Michel Roux

Why glycine transporters have different stoichiometries.

In the brain, neurons and glial cells compete for the uptake of the fast neurotransmitters, glutamate, GABA and glycine, through specific transporters. The relative contributions of glia and neurons to the neurotransmitter uptake depend on the kinetic properties, thermodynamic coupling and density of transporters but also on the intracellular metabolization or sequestration of the neurotransmitter. In the case of glycine, which is both an inhibitory transmitter and a neuromodulator of the excitatory glutamatergic transmission as a co‐agonist of N‐methyl D‐aspartate receptors, the glial (GlyT1b) and neuronal (GlyT2a) transporters differ at least in three aspects: (i) stoichiometries, (ii) reverse uptake capabilities and (iii) pre‐steady‐state kinetics. A 3 Na+/1 Cl−/gly stoichiometry was established for GlyT2a on the basis of a 2 charges/glycine flux ratio and changes in the reversal potential of the transporter current as a function of the extracellular glycine, Na+ and Cl− concentrations. Therefore, the driving force available for glycine uphill transport in neurons is about two orders of magnitude larger than for glial cells. In addition, GlyT2a shows a severe limitation for reverse uptake, which suggests an essential role of GlyT2a in maintaining a high intracellular glycine pool, thus facilitating the refilling of synaptic vesicles by the low affinity, low specificity vesicular transporter VGAT/VIAAT. In contrast, the 2 Na+/1 Cl−/gly stoichiometry and bi‐directional transport properties of GlyT1b are appropriate for the control of the extracellular glycine concentration in a submicromolar range that can modulate N‐methyl D‐aspartate receptors effectively. Finally, analysis of the pre‐steady‐state kinetics of GlyT1b and GlyT2a revealed that at the resting potential neuronal transporters are preferentially oriented outward, ready to bind glycine, which suggests a kinetic advantage in the uptake contest.

Three-Dimensional Description of Dense Urban Areas using Maps and Aerial Images

In this paper we present a method to generate three-dimensional building hypotheses in dense urban areas using simultaneously scanned maps and aerial images. This technique relies on various informations extracted from the scanned map on different features like the road network, the urban blocks and the buildings. These features are used to guide the analysis of a disparity image calculated with a stereo pair of aerial images. Two different methods are proposed for the detection of the buildings. They basically differ from each other by the reliability they give to the different features extracted from the map. The effectiveness of this approach is demonstrated using complex imagery over large and dense urban areas presenting a large variety of landscapes.

31P NMR as a tool for monitoring detergent solubilization of sarcoplasmic reticulum membranes

Sarcoplasmic reticulum vesicles were solubilized stepwise by the nonionic detergent octaethyleneglycol monododecyl ether; 31P NMR enabled the extent of phospholipid solubilization to be monitored by following the conversion of the broad resonance peak characterizing the phospholipids inserted in the bilayer to the narrow resonance signal characterizing phospolipids inserted into a mixed micelle. Up to 0.25 g detergent/g protein could be incorporated into the membrane without solubilization. Higher detergent concentrations of up to 1.5–2 g detergent/g protein led to gradual solubilization. Although the method allows us to monitor the extent of solubilization of individual phospholipid classes, there was no evidence of either preferential solubilization or retention of a specific class of phospholipids.

Building detection from high-resolution color images

We describe a new method for the detection and reconstruction of building in dense urban areas using high resolution aerial images. Our approach begins with the generation of a dense digital elevation model (DEM). A sparse disparity map is densified using a region-based segmentation of the left aerial image: each detected region is tested to be planar in the disparity map. A strategy is proposed to optimize the generation of these planar surfaces taking into account the noise present in the sparse disparity map and the robustness and complexity of different algorithms for planar approximation. The second step of our approach deals with the generation of building hypotheses. Based on the DEM previously computed, geometric and colorimetric criteria are used for the fusion of parallel regions, for the detection of symmetrical regions in the 3D object space and for the reconstruction of roof buildings. Experimental results are presented on a scene in the suburb of Bruxelles with color images at the resolution of 10 cm/pixel.

Cooperation for analysis of maps and aerial images for urban scene description

In this paper we present a method to generate 3D cartographic databases in dense urban areas using simultaneously scanned maps and aerial images. The generation of digital terrain models (DTM) and the 3D description of buildings using aerial imagery are both supported by the previous analysis of scanned maps. Our approach relies on various information extracted form the maps on different features like the road network, the urban blocks and the buildings. These features are used to guide the analysis of a disparity image calculated with a stereo pair of aerial images. For the generation of a DTM, the road network allows to focus the algorithms on regions where the information on the ground elevation is available: the crossroads and the road sections. For the detection and the description of buildings, each urban block provided by the road network of the map is analyzed separately, taking into account the different features detected in the map. The effectiveness of this approach is demonstrated using complex imagery over large and dense urban areas presenting a large variety of landscapes.

Markov random fields for digital terrain model extraction

This paper deals with the automatic extraction of the ground elevation or digital terrain model (DTM) from the global digital elevation model (DEM). The proposed method is divided into two main steps: first, the road network and the cross-roads are extracted, and the elevation of the cross-roads is estimated on the DEM; secondly, the height of the cross-roads is regularized using some contextual knowledge. The second step is performed inside a Markovian framework based on the natural graph of the roads. Results on interferometric SAR data and optical images are presented.

Map Analysis for Guided Interpretation of Aerial Images

We present a set of procedures to extract useful information from maps of cities in order to help in interpreting aerial images. The aim of this work is to combine semantic information issued from the map with the raw information coming from a picture. Several cues can be extracted from maps. The road network gives the structure of the city and cut the scene in small but coherent regions: the urban blocks. The map gives also a geometric and semantic description of the contents of each urban block. This information can then be used for the interpretation of aerial images.Two applications are briefly presented: the generation of a digital terrain model (DTM) in urban areas and the 3D-description of buildings1.

Computing statistics from man-made structures on the Earth's surface for indexing satellite images.

Structural elements issued from line detectors may provide very efficient cues for image indexing in satellite image data mining. These elements reflect the topology, the density and the geometry of the road network which is of prime importance to express the geomorphology and the human-impact on a landscape.

DSM extraction from SPOT5 stereo-pairs

The generation of a digital surface model (DSM) from remotely sensed images is an important task for various applications such as telecommunications, cell planning, the environment monitoring.... In general, extracting DSMs consists of: camera modelling, stereo matching and interpolation. However, in the case of satellite imagery and because of the satellite characteristics, it is difficult to apply the algorithms and the model generally used with aerial images. In this paper, we adopted a model which uses all parameters of acquisition: it is the full model. The matching algorithm used takes into account geometry of the image. It is base on a hierarchical approach followed by a relaxation process. The full model of SPOT5 sensors is then used for the calculation of the 3D-co-ordinates on the ground. Results show good quality and accuracy of the obtained DSM compared to ground control points and DSM data of superior quality.

Road extraction from SAR images by using a graphical sketch of road

The subject of this communication is the detection of roads on SAR images starting from a user-drawn graphical sketch. This sketch is considered as a road model, and the main contribution of this communication is to incorporate this model in a yet-existing blind road detection method to improve the quality and reliability of the detection. The method combines both local and global criteria for raod extraction. It consists of mainly two parts. First local information is extracted by applying a low-level operator, based on a line detector taking to account the statistical properties of speckle in SAR images. In the second step, road detection is performed by selecting the best road in a graph composed of all the detected segments and all possible connections between them. To demonstrate the contribution of the use of a model in the road detection, we first introduce an exact model of hte road (extracted for instance from a road map), in order to validate the method. Then the method is applied with an un-precise model (both in form and position). The obtained results from different SAR images are presented and evaluated with objective criteria.