Glycine uptake regulates hippocampal network activity via glycine receptor-mediated tonic inhibition

Functional glycine receptors (GlyRs) are enriched in the hippocampus, but their role in hippocampal function remains unclear. Since the concentration of ambient glycine is determined by the presence of powerful glycine transporter (GlyT), we blocked the r

Chronic Morphine Treatment Impaired Hippocampal Long-Term Potentiation and Spatial Memory via Accumulation of Extracellular Adenosine Acting on Adenosine A(1) Receptors

Chronic exposure to opiates impairs hippocampal long-term potentiation (LTP) and spatial memory, but the underlying mechanisms remain to be elucidated. Given the well known effects of adenosine, an important neuromodulator, on hippocampal neuronal excitability and synaptic plasticity, we investigated the potential effect of changes in adenosine concentrations on chronic morphine treatment-induced impairment of hippocampal CA1 LTP and spatial memory. We found that chronic treatment in mice with either increasing doses (20-100 mg/kg) of morphine for 7 d or equal daily dose (20 mg/kg) of morphine for 12 d led to a significant increase of hippocampal extracellular adenosine concentrations. Importantly, we found that accumulated adenosine contributed to the inhibition of the hippocampal CA1 LTP and impairment of spatial memory retrieval measured in the Morris water maze. Adenosine A(1) receptor antagonist 8-cyclopentyl-1,3-dipropylxanthine significantly reversed chronic morphine-induced impairment of hippocampal CA1 LTP and spatial memory. Likewise, adenosine deaminase, which converts adenosine into the inactive metabolite inosine, restored impaired hippocampal CA1 LTP. We further found that adenosine accumulation was attributable to the alteration of adenosine uptake but not adenosine metabolisms. Bidirectional nucleoside transporters (ENT2) appeared to play a key role in the reduction of adenosine uptake. Changes in PKC-alpha/beta activity were correlated with the attenuation of the ENT2 function in the short-term (2 h) but not in the long-term (7 d) period after the termination of morphine treatment. This study reveals a potential mechanism by which chronic exposure to morphine leads to impairment of both hippocampal LTP and spatial memory.

Thermal energy minimisation in papermaking via MD control approach

This paper reports the application of Advanced Process Control (APC) techniques for improving the thermal energy efficiency of a paperboard-making process by regulating the Machine Direction (MD) profile of the basis weight and moisture content of the paper-board. A Model Predictive Controller (MPC) is designed so that the sheet moisture and basis weight tracking errors along with variations of the sheet moisture and basis weight are reduced. Also, the drainage is maximised through improved wet-end stability which can facilitate driving the sheet moisture set-point closer to its upper specification limit over time. It is shown that the proposed strategy can result in reducing steam usage by 8-10%. A simulation study based on a UK board machine is presented to show the effectiveness of the proposed technique. © 2011 Intl Journal of Adv Mechatr.

Explosion pressure prediction via polynomial mathematical correlation based on advanced CFD Modelling

Computational Fluid Dynamics CFD can be used as a powerful tool supporting engineers throughout the steps of the design. The combination of CFD with response surface methodology can play an important role in such cases. During the conceptual engineering design phase, a quick response is always a matter of urgency. During this phase even a sketch of the geometrical model is rare. Therefore, the utilisation of typical response surface developed for congested and confined environment rather than CFD can be an important tool to help the decision making process, when the geometrical model is not available, provided that similarities can be considered when taking into account the characteristic of the geometry in which the response surface was developed. The present work investigates how three different types of response surfaces behave when predicting overpressure in accidental scenarios based on CFD input. First order, partial second order and complete second order polynomial expressions are investigated. The predicted results are compared with CFD findings for a classical offshore experiment conducted by British Gas on behalf of Mobil and good agreement is observed for higher order response surfaces. The higher order response surface calculations are also compared with CFD calculations for a typical offshore module and good agreement is also observed. © 2011 Elsevier Ltd.

Hot electron field emission via individually transistor-ballasted carbon nanotube arrays.

We present electronically controlled field emission characteristics of arrays of individually ballasted carbon nanotubes synthesized by plasma-enhanced chemical vapor deposition on silicon-on-insulator substrates. By adjusting the source-drain potential we have demonstrated the ability to controllable limit the emission current density by more than 1 order of magnitude. Dynamic control over both the turn-on electric field and field enhancement factor have been noted. A hot electron model is presented. The ballasted nanotubes are populated with hot electrons due to the highly crystalline Si channel and the high local electric field at the nanotube base. This positively shifts the Fermi level and results in a broad energy distribution about this mean, compared to the narrow spread, lower energy thermalized electron population in standard metallic emitters. The proposed vertically aligned carbon nanotube field-emitting electron source offers a viable platform for X-ray emitters and displays applications that require accurate and highly stable control over the emission characteristics.

The effects of centrally administered fluorocitrate via inhibiting glial cells on working memory in rats

Although prefrontal and hippocampal neurons are critical for spatial working memory, the function of glial cells in spatial working memory remains uncertain. In this study we investigated the function of glial cells in rats' working memory. The glial cell

On-line policy optimisation of spoken dialogue systems via live interaction with human subjects

Statistical dialogue models have required a large number of dialogues to optimise the dialogue policy, relying on the use of a simulated user. This results in a mismatch between training and live conditions, and significant development costs for the simulator thereby mitigating many of the claimed benefits of such models. Recent work on Gaussian process reinforcement learning, has shown that learning can be substantially accelerated. This paper reports on an experiment to learn a policy for a real-world task directly from human interaction using rewards provided by users. It shows that a usable policy can be learnt in just a few hundred dialogues without needing a user simulator and, using a learning strategy that reduces the risk of taking bad actions. The paper also investigates adaptation behaviour when the system continues learning for several thousand dialogues and highlights the need for robustness to noisy rewards. © 2011 IEEE.

Polarisation diversity of conformal arrays based on geometric algebra via convex optimisation

A simple and general design procedure is presented for the polarisation diversity of arbitrary conformal arrays; this procedure is based on the mathematical framework of geometric algebra and can be solved optimally using convex optimisation. Aside from being simpler and more direct than other derivations in the literature, this derivation is also entirely general in that it expresses the transformations in terms of rotors in geometric algebra which can easily be formulated for any arbitrary conformal array geometry. Convex optimisation has a number of advantages; solvers are widespread and freely available, the process generally requires a small number of iterations and a wide variety of constraints can be readily incorporated. The study outlines a two-step approach for addressing polarisation diversity in arbitrary conformal arrays: first, the authors obtain the array polarisation patterns using geometric algebra and secondly use a convex optimisation approach to find the optimal weights for the polarisation diversity problem. The versatility of this approach is illustrated via simulations of a 7×10 cylindrical conformal array. © 2012 The Institution of Engineering and Technology.

Adhesive properties of gecko-inspired mimetic via micropatterned carbon nanotube forests

The adhesive properties of the gecko foot have inspired designs of advanced micropatterned surfaces with increased contact areas. We have fabricated micropatterned pillars of vertically aligned carbon nanotube forests with a range of pillar diameters, heights, and spacings (or pitch). We used nanoindentation to measure their elastic and orthogonal adhesion properties and derive their scaling behavior. The patterning of nanotube forests into pillar arrays allows a reduction of the effective modulus from 10 to 15 MPa to 0.1-1 MPa which is useful for developing maximum conformal adhesion. © 2012 American Chemical Society.

Carbon nanotube growth for through silicon via application.

Through silicon via (TSV) technology is key for next generation three-dimensional integrated circuits, and carbon nanotubes (CNT) provide a promising alternative to metal for filling the TSV. Three catalyst preparation methods for achieving CNT growth from the bottom of the TSV are investigated. Compared with sputtering and evaporation, catalyst deposition using dip-coating in a FeCl2 solution is found to be a more efficient method for realizing a bottom-up filling of the TSV (aspect ratio 5 or 10) with CNT. The CNT bundles grown in 5 min exceed the 50 μm length of the TSV and are multi-wall CNT with three to eight walls. The CNT bundles inside the TSV were electrically characterized by creating a direct contact using a four-point nanoprober setup. A low resistance of the CNT bundle of 69.7 Ω (297 Ω) was measured when the CNT bundle was contacted midway along (over the full length of) the 25 μm deep TSV. The electrical characterization in combination with the good filling of the TSV demonstrates the potential use of CNT in fully integrated TSV applications.

Sensitivity analysis of a time-delayed thermo-acoustic system via an adjoint-based approach

We apply adjoint-based sensitivity analysis to a time-delayed thermo-acoustic system: a Rijke tube containing a hot wire. We calculate how the growth rate and frequency of small oscillations about a base state are affected either by a generic passive control element in the system (the structural sensitivity analysis) or by a generic change to its base state (the base-state sensitivity analysis). We illustrate the structural sensitivity by calculating the effect of a second hot wire with a small heat-release parameter. In a single calculation, this shows how the second hot wire changes the growth rate and frequency of the small oscillations, as a function of its position in the tube. We then examine the components of the structural sensitivity in order to determine the passive control mechanism that has the strongest influence on the growth rate. We find that a force applied to the acoustic momentum equation in the opposite direction to the instantaneous velocity is the most stabilizing feedback mechanism. We also find that its effect is maximized when it is placed at the downstream end of the tube. This feedback mechanism could be supplied, for example, by an adiabatic mesh. We illustrate the base-state sensitivity by calculating the effects of small variations in the damping factor, the heat-release time-delay coefficient, the heat-release parameter, and the hot-wire location. The successful application of sensitivity analysis to thermo-acoustics opens up new possibilities for the passive control of thermo-acoustic oscillations by providing gradient information that can be combined with constrained optimization algorithms in order to reduce linear growth rates. © Cambridge University Press 2013.

Kernel conditional quantile estimation via reduction revisited

Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among other domains. In this paper, we show how to estimate these conditional quantile functions within a Bayes risk minimization framework using a Gaussian process prior. The resulting non-parametric probabilistic model is easy to implement and allows non-crossing quantile functions to be enforced. Moreover, it can directly be used in combination with tools and extensions of standard Gaussian Processes such as principled hyperparameter estimation, sparsification, and quantile regression with input-dependent noise rates. No existing approach enjoys all of these desirable properties. Experiments on benchmark datasets show that our method is competitive with state-of-the-art approaches. © 2009 IEEE.