Non-invasive recording in untethered animals is arguably the ultimate step in the analysis of neuronal function, but such recordings remain elusive. To address this problem, we devised a system that tracks neuron-sized fluorescent targets in real time. The system can be used to create virtual environments by optogenetic activation of sensory neurons, or to image activity in identified neurons at high magnification. By recording activity in neurons of freely moving C. elegans, we tested the long-standing hypothesis that forward and reverse locomotion are generated by distinct neuronal circuits. Surprisingly, we found motor neurons that are active during both types of locomotion, suggesting a new model of locomotion control in C. elegans. These results emphasize the importance of recording neuronal activity in freely moving animals and significantly expand the potential of imaging techniques by providing a mean to stabilize fluorescent targets.

This article describes the fabrication and use of microfluidic devices for investigating spatial orientation behaviors in nematode worms (Caenorhabditis elegans). Until now, spatial orientation has been studied in freely moving nematodes in which the frequency and nature of encounters with the gradient are uncontrolled experimental variables. In the new devices, the nematode is held in place by a restraint that aligns the longitudinal axis of the body with the border between two laminar fluid streams, leaving the animal's head and tail free to move. The content of the fluid streams can be manipulated to deliver step gradients in space or time. We demonstrate the utility of the device by identifying previously uncharacterized aspects of the behavioral mechanisms underlying chemotaxis, osmotic avoidance, and thermotaxis in this organism. The new devices are readily adaptable to behavioral and imaging studies involving fluid borne stimuli in a wide range of sensory modalities.

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SUMMARYThe Common Component Architecture (CCA) is a component model for high-performance computing, developed by a grass-roots effort of computational scientists. Although the CCA is usable with CORBA-like distributed-object components, its main purpose is to set forth a component model for high-performance, parallel computing. Traditional component models are not well suited for performance and massive parallelism. We outline the design pattern for the CCA component model, discuss our strategy for language interoperability, describe the development tools we provide, and walk through an illustrative example using these tools. Performance and scalability, which are distinguishing features of CCA components, affect choices throughout design and implementation.

AbstracI-Microhenchmarks, i.e. very small computational kernels, have become commonly used for quantitative measures of node performance in clusters. For example, a commonly used benchmark measures the amount of time required to perform a fixed quantum of work. Unfortunately, this benchmark is one of many that vinlate well known rules from sampling theory, leading to erroneous, contradictory or misleading results. At a minimum, these types of henchmdrks can not he used to identify time-based activitia that may interfere with and hence limit application performance. Our original and primary goal remains to identify noise in the system due to periodic activities that are not part of user application code. In this paper, we discuss why the 'fixed quantum of work' benchmark provides data that is of limited use for analysis; and we show code for, discuss, and analyze results frnm a microbenchmark which follows good rules of sampling hygiene, and hence provides useful data for analysis.