Using covariance identities based on the Clark-Ocone representation formula we derive Gaussian density bounds and tail estimates for the probability law of the solutions of several types of stochastic differential equations, including Stratonovich equations with boundary condition and irregular drifts, and equations driven by fractional Brownian motion. Our arguments are generally simpler than the existing ones in the literature as our approach avoids the use of the inverse of the Ornstein-Uhlenbeck operator.

Based on a new multiplication formula for discrete multiple stochastic integrals with respect to non-symmetric Bernoulli random walks, we extend the results of Nourdin et al. (2010) on the Gaussian approximation of symmetric Rademacher sequences to the setting of possibly non-identically distributed independent Bernoulli sequences. We also provide Poisson approximation results for these sequences, by following the method of Peccati (2011). Our arguments use covariance identities obtained from the Clark-Ocone representation formula in addition to those usually based on the inverse of the Ornstein-Uhlenbeck operator.

In this paper we consider Volterra integral equations on time scales and describe our study about the long time behavior of their solutions. We provide sufficient conditions for the stability under constant perturbations by using the direct Lyapunov method and we present some examples of application.

Casual mutations and natural selection have driven the evolution of protein amino acid sequences that we observe at present in nature. The question about which is the dominant force of proteins evolution is still lacking of an unambigu- ous answer. Casual mutations tend to randomize protein sequences while, in order to have the correct functionality, one expects that selection mechanisms impose rigid contraints on amino acid sequences. Moreover, one also has to consider that the space of all possible amino acid sequences is so astonishingly large that it could be reasonable to have a well tuned amino acid sequence in- distinguishable from a random one. In order to study the possibility to discriminate between random and natural amino acid sequences, we introduce different measures of association between pairs of amino acids in a sequence, and apply them to a dataset of 1, 047 nat- ural protein sequences and 10, 470 random sequences, carefully generated in order to preserve the relative length and amino acid distribution of the natu- ral proteins. We analize the multidimensional measures with machine learning techniques and show that, to a reasonable extent, natural protein sequences can be differentiated from random ones

In this paper we propose a LWR-like model for traffic flow on networks which allows to track several groups of drivers, each of them being characterized only by their destination in the network. The path actually followed to reach the destination is not assigned a priori, and can be chosen by the drivers during the journey, taking decisions at junctions. The model is then used to describe three possible behaviors of drivers, as- sociated to three different ways to solve the route choice problem: 1. Drivers ignore the presence of the other vehicles; 2. Drivers react to the current dis- tribution of traffic, but they do not forecast what will happen at later times; 3. Drivers take into account the current and future distribution of vehicles. Notice that, in the latter case, we enter the field of differential games, and, if a solution exists, it likely represents a global equilibrium among drivers. Numerical simulations highlight the differences between the three behaviors and offer insights into the existence of equilibria.

The inverse problem of MEG aims at estimating electromagnetic cerebral activity from measurements of the magnetic fields outside the head. After formulating the problem within the Bayesian framework, a hierarchical conditionally Gaussian prior model is introduced, including a physiologically inspired prior model that takes into account the preferred directions of the source currents. The hyperparameter vector consists of prior variances of the dipole moments, assumed to follow a non-conjugate gamma distribution with variable scaling and shape parameters. A point estimate of both dipole moments and their variances can be computed using an iterative alternating sequential updating algorithm, which is shown to be globally convergent. The numerical solution is based on computing an approximation of the dipole moments using a Krylov subspace iterative linear solver equipped with statistically inspired preconditioning and a suitable termination rule. The shape parameters of the model are shown to control the focality, and furthermore, using an empirical Bayes argument, it is shown that the scaling parameters can be naturally adjusted to provide a statistically well justified depth sensitivity scaling. The validity of this interpretation is verified through computed numerical examples. Also, a computed example showing the applicability of the algorithm to analyze realistic time series data is presented.

Financial and activity report - project T.He.T.A. - Technological tools for the Promotion of Transadriatic Archaeological Heritage

Financial and activity report - project T.He.T.A. "Technological tools for the Promotion of Transadriatic Archaeological Heritage"

Financial and activity report - project T.He.T.A. "Technological tools for the Promotion of Transadriatic Archaeological Heritage"

T.He.T.A. Project was developed with the goal of improving and enhancing the cultural heritage, in order to make it more accessible to the large international tourism through the promotion of archaeological sites in Apulia and Greece, such as Monte Sannace (Italy) and Paleopolis (Greece). Both are characterized by a significant cultural value, but at the same time they raise issues regarding theirs preservation status as well as their capability to attract touristic flows.

Motivation Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. However, available datasets consist of a small number of samples (n patients) and a large number of gene expression data (p predictors). Therefore, the main challenge is to cope with the high-dimensionality, i.e. p>>n, and a novel appealing approach is to use screening procedures to reduce the size of the feature space to a moderate scale (Wu & Yin 2015, Song et al. 2014, He et al. 2013). In addition, genes are often co-regulated and their expression levels are expected to be highly correlated. Genes that are involved in the same biological process are grouped in pathway structures. In order to incorporate the pathway information of genes, network-based methods have been applied (Zhang et al. 2013, Sun et al. 2013). Motivated by the most recent models based on variable screening techniques and integration of pathway information into penalized Cox methods, we propose a new procedure to obtain more accurate predictions. First, we identify the high-risk genes by using variable screening techniques and then, we perform Cox regression analysis integrating network information associated with the selected high-risk genes. By combining these two approaches, we present a new method to select important core pathways and genes that are related to the survival outcome and we show the benefits of our proposal both in simulation and real studies. Methods In our study, we combine variable screening techniques and network methods to identify genes and pathways highly associated with the disease and to better predict patient risk. We propose a new method for survival analysis based on the following steps. First, (i) we perform variable screening, such as the sure independence screening (Fan et al. 2008) and its advancement (Gorst-Rasmussen & Scheike 2013, Zhao & Li 2012, Fan et al. 2010) to select the active set of variables strongly correlated with the survival response, and then (ii) we apply network-based Cox regression models, such as Net-Cox and AdaLnet, which use a network based on the number of selected signature genes to predict survival probability. In order to build our apriori network information, we use the human gene functional linkage approach (Huttenhower et al. 2009). Such network contains maps of functional activity and interaction networks in over 200 areas of human cellular biology with information from 30.000 genome-scale experiments. The functional linkage network summarizes information from a variety of biologically informative perspectives: prediction of protein function and functional modules, cross-talk among biological processes, and association of novel genes and pathways with known genetic disorders. In particular, our gene network is built by using the HEFalMp tool to determine the edge's weight w between two nodes (i.e. genes). The resulting network consists of a fixed number of unique genes (about 2000 genes), where w describes how strong is the relation between two genes and it takes values in [0,1]. Hence, while the screening methods recruit the features with the best marginal utility to reduce the dimensionality of the data, the network incorporates the pathway information used as a prior knowledge network into the survival analysis. Results We combine variable screening procedures and network-penalized Cox models for high-dimensional survival data aimed at determining pathway structures and biomarkers involved in cancer progression. By using this approach, it is possible to obtain a deeper insight of the gene-regulatory networks and investigate the gene signatures related to the cancer survival time in order to understand how patient features (molecular and clinical information) can influence cancer treatment and detection. In particular, we show the results obtained in simulation and real cancer studies, along with screening rules. The simulated data are aimed to illustrate two different biological scenarios. In the first setting, we examine the situation where all genes within the same module belong to different groups or pathways. In the second one, the pathways are not independent among them (as in genomic studies), but the activation of some groups is conditional from other pathways. We use specificity, sensitivity and Matthews Correlation Coefficient to compare the prediction performance. We also predict patient survival using molecular data of different cancer types, such as ovarian and breast cancer. We investigate the set of the active signature genes and the corresponding pathways involved in the cancer disease process. Then, using the biological network, as prior information network, we perform network-based Cox model including Kaplan- Meier curve and log-rank test. Overall this study shows that the new screening-network analysis is useful for improving

Beside geographical and physical characteristics of the environment, mostly temperature changes drive glacier dynamical evolution with subglacial and supraglacial water release or approaching a metastable state. The appearance of subglacial lakes filling bedrock depressions, glacier sliding, crevasses formation and calving are linked climate change sensitive macro-phenomena, where interactions between the interfacing phases are crucial. We shall discuss the mathematical modelling and the numerical simulation of one of the above glacier problems with moving boundary. References A. Di Mascio, R. Broglia and R. Muscari, "On the application of the single- phase level set method to naval hydrodynamic flows", Computers & Fluids, Vol.36, 2007, pp. 868-863. D. Mansutti, E. Bucchignani, J. Otero and P. Glowacki, "Modelling and numerical sensitivity study on the conjecture of a subglacial lake at Amundsenisen, Svalbard", Applied Mathematical Modelling, http://dx.doi.org/10.1016/j.apm.2014.12.043 (in press), 2014.

Il 14 dicembre del 1955, presso la sede Centrale del CNR, il Presidente della Repubblica, Giovanni Gronchi, inaugurava il calcolatore elettronico Ferranti Mark1* dell'Istituto Nazionale per le Applicazioni del Calcolo, alla presenza del fondatore e direttore dell'Istituto, il matematico Mauro Picone. Dal nome del costruttore e dalla sigla dell'istituto, la macchina venne denominata FINAC. Si trattava del secondo calcolatore elettronico installato in Italia, preceduto di pochi mesi dal CRC-102A del Politecnico di Milano. L'acquisto era avvenuto grazie agli sforzi di Picone per dotare il suo istituto di una delle 'potenti macchine calcolatrici elettroniche', all'epoca solo anglo-americane. Negli anni precedenti Picone era giunto più volte ad un passo dal realizzare il suo intento di costruire quello che, sarebbe stato il primo calcolatore italiano. Aveva maturato questo proposito viaggiando negli USA, dove l'analisi numerica progrediva enormemente con lo sviluppo di progetti su 'macchine calcolatrici a cifre ad alta velocità'. Poiché una serie di impedimenti, internazionali e interni, rischiavano di prolungare eccessivamente i tempi, Picone scelse di acquistare un'apparecchiatura già in commercio, che sarebbe poi stata la FINAC, che per alcuni anni rimase il più potente computer italiano. Con esso vennero sviluppate molte ricerche sui temi più svariati, dal modello econometrico della Banca d'Italia ai calcoli per la progettazione di ponti e dighe. Un lavoro, particolarmente significativo per lo sviluppo dell'informatica italiana, fu la realizzazione di un simulatore della CEP, futura Calcolatrice Elettronica Pisana. Con questo anniversario si vuole celebrare, tra le altre cose, l'intuizione di Mauro Picone, la sua capacità innovativa e la scelta di investire generosamente nel futuro, conferendo grande impulso alla soluzione di problemi reali attraverso la modellizzazione matematica, creando anche in Italia i presupposti per lo sviluppo della moderna matematica applicata e dell'informatica

In this study, we investigated by in silico analysis the possible correlation between microRNAs (miRNAs) and Anamnia V-SINEs (a superfamily of short interspersed nuclear elements), which belong to those retroposon families that have been preserved in vertebrate genomes for millions of years and are actively transcribed because they are embedded in the 3? untranslated region (UTR) of several genes. We report the results of the analysis of the genomic distribution of these mobile elements in zebrafish (Danio rerio) and discuss their involvement in generating miRNA gene loci. The computational study showed that the genes predicted to bear V-SINEs can be targeted by miRNAs with a very high hybridization E-value. Gene ontology analysis indicates that these genes are mainly involved in metabolic, membrane, and cytoplasmic signaling pathways. Nearly all the miRNAs that were predicted to target the V-SINEs of these genes, i.e., miR-338, miR-9, miR-181, miR-724, miR-735, and miR-204, have been validated in similar regulatory roles in mammals. The large number of genes bearing a V-SINE involved in metabolic and cellular processes suggests that V-SINEs may play a role in modulating cell responses to different stimuli and in preserving the metabolic balance during cell proliferation and differentiation. Although they need experimental validation, these preliminary results suggest that in the genome of D. rerio, as in other TE families in vertebrates, the preservation of V-SINE retroposons may also have been favored by their putative role in gene network modulation.

Background: Chip-seq experiments are becoming a standard approach for genome-wide profiling protein-DNA interactions, such as detecting transcription factor binding sites, histone modification marks and RNA Polymerase II occupancy. However, when comparing a ChIP sample versus a control sample, such as Input DNA, normalization procedures have to be applied in order to remove experimental source of biases. Despite the substantial impact that the choice of the normalization method can have on the results of a ChIP-seq data analysis, their assessment is not fully explored in the literature. In particular, there are no diagnostic tools that show whether the applied normalization is indeed appropriate for the data being analyzed. Results: In this work we propose a novel diagnostic tool to examine the appropriateness of the estimated normalization procedure. By plotting the empirical densities of log relative risks in bins of equal read count, along with the estimated normalization constant, after logarithmic transformation, the researcher is able to assess the appropriateness of the estimated normalization constant. We use the diagnostic plot to evaluate the appropriateness of the estimates obtained by CisGenome, NCIS and CCAT on several real data examples. Moreover, we show the impact that the choice of the normalization constant can have on standard tools for peak calling such as MACS or SICER. Finally, we propose a novel procedure for controlling the FDR using sample swapping. This procedure makes use of the estimated normalization constant in order to gain power over the naive choice of constant (used in MACS and SICER), which is the ratio of the total number of reads in the ChIP and Input samples. Conclusions: Linear normalization approaches aim to estimate a scale factor, r, to adjust for different sequencing depths when comparing ChIP versus Input samples. The estimated scaling factor can easily be incorporated in many peak caller algorithms to improve the accuracy of the peak identification. The diagnostic plot proposed in this paper can be used to assess how adequate ChIP/Input normalization constants are, and thus it allows the user to choose the most adequate estimate for the analysis.

Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. Available datasets consist of a small number of samples (n patients) and a large number of genes (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches can be applied. In our analysis, we compared the most recent network penalized Cox models for highdimensional survival data aimed to determine pathway structures and biomarkers involved into cancer progression. Using these network-based models, we show how to obtain a deeper understanding of the gene-regulatory networks and investigate the gene signatures related to prognosis and survival in different types of tumors. Comparisons are carried out on three real different cancer datasets.

Imprinting Control Regions (ICRs) need to maintain their parental allele-specific DNA methylation during early embryogenesis despite genome-wide demethylation and subsequent de novo methylation. ZFP57 and KAP1 are both required for maintaining the repressive DNA methylation and H3-lysine-9-trimethylation (H3K9me3) at ICRs. In vitro, ZFP57 binds a specific hexanucleotide motif that is enriched at its genomic binding sites. We now demonstrate in mouse embryonic stem cells (ESCs) that SNPs disrupting closely-spaced hexanucleotide motifs are associated with lack of ZFP57 binding and H3K9me3 enrichment. Through a transgenic approach in mouse ESCs, we further demonstrate that an ICR fragment containing three ZFP57 motif sequences recapitulates the original methylated or unmethylated status when integrated into the genome at an ectopic position. Mutation of Zfp57 or the hexanucleotide motifs led to loss of ZFP57 binding and DNA methylation of the transgene. Finally, we identified a sequence variant of the hexanucleotide motif that interacts with ZFP57 both in vivo and in vitro. The presence of multiple and closely located copies of ZFP57 motif variants emerges as a distinct characteristic that is required for the faithful maintenance of repressive epigenetic marks at ICRs and other ZFP57 binding sites.

In this paper, we discuss the concept of Reproducible Research and its importance to produce transparent and high quality scientific papers. In particular, we illustrate the advantages that both paper authors and readers can receive from the adoption of Reproducible Research and we discuss a strategy to develop computational tools supporting such a feature. We present a novel version of RNASeqGUI, a user friendly computational tool capable to handle and analyse RNA-Seq data. This tool exploits Reproducible Research feature to produce RNA-Seq analyses easy to read, inspect, understand, study, reproduce and modify. Overall, this paper is a proof of concept on how it is possible to develop complex and interactive tools in the spirit of Reproducible Research.

The turning circle manoeuvre of a naval supply vessel (characterized by a block coefficient ^CB~0.60) is simulated by the integration of the unsteady Reynolds-Averaged Navier Stokes equations coupled with the equations of rigid body motion with six degrees of freedom. The model is equipped with all the appendages, and it is characterised by an unusual single rudder/twin screws configuration. This arrangement causes poor directional stability qualities, which makes the prediction of the trajectory a challenging problem. As already shown in previous works, the treatment of the in-plane loads exerted by the propellers is of paramount importance; to this aim each propeller is simulated by an actuator disk model, properly modified to account for oblique flow effects. The main goal of the present paper is to assess the capability of the CFD tool to accurately predict the trajectory of the ship and to analyse the complex flow field around a vessel performing a turning manoeuvre. Distribution of forces and moments on the main hull, stern appendages and rudder are analysed in order to gain a deeper insight into the dynamic behaviour of the vessel. Validation is provided by the comparison with experimental data from free running tests.