HTS and Rational Drug Design [RDD] to Generate a Class of 5-HT2C-Selective Ligands for Possible Use in Schizophrenia

(Chemical Equation Presented) Treating neurological conditions: Optimization of a previously identified lead 5-HT2C agonist (left) led to the discovery of a highly selective 5-HT2C agonist (right). Importantly, this compound is a 5-HT2B receptor antagonist. Because of its selective 5-HT2C receptor activity, the compound was further evaluated in the phencyclidine model of disrupted prepulse inhibition, and found to exhibit normalizing effects comparable to those shown by the 5-HT 2C agonist vabicaserin, a drug currently in phase II clinical studies for schizophrenia

Quality-sensitive foraging by a robot swarm through virtual pheromone trails

Large swarms of simple autonomous robots can be employed to find objects clustered at random locations, and transport them to a central depot. This solution offers system parallelisation through concurrent environment exploration and object collection by several robots, but it also introduces the challenge of robot coordination. Inspired by ants’ foraging behaviour, we successfully tackle robot swarm coordination through indirect stigmergic communication in the form of virtual pheromone trails. We design and implement a robot swarm composed of up to 100 Kilobots using the recent technology Augmented Reality for Kilobots (ARK). Using pheromone trails, our memoryless robots rediscover object sources that have been located previously. The emerging collective dynamics show a throughput inversely proportional to the source distance. We assume environments with multiple sources, each providing objects of different qualities, and we investigate how the robot swarm balances the quality-distance trade-off by using quality-sensitive pheromone trails. To our knowledge this work represents the largest robotic experiment in stigmergic foraging, and is the first complete demonstration of ARK, showcasing the set of unique functionalities it provides

Spatial Navigation Based on Novelty Mediated Autobiographical Memory

Abstract. This paper presents a method for spatial navigation performed mainly on past experiences. The past experiences are remembered in their temporal context, i.e. as episodes of events. The learned episodes form an ac-tive autobiography that determines the future navigation behaviour. The epi-sodic and autobiographical memories are modelled to resemble the memory formation process that takes place in the rat hippocampus. The method im-plies naturally inferential reasoning in the robotic framework that may make it more flexible for navigation in unseen environments. The relation between novelty and life-long exploratory (latent) learning is shown to be important and therefore is incorporated into the learning process. As a result, active au-tobiography formation depends on latent learning while individual trials might be reward driven. The experimental results show that learning mediat-ed by novelty provides a flexible and efficient way to encode spatial informa-tion in its contextual relatedness and directionality. Therefore, performing a novel task is fast but solution is not optimal. In addition, learning becomes naturally a continuous process- encoding and retrieval phase have the same underlying mechanism, and thus do not need to be separated. Therefore, building a “life long ” autobiography is feasible.

Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes

When an emergency occurs within a building, it may be initially safer to send autonomous mobile nodes, instead of human responders, to explore the area and identify hazards and victims. Exploring all the area in the minimum amount of time and reporting back interesting findings to the human personnel outside the building is an essential part of rescue operations. Our assumptions are that the area map is unknown, there is no existing network infrastructure, long-range wireless communication is unreliable and nodes are not location-aware. We take into account these limitations, and propose an architecture consisting of both mobile nodes (robots, called agents) and stationary nodes (inexpensive smart devices, called tags). As agents enter the emergency area, they sprinkle tags within the space to label the environment with states. By reading and updating the state of the local tags, agents are able to coordinate indirectly with each other, without relying on direct agent-to-agent communication. In addition, tags wirelessly exchange local information with nearby tags to further assist agents in their exploration task. Our simulation results show that the proposed algorithm, which exploits both tag-to-tag and agent-to-tag communication, outperforms previous algorithms that rely only on agent-to-tag communication

Path formation in a robot swarm: Self-organized strategies to find your way home

SCOPUS: ar.jinfo:eu-repo/semantics/publishe