Chronic Treatment with a Promnesiant GABA-A α5-Selective Inverse Agonist Increases Immediate Early Genes Expression during Memory Processing in Mice and Rectifies Their Expression Levels in a Down Syndrome Mouse Model

Decrease of GABAergic transmission has been proposed to improve memory functions. Indeed, inverse agonists selective for α5 GABA-A-benzodiazepine receptors (α5IA) have promnesiant activity. Interestingly, we have recently shown that α5IA can rescue cognitive deficits in Ts65Dn mice, a Down syndrome mouse model with altered GABAergic transmission. Here, we studied the impact of chronic treatment with α5IA on gene expression in the hippocampus of Ts65Dn and control euploid mice after being trained in the Morris water maze task. In euploid mice, chronic treatment with α5IA increased IEGs expression, particularly of c-Fos and Arc genes. In Ts65Dn mice, deficits of IEGs activation were completely rescued after treatment with α5IA. In addition, normalization of Sod1 overexpression in Ts65Dn mice after α5IA treatment was observed. IEG expression regulation after α5IA treatment following behavioral stimulation could be a contributing factor for both the general promnesiant activity of α5IA and its rescuing effect in Ts65Dn mice alongside signaling cascades that are critical for memory consolidation and cognition

ASILUM: A platform to evaluate advanced combinations of smart antennas and multi-user detection for UMTS FDD and TDD

The purpose of this paper is to present and explain some results of ASILUM, a project included into IST (Information Society Technologies), a research programme of European Community. This project aims to contribute to the technical innovation and policies of the European Community by validating new transceivers concepts to increase the capacity of UMTS (FDD and TDD modes) through new and efficient interference mitigation schemes. These schemes are jointly using smart antennas and multiuser detection. They have been validated through link and system level simulations

SPH MODELING OF MEAN VELOCITY CIRCULATION IN A RIP CURRENT SYSTEM

A Lagrangian numerical model called Smoothed Particle Hydrodynamics is used to analyze rip current system generated by a single bar and a rip channel. The pattern of the wave-induced circulation cell over the bar, the oppositely-rotating circulation cell on-shore and a strong seaward-directed current in the rip channel is modeled numerically. The mean horizontal variations of rip current system as well as three-dimensional circulations are studied. The results in three-dimensional space reveal the wave-current interaction and flow patterns in different parts of rip channel, bar, and the trough located near shore. For comparison to experimental data, Eulerian nodes are introduced to the numerical model and SPH interpolation over neighboring Lagrangian particles is implemented to find fluid parameters at those specific nodes. This methodology leads to a better understanding of depth-integrated flows and a more accurate comparison of numerical results with experimental results. Model predictions are compared to laboratory measurements of Drønen et al. (2002) and show good agreement, including mean velocity profiles, mean surface elevation and three-dimensional velocity components

Specific targeting of the GABA-A receptor α5 subtype by a selective inverse agonist restores cognitive deficits in Down syndrome mice

An imbalance between inhibitory and excitatory neurotransmission has been proposed to contribute to altered brain function in individuals with Down syndrome (DS). Gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the central nervous system and accordingly treatment with GABA-A antagonists can efficiently restore cognitive functions of Ts65Dn mice, a genetic model for DS. However, GABA-A antagonists are also convulsant which preclude their use for therapeutic intervention in DS individuals. Here, we have evaluated safer strategies to release GABAergic inhibition using a GABA-A-benzodiazepine receptor inverse agonist selective for the α5-subtype (α5IA). We demonstrate that α5IA restores learning and memory functions of Ts65Dn mice in the novel-object recognition and in the Morris water maze tasks. Furthermore, we show that following behavioural stimulation, α5IA enhances learning-evoked immediate early gene products in specific brain regions involved in cognition. Importantly, acute and chronic treatments with α5IA do not induce any convulsant or anxiogenic effects that are associated with GABA-A antagonists or non-selective inverse agonists of the GABA-A-benzodiazepine receptors. Finally, chronic treatment with α5IA did not induce histological alterations in the brain, liver and kidney of mice. Our results suggest that non-convulsant α5-selective GABA-A inverse agonists could improve learning and memory deficits in DS individuals

Efficient Parallel Statistical Model Checking of Biochemical Networks

We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture

On the sequential massart algorithm for statistical model checking

Several schemes have been provided in Statistical Model Checking (SMC) for the estimation of property occurrence based on predefined confidence and absolute or relative error. Simulations might be however costly if many samples are required and the usual algorithms implemented in statistical model checkers tend to be conservative. Bayesian and rare event techniques can be used to reduce the sample size but they can not be applied without prerequisite or knowledge about the system under scrutiny. Recently, sequential algorithms based on Monte Carlo estimations and Massart bounds have been proposed to reduce the sample size while providing guarantees on error bounds which has been shown to outperform alternative frequentist approaches [15]. In this work, we discuss some features regarding the distribution and the optimisation of these algorithms.No Full Tex

A whole-plant functional scheme predicting the early growth of tropical tree species: evidence from 15 tree species in Central Africa

Key message: This study highlighted the consistency of a functional scheme integrating leaf, stem and root traits, biomass allocation and stem anatomy for 15 tropical tree species at the seedling stage. This functional scheme was shaped by the trade-offs for resource use and the hydraulics of the plants and was found to determine seedling growth. Abstract: Functional traits determine plant functioning, performance and response to the environment and define species functional strategy. The functional strategy of 15 African tree species was assessed by (1) highlighting the structure of traits covariance and the underlying functional trade-offs, (2) inferring a whole-plant functional scheme and (3) testing the correlation of the functional scheme with plant performance for two early developmental stages (seedlings and saplings). We selected 10 seedlings for each of the 15 species studied from a nursery in south-eastern Cameroon and measured 18 functional traits, including leaf, stem and root traits, biomass allocation and stem anatomy. We assessed the height and diameter growth of the seedlings and the DBH growth and survival for the saplings of nearby plantations. Multivariate analyses highlighted the covariations among the functional traits of the leaf/stem/root, biomass allocation ratios and stem anatomy. The major trait covariation axes were driven by two trade-offs, first between resource acquisition and conservation and second between hydraulic safety and efficiency. The axes were integrated into a Bayesian network inferring a functional scheme at the whole-plant scale, which was found to predict the growth of the seedlings but not the performance of the saplings. The functional strategies of the seedlings were determined by an integrated whole-plant scheme reflecting the trade-offs for resource use and plant hydraulics. The scheme predicted the growth of the seedlings through mechanistic pathways from the wood stem to all the plant traits, but it appeared to shift at the stage of the saplings. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature