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A physical trainer often physically guides a learner’s limbs to teach an ideal movement, giving the learner proprioceptive information about the movement to be reproduced later. This instruction requires the learner to perceive kinesthetic information and store the instructed information temporarily. Therefore, (1) proprioceptive acuity to accurately perceive the taught kinesthetics and (2) short-term memory to store the perceived information are two critical functions for reproducing the taught movement. While the importance of proprioceptive acuity and short-term memory has been suggested for active motor learning, little is known about passive motor learning. Twenty-one healthy adults (mean age 25.6 years, range 19–38 years) participated in this study to investigate whether individual learning efficiency in passively guided learning is related to these two functions. Consequently, learning efficiency was significantly associated with short-term memory capacity. In particular, individuals who could recall older sensory stimuli showed better learning efficiency. However, no significant relationship was observed between learning efficiency and proprioceptive acuity. A causal graph model found a direct influence of memory on learning and an indirect effect of proprioceptive acuity on learning via memory. Our findings suggest the importance of a learner’s short-term memory for effective passive motor learning.

Sports trainers often grasp and move trainees’ limbs to give instructions on desired movements, and a merit of this passive training is the transferring of instructions via proprioceptive information. However, it remains unclear how passive training affects the proprioceptive system and improves learning. This study examined changes in proprioceptive acuity due to passive training to understand the underlying mechanisms of upper extremity training. Participants passively learned a trajectory of elbow-joint movement as per the instructions of a single-arm upper extremity exoskeleton robot, and the performance of the target movement and proprioceptive acuity were assessed before and after the training. We found that passive training improved both the reproduction performance and proprioceptive acuity. We did not identify a significant transfer of the training effect across arms, suggesting that the learning effect is specific to the joint space. Furthermore, we found a significant improvement in learning performance in another type of movement involving the trained elbow joint. These results suggest that participants form a representation of the target movement in the joint space during the passive training, and intensive use of proprioception improves proprioceptive acuity.

Sit-to-stand (STS) assistance should not only reduce effort but also preserve or shape neuromuscular activity patterns. We propose a data-driven control strategy for an assistive chair with two degrees of freedom (vertical and horizontal seat motion) to infer desired multi-muscle activation during STS. The chair is parameterized by four binary variables (fast/slow vertical and horizontal velocities, and early/late onset timing for each axis), yielding 16 control combinations. Surface EMG from eight lower-limb muscles was collected from six healthy adult males across all control combinations (10 trials per condition). We extracted hundred-dimensional EMG features by segmenting STS into four phases and computing summary statistics per muscle and phase. Four L 1 -regularized logistic regression classifiers were trained to infer each control variable from EMG features, enabling a classifier-based statistical mapping from target EMG features to chair control parameters. The classifiers achieved F-scores of 0.96 and 0.99 for forward and upward speed, and 0.89 and 0.82 for forward and upward timing, respectively. In an offline evaluation, the estimated control parameters inferred EMG feature patterns significantly closer to the target than non-target parameter combinations. These results suggest that low-DoF seat motion can be used to modulate higher-dimensional muscle activation patterns during STS, providing a basis for future real-time and individualized assistive control.

This study introduces a body-weight-support (BWS) robot actuated by two pneumatic artificial muscles (PAMs). Conventional BWS devices typically use springs or a single actuator, whereas our robot has a split force-controlled BWS (SF-BWS), in which two force-controlled actuators independently support the left and right sides of the user’s body. To reduce the experience of weight, vertical unweighting support forces are transferred directly to the user’s left and right hips through a newly designed harness with an open space around the shoulder and upper chest area to allow freedom of movement. A motion capture evaluation with three healthy participants confirmed that the proposed harness does not impede upper-body motion during laterally identical force-controlled partial BWS walking, which is quantitatively similar to natural walking. To evaluate our SF-BWS robot, we performed a force-tracking and split-force control task using different simulated load weight setups (40, 50, and 60 kg masses). The split-force control task, providing independent force references to each PAM and conducted with a 60 kg mass and a test bench, demonstrates that our SF-BWS robot is capable of shifting human body weight in the mediolateral direction. The SF-BWS robot successfully controlled the two PAMs to generate the desired vertical support forces.

Sit-to-stand ( ) and stand-to-sit ( ) motions, collectively referred to as STS motions, are fundamental movements for independent daily living. However, many individuals are unable to generate sufficient strength and balance during these motions, which increases the risk of fall-related accidents. Therefore, proper and timely mechanical assistance is needed to improve the quality of daily life for people with weak muscles or insufficient motor control. To support the development of such assistance, we analyzed and ) motions performed using two different strategies by means of electromyography. Muscle synergy analysis was used to provide a compact and physiologically interpretable description of myoelectric patterns, enabling systematic comparisons of neuromuscular control across the two movement strategies, namely the momentum transfer strategy and the stabilization strategy. Based on these findings, we further proposed a deep neural network framework to predict motion states prior to motion initiation. The experimental results demonstrated that at least four synergy patterns were sufficient to represent these STS motions. In addition, the proposed method achieved an accuracy of 92.97 ± 0.86% with a forecasting time of 300 ms for motion state prediction, while the average temporal error remained consistently below 50 ms. These findings indicate that muscle synergy analysis can effectively characterize different STS movement strategies and that the proposed deep neural network framework can provide sufficient lead time for assistive device activation. This approach may contribute to the development of effective mechanical assistance systems for individuals with impaired muscle strength or motor control.

Exoskeleton robots necessitate the capacity to promptly generate appropriate assistance for the user’s needs across a range of motion scenarios. In this study, we developed a multitask assistance control strategy using a transformer that generates control commands to the exoskeleton robot according to the user’s status and the environment. Our approach captured the user’s joint and trunk motion and a first-person view image as input to the control system. A series of motion tasks were employed to validate the implemented AI with the proposed approach, including walking, squatting, and a step-up movement. Healthy subjects participated and the application of AI with our method to an exoskeleton robot reduced muscle loads. Moreover, the learned assist strategy was found to generalize, reducing muscle activity in another participant. These findings represent a first step toward achieving exoskeleton robot control that assists diverse movements across individuals in various environments using our transformer-based approach.

In this study, we propose an optimal assistive control strategy that uses estimated user's movement intention as the terminal cost function. We estimate the movement intention by observing human user's joint angle, angluar velocity, and muscle activities for very short period of time. A task-related low-dimensional feature space is extracted from the observed user's movement data. We assume that discrete number of optimal control laws associated to different target tasks are pre-computed. Then, the optimal assistive policy is derived by blending the pre-computed optimal control laws based on the linear Bellman combination method. Coefficients that determine how to blend the control laws are derived based on the low-dimensional feature value that represents the user's movement intention. To validate our proposed method, we conducted basketball throwing tasks. In these experiments, subjects were asked to throw a basketball into a hoop placed at different throwing distances. The distances from the throwing point to the hoop were estimated as the user's movement intention and the optimal control policies were derived by using our proposed method. The results showed that the basketball throwing performances of the subjects were mostly improved.

Cancer cells adapt to various stress conditions by optimizing gene expression profiles via transcriptional and translational regulation. However, whether and how EXOSC9, a component of the RNA exosome complex, regulates adaptation to stress conditions and tumorigenicity in cancer cells remain unclear. Here, we examined the effects of EXOSC9 depletion on cancer cell growth under various stress conditions. EXOSC9 depletion attenuated growth and survival under various stress conditions in cancer cells. Interestingly, this also decreased the number of P-bodies, which are messenger ribonucleoprotein particles (mRNPs) required for stress adaptation. Meanwhile, EXOSC2/EXOSC4 depletion also attenuated P-body formation and stress resistance with decreased EXOSC9 protein. EXOSC9-mediated stress resistance and P-body formation were found to depend on the intact RNA-binding motif of this protein. Further, RNA-seq analyses identified 343 EXOSC9-target genes, among which, APOBEC3G contributed to defects in stress resistance and P-body formation in MDA-MB-231 cells. Finally, EXOSC9 also promoted xenografted tumor growth of MDA-MB-231 cells in an intact RNA-binding motif-dependent manner. Database analyses further showed that higher EXOSC9 activity, estimated based on the expression of 343 target genes, was correlated with poorer prognosis in some cancer patients. Thus, drugs targeting activity of the RNA exosome complex or EXOSC9 might be useful for cancer treatment.

The agonistic/antagonistic biocharacter of selective estrogen receptor modulators (SERMs) can have therapeutic advantages, particularly in the case of premenopausal breast cancers. Although the contradictory effects of these modulators have been studied in terms of crosstalk between the estrogen receptor α (ER) and coactivator dynamics and growth factor signaling, the molecular basis of these mechanisms is still obscure. We identify a series of regulatory mechanisms controlling cofactor dynamics on ER and SERM function, whose activities require F-box protein 22 (Fbxo22). Skp1, Cullin1, F-box-containing complex (SCFFbxo22) ubiquitylated lysine demethylase 4B (KDM4B) complexed with tamoxifen-bound (TAM-bound) ER, whose degradation released steroid receptor coactivator (SRC) from ER. Depletion of Fbxo22 resulted in ER-dependent transcriptional activation via transactivation function 1 (AF1) function, even in the presence of SERMs. In living cells, TAM released SRC and KDM4B from ER in a Fbxo22-dependent manner. SRC release by TAM required Fbxo22 on almost all ER-SRC-bound enhancers and promoters. TAM failed to prevent the growth of Fbxo22-depleted, ER-positive breast cancers both in vitro and in vivo. Clinically, a low level of Fbxo22 in tumor tissues predicted a poorer outcome in ER-positive/human epidermal growth factor receptor type 2-negative (HER2-negative) breast cancers with high hazard ratios, independently of other markers such as Ki-67 and node status. We propose that the level of Fbxo22 in tumor tissues defines a new subclass of ER-positive breast cancers for which SCFFbxo22-mediated KDM4B degradation in patients can be a therapeutic target for the next generation of SERMs.

Given the lack of therapeutic targets, the conventional approach for managing triple-negative breast cancer (TNBC) involves the utilization of cytotoxic chemotherapeutic agents. However, most TNBCs acquire resistance to chemotherapy, thereby lowering the therapeutic outcome. In addition to oncogenic mutations in TNBC, microenvironment-induced mechanisms render chemoresistance more complex and robust in vivo. Here, we aimed to analyze whether depletion of Munc18-1 interacting protein 3 (Mint3), which activates hypoxia-inducible factor 1 (HIF-1) during normoxia, sensitizes TNBC to chemotherapy. We found that Mint3 promotes the chemoresistance of TNBC in vivo. Mint3 depletion did not affect the sensitivity of human TNBC cell lines to doxorubicin and paclitaxel in vitro but sensitized tumors of these cells to chemotherapy in vivo. Transcriptome analyses revealed that the Mint3–HIF-1 axis enhanced heat shock protein 70 (HSP70) expression in tumors of TNBC cells. Administering an HSP70 inhibitor enhanced the antitumor activity of doxorubicin in TNBC tumors, similar to Mint3 depletion. Mint3 expression was also correlated with HSP70 expression in human TNBC specimens. Mechanistically, Mint3 depletion induces glycolytic maladaptation to the tumor microenvironment in TNBC tumors, resulting in energy stress. This energy stress by Mint3 depletion inactivated heat shock factor 1 (HSF-1), the master regulator of HSP expression, via the AMP-activated protein kinase/mechanistic target of the rapamycin pathway following attenuated HSP70 expression. In conclusion, Mint3 is a unique regulator of TNBC chemoresistance in vivo via metabolic adaptation to the tumor microenvironment, and a combination of Mint3 inhibition and chemotherapy may be a good strategy for TNBC treatment.