Diminution de la résistance aux antibiotiques des souches de Streptococcus pneumoniae isolées d’infections invasives en France entre 2005 et 2011 : résultats des Observatoires Régionaux du Pneumocoque (ORP)

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Three-dimensional Josephson-junction arrays in the quantum regime

We study the quantum phase transition properties of a three-dimensional periodic array of Josephson junctions with charging energy that includes both the self and mutual junction capacitances. We use the phase fluctuation algebra between number and phase operators, given by the Euclidean group E_2, and we effectively map the problem onto a solvable quantum generalization of the spherical model. We obtain a phase diagram as a function of temperature, Josephson coupling and charging energy. We also analyze the corresponding fluctuation conductivity and its universal scaling form in the vicinity of the zero-temperature quantum critical point.Comment: 9 pages, LATEX, three PostScript figures. Submitted to Phys. Rev. Let

Fluctuating diamagnetism in underdoped high temperature superconductors

The fluctuation induced diamagnetism of underdoped high temperature superconductors is studied in the framework of the Lawrence-Doniach model. By taking into account the fluctuations of the phase of the order parameter only, the latter reduces to a layered XY-model describing a liquid of vortices which can be either thermally excited or induced by the external magnetic field. The diamagnetic response is given by a current-current correlation function which is evaluated using the Coulomb gas analogy. Our results are then applied to recent measurements of fluctuation diamagnetism in underdoped YBCO. They allow to understand both the observed anomalous temperature dependence of the zero-field susceptibility and the two distinct regimes appearing in the magnetic field dependence of the magnetization.Comment: 12 pages, 4 figures included, accepted for publication in PR

A model study of cellular short-term memory produced by slowly inactivating potassium conductances

Abstract. We analyzed the cellular short-term memory effects induced by a slowly inactivating potassium (Ks) conductance using a biophysical model of a neuron. We first described latency-to-first-spike and temporal changes in firing frequency as a function of parameters of the model, injected current and prior history of the neuron (deinactivation level) under current clamp. This provided a complete set of properties describing the Ks conductance in a neuron. We then showed that the action of the Ks conductance is not generally appropriate for controlling latency-to-first-spike under random synaptic stimulation. However, reliable latencies were found when neuronal population computation was used. Ks inactivation was found to control the rate of convergence to steady-state discharge behavior and to allow frequency to increase at variable rates in sets of synaptically connected neurons. These results suggest that inactivation of the Ks conductance can have a reliable influence on the behavior of neuronal populations under real physiological conditions

A model study of cellular short-term memory produced by slowly inactivating potassium conductances

Abstract. We analyzed the cellular short-term memory effects induced by a slowly inactivating potassium (Ks) conductance using a biophysical model of a neuron. We first described latency-to-first-spike and temporal changes in firing frequency as a function of parameters of the model, injected current and prior history of the neuron (deinactivation level) under current clamp. This provided a complete set of properties describing the Ks conductance in a neuron. We then showed that the action of the Ks conductance is not generally appropriate for controlling latency-to-first-spike under random synaptic stimulation. However, reliable latencies were found when neuronal population computation was used. Ks inactivation was found to control the rate of convergence to steady-state discharge behavior and to allow frequency to increase at variable rates in sets of synaptically connected neurons. These results suggest that inactivation of the Ks conductance can have a reliable influence on the behavior of neuronal populations under real physiological conditions

When One Size Does Not Fit All: A Simple Statistical Method to Deal with Across-Individual Variations of Effects

In science, it is a common experience to discover that although the investigated effect is very clear in some individuals, statistical tests are not significant because the effect is null or even opposite in other individuals. Indeed, t-tests, Anovas and linear regressions compare the average effect with respect to its inter-individual variability, so that they can fail to evidence a factor that has a high effect in many individuals (with respect to the intra-individual variability). In such paradoxical situations, statistical tools are at odds with the researcher’s aim to uncover any factor that affects individual behavior, and not only those with stereotypical effects. In order to go beyond the reductive and sometimes illusory description of the average behavior, we propose a simple statistical method: applying a Kolmogorov-Smirnov test to assess whether the distribution of p-values provided by individual tests is significantly biased towards zero. Using Monte-Carlo studies, we assess the power of this two-step procedure with respect to RM Anova and multilevel mixed-effect analyses, and probe its robustness when individual data violate the assumption of normality and homoscedasticity. We find that the method is powerful and robust even with small sample sizes for which multilevel methods reach their limits. In contrast to existing methods for combining p-values, the Kolmogorov-Smirnov test has unique resistance to outlier individuals: it cannot yield significance based on a high effect in one or two exceptional individuals, which allows drawing valid population inferences. The simplicity and ease of use of our method facilitates the identification of factors that would otherwise be overlooked because they affect individual behavior in significant but variable ways, and its power and reliability with small sample sizes (<30–50 individuals) suggest it as a tool of choice in exploratory studies

Cortical Processing during Dynamic Motor Adaptation

Abstract In this chapter we investigate the role of motor cortex in adapting movements to novel dynamic environments. We present results from two experi-ments in which monkey or human subjects learned to make two-dimensional reaching movements while holding a robotic manipulandum that applied a predictable pattern of forces (a curl field) to their hand. In the first study, we analyzed electrophysiological data recorded in motor cortex while monkeys adapted or readapted to the novel forces on each day of the experiment. In the second study, we perturbed the excitability of motor cortex using repetitive transcranial magnetic stimulation (rTMS) as human participants adapted to the forces. From the first experiment, we present qualitative evidence that a network of cortical areas including the supplementary motor area, premotor cortex, and primary motor cortex (M1) not only encodes kinematic and dynamic parameters of motor execution, but also registers changes in encoding that could provide a substrate for motor memory. Based on the second experiment, we qualify the role ofM1 inmotor memory, by showing that its disruption by rTMS does not interfere with the process of initial motor adaptation, but rather with offline improvement as measured at retest on the following day

Robotic neurorehabilitation: a computational motor learning perspective

Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols