Results were consistent with existing literature and offer useful, complementary insights to comparative genomics and physiological testing. Moreover, the implementation of the Gleukos web portal (http//sing-group.org/gleukos) offers a public service to those interested in targeting microbial carbohydrate metabolism for biotechnology and health applications.To test the feasibility of implementing multisensory (auditory and visual) stimulation in combination with electrodes placed on non-hair positions to design more efficient and comfortable Brain-computer interfaces (BCI). Fifteen volunteers participated in the experiments. They were stimulated by visual, auditory and multisensory stimuli set at 37, 38, 39 and 40Hz and at different phases (0°, 90°, 180° and 270°). The electroencephalogram (EEG) was measured from Oz, T7, T8, Tp9 and Tp10 positions. To evaluate the amplitude of the visual and auditory evoked potentials, the signal-to-noise ratio (SNR) was used and the accuracy of detection was calculated using canonical correlation analysis. Additionally, the volunteers were asked about the discomfort of each kind of stimulus. The multisensory stimulation allows for attaining higher SNR on every electrode. Non-hair (Tp9 and Tp10) positions attained SNR and accuracy similar to the ones obtained from occipital positions on visual stimulation. No significant difference was found on the discomfort produced by each kind of stimulation. The results demonstrated that multisensory stimulation can help in obtaining high amplitude steady-state evoked responses with a similar discomfort level. Then, it is possible to design a more efficient and comfortable hybrid-BCI based on multisensory stimulation and electrodes on non-hair positions. The current article proposes a new paradigm for hybrid-BCI based on steady-state evoked potentials measured from the area behind-the-ears and elicited by multisensory stimulation, thus, allowing subjects to achieve similar performance to the one achieved by visual-occipital BCI, but measuring the EEG on a more comfortable electrode location.Common spatial pattern (CSP) is an efficient algorithm widely used in feature extraction of EEG-based motor imagery classification. Traditional CSP depends only on spatial filtering, that aims to maximize or minimize the ratio of variances of filtered EEG signals in different classes. Recent advances of CSP approaches show that temporal filtering is also preferable to extract discriminative features. In view of this perspective, a novel spatio-temporal filtering strategy is proposed in this paper. To improve computational efficiency and alleviate the overfitting issue frequently encountered in the case of small sample size, the same temporal filter is designed by EEG signals of the same class and shared by all the spatial channels. Spatial and temporal filters can be updated alternatively in practice. Furthermore, each of the resulting designs can still be cast as a CSP problem and tackled efficiently by the eigenvalue decomposition. To alleviate the adverse effects of outliers or noisy EEG channels, sparse spatial or temporal filters can also be achieved by incorporating an ℓ1-norm-based regularization term in our CSP problem. The regularized spatial or temporal filter design is iteratively reformulated as a CSP problem via the reweighting technique. Two sets of motor imagery EEG data of BCI competitions are used in our experiments to verify the effectiveness of the proposed algorithm.Although a well-established body of literature has examined electrophysiological muscle classification methods and systems, ways to enhance their transparency is still an important challenge and requires further study. In this work, a transparent semi-supervised electrophysiological muscle classification system which uses needle-detected EMG signals to classify muscles as normal, myopathic, or neurogenic is proposed. The electrophysiological muscle classification (EMC) problem is naturally formulated using multiple instance learning (MIL) and needs an adaptation of standard supervised classifiers for the purpose of training and evaluating bags of instances. Here, a novel MIL-based EMC system in which the muscle classifier uses predictions based on motor unit potentials (MUPs) to infer muscle labels is described. This system uses morphological, stability, near fiber and spectral MUP features. Quantitative results obtained from applying the proposed transparent system to four electrophysiologically different groups of muscles, composed of proximal and distal hand and leg muscles, resulted in an average classification accuracy of 95.85%. The findings show the superior and stable performance of the proposed EMC system compared to previous works using other supervised, semi-supervised and unsupervised methods.With advances in robotic prostheses, researchers attempt to improve amputee's gait performance (e.g., gait symmetry) beyond restoring normative knee kinematics/kinetics. Yet, little is known about how the prosthesis mechanics/control influence wearer-prosthesis' gait performance, such as gait symmetry, stability, etc. This study aimed to investigate the influence of robotic transfemoral prosthesis mechanics on human wearers' gait symmetry. The investigation was enabled by our previously designed reinforcement learning (RL) supplementary control, which simultaneously tuned 12 control parameters that determined the prosthesis mechanics throughout a gait cycle. The RL control design facilitated safe explorations of prosthesis mechanics with the human in the loop.  https://www.selleckchem.com/products/Phlorizin(Phloridzin).html  Subjects were recruited and walked with a robotic transfemoral prosthesis on a treadmill while the RL controller tuned the control parameters. Stance time symmetry, step length symmetry, and bilateral anteroposterior (AP) impulses were measured. The data analysis showed that changes in robotic knee mechanics led to movement variations in both lower limbs and therefore gait temporal-spatial symmetry measures. Consistent across all the subjects, inter-limb AP impulse measurements explained gait symmetry the stance time symmetry was significantly correlated with the net inter-limb AP impulse, and the step length symmetry was significantly correlated with braking and propulsive impulse symmetry. The results suggest that it is possible to personalize transfemoral prosthesis control for improved temporal-spatial gait symmetry. However, adjusting prosthesis mechanics alone was insufficient to maximize the gait symmetry. Rather, achieving gait symmetry may require coordination between the wearer's motor control of the intact limb and adaptive control of the prosthetic joints. The results also indicated that the RL-based prosthesis tuning system was a potential tool for studying wearer-prosthesis interactions.