Promoter methylation of cancer-related genes in gastric carcinoma.

UNLABELLED: Genetic changes associated with gastric cancer are not completely known, but epigenetic mechanisms involved in this disease seem to play an important role in its pathophysiology. One of these mechanisms, an aberrant methylation in the promoter regions of genes involved in cancer induction and promotion, may be of particular importance in gastric cancer. AIM: To analyze the methylation status of eight genes: Apaf-1, Casp8, CDH1, MDR1, GSTP1, BRCA1, hMLH1, Fas in gastric cancer patients. METHODS: The methylation pattern of the genes was assessed by methylation specific restriction enzyme PCR (MSRE-PCR) in gastric tumors taken during surgery of 27 patients and compared with the methylation pattern in material obtained from biopsy in 25 individuals without cancer and pre-cancerous lesions. RESULTS: We observed a promoter hypermethylation in the Casp8, hMLH1, CDH1 and MDR1 in gastric cancer patients as compared with the controls. Additionally, we investigated the relationship between promoter hypermethylation and age, gender, smoking and gastric cancer family history. The hypermethylation of the hMLH1 gene occurred more frequently in female than in men, and the hypermethylation of the CDH1 gene was observed preferentially in smoking than in non-smoking individuals. CONCLUSION: The data obtained indicate that changes in DNA methylation may contribute to gastric carcinogenesis.

Prediction of movement following noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia by means of middle latency auditory evoked responses.

This paper investigates the applicability of generalized dynamic neural networks for the design of a two-valued anesthetic depth indicator during isoflurane/nitrous oxide anesthesia. The indicator construction is based on the processing of middle latency auditory evoked responses (MLAER) in combination with the observation of the patient's movement reaction to skin incision. The framework of generalized dynamic neural networks does not require any data preprocessing, visual data inspection or subjective feature extraction. The study is based on a data set of 106 patients scheduled for elective surgery under isoflurane/nitrous oxide anesthesia. The processing of the measured MLAER is performed by a recurrent neural network that transforms the MLAER signals into signals having a very uncomplex structure. The evaluation of these signals is self-evident, and yields to a simple threshold classifier. Using only evoked potentials before the pain stimulus, the patient's reaction could be predicted with a probability of 81.5%. The MLAER is closely associated to the patient's reaction to skin incision following noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia. In combination with other parameters, MLAER could contribute to an objective and trustworthy movement prediction to noxious stimulation.

Training trajectories by continuous recurrent multilayer networks.

This paper addresses the problem of training trajectories by means of continuous recurrent neural networks whose feedforward parts are multilayer perceptrons. Such networks can approximate a general nonlinear dynamic system with arbitrary accuracy. The learning process is transformed into an optimal control framework where the weights are the controls to be determined. A training algorithm based upon a variational formulation of Pontryagin's maximum principle is proposed for such networks. Computer examples demonstrating the efficiency of the given approach are also presented.

Initial state training procedure improves dynamic recurrent networks with time-dependent weights.

The problem of learning multiple continuous trajectories by means of recurrent neural networks with (in general) time-varying weights is addressed. The learning process is transformed into an optimal control framework where both the weights and the initial network state to be found are treated as controls. For such a task, a learning algorithm is proposed which is based on a variational formulation of Pontryagin's maximum principle. The convergence of this algorithm, under reasonable assumptions, is also investigated. Numerical examples of learning nontrivial two-class problems are presented which demonstrate the efficiency of the approach proposed.

Identification of hemifield single trial PVEP on the basis of generalized dynamic neural network classifiers.

This paper is concerned with the application of generalized dynamic neural networks for the identification of hemifield pattern-reversal visual evoked potentials. The identification process is performed by different networks with time-varying weights using signals from different electrode positions as external inputs. Since dynamic neural networks are able to process time-varying signals, the identification of the stimulated hemiretinae is performed without feature extraction. The performance of the method presented is compared with a reference method based on the values of instantaneous frequency at the occipital electrode positions at P100 latency.

Adaptable preprocessing units and neural classification for the segmentation of EEG signals.

In this contribution, a methodology for the simultaneous adaptation of preprocessing units (PPUs) for feature extraction and of neural classifiers that can be used for time series classification is presented. The approach is based upon an extension of the backpropagation algorithm for the correction of the preprocessing parameters. In comparison with purely neural systems, the reduced input dimensionality improves the generalization capability and reduces the numerical effort. In comparison with PPUs with fixed parameters, the success of the adaptation is less sensitive to the choice of the parameters. The efficiency of the developed method is demonstrated via the use of quadratic filters with adaptable transmission bands as preprocessing units for the segmentation of two different types of discontinuous EEG: discontinuous neonatal EEG (burst-interburst segmentation) and EEG in deep stages of sedation (burst-suppression segmentation).

Learning continuous trajectories in recurrent neural networks with time-dependent weights.

This paper is concerned with a general learning (with arbitrary criterion and state-dependent constraints) of continuous trajectories by means of recurrent neural networks with time-varying weights. The learning process is transformed into an optimal control framework, where the weights to be found are treated as controls. A new learning algorithm based on a variational formulation of Pontryagin's maximum principle is proposed. This algorithm is shown to converge, under reasonable conditions, to an optimal solution. The neural networks with time-dependent weights make it possible to efficiently find an admissible solution (i.e., initial weights satisfying state constraints) which then serves as an initial guess to carry out a proper minimization of a given criterion. The proposed methodology may be directly applicable to both classification of temporal sequences and to optimal tracking of nonlinear dynamic systems. Numerical examples are also given which demonstrate the efficiency of the approach presented.

Structure optimization of neural networks with the A*-algorithm.

A method for the construction of optimal structures for feedforward neural networks is introduced. On the basis of a construction of a graph of network structures and an evaluation value which is assigned to each of them, an heuristic search algorithm can be installed on this graph. The application of the A*-algorithm ensures, in theory, both the optimality of the solution and the optimality of the search. For several examples, a comparison between the new strategy and the well-known cascade-correlation procedure is carried out with respect to the performance of the resulting structures.

Structure optimization of neural networks with A* -algorithm application in EEG pattern analysis.

The A* - Algorithm for heuristic search is applied to construct a Neural Network structure (NS) that optimally fits the structure of data to be learned. In this way, the user of Neural Networks (NN) is able to avoid the empirical testing of different structures. The method given here is applied to the recognition of different patterns derived from the EEG of an epileptic patient.

Application of optimized pattern recognition units in EEG analysis: common optimization of preprocessing and weights of neural networks as well as structure optimization.

The main goal of this study is to demonstrate the possibility of training the Neural Network (multilayer perceptron) classifier and preprocessing units simultaneously, i.e., that properties of preprocessing are chosen automatically during the training phase. In the first realization step, adaptive recursive estimation of the power within a frequency band was used as a preprocessing unit. To improve the efficiency of special units, the power and momentary frequency estimation was replaced by methods that are based on adaptive Hilbert transformers. The strategy was developed to obtain optimized recognition units that can be efficiently integrated into strategies for monitoring the cerebral status of neonates. Therefore, applications (e.g., in neonatal EEG pattern recognition) will be shown. Additionally, a method of minimizing the error function was used, where this minimization is based on optimizing the network structure. The results of structure optimization in the field of EEG pattern recognition in epileptic patients can be demonstrated.