Critical Point of a Weakly Interacting Two-Dimensional Bose Gas

We study the Berezinskii-Kosterlitz-Thouless transition in a weakly interacting 2D quantum Bose gas using the concept of universality and numerical simulations of the classical $|\psi|^4$-model on a lattice. The critical density and chemical potential are given by relations $n_c=(mT/2\pi \hbar^2) \ln(\xi \hbar^2/ mU)$ and $\mu_c=(mTU/\pi \hbar^2) \ln(\xi_{\mu} \hbar^2/ mU)$, where $T$ is the temperature, $m$ is the mass, and $U$ is the effective interaction. The dimensionless constant $\xi= 380 \pm 3$ is very large and thus any quantitative analysis of the experimental data crucially depends on its value. For $\xi_{\mu}$ our result is $\xi_{\mu} = 13.2 \pm 0.4$. We also report the study of the quasi-condensate correlations at the critical point.Comment: 4 pages (3 figures), Latex. Submitted to PR

Weakly interacting Bose gas in the vicinity of the critical point

We consider a three-dimensional weakly interacting Bose gas in the fluctuation region (and its vicinity) of the normal-superfluid phase transition point. We establish relations between basic thermodynamic functions: density, $n(T,\mu)$, superfluid density $n_s(T,\mu)$, and condensate density, $n_{\rm cnd} (T,\mu)$. Being universal for all weakly interacting $|\psi|^4$ systems, these relations are obtained from Monte Carlo simulations of the classical $|\psi|^4$ model on a lattice. Comparing with the mean-field results yields a quantitative estimate of the fluctuation region size. Away from the fluctuation region, on the superfluid side, all the data perfectly agree with the predictions of the quasicondensate mean field theory.--This demonstrates that the only effect of the leading above-the-mean-field corrections in the condensate based treatments is to replace the condensate density with the quasicondensate one in all local thermodynamic relations. Surprisingly, we find that a significant fraction of the density profile of a loosely trapped atomic gas might correspond to the fluctuation region.Comment: 14 pages, Latex, 8 figure

The Type 2 Diabetes Knowledge Portal: an Open access Genetic Resource Dedicated to Type 2 Diabetes and Related Traits

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP\u27s comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

Weakly interacting Bose gases in the fluctuation region

We study the superfluid transition of weakly interacting Bose gases, classical fields and related systems in two dimensions, where it is a Berezinskii-Kosterlitz-Thouless transition, and in three dimensions, where it is a U(1) transition accompanied by the onset of Bose-Einstein condensation. We show that for small interaction strength U, these systems can be described by a :Ψ:4 model based on classical complex fields and we establish universal behavior among systems of different microscopical details, such as classical and quantum, continuous and lattice. By numerical simulations of the classical lattice field, using Worm Algorithm, we solve all relevant thermodynamical functions of this system, which are density, condensate density (in 3D), superfluid density and quasi-condensate. Through universality considerations, we can translate these results to all systems which can be described by a :Ψ:4 model based on a classical complex field. Our results cover the fluctuation region and extend into mean field and critical region, where we connect them to results of mean field theory and renormalization group theory respectively. We also establish that the quasicondensate, which exists in two and three dimensions, is a basis for a more accurate mean field theory treatment than the condensate, which exists only in three dimensions

knowledge Modeling Semantic Web SBML

Abstract A large and growing network (‘‘cloud’’) of interlinked terms and records of items of Systems Biology knowledge is available from the web. These items include pathways, reactions, substances, literature references, organisms, and anatomy, all described in different data sets. Here, we discuss how the knowledge from the cloud can be molded into representations (views) useful for data visualization and modeling. We discuss methods to create and use various views relevant for visualization, modeling, and model annotations, while hiding irrelevant details without unacceptable loss or distortion. We show that views are compatible with understanding substances and processes as sets of microscopic compounds and events respectively, which allows the representation of specializations and generalizations as subsets and supersets respectively. We explain how these methods can be implemented based on the bridging ontology System

broadinstitute/dig-loam: v3.0.1

<p>Introduces bug fixes from v3.0.0 and represents final release before splitting qc into qc and export modules</p&gt