Enhancement of Nematic Order and Global Phase Diagram of a Lattice Model for Coupled Nematic Systems

We use an infinite-range Maier-Saupe model, with two sets of local quadrupolar variables and restricted orientations, to investigate the global phase diagram of a coupled system of two nematic subsystems. The free energy and the equations of state are exactly calculated by standard techniques of statistical mechanics. The nematic-isotropic transition temperature of system A increases with both the interaction energy among mesogens of system B, and the two-subsystem coupling J. This enhancement of the nematic phase is manifested in a global phase diagram in terms of the interaction parameters and the temperature T. We make some comments on the connections of these results with experimental findings for a system of diluted ferroelectric nanoparticles embedded in a nematic liquid-crystalline environment.

Constraining H-0 in general dark energy models from Sunyaev-Zeldovich/X-ray technique and complementary probes

In accelerating dark energy models, the estimates of the Hubble constant, Ho, from Sunyaev-Zerdovich effect (SZE) and X-ray surface brightness of galaxy clusters may depend on the matter content (Omega(M)), the curvature (Omega(K)) and the equation of state parameter GO. In this article, by using a sample of 25 angular diameter distances of galaxy clusters described by the elliptical beta model obtained through the SZE/X-ray technique, we constrain Ho in the framework of a general ACDM model (arbitrary curvature) and a flat XCDM model with a constant equation of state parameter omega = p(x)/rho(x). In order to avoid the use of priors in the cosmological parameters, we apply a joint analysis involving the baryon acoustic oscillations (BA()) and the (MB Shift Parameter signature. By taking into account the statistical and systematic errors of the SZE/X-ray technique we obtain for nonflat ACDM model H-0 = 74(-4.0)(+5.0) km s(-1) Mpc(-1) (1 sigma) whereas for a fiat universe with constant equation of state parameter we find H-0 = 72(-4.0)(+5.5) km s(-1) Mpc(-1)(1 sigma). By assuming that galaxy clusters are described by a spherical beta model these results change to H-0 = 6(-7.0)(+8.0) and H-0 = 59(-6.0)(+9.0) km s(-1) Mpc(-1)(1 sigma), respectively. The results from elliptical description are in good agreement with independent studies from the Hubble Space Telescope key project and recent estimates based on the Wilkinson Microwave Anisotropy Probe, thereby suggesting that the combination of these three independent phenomena provides an interesting method to constrain the Bubble constant. As an extra bonus, the adoption of the elliptical description is revealed to be a quite realistic assumption. Finally, by comparing these results with a recent determination for a, flat ACDM model using only the SZE/X-ray technique and BAO, we see that the geometry has a very weak influence on H-0 estimates for this combination of data.

Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model. (C) 2012 Elsevier B.V. All rights reserved.

Structure and mechanism of diet specialisation: testing models of individual variation in resource use with sea otters

Studies of consumer-resource interactions suggest that individual diet specialisation is empirically widespread and theoretically important to the organisation and dynamics of populations and communities. We used weighted networks to analyze the resource use by sea otters, testing three alternative models for how individual diet specialisation may arise. As expected, individual specialisation was absent when otter density was low, but increased at high-otter density. A high-density emergence of nested resource-use networks was consistent with the model assuming individuals share preference ranks. However, a density-dependent emergence of a non-nested modular network for core resources was more consistent with the competitive refuge model. Individuals from different diet modules showed predictable variation in rank-order prey preferences and handling times of core resources, further supporting the competitive refuge model. Our findings support a hierarchical organisation of diet specialisation and suggest individual use of core and marginal resources may be driven by different selective pressures.

Twinlike models for kinks and compactons in flat and warped spacetime

This work deals with the presence of twinlike models in scalar field theories. We show how to build distinct scalar field theories having the same extended solution, with the same energy density and linear stability. Here, however, we start from a given but generalized scalar field theory, and we construct the corresponding twin model, which also engenders generalized dynamics. We investigate how the twinlike models arise in both flat and curved spacetimes. In the curved spacetime, we consider a braneworld model with the warp factor controlling the spacetime geometry with a single extra dimension of infinite extent. In particular, we study linear stability in both flat and curved spacetimes, and in the case of curved spacetime-in both the gravity and the scalar field sectors-for the two braneworld models. DOI: 10.1103/PhysRevD.86.125021

Computational tools for comparing asymmetric GARCH models via Bayes factors

In this paper we use Markov chain Monte Carlo (MCMC) methods in order to estimate and compare GARCH models from a Bayesian perspective. We allow for possibly heavy tailed and asymmetric distributions in the error term. We use a general method proposed in the literature to introduce skewness into a continuous unimodal and symmetric distribution. For each model we compute an approximation to the marginal likelihood, based on the MCMC output. From these approximations we compute Bayes factors and posterior model probabilities. (C) 2012 IMACS. Published by Elsevier B.V. All rights reserved.

Local power properties of some asymptotic tests in symmetric linear regression models

In this paper we obtain asymptotic expansions, up to order n(-1/2) and under a sequence of Pitman alternatives, for the nonnull distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the class of symmetric linear regression models. This is a wide class of models which encompasses the t model and several other symmetric distributions with longer-than normal tails. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters. Furthermore, in order to compare the finite-sample performance of these tests in this class of models, Monte Carlo simulations are presented. An empirical application to a real data set is considered for illustrative purposes. (C) 2011 Elsevier B.V. All rights reserved.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Constitutive models for biodegradable thermoplastic ropes for ligament repair

The concept behind a biodegradable ligament device is to temporarily replace the biomechanical functions of the ruptured ligament, while it progressively regenerates its capacities. However, there is a lack of methods to predict the mechanical behaviour evolution of the biodegradable devices during degradation, which is an important aspect of the project. In this work, a hyper elastic constitutive model will be used to predict the mechanical behaviour of a biodegradable rope made of aliphatic polyesters. A numerical approach using ABAQUS is presented, where the material parameters of the model proposal are automatically updated in correspondence to the degradation time, by means of a script in PYTHON. In this method we also use a User Material subroutine (UMAT) to apply a failure criterion base on the strength that decreases according to a first order differential equation. The parameterization of the material model proposal for different degradation times were achieved by fitting the theoretical curves with the experimental data of tensile tests on fibres. To model all the rope behaviour we had considered one step of homogenisation considering the fibres architectures in an elementary volume. (C) 2012 Elsevier Ltd. All rights reserved.

Use of recent geoid models to estimate mean dynamic topography and geostrophic currents in South Atlantic and Brazil Malvinas confluence

The use of geoid models to estimate the Mean Dynamic Topography was stimulated with the launching of the GRACE satellite system, since its models present unprecedented precision and space-time resolution. In the present study, besides the DNSC08 mean sea level model, the following geoid models were used with the objective of computing the MDTs: EGM96, EIGEN-5C and EGM2008. In the method adopted, geostrophic currents for the South Atlantic were computed based on the MDTs. In this study it was found that the degree and order of the geoid models affect the determination of TDM and currents directly. The presence of noise in the MDT requires the use of efficient filtering techniques, such as the filter based on Singular Spectrum Analysis, which presents significant advantages in relation to conventional filters. Geostrophic currents resulting from geoid models were compared with the HYCOM hydrodynamic numerical model. In conclusion, results show that MDTs and respective geostrophic currents calculated with EIGEN-5C and EGM2008 models are similar to the results of the numerical model, especially regarding the main large scale features such as boundary currents and the retroflection at the Brazil-Malvinas Confluence.

Cation Distribution and Local Order in Mixed Sodium Metaphosphate Glasses

Two structural properties in mixed alkali metal phosphate glasses that seem to be crucial to the development of the mixed ion effect in dc conductivity were systematically analyzed in Na mixed metaphosphates: the local order around the mobile species, and their distribution and mixing in the glass network. The set of glasses considered here, Na1-xMxPO3 with M = Li, Ag, K, Rb, and Cs and 0 <= x <= 1, encompass a broad degree of size mismatch between the mixed cation species. A comprehensive solid-state nuclear magnetic resonance study was carried out using P-31 MAS, Na-23 triple quantum MAS, Rb-87 QCPMG, P-31-Na-23 REDOR, Na-23-Li-7 and Li-7-Li-6 SEDOR, and Na-23 spin echo decay. It was observed that the arrangement of P atoms around Na in the mixed glasses was indistinguishable from that observed in the NaPO3 glass. However, systematic distortions in the local structure of the 0 environments around Na were observed, related to the presence of the second cation. The average Na-O distances show an expansion/compression When Na+ ions are replaced by cations with respectively smaller/bigger radii. The behavior of the nuclear electric quadrupole coupling. constants indicates that this expansion reduces the local symmetry, while the compression produces the opposite effect These effects become marginally small when the site mismatch between the cations is small, as in Na-Ag mixed glasses. The present study confirms the intimate mixing of cation species at the atomic scale, but clear deviations from random mixing were detected in systems with larger alkali metal ions (Cs-Na, K-Na, Rb-Na). In contrast, no deviations from the statistical ion mixture were found in the systems Ag-Na and Li-Na, where mixed cations are either of radii comparable to (Ag+) or smaller than (Li+) Na+. The set of results supports two fundamental structural features of the models proposed to explain the mixed ion effect: the. structural specificity of the sites occupied by each cation species and their mixing at the atomic scale.

Analysis of elastic functionally graded materials under large displacements via high-order tetrahedral elements

The purpose of this study is to present a position based tetrahedral finite element method of any order to accurately predict the mechanical behavior of solids constituted by functionally graded elastic materials and subjected to large displacements. The application of high-order elements makes it possible to overcome the volumetric and shear locking that appears in usual homogeneous isotropic situations or even in non-homogeneous cases developing small or large displacements. The use of parallel processing to improve the computational efficiency, allows employing high-order elements instead of low-order ones with reduced integration techniques or strain enhancements. The Green-Lagrange strain is adopted and the constitutive relation is the functionally graded Saint Venant-Kirchhoff law. The equilibrium is achieved by the minimum total potential energy principle. Examples of large displacement problems are presented and results confirm the locking free behavior of high-order elements for non-homogeneous materials. (C) 2011 Elsevier B.V. All rights reserved.

Plasma corticosterone levels in mouse models of pain

Background: Pain markedly activates the hypothalamic-pituitary-adrenal (HPA) axis and increases plasma corticosterone release interfering significantly with nociceptive behaviour as well as the mechanism of action of analgesic drugs. Aims/Methods: In the present study, we monitored the time course of circulating corticosterone in two mouse strains (C57Bl/6 and Balb/C) under different pain models. In addition, the stress response was investigated following animal handling, intrathecal (i.t.) manipulation and habituation to environmental conditions commonly used in nociceptive experimental assays. We also examined the influence of within-cage order of testing on plasma corticosterone. Results: Subcutaneous injection of capsaicin precipitated a prompt stress response whereas carrageenan and complete Freund's adjuvant induced an increased corticosterone release around the third hour post-injection. However, carrageenan induced a longer increased corticosterone in C57Bl/6 mice. In partial sciatic nerve ligation, neuropathic pain model corticosterone increased only in the first days whereas mechanical hypersensitivity remained much longer. Animal handling also represents an important stressor whereas the i.t. injection per se does not exacerbate the handling-induced stress response. Moreover, the order of testing animals from the same cage does not interfere with plasma corticosterone levels in the intrathecal procedure. Animal habituation to the testing apparatus also does not reduce the immediate corticosterone increase as compared with non-habituated mice. Conclusion: Our data indicate that HPA axis activation in acute and chronic pain models is time dependent and may be dissociated from evoked hyperalgesia. Therefore, HPA-axis activation represents an important variable to be considered when designing experimental assays of persistent pain as well as for interpretation of data.

Exact cosmological solutions of models with an interacting dark sector

In this work we extend the first order formalism for cosmological models that present an interaction between a fermionic and a scalar field. Cosmological exact solutions describing universes filled with interacting dark energy and dark matter have been obtained. Viable cosmological solutions with an early period of decelerated expansion followed by late acceleration have been found, notably one which presents a dark matter component dominating in the past and a dark energy component dominating in the future. In another one, the dark energy alone is the responsible for both periods, similar to a Chaplygin gas case. Exclusively accelerating solutions have also been obtained.

Weibull and generalised exponential overdispersion models with an application to ozone air pollution

We consider the problem of estimating the mean and variance of the time between occurrences of an event of interest (inter-occurrences times) where some forms of dependence between two consecutive time intervals are allowed. Two basic density functions are taken into account. They are the Weibull and the generalised exponential density functions. In order to capture the dependence between two consecutive inter-occurrences times, we assume that either the shape and/or the scale parameters of the two density functions are given by auto-regressive models. The expressions for the mean and variance of the inter-occurrences times are presented. The models are applied to the ozone data from two regions of Mexico City. The estimation of the parameters is performed using a Bayesian point of view via Markov chain Monte Carlo (MCMC) methods.