Purpose: Beckwith-Wiedemann syndrome (BWS) is a developmental disorder caused by dysregulation of the imprinted gene cluster of chromosome 11p15.5 and often associated with loss of methylation (LOM) of the imprinting center 2 (IC2) located in KCNQ1 intron 10. To unravel the etiological mechanisms underlying these epimutations, we searched for genetic variants associated with IC2 LOM. Methods: We looked for cases showing the clinical features of both BWS and long QT syndrome (LQTS), which is often associated with KCNQ1 variants.

In this paper, we present a 2-Dimensional (2D) Optimal Interpolation (OI) technique for spatially scattered infrared satellite observations, from which level 2 products have been obtained, in order to yield level 3, regularly gridded, data. The scheme derives from a Bayesian predictor-corrector scheme used in data assimilation and is based on the Kalman Filter estimation.

Process mining is the set of techniques to retrieve a process model starting from available logging data. The discovered process model has to be analyzed to verify whether it respects the defined properties, i.e., the so-called compliance checking. Our aim is to use a model checking based approach to verify compliance. First, we propose an integrated-tool approach using existing tools as ProM (a framework supporting process mining techniques) and CADP (a formal verification environment). More precisely, the execution traces from a software system are extracted.

Measures and methods used in financial sector to quantify risk, have been recently applied to cyber world. The aim is to help organizations to improve risk management strategies and to wisely plan investments in cyber security. On the other hand, they are useful instruments for insurance companies in pricing cyber insurance contracts and setting the minimum capital requirements defined by the regulators. In this paper we propose an estimation of Value at Risk (VaR), referred to as Cyber Value at Risk in cyber security domain, and Tail Value at risk (TVaR).

Despite the development of new technologies, in order to prevent the stealing of cars, the number of car thefts is sharply increasing. With the advent of electronics, new ways to steal cars were found. To avoid auto-theft attacks, in this paper we propose a machine leaning based method to silently e continuously profile the driver by analyzing built-in vehicle sensors. We evaluate the efficiency of the proposed method in driver identification using 10 different drivers.

In the last decades companies worldwide are facing a new kind of risk, namely cyber risk, that has emerged as one of the top challenges in risk management. Insurance was only recently applied to cyber world and it is increasingly becoming part of the risk management process, posing many challenges to actuaries. One of the main issues is the lack of data, in particular nancial ones.

Cloud detection is a critical issue for satellite optical remote sensing, since potential errors in cloud masking can be translated directly into significant uncertainty in the retrieved downstream geophysical products. The problem is particularly challenging when only of a limited number of spectral bands is available, and thermal infrared bands are lacking.