Lymph heart in chick - somitic origin, development and embryonic oedema

The lymph heart is a sac-like structure on either side of avian tail. In some adult birds, it empties the lymph from the copulatory organ; however, during embryonic development, it is thought to circulate extra-embryonic lymph. Very little is known about the origin, innervation and the cellular changes it undergoes during development. Using immunohistochemistry and gene expression profiling we show that the musculature of the lymph heart is initially composed solely of striated skeletal muscle but later develops an additional layer composed of smooth myofibroblasts. Chick-quail fate-mapping demonstrates that the lymph heart originates from the hypaxial compartments of somites 34-41. The embryonic lymph heart is transiently innervated by somatic motoneurons with no autonomic input. In comparison to body muscles, the lymph heart has different sensitivity to neuromuscular junction blockers (sensitive only to decamethonium). Furthermore, its abundant bungarotoxin-positive acetylcholinesterase receptors are unique as they completely lack specific acetylcholinesterase activity. Several lines of evidence suggest that the lymph heart may possess an intrinsic pacing mechanism. Finally, we assessed the function of the lymph heart during embryogenesis and demonstrate that it is responsible for preventing embryonic oedema in birds, a role previously thought to be played by body skeletal muscle contractions.

Landscape of intelligent cores: An autonomic multi-agent approach for space application

Space applications are challenged by the reliability of parallel computing systems (FPGAs) employed in space crafts due to Single-Event Upsets. The work reported in this paper aims to achieve self-managing systems which are reliable for space applications by applying autonomic computing constructs to parallel computing systems. A novel technique, 'Swarm-Array Computing' inspired by swarm robotics, and built on the foundations of autonomic and parallel computing is proposed as a path to achieve autonomy. The constitution of swarm-array computing comprising for constituents, namely the computing system, the problem / task, the swarm and the landscape is considered. Three approaches that bind these constituents together are proposed. The feasibility of one among the three proposed approaches is validated on the SeSAm multi-agent simulator and landscapes representing the computing space and problem are generated using the MATLAB.

An autonomic approach in swarm-array computing

Can autonomic computing concepts be applied to traditional multi-core systems found in high performance computing environments? In this paper, we propose a novel synergy between parallel computing and swarm robotics to offer a new computing paradigm, `Swarm-Array Computing' that can harness and apply autonomic computing for parallel computing systems. One approach among three proposed approaches in swarm-array computing based on landscapes of intelligent cores, in which the cores of a parallel computing system are abstracted to swarm agents, is investigated. A task gets executed and transferred seamlessly between cores in the proposed approach thereby achieving self-ware properties that characterize autonomic computing. FPGAs are considered as an experimental platform taking into account its application in space robotics. The feasibility of the proposed approach is validated on the SeSAm multi-agent simulator.

Applying autonomic computing concepts to parallel computing using intelligent agents

The work reported in this paper is motivated by the fact that there is a need to apply autonomic computing concepts to parallel computing systems. Advancing on prior work based on intelligent cores [36], a swarm-array computing approach, this paper focuses on ‘Intelligent agents’ another swarm-array computing approach in which the task to be executed on a parallel computing core is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and is seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-ware objectives of autonomic computing. The feasibility of the proposed swarm-array computing approach is validated on a multi-agent simulator.

Autonomic arousal in childhood anxiety disorders: associations with state anxiety and social anxiety disorder

Background Psychophysiological theories suggest that individuals with anxiety disorders may evidence inflexibility in their autonomic activity at rest and when responding to stressors. In addition, theories of social anxiety disorder, in particular, highlight the importance of physical symptoms. Research on autonomic activity in childhood (social) anxiety disorders, however, is scarce and has produced inconsistent findings, possibly because of methodological limitations. Method The present study aimed to account for limitations of previous studies and measured respiratory sinus arrhythmia (RSA) and heart rate (HR) using Actiheart heart rate monitors and software (Version 4) during rest and in response to a social and a non-social stressor in 60 anxious (30 socially anxious and 30 ‘other’ anxious), and 30 nonanxious sex-and age-matched 7–12 year olds. In addition, the effect of state anxiety during the tasks was explored. Results No group differences at rest or in response to stress were found. Importantly, however, with increases in state anxiety, all children, regardless of their anxiety diagnoses showed less autonomic responding (i.e., less change in HR and RSA from baseline in response to task) and took longer to recover once the stressor had passed. Limitations This study focused primarily on parasympathetic arousal and lacked measures of sympathetic arousal. Conclusion The findings suggest that childhood anxiety disorders may not be characterized by inflexible autonomic responding, and that previous findings to the contrary may have been the result of differences in subjective anxiety between anxious and nonanxious groups during the tasks, rather than a function of chronic autonomic dysregulation.

Psychophysiological responses to appraisal dimensions in a computer game

A computer game was used to study psychophysiological reactions to emotion-relevant events. Two dimensions proposed by Scherer (1984a, 1984b) in his appraisal theory, the intrinsic pleasantness and goal conduciveness of game events, were studied in a factorial design. The relative level at which a player performed at the moment of an event was also taken into account. A total of 33 participants played the game while cardiac activity, skin conductance, skin temperature, and muscle activity as well as emotion self-reports were assessed. The self-reports indicate that game events altered levels of pride, joy, anger, and surprise. Goal conduciveness had little effect on muscle activity but was associated with significant autonomic effects, including changes to interbeat interval, pulse transit time, skin conductance, and finger temperature. The manipulation of intrinsic pleasantness had little impact on physiological responses. The results show the utility of attempting to manipulate emotion-constituent appraisals and measure their peripheral physiological signatures.

Individual differences in some (but not all) medial prefrontal regions reflect cognitive demand while regulating unpleasant emotion.

The present study investigated the premise that individual differences in autonomic physiology could be used to specify the nature and consequences of information processing taking place in medial prefrontal regions during cognitive reappraisal of unpleasant pictures. Neural (blood oxygenation level-dependent functional magnetic resonance imaging) and autonomic (electrodermal [EDA], pupil diameter, cardiac acceleration) signals were recorded simultaneously as twenty-six older people (ages 64–66 years) used reappraisal to increase, maintain, or decrease their responses to unpleasant pictures. EDA was higher when increasing and lower when decreasing compared to maintaining. This suggested modulation of emotional arousal by reappraisal. By contrast, pupil diameter and cardiac acceleration were higher when increasing and decreasing compared to maintaining. This suggested modulation of cognitive demand. Importantly, reappraisal-related activation (increase, decrease > maintain) in two medial prefrontal regions (dorsal medial frontal gyrus and dorsal cingulate gyrus) was correlated with greater cardiac acceleration (increase, decrease > maintain) and monotonic changes in EDA (increase > maintain > decrease). These data indicate that these two medial prefrontal regions are involved in the allocation of cognitive resources to regulate unpleasant emotion, and that they modulate emotional arousal in accordance with the regulatory goal. The emotional arousal effects were mediated by the right amygdala. Reappraisal-related activation in a third medial prefrontal region (subgenual anterior cingulate cortex) was not associated with similar patterns of change in any of the autonomic measures, thus highlighting regional specificity in the degree to which cognitive demand is reflected in medial prefrontal activation during reappraisal.

Economic models and algorithms for distributed systems

Distributed computing paradigms for sharing resources such as Clouds, Grids, Peer-to-Peer systems, or voluntary computing are becoming increasingly popular. While there are some success stories such as PlanetLab, OneLab, BOINC, BitTorrent, and SETI@home, a widespread use of these technologies for business applications has not yet been achieved. In a business environment, mechanisms are needed to provide incentives to potential users for participating in such networks. These mechanisms may range from simple non-monetary access rights, monetary payments to specific policies for sharing. Although a few models for a framework have been discussed (in the general area of a "Grid Economy"), none of these models has yet been realised in practice. This book attempts to fill this gap by discussing the reasons for such limited take-up and exploring incentive mechanisms for resource sharing in distributed systems. The purpose of this book is to identify research challenges in successfully using and deploying resource sharing strategies in open-source and commercial distributed systems.

Autonomous execution of tasks by swarm carrier agents in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing system? The work reported in this paper is motivated towards bridging this gap by proposing swarm-array computing, a novel technique to achieve autonomy for distributed parallel computing systems. Among three proposed approaches, the second approach, namely 'Intelligent Agents' is of focus in this paper. The task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier. agents and can be seamlessly transferred between cores in the event of a pre-dicted failure, thereby achieving self-ware objectives of autonomic computing. The feasibility of the proposed approach is validated on a multi-agent simulator.

Intelligent agents: are they feasible in swarm-array computing?

The work reported in this paper proposes 'Intelligent Agents', a Swarm-Array computing approach focused to apply autonomic computing concepts to parallel computing systems and build reliable systems for space applications. Swarm-array computing is a robotics a swarm robotics inspired novel computing approach considered as a path to achieve autonomy in parallel computing systems. In the intelligent agent approach, a task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and can be seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-* objectives of autonomic computing. The approach is validated on a multi-agent simulator.

Intelligent cores and intelligent agents: two approaches to achieve autonomy in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing systems? How can autonomic computing approaches be extended towards building reliable systems? How can existing technologies be merged to provide a solution for self-managing systems? The work reported in this paper aims to answer these questions by proposing Swarm-Array Computing, a novel technique inspired from swarm robotics and built on the foundations of autonomic and parallel computing paradigms. Two approaches based on intelligent cores and intelligent agents are proposed to achieve autonomy in parallel computing systems. The feasibility of the proposed approaches is validated on a multi-agent simulator.

Building reliable systems for space applications using swarm-array computing

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent-based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve self-managing distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Building reliable systems for space applications using swarm-array computing

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent-based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve self-managing distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.