A major goal of bioinformatics is the characterization of transcription factors and the transcriptional programs they regulate. Given the speed of genome sequencing, we would like to quickly annotate regulatory sequences in newly-sequenced genomes. In such cases, it would be helpful to predict sequence motifs by using experimental data from closely related model organism. Here we present a general algorithm that allow to identify transcription factor binding sites in one newly sequenced species by performing Bayesian regression on the annotated species. First we set the rationale of our method by applying it within the same species, then we extend it to use data available in closely related species. Finally, we generalise the method to handle the case when a certain number of experiments, from several species close to the species on which to make inference, are available. In order to show the performance of the method, we analyse three functionally related networks in the Ascomycota. Two gene network case studies are related to the G2/M phase of the Ascomycota cell cycle; the third is related to morphogenesis. We also compared the method with MatrixReduce and discuss other types of validation and tests. The first network is well known and provides a biological validation test of the method. The two cell cycle case studies, where the gene network size is conserved, demonstrate an effective utility in annotating new species sequences using all the available replicas from model species. The third case, where the gene network size varies among species, shows that the combination of information is less powerful but is still informative. Our methodology is quite general and could be extended to integrate other high-throughput data from model organisms.

Background: At present five evolutionary hypotheses have been proposed to explain the great variability of the genomic GC content among and within genomes: the mutational bias, the biased gene conversion, the DNA breakpoints distribution, the thermal stability and the metabolic rate. Several studies carried out on bacteria and teleostean fish pointed towards the critical role played by the environment on the metabolic rate in shaping the base composition of genomes. In mammals the debate is still open, and evidences have been produced in favor of each evolutionary hypothesis. Human genes were assigned to three large functional categories (as well as to the corresponding functional classes) according to the KOG database: (i) information storage and processing, (ii) cellular processes and signaling, and (iii) metabolism. The classification was extended to the organisms so far analyzed performing a reciprocal Blastp and selecting the best reciprocal hit. The base composition was calculated for each sequence of the whole CDS dataset. Results: The GC3 level of the above functional categories was increasing from (i) to (iii). This specific compositional pattern was found, as footprint, in all mammalian genomes, but not in frog and lizard ones. Comparative analysis of human versus both frog and lizard functional categories showed that genes involved in the metabolic processes underwent the highest GC3 increment. Analyzing the KOG functional classes of genes, again a well defined intragenomic pattern was found in all mammals. Not only genes of metabolic pathways, but also genes involved in chromatin structure and dynamics, transcription, signal transduction mechanisms and cytoskeleton, showed an average GC3 level higher than that of the whole genome. In the case of the human genome, the genes of the aforementioned functional categories showed a high probability to be associated with the chromosomal bands. Conclusions: In the light of different evolutionary hypotheses proposed so far, and contributing with different potential to the genome compositional heterogeneity of mammalian genomes, the one based on the metabolic rate seems to play not a minor role. Keeping in mind similar results reported in bacteria and in teleosts, the specific compositional patterns observed in mammals highlight metabolic rate as unifying factor that fits over a wide range of living organisms.

The realization of innovative transport services, require increasingly greater flexibility and inexpensiveness of the service. In many cases the solution is to realize a demand responsive transportation system, in which there are two main goals: minimize costs and maximize flexibility. In this work, we address a Demand Responsive Transport System capable of managing incoming transport demand using a solution based on an insertion heuristics to solve an On-Line dynamic DaRP instance. The solutions provided by the heuristics are simulated dynamically in a discrete events environment in which it is possible to reproduce the movement of the vehicles, the passengers' arrival to the stops, the delays due to the traffic congestion and possible anomalies in the behavior of the passengers. Finally, at the end of the simulation, a set of performance indicators summarize the solution planned by the heuristics.

The concept of innovation in transport systems requires the satisfaction of two main objectives: flexibility and costs minimization. The demand responsive transport systems (DRTS) seem to be the solution for the trade-off between flexibility and efficiency. They require the planning of travel paths (routing) and customers pick-up and drop-off times (scheduling) according to received requests and respecting the limited capacity of the fleet and time constraints (hard time windows) for each networks node. Even considering invariable conditions of the network a DRTS may operate according to a static or to a dynamic mode. In the dynamic mode, customers requests arrive when the service is already running and, consequently, the solution may change over time. In this work, we use an algorithm able to solve a dynamic multi-vehicle DaRP by managing incoming transport demand as fast as possible. The heuristics is a greedy method that tries to assign the requests to one of the fleets vehicles finding each time the local optimum. The usage of vehicles only when strictly necessary, provides to costs minimization. The work is enriched by a series of tests with different values of the fleets vehicles and their capacity, of time windows and of incoming requests number. The solutions provided by the heuristics are simulated in a discrete events environment in which its possible to reproduce the movement of the buses, the passengers' arrival to the stops, and in the next step the delays due to the traffic congestion and possible anomalies in the behaviour of the passengers. Finally, at the end of the simulation, a set of performance indicators evaluate the solution planned by the heuristics.

Time-course microarray experiments are an increasingly popular approach for understanding the dynamical behavior of a wide range of biological systems. In this paper we discuss some recently developed functional Bayesian methods specifically designed for time-course microarray data. The methods allow one to identify differentially expressed genes, to rank them, to estimate their expression profiles and to cluster the genes associated with the treatment according to their behavior across time. The methods successfully deal with various technical difficulties that arise in this type of experiments such as a large number of genes, a small number of observations, non-uniform sampling intervals, missing or multiple data and temporal dependence between observations for each gene. The procedures are illustrated using both simulated and real data.

Propeller modelling in CFD simulations is a key issue for the correct prediction of hull-propeller interactions, manoeuvring characteristics and the flow field in the stern region of a marine vehicle. From this point of view, actuator disk approaches have proved their reliability and computational efficiency; for these reasons, they are commonly used for the analysis of propulsive performance of a ship. Nevertheless, these models often neglect peculiar physical phenomena which characterise the operating propeller in off-design condition, namely the in-plane loads that are of paramount importance when considering non-standard or unusual propeller/rudder arrangements. In order to emphasize the importance of these components (in particular the propeller lateral force) and the need of a detailed propeller model for the correct prediction of the manoeuvring qualities of a ship, the turning circle manoeuvre of a self-propelled fully appended twin screw tanker-like ship model with a single rudder is simulated by the unsteady RANS solver ?navis developed at CNR-INSEAN; several propeller models able to include the effect of the strong oblique flow component encountered during a manoeuvre have been considered and compared. It is emphasized that, despite these models account for very complex and fundamental physical effects, which would be lost by a traditional actuator disk approach, the increase in computational resources is almost negligible. The accuracy of these models is assessed by comparison with experimental data from free running tests. The main features of the flow field, with particular attention to the vortical structures detached from the hull are presented as well.

Within a continuum framework, flows featuring shock waves can be modelled by means of either shock capturing or shock fitting. Shock-capturing codes are algorithmically simple, but are plagued by a number of numerical troubles, particularly evident when shocks are strong and the grids unstructured. On the other hand, shock-fitting algorithms on structured grids allow to accurately compute solutions on coarse meshes, but tend to be algorithmically complex. We show how recent advances in computational mesh generation allow to relieve some of the difficulties encountered by shock capturing and contribute towards making shock fitting on unstructured meshes a versatile technique.

We consider a weakly nonlinear system of the form (I + phi(x)A)x = p, where phi(x) is a real function of the unknown vector x, and (I + phi(x)A) is an M-matrix. We propose to solve it by means of a sequence of linear systems defined by the iteration procedure (I + phi(x(r))A)x(r + 1) = p, r = 0, 1, ... . The global convergence is proved by considering a related fixed-point problem.

We design and analyse a numerical method for the solution of a particular second order integro-differential boundary value problem on the semiaxis, which arises in the study of the kinetic theory of dusty plasmas. The method we propose represents a first insight into the numerical solution of more complicated problems and consists of a discretization of the differential and integral terms and of an iteration process to solve the resulting non-linear system. Under suitable hypotheses we prove the convergence. We will show the characteristics of the method by means of some numerical simulations.

It is shown that the density of the ratio of two random variables with the same variance and joint Gaussian density satisfies a nonstationary diffusion equation. Implications of this result for adaptive kernel density estimation of the condensed density of the generalized eigenvalues of a random matrix pencil useful for solving the exponential analysis problem are discussed.

Pencils of matrices whose elements have a joint noncentral Gaussian distribution with nonidentical covariance are considered. An approximation to the distribution of the squared modulus of their determinant is computed which allows to get a closed form approximation of the condensed density of the generalized eigenvalues of the pencils. Implications of this result for solving several moments problems are discussed and some numerical examples are provided.

The cerebral cortex of primates is endowed with neurons specifi- cally tuned for biological actions. These neurons are located in a network of areas comprising the visual areas of the region of the superior temporal sulcus (STS) and the visuomotor areas of the inferior parietal lobule and premotor cortex. It is generally assumed that action understanding depends on a serial recruitment of these areas. The observed actions, following an initial processing in striate and extrastriate visual areas, are encoded in STS. Subsequently, they are transformed into a motor format in the parietal and premotor areas. This transformation is done via the mirror mechanism. Here we present evidence for a fundamental role in action perception of backward projections to the occipital lobe. The evidence is based on two studies. In the first one, using high-density EEG, we showed that, during hand- action observation, following an early activation of occipital, parietal and premotor areas, late waves occur in the occipital lobe; in the second study, using TMS, we showed a clear impairment of action perception following occipital stimulation at the time of the late occipital waves. We conclude that, backward projections from motor cortex ‘bind’ the understanding of the goal of an action with the pictorial descriptions of the same action. This binding allows the full perception of the observed actions as a joint function of visual and motor areas and overcomes the traditional functional separation between the two systems