Cytokine mRNA expression in CSF and peripheral blood mononuclear cells in multiple sclerosis: detection by RT-PCR without in vitro stimulation.

We amplified the mRNA for cytokines in peripheral blood mononuclear cells (PBMC) and cerebrospinal fluid (CSF) cells from 18 multiple sclerosis (MS) patients and 21 other neurological patients, using reverse transcription polymerase chain reaction (RT-PCR). A radioactive hybridization of the amplified DNA allowed quantitation of mRNA levels. Expression of tumor necrosis factor (TNF)-alpha, interferon (IFN)-gamma and interleukin (IL)-10 mRNA was elevated in CSF cells of MS patients. IFN-gamma and IL-10 mRNA levels were higher in MS patients than in other inflammatory neurological diseases. In many MS patients, both proinflammatory and immunoregulatory cytokine messages were detected in the CSF compartment. Such immune reactivity in CSF, as opposed to the peripheral compartment, did not correlate with the clinical activity of the disease.

Automatic analysis of signals with symbolic content.

This paper presents a set of methods for helping in the analysis of signals with particular features that admit a symbolic description. The methodology is based on a general discrete model for a symbolic processing subsystem, which is fuzzyfied by means of a fuzzy inference system. In this framework a number of design problems have been approached. The curse of dimensionality problem and the specification of adequate membership functions are the main ones. In addition, other strategies, which make the design process simpler and more robust, are introduced. Their goals are automating the production of the rule base of the fuzzy system and composing complex systems from simpler subsystems under symbolic constrains. These techniques are applied to the analysis of wakefulness episodes in the sleep EEG. In order to solve the practical difficulty of finding remarkable situations from the outputs of the symbolic subsystems an unsupervised adaptive learning technique (FART network) has been applied.

Hopf bifurcation stability in Hopfield neural networks.

In this paper we consider a simple discrete Hopfield neural network model and analyze local stability using the associated characteristic model. In order to study the dynamic behavior of the quasi-periodic orbit, the Hopf bifurcation must be determined. For the case of two neurons, we find one necessary condition that yields the Hopf bifurcation. In addition, we determine the stability and direction of the Hopf bifurcation by applying normal form theory and the center manifold theorem. An example is given and a numerical simulation is performed to illustrate the results. We analyze the influence of bias weights on the stability of the quasi-periodic orbit and study the phase-locking phenomena for certain experimental results with Arnold Tongues in a particular weight configuration.

Tools for acquisition, processing and knowledge-based diagnostic of the electroencephalogram and visual evoked potentials.

The objective of our research is to develop computer-based tools to automate the clinical evaluation of the electroencephalogram (EEG) and visual evoked potentials (VEP). This paper describes a set of solutions to support all the aspects regarding the standard procedures of the diagnosis in neurophysiology, including: (1) acquisition and real-time processing and compression of EEG and VEP signals, (2) real-time brain mapping of spectral powers, (3) classifier design, (4) automatic detection of morphologies through supervised neural networks. (5) signal analysis through fuzzy modelling, and (6) a knowledge based approach to classifier design.