Ictal quantitative surface electromyography correlates with postictal EEG suppression.

To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment. Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES. We found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%. Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk. This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression

Probing the crossover in CO desorption from single crystal to nanoparticulate Ru model catalysts

cited By 10International audienceUsing model catalysts, we demonstrate that CO desorption from Ru surfaces can be switched from that typical of single crystal surfaces to one more characteristic of supported nanoparticles. First, the CO desorption behaviour from Ru nanoparticles supported on highly oriented pyrolytic graphite was studied. Both mass-selected and thermally evaporated nanoparticles were deposited. TPD spectra from the mass-selected nanoparticles exhibit a desorption peak located around 410 K with a broad shoulder extending from around 480 K to 600 K, while spectra obtained from thermally evaporated nanoparticles exhibit a single broad feature from ∼350 K to ∼450 K. A room temperature deposited 50 Å thick Ru film displays a characteristic nanoparticle-like spectrum with a broad desorption feature at ∼420 K and a shoulder extending from ∼450 K to ∼600 K. Subsequent annealing of this film at 900 K produced a polycrystalline morphology of flat Ru(001) terraces separated by monatomic steps. The CO desorption spectrum from this surface resembles that obtained on single crystal Ru(001) with two large desorption features located at 390 K and 450 K due to molecular desorption from terrace sites, and a much smaller peak at ∼530 K due to desorption of dissociatively adsorbed CO at step sites. In a second experiment, ion sputtering was used to create surface defects on a Ru(0 1 54) single crystal surface. A gradual shift away from the desorption spectrum typical of a Ru(001) surface towards one resembling desorption from supported Ru nanoparticles was observed with increasing sputter time. © 2011 the Owner Societies

Polarimetric SAR Image Segmentation with B-Splines and a New Statistical Model

We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GHP distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or manually, and the proposed algorithm uses a deformable contours technique to find the boundary. In doing this, the parameters of the polarimetric GHP model for the data are estimated, in order to find the transition points between the region being segmented and the surrounding area. This is a local algorithm since it works only on the region to be segmented. Results of its performance are presented

Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage

Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we use meta-genomic analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explains a minor part of the AMR variation that we observe. We find no evidence for cross-selection between antimicrobial classes, or for effect of air travel between sites. However, AMR gene abundance strongly correlates with socio-economic, health and environmental factors, which we use to predict AMR gene abundances in all countries in the world. Our findings suggest that global AMR gene diversity and abundance vary by region, and that improving sanitation and health could potentially limit the global burden of AMR. We propose metagenomic analysis of sewage as an ethically acceptable and economically feasible approach for continuous global surveillance and prediction of AMR.Peer reviewe

Peri-ictal heart rate variability parameters as surrogate markers of seizure severity.

This study aims at defining objective parameters reflecting the severity of peri-ictal autonomic changes and their relation to post-ictal generalized electroencephalography (EEG) suppression (PGES), with the view that such changes could be detected by wearable seizure detection systems and prove useful to assess the risk of sudden unexpected death in epilepsy (SUDEP). To this purpose, we assessed peri-ictal changes in heart rate variability (HRV) and correlated them with seizure duration, intensity of electromyography-based ictal muscle activity, and presence and duration of post-ictal generalized EEG suppression (PGES). We evaluated 75 motor seizures from 40 patients, including 61 generalized tonic-clonic seizures (GTCS) and 14 other major motor seizure types. For all major motor seizures, HRV measurements demonstrated a significantly decreased parasympathetic activity and increased sympathetic activity in the post-ictal period. The post-ictal increased sympathetic activity was significantly higher for GTCS as compared with non-GTCS. The degree of peri-ictal decreased parasympathetic activity and increased sympathetic activity was associated with longer PGES (>20 s), longer seizure duration, and greater intensity of ictal muscle activity. Mean post-ictal heart rate (HR) was an independent predictor of PGES duration, seizure duration, and intensity of ictal muscle contraction. Our results indicate that peri-ictal changes in HRV are potential biomarkers of major motor seizure severity