ErbB-2 expression and its association with other biological parameters of breast cancer among Indian women

Overexpression of the epidermal growth factor receptor family genes, which include ErbB-1, 2, 3 and 4, has been implicated in a number of cancers. We have studied the extent of ErbB-2 overexpression among Indian women with sporadic breast cancer. Methods: Immmunohistochemistry and genomic polymerase chain reaction (PCR) were used to study the ErbB2 overexpression. ErbB2 status was correlated with other clinico-pathological parameters, including patient survival. Results: ErbB-2 overexpression was detected in 43.2% (159/368) of the cases by immunohistochemistry. For a sub-set of patients (n = 55) for whom total DNA was available, ErbB-2 gene amplification was detected in 25.5% (14/55) of the cases by genomic PCR. While the ErbB2 overexpression was significantly higher in patients with lymphnode (χ2 = 12.06, P≤ 0.001), larger tumor size (χ2 = 8.22, P = 0.042) and ductal carcinoma (χ2 = 15.42, P ≤ 0.001), it was lower in patients with disease-free survival (χ2 = 22.13, P ≤ 0.001). Survival analysis on a sub-set of patients for whom survival data were available (n = 179) revealed that ErbB-2 status (χ2 =25.94, P ≤ 0.001), lymphnode status (χ2 = 12.68, P ≤ 0.001), distant metastasis (χ2 = 19.49, P ≤ 0.001) and stage of the disease (χ2 = 28.04, P ≤0.001) were markers of poor prognosis. Conclusions: ErbB-2 overexpression was significantly greater compared with the Western literature, but comparable to other Indian studies. Significant correlation was found between ErbB-2 status and lymphnode status, tumor size and ductal carcinoma. ErbB-2 status, lymph node status, distant metastasis and stage of the disease were found to be prognostic indicators.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Tool wear monitoring using ant behaviour

In this paper we show the applicability of Ant Colony Optimisation (ACO) techniques for pattern classification problem that arises in tool wear monitoring. In an earlier study, artificial neural networks and genetic programming have been successfully applied to tool wear monitoring problem. ACO is a recent addition to evolutionary computation technique that has gained attention for its ability to extract the underlying data relationships and express them in form of simple rules. Rules are extracted for data classification using training set of data points. These rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in ACO based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The classification accuracy obtained in ACO based approach is as good as obtained in other biologically inspired techniques.

Can biological motion be a biometric?

Biological motion has successfully been used for analysis of a person's mood and other psychological traits. Efforts are made to use human gait as a non-invasive mode of biometric. In this reported work, we try to study the effectiveness of biological gait motion of people as a cue to biometric based person recognition. The data is 3D in nature and, hence, has more information with itself than the cues obtained from video-based gait patterns. The high accuracies of person recognition using a simple linear model of data representation and simple neighborhood based classfiers, suggest that it is the nature of the data which is more important than the recognition scheme employed.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Use of adult male bonnet monkeys (Macaca radiata) for testing the biological activity of gonadotropin releasing hormone and its analogues

The effect of injecting agonistic and antagonistic analogues of gonadotropin releasing hormone analogues on serum testosterone levels was checked in adult and immature male bonnet monkeys. Of the agonistic analogues Buserelin, Ovurelin and D-Phe6 Gln8 GnRH were found to be most potent in increasing serum testosterone levels in the adult male bonnet monkeys. While 27-month-old monkeys responded well to des Gly10 GnRH, only marginal response was observed in the case of 15-month-old monkeys. Studies carried out with Ovurelin indicated that it was not effective in causing desensitization in adult monkeys. The antagonistic analogue was effective in blocking nocturnal surge of serum testosterone. Based on these studies it is suggested the adult male bonnet monkeys can be effectively used for testing the activity of GnRH analogues.