Dynamics of \u3cem\u3eE. Coli\u3c/em\u3e Single Stranded DNA Binding (SSB) Protein-DNA Complexes

Single stranded DNA binding proteins (SSB) are essential to the cell as they stabilize transiently open single stranded DNA (ssDNA) intermediates, recruit appropriate DNA metabolism proteins, and coordinate fundamental processes such as replication, repair and recombination. Escherichia coli single stranded DNA binding protein (EcSSB) has long served as the prototype for the study of SSB function. The structure, functions, and DNA binding properties of EcSSB are well established: The protein is a stable homotetramer with each subunit possessing an N-terminal DNA binding core, a C-terminal protein-protein interaction tail, and an intervening intrinsically disordered linker (IDL). EcSSB wraps ssDNA in multiple DNA binding modes and can diffuse along DNA to remove secondary structures and remodel other protein-DNA complexes. This review provides an update on these features based on recent findings, with special emphasis on the functional and mechanistic relevance of the IDL and DNA binding modes

Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model

We present a common framework for Bayesian emulation methodologies for multivariate-output simulators, or computer models, that employ either parametric linear models or nonparametric Gaussian processes. Novel diagnostics suitable for multivariate covariance-separable emulators are developed and techniques to improve the adequacy of an emulator are discussed and implemented. A variety of emulators are compared for a humanitarian relief simulator, modelling aid missions to Sicily after a volcanic eruption and earthquake, and a sensitivity analysis is conducted to determine the sensitivity of the simulator output to changes in the input variables. The results from parametric and nonparametric emulators are compared in terms of prediction accuracy, uncertainty quantification and scientific interpretability

Analysis of the flowability of cohesive powders using Distinct Element Method

Computer simulations using Distinct Element Method (DEM) have been carried out to investigate the effect of cohesion on the flowability of polydisperse particulate systems. For this purpose, two assemblies with different values of surface energy and made of 3000 spheres with the mechanical properties of glass beads were considered. The analysis of the flowability of the powders is presented in terms of the unconfined yield stress as a function of strain rate for different pre-consolidation loads. For values of the surface energy of 1.0 J/m2 and strain rates lower than 6 s− 1, the unconfined yield stress does not change significantly indicating a quasi-static behaviour of the particulate assemblies during the compression process. For larger strain rates, the unconfined yield stress varies with the power index of 1.2 of the strain rate. The influence of the pre-consolidating stress on the powder behaviour has also been investigated and a flow factor was obtained from the linear relationship between the unconfined yield stress and pre-consolidation stress. The computer simulations show qualitatively a good agreement with the experimental trends on highly cohesive powder flow behaviour

Analysis of the effect of cohesion and gravity on the bulk behaviour of powders using Discrete Element Method

Computer simulations using Distinct Element Method have been carried out to analyse the bulk behaviour of a polydisperse assembly of glass beads. For this purpose an assembly made of 3000 spheres were generated to which the mechanical properties of glass beads were assigned. The system was initially compressed isotropically at a strain rate of 1 s-1 in the absence of gravity and surface energy. Once the assembly reached a packing fraction of about 0.62, the effects of cohesion and gravity on the bulk behaviour were analysed for two different cases. In the first case only gravity was applied, whilst in the second case both gravity and surface energy were acting on the particles. The evolution of the components of the stress tensor for the case in which only gravity was applied indicated that the gravity did not appreciably affect the isotropy of the system. In contrast, the system in which surface energy was introduced became anisotropic. The concept of unconfined yield stress of bulk cohesive powders was used to analyse the effect of surface energy and strain rate. For values of surface energy of 1.0 J/m2 and of strain rate lower than 1 s-1 the unconfined yield strength did not change significantly indicating a quasi-static behaviour for the compression process. However, for values of strain rates larger than 1 s-1 the unconfined yield strength increased with the strain rate, following a power law trend with an index of 1.7

Bayesian Optimal Design for Ordinary Differential Equation Models

Bayesian optimal design is considered for experiments where it is hypothesised that the responses are described by the intractable solution to a system of non-linear ordinary differential equations (ODEs). Bayesian optimal design is based on the minimisation of an expected loss function where the expectation is with respect to all unknown quantities (responses and parameters). This expectation is typically intractable even for simple models before even considering the intractability of the ODE solution. New methodology is developed for this problem that involves minimising a smoothed stochastic approximation to the expected loss and using a state-of-the-art stochastic solution to the ODEs, by treating the ODE solution as an unknown quantity. The methodology is demonstrated on three illustrative examples and a real application involving estimating the properties of human placentas

PyFR: An Open Source Framework for Solving Advection-Diffusion Type Problems on Streaming Architectures using the Flux Reconstruction Approach

High-order numerical methods for unstructured grids combine the superior accuracy of high-order spectral or finite difference methods with the geometric flexibility of low-order finite volume or finite element schemes. The Flux Reconstruction (FR) approach unifies various high-order schemes for unstructured grids within a single framework. Additionally, the FR approach exhibits a significant degree of element locality, and is thus able to run efficiently on modern streaming architectures, such as Graphical Processing Units (GPUs). The aforementioned properties of FR mean it offers a promising route to performing affordable, and hence industrially relevant, scale-resolving simulations of hitherto intractable unsteady flows within the vicinity of real-world engineering geometries. In this paper we present PyFR, an open-source Python based framework for solving advection-diffusion type problems on streaming architectures using the FR approach. The framework is designed to solve a range of governing systems on mixed unstructured grids containing various element types. It is also designed to target a range of hardware platforms via use of an in-built domain specific language based on the Mako templating engine. The current release of PyFR is able to solve the compressible Euler and Navier-Stokes equations on grids of quadrilateral and triangular elements in two dimensions, and hexahedral elements in three dimensions, targeting clusters of CPUs, and NVIDIA GPUs. Results are presented for various benchmark flow problems, single-node performance is discussed, and scalability of the code is demonstrated on up to 104 NVIDIA M2090 GPUs. The software is freely available under a 3-Clause New Style BSD license (see www.pyfr.org)

On the joint analysis of CMB temperature and lensing-reconstruction power spectra

Gravitational lensing provides a significant source of cosmological information in modern CMB parameter analyses. It is measured in both the power spectrum and trispectrum of the temperature fluctuations. These observables are often treated as independent, although as they are both determined from the same map this is impossible. In this paper, we perform a rigorous analysis of the covariance between lensing power spectrum and trispectrum analyses. We find two dominant contributions coming from: (i) correlations between the disconnected noise bias in the trispectrum measurement and sample variance in the temperature power spectrum; and (ii) sample variance of the lenses themselves. The former is naturally removed when the dominant N0 Gaussian bias in the reconstructed deflection spectrum is dealt with via a partially data-dependent correction, as advocated elsewhere for other reasons. The remaining lens-cosmic-variance contribution is easily modeled but can safely be ignored for a Planck-like experiment, justifying treating the two observable spectra as independent. We also test simple likelihood approximations for the deflection power spectrum, finding that a Gaussian with a parameter-independent covariance performs well.Comment: 25+11 pages, 14 figure

Improving CMB non-Gaussianity estimators using tracers of local structure

Local non-Gaussianity causes correlations between large scale perturbation modes and the small scale power. The large-scale CMB signal has contributions from the integrated Sachs Wolfe (ISW) effect, which does not correlate with the small scale power. If this ISW contribution can be removed, the sensitivity to local non-Gaussianity is improved. Gravitational lensing and galaxy counts can be used to trace the ISW contribution; in particular we show that the CMB lensing potential is highly correlated with the ISW signal. We construct a nearly-optimal estimator for the local non-Gaussianity parameter \fnl and investigate to what extent we can use this to decrease the variance on {\fnl}. We show that the variance can be decreased by up to $20\%$ at Planck sensitivity using galaxy counts. CMB lensing is a good bias-independent ISW tracer for future more sensitive observations, though the fractional decrease in variance is small if good polarization data is also available.Comment: 8 pages, 3 figures. Comments welcom