Effects of Force Modulation on Large Muscles during Human Cycling

Voluntary force modulation is defined as the ability to tune the application of force during motion. However, the mechanisms behind this modulation are not yet fully understood. In this study, we examine muscle activity under various resistance levels at a fixed cycling speed. The main goal of this research is to identify significant changes in muscle activation related to the real-time tuning of muscle force. This work revealed significant motor adaptations of the main muscles utilized in cycling as well as positive associations between the force level and the temporal and spatial inter-cycle stability in the distribution of sEMG activity. From these results, relevant biomarkers of motor adaptation could be extracted for application in clinical rehabilitation to increase the efficacy of physical therapy.This research was funded by Generalitat Valenciana (grant number GV/2019/025) and the Kakenhi National Japanese Grant for Early-Career Scientists (grant number 18K18431)

Quantification of Extent of Muscle-skin Shifting by Traversal sEMG Analysis Using High-density sEMG Sensor

Averaging electromyographic activity prior to muscle synergy computation is a common method employed to compensate for the inter-repetition variability usually associated with this kind of physiological recording. Capturing muscle synergies requires the preservation of accurate temporal and spatial information for muscle activity. The natural variation in electromyography data across consecutive repetitions of the same task raises several related challenges that make averaging a non-trivial process. Duration and triggering times of muscle activity generally vary across different repetitions of the same task. Therefore, it is necessary to define a robust methodology to segment and average muscle activity that deals with these issues. Emerging from this need, the present work proposes a standard protocol for segmenting and averaging muscle activations from periodic motions in a way that accurately preserves the temporal and spatial information contained in the original data and enables the isolation of a single averaged motion period. This protocol has been validated with muscle activity data recorded from 15 participants performing elbow flexion/extension motions, a series of actions driven by well-established muscle synergies. Using the averaged data, muscle synergies were computed, permitting their behavior to be compared with previous results related to the evaluated task. The comparison between the method proposed and a widely used methodology based on motion flags, shown the benefits of our system maintaining the consistency of muscle activation timings and synergie

Generation of Human-Like Movement from Symbolized Information

An important function missing from current robotic systems is a human-like method for creating behavior from symbolized information. This function could be used to assess the extent to which robotic behavior is human-like because it distinguishes human motion from that of human-made machines created using currently available techniques. The purpose of this research is to clarify the mechanisms that generate automatic motor commands to achieve symbolized behavior. We design a controller with a learning method called tacit learning, which considers system–environment interactions, and a transfer method called mechanical resonance mode, which transfers the control signals into a mechanical resonance mode space (MRM-space). We conduct simulations and experiments that involve standing balance control against disturbances with a two-degree-of-freedom inverted pendulum and bipedal walking control with humanoid robots. In the simulations and experiments on standing balance control, the pendulum can become upright after a disturbance by adjusting a few signals in MRM-space with tacit learning. In the simulations and experiments on bipedal walking control, the robots realize a wide variety of walking by manually adjusting a few signals in MRM-space. The results show that transferring the signals to an appropriate control space is the key process for reducing the complexity of the signals from the environment and achieving diverse behavior

Transcriptome Profiling of Lotus japonicus Roots During Arbuscular Mycorrhiza Development and Comparison with that of Nodulation

To better understand the molecular responses of plants to arbuscular mycorrhizal (AM) fungi, we analyzed the differential gene expression patterns of Lotus japonicus, a model legume, with the aid of a large-scale cDNA macroarray. Experiments were carried out considering the effects of contaminating microorganisms in the soil inoculants. When the colonization by AM fungi, i.e. Glomus mosseae and Gigaspora margarita, was well established, four cysteine protease genes were induced. In situ hybridization revealed that these cysteine protease genes were specifically expressed in arbuscule-containing inner cortical cells of AM roots. On the other hand, phenylpropanoid biosynthesis-related genes for phenylalanine ammonia-lyase (PAL), chalcone synthase, etc. were repressed in the later stage, although they were moderately up-regulated on the initial association with the AM fungus. Real-time RT–PCR experiments supported the array experiments. To further confirm the characteristic expression, a PAL promoter was fused with a reporter gene and introduced into L. japonicus, and then the transformants were grown with a commercial inoculum of G. mosseae. The reporter activity was augmented throughout the roots due to the presence of contaminating microorganisms in the inoculum. Interestingly, G. mosseae only colonized where the reporter activity was low. Comparison of the transcriptome profiles of AM roots and nitrogen-fixing root nodules formed with Mesorhizobium loti indicated that the PAL genes and other phenylpropanoid biosynthesis-related genes were similarly repressed in the two organs

Changes in conditional net survival and dynamic prognostic factors in patients with newly diagnosed metastatic prostate cancer initially treated with androgen deprivation therapy

Background The purpose of this study was to identify predictive factors associated with conditional net survival in patients with metastatic hormone-naive prostate cancer (mHNPC) initially treated with androgen deprivation therapy (ADT). Methods At nine hospitals in Tohoku, Japan, the medical records of 605 consecutive patients with mHNPC who initially received ADT were retrospectively reviewed. The Pohar Perme estimator was used to calculate conditional net cancer-specific survival (CSS) and overall survival (OS) for up to 5 years subsequent to the diagnosis. Using multiple imputation, proportional hazard ratios for conditional CSS and OS were calculated with adjusted Cox regression models. Results During a median follow up of 2.95 years, 208 patients died, of which 169 died due to progressive prostate cancer. At baseline, the 5-year CSS and OS rates were 65.5% and 58.2%, respectively. Conditional 5-year net CSS and OS survival gradually increased for all the patients. In patients given a 5-year survivorship, the conditional 5-year net CSS and OS rates improved to 0.906 and 0.811, respectively. Only the extent of disease score (EOD) >= 2 remained a prognostic factor for CSS and OS up to 5 years; as survival time increased, other variables were no longer independent prognostic factors. Conclusions The conditional 5-year net CSS and OS in patients with mHNPC gradually increased; thus, the risk of mortality decreased with increasing survival. The patient\u27s risk profile changed over time. EOD remained an independent prognostic factor for CSS and OS after 5-year follow-up. Conditional net survival can play a role in clinical decision-making, providing intriguing information for cancer survivors

Prognostic significance of early changes in serum biomarker levels in patients with newly diagnosed metastatic prostate cancer

We evaluated the impact of early changes in serum biomarker levels on the survival of patients with metastatic hormone-sensitive prostate cancer (mHSPC) who were initially treated with androgen deprivation therapy (ADT). We retrospectively investigated 330 patients with mHSPC whose serum maker levels were at baseline and at 2-4 months. An optimal Cox regression model was established with the highest optimism-corrected concordance index based on 10-fold cross-validation. The median cancer-specific survival (CSS) and overall survival (OS) were 7.08 and 6.47 years (median follow-up, 2.53 years), respectively. In the final optimal Cox model with serum biomarker levels treated as time-varying covariates, prostate-specific antigen (PSA), hemoglobin (Hb), and alkaline phosphatase (ALP) significantly increased the risk of poor survival in the context of both CSS and OS. Kaplan-Meier curves stratified by the three risk factors of high PSA, low Hb and high ALP desmondtated that median OS were not reached with none of these factors, 6.47 years with one or two factors, and 1.76 years with all three factors. Early changes in serum biomarker levels after ADT may be good prognostic markers for the survival of patients with mHSPC

Emergence of Motor Synergy in Vertical Reaching Task via Tacit Learning

International audienceThe dynamics of multijoint limbs often causes complex dynamic interaction torques which are the inertial effect of other joints motion. It is known that Cerebellum takes important role in a motor learning by developing the internal model. In this paper, we propose a novel computational control paradigm in vertical reaching task which involves the management of interaction torques and gravitational effect. The obtained results demonstrate that the proposed method is valid for acquiring motor synergy in the system with actuation redundancy and resulted in the energy efficient solutions. It is highlighted that the tacit learning in vertical reaching task can bring computational adaptability and optimality with model-free and cost-function-free approach differently from previous studies

Synergetic Learning Control Paradigm for Redundant Robot to Enhance Error-Energy Index

International audienceIn order to perform energetically efficient motion as in human control, so-called optimization-based approach is commonly used in both robotics and neuroscience. Such an optimization approach can provide optimal solution when the prior dynamics information of the manipulator and the environment is explicitly given. However, the environment, where the robot faces with in a real world rarely has such a situation. The dynamics conditions change by the contact situation or the hand load for the manipulation task. Simple computational paradigm to realize both adaptability and learning is essential to bridge the gap between learning and control process in redundancy. We verify a novel synergetic learning control paradigm in reaching task of redundant manipulator. The performance in handling different dynamics conditions is evaluated in dual criteria of error-energy (E-E) coupling without prior knowledge of the given environmental dynamics and with model-optimization-free approach. This paper aims at investigating the ability of phenomenological optimization with the proposed human-inspired learning control paradigm for environmental dynamics recognition and adaptation, which is different from conventional model optimization approach. E-E index is introduced to evaluate not only the tracking performance, but also the error reduction rate per the energy consumption