The HCN domain couples voltage gating andcAMP response in hyperpolarization-activatedcyclic nucleotide-gated channels

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels control spontaneous electrical activity in heart and brain. Binding of cAMP to the cyclic nucleotide-binding domain (CNBD) facilitates channel opening by relieving a tonic inhibition exerted by the CNBD. Despite high resolution structures of the HCN1 channel in the cAMP bound and unbound states, the structural mechanism coupling ligand binding to channel gating is unknown. Here we show that the recently identified helical HCN-domain (HCND) mechanically couples the CNBD and channel voltage sensing domain (VSD), possibly acting as a sliding crank that converts the planar rotational movement of the CNBD into a rotational upward displacement of the VSD. This mode of operation and its impact on channel gating are confirmed by computational and experimental data showing that disruption of critical contacts between the three domains affects cAMP- and voltage-dependent gating in three HCN isoforms

The HCN domain couples voltage gating and cAMP response in hyperpolarization-activated cyclic nucleotide-gated channels

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels control spontaneous electrical activity in heart and brain. Binding of cAMP to the cyclic nucleotide-binding domain (CNBD) facilitates channel opening by relieving a tonic inhibition exerted by the CNBD. Despite high resolution structures of the HCN1 channel in the cAMP bound and unbound states, the structural mechanism coupling ligand binding to channel gating is unknown. Here we show that the recently identified helical HCN-domain (HCND) mechanically couples the CNBD and channel voltage sensing domain (VSD), possibly acting as a sliding crank that converts the planar rotational movement of the CNBD into a rotational upward displacement of the VSD. This mode of operation and its impact on channel gating are confirmed by computational and experimental data showing that disruption of critical contacts between the three domains affects cAMP- and voltagedependent gating in three HCN isoforms

An Interlaced Extended Information Filter for Self-Localization in Sensor Networks

Wireless Sensor Networks (WSNs) are at the forefront of emerging technologies due to the recent advances in Microelectromechanical Systems (MEMSs). The inherent multidisciplinary nature of WSN attracted scientists coming from different areas stemming from networking to robotics. WSNs are considered to be unattended systems with applications ranging from environmental sensing, structural monitoring, and industrial process control to emergency response and mobile target tracking. Most of these applications require basic services such as self-localization or time synchronization. The distributed nature and the limited hardware capabilities of WSN challenge the development of effective applications. In this paper, the Self-Localization problem for Sensor Network is addressed. A distributed formulation based on the Information version of the Kalman Filter is provided. Distribution is achieved by neglecting any coupling factor in the system and assuming an independent reduced-order filter running onboard each node. The formulation is extended by an interlacement technique. It aims to alleviate the error introduced by neglecting the crosscorrelation terms by "suitably" increasing the noise covariance matrices. Real experiments involving MICAz Mote platforms produced by Crossbows along with simulations have been carried out to validate the effectiveness of the proposed Self-Localization technique

A Distributed Extended Information Filter for Self-Localization in Sensor Networks

In this paper the Self-Localization problem for Sensor Networks is addressed. Given a set of nodes deployed in an environment, self-localization consists of finding out the location of all nodes in regard to any topology or metric of interest. Nodes are assumed to be equipped with a sensor board able to provide these inter-node distances. In addition, a few nodes are assumed to be equipped with some absolute position devices. According to this scenario, a new distributed algorithm based on an Extended Information Filter is proposed. This algorithm provides an accurate estimation of node positions with a reasonable computational complexity, even when in presence of noisy measurements. Real experiments, carried out by exploiting Micaz Motes platforms, have been performed to show the effectiveness of the proposed technique

A Spatially Structured Genetic Algorithm for Multi-Robot Localization

In this paper the multi-robot localization problem is addressed. A new framework based on a spatially structured genetic algorithm is proposed. Collaboration among robots is considered and is limited to the exchange of sensor data. Additionally, the relative distance and orientation among robots are assumed to be available. The proposed framework (MR-SSGA) takes advantage of the cooperation so that the perceptual capability of each robot is extended. Cooperation can be set-up at any time when robots meet, it is fully decoupled and does not require robots to stop. Several simulations have been performed, either considering cooperation activated or not, in order to emphasize the effectiveness of the collaboration strategy

An integrated framework for simultaneous robot and sensor network localization

Localization has been recognised as one of the most significant problems in mobile robotics. A robot must have a knowledge of its position in an environment in order to correctly perform almost any task. Moreover, having accurate information about the nodes positions turns out to be substantial in a sensor network context. In fact, several middle-ware services often rely on location knowledge to properly operate. In this paper, an overall scenario in which a robot is moving within a collaborative sensor network is considered. The aim is to provide an unified framework in which the robot can localise itself gathering information coming from the network, while nodes simultaneously try to find out their location. Simulation are provided in order to validate the effectiveness of the proposed framework

Molecular determinants for agonist recognition and discrimination in P2X2 receptors

International audienc

A Hybrid Active Global Localisation Algorithm for Mobile Robots

Abstract — Localisation is one of the most important tasks to be accomplished in order to realize the complete autonomy of a mobile robot. In this paper, a new strategy for global localisation is proposed. Applying this method a robot is able to safely initialise its position or relocalise itself in case of recovery of pose tracking failure. The algorithm presented adopts an hybrid approach. First a particle filter is used to generate hypotheses on the possible pose supposing that no movements are allowed to avoid collisions. Thereafter safe trajectories are planned and executed to reduce the remaining ambiguities while the hypotheses are monitored and validated by a set of parallel Extended Kalman Filters. The novelty of this approach stands on the ability to generate the pose hypotheses without any feature- based knowledge. As a consequence, a landmark-based description of the environment is no longer required for the algorithm execution. I