Sleep-wake sensitive mechanisms of adenosine release in the basal forebrain of rodents : an in vitro study

Adenosine acting in the basal forebrain is a key mediator of sleep homeostasis. Extracellular adenosine concentrations increase during wakefulness, especially during prolonged wakefulness and lead to increased sleep pressure and subsequent rebound sleep. The release of endogenous adenosine during the sleep-wake cycle has mainly been studied in vivo with microdialysis techniques. The biochemical changes that accompany sleep-wake status may be preserved in vitro. We have therefore used adenosine-sensitive biosensors in slices of the basal forebrain (BFB) to study both depolarization-evoked adenosine release and the steady state adenosine tone in rats, mice and hamsters. Adenosine release was evoked by high K+, AMPA, NMDA and mGlu receptor agonists, but not by other transmitters associated with wakefulness such as orexin, histamine or neurotensin. Evoked and basal adenosine release in the BFB in vitro exhibited three key features: the magnitude of each varied systematically with the diurnal time at which the animal was sacrificed; sleep deprivation prior to sacrifice greatly increased both evoked adenosine release and the basal tone; and the enhancement of evoked adenosine release and basal tone resulting from sleep deprivation was reversed by the inducible nitric oxide synthase (iNOS) inhibitor, 1400 W. These data indicate that characteristics of adenosine release recorded in the BFB in vitro reflect those that have been linked in vivo to the homeostatic control of sleep. Our results provide methodologically independent support for a key role for induction of iNOS as a trigger for enhanced adenosine release following sleep deprivation and suggest that this induction may constitute a biochemical memory of this state

Reconstruction of one-dimensional chaotic maps from sequences of probability density functions

In many practical situations, it is impossible to measure the individual trajectories generated by an unknown chaotic system, but we can observe the evolution of probability density functions generated by such a system. The paper proposes for the first time a matrix-based approach to solve the generalized inverse Frobenius–Perron problem, that is, to reconstruct an unknown one-dimensional chaotic transformation, based on a temporal sequence of probability density functions generated by the transformation. Numerical examples are used to demonstrate the applicability of the proposed approach and evaluate its robustness with respect to constantly applied stochastic perturbations

CD8+ T Cells Mediate the Athero-Protective Effect of Immunization with an ApoB-100 Peptide

Immunization of hypercholesterolemic mice with selected apoB-100 peptide antigens reduces atherosclerosis but the precise immune mediators of athero-protection remain unclear. In this study we show that immunization of apoE (-/-) mice with p210, a 20 amino acid apoB-100 related peptide, reduced aortic atherosclerosis compared with PBS or adjuvant/carrier controls. Immunization with p210 activated CD8+ T cells, reduced dendritic cells (DC) at the site of immunization and within the plaque with an associated reduction in plaque macrophage immunoreactivity. Adoptive transfer of CD8+ T cells from p210 immunized mice recapitulated the athero-protective effect of p210 immunization in naïve, non-immunized mice. CD8+ T cells from p210 immunized mice developed a preferentially higher cytolytic response against p210-loaded dendritic cells in vitro. Although p210 immunization profoundly modulated DCs and cellular immune responses, it did not alter the efficacy of subsequent T cell dependent or independent immune response to other irrelevant antigens. Our data define, for the first time, a role for CD8+ T cells in mediating the athero-protective effects of apoB-100 related peptide immunization in apoE (-/-) mice

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

dentification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 x 10(-8)) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.Peer reviewe

Dynamic Predictive Modeling Under Measured and Unmeasured Continuous-Time Stochastic Input Behavior

Many input variables of chemical processes have a continuous-time stochastic (CTS) behavior. The nature of these variables is a persistent, time-correlated variation that manifests as process variation as the variables deviate in time from their nominal levels. This work introduces methodologies in process identification for improving the modeling of process outputs by exploiting CTS input modeling under cases where the input is measured and unmeasured. In the measured input case, the output variable is measured offline, infrequently, and at a varying sampling rate. A method is proposed for estimating CTS parameters from the measured input by exploiting statistical properties of its CTS model. The proposed approach is evaluated based on both output accuracy and predictive ability several steps ahead of the current input measurement. Two parameter estimation techniques are proposed when the input is unmeasured. The first is a derivative-free approach that uses sample moments and analytical expressions for population moments to estimate the CTS model parameters. The second exploits the CTS input model and uses the analytical solution of the dynamic model to estimate these parameters. The predictive ability of the latter approach is evaluated in the same way as the measured input case. All of the data in this work were artificially generated under the probabilistic CTS model.Reprinted (adapted) with permission from Industrial and Engineering Chemistry Research 51 (2012): 5469, doi: 10.1021/ie201998b. Copyright 2012 American Chemical Society.</p