Magnetohydrodynamic wave resonance and the evocation of epileptiform activity by millitesla DC magnetic fields.

DC magnetic fields of low- to medium-flux density (nTesla to mTesla) affect a variety of brain activities. The cellular mechanism underlying these effects is unknown. A model involving magnetohydrodynamic waves and corresponding resonance phenomena is offered here as a solution. The model is discussed in relation to the evocation of epileptiform activity due to external DC magnetic fields in the mTesla range. The assumed existence of a resonance wave makes it possible to calculate the size of the involved area.

Neural network method to analyze data compression in DNA and RNA sequences.

Neural network computations on RNA sequences are used to demonstrate that data compression is possible in these sequences. The result implies that a certain discrimination should be achievable between structured vs random regions. The technique is illustrated by computing the compressibility of short RNA sequences such as tRNA. The method should be valuable in measuring the information content of DNA, including noncoding DNA, which has been shown to display certain properties resembling natural language attributes.

A mechanism for action of extremely low frequency electromagnetic fields on biological systems.

This report outlines a simple mechanism, based on the Hall Effect, by which static and low frequency (50-60 Hz) pulsed electromagnetic fields (PEMFs) can modify cation flow across biological membranes and alter cell metabolism. We show that magnetic fields commonly found in the environment can be expected to cause biologically significant interactions between transported cations and basic domains of cation channel proteins. We calculate that these interactions generate forces of a magnitude similar to those created by normal transmembrane voltage changes known to gate cation channels. Thus PEMFs are shown to have the potential of regulating flow through cation channels, changing the steady state concentrations of cellular cations and thus the metabolic processes dependent on cation concentrations.

The use of artificial neural networks in biomedical technologies: an introduction.

Artificial neural networks (NN) are systems than can learn. In the most common situation, an operator trains the system on a set of input and output data belonging to a particular category. If new data of the same category, but not in the training set, are presented to the system, the NN can use the learned data to predict outcomes without any specific programming relating to the category of events involved. The fields of application of NN have increased dramatically in the past few years. Originally, the NN technique was mainly in the hands of computer programming specialists and the applications concentrated on tasks such as decision systems and signal processing. However, this picture has changed due to the emergence of user-friendly NN software for personal computers. A large variety of possible NN applications now exist for non-computer specialists. Thus, with only a very modest knowledge of the theory behind neural networks, it is possible to attack complicated problems in a researcher's own area of specialty with the NN technique. This is especially true in the field of medical technology, the topic of this review. The review is divided into three sections: 1) an elementary introduction to useful NN methods; 2) a review of the most important applications of the NN technique to this point in time; 3) a summary of available computer details that would be needed for a beginner in this field.

Neural-network applications for analysis of adverse drug reactions.

A neural-network analysis has been applied to predict adverse or side effects of drugs using a database of antidepressant agents with known effects as well as input from a database with both patient parameters and drug information. A NeuralWork software package was implemented on a Macintosh Quadra 700 and trained on a database of ten drugs with known adverse effects. Another agent (not in the database) was used to test the ability of the network to predict the relative incidence of its side effects. Despite the small number of drugs used for training, the adverse effects of some drugs, such as doxepin, were predicted with 90-100% accuracy. These results indicate that neural-network analysis can be used to predict adverse drug effects for drugs within a given class and ultimately can be extended to include patient parameters to predict the mechanisms of action of drugs from relatively large databases.

A generalized information function applied to the genetic code.

The problem of the partitioning of the degeneracy of the codons in the genetic code is considered in the framework of a generalized information function IG = c sigma kpk(ln pk + G(Ek] where k represents the number of codons in a specific degeneracy class and G(Ek) is an arbitrary real valued function. For G(Ek) = 0 the Shannon information function is recovered. For a particular choice of G(Ek) that takes the dominance of even degeneracies into account, it is found by direct numerical calculations that the correct degeneracy partitioning appears as optimal values of the Ig function. This results is also supported by optimization calculations in which the generalized information function is regarded as a continuous function in the degeneracy variables.

On the information content of the genetic code.

In living organisms 20 amino acids along with the terminator value(s) are encoded by 64 codons giving a degeneracy of the codons as described by the genetic code. A basic theoretical problem of genetic codes is to explain the particular distribution of degeneracies of partitions involved in the codes. In this work the degeneracy problem is considered in the framework of information theory. It is shown by direct numerical evaluation of a certain degeneracy information function associated with the genetic code that the degeneracy of the codes is observed to be related to the optimization of this function.

Reaction of malonaldehyde with mitochondrial membranes.

Malonaldehyde formed by lipid oxidation is regarded as a main crosslinker in the formation of natural age pigment. To elucidate the mechanism of pigment formation the reaction of malonaldehyde with biomembranes using fluorescence spectroscopy has been studied. Rat liver mitochondrial ghosts or bovine serum albumin were reacted with malonaldehyde. In both cases two main fluorescence changes were observed: protein fluorescence decreased to 50% of its initial value in about two hours; aminoiminopropene fluorescence reached a maximum at a much slower rate. The kinetics support a two-step reaction hypothesis. First, malonaldehyde reacts with protein quenching its fluorescence. Next fluorescent interprotein aminoiminopropene (AIP) crosslinks are formed. The fluorescence lifetime value of the induced AIP fluorophore was shown to be similar to the lifetime of naturally occurring age pigment previously reported for mitochondrial ghosts prepared from aged animals (5.4 ns +/- 0.3 and 5.9 ns +/- 0.6, respectively).