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BackgroundPurposeMethodsResultsConclusionAugmented reality gait adaptive training (ARGAT) and repetitive transcranial magnetic stimulation (rTMS) have both demonstrated efficacy in improving lower limb motor function in survivors of stroke.To investigate the effects of combining rTMS and ARGAT on motor function in survivors of stroke.The experimental group received a combination of rTMS and ARGAT, while the control group received ARGAT alone. The interventions comprised a total of 20 sessions, conducted over four weeks with five consecutive daily sessions. Outcome measures included three-dimensional gait analysis (3DGA), surface electromyography (sEMG), Fugl-Meyer assessment for the lower extremity (FMA-LE), and the Berg Balance Scale (BBS).Following the intervention, both groups showed significant improvements in walking speed, symmetry index, affected step length, affected stride length, FMA-LE, and BBS scores (p < .05). Furthermore, the experimental group demonstrated greater improvements in walking speed (F = 4.58, p = .040), cadence (F = 5.67, p = .023), affected step length (F = 5.79, p = .022), affected stride length (F = 4.84, p = .035), FMA-LE (Z = 2.43, p = .019), and BBS (F = 4.76, p = .036) compared to the control group. The experimental group demonstrated a significant improvement in the co-contraction index (CCI) of the knee joint (F = 14.88, p < .001), a change not observed in the control group (F = 2.16, p = .151). However, neither group showed significant alterations in CCI of the ankle joint (F = 1.58, p = .218), step width (F = 0.24, p = .630), unaffected step length (F = 0.22, p = .641), or unaffected stride length (F = 2.99, p = .093).The combination of low-frequency rTMS and ARGAT demonstrated superior effects on motor function recovery compared to ARGAT alone in survivors of stroke. [ABSTRACT FROM AUTHOR]

Honey bees have three castes, drones, workers, and queens, that accomplish different tasks. In this research, we revealed the divergence in feeding behavior of drones and workers by experimental and theoretical techniques from the perspective of tongue kinematics. We observed the feeding cycles from living drones and workers of Apis mellifera ligustica and introduced a fluid transport model for computing the energy consumption during nectar feeding. We derived the theoretically-optimal time apportionment ratios (TARs) in the tongue protraction and retraction phases of drones and workers under the objective function of maximizing the efficiency of nectar uptake. By comparing the theoretically-optimal TARs with the real ones, we drew a conclusion that there is a dichotomy in nectar-feeding behavior between drones and workers. The actual TAR of drones is not consistent with the theoretically-optimal TAR while the actual TAR of workers is. This dichotomy may be caused by caste-related labor and environmental constraints. [ABSTRACT FROM AUTHOR]

The phenomena of friction in clearance joints have time- dependent nonlinear characteristics, which could lead to undesirable and unstable dynamic behavior of mechanical systems. Various mathematical models have been proposed to describe friction force in clearance joints. However, most of the previous models have different limitations, such as discontinuity at zero velocity or disability of detecting stick-slip motion. In this paper, a hybrid friction model is presented to analyze the dynamics of planar mechanical systems, considering sliding, sticking and Stribeck effect in revolute clearance joints. According to the kinematic analysis, the relative tangential motion between the journal and the bearing is categorized as sticking state, sliding state and transitional state. In the sticking state, an assumed acceleration constraint for the contact points is proposed to estimate the static friction force. This method not only eliminates the calculation of the implicit external force, but also accurately reflects the sticking process in accordance with the friction physics. Furthermore, the Stribeck effect is introduced in the description of the friction force in the sliding state, revealing the influence of the sliding velocity on the friction force. The stick-slip motion could also be accurately described by the Stribeck effect, which guarantees the continuity of the friction force during the transition process. Finally, a space robot manipulator is taken as an example to demonstrate the hybrid friction model. Dynamics of the space robot manipulator with Coulomb friction model, LuGre friction model, Wojewoda friction model and the hybrid friction model are compared, respectively. And the effects of clearance size, static friction coefficient and sliding friction coefficient on the dynamics of the manipulator are also analyzed. [ABSTRACT FROM AUTHOR]

Background: Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related retinal detachment (HMRD) is crucial for effective treatment and prevention of additional vision impairment. Consequently, our objective was to streamline and validate a machine-learning model based on clinical laboratory omics (clinlabomics) for the early detection of RD in HM patients. Methods: We extracted clinlabomics data from the electronic health records for 24,440 HM and 5607 HMRD between 2015 and 2022. Lasso regression analysis assessed fifty-nine variables, excluding collinear variables (variance inflation factor > 10). Four models based on random forest, gradient boosting machine (GBM), generalized linear model, and Deep Learning Model were trained for HMRD diagnosis and employed for internal validation. An external test of the models was done. Three random data sets were further processed to validate the performance of the diagnostic model. The primary outcomes were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) to diagnose HMRD. Results: Nine variables were selected by all models. Given the AUC and AUCPR values across the different sets, the GBM model was chosen as the final diagnostic model. The GBM model had an AUC of 0.8550 (95%CI = 0.8322–0.8967) and an AUCPR of 0.5584 (95%CI = 0.5250–0.5879) in the training set. The AUC and AUCPR in the internal validation were 0.8405 (95%CI = 0.8060–0.8966) and 0.5355 (95%CI = 0.4988–0.5732). During the external test evaluation, it reached an AUC of 0.7579 (95%CI = 0.7340–0.7840) and an AUCPR of 0.5587 (95%CI = 0.5345–0.5880). A similar discriminative capacity was observed in the three random data sets. The GBM model was well-calibrated across all the sets. The GBM-RD model was implemented into a web application that provides risk prediction for HM individuals. Conclusion: GBM algorithms based on nine features successfully predicted the diagnosis of RD in patients with HM, which will help ophthalmologists to establish a preliminary diagnosis and to improve diagnostic accuracy in the clinic. [ABSTRACT FROM AUTHOR]

Bioinspired Continuum Robots with Programmable Stiffness by Harnessing Phase Change Materials (Adv. Bioinspired continuum robot, conformal interaction, phase change materials, programmable stiffness, tensegrity structure Keywords: bioinspired continuum robot; conformal interaction; phase change materials; programmable stiffness; tensegrity structure EN bioinspired continuum robot conformal interaction phase change materials programmable stiffness tensegrity structure 1 1 1 03/28/23 20230315 NES 230315 B Bioinspired Continuum Robots b In article number 2201616, Ji Liu, Haijun Peng, Jianing Wu, and co-workers present a three-segment continuum robot constructed by a class-3 tensegrity structure, inspired by the mechanism of an elephant trunk for regulating local stiffness. [Extracted from the article]