Computational experiments are one of the most used and flexible investigation tools in fluid dynamics. The Lattice Boltzmann Equation is a well established computational method particularly promising for multi-phase flows at micro and macro scales. Here we present preliminary results on performances of the LBE method on the Cell Broadband Engine platform.

Advances in MDCT will extend coronary CTA beyond the morphology data provided by systems that use 64 or fewer detector rows. Newer coronary CTA technology such as prospective ECG-gating will also enable lower dose examinations. Since the current standard of care for coronary diagnoses is catheterization, CT will continue to be benchmarked against catheterization reference points, in particular temporal resolution, spatial resolution, radiation dose, and volume coverage. This article focuses on single heart beat cardiac acquisitions enabled by 320-detector row CT. Imaging with this system can now be performed with patient radiation doses comparable to catheterization. The high image quality, excellent contrast opacification, and absence of stair-step artifact provide the potential to evaluate endothelial shear stress (ESS) noninvasively with CT. Low ESS is known to lead to the development and progression of atherosclerotic plaque culminating in high-risk vulnerable plaque likely to rupture and cause an acute coronary event. The magnitude of local low ESS, in combination with the local remodeling response and the severity of systemic risk factors, determines the natural history of each plaque. This paper describes the steps required to derive an ESS map from 320-detector row CT data using the Lattice Boltzmann method to include the complex geometry of the coronary arterial tree. This approach diminishes the limitations of other computational fluid dynamics methods to properly evaluate multiple coronary arteries, including the complex geometry of coronary bifurcations where lesions tend to develop.

We have developed a rat brain organotypic culture model, in which tissue slices contain cortex-subventricular zone-striatum regions, to model neuroblast activity in response to in vitro ischemia. Neuroblast activation has been described in terms of two main parameters, proliferation and migration from the subventricular zone into the injured cortex. We observed distinct phases of neuroblast activation as is known to occur after in vivo ischemia. Thus, immediately after oxygen/glucose deprivation (6-24 hours), neuroblasts reduce their proliferative and migratory activity, whereas, at longer time points after the insult (2 to 5 days), they start to proliferate and migrate into the damaged cortex. Antagonism of ionotropic receptors for extracellular ATP during and after the insult unmasks an early activation of neuroblasts in the subventricular zone, which responded with a rapid and intense migration of neuroblasts into the damaged cortex (within 24 hours). The process is further enhanced by elevating the production of the chemoattractant SDf-1alpha and may also be boosted by blocking the activation of microglia. This organotypic model which we have developed is an excellent in vitro system to study neurogenesis after ischemia and other neurodegenerative diseases. Its application has revealed a SOS response to oxygen/glucose deprivation, which is inhibited by unfavorable conditions due to the ischemic environment. Finally, experimental quantifications have allowed us to elaborate a mathematical model to describe neuroblast activation and to develop a computer simulation which should have promising applications for the screening of drug candidates for novel therapies of ischemia-related pathologies.

We discuss multiscale simulations of long biopolymer translocation through wide nanopores that can ac- commodate multiple polymer strands. The simulations provide clear evidence of folding quantization, namely the translocation proceeds through multifolded configurations characterized by a well-defined integer number of folds. As a consequence, the translocation time acquires a dependence on the average folding number, which results in a deviation from the single-exponent power law characterizing single-file translocation through narrow pores. The mechanism of folding quantization allows polymers above a threshold length (approximately 1000 persistence lengths for double-stranded DNA) to exhibit cooperative behavior, and as a result to translocate noticeably faster.

A retrieval algorithm that uses a statistical strategy based on dimension reduction is proposed. The ethodology and details of the implementation of the new algorithm are presented and discussed. The algorithm has been applied to high resolution spectra measured by the Infrared Atmospheric Sounding Interferometer instrument to retrieve atmospheric profiles of temperature, water vapour and ozone. The performance of the inversion strategy has been assessed by comparing the retrieved profiles to the ones obtained by colocating in space and time profiles from the European Centre for Medium-Range Weather Forecasts analysis.

An experimental method to select the number of principal components in minimum noise fraction (MNF) is proposed to process images measured by imagery sensors onboard aircraft or satellites. The method is based on an experimental measurement by spectrometers in dark conditions from which noise structure can be estimated. To represent typical land conditions and atmospheric variability, a significative data set of synthetic noise-free images based on real Multispectral Infrared and Visible Imaging Spectrometer images is built. To this purpose, a subset of spectra is selected within some public libraries that well represent the simulated images. By coupling these synthetic images and estimated noise, the optimal number of components in MNF can be obtained. In order to have an objective (fully data driven) procedure, some criteria are proposed, and the results are validated to estimate the number of components without relying on ancillary data. The whole procedure is made computationally feasible by some simplifications that are introduced. A comparison with a state-of-the-art algorithm for estimating the optimal number of components is also made.

By generating random codes and applying Fisher’s exact test, we confirm that the biosynthetic families of amino acids are intimately involved in the organisation of the genetic code. This observation corroborates the coevolution theory of genetic code origin because, as the amino acids belonging to the single biosynthetic families have codons that are contiguous in the genetic code, they must have entered the code itself by means of a clustering mechanism, which must clearly have been compatible with the mechanism on which this theory is based because this too envisages the clustering of biosynthetically correlated amino acids within the code.

In this paper we develop a direct quadrature method for solving Volterra-Fredholm integral equations on an unbounded spatial domain. These problems, when related to some important physical and biological phenomena, are characterized by kernels that present variable peaks along space. The method we propose is adaptive in the sense that the number of spatial nodes of the quadrature formula varies with the position of the peaks. The convergence of the method is studied and its performances are illustrated by means of a few significative examples. The parallel algorithm which implements the method and its performances are described.

We present a parallel version of MUPHY, a multi-physics/scale code based upon the combination of microscopic Molecular Dynamics (MD) with a hydro-kinetic Lattice Boltzmann (LB) method. The features of the parallel version of MUPHY are hereby demonstrated for the case of translocation of biopolymers through nanometer-sized, multi-pore configurations, taking into explicit account the hydrodynamic interactions of the translocating molecules with the surrounding fluid. The parallel implementation exhibits excellent scalability on the IBM BlueGene platform and includes techniques which may improve the flexibility and efficiency of other complex multi-physics parallel applications, such as hemodynamics, targeted-drug delivery and others.

A new technique is presented to create nosologic images of the brain based on magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI). A nosologic image summarizes the presence of different tissues and lesions in a single image by color coding each voxel or pixel according to the histopathological class it is assigned to. The proposed technique applies advanced methods from image processing as well as pattern recognition to segment and classify brain tumours. First, a registered brain atlas and a subject-specific abnormal tissue prior, obtained from MRSI data, are used for the segmentation. Next, the detected abnormal tissue is classified based on supervised pattern recognition methods. Class probabilities are also calculated for the segmented abnormal region. Compared to previous approaches, the new framework is more flexible and able to better exploit spatial information leading to improved nosologic images. The combined scheme offers a new way to produce high-resolution nosologic images, representing tumour heterogeneity and class probabilities, which may help clinicians in decision making.

The Generalized- ? -Space G? (p, m, w) contains many classical rearrangement invariant spaces. Here we shall study its associate space and we shall estimate its associate norm. In particular, we characterize all optimal functions u achieving the associate norm of Generalized ? -space G ? (p, m, w) when it is reflexive. For the purpose, we use the notion of relative rearrangement and new additional results on this concept. Moreover, we prove that the space G? (p, m, w) is reflexive under the conditions that m > 1 and p >= 2.

This work presents the results of a quantitative analysis of an interferometric SAR (InSAR) Digital Elevation Model (DEM), obtained by the Shuttle Radar Topography Mission (SRTM). The analysis aims to identify additional parameters to recognize areas in southern Italy with different tectonic activity and behaviour. The axial zone of the Campania-Lucania Apennine and the Sila Massif in Calabria, Italy, characterized by quite different evolutionary histories, have been chosen as test areas sufficiently wide to validate observations on a sub-regional scale at least. Geomorphological information on the shape of palaeosurfaces has been used to estimate uplift and/or erosion amounts and rates. Palaeosurfaces are identified on the DEM as regions with an altitude higher than 1000 m a.s.l. and sub-planar land surfaces dipping less than 6 degrees. Information about the shape of palaeosurfaces during the first stage of uplift and before the tectonic-induced block fragmentation has been extracted. A fragmentation index has been computed for these erosional surfaces. The first stage of this landscape evolution has been studied in terms of the geometric characteristics of fragmented blocks. The last erosional stage has been recognized both in terms of geometric characteristics and fragmentation index of the sub-horizontal land surfaces. Altitude and age of the palaeosurfaces, referred to ancient base-levels of the erosion, have been used to estimate the erosional rate.

We investigate the analytical behavior of the solution of the test equation and derive the qualitative and quantitative properties of the numerical solution. The numerical experiments show that the stability conditions obtained represent sufficient conditions for the stability of the numerical method applied to a more general equation.

A truly functional Bayesian method for detecting temporally differentially expressed genes between two experimental conditions is presented. The method distinguishes between two biologically different set ups, one in which the two samples are interchangeable, and one in which the second sample is a modification of the first, i.e. the two samples are non-interchangeable. This distinction leads to two different Bayesian models, which allow more flexibility in modeling gene expression profiles. The method allows one to identify differentially expressed genes, to rank them and to estimate their expression profiles. The proposed procedure successfully deals with various technical difficulties which arise in microarray time-course experiments, such as small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements. The procedure allows one to account for various types of error, thus offering a good compromise between nonparametric and normality assumption based techniques. In addition, all evaluations are carried out using analytic expressions, hence the entire procedure requires very little computational effort. The performance of the procedure is studied using simulated and real data. A truly functional Bayesian method for detecting temporally differentially expressed genes between two experimental conditions is presented. The method distinguishes between two biologically different set ups, one in which the two samples are interchangeable, and one in which the second sample is a modification of the first, i.e. the two samples are non-interchangeable. This distinction leads to two different Bayesian models, which allow more flexibility in modeling gene expression profiles. The method allows one to identify differentially expressed genes, to rank them and to estimate their expression profiles. The proposed procedure successfully deals with various technical difficulties which arise in microarray time-course experiments, such as small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements. The procedure allows one to account for various types of error, thus offering a good compromise between nonparametric and normality assumption based techniques. In addition, all evaluations are carried out using analytic expressions, hence the entire procedure requires very little computational effort. The performance of the procedure is studied using simulated and real data.