Expression of human neuronal protein 22, a novel cytoskeleton-associated protein, was decreased in the anterior cingulate cortex of schizophrenia

Human neuronal protein 22 (hNP22) is a novel neuron-specific protein featuring numerous motifs previously described in cytoskeleton-associating and signaling proteins. Because previous studies have supported abnormalities in neuronal cytoarchitecture and/or development in the schizophrenia brain, we examined the expression of hNP22 in the anterior cingulate cortex, the hippocampus and the prefrontal cortex of schizophrenic and normal control postmortem brains using high-sensitive immunohistochemistry. Seven schizophrenic and seven age- and sex-matched control brains were examined. The ratio of hNP22-immunopositive cells/total cells was significantly reduced in layer V (p = .020) and layer VI (p = .022) of the anterior cingulate cortex of schizophrenic brain compared with controls. In contrast, there were no significant changes observed in the hippocampus and the prefrontal cortex. These results suggest that altered expression of hNP22 may be associated with modifications in neuronal cytoarchitecture leading to dysregulation of neural signal transduction in the anterior cingulate cortex of the schizophrenia brain.

Tracking the states of a nonlinear and nonstationary system in the weight-space of artificial neural networks

We propose a novel interpretation and usage of Neural Network (NN) in modeling physiological signals, which are allowed to be nonlinear and/or nonstationary. The method consists of training a NN for the k-step prediction of a physiological signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. We de. ne a novel feature, Normalized Vector Separation (gamma(ij)), to measure the separation of two arbitrary states i and j in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that gamma(ij) can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled (within a root-means-squared-error of 0.01) by the proposed method, and the blood perfusion state of the brain could be monitored via gamma(ij), with small NNs having no more than 21 connection weight altogether.

Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.