Non-coding RNAs in the nervous system

Increasing evidence suggests that the development and function of the nervous system is heavily dependent on RNA editing and the intricate spatiotemporal expression of a wide repertoire of non-coding RNAs, including micro RNAs, small nucleolar RNAs and longer non-coding RNAs. Non-coding RNAs may provide the key to understanding the multi-tiered links between neural development, nervous system function, and neurological diseases.

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.

Selective loss of synaptic proteins in Alzheimer's disease: Evidence for an increased severity with APOE epsilon 4

A pathological feature of Alzheimer's disease (AD) is an area-specific neuronal loss that may be caused by excitotoxicity-related synaptic dysfunction. Relative expression levels of synaptopbysin, dynamin I, complexins I and II, N-cadherin, and alpha CaMKII were analysed in human brain tissue from AD cases and controls in hippocampus, and inferior temporal and occipital cortices. Synaptophysin and dynamin I are presynaptic terminal proteins not specific to any neurotransmitter system whereas complexin II, N-cadherin, and alpha CaMKII are specific for excitatory synapses. Complexin I is a presynaptic protein localised to inhibitory synapses. There were no significant differences in synaptophysin, dynamin I, N-cadherin, or alpha CaMKII protein levels between AD cases and controls. The complexin proteins were both markedly lower in AD cases than in controls (P < 0.01). Cases were also categorised by APOE genotype. Averaged across areas there was a 36% lowering of presynaptic proteins in AD cases carrying at least one epsilon 4 allele compared with in AD cases lacking the epsilon 4 allele. We infer that synaptic protein level is not indicative of neuronal loss, but the synaptic dysfunction may result from the marked relative loss of the complexins in AD, and lower levels of presynaptic proteins in AD cases with the APOE epsilon 4 allele. (c) 2006 Elsevier Ltd. All rights reserved.

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).

Brain tumour stem cells

The dogma that the genesis of new cells is a negligible event in the adult mammalian brain has long influenced our perception and understanding of the origin and development of CNS tumours. The discovery that new neurons and glia are produced throughout life from neural stem cells provides new possibilities for the candidate cells of origin of CNS neoplasias. The emerging hypothesis is that alterations in the cellular and genetic mechanisms that control adult neurogenesis might contribute to brain tumorigenesis, thereby allowing the identification of new therapeutic strategies.

Left-right information flow in the brain during EEG arousals

In disorders such as sleep apnea, sleep is fragmented with frequent EEG-arousal (EEGA) as determined via changes in the sleep-electroencephalogram. EEGA is a poorly understood, complicated phenomenon which is critically important in studying the mysteries of sleep. In this paper we study the information flow between the left and right hemispheres of the brain during the EEGA as manifested through inter-hemispheric asynchrony (IHA) of the surface EEG. EEG data (using electrodes A1/C4 and A2/C3 of international 10-20 system) was collected from 5 subjects undergoing routine polysomnography (PSG). Spectral correlation coefficient (R) was computed between EEG data from two hemispheres for delta-delta(0.5-4 Hz), theta-thetas(4.1-8 Hz), alpha-alpha(8.1-12 Hz) & beta-beta(12.1-25 Hz) frequency bands, during EEGA events. EEGA were graded in 3 levels as (i) micro arousals (3-6 s), (ii) short arousals (6.1-10 s), & (iii) long arousals (10.1-15 s). Our results revealed that in beta band, IHA increases above the baseline after the onset of EEGA and returns to the baseline after the conclusion of event. Results indicated that the duration of EEGA events has a direct influence on the onset of IHA. The latency (L) between the onset of arousals and IHA were found to be L=2plusmn0.5 s (for micro arousals), 4plusmn2.2 s (short arousals) and 6.5plusmn3.6 s (long arousals)