Growing Neurospora colonies attached to a glass surface in liquid medium

Growing Neurospora colonies attached to a glass surface in liquid mediu

Worldline algorithms for Casimir configurations

We present improved worldline numerical algorithms for high-precision calculations of Casimir interaction energies induced by scalar-field fluctuations with Dirichlet boundary conditions for various Casimir geometries. Significant reduction of numerical cost is gained by exploiting the symmetries of the worldline ensemble in combination with those of the configurations. This facilitates high-precision calculations on standard PCs or small clusters. We illustrate our strategies using the experimentally most relevant sphere-plate and cylinder-plate configuration. We compute Casimir curvature effects for a wide parameter range, revealing the tight validity bounds of the commonly used proximity force approximation (PFA). We conclude that data analysis of future experiments aiming at a precision of 0.1% must no longer be based on the PFA. Revisiting the parallel-plate configuration, we find a mapping between the D-dimensional Casimir energy and properties of a random-chain polymer ensemble.Comment: 23 pages, 9 figure

Sorbose resistant mutants in Neurospora crassa

Sorbose resistant mutants in Neurospora crass

Quantum energies with worldline numerics

We present new results for Casimir forces between rigid bodies which impose Dirichlet boundary conditions on a fluctuating scalar field. As a universal computational tool, we employ worldline numerics which builds on a combination of the string-inspired worldline approach with Monte-Carlo techniques. Worldline numerics is not only particularly powerful for inhomogeneous background configurations such as involved Casimir geometries, it also provides for an intuitive picture of quantum-fluctuation-induced phenomena. Results for the Casimir geometries of a sphere above a plate and a new perpendicular-plates configuration are presented.Comment: 8 pages, 2 figures, Submitted to the Proceedings of the Seventh Workshop QFEXT'05 (Barcelona, September 5-9, 2005), Refs updated, version to appear in JPhys

Casimir effect for curved geometries: PFA validity limits

We compute Casimir interaction energies for the sphere-plate and cylinder-plate configuration induced by scalar-field fluctuations with Dirichlet boundary conditions. Based on a high-precision calculation using worldline numerics, we quantitatively determine the validity bounds of the proximity force approximation (PFA) on which the comparison between all corresponding experiments and theory are based. We observe the quantitative failure of the PFA on the 1% level for a curvature parameter a/R > 0.00755. Even qualitatively, the PFA fails to predict reliably the correct sign of genuine Casimir curvature effects. We conclude that data analysis of future experiments aiming at a precision of 0.1% must no longer be based on the PFA.Comment: 4 pages, 4 figure

Cotranscription of the electron transport protein genes nifJ and nifF in Enterobacter agglomerans 333

A nucleotide sequence showing extensive homology to the nifF gene, which codes for a flavodoxin involved in nitrogen fixation in Klebsiella pneumoniae, was localized on the plasmid pEA3 of Enterobacter agglomerans and determined. The analysis of transcriptional fusions, as well as transcript protection assays, indicated a novel nif gene organization, that is, the cotranscription of nifJ and nifF

Worldline approach to Casimir effect and Gross-Neveu model

We employ worldline numerics to study Casimir effect and Gross-Neveu model. In this approach, the quantum fluctuations are mapped onto quantum mechanical path integrals, which are evaluated with Monte Carlo methods. For the Casimir effect, this allows the precise computation of the interaction energy for a Dirichlet scalar in Casimir geometries inaccessible to other methods. We study geometries involving curvature and edges, both are important for experiments and applications in nanotechnology, respectively. Significant reduction of numerical cost is gained by exploiting the symmetries of the worldline ensemble in combination with those of the configurations. Our results reveal the tight validity bounds of the commonly used proximity force approximation (PFA) and provide first insight into the effect of edges of finite plates on the Casimir force. In the Gross-Neveu model, we compute the trace over the fermion fluctuations using a worldline path integral, whose numerical evaluation is demonstrated for various configurations in the two dimensional model. We incorporate temperature and chemical potential in our formalism and perform first worldline numeric computations at finite values of these quantities. We thereby rediscover aspects of the established phase diagram. The methods employed can be extended to higher dimensions, to study the existence of a spatially inhomogeneous ground state beyond the two dimensional Gross-Neveu model

Model-based extension of high-throughput to high-content data

<p>Abstract</p> <p>Background</p> <p>High-quality quantitative data is a major limitation in systems biology. The experimental data used in systems biology can be assigned to one of the following categories: assays yielding average data of a cell population, high-content single cell measurements and high-throughput techniques generating single cell data for large cell populations. For modeling purposes, a combination of data from different categories is highly desirable in order to increase the number of observable species and processes and thereby maximize the identifiability of parameters.</p> <p>Results</p> <p>In this article we present a method that combines the power of high-content single cell measurements with the efficiency of high-throughput techniques. A calibration on the basis of identical cell populations measured by both approaches connects the two techniques. We develop a mathematical model to relate quantities exclusively observable by high-content single cell techniques to those measurable with high-content as well as high-throughput methods. The latter are defined as free variables, while the variables measurable with only one technique are described in dependence of those. It is the combination of data calibration and model into a single method that makes it possible to determine quantities only accessible by single cell assays but using high-throughput techniques. As an example, we apply our approach to the nucleocytoplasmic transport of STAT5B in eukaryotic cells.</p> <p>Conclusions</p> <p>The presented procedure can be generally applied to systems that allow for dividing observables into sets of free quantities, which are easily measurable, and variables dependent on those. Hence, it extends the information content of high-throughput methods by incorporating data from high-content measurements.</p

Interaction between Experiment, Modeling and Simulation of Spatial Aspects in the JAK2/STAT5 Signaling Pathway

Fundamental progress in systems biology can only be achieved if experimentalists and theoreticians closely collaborate. Mathematical models cannot be formulated precisely without deep knowledge of the experiments while complex biological systems can often not be understood fully without mathematical interpretation of the dynamic processes involved. In this article, we describe how these two approaches can be combined to gain new insights on one of the most extensively studied signal transduction pathways, the Janus kinase (JAK)/ signal transducer and activator of transcription (STAT) pathway. We focus on the parameters of a model describing how STAT proteins are transported from the membrane to the nucleus where STATs regulate gene expression. We discuss which parameters can be measured experimentally in different cell types and how the unknown parameters are estimated, what the limits of these techniques and how accurate the determinations are

Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2

Aeolian dust has significant impacts on climate, public health, infrastructure and ecosystems. Assessing dust concentrations and the impacts is challenging because the emissions depend on many environmental factors and can vary greatly with meteorological conditions. We present a data-driven aeolian dust scheme that combines machine learning components and physical equations to predict atmospheric dust concentrations and quantify the sources. The numerical scheme was trained to reproduce dust aerosol optical depth retrievals by the Infrared Atmospheric Sounding Interferometer on board the MetOp-A satellite. The input parameters included meteorological variables from the fifth-generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts. The trained dust scheme can be applied as an emission submodel to be used in climate and Earth system models, which is reproducibly derived from observational data so that a priori assumptions and manual parameter tuning can be largely avoided. We compared the trained emission submodel to a state-of-the-art emission parameterisation, showing that it substantially improves the representation of aeolian dust in the global atmospheric chemistry–climate model EMAC.</p