Non-neuronal, slow GABA signalling in the ventrobasal thalamus targets δ-subunit-containing GABA(A) receptors

The rodent ventrobasal (VB) thalamus contains a relatively uniform population of thalamocortical (TC) neurons that receive glutamatergic input from the vibrissae and the somatosensory cortex, and inhibitory input from the nucleus reticularis thalami (nRT). In this study we describe ?-aminobutyric acid (GABA)(A) receptor-dependent slow outward currents (SOCs) in TC neurons that are distinct from fast inhibitory postsynaptic currents (IPSCs) and tonic currents. SOCs occurred spontaneously or could be evoked by hypo-osmotic stimulus, and were not blocked by tetrodotoxin, removal of extracellular Ca(2+) or bafilomycin A1, indicating a non-synaptic, non-vesicular GABA origin. SOCs were more common in TC neurons of the VB compared with the dorsal lateral geniculate nucleus, and were rarely observed in nRT neurons, whilst SOC frequency in the VB increased with age. Application of THIP, a selective agonist at d-subunit-containing GABA(A) receptors, occluded SOCs, whereas the benzodiazepine site inverse agonist ß-CCB had no effect, but did inhibit spontaneous and evoked IPSCs. In addition, the occurrence of SOCs was reduced in mice lacking the d-subunit, and their kinetics were also altered. The anti-epileptic drug vigabatrin increased SOC frequency in a time-dependent manner, but this effect was not due to reversal of GABA transporters. Together, these data indicate that SOCs in TC neurons arise from astrocytic GABA release, and are mediated by d-subunit-containing GABA(A) receptors. Furthermore, these findings suggest that the therapeutic action of vigabatrin may occur through the augmentation of this astrocyte-neuron interaction, and highlight the importance of glial cells in CNS (patho) physiology.

RAMPs as drug targets

The receptor activity-modifying protein (RAMP) family of membrane proteins regulates G protein-coupled receptor (GPCR) function in several ways. RAMPs can alter their pharmacology and signalling as well as the trafficking of these receptors to and from the cell surface. Accordingly, RAMPs may be exploited as drug targets, offering new opportunities for regulating the function of therapeutically relevant RAMP-interacting GPCRs. For example, several small molecule antagonists of RAMP1/ calcitonin receptor-like receptor complexes, which block the actions of the neuropeptide calcitonin gene-related peptide are in development for the treatment of migraine headache.

Oxidized phospholipid inhibition of toll-like receptor (TLR) signaling is restricted to TLR2 and TLR4 roles for cd14, lps-binding protein, and md2 as targets for specificity of inhibition

The generation of reactive oxygen species is a central feature of inflammation that results in the oxidation of host phospholipids. Oxidized phospholipids, such as 1-palmitoyl-2-arachidonyl-sn-glycero-3-phosphorylcholine (OxPAPC), have been shown to inhibit signaling induced by bacterial lipopeptide or lipopolysac-charide (LPS), yet the mechanisms responsible for the inhibition of Toll-like receptor (TLR) signaling by OxPAPC remain incompletely understood. Here, we examined the mechanisms by which OxPAPC inhibits TLR signaling induced by diverse ligands in macrophages, smooth muscle cells, and epithelial cells. OxPAPC inhibited tumor necrosis factor- production, IB degradation, p38 MAPK phosphorylation, and NF-B-dependent reporter activation induced by stimulants of TLR2 and TLR4 (Pam3CSK4 and LPS) but not by stimulants of other TLRs (poly(I·C), flagellin, loxoribine, single-stranded RNA, or CpG DNA) in macrophages and HEK-293 cells transfected with respective TLRs and significantly reduced inflammatory responses in mice injected subcutaneously or intraperitoneally with Pam3CSK4. Serum proteins, including CD14 and LPS-binding protein, were identified as key targets for the specificity of TLR inhibition as supplementation with excess serum or recombinant CD14 or LBP reversed TLR2 inhibition by OxPAPC, whereas serum accessory proteins or expression of membrane CD14 potentiated signaling via TLR2 and TLR4 but not other TLRs. Binding experiments and functional assays identified MD2 as a novel additional target of OxPAPC inhibition of LPS signaling. Synthetic phospholipid oxidation products 1-palmitoyl-2-(5-oxovaleryl)-sn-glycero-3-phosphocholine and 1-palmitoyl-2-glutaryl-sn-glycero-3-phosphocholine inhibited TLR2 signaling from 30 µM. Taken together, these results suggest that oxidized phospholipid-mediated inhibition of TLR signaling occurs mainly by competitive interaction with accessory proteins that interact directly with bacterial lipids to promote signaling via TLR2 or TLR4.

From 'perfect mix' to 'potion magique':regulatory T cells and anti-inflammatory cytokines as adjuvant targets

The efficacy of vaccines can be greatly improved by the addition of adjuvants, which enhance and modify immune responses. Historically, adjuvants have been discovered empirically by using experimental models.

Three-dimensional calibration targets for optical coherence tomography

The recent expansion of clinical applications for optical coherence tomography (OCT) is driving the development of approaches for consistent image acquisition. There is a simultaneous need for time-stable, easy-to-use imaging targets for calibration and standardization of OCT devices. We present calibration targets consisting of three-dimensional structures etched into nanoparticle-embedded resin. Spherical iron oxide nanoparticles with a predominant particle diameter of 400 nm were homogeneously dispersed in a two part polyurethane resin and allowed to harden overnight. These samples were then etched using a precision micromachining femtosecond laser with a center wavelength of 1026 nm, 100kHz repetition rate and 450 fs pulse duration. A series of lines in depth were etched, varying the percentage of inscription energy and speed of the translation stage moving the target with respect to the laser. Samples were imaged with a dual wavelength spectral-domain OCT system and point-spread function of nanoparticles within the target was measured.

Individualized glycaemic targets and pharmacotherapy in type 2 diabetes

The Global Partnership for Effective Diabetes Management, established to provide practical guidance to improve patient outcomes in diabetes, has developed and modified recommendations to improve glycaemic control in type 2 diabetes. The Global Partnership advocates an individualized therapeutic approach and, as part of the process to customize therapy, has previously identified specific type 2 diabetes patient subgroups that require special consideration. This article builds on earlier publications, expanding the scope of practical guidance to include newly diagnosed individuals with complications and women with diabetes in pregnancy. Good glycaemic control remains the cornerstone of managing type 2 diabetes, and plays a vital role in preventing or delaying the onset and progression of diabetic complications. Individualizing therapeutic goals and treatments to meet glycaemic targets safely and without delay remains paramount, in addition to a wider programme of care to reduce cardiovascular risk factors and improve patient outcomes. © The Author(s) 2013.

Three-dimensional calibration targets for optical coherence tomography

The recent expansion of clinical applications for optical coherence tomography (OCT) is driving the development of approaches for consistent image acquisition. There is a simultaneous need for time-stable, easy-to-use imaging targets for calibration and standardization of OCT devices. We present calibration targets consisting of three-dimensional structures etched into nanoparticle-embedded resin. Spherical iron oxide nanoparticles with a predominant particle diameter of 400 nm were homogeneously dispersed in a two part polyurethane resin and allowed to harden overnight. These samples were then etched using a precision micromachining femtosecond laser with a center wavelength of 1026 nm, 100kHz repetition rate and 450 fs pulse duration. A series of lines in depth were etched, varying the percentage of inscription energy and speed of the translation stage moving the target with respect to the laser. Samples were imaged with a dual wavelength spectral-domain OCT system and point-spread function of nanoparticles within the target was measured.

The design of high-power accelerator targets

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Evolution, recurrency and kernels in learning to model inflation

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. We use non-linear, artificial intelligence techniques, namely, recurrent neural networks, evolution strategies and kernel methods in our forecasting experiment. In the experiment, these three methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. There is evidence in the literature that evolutionary methods can be used to evolve kernels hence our future work should combine the evolutionary and kernel methods to get the benefits of both.

Meeting 2050 CO2 emissions reduction targets:The potential for electric vehicles

This chapter explores the potential for electric vehicles to contribute to decarbonising surface transport. Decarbonising transport is a major global challenge-meeting CO2 emissions reduction targets for 2050, with a rapidly growing, and urbanising global population.

Inflation convergence in Europe

Convergence has been a popular theme in applied economics since the seminal papers of Barro (1991) and Barro and Sala-i-Martin (1992). The very notion of convergence quickly becomes problematic from an academic viewpoint however when we try and formalise a framework to think about these issues. In the light of the abundance of available convergence concepts, it would be useful to have a more universal framework that encompassed existing concepts as special cases. Moreover, much of the convergence literature has treated the issue as a zero-one outcome. We argue that it is more sensible and useful for policy decision makers and academic researchers to consider also ongoing convergence over time. Assessing the progress of ongoing convergence is one interesting and important means of evaluating whether the Eastern European New Member Countries (NMC) of the European Union (EU) are getting closer to being deemed “ready” to join the European Monetary Union (EMU), that is, fulfilling the Maastricht convergence criteria.

Inflation forecasting, relative price variability and skewness

This article presents out-of-sample inflation forecasting results based on relative price variability and skewness. It is demonstrated that forecasts on long horizons of 1.5-2 years are significantly improved if the forecast equation is augmented with skewness. © 2010 Taylor & Francis.

Does money matter in inflation forecasting?

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regressiontechniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a nave random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists' long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies. © 2010 Elsevier B.V. All rights reserved.

Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States

This paper demonstrates a mechanism whereby rules can be extracted from a feedforward neural network trained to characterize the inflation "pass-through" problem in American monetary policy, defined as the relationship between changes in the growth rate(s) of individual commodities and the economy-wide rate of growth of consumer prices. Monthly price data are encoded and used to train a group of candidate connectionist architectures. One candidate is selected for rule extraction, using a custom decompositional extraction algorithm that generates rules in human-readable and machine-executable form. Rule and network accuracy are compared, and comments are made on the relationships expressed within the discovered rules. The types of discovered relationships could be used to guide monetary policy decisions.