Protein kinase N2 regulates AMP kinase signaling and insulin responsiveness of glucose metabolism in skeletal muscle

Insulin resistance is central to the development of type 2 diabetes and related metabolic disorders. Because skeletal muscle is responsible for the majority of whole body insulin-stimulated glucose uptake, regulation of glucose metabolism in this tissue is of particular importance. Although Rho GTPases and many of their affecters influence skeletal muscle metabolism, there is a paucity of information on the protein kinase N (PKN) family of serine/threonine protein kinases. We investigated the impact of PKN2 on insulin signaling and glucose metabolism in primary human skeletal muscle cells in vitro and mouse tibialis anterior muscle in vivo. PKN2 knockdown in vitro decreased insulin-stimulated glucose uptake, incorporation into glycogen, and oxidation. PKN2 siRNA increased 5′-adenosine monophosphate-activated protein kinase (AMPK) signaling while stimulating fatty acid oxidation and incorporation into triglycerides and decreasing protein synthesis. At the transcriptional level, PKN2 knockdown increased expression of PGC-1α and SREBP-1c and their target genes. In mature skeletal muscle, in vivo PKN2 knockdown decreased glucose uptake and increased AMPK phosphorylation. Thus, PKN2 alters key signaling pathways and transcriptional networks to regulate glucose and lipid metabolism. Identification of PKN2 as a novel regulator of insulin and AMPK signaling may provide an avenue for manipulation of skeletal muscle metabolism

Genome-wide DNA methylation changes in a mouse model of infection-mediated neurodevelopmental disorders

Background Prenatal exposure to infectious or inflammatory insults increases the risk of neurodevelopmental disorders. Using a well-established mouse model of prenatal viral-like immune activation, we examined whether this pathological association involves genome-wide DNA methylation differences at single nucleotide resolution. Methods Prenatal immune activation was induced by maternal treatment with the viral mimetic polyriboinosinic-polyribocytidylic acid in middle or late gestation. Following behavioral and cognitive characterization of the adult offspring (n = 12 per group), unbiased capture array bisulfite sequencing was combined with subsequent matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and quantitative real-time polymerase chain reaction analyses to quantify DNA methylation changes and transcriptional abnormalities in the medial prefrontal cortex of immune-challenged and control offspring. Gene ontology term enrichment analysis was used to explore shared functional pathways of genes with differential DNA methylation. Results Adult offspring of immune-challenged mothers displayed hyper- and hypomethylated CpGs at numerous loci and at distinct genomic regions, including genes relevant for gamma-aminobutyric acidergic differentiation and signaling (e.g., Dlx1, Lhx5, Lhx8), Wnt signaling (Wnt3, Wnt8a, Wnt7b), and neural development (e.g., Efnb3, Mid1, Nlgn1, Nrxn2). Altered DNA methylation was associated with transcriptional changes of the corresponding genes. The epigenetic and transcriptional effects were dependent on the offspring\u2019s age and were markedly influenced by the precise timing of prenatal immune activation. Conclusions Prenatal viral-like immune activation is capable of inducing stable DNA methylation changes in the medial prefrontal cortex. These long-term epigenetic modifications are a plausible mechanism underlying the disruption of prefrontal gene transcription and behavioral functions in subjects with prenatal infectious histories

Polynomial growth of volume of balls for zero-entropy geodesic systems

The aim of this paper is to state and prove polynomial analogues of the classical Manning inequality relating the topological entropy of a geodesic flow with the growth rate of the volume of balls in the universal covering. To this aim we use two numerical conjugacy invariants, the {\em strong polynomial entropy $h_{pol}$} and the {\em weak polynomial entropy $h_{pol}^*$}. Both are infinite when the topological entropy is positive and they satisfy $h_{pol}^*\leq h_{pol}$. We first prove that the growth rate of the volume of balls is bounded above by means of the strong polynomial entropy and we show that for the flat torus this inequality becomes an equality. We then study the explicit example of the torus of revolution for which we can give an exact asymptotic equivalent of the growth rate of volume of balls, which we relate to the weak polynomial entropy.Comment: 22 page

Exponential and moment inequalities for U-statistics

A Bernstein-type exponential inequality for (generalized) canonical U-statistics of order 2 is obtained and the Rosenthal and Hoffmann-J{\o}rgensen inequalities for sums of independent random variables are extended to (generalized) U-statistics of any order whose kernels are either nonnegative or canonicalComment: 22 page

Sparsity and Incoherence in Compressive Sampling

We consider the problem of reconstructing a sparse signal $x^0\in\R^n$ from a limited number of linear measurements. Given $m$ randomly selected samples of $U x^0$, where $U$ is an orthonormal matrix, we show that $\ell_1$ minimization recovers $x^0$ exactly when the number of measurements exceeds $$m\geq \mathrm{Const}\cdot\mu^2(U)\cdot S\cdot\log n,$$ where $S$ is the number of nonzero components in $x^0$, and $\mu$ is the largest entry in $U$ properly normalized: $\mu(U) = \sqrt{n} \cdot \max_{k,j} |U_{k,j}|$. The smaller $\mu$, the fewer samples needed. The result holds for ``most'' sparse signals $x^0$ supported on a fixed (but arbitrary) set $T$. Given $T$, if the sign of $x^0$ for each nonzero entry on $T$ and the observed values of $Ux^0$ are drawn at random, the signal is recovered with overwhelming probability. Moreover, there is a sense in which this is nearly optimal since any method succeeding with the same probability would require just about this many samples

Level set based eXtended finite element modelling of the response of fibrous networks under hygroscopic swelling

Materials like paper, consisting of a network of natural fibres, exposed to variations in moisture, undergo changes in geometrical and mechanical properties. This behaviour is particularly important for understanding the hygro-mechanical response of sheets of paper in applications like digital printing. A two-dimensional microstructural model of a fibrous network is therefore developed to upscale the hygro-expansion of individual fibres, through their interaction, to the resulting overall expansion of the network. The fibres are modelled with rectangular shapes and are assumed to be perfectly bonded where they overlap. For realistic networks the number of bonds is large and the network is geometrically so complex that discretizing it by conventional, geometry-conforming, finite elements is cumbersome. The combination of a level-set and XFEM formalism enables the use of regular, structured grids in order to model the complex microstructural geometry. In this approach, the fibres are described implicitly by a level-set function. In order to represent the fibre boundaries in the fibrous network, an XFEM discretization is used together with a Heaviside enrichment function. Numerical results demonstrate that the proposed approach successfully captures the hygro-expansive properties of the network with fewer degrees of freedom compared to classical FEM, preserving desired accuracy.Comment: 27 pages, 22 figures, 4 tables, J. Appl. Mech. June 19, 202

Assimilation of atmospheric methane products into the MACC-II system: From SCIAMACHY to TANSO and IASI

The Monitoring Atmospheric Composition and Climate Interim Implementation (MACC-II) delayed-mode (DM) system has been producing an atmospheric methane (CH4) analysis 6 months behind real time since June 2009. This analysis used to rely on the assimilation of the CH4 product from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument onboard Envisat. Recently the Laboratoire de Météorologie Dynamique (LMD) CH4 products from the Infrared Atmospheric Sounding Interferometer (IASI) and the SRON Netherlands Institute for Space Research CH4 products from the Thermal And Near-infrared Sensor for carbon Observation (TANSO) were added to the DM system. With the loss of Envisat in April 2012, the DM system now has to rely on the assimilation of methane data from TANSO and IASI. This paper documents the impact of this change in the observing system on the methane tropospheric analysis. It is based on four experiments: one free run and three analyses from respectively the assimilation of SCIAMACHY, TANSO and a combination of TANSO and IASI CH4 products in the MACC-II system. The period between December 2010 and April 2012 is studied. The SCIAMACHY experiment globally underestimates the tropospheric methane by 35 part per billion (ppb) compared to the HIAPER Pole-to-Pole Observations (HIPPO) data and by 28 ppb compared the Total Carbon Column Observing Network (TCCON) data, while the free run presents an underestimation of 5 ppb and 1 ppb against the same HIPPO and TCCON data, respectively. The assimilated TANSO product changed in October 2011 from version v.1 to version v.2.0. The analysis of version v.1 globally underestimates the tropospheric methane by 18 ppb compared to the HIPPO data and by 15 ppb compared to the TCCON data. In contrast, the analysis of version v.2.0 globally overestimates the column by 3 ppb. When the high density IASI data are added in the tropical region between 30° N and 30° S, their impact is mainly positive but more pronounced and effective when combined with version v.2.0 of the TANSO products. The resulting analysis globally underestimates the column-averaged dry-air mole fractions of methane (xCH4) just under 1 ppb on average compared to the TCCON data, whereas in the tropics it overestimates xCH4 by about 3 ppb. The random error is estimated to be less than 7 ppb when compared to TCCON data

Adaptive density estimation for stationary processes

We propose an algorithm to estimate the common density $s$ of a stationary process $X_1,...,X_n$. We suppose that the process is either $\beta$ or $\tau$-mixing. We provide a model selection procedure based on a generalization of Mallows' $C_p$ and we prove oracle inequalities for the selected estimator under a few prior assumptions on the collection of models and on the mixing coefficients. We prove that our estimator is adaptive over a class of Besov spaces, namely, we prove that it achieves the same rates of convergence as in the i.i.d framework