Suivi de l'adhésion thérapeutique des patients hypertendus ou dyslipi- démiques par les cercles de qualité médecins-pharmaciens [Monitoring drug adherence for hypertensive and dyslipidemic patients in networks of community-based pharmacists and physicians].

Patient adherence is often poor for hypertension and dyslipidaemia. A monitoring of drug adherence might improve these risk factors control, but little is known in ambulatory care. We conducted a randomised controlled study in networks of community-based pharmacists and physicians in the canton of Fribourg to examine whether monitoring drug adherence with an electronic monitor (MEMS) would improve risk factor control among treated, but uncontrolled hypertensive and dyslipidemic patients. The results indicate that MEMS achieve a better blood pressure control and lipid profile, although its implementation requires considerable resources. The study also shows the value of collaboration between physicians and pharmacists in the field of patient adherence to improve ambulatory care of patients with cardiovascular risk factors.

Aversive Stimuli and Functional Networks of Fear Learning in PTSD : 734

Background: One characteristic of post traumatic stress disorder is an inability to adapt to a safe environment i.e. to change behavior when predictions of adverse outcomes are not met. Recent studies have also indicated that PTSD patients have altered pain processing, with hyperactivation of the putamen and insula to aversive stimuli (Geuze et al, 2007). The present study examined neuronal responses to aversive and predicted aversive events. Methods: Twenty-four trauma exposed non-PTSD controls and nineteen subjects with PTSD underwent fMRI imaging during a partial reinforcement fear conditioning paradigm, with a mild electric shock as the unconditioned stimuli (UCS). Three conditions were analyzed: actual presentations of the UCS, events when a UCS was expected, but omitted (CS+), and events when the UCS was neither expected nor delivered (CS-). Results: The UCS evoked significant alterations in the pain matrix consisting of the brainstem, the midbrain, the thalamus, the insula, the anterior and middle cingulate and the contralateral somatosensory cortex. PTSD subjects displayed bilaterally elevated putamen activity to the electric shock, as compared to controls. In trials when USC was expected, but omitted, significant activations were observed in the brainstem, the midbrain, the anterior insula and the anterior cingulate. PTSD subjects displayed similar activations, but also elevated activations in the amygdala and the posterior insula. Conclusions: These results indicate altered fear and safety learning in PTSD, and neuronal activations are further explored in terms of functional connectivity using psychophysiological interaction analyses.

Networks of Coordination : Swiss Business Associations as an Intermediary between Business, Politics and Administration during the 20th Century

Until the 1990's, Switzerland could be classified as either a corporatist, cooperative or coordinated market economy where non-market mechanisms of coordination among economic and political actors were very important. In this respect, Business Interest Associations (BIAs) played a key role. The aim of this paper is to look at the historical evolution of the five main peak Swiss BIAs through network analysis for five assorted dates during the 20th century (1910, 1937, 1957, 1980 and 2000) while relying on a database that includes more than 12,000 people. First, we examine the logic of membership in these associations, which allows us to analyze their position and function within the network of the Swiss economic elite. Until the 1980's, BIAs took part in the emergence and consolidation of a closely meshed national network, which declined during the two last decades of the 20th century. Second, we investigate the logic of influence of these associations by looking at the links they maintained with the political and administrative worlds through their links to the political parties and Parliament, and to the administration via the extra-parliamentary commissions (corporatist bodies). In both cases, the recent dynamic of globalization called into question the traditional role of BIAs.

Local optima networks of hard combinatorial landscapes

Combinatorial optimization involves finding an optimal solution in a finite set of options; many everyday life problems are of this kind. However, the number of options grows exponentially with the size of the problem, such that an exhaustive search for the best solution is practically infeasible beyond a certain problem size. When efficient algorithms are not available, a practical approach to obtain an approximate solution to the problem at hand, is to start with an educated guess and gradually refine it until we have a good-enough solution. Roughly speaking, this is how local search heuristics work. These stochastic algorithms navigate the problem search space by iteratively turning the current solution into new candidate solutions, guiding the search towards better solutions. The search performance, therefore, depends on structural aspects of the search space, which in turn depend on the move operator being used to modify solutions. A common way to characterize the search space of a problem is through the study of its fitness landscape, a mathematical object comprising the space of all possible solutions, their value with respect to the optimization objective, and a relationship of neighborhood defined by the move operator. The landscape metaphor is used to explain the search dynamics as a sort of potential function. The concept is indeed similar to that of potential energy surfaces in physical chemistry. Borrowing ideas from that field, we propose to extend to combinatorial landscapes the notion of the inherent network formed by energy minima in energy landscapes. In our case, energy minima are the local optima of the combinatorial problem, and we explore several definitions for the network edges. At first, we perform an exhaustive sampling of local optima basins of attraction, and define weighted transitions between basins by accounting for all the possible ways of crossing the basins frontier via one random move. Then, we reduce the computational burden by only counting the chances of escaping a given basin via random kick moves that start at the local optimum. Finally, we approximate network edges from the search trajectory of simple search heuristics, mining the frequency and inter-arrival time with which the heuristic visits local optima. Through these methodologies, we build a weighted directed graph that provides a synthetic view of the whole landscape, and that we can characterize using the tools of complex networks science. We argue that the network characterization can advance our understanding of the structural and dynamical properties of hard combinatorial landscapes. We apply our approach to prototypical problems such as the Quadratic Assignment Problem, the NK model of rugged landscapes, and the Permutation Flow-shop Scheduling Problem. We show that some network metrics can differentiate problem classes, correlate with problem non-linearity, and predict problem hardness as measured from the performances of trajectory-based local search heuristics.

Conditioning of stochastic 3-D fracture networks to hydrological and geophysical data

The geometry and connectivity of fractures exert a strong influence on the flow and transport properties of fracture networks. We present a novel approach to stochastically generate three-dimensional discrete networks of connected fractures that are conditioned to hydrological and geophysical data. A hierarchical rejection sampling algorithm is used to draw realizations from the posterior probability density function at different conditioning levels. The method is applied to a well-studied granitic formation using data acquired within two boreholes located 6 m apart. The prior models include 27 fractures with their geometry (position and orientation) bounded by information derived from single-hole ground-penetrating radar (GPR) data acquired during saline tracer tests and optical televiewer logs. Eleven cross-hole hydraulic connections between fractures in neighboring boreholes and the order in which the tracer arrives at different fractures are used for conditioning. Furthermore, the networks are conditioned to the observed relative hydraulic importance of the different hydraulic connections by numerically simulating the flow response. Among the conditioning data considered, constraints on the relative flow contributions were the most effective in determining the variability among the network realizations. Nevertheless, we find that the posterior model space is strongly determined by the imposed prior bounds. Strong prior bounds were derived from GPR measurements and helped to make the approach computationally feasible. We analyze a set of 230 posterior realizations that reproduce all data given their uncertainties assuming the same uniform transmissivity in all fractures. The posterior models provide valuable statistics on length scales and density of connected fractures, as well as their connectivity. In an additional analysis, effective transmissivity estimates of the posterior realizations indicate a strong influence of the DFN structure, in that it induces large variations of equivalent transmissivities between realizations. The transmissivity estimates agree well with previous estimates at the site based on pumping, flowmeter and temperature data.

Automobile Corporate Networks in Europe : Sectoral Specialization of Central and Eastern European Cities

The global automobile industry is made up of very large corporations and their various subsidiaries containing different functions that create complex locational structures. The networks formed by the 19 largest automobile transnational corporations constitute an automobile "oligopoly" representing more than 90% (OICA, 2012) of the world's production. Since the mid-1990s, Central and Eastern European cities have become attractive for transnational corporations and particularly for the production functions in the automobile sector. This leads to a crucial question. Are strategic functions (such as R&D) within these networks also located in Central and Eastern Europe, or is the region still manufacturing-oriented in the automobile industry? This paper focuses on the patterns and the main factors influencing the role of some of these new central and Eastern European cities that have become integrated in the global value chain of the automobile industry. By analysing the various locations of the specialized functions within the corporations, this study aims to extend the research on global value chains (Gereffi and Korzeniewicz; 1994, Sturgeon, 2000; Krätke, 2014). The spatial patterns of the various functions and the ownerships networks of the automobile industry are constructed in order to identify the cities supporting it. In particular, the way that national metropolises bring their national territories into the globalization of the automobile industry is addressed. For example, are there some specific advantages of capital cities compared to cities that have less integration in globalization terms?

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Binding under Conflict Conditions: State-Space Analysis of Multivariate EEG Synchronization.

Real-world objects are often endowed with features that violate Gestalt principles. In our experiment, we examined the neural correlates of binding under conflict conditions in terms of the binding-by-synchronization hypothesis. We presented an ambiguous stimulus ("diamond illusion") to 12 observers. The display consisted of four oblique gratings drifting within circular apertures. Its interpretation fluctuates between bound ("diamond") and unbound (component gratings) percepts. To model a situation in which Gestalt-driven analysis contradicts the perceptually explicit bound interpretation, we modified the original diamond (OD) stimulus by speeding up one grating. Using OD and modified diamond (MD) stimuli, we managed to dissociate the neural correlates of Gestalt-related (OD vs. MD) and perception-related (bound vs. unbound) factors. Their interaction was expected to reveal the neural networks synchronized specifically in the conflict situation. The synchronization topography of EEG was analyzed with the multivariate S-estimator technique. We found that good Gestalt (OD vs. MD) was associated with a higher posterior synchronization in the beta-gamma band. The effect of perception manifested itself as reciprocal modulations over the posterior and anterior regions (theta/beta-gamma bands). Specifically, higher posterior and lower anterior synchronization supported the bound percept, and the opposite was true for the unbound percept. The interaction showed that binding under challenging perceptual conditions is sustained by enhanced parietal synchronization. We argue that this distributed pattern of synchronization relates to the processes of multistage integration ranging from early grouping operations in the visual areas to maintaining representations in the frontal networks of sensory memory.

Glucose competence of the hepatoportal vein sensor requires the presence of an activated glucagon-like peptide-1 receptor.

Activation of the hepatoportal glucose sensors by portal glucose infusion leads to increased glucose clearance and induction of hypoglycemia. Here, we investigated whether glucagon-like peptide-1 (GLP-1) could modulate the activity of these sensors. Mice were therefore infused with saline (S-mice) or glucose (P-mice) through the portal vein at a rate of 25 mg/kg. min. In P-mice, glucose clearance increased to 67.5 +/- 3.7 mg/kg. min as compared with 24.1 +/- 1.5 mg/kg. min in S-mice, and glycemia decreased from 5.0 +/- 0.1 to 3.3 +/- 0.1 mmol/l at the end of the 3-h infusion period. Coinfusion of GLP-1 with glucose into the portal vein at a rate of 5 pmol/kg. min (P-GLP-1 mice) did not increase the glucose clearance rate (57.4 +/- 5.0 ml/kg. min) and hypoglycemia (3.8 +/- 0.1 mmol/l) observed in P-mice. In contrast, coinfusion of glucose and the GLP-1 receptor antagonist exendin-(9-39) into the portal vein at a rate of 0.5 pmol/kg. min (P-Ex mice) reduced glucose clearance to 36.1 +/- 2.6 ml/kg. min and transiently increased glycemia to 9.2 +/- 0.3 mmol/l at 60 min of infusion before it returned to the fasting level (5.6 +/- 0.3 mmol/l) at 3 h. When glucose and exendin-(9-39) were infused through the portal and femoral veins, respectively, glucose clearance increased to 70.0 +/- 4.6 ml/kg. min and glycemia decreased to 3.1 +/- 0.1 mmol/l, indicating that exendin-(9-39) has an effect only when infused into the portal vein. Finally, portal vein infusion of glucose in GLP-1 receptor(-/-) mice failed to increase the glucose clearance rate (26.7 +/- 2.9 ml/kg. min). Glycemia increased to 8.5 +/- 0.5 mmol/l at 60 min and remained elevated until the end of the glucose infusion (8.2 +/- 0.4 mmol/l). Together, our data show that the GLP-1 receptor is part of the hepatoportal glucose sensor and that basal fasting levels of GLP-1 sufficiently activate the receptor to confer maximum glucose competence to the sensor. These data demonstrate an important extrapancreatic effect of GLP-1 in the control of glucose homeostasis.

Brain network analysis of patients with Temporal Lobe Epilepsy

Patients with Temporal Lobe Epilepsy (TLE) suffer from widespread subtle white matter abnormalities and abnormal functional connectivity extending beyond the affected lobe, as revealed by Diffusion Tensor MR Imaging, volumetric and functional MRI studies. Diffusion Spectrum Imaging (DSI) is a diffusion imaging technique with high angular resolution for improving the mapping of white matter pathways. In this study, we used DSI, connectivity matrices and topological measures to investigate how the alteration in structural connectivity influences whole brain structural networks. Eleven patients with right-sided TLE and hippocampal sclerosis and 18 controls underwent our DSI protocol at 3T. The cortical and subcortical grey matters were parcellated into 86 regions of interest and the connectivity between every region pair was estimated using global tractography and a connectivity matrix (the adjacency matrix of the structural network). We then compared the networks of patients and controls using topological measures. In patients, we found a higher characteristic path length and a lower clustering coefficient compared to controls. Local measures at node level of the clustering and efficiency showed a significant difference after a multiple comparison correction (Bonferroni). These significant nodes were located within as well outside the temporal lobe, and the localisation of most of them was consistent with regions known to be part of epileptic networks in TLE. Our results show altered connectivity patterns that are concordant with the mapping of functional epileptic networks in patients with TLE. Further studies are needed to establish the relevance of these findings for the propagation of epileptic activity, cognitive deficits in medial TLE and outcome of epilepsy surgery in individual patients.

Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.

Acute Effects of Cannabis Smoking on Skills Related to Driving : an fMRI Study

Introduction : Driving is a complex everyday task requiring mechanisms of perception, attention, learning, memory, decision making and action control, thus indicating that involves numerous and varied brain networks. If many data have been accumulated over time about the effects of alcohol consumption on driving capability, much less is known about the role of other psychoactive substances, such as cannabis (Chang et al.2007, Ramaekers et al, 2006). Indeed, the solicited brain areas during safe driving which could be affected by cannabis exposure have not yet been clearly identified. Our aim is to study these brain regions during a tracking task related to driving skills and to evaluate the modulation due to the tolerance of cannabis effects. Methods : Eight non-smoker control subjects participated to an fMRI experiment based on a visuo-motor tracking task, alternating active tracking blocks with passive tracking viewing and rest condition. Half of the active tracking conditions included randomly presented traffic lights as distractors. Subjects were asked to track with a joystick with their right hand and to press a button with their left index at each appearance of a distractor. Four smoking subjects participated to the same fMRI sessions once before and once after smoking cannabis and a placebo in two independent cross-over experiments. We quantified the performance of the subjects by measuring the precision of the behavioural responses (i.e. percentage of time of correct tracking and reaction times to distractors). Functional MRI data were acquired using on a 3.0T Siemens Trio system equipped with a 32-channel head coil. BOLD signals will be obtained with a gradient-echo EPI sequence (TR=2s, TE=30ms, FoV=216mm, FA=90°, matrix size 72×72, 32 slices, thickness 3mm). Preprocessing, single subject analysis and group statistics were conducted on SPM8b. Results were thresholded at p<0.05 (FWE corrected) and at k>30 for spatial extent. Results : Behavioural results showed a significant impairment in task and cognitive test performance of the subjects after cannabis inhalation when comparing their tracking accuracy either to the controls subjects or to their performances before the inhalation or after the placebo inhalation (p<0.001 corrected). In controls, fMRI BOLD analysis of the active tracking condition compared to the passive one revealed networks of polymodal areas in superior frontal and parietal cortex dealing with attention and visuo-spatial coordination. In accordance to what is known of the visual and sensory motor networks we found activations in V4, frontal eye-field, right middle frontal gyrus, intra-parietal sulcus, temporo-parietal junction, premotor and sensory-motor cortex. The presence of distractors added a significant activation in the precuneus. Preliminary results on cannabis smokers in the acute phase, compared either to themselves before the cannabis inhalation or to control subjects, showed a decreased activation in large portions of the frontal and parietal attention network during the simple tracking task, but greater involvement of precuneus, of the superior part of intraparietal sulcus and middle frontal gyrus bilaterally when distractors were present in the task. Conclusions : Our preliminary results suggest that acute cannabis smoking alters performances and brain activity during active tracking tasks, partly reorganizing the recruitment of brain areas of the attention network.

The Alps Vegetation Database - a geo-referenced community-level archive of all terrestrial plants occurring in the Alps.

Mountain ranges are biodiversity hotspots worldwide and provide refuge to many organisms under contemporary climate change. Gathering field information on mountain biodiversity over time is of primary importance to understand the response of biotic communities to climate changes. For plants, several long-term observation sites and networks of mountain biodiversity are emerging worldwide to gather field data and monitor altitudinal range shifts and community composition changes under contemporary climate change. Most of these monitoring sites, however, focus on alpine ecosystems and mountain summits, such as the global observation research initiative in alpine environments (GLORIA). Here we describe the Alps Vegetation Database, a comprehensive community level archive (GIVD ID EU-00-014) which aims at compiling all available geo-referenced vegetation plots from lowland forests to alpine grasslands across the greatest mountain range in Europe: the Alps. This research initiative was funded between 2008 and 2011 by the Danish Council for Independent Research and was part of a larger project to compare cross-scale plant community structure between the Alps and the Scandes. The Alps Vegetation Database currently harbours 35,731 geo-referenced vegetation plots and 5,023 valid taxa across Mediterranean, temperate and alpine environments. The data are mainly used by the main contributors of the Alps Vegetation Database in an ecoinformatics approach to test hypotheses related to plant macroecology and biogeography, but external proposals for joint collaborations are welcome.