An attempt to extend human sensory capabilities by means of implant technology

In this paper an attempt is described to increase the range of human sensory capabilities by means of implant technology. The key aim is to create an additional sense by feeding signals directly to the human brain, via the nervous system rather than via a presently operable human sense. Neural implant technology was used to directly interface a human nervous system with a computer in a one off trial. The output from active ultrasonic sensors was then employed to directly stimulate the human nervous system. An experimental laboratory set up was used as a test bed to assess the usefulness of this sensory addition.

Nitric oxide suppresses cerebral vasomotion by sGC-independent effects on ryanodine receptors and voltage-gated calcium channels

Background/Aims: In cerebral arteries, nitric oxide (NO) release plays a key role in suppressing vasomotion. Our aim was to establish the pathways affected by NO in rat middle cerebral arteries. Methods: In isolated segments of artery, isometric tension and simultaneous measurements of either smooth muscle membrane potential or intracellular [Ca 2+ ] ([Ca 2+ ] SMC ) changes were recorded. Results: In the absence of L -NAME, asynchronous propagating Ca 2+ waves were recorded that were sensitive to block with ryanodine, but not nifedipine. L -NAME stimulated pronounced vasomotion and synchronous Ca 2+ oscillations with close temporal coupling between membrane potential, tone and [Ca 2+ ] SMC . If nifedipine was applied together with L -NAME, [Ca 2+ ] SMC decreased and synchronous Ca 2+ oscillations were lost, but asynchronous propagating Ca 2+ waves persisted. Vasomotion was similarly evoked by either iberiotoxin, or by ryanodine, and to a lesser extent by ODQ. Exogenous application of NONOate stimulated endothelium-independent hyperpolarization and relaxation of either L -NAME-induced or spontaneous arterial tone. NO-evoked hyperpolarization involved activation of BK Ca channels via ryanodine receptors (RYRs), with little involvement of sGC. Further, in whole cell mode, NO inhibited current through L-type voltage-gated Ca 2+ channels (VGCC), which was independent of both voltage and sGC. Conclusion: NO exerts sGC-independent actions at RYRs and at VGCC, both of which normally suppress cerebral artery myogenic tone.

The causal surface and its effect on distribution transparency in a distributed virtual environment

One goal in the development of distributed virtual environments (DVEs) is to create a system such that users are unaware of the distribution-the distribution should be transparent. The paper begins by discussing the general issues in DVEs that might make this possible, and a system that allows some level of distribution transparency is described. The system described suffers from effects of inconsistency, which in turn cause undesirable visual effects. The causal surface is introduced as a solution that removes these visual effects. The paper then introduces two determining factors of distribution transparency relating to user perception and performance. With regard to these factors, two hypotheses are stated relating to the causal surface. A user-trial on forty-five subjects is used to validate the hypotheses. A discussion of the results of the trial concludes that the causal surface solution does significantly improve the distribution transparency in a DVE.

Likelihood-free estimation of model evidence

Statistical methods of inference typically require the likelihood function to be computable in a reasonable amount of time. The class of “likelihood-free” methods termed Approximate Bayesian Computation (ABC) is able to eliminate this requirement, replacing the evaluation of the likelihood with simulation from it. Likelihood-free methods have gained in efficiency and popularity in the past few years, following their integration with Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) in order to better explore the parameter space. They have been applied primarily to estimating the parameters of a given model, but can also be used to compare models. Here we present novel likelihood-free approaches to model comparison, based upon the independent estimation of the evidence of each model under study. Key advantages of these approaches over previous techniques are that they allow the exploitation of MCMC or SMC algorithms for exploring the parameter space, and that they do not require a sampler able to mix between models. We validate the proposed methods using a simple exponential family problem before providing a realistic problem from human population genetics: the comparison of different demographic models based upon genetic data from the Y chromosome.