Preclinical Studies with Umbilical Cord Mesenchymal Stromal Cells in Different Animal Models for Muscular Dystrophy

Umbilical cord mesenchymal stromal cells (MSC) have been widely investigated for cell-based therapy studies as an alternative source to bone marrow transplantation. Umbilical cord tissue is a rich source of MSCs with potential to derivate at least muscle, cartilage, fat, and bone cells in vitro. The possibility to replace the defective muscle cells using cell therapy is a promising approach for the treatment of progressive muscular dystrophies (PMDs), independently of the specific gene mutation. Therefore, preclinical studies in different models of muscular dystrophies are of utmost importance. The main objective of the present study is to evaluate if umbilical cord MSCs have the potential to reach and differentiate into muscle cells in vivo in two animal models of PMDs. In order to address this question we injected (1) human umbilical cord tissue (hUCT) MSCs into the caudal vein of SJL mice; (2) hUCT and canine umbilical cord vein (cUCV) MSCs intra-arterially in GRMD dogs. Our results here reported support the safety of the procedure and indicate that the injected cells could engraft in the host muscle in both animal models but could not differentiate into muscle cells. These observations may provide important information aiming future therapy for muscular dystrophies.

Nonaxisymmetric magnetorotational instability in ideal and viscous plasmas

The excitation of magnetorotational instability (MRI) in rotating laboratory plasmas is investigated. In contrast to astrophysical plasmas, in which gravitation plays an important role, in laboratory plasmas it can be neglected and the plasma rotation is equilibrated by the pressure gradient. The analysis is restricted to the simple model of a magnetic confinement configuration with cylindrical symmetry, in which nonaxisymmetric perturbations are investigated using the local approximation. Starting from the simplest case of an ideal plasma, the corresponding dispersion relations are derived for more complicated models including the physical effects of parallel and perpendicular viscosities. The Friemann-Rotenberg approach used for ideal plasmas is generalized for the viscous model and an analytical expression for the instability boundary is obtained. It is shown that, in addition to the standard effect of radial derivative of the rotation frequency (the Velikhov effect), which can be destabilizing or stabilizing depending on the sign of this derivative in the ideal plasma, there is a destabilizing effect proportional to the fourth power of the rotation frequency, or, what is the same, to the square of the plasma pressure gradient, and to the square of the azimuthal mode number of the perturbations. It is shown that the instability boundary also depends on the product of the plasma pressure and density gradients, which has a destabilizing effect when it is negative. In the case of parallel viscosity, the MRI looks like an ideal instability independent of viscosity, while, in the case of strong perpendicular viscosity, it is a dissipative instability with the growth rate inversely proportional to the characteristic viscous decay rate. We point out, however, that the modes of the continuous range of the magnetohydrodynamics spectrum are not taken into account in this paper, and they can be more dangerous than those that are considered. (c) 2008 American Institute of Physics.

Statistical models of mixtures with a biaxial nematic phase

We consider a simple Maier-Saupe statistical model with the inclusion of disorder degrees of freedom to mimic the phase diagram of a mixture of rodlike and disklike molecules. A quenched distribution of shapes leads to a phase diagram with two uniaxial and a biaxial nematic structure. A thermalized distribution, however, which is more adequate to liquid mixtures, precludes the stability of this biaxial phase. We then use a two-temperature formalism, and assume a separation of relaxation times, to show that a partial degree of annealing is already sufficient to stabilize a biaxial nematic structure.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Probing neutrino oscillations in supersymmetric models at the Large Hadron Collider

The lightest supersymmetric particle may decay with branching ratios that correlate with neutrino oscillation parameters. In this case the CERN Large Hadron Collider (LHC) has the potential to probe the atmospheric neutrino mixing angle with sensitivity competitive to its low-energy determination by underground experiments. Under realistic detection assumptions, we identify the necessary conditions for the experiments at CERN's LHC to probe the simplest scenario for neutrino masses induced by minimal supergravity with bilinear R parity violation.

Deciphering the spin of new resonances in Higgsless models

We study the potential of the CERN large hadron collider to probe the spin of new massive vector boson resonances predicted by Higgsless models. We consider its production via weak boson fusion which relies only on the coupling between the new resonances and the weak gauge bosons. We show that the large hadron collider will be able to unravel the spin of the particles associated with the partial restoration of unitarity in vector boson scattering for integrated luminosities of 150-560 fb(-1), depending on the new state mass and on the method used in the analyses.

Hydro-kinetic approach to relativistic heavy ion collisions

We develop a combined hydro-kinetic approach which incorporates a hydrodynamical expansion of the systems formed in A + A collisions and their dynamical decoupling described by escape probabilities. The method corresponds to a generalized relaxation time (tau(rel)) approximation for the Boltzmann equation applied to inhomogeneous expanding systems; at small tau(rel) it also allows one to catch the viscous effects in hadronic component-hadron-resonance gas. We demonstrate how the approximation of sudden freeze-out can be obtained within this dynamical picture of continuous emission and find that hypersurfaces, corresponding to a sharp freeze-out limit, are momentum dependent. The pion m(T) spectra are computed in the developed hydro-kinetic model, and compared with those obtained from ideal hydrodynamics with the Cooper-Frye isothermal prescription. Our results indicate that there does not exist a universal freeze-out temperature for pions with different momenta, and support an earlier decoupling of higher p(T) particles. By performing numerical simulations for various initial conditions and equations of state we identify several characteristic features of the bulk QCD matter evolution preferred in view of the current analysis of heavy ion collisions at RHIC energies.

Disordered ensembles of random matrices

It is shown that the families of generalized matrix ensembles recently considered which give rise to an orthogonal invariant stable Levy ensemble can be generated by the simple procedure of dividing Gaussian matrices by a random variable. The nonergodicity of this kind of disordered ensembles is investigated. It is shown that the same procedure applied to random graphs gives rise to a family that interpolates between the Erdos-Renyi and the scale free models.

Anisotropic singularities in modified gravity models

We show that the common singularities present in generic modified gravity models governed by actions of the type S = integral d(4)x root-gf(R, phi, X). with X = -1/2 g(ab)partial derivative(a)phi partial derivative(b)phi, are essentially the same anisotropic instabilities associated to the hypersurface F(phi) = 0 in the case of a nonminimal coupling of the type F(phi)R, enlightening the physical origin of such singularities that typically arise in rather complex and cumbersome inhomogeneous perturbation analyses. We show, moreover, that such anisotropic instabilities typically give rise to dynamically unavoidable singularities, precluding completely the possibility of having physically viable models for which the hypersurface partial derivative f/partial derivative R = 0 is attained. Some examples are explicitly discussed.

Two-component Abelian sandpile models

In one-component Abelian sandpile models, the toppling probabilities are independent quantities. This is not the case in multicomponent models. The condition of associativity of the underlying Abelian algebras imposes nonlinear relations among the toppling probabilities. These relations are derived for the case of two-component quadratic Abelian algebras. We show that Abelian sandpile models with two conservation laws have only trivial avalanches.

Directed Abelian algebras and their application to stochastic models

With each directed acyclic graph (this includes some D-dimensional lattices) one can associate some Abelian algebras that we call directed Abelian algebras (DAAs). On each site of the graph one attaches a generator of the algebra. These algebras depend on several parameters and are semisimple. Using any DAA, one can define a family of Hamiltonians which give the continuous time evolution of a stochastic process. The calculation of the spectra and ground-state wave functions (stationary state probability distributions) is an easy algebraic exercise. If one considers D-dimensional lattices and chooses Hamiltonians linear in the generators, in finite-size scaling the Hamiltonian spectrum is gapless with a critical dynamic exponent z=D. One possible application of the DAA is to sandpile models. In the paper we present this application, considering one- and two-dimensional lattices. In the one-dimensional case, when the DAA conserves the number of particles, the avalanches belong to the random walker universality class (critical exponent sigma(tau)=3/2). We study the local density of particles inside large avalanches, showing a depletion of particles at the source of the avalanche and an enrichment at its end. In two dimensions we did extensive Monte-Carlo simulations and found sigma(tau)=1.780 +/- 0.005.

Structural models for yttrium aluminium borate laser glasses: NMR and EPR studies of the system (Y(2)O(3))(0.2)-(Al(2)O(3))(x)-(B(2)O(3))(0.8-x)

The structure of laser glasses in the system (Y(2)O(3))(0.2){(Al(2)O(3))(x))(B(2)O(3))(0.8-x)} (0.15 <= x <= 0.40) has been investigated by means of (11)B, (27)Al, and (89)Y solid state NMR as well as electron spin echo envelope modulation (ESEEM) of Yb-doped samples. The latter technique has been applied for the first time to an aluminoborate glass system. (11)B magic-angle spinning (MAS)-NMR spectra reveal that, while the majority of the boron atoms are three-coordinated over the entire composition region, the fraction of three-coordinated boron atoms increases significantly with increasing x. Charge balance considerations as well as (11)B NMR lineshape analyses suggest that the dominant borate species are predominantly singly charged metaborate (BO(2/2)O(-)), doubly charged pyroborate (BO(1/2)(O(-))(2)), and (at x = 0.40) triply charged orthoborate groups. As x increases along this series, the average anionic charge per trigonal borate group increases from 1.38 to 2.91. (27)Al MAS-NMR spectra show that the alumina species are present in the coordination states four, five and six, and the fraction of four-coordinated Al increases markedly with increasing x. All of the Al coordination states are in intimate contact with both the three-and the four-coordinate boron species and vice versa, as indicated by (11)B/(27)Al rotational echo double resonance (REDOR) data. These results are consistent with the formation of a homogeneous, non-segregated glass structure. (89)Y solid state NMR spectra show a significant chemical shift trend, reflecting that the second coordination sphere becomes increasingly ""aluminate-like'' with increasing x. This conclusion is supported by electron spin echo envelope modulation (ESEEM) data of Yb-doped glasses, which indicate that both borate and aluminate species participate in the medium range structure of the rare-earth ions, consistent with a random spatial distribution of the glass components.

Nonempirical hyper-generalized-gradient functionals constructed from the Lieb-Oxford bound

A simple and completely general representation of the exact exchange-correlation functional of density-functional theory is derived from the universal Lieb-Oxford bound, which holds for any Coulomb-interacting system. This representation leads to an alternative point of view on popular hybrid functionals, providing a rationale for why they work and how they can be constructed. A similar representation of the exact correlation functional allows to construct fully nonempirical hyper-generalized-gradient approximations (HGGAs), radically departing from established paradigms of functional construction. Numerical tests of these HGGAs for atomic and molecular correlation energies and molecular atomization energies show that even simple HGGAs match or outperform state-of-the-art correlation functionals currently used in solid-state physics and quantum chemistry.

Inference of gene regulatory networks from time series by Tsallis entropy

Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.

Bayesian updating rules in continuous opinion dynamics models

Here, I investigate the use of Bayesian updating rules applied to modeling how social agents change their minds in the case of continuous opinion models. Given another agent statement about the continuous value of a variable, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a uniform distribution. This represents the idea that the other agent might have no idea about what is being talked about. The effect of updating only the first moments of the distribution will be studied, and we will see that this generates results similar to those of the bounded confidence models. On also updating the second moment, several different opinions always survive in the long run, as agents become more stubborn with time. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.