Nitrogen-containing bisphosphonates were found to inhibit farnesyl diphosphate synthase - an essential enzyme in the cholesterol biosynthesis pathway, but their effect on cholesterol synthesis per se in the central nervous system (CNS) remains unknown. The aim of the present study was to examine possible influence of a representative agent alendronate on cholesterol synthesis rates in selected parts of rat CNS and on plasma cholesterol level. Two groups of rats were orally administered either alendronate (3 mg/kg b.w. ) or vehicle for 9 days. At the end of experiment, brain (basal ganglia, frontal cortex and hippocampus) and spinal cord were isolated and cholesterol synthesis was determined using the technique of deuterium incorporation from deuterated wa ter. In the alendronate group significant reductions of choleste rol synthesis rates were detected in frontal cortex, hippocampus and spinal cord (p<0.001). However, the experimental treatment did not produce a significant alteration in the levels of plasma cholesterol. In conclusion, this study brings the first experimental evidence of the inhibition of cholesterol biosynthesis with alendronate in central nervous system., Ľ. Cibičková, R. Hyšpler, N. Cibiček, E. Čermáková, V. Palička., and Obsahuje bibliografii
Attention decrease and an eventual micro-sleep of an artificial system operator is very dangerous and its early detection can prevent great losses. This work deals with an early detection of micro-sleep based on analysis of an electroencefalographic activity of tlie brain. There are classic spectral methods - the Discrete Fourier Transform and parametric methods - autoregressive models used for signal processing here. An influence of a band pass filter characteristic on classification is investigated. For the detection of the micro-sleep multi-layer perceptron, radial basis function (RBF) and the learning vector quantization (LVQ) neural networks are used. The k-nearest neighbor as a representative of non-parametric methods is examined. The last method used here is based on the Bayesian theory and its coefficients are found using the maximum likelihood estimation.
A dataset intended for fully trainable natural language generation (NLG) systems in task-oriented spoken dialogue systems (SDS), covering the English public transport information domain. It includes preceding context (user utterance) along with each data instance (pair of source meaning representation and target natural language paraphrase to be generated).
Taking the form of the previous user utterance into account for generating the system response allows NLG systems trained on this dataset to entrain (adapt) to the preceding utterance, i.e., reuse wording and syntactic structure. This should presumably improve the perceived naturalness of the output, and may even lead to a higher task success rate.
Crowdsourcing has been used to obtain natural context user utterances as well as natural system responses to be generated.