Acetaminophen, via its reactive metabolite N-acetyl-p-benzo-quinoneimine and transient receptor potential ankyrin-1 stimulation, causes neurogenic inflammation in the airways and other tissues in rodents

Acetaminophen [N-acetyl-p-aminophenol (APAP)] is the most common antipyretic/analgesic medicine worldwide. If APAP is overdosed, its metabolite, N-acetyl-p-benzo-quinoneimine (NAPQI), causes liver damage. However, epidemiological evidence has associated previous use of therapeutic APAP doses with the risk of chronic obstructive pulmonary disease (COPD) and asthma. The transient receptor potential ankyrin-1 (TRPA1) channel is expressed by peptidergic primary sensory neurons. Because NAPQI, like other TRPA1 activators, is an electrophilic molecule, we hypothesized that APAP, via NAPQI, stimulates TRPA1, thus causing airway neurogenic inflammation. NAPQI selectively excites human recombinant and native (neuroblastoma cells) TRPA1. TRPA1 activation by NAPQI releases proinflammatory neuropeptides (substance P and calcitonin gene-related peptide) from sensory nerve terminals in rodent airways, thereby causing neurogenic edema and neutrophilia. Single or repeated administration of therapeutic (15-60 mg/kg) APAP doses to mice produces detectable levels of NAPQI in the lung, and increases neutrophil numbers, myeloperoxidase activity, and cytokine and chemokine levels in the airways or skin. Inflammatory responses evoked by NAPQI and APAP are abated by TRPA1 antagonism or are absent in TRPA1-deficient mice. This novel pathway, distinguished from the tissue-damaging effect of NAPQI, may contribute to the risk of COPD and asthma associated with therapeutic APAP use.-Nassini, R., Materazzi, S., Andre, E., Sartiani, L., Aldini, G., Trevisani, M., Carnini, C., Massi, D., Pedretti, P., Carini, M., Cerbai, E., Preti, D., Villetti, G., Civelli, M., Trevisan, G., Azzari, C., Stokesberry, S., Sadofsky, L., McGarvey, L., Patacchini, R., Geppetti, P. Acetaminophen, via its reactive metabolite N-acetyl-p-benzo-quinoneimine and transient receptor potential ankyrin-1 stimulation causes neurogenic inflammation in the airways and other tissues in rodents. FASEB J. 24, 4904-4916 (2010). www.fasebj.org

An efficient neural network approach for nanoscale FinFET modelling and circuit simulation

The present paper demonstrates the suitability of artificial neural network (ANN) for modelling of a FinFET in nano-circuit simulation. The FinFET used in this work is designed using careful engineering of source-drain extension, which simultaneously improves maximum frequency of oscillation f(max) because of lower gate to drain capacitance, and intrinsic gain A(V0) = g(m)/g(ds), due to lower output conductance g(ds). The framework for the ANN-based FinFET model is a common source equivalent circuit, where the dependence of intrinsic capacitances, resistances and dc drain current I-d on drain-source V-ds and gate-source V-gs is derived by a simple two-layered neural network architecture. All extrinsic components of the FinFET model are treated as bias independent. The model was implemented in a circuit simulator and verified by its ability to generate accurate response to excitations not used during training. The model was used to design a low-noise amplifier. At low power (J(ds) similar to 10 mu A/mu m) improvement was observed in both third-order-intercept IIP3 (similar to 10 dBm) and intrinsic gain A(V0) (similar to 20 dB), compared to a comparable bulk MOSFET with similar effective channel length. This is attributed to higher ratio of first-order to third-order derivative of I-d with respect to gate voltage and lower g(ds), in FinFET compared to bulk MOSFET. Copyright (C) 2009 John Wiley & Sons, Ltd.

Image segmentation based on multi-scan constraint satisfaction neural network

A novel image segmentation method based on a constraint satisfaction neural network (CSNN) is presented. The new method uses CSNN-based relaxation but with a modified scanning scheme of the image. The pixels are visited with more distant intervals and wider neighborhoods in the first level of the algorithm. The intervals between pixels and their neighborhoods are reduced in the following stages of the algorithm. This method contributes to the formation of more regular segments rapidly and consistently. A cluster validity index to determine the number of segments is also added to complete the proposed method into a fully automatic unsupervised segmentation scheme. The results are compared quantitatively by means of a novel segmentation evaluation criterion. The results are promising.