Neuronal precursor cell-expressed developmentally down-regulated 4-1 (NEDD4-1) controls the sorting of newly synthesized Ca(V)1.2 calcium channels

Neuronal precursor cell-expressed developmentally down-regulated 4 (Nedd4) proteins are ubiquitin ligases, which attach ubiquitin moieties to their target proteins, a post-translational modification that is most commonly associated with protein degradation. Nedd4 ubiquitin ligases have been shown to down-regulate both potassium and sodium channels. In this study, we investigated whether Nedd4 ubiquitin ligases also regulate Ca(v) calcium channels. We expressed three Nedd4 family members, Nedd4-1, Nedd4-2, and WWP2, together with Ca(v)1.2 channels in tsA-201 cells. We found that Nedd4-1 dramatically decreased Ca(v) whole-cell currents, whereas Nedd4-2 and WWP2 failed to regulate the current. Surface biotinylation assays revealed that Nedd4-1 decreased the number of channels inserted at the plasma membrane. Western blots also showed a concomitant decrease in the total expression of the channels. Surprisingly, however, neither the Ca(v) pore-forming α1 subunit nor the associated Ca(v)β and Ca(v)α(2)δ subunits were ubiquitylated by Nedd4-1. The proteasome inhibitor MG132 prevented the degradation of Ca(v) channels, whereas monodansylcadaverine and chloroquine partially antagonized the Nedd4-1-induced regulation of Ca(v) currents. Remarkably, the effect of Nedd4-1 was fully prevented by brefeldin A. These data suggest that Nedd4-1 promotes the sorting of newly synthesized Ca(v) channels for degradation by both the proteasome and the lysosome. Most importantly, Nedd4-1-induced regulation required the co-expression of Ca(v)β subunits, known to antagonize the retention of the channels in the endoplasmic reticulum. Altogether, our results suggest that Nedd4-1 interferes with the chaperon role of Ca(v)β at the endoplasmic reticulum/Golgi level to prevent the delivery of Ca(v) channels at the plasma membrane.

Diffusion-weighted MR imaging including bi-exponential fitting for the detection of recurrent or residual tumour after (chemo)radiotherapy for laryngeal and hypopharyngeal cancers

To assess whether diffusion-weighted magnetic resonance imaging (DW-MRI) including bi-exponential fitting helps to detect residual/recurrent tumours after (chemo)radiotherapy of laryngeal and hypopharyngeal carcinoma.

Sorting through the garbage can: under what conditions do governments adopt policy programs?

The paper aims at explaining the adoption of policy programs. We use the garbage can model of organizational choice as our theoretical framework and complement it with the institutional setting of administrative decision-making in order to understand the complex causation of policy program adoption. Institutions distribute decision power by rules and routines and coin actor identities and their interpretations of situations. We therefore expect institutions to play a role when a policy window opens. We explore the configurative explanations for program adoption in a systematic comparison of the adoption of new alcohol policy programs in the Swiss cantons employing Qualitative Comparative Analysis. The most important conditions are the organizational elements of the administrative structure decisive for the coupling of the streams. The results imply that classic bureaucratic structures are better suited to put policies into practice than limited government.

hp-dGFEM for Second-Order Elliptic Problems in Plyhedra II: Exponential Convergence

The goal of this paper is to establish exponential convergence of $hp$-version interior penalty (IP) discontinuous Galerkin (dG) finite element methods for the numerical approximation of linear second-order elliptic boundary-value problems with homogeneous Dirichlet boundary conditions and piecewise analytic data in three-dimensional polyhedral domains. More precisely, we shall analyze the convergence of the $hp$-IP dG methods considered in [D. Schötzau, C. Schwab, T. P. Wihler, SIAM J. Numer. Anal., 51 (2013), pp. 1610--1633] based on axiparallel $\sigma$-geometric anisotropic meshes and $\bm{s}$-linear anisotropic polynomial degree distributions.

Non-exponential return time distributions for vorticity extremes explained by fractional Poisson processes

Serial correlation of extreme midlatitude cyclones observed at the storm track exits is explained by deviations from a Poisson process. To model these deviations, we apply fractional Poisson processes (FPPs) to extreme midlatitude cyclones, which are defined by the 850 hPa relative vorticity of the ERA interim reanalysis during boreal winter (DJF) and summer (JJA) seasons. Extremes are defined by a 99% quantile threshold in the grid-point time series. In general, FPPs are based on long-term memory and lead to non-exponential return time distributions. The return times are described by a Weibull distribution to approximate the Mittag–Leffler function in the FPPs. The Weibull shape parameter yields a dispersion parameter that agrees with results found for midlatitude cyclones. The memory of the FPP, which is determined by detrended fluctuation analysis, provides an independent estimate for the shape parameter. Thus, the analysis exhibits a concise framework of the deviation from Poisson statistics (by a dispersion parameter), non-exponential return times and memory (correlation) on the basis of a single parameter. The results have potential implications for the predictability of extreme cyclones.

Quasi-Self-Dual Exponential Lévy Processes

The important application of semistatic hedging in financial markets naturally leads to the notion of quasi--self-dual processes. The focus of our study is to give new characterizations of quasi--self-duality. We analyze quasi--self-dual Lévy driven markets which do not admit arbitrage opportunities and derive a set of equivalent conditions for the stochastic logarithm of quasi--self-dual martingale models. Since for nonvanishing order parameter two martingale properties have to be satisfied simultaneously, there is a nontrivial relation between the order and shift parameter representing carrying costs in financial applications. This leads to an equation containing an integral term which has to be inverted in applications. We first discuss several important properties of this equation and, for some well-known Lévy-driven models, we derive a family of closed-form inversion formulae.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.