Application of Machine Learning to Mortality Modeling and Forecasting

Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy

I fiori di Innsbruck: Lorenzo Lippi e Pietro Andrea Mattioli

Grazie al riconoscimento di un’edizione del celebre erbario di Pietro Andrea Mattioli (I Discorsi…, Venezia, 1573), che Lorenzo Lippi (1606-1664) ha puntualmente riprodotto alle spalle del San Cosma conservato al Museum of Art di New Orleans, è stato possibile formulare alcune ipotesi sul significato e sulla destinazione del dipinto. In particolare, analizzando i valori simbolici delle tre piante raffigurate (elicriso, stecade citrina, amaranto), come sono stati codificati da Pierio Valeriano prima, da Cesare Ripa e Giovanni Zaratino Castellini poi, si può ragionevolmente supporre che il quadro sia stato eseguito durante il soggiorno del pittore a Innsbruck (1643-1644), e che fosse destinato a celebrare la figura e l’opera dell’Arciduchessa Claudia de’ Medici, allora regnante sul Tirolo

Relationship between explore to the environmental toxicant methylmercury and the transcriptional repressor rest in vitro model of amyotrophic lateral sclerosis

Since MeHg exposure hastens the onset of amyotrophic lateral sclerosis-like phenotype in SOD1G93A mice activating glutamate receptors and determined neuronal cell death by increasing REST expression, we invesigated the possible role of MeHg to accelerate reduction in cell survival in NSC-34 motor neuron like cells transiently transfected whit G93A-SOD1 construct via REST up regulation. Furthermore, we studied the trascriptional activators Sp1, Sp3, CREB and JunD. Additionally, we identified the epigenetic mechanism by which MeHg regulated REST gene. Since REST overexpression in neurons determined cell death by activating necroptosis, we investigated the role of MeHg to activate necroptosis in NSC34 cells overexpressing G93A-SOD1 construc