gas-concentration measurements in volcanic areas. Remote detection of direct absorption line shapes was possible thanks to a telecom single-mode optical fiber that delivered radiation from a room-temperature distributed-feedback diode laser, emitting at 1.997 μm, to an open-path multiple-reflection cell, placed on gas effluxes. The system was deployed on two different active volcanoes in Italy, where simultaneous and continuous monitoring of CO[sub 2] and H[sub 2]O concentrations has been demonstrated. [ABSTRACT FROM AUTHOR]

A case study is presented in which different probabilistic prediction models (Bayesian probability, fuzzy logic "and", "or", "sum", "product", "gamma" operations, and certainty factors) are used to produce landslide hazard maps for a hilly and mountainous region in the northern Apennines, Italy. Seven data layers are exploited to detect most vulnerable areas: lithology, distance from the geological lineaments, annual rainfall amount, land cover type, topographic slope and aspect, and the distance from hydrographic network segments. The results of the different predictions are compared using the prediction rate index and critically discussed, to evaluate the possibility of using readily available databases for land planning. [ABSTRACT FROM AUTHOR]

I present a novel machine learning approach to predict sex in the bioarchaeological record. Eighteen cranial interlandmark distances and five maxillary dental metric distances were recorded from n = 420 human skeletons from the necropolises at Alfedena (600–400 BCE) and Campovalano (750–200 BCE and 9–11th Centuries CE) in central Italy. A generalized low rank model (GLRM) was used to impute missing data and Area under the Curve—Receiver Operating Characteristic (AUC-ROC) with 20-fold stratified cross-validation was used to evaluate predictive performance of eight machine learning algorithms on different subsets of the data. Additional perspectives such as this one show strong potential for sex prediction in bioarchaeological and forensic anthropological contexts. Furthermore, GLRMs have the potential to handle missing data in ways previously unexplored in the discipline. Although results of this study look promising (highest AUC-ROC = 0.9722 for predicting binary male/female sex), the main limitation is that the sexes of the individuals included were not known but were estimated using standard macroscopic bioarchaeological methods. However, future research should apply this machine learning approach to known-sex reference samples in order to better understand its value, along with the more general contributions that machine learning can make to the reconstruction of past human lifeways. [ABSTRACT FROM AUTHOR]

Salvia ceratophylloides (Ard.) is an endemic and rare plant species recently rediscovered as very few individuals at two different Southern Italy sites. The study of within-plant variation is fundamental to understand the plant adaptation to the local conditions, especially in rare species, and consequently to preserve plant biodiversity. Here, we reported the variation of the morpho-ecophysiological and metabolic traits between the sessile and petiolate leaf of S. ceratophylloides plants at two different sites for understanding the adaptation strategies for surviving in these habitats. The S. ceratophylloides individuals exhibited different net photosynthetic rate, maximum quantum yield, light intensity for the saturation of the photosynthetic machinery, stomatal conductance, transpiration rate, leaf area, fractal dimension, and some volatile organic compounds (VOCs) between the different leaf types. This within-plant morpho-physiological and metabolic variation was dependent on the site. These results provide empirical evidence of sharply within-plant variation of the morpho-physiological traits and VOCs profiles in S. ceratophylloides, explaining the adaptation to the local conditions. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses various reports within the issue on topics related to the European Multigrid conference (EMC) held at the island Isola d'Ischia in Italy on September 19-23, 2010.

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]

Objective: To determine whether out-of-hospital healthcare and adverse outcomes are better in stroke patients admitted to a neurology ward compared with those admitted to general wards.Methods: Beneficiaries of the National Health Service from the Italian Lombardy Region who were discharged alive after hospital admission during the year 2009 for ischemic stroke (9776 patients) or intracerebral or subarachnoid hemorrhage (1102 patients) entered into the cohort and were followed until 2012. Exposure of interest was the ward type where inpatients were admitted (neuro vs. general wards). Outcomes were out-of-hospital healthcare (i.e., drug prescriptions, diagnostic procedures, and laboratory clinical evaluations) and adverse clinical outcomes (i.e., all-cause death and hospital readmission). Exposure-outcome associations were investigated. High-dimensional propensity score methodology was used for taking into account confounders. Mediation analysis was used to verify whether the association between ward type and clinical outcomes is mediated by out-of-hospital adherence to healthcare.Results: Better adherence to out-of-hospital healthcare received from patients discharged from neuro, rather than general, wards was observed being the proportions of adherent patients 42.4% and 39.5%, respectively. Compared with general wards, discharge from neuro was associated with reduced 3-year emergency admissions (from 50.1 to 47.5% among ischemic stroke patients) and reduced 3-year mortality (from 37.5 to 27.0% among hemorrhagic stroke patients). From 10 to 15% of outcome risk, reductions were mediated by better adherence to out-of-hospital healthcare.Conclusions: For patients with acute ischemic and hemorrhagic stroke, admission to neuro vs. general wards is associated with better out-of-hospital healthcare and long-term adverse outcomes. [ABSTRACT FROM AUTHOR]