Robustness of d-Density Wave Order to Nonmagnetic Impurities

Effect of finite density of nonmagnetic impurities on a coexisting phase of d-density wave (DDW) order and d-wave superconducting (DSC) order is studied using Bogoliubov-de Gennes (BdG) method. The spatial variation of the inhomogeneous DDW order due to impurities has a strong correlation with that of density, which is very different from that of DSC order. The length scale associated with DDW is found to be of the order of a lattice spacing. The nontrivial inhomogeneities are shown to make DDW order much more robust to the impurities, while DSC order becomes very sensitive to them. The effect of disorder on the density of states is also discussed.Comment: 4 pages, 3 PostScript figure

Identification of the relaxation kernel in diffusion processes and viscoelasticity with memory via deconvolution

We present an algorithm for the identification of the relaxation kernel in the theory of diffusion systems with memory (or of viscoelasticity) which is linear, in the sense that we propose a linear Volterra integral equation of convolution type whose solution is the relaxation kernel. The algorithm is based on the observation of the flux through a part of the boundary of a body

Application of DETECTER, an evolutionary genomic tool to analyze genetic variation, to the cystic fibrosis gene family

BACKGROUND: The medical community requires computational tools that distinguish missense genetic differences having phenotypic impact within the vast number of sense mutations that do not. Tools that do this will become increasingly important for those seeking to use human genome sequence data to predict disease, make prognoses, and customize therapy to individual patients. RESULTS: An approach, termed DETECTER, is proposed to identify sites in a protein sequence where amino acid replacements are likely to have a significant effect on phenotype, including causing genetic disease. This approach uses a model-dependent tool to estimate the normalized replacement rate at individual sites in a protein sequence, based on a history of those sites extracted from an evolutionary analysis of the corresponding protein family. This tool identifies sites that have higher-than-average, average, or lower-than-average rates of change in the lineage leading to the sequence in the population of interest. The rates are then combined with sequence data to determine the likelihoods that particular amino acids were present at individual sites in the evolutionary history of the gene family. These likelihoods are used to predict whether any specific amino acid replacements, if introduced at the site in a modern human population, would have a significant impact on fitness. The DETECTER tool is used to analyze the cystic fibrosis transmembrane conductance regulator (CFTR) gene family. CONCLUSION: In this system, DETECTER retrodicts amino acid replacements associated with the cystic fibrosis disease with greater accuracy than alternative approaches. While this result validates this approach for this particular family of proteins only, the approach may be applicable to the analysis of polymorphisms generally, including SNPs in a human population

Formation of Electronic Nematic Phase in Interacting Systems

We study the formation of an electronic nematic phase characterized by a broken point-group symmetry in interacting fermion systems within the weak coupling theory. As a function of interaction strength and chemical potential, the phase transition between the isotropic Fermi liquid and nematic phase is first order at zero temperature and becomes second order at a finite temperature. The transition is present for all typical, including quasi-2D, electronic dispersions on the square lattice and takes place for arbitrarily small interaction when at van Hove filling, thus suppressing the Lifshitz transition. In connection with the formation of the nematic phase, we discuss the origin of the first order transition and competition with other broken symmetry states.Comment: revtex4, 6 pages, 6 figures; revised introduction, updated reference

Neogenin and RGMa control neural tube closure and neuroepithelial morphology by regulating cell polarity

In humans, neural tube closure defects occur in 1: 1000 pregnancies. The design of new strategies for the prevention of such common defects would benefit from an improved understanding of the molecular events underlying neurulation. Neural fold elevation is a key morphological process that acts during neurulation to drive neural tube closure. However, to date, the molecular pathways underpinning neural fold elevation have not been elucidated. Here, we use morpholino knock-down technology to demonstrate that Repulsive Guidance Molecule (RGMa)-Neogenin interactions are essential for effective neural fold elevation during Xenopus neurulation and that loss of these molecules results in disrupted neural tube closure. We demonstrate that Neogenin and RGMa are required for establishing the morphology of deep layer cells in the neural plate throughout neurulation. We also show that loss of Neogenin severely disrupts the microtubule network within the deep layer cells suggesting that Neogenin-dependent microtubule organization within the deep cells is essential for radial intercalation with the overlying superficial cell layer, thereby driving neural fold elevation. In addition, we show that sustained Neogenin activity is also necessary for the establishment of the apicobasally polarized pseudostratified neuroepithelium of the neural tube. Therefore, our study identifies a novel signaling pathway essential for radial intercalation and epithelialization during neural fold elevation and neural tube morphogenesis. Copyright ©2008 Society for Neuroscienc

Analytic, dust-independent mass-loss rates for red supergiant winds initiated by turbulent pressure

Context. Red supergiants are observed to undergo vigorous mass-loss. However, to date, no theoretical model has succeeded in explaining the origins of these objects' winds. This strongly limits our understanding of red supergiant evolution and Type II-P and II-L supernova progenitor properties. Aims. We examine the role that vigorous atmospheric turbulence may play in initiating and determining the mass-loss rates of red supergiant stars. Methods. We analytically and numerically solve the equations of conservation of mass and momentum, which we later couple to an atmospheric temperature structure, to obtain theoretically motivated mass-loss rates. We then compare these to state-of-the-art empirical mass-loss rate scaling formulae as well as observationally inferred mass-loss rates of red supergiants. Results. We find that the pressure due to the characteristic turbulent velocities inferred for red supergiants is sufficient to explain the mass-loss rates of these objects in the absence of the normally employed opacity from circumstellar dust. Motivated by this initial success, we provide a first theoretical and fully analytic mass-loss rate prescription for red supergiants. We conclude by highlighting some intriguing possible implications of these rates for future studies of stellar evolution, especially in light of the lack of a direct dependence on metallicity.Comment: 14 pages, 9 figures, 2 table

A Bayesian nonparametric approach to dynamic item-response modeling: An application to the GUSTO cohort study

Statistical analysis of questionnaire data is often performed employing techniques from item-response theory. In this framework, it is possible to differentiate respondent profiles and characterize the questions (items) included in the questionnaire via interpretable parameters. These models are often crosssectional and aim at evaluating the performance of the respondents. The motivating application of this work is the analysis of psychometric questionnaires taken by a group of mothers at different time points and by their children at one later time point. The data are available through the GUSTO cohort study. To this end, we propose a Bayesian semiparametric model and extend the current literature by: (i) introducing temporal dependence among questionnaires taken at different time points; (ii) jointly modeling the responses to questionnaires taken from different, but related, groups of subjects (in our case mothers and children), introducing a further dependency structure and therefore sharing of information; (iii) allowing clustering of subjects based on their latent response profile. The proposed model is able to identify three main groups of mother/child pairs characterized by their response profiles. Furthermore, we report an interesting maternal reporting bias effect strongly affecting the clustering structure of the mother/child dyads

Semi-crystalline block copolymer bicontinuous nanospheres for thermoresponsive controlled release

We demonstrate the controlled release of pyrene, as a model hydrophobic molecule, from self-assembled bicontinuous nanospheres formed from an amphiphilic block copolymer. The bicontinuous polymer nanospheres act as efficient nanocarriers and the incorporation of hydrophobic poly(alkyl methacrylate) blocks introduces a temperature responsive component to the hydrophobic core