The actions of Pasteurella multocida toxin on neuronal cells

Pasteurella multocida toxin (PMT) activates the G-proteins Gα, Gα, Gα Gα and Gα by deamidation of specific glutamine residues. A number of these alpha subunits have signalling roles in neurones. Hence we studied the action of this toxin on rat superior cervical ganglion (SCG) neurones and NG108-15 neuronal cells. Both Gα and Gα could be identified in SCGs with immunocytochemistry. PMT had no direct action on Kv7 or Cav2 channels in SCGs. However PMT treatment enhanced muscarinic receptor mediated inhibition of M-current (Kv7.2 + 7. 3) as measured by a 19-fold leftward shift in the oxotremorine-M concentration-inhibition curve. Agonists of other receptors, such as bradykinin or angiotensin, that inhibit M-current did not produce this effect. However the amount of PIP hydrolysis could be enhanced by PMT for all three agonists. In a transduction system in SCGs that is unlikely to be affected by PMT, Go mediated inhibition of calcium current, PMT was ineffective whereas the response was blocked by pertussis toxin as expected. M1 muscarinic receptor evoked calcium mobilisation in transformed NG108-15 cells was enhanced by PMT. The calcium rises evoked by uridine triphosphate acting on endogenous P2Y receptors in NG108-15 cells were enhanced by PMT. The time and concentration dependence of the PMT effect was different for the resting calcium compared to the calcium rise produced by activation of P2Y receptors. PMT's action on these neuronal cells would suggest that if it got into the brain, symptoms of a hyperexcitable nature would be seen, such as seizures. © 2013 The Authors. Published by Elsevier Ltd. All rights reserved

Posterior Contraction Rates for the Bayesian Approach to Linear Ill-Posed Inverse Problems

We consider a Bayesian nonparametric approach to a family of linear inverse problems in a separable Hilbert space setting with Gaussian noise. We assume Gaussian priors, which are conjugate to the model, and present a method of identifying the posterior using its precision operator. Working with the unbounded precision operator enables us to use partial differential equations (PDE) methodology to obtain rates of contraction of the posterior distribution to a Dirac measure centered on the true solution. Our methods assume a relatively weak relation between the prior covariance, noise covariance and forward operator, allowing for a wide range of applications

Data-adaptive harmonic spectra and multilayer Stuart-Landau models

Harmonic decompositions of multivariate time series are considered for which we adopt an integral operator approach with periodic semigroup kernels. Spectral decomposition theorems are derived that cover the important cases of two-time statistics drawn from a mixing invariant measure. The corresponding eigenvalues can be grouped per Fourier frequency, and are actually given, at each frequency, as the singular values of a cross-spectral matrix depending on the data. These eigenvalues obey furthermore a variational principle that allows us to define naturally a multidimensional power spectrum. The eigenmodes, as far as they are concerned, exhibit a data-adaptive character manifested in their phase which allows us in turn to define a multidimensional phase spectrum. The resulting data-adaptive harmonic (DAH) modes allow for reducing the data-driven modeling effort to elemental models stacked per frequency, only coupled at different frequencies by the same noise realization. In particular, the DAH decomposition extracts time-dependent coefficients stacked by Fourier frequency which can be efficiently modeled---provided the decay of temporal correlations is sufficiently well-resolved---within a class of multilayer stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators. Applications to the Lorenz 96 model and to a stochastic heat equation driven by a space-time white noise, are considered. In both cases, the DAH decomposition allows for an extraction of spatio-temporal modes revealing key features of the dynamics in the embedded phase space. The multilayer Stuart-Landau models (MSLMs) are shown to successfully model the typical patterns of the corresponding time-evolving fields, as well as their statistics of occurrence.Comment: 26 pages, double columns; 15 figure

Weak convergence of finite element approximations of linear stochastic evolution equations with additive noise II. Fully discrete schemes

We present an abstract framework for analyzing the weak error of fully discrete approximation schemes for linear evolution equations driven by additive Gaussian noise. First, an abstract representation formula is derived for sufficiently smooth test functions. The formula is then applied to the wave equation, where the spatial approximation is done via the standard continuous finite element method and the time discretization via an I-stable rational approximation to the exponential function. It is found that the rate of weak convergence is twice that of strong convergence. Furthermore, in contrast to the parabolic case, higher order schemes in time, such as the Crank-Nicolson scheme, are worthwhile to use if the solution is not very regular. Finally we apply the theory to parabolic equations and detail a weak error estimate for the linearized Cahn-Hilliard-Cook equation as well as comment on the stochastic heat equation

Unsteady undular bores in fully nonlinear shallow-water theory

We consider unsteady undular bores for a pair of coupled equations of Boussinesq-type which contain the familiar fully nonlinear dissipationless shallow-water dynamics and the leading-order fully nonlinear dispersive terms. This system contains one horizontal space dimension and time and can be systematically derived from the full Euler equations for irrotational flows with a free surface using a standard long-wave asymptotic expansion. In this context the system was first derived by Su and Gardner. It coincides with the one-dimensional flat-bottom reduction of the Green-Naghdi system and, additionally, has recently found a number of fluid dynamics applications other than the present context of shallow-water gravity waves. We then use the Whitham modulation theory for a one-phase periodic travelling wave to obtain an asymptotic analytical description of an undular bore in the Su-Gardner system for a full range of "depth" ratios across the bore. The positions of the leading and trailing edges of the undular bore and the amplitude of the leading solitary wave of the bore are found as functions of this "depth ratio". The formation of a partial undular bore with a rapidly-varying finite-amplitude trailing wave front is predicted for ``depth ratios'' across the bore exceeding 1.43. The analytical results from the modulation theory are shown to be in excellent agreement with full numerical solutions for the development of an undular bore in the Su-Gardner system.Comment: Revised version accepted for publication in Phys. Fluids, 51 pages, 9 figure

Construction of Modern Robust Nodal Discontinuous Galerkin Spectral Element Methods for the Compressible Navier-Stokes Equations

Discontinuous Galerkin (DG) methods have a long history in computational physics and engineering to approximate solutions of partial differential equations due to their high-order accuracy and geometric flexibility. However, DG is not perfect and there remain some issues. Concerning robustness, DG has undergone an extensive transformation over the past seven years into its modern form that provides statements on solution boundedness for linear and nonlinear problems. This chapter takes a constructive approach to introduce a modern incarnation of the DG spectral element method for the compressible Navier-Stokes equations in a three-dimensional curvilinear context. The groundwork of the numerical scheme comes from classic principles of spectral methods including polynomial approximations and Gauss-type quadratures. We identify aliasing as one underlying cause of the robustness issues for classical DG spectral methods. Removing said aliasing errors requires a particular differentiation matrix and careful discretization of the advective flux terms in the governing equations.Comment: 85 pages, 2 figures, book chapte

A Bacterial Cytotoxin Identifies the RhoA Exchange Factor Net1 as a Key Effector in the Response to DNA Damage

Background: Exposure of adherent cells to DNA damaging agents, such as the bacterial cytolethal distending toxin (CDT) or ionizing radiations (IR), activates the small GTPase RhoA, which promotes the formation of actin stress fibers and delays cell death. The signalling intermediates that regulate RhoA activation and promote cell survival are unknown. Principal Findings: We demonstrate that the nuclear RhoA-specific Guanine nucleotide Exchange Factor (GEF) Net1 becomes dephosphorylated at a critical inhibitory site in cells exposed to CDT or IR. Expression of a dominant negative Net1 or Net1 knock down by iRNA prevented RhoA activation, inhibited the formation of stress fibers, and enhanced cell death, indicating that Net1 activation is required for this RhoA-mediated responses to genotoxic stress. The Net1 and RhoAdependent signals involved activation of the Mitogen-Activated Protein Kinase p38 and its downstream target MAPKactivated protein kinase 2. Significance: Our data highlight the importance of Net1 in controlling RhoA and p38 MAPK mediated cell survival in cells exposed to DNA damaging agents and illustrate a molecular pathway whereby chronic exposure to a bacterial toxin ma

Development and Preliminary Evaluation of a Multivariate Index Assay for Ovarian Cancer

BACKGROUND: Most women with a clinical presentation consistent with ovarian cancer have benign conditions. Therefore methods to distinguish women with ovarian cancer from those with benign conditions would be beneficial. We describe the development and preliminary evaluation of a serum-based multivariate assay for ovarian cancer. This hypothesis-driven study examined whether an informative pattern could be detected in stage I disease that persists through later stages. METHODOLOGY/PRINCIPAL FINDINGS: Sera, collected under uniform protocols from multiple institutions, representing 176 cases and 187 controls from women presenting for surgery were examined using high-throughput, multiplexed immunoassays. All stages and common subtypes of epithelial ovarian cancer, and the most common benign ovarian conditions were represented. A panel of 104 antigens, 44 autoimmune and 56 infectious disease markers were assayed and informative combinations identified. Using a training set of 91 stage I data sets, representing 61 individual samples, and an equivalent number of controls, an 11-analyte profile, composed of CA-125, CA 19-9, EGF-R, C-reactive protein, myoglobin, apolipoprotein A1, apolipoprotein CIII, MIP-1alpha, IL-6, IL-18 and tenascin C was identified and appears informative for all stages and common subtypes of ovarian cancer. Using a testing set of 245 samples, approximately twice the size of the model building set, the classifier had 91.3% sensitivity and 88.5% specificity. While these preliminary results are promising, further refinement and extensive validation of the classifier in a clinical trial is necessary to determine if the test has clinical value. CONCLUSIONS/SIGNIFICANCE: We describe a blood-based assay using 11 analytes that can distinguish women with ovarian cancer from those with benign conditions. Preliminary evaluation of the classifier suggests it has the potential to offer approximately 90% sensitivity and 90% specificity. While promising, the performance needs to be assessed in a blinded clinical validation study