Biomedical subjects

J C Schouten

Publications and source records attributed to J C Schouten.

4 recordsLinked to original sources

PubMed2002

Using a high-resolution magnetic sector mass spectrometer for fast analysis of transient reaction processes in automotive catalytic converters.

A high-resolution magnetic sector mass spectrometer has been developed and used for transient kinetic measurements on reactions that are important for automotive exhaust gas catalysis. This instrument allows separation of the isobaric masses of CO and N2 as well as CO2 and N2O, which are separately monitored in time at a sampling rate of 48 Hz. The time resolution is sufficient to determine individual kinetic rate parameters for reactions under transient conditions. A description of the experimental set-up as well as the software used is given. The results are illustrated by the catalytic reduction of NO by CO. To the best knowledge of the authors, this is the first application of a magnetic sector mass spectrometer for kinetic research under dynamic operation conditions.

Journal Article↗

PubMed2001

Activation of brainstem N-methyl-D-aspartate receptors is required for the analgesic actions of morphine given systemically.

The analgesic actions of opioids are in large part mediated by activation of brainstem pain modulating neurons that depress nociceptive transmission at the level of the dorsal horn. The present study was designed to characterize the contribution of N-methyl-D-aspartate (NMDA)- and non-NMDA-mediated excitatory transmission within the rostral ventromedial medulla (RVM) to the activation of brainstem inhibitory output neurons and analgesia produced by systemic morphine administration. The NMDA receptor antagonist D-2-amino-5-phosophonopentanoic acid (AP5), the non-NMDA receptor antagonist 6-cyano-7-nitroquinoxaline-2,3-dione disodium (CNQX) or saline was infused into the RVM of lightly anesthetized rats while recording the activity of identified pain modulating neurons: 'off-cells', thought to inhibit nociceptive transmission, and 'on-cells', thought to facilitate nociception. Nociceptive responsiveness (tail flick latency) was not affected by either antagonist. AP5, but not CNQX, attenuated or blocked activation and disinhibition of off-cells and the antinociception produced by systemically administered morphine. Reflex-related discharge of on-cells was unaffected by AP5, but significantly attenuated by CNQX. The present results highlight two important aspects of RVM pain modulatory circuits. First, morphine given systemically produces its analgesic effect at least in part by recruiting an NMDA-mediated excitatory process to activate off-cells within the RVM. This excitatory process may play a role in the analgesic synergy produced by simultaneous mu-opioid activation at different levels of the neuraxis. Second, reflex-related activation of on-cells is mediated by a non-NMDA receptor, and this activation does not appear to play a significant role in regulating reflex responses to acute noxious stimuli. Excitatory amino acid-mediated excitation thus has at least two distinct roles within the RVM, activating off-cells and on-cells under different conditions.

2-Amino-5-phosphonovalerate↗

PubMed2000

Learning chaotic attractors by neural networks.

An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single measured time series. During training, the algorithm learns to short-term predict the time series. At the same time a criterion, developed by Diks, van Zwet, Takens, and de Goede (1996) is monitored that tests the hypothesis that the reconstructed attractors of model-generated and measured data are the same. Training is stopped when the prediction error is low and the model passes this test. Two other features of the algorithm are (1) the way the state of the system, consisting of delays from the time series, has its dimension reduced by weighted principal component analysis data reduction, and (2) the user-adjustable prediction horizon obtained by "error propagation"-partially propagating prediction errors to the next time step. The algorithm is first applied to data from an experimental-driven chaotic pendulum, of which two of the three state variables are known. This is a comprehensive example that shows how well the Diks test can distinguish between slightly different attractors. Second, the algorithm is applied to the same problem, but now one of the two known state variables is ignored. Finally, we present a model for the laser data from the Santa Fe time-series competition (set A). It is the first model for these data that is not only useful for short-term predictions but also generates time series with similar chaotic characteristics as the measured data.

Algorithms↗

PubMed1999

Synchronizing chaos in an experimental chaotic pendulum using methods from linear control theory.

Linear feedback control, specifically model predictive control (MPC), was used successfully to synchronize an experimental chaotic pendulum both on unstable periodic and aperiodic orbits. MPC enables tuning of the controller to give an optimal controller performance. That is, both the fluctuations around the target trajectory and the necessary control actions are minimized using a least-squares solution of the linearized problem. It is thus shown that linear control methods can be applied to experimental chaotic systems, as long as an adequate model is available that can be linearized along the desired trajectory. This model is used as an observer, i.e., it is synchronized with the experimental pendulum to estimate the state of the experimental pendulum. In contrast with other chaos control procedures like the map-based Ott, Grebogi, and York method [Phys. Rev. Lett. 64, 1196 (1990)], the continuous type feedback control proposed by Pyragas [Phys. Lett. A 170, 421 (1992)], or the feedback control method recently proposed by Brown and Rulkov [Chaos 7 (3), 395 (1997)], the procedure outlined in this paper automatically results in a choice for the feedback gains that gives optimum performance, i.e., minimum fluctuations around the desired trajectory using minimum control actions.

Journal Article↗