H-1 magnetic resonance studies on MCF-7 and HeLa cells were undertaken to reveal differences in lipid and lipid metabolite signals during the growth in culture. High intensity mobile lipid (ML) signals were found during the first days in culture, while afterwards the same signals declined and started increasing again at confluence and at late confluence. At the same time, signals from the lipid metabolite phosphocholine decreased in intensity while signals from glycerophosphocholine in MCF-7 and from choline in HeLa increased as cells approached confluence. Spectral parameters from actively proliferating and non-proliferating cells were used to classify cells with respect to the proliferative conditions by means of a multivariate statistical analysis. Furthermore, it was shown that polyunsaturation of mobile lipid chains was lower in the confluent group with respect to the actively proliferating cells. The examination of spectra from suspensions of MCF-7 spheroids with diameter smaller than 500 mum suggests that cells in spheroids are in condition of lipid metabolism similar to that of confluent cultured cells.

Discussion on the meeting on "statistical approaches to inverse problem"

Accurate quantitation of Magnetic Resonance Spectroscopy (MRS) signals is an essential step before converting the estimated signal parameters, such as frequencies, damping factors, and amplitudes, into biochemical quantities (concentration, pH). Several subspace-based parameter estimators have been developed for this task, which are efficient and accurate time-domain algorithms. However, they suffer from a serious drawback: they allow only a limited inclusion of prior knowledge which is important for accuracy and resolution. In this paper, a new method is presented: KNOB-SVD and its improved variant KNOB-TLS. KNOB-SVD is a recently proposed method, based on the Singular Value Decomposition (SVD), which allows the use of more prior knowledge about the signal parameters than previously published subspace-based methods. We compare its performance in terms of robustness and accuracy with the performance of three commonly used methods for signal parameter estimation: HTLS, a subspace-based method which does not allow any inclusion of prior knowledge, except for the model order; HTLSPK(Dfdeq), a subspace-based method obtained by incorporating in HTLS the prior information that the frequency differences between doublet components are known and the damping factors are equal; and AMARES, an interactive maximum likelihood method that allows the inclusion of a variety of prior knowledge. Extensive simulation and in vivo studies, using 31P as well as proton MRS signals, show that the new method outperforms HTLS and HTLSPK(Dfdeq) in robustness, accuracy, and resolution, and that it provides parameter estimates comparable to the AMARES ones.

The problem of obtaining relevant results in web searching has been tackled with several approaches. Although very e0ective techniques are currently used by the most popular search engines when no a priori knowledge on the user's desires beside the search keywords is available, in di0erent settings it is conceivable to design search methods that operate on a thematic database of web pages that refer to a common body of knowledge or to speci3c sets of users. We have considered such premises to design and develop a search method that deploys data mining and optimization techniques to provide a more signi3cant and restricted set of pages as the 3nal result of a user search. We adopt a vectorization method based on search context and user pro&le to apply clustering techniques that are then re3ned by a specially designed genetic algorithm. In this paper we describe the method, its implementation, the algorithms applied, and discuss some experiments that has been run on test sets of web pages.

A hollow-fiber enzyme reactor, operating under isothermal and nonisothermal conditions, was built employing a polypropylene hollow fiber onto which â-galactosidase was immobilized. Hexamethylenediamine and glutaraldehyde were used as spacer and coupling agent, respectively. Glucose production was studied as a function of temperature, substrate concentration, and size of the transmembrane temperature gradient. The actual average temperature differences across the polypropylene fiber, to which reference was done to evaluate the effect of the nonisothermal conditions, were calculated by means of a mathematical approach, which made it possible to know, using computer simulation, the radial and axial temperature profiles inside the bioreactor and across the membrane. Percent activity increases, proportional to the size of the temperature gradients, were found when the enzyme activities under nonisothermal conditions were compared to those measured under comparable isothermal conditions. Percent reductions of the production times, proportional to the applied temperature gradients, were also calculated. The advantage of employing nonisothermal bioreactors in biotechnological industrial process was discussed.

The problem of the interplay between normal and anomalous scaling in turbulent systems stirred by a random forcing with a power-law spectrum is addressed. We consider both linear and nonlinear systems. As for the linear case, we study passive scalars advected by a 2d velocity field in the inverse cascade regime. For the nonlinear case, we review a recent investigation of 3d Navier Stokes turbulence, and we present new quantitative results for shell models of turbulence. We show that to get firm statements, it is necessary to reach considerably high resolutions due to the presence of unavoidable subleading terms affecting all correlation functions. All findings support universality of anomalous scaling for the small-scale fluctuations.

The statistical properties of velocity and acceleration fields along the trajectories of fluid particles transported by a fully developed turbulent flow are investigated by means of high resolution direct numerical simulations. We present results for Lagrangian velocity structure functions, the acceleration probability density function, and the acceleration variance conditioned on the instantaneous velocity. These are compared with predictions of the multifractal formalism, and its merits and limitations are discussed.

We present a detailed numerical study of anisotropic statistical fluctuations in stationary, homogeneous turbulent flows. We address both problems of intermittency in anisotropic sectors, and the relative importance of isotropic and anisotropic fluctuations at different scales on a direct numerical simulation of a three-dimensional random Kolmogorov flow. We review a simple argument to predict the dimensional scaling for all velocity moments, in all anisotropic sectors. We extend a previous analysis made on the same data set (Phys. Rev. Lett. 86 (2001) 4831) presenting (i) the statistical behavior of spectra and co-spectra; (ii) high-order longitudinal structure functions; (iii) anisotropic fluctuations of the full tensorial two-points velocity correlations. Among the many issues discussed, we stress the problem of the return-to-isotropy, the universality of anisotropic fluctuations and the foliation mechanism. A new a priori test on sub-grid quantities used in Large-Eddy Simulations is also presented.

In this paper we propose a method for wavelet filtering of noisy signals when prior information about the L2 energy of the signal of interest is available Assuming the independence model according to which the wavelet coecients are treated individually we propose a level dependent shrinkage rule that turns out to be the ?minimax rule for a suitable class say of realistic priors on the wavelet coecients The proposed methodology is particularly well suited for denoising tasks where signal?to?noise ratio is low and it is illustrated on a battery of standard test function tions Performance comparisons with some others methods existing in the literature are provided An example in atomic force microscopy AFM is also discussed Key words and phrases? Atomic force microscopy bounded normal mean ?mini? maxity shrinkage wavelet regression

In this paper we propose a non-linear block shrinkage method in the wavelet domain for estimating an unknown function in the presence of Gaussian noise. This shrinkage utilizes an empirical Bayesian blocking approach that accounts for the sparseness of the representation of the unknown function. The modeling is accomplished by using a mixture of two normal-inverse gamma distributions as a joint prior on wavelet coefficients and noise variance in each block at a particular resolution level. This method results in an explicit and readily implementable weighted sum of shrinkage rules. An automatic, level-dependent choice for the model hyperparameters, that leads to amplitude-scale invariant solutions, is also suggested.