Calcium-permeable acid-sensing ion channel is a molecular target of the neurotoxic metal ion lead

Acid-sensing ion channels (ASICs) are emerging as fundamental players in the regulation of neural plasticity and in pathological conditions. Here we showed that lead (Pb2+), a well known neurotoxic metal ion, reversibly and concentration-dependently inhib

Characterization of acid-sensing ion channels in dorsal horn neurons of rat spinal cord

Acid-sensing ion channels (ASICs) are ligand-gated cation channels activated by extracellular protons. In periphery, they contribute to sensory transmission, including that of nociception and pain. Here we characterized ASIC-like currents in dorsal horn neurons of the rat spinal cord and their functional modulation in pathological conditions. Reverse transcriptase-nested PCR and Western blotting showed that three ASIC isoforms, ASIC1a, ASIC2a, and ASIC2b, are expressed at a high level in dorsal horn neurons. Electrophysiological and pharmacological properties of the proton-gated currents suggest that homomeric ASIC1a and/or heteromeric ASIC1a + 2b channels are responsible for the proton-induced currents in the majority of dorsal horn neurons. Acidification-induced action potentials in these neurons were compatible in a pH-dependent manner with the pH dependence of ASIC-like current. Furthermore, peripheral complete Freund's adjuvant-induced inflammation resulted in increased expression of both ASIC1a and ASIC2a in dorsal horn. These results support the idea that the ASICs of dorsal horn neurons participate in central sensory transmission/modulation under physiological conditions and may play important roles in inflammation-related persistent pain.

Coupling between NMDA receptor and acid-sensing ion channel contributes to ischemic neuronal death

Acid-sensing ion channels (ASICs) composed of ASIC1a subunit exhibit a high Ca2+ permeability and play important roles in synaptic plasticity and acid-induced cell death. Here, we show that ischemia enhances ASIC currents through the phosphorylation at Ser478 and Ser479 of ASIC1a, leading to exacerbated ischemic cell death. The phosphorylation is catalyzed by Ca2+/calmodulin-dependent protein kinase II (CaMKII) activity, as a result of activation of NR2B-containing N-methyl-D-aspartate subtype of glutamate receptors (NMDARs) during ischemia. Furthermore, NR2B-specific antagonist, CaMKII inhibitor, or overexpression of mutated form of ASIC1a with Ser478 or Ser479 replaced by alanine (ASICla-S478A, ASIC1a-S479A) in cultured hippocampal neurons prevented ischemia-induced enhancement of ASIC currents, cytoplasmic Ca2+ elevation, as well as neuronal death. Thus, NMDAR-CaMKII cascade is functionally coupled to ASICs and contributes to acidotoxicity during ischemia. Specific blockade of NMDAR/CaMKII-ASIC coupling may reduce neuronal death after ischemia and other pathological conditions involving excessive glutamate release and acidosis.

Upregulation of acid-sensing ion channel ASIC1a in spinal dorsal horn neurons contributes to inflammatory pain hypersensitivity

Development of chronic pain involves alterations in peripheral nociceptors as well as elevated neuronal activity in multiple regions of the CNS. Previous pharmacological and behavioral studies suggest that peripheral acid-sensing ion channels (ASICs) cont

Statistical gear health analysis which is robust to fluctuating loads and operating speeds

Condition-based maintenance is concerned with the collection and interpretation of data to support maintenance decisions. The non-intrusive nature of vibration data enables the monitoring of enclosed systems such as gearboxes. It remains a significant challenge to analyze vibration data that are generated under fluctuating operating conditions. This is especially true for situations where relatively little prior knowledge regarding the specific gearbox is available. It is therefore investigated how an adaptive time series model, which is based on Bayesian model selection, may be used to remove the non-fault related components in the structural response of a gear assembly to obtain a residual signal which is robust to fluctuating operating conditions. A statistical framework is subsequently proposed which may be used to interpret the structure of the residual signal in order to facilitate an intuitive understanding of the condition of the gear system. The proposed methodology is investigated on both simulated and experimental data from a single stage gearbox. © 2011 Elsevier Ltd. All rights reserved.

Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Metal-polymer composite sensors for volatile organic compounds: Part 1. Flow-through chemi-resistors

A new type of chemi-resistor based on a novel metal-polymer composite is described. The composite contains nickel particles with sharp nano-scale surface features, which are intimately coated by the polymer matrix so that they do not come into direct physical contact. No conductive chains of filler particles are formed even at loadings above the percolation threshold and the composite is intrinsically insulating. However, when subjected to compression the composite becomes conductive, with sample resistance falling from ≥ 1012 Ω to < 0.01 Ω. The composite can be formed into insulating granules, which display similar properties to the bulk form. A bed of granules compressed between permeable frits provides a porous structure with a start resistance set by the degree of compression while the granules are free to swell when exposed to volatile organic compounds (VOCs). The granular bed presents a large surface area for the adsorption of VOCs from the gas stream flowing through it. The response of this system to a variety of vapours has been studied for two different sizes of the granular bed and for different matrix polymers. Large responses, ΔR/R0 ≥ 10^7, are observed when saturated vapours are passed through the chemi-resistor. Rapid response allows real time sensing of VOCs and the initial state is recovered in a few seconds by purging with an inert gas stream. The variation in response as a function of VOC concentration is determined.