GABA(A) receptor phospho-dependent modulation is regulated by phospholipase C-related inactive protein type 1, a novel protein phosphatase 1 anchoring protein

GABA(A) receptors are critical in controlling neuronal activity. Here, we examined the role for phospholipase C-related inactive protein type 1 (PRIP-1), which binds and inactivates protein phosphatase 1alpha (PP1alpha) in facilitating GABA(A) receptor phospho-dependent regulation using PRIP-1(-/-) mice. In wild-type animals, robust phosphorylation and functional modulation of GABA(A) receptors containing beta3 subunits by cAMP-dependent protein kinase was evident, which was diminished in PRIP-1(-/-) mice. PRIP-1(-/-) mice exhibited enhanced PP1alpha activity compared with controls. Furthermore, PRIP-1 was able to interact directly with GABA(A) receptor beta subunits, and moreover, these proteins were found to be PP1alpha substrates. Finally, phosphorylation of PRIP-1 on threonine 94 facilitated the dissociation of PP1alpha-PRIP-1 complexes, providing a local mechanism for the activation of PP1alpha. Together, these results suggest an essential role for PRIP-1 in controlling GABA(A) receptor activity via regulating subunit phosphorylation and thereby the efficacy of neuronal inhibition mediated by these receptors

Prediction and Generation of Binary Markov Processes: Can a Finite-State Fox Catch a Markov Mouse?

Understanding the generative mechanism of a natural system is a vital component of the scientific method. Here, we investigate one of the fundamental steps toward this goal by presenting the minimal generator of an arbitrary binary Markov process. This is a class of processes whose predictive model is well known. Surprisingly, the generative model requires three distinct topologies for different regions of parameter space. We show that a previously proposed generator for a particular set of binary Markov processes is, in fact, not minimal. Our results shed the first quantitative light on the relative (minimal) costs of prediction and generation. We find, for instance, that the difference between prediction and generation is maximized when the process is approximately independently, identically distributed.Comment: 12 pages, 12 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/gmc.ht

Analyzing the House Fly's Exploratory Behavior with Autoregression Methods

This paper presents a detailed characterization of the trajectory of a single housefly with free range of a square cage. The trajectory of the fly was recorded and transformed into a time series, which was fully analyzed using an autoregressive model, which describes a stationary time series by a linear regression of prior state values with the white noise. The main discovery was that the fly switched styles of motion from a low dimensional regular pattern to a higher dimensional disordered pattern. This discovered exploratory behavior is, irrespective of the presence of food, characterized by anomalous diffusion.Comment: 20 pages, 9 figures, 1 table, full pape

Don't bleach chaotic data

A common first step in time series signal analysis involves digitally filtering the data to remove linear correlations. The residual data is spectrally white (it is ``bleached''), but in principle retains the nonlinear structure of the original time series. It is well known that simple linear autocorrelation can give rise to spurious results in algorithms for estimating nonlinear invariants, such as fractal dimension and Lyapunov exponents. In theory, bleached data avoids these pitfalls. But in practice, bleaching obscures the underlying deterministic structure of a low-dimensional chaotic process. This appears to be a property of the chaos itself, since nonchaotic data are not similarly affected. The adverse effects of bleaching are demonstrated in a series of numerical experiments on known chaotic data. Some theoretical aspects are also discussed.Comment: 12 dense pages (82K) of ordinary LaTeX; uses macro psfig.tex for inclusion of figures in text; figures are uufile'd into a single file of size 306K; the final dvips'd postscript file is about 1.3mb Replaced 9/30/93 to incorporate final changes in the proofs and to make the LaTeX more portable; the paper will appear in CHAOS 4 (Dec, 1993

Fast stable direct fitting and smoothness selection for Generalized Additive Models

Existing computationally efficient methods for penalized likelihood GAM fitting employ iterative smoothness selection on working linear models (or working mixed models). Such schemes fail to converge for a non-negligible proportion of models, with failure being particularly frequent in the presence of concurvity. If smoothness selection is performed by optimizing `whole model' criteria these problems disappear, but until now attempts to do this have employed finite difference based optimization schemes which are computationally inefficient, and can suffer from false convergence. This paper develops the first computationally efficient method for direct GAM smoothness selection. It is highly stable, but by careful structuring achieves a computational efficiency that leads, in simulations, to lower mean computation times than the schemes based on working-model smoothness selection. The method also offers a reliable way of fitting generalized additive mixed models

HMM based scenario generation for an investment optimisation problem

This is the post-print version of the article. The official published version can be accessed from the link below - Copyright @ 2012 Springer-Verlag.The Geometric Brownian motion (GBM) is a standard method for modelling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, important features of the time series, like extreme behaviour or volatility clustering cannot be captured. We propose an approach by which the parameters of the GBM are able to switch between regimes, more precisely they are governed by a hidden Markov chain. Thus, we model the financial time series via a hidden Markov model (HMM) with a GBM in each state. Using this approach, we generate scenarios for a financial portfolio optimisation problem in which the portfolio CVaR is minimised. Numerical results are presented.This study was funded by NET ACE at OptiRisk Systems

Learning a Factor Model via Regularized PCA

We consider the problem of learning a linear factor model. We propose a regularized form of principal component analysis (PCA) and demonstrate through experiments with synthetic and real data the superiority of resulting estimates to those produced by pre-existing factor analysis approaches. We also establish theoretical results that explain how our algorithm corrects the biases induced by conventional approaches. An important feature of our algorithm is that its computational requirements are similar to those of PCA, which enjoys wide use in large part due to its efficiency

Wider sampling reveals a non-sister relationship for geographically contiguous lineages of a marine mussel

The accuracy of phylogenetic inference can be significantly improved by the addition of more taxa and by increasing the spatial coverage of sampling. In previous studies, the brown mussel Perna perna showed a sister-lineage relationship between eastern and western individuals contiguously distributed along the South African coastline. We used mitochondrial (COI) and nuclear (ITS) sequence data to further analyze phylogeographic patterns within P.perna. Significant expansion of the geographical coverage revealed an unexpected pattern. The western South African lineage shared the most recent common ancestor (MRCA) with specimens from Angola, Venezuela, and Namibia, whereas eastern South African specimens and Mozambique grouped together, indicating a non-sister relationship for the two South African lineages. Two plausible biogeographic scenarios to explain their origin were both supported by the hypotheses-testing analysis. One includes an Indo-Pacific origin for P.perna, dispersal into the Mediterranean and Atlantic through the Tethys seaway, followed by recent secondary contact after southward expansion of the western and eastern South African lineages. The other scenario (Out of South Africa) suggests an ancient vicariant divergence of the two lineages followed by their northward expansion. Nevertheless, the Out of South Africa hypothesis would require a more ancient divergence between the two lineages. Instead, our estimates indicated that they diverged very recently (310 kyr), providing a better support for an Indo-Pacific origin of the two South African lineages. The arrival of the MRCA of P.perna in Brazil was estimated at 10 [0-40] kyr. Thus, the hypothesis of a recent introduction in Brazil through hull fouling in wooden vessels involved in the transatlantic itineraries of the slave trade did not receive strong support, but given the range for this estimate, it could not be discarded. Wider geographic sampling of marine organisms shows that lineages with contiguous distributions need not share a common ancestry.Portuguese National Science Foundation (FCT) [EXPL/BIA-BIC/1471/2012]; South Africa Research Chairs Initiative (SARChI) of the Department of Science and Technologyinfo:eu-repo/semantics/publishedVersio