Comparison between the degree of motor unit short-term synchronization and recurrence quantification analysis of the surface EMG in two human muscles

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Interpretable machine learning models for classifying low back pain status using functional physiological variables.

PURPOSE:To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. METHODS:Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. RESULTS:Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text]  = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] =  0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION:The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material

Stability of dissolved and soluble Fe(II) in shelf sediment pore waters and release to an oxic water column

Shelf sediments underlying temperate and oxic waters of the Celtic Sea (NW European Shelf) were found to have shallow oxygen penetrations depths from late spring to late summer (2.2–5.8 mm below seafloor) with the shallowest during/after the spring-bloom (mid-April to mid-May) when the organic carbon content was highest. Sediment porewater dissolved iron (dFe, 85%) consisted of Fe(II) and gradually increased from 0.4 to 15 μM at the sediment surface to ~100–170 µM at about 6 cm depth. During the late spring this Fe(II) was found to be mainly present as soluble Fe(II) (>85% sFe, 7 h. Iron(II) oxidation experiments in core top and bottom waters also showed removal from solution but at rates up to 5-times slower than predicted from theoretical reaction kinetics. These data imply the presence of ligands capable of complexing Fe(II) and supressing oxidation. The lower oxidation rate allows more time for the diffusion of Fe(II) from the sediments into the overlying water column. Modelling indicates significant diffusive fluxes of Fe(II) (on the order of 23–31 µmol m−2 day−1) are possible during late spring when oxygen penetration depths are shallow, and pore water Fe(II) concentrations are highest. In the water column this stabilised Fe(II) will gradually be oxidised and become part of the dFe(III) pool. Thus oxic continental shelves can supply dFe to the water column, which is enhanced during a small period of the year after phytoplankton bloom events when organic matter is transferred to the seafloor. This input is based on conservative assumptions for solute exchange (diffusion-reaction), whereas (bio)physical advection and resuspension events are likely to accelerate these solute exchanges in shelf-seas