Protein dynamics in the reductive activation of a B12-containing enzyme

B12-dependent proteins are involved in methyl transfer reactions ranging from the biosynthesis of methionine in humans to the formation of acetyl-CoA in anaerobic bacteria. During their catalytic cycle, they undergo large conformational changes to interact with various proteins. Recently, the crystal structure of the B12-containing corrinoid iron–sulfur protein (CoFeSP) in complex with its reductive activator (RACo) was determined, providing a first glimpse of how energy is transduced in the ATP-dependent reductive activation of corrinoid-containing methyltransferases. The thermodynamically uphill electron transfer from RACo to CoFeSP is accompanied by large movements of the cofactor-binding domains of CoFeSP. To refine the structure-based mechanism, we analyzed the conformational change of the B12-binding domain of CoFeSP by pulsed electron–electron double resonance and Förster resonance energy transfer spectroscopy. We show that the site-specific labels on the flexible B12-binding domain and the small subunit of CoFeSP move within 11 Å in the RACo:CoFeSP complex, consistent with the recent crystal structures. By analyzing the transient kinetics of formation and dissociation of the RACo:CoFeSP complex, we determined values of 0.75 μM–1 s–1 and 0.33 s–1 for rate constants kon and koff, respectively. Our results indicate that the large movement observed in crystals also occurs in solution and that neither the formation of the protein encounter complex nor the large movement of the B12-binding domain is rate-limiting for the ATP-dependent reductive activation of CoFeSP by RACo

Automated classification of neonatal sleep states using EEG

Objective: To develop a method for automated neonatal sleep state classification based on EEG that can be applied over a wide range of age. Methods: We collected 231 EEG recordings from 67 infants between 24 and 45 weeks of postmenstrual age. Ten minute epochs of 8 channel polysomnography (N = 323) from active and quiet sleep were used as a training dataset. We extracted a set of 57 EEG features from the time, frequency, and spatial domains. A greedy algorithm was used to define a reduced feature set to be used in a support vector machine classifier. Results: Performance tests showed that our algorithm was able to classify quiet and active sleep epochs with 85% accuracy, 83% sensitivity, and 87% specificity. The performance was not substantially lowered by reducing the epoch length or EEG channel number. The classifier output was used to construct a novel trend, the sleep state probability index, that improves the visualisation of brain state fluctuations. Conclusions: A robust EEG-based sleep state classifier was developed. It performs consistently well across a large span of postmenstrual ages. Significance: This method enables the visualisation of sleep state in preterm infants which can assist clinical management in the neonatal intensive care unit. (C) 2017 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.Peer reviewe

Reduction of CPR artifacts in the ventricular fibrillation ECG by coherent line removal

<p>Abstract</p> <p>Background</p> <p>Interruption of cardiopulmonary resuscitation (CPR) impairs the perfusion of the fibrillating heart, worsening the chance for successful defibrillation. Therefore ECG-analysis <it>during ongoing chest compression </it>could provide a considerable progress in comparison with standard analysis techniques working only during "hands-off" intervals.</p> <p>Methods</p> <p>For the reduction of CPR-related artifacts in ventricular fibrillation ECG we use a localized version of the <it>coherent line removal </it>algorithm developed by Sintes and Schutz. This method can be used for removal of periodic signals with sufficiently coupled harmonics, and can be adapted to specific situations by optimal choice of its parameters (e.g., the number of harmonics considered for analysis and reconstruction). Our testing was done with 14 different human ventricular fibrillation (VF) ECGs, whose fibrillation band lies in a frequency range of [1 Hz, 5 Hz]. The VF-ECGs were mixed with 12 different ECG-CPR-artifacts recorded in an animal experiment during asystole. The length of each of the ECG-data was chosen to be 20 sec, and testing was done for all 168 = 14 × 12 pairs of data. VF-to-CPR ratio was chosen as -20 dB, -15 dB, -10 dB, -5 dB, 0 dB, 5 dB and 10 dB. Here -20 dB corresponds to the highest level of CPR-artifacts.</p> <p>Results</p> <p>For non-optimized <it>coherent line removal </it>based on signals with a VF-to-CPR ratio of -20 dB, -15 dB, -10 dB, -5 dB and 0 dB, the signal-to-noise gains (SNR-gains) were 9.3 ± 2.4 dB, 9.4 ± 2.4 dB, 9.5 ± 2.5 dB, 9.3 ± 2.5 dB and 8.0 ± 2.7 (mean ± std, <it>n </it>= 168), respectively. Characteristically, an original VF-to-CPR ratio of -10 dB, corresponds to a variance ratio <it>var</it>(VF):<it>var</it>(CPR) = 1:10. An improvement by 9.5 dB results in a restored VF-to-CPR ratio of -0.5 dB, corresponding to a variance ratio <it>var</it>(VF):<it>var</it>(CPR) = 1:1.1, the variance of the CPR in the signal being reduced by a factor of 8.9.</p> <p>Discussion</p> <p>The <it>localized coherent line removal </it>algorithm uses the information of a single ECG channel. In contrast to multi-channel algorithms, no additional information such as thorax impedance, blood pressure, or pressure exerted on the sternum during CPR is required. Predictors of defibrillation success such as mean and median frequency of VF-ECGs containing CPR-artifacts are prone to being governed by the harmonics of the artifacts. Reduction of CPR-artifacts is therefore necessary for determining reliable values for estimators of defibrillation success.</p> <p>Conclusions</p> <p>The <it>localized coherent line removal </it>algorithm reduces CPR-artifacts in VF-ECG, but does not eliminate them. Our SNR-improvements are in the same range as offered by multichannel methods of Rheinberger et al., Husoy et al. and Aase et al. The latter two authors dealt with different ventricular rhythms (VF and VT), whereas here we dealt with VF, only. Additional developments are necessary before the algorithm can be tested in real CPR situations.</p

Contents 1 Robustness of Regular Sampling in Sobolev Algebras

2 Wiener Amalgam Spaces W (B, ℓ p)..............