Cofilin immunolabelling correlates with depth of invasion in gastrointestinal endocrine cell tumors

Gastrointestinal endocrine cell tumors are a heterogeneous population of lesions believed to arise from neuroendocrine cells of the gastrointestinal mucosa. The current classification of these tumors is based on tumor size, microscopic features and clinical evidence of metastasis. Although diagnostic categories generally correlate with prognosis, molecular prognostic markers will be clinically useful adjuncts. Cofilin has been implicated in tumor invasion, and its immunolocalisation was studied in gastrointestinal endocrine cell tumors. The immunolocalisation of cofilin was studied by immunohistochemistry in 34 formalin-fixed, paraffin wax-embedded gastrointestinal endocrine cell tumors using a tissue microarray platform. A significant correlation was found between high cofilin immunolabelling and the depth of invasion (p

RhoA protein expression correlates positively with degree of malignancy in astrocytomas

Astrocytic tumors are the most common intracranial neoplasms. Their prognoses correlate with a conventional morphological grading system that suffers from diagnostic subjectivity and hence, inter-observer inconsistency. A molecular marker that provides an objective reference for classification and prognostication of astrocytic tumors would be useful in diagnostic pathology. RhoA, a GTPase protein involved in cell migration and adhesion has been shown to be upregulated in a variety of human cancers. Based on direct analysis of clinical materials, our study demonstrates increased expression of RhoA in high-grade astrocytomas. This observation may be relevant to astrocytoma biology and the development of potential therapeutics against high-grade astrocytomas. Of more immediate consequence, utilization of this marker may aid in the routine pathological grading (and hence prognostication) of astrocytomas. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

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.

Automated nonlinear system modelling with multiple neural networks

This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLPs). It focuses on both structure uncertainty and parameter uncertainty, which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. First, an automated network structure selection procedure is proposed within a fixed time interval for a given network construction criterion. Then, the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope with structure uncertainty, a hysteresis strategy is proposed to enable neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and a simulation example show the efficacy of the proposed method.