Using Large-Scale Sensor Data to Test Factors Predictive of Perseverance in Home Movement Rehabilitation: Early Exercise Frequency and Schedule Consistency.

Home-based exercises are an important component of stroke rehabilitation but are seldom fully completed. Past studies of exercise perseverance in the general public have suggested the importance of early exercise frequency and schedule consistency (in terms of which days of the week exercises are performed) because they encourage habit formation. To test whether these observations apply after a stroke, we leveraged data from 2,583 users of a sensor-based system (FitMi) developed to motivate movement exercises at home. We grouped users based on their early exercise frequency (defined across the initial 6 weeks of use) and calculated the evolution of habit score (defined as exercise frequency multiplied by exercise duration) across 6 months. We found that habit score decayed exponentially over time but with a slower decay constant for individuals with higher early frequency. Only the group with an early exercise frequency of 4 days/week or more had non-zero habit score at six months. Within each frequency group, dividing individuals into higher and lower consistency subgroups revealed that the higher consistency subgroups had significantly higher habit scores. These results are consistent with previous studies on habit formation in exercise and may help in designing effective home rehabilitation programs after stroke.

The Profiles and Antecedents of Supervisor-Directed Emotional Labor Strategies: The Role of Self-Identity and LMX Orientations in Emotional Labor Strategy.

This study has two purposes. The first is to determine whether subordinates employ alternative combinations of emotion regulation strategies toward their supervisors beyond merely using surface and deep labor from the person-centered perspective. The second purpose is to understand why such acts of emotion regulation occur in interactions between employers and employees in the typical workplace. Utilizing latent profile analysis on data from 232 office employees in Beijing, China, collected using a two-stage sampling technique, four distinct supervisor-directed emotional labor profiles (i.e., deep actors, non-actors, moderators, and regulators) are identified. We find that these profiles are differentiated by several factors (i.e., individual identity, relational identity, and LMX orientations). Moreover, our findings suggest that employees exhibiting high levels of relational identity are more predisposed to act as deep actors, whereas individuals with high levels of individual identity are prone to being regulators as opposed to becoming deep actors, non-actors, or moderators. In addition, our results also suggest that LMX orientations have moderating effects on the relationships between self-identities and supervisor-directed emotional labor strategies. Overall, the results of this study expand the potential dimensionality of supervisor-directed emotion regulation strategies (e.g., regulating and non-acting) and bridge a gap in our understanding of the factors impacting supervisor-directed emotional labor.

Ankle dorsiflexion assistance of patients with foot drop using a powered ankle-foot orthosis to improve the gait asymmetry.

BACKGROUND: Foot drop is a neuromuscular disorder that causes abnormal gait patterns. This study developed a pneumatically powered ankle-foot orthosis (AFO) to improve the gait patterns of patients with foot drop. We hypothesized that providing unilateral ankle dorsiflexion assistance during the swing phase would improve the kinematics and spatiotemporal gait parameters of such patients. Accordingly, this study aims to examine the efficacy of the proposed assistance system using a strategy for joint kinematics and spatiotemporal gait parameters (stride length, swing velocity, and stance phase ratio). The analysis results are expected to provide knowledge for better design and control of AFOs in patients with foot drop. METHOD: Ten foot drop patients with hemiparesis (54.8 y ± 14.1 y) were fitted with a custom AFO with an adjustable calf brace and portable air compressor for ankle dorsiflexion assistance in the gait cycle during the swing phase. All subjects walked under two different conditions without extensive practice: (1) barefoot and (2) wearing a powered AFO. Under each condition, the patients walked back and forth on a 9-m track with ten laps of level ground under the supervision of licensed physical therapists. The lower-limb joint and trunk kinematics were acquired using 12 motion-capture cameras. RESULTS: We found that kinematic asymmetry decreased in the three lower-limb joints after ankle dorsiflexion assistance during the swing phase. The average ankle-joint angle increased after using the AFO during the entire gait cycle. Similarly, the knee-joint angle showed a slight increase while using the AFO, leading to a significantly decreased standard deviation within patients. Conversely, the hip-joint angle showed no significant improvements with assistance. While several patients exhibited noticeably lower levels of asymmetry, no significant changes were observed in the average asymmetry of the swing velocity difference between the affected and unaffected sides while using the AFO. CONCLUSION: We experimentally validated that ankle dorsiflexion assistance during the swing phase temporarily improves gait asymmetry in foot-drop patients. The experimental results also prove the efficacy of the developed AFO for gait assistance in foot-drop patients.

Effect of Dyadic Haptic Collaboration on Ankle Motor Learning and Task Performance.

Optimizing skill acquisition during novel motor tasks and regaining lost motor functions have been the interest of many researchers over the past few decades. One approach shown to accelerate motor learning involves haptically coupling two individuals through robotic interfaces. Studies have shown that an individual's solo performance during upper-limb tracking tasks may improve after haptically-coupled training with a partner. In this study, our goal was to investigate whether these findings can be translated to lower-limb motor tasks, more specifically, during an ankle position tracking task. Using one-degree-of-freedom ankle movements, pairs of participants (i.e., dyads) tracked target trajectories independently. Participants alternated between tracking trials with and without haptic coupling, achieved by rendering a virtual spring between two ankle rehabilitation robots. In our analysis, we compared changes in task performance across trials while training with and without haptic coupling. The tracking performance of both individuals (i.e., dyadic task performance) improved during haptic coupling, which was likely due to averaging of random errors of the dyadic pair during tracking. However, we found that dyadic haptic coupling did not lead to faster individual learning for the tracking task. These results suggest that haptic coupling between unimpaired individuals may not be an effective method of training ankle movements during a simple, one-degree-of-freedom task.

Toward a generalizable deep CNN for neural drive estimation across muscles and participants.

Objective.High-density electromyography (HD-EMG) decomposition algorithms are used to identify individual motor unit (MU) spike trains, which collectively constitute the neural code of movements, to predict motor intent. This approach has advanced from offline to online decomposition, from isometric to dynamic contractions, leading to a wide range of neural-machine interface applications. However, current online methods need offline retraining when applied to the same muscle on a different day or to a different person, which limits their applications in a real-time neural-machine interface. We proposed a deep convolutional neural network (CNN) framework for neural drive estimation, which takes in frames of HD-EMG signals as input, extracts general spatiotemporal properties of MU action potentials, and outputs the number of spikes in each frame. The deep CNN can generalize its application without retraining to HD-EMG data recorded in separate sessions, muscles, or participants.Approach.We recorded HD-EMG signals from the vastus medialis and vastus lateralis muscles from five participants while they performed isometric contractions during two sessions separated by ∼20 months. We identified MU spike trains from HD-EMG signals using a convolutive blind source separation (BSS) method, and then used the cumulative spike train (CST) of these MUs and the HD-EMG signals to train and validate the deep CNN.Main results.On average, the correlation coefficients between CST from the BSS and that from deep CNN were0.983±0.006for leave-one-out across-sessions-and-muscles validation and0.989±0.002for leave-one-out across-participants validation. When trained with more than four datasets, the performance of deep CNN saturated at0.984±0.001for cross validations across muscles, sessions, and participants.Significance.We can conclude that the deep CNN is generalizable across the aforementioned conditions without retraining. We could potentially generate a robust deep CNN to estimate neural drive to muscles for neural-machine interfaces.

A Deep CNN Framework for Neural Drive Estimation From HD-EMG Across Contraction Intensities and Joint Angles.

OBJECTIVE: Previous studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with assistive technologies. Despite the high estimation accuracy, current methods based on neural networks need to be trained with specific motor unit action potential (MUAP) shapes updated for each condition (i.e., varying muscle contraction intensities or joint angles). This preliminary step dramatically limits the potential generalization of these algorithms across tasks. We propose a novel approach to estimate the neural drive using a deep convolutional neural network (CNN), which can identify the cumulative spike train (CST) through general features of MUAPs from a pool of motor units. METHODS: We recorded HD-EMG signals from the gastrocnemius medialis muscle under three isometric contraction scenarios: 1) trapezoidal contraction tasks with different intensities, 2) contraction tasks with a trapezoidal or sinusoidal torque target, and 3) trapezoidal contraction tasks at different ankle angles. We applied a convolutive blind source separation (BSS) method to decompose HD-EMG signals to CST and segmented both signals into windows to train and validate the deep CNN. Then, we optimized the structure of the deep CNN and validated its generalizability across contraction tasks within each scenario. RESULTS: With the optimal configuration for the HD-EMG data window (overlap of 20 data points and window length of 40 data points), the deep CNN estimated the CST close to that from BSS, with a correlation coefficient higher than 0.96 and normalized root-mean-square-error lower than 7% with respect to the BSS (golden standard) within each scenario. CONCLUSION: The proposed deep CNN framework can utilize data from different contraction tasks (e.g., different intensities), learn general features of MUAP variants, and estimate the neural drive for other contraction tasks. SIGNIFICANCE: With the proposed deep CNN, we could potentially build a neural-drive-based human-machine interface that is generalizable to different contraction tasks without retraining.

sEMG-Triggered Fast Assistance Strategy for a Pneumatic Back Support Exoskeleton.

To prevent lower back pain (LBP) in the industrial workplace, various powered back support exoskeletons (BSEs) have been developed. However, conventional kinematics-triggered assistance (KA) strategies induce latency, degrading assistance efficiency. Therefore, we proposed and experimentally evaluated a surface electromyography (sEMG)-triggered assistance (EA) strategy. Nine healthy subjects participated in the lifting experiments: 1) external loads test, 2) extra latency test, and 3) repetitive lifting test. In the external loads test, subject performed lifting with four different external loads (0 kg, 7.5 kg, 15 kg, and 22.5 kg). The assistance was triggered earlier by EA compared to KA from 114 ms to 202 ms, 163 ms to 269 ms for squat and stoop lifting respectively, as external loads increased from 0 kg to 22.5 kg. In the extra latency test, the effects of extra latency (manual switch, 0 ms, 100 ms and 200 ms) in EA on muscle activities were investigated. Muscle activities were minimized in the fast assistance (0 ms and 100 ms) condition and increased with extra latency. In the repetitive lifting test, the EA strategy significantly reduced L1 muscle fatigue by 70.4% in stoop lifting, compared to KA strategy. Based on the experimental results, we concluded that fast assistance triggered by sEMG improved assistance efficiency in BSE and was particularly beneficial in heavy external loads situations. The proposed assistive strategy can be used to prevent LBP by reducing back muscle fatigue and is easily applicable to various industrial exoskeleton applications.

Fabrication of conductive silver paste recovered from leaching of waste catalyst using hydrochloric acid.

Transition metal compounds based on silver (Ag) and palladium (Pd) are extensively used as catalysts in the petrochemical industries. The catalytic activities of Ag and Pd decrease over time and hence need to be discarded. The recovery of elements like Ag from waste catalyst is essential because of its limited availability and cost, and it is environmentally beneficial with regards to recycling. In this study, Pd and Ag were leached from waste catalyst providing an alternative source suitable for a Ag paste electrode. Through an efficient reduction process, AgCl particles were obtained which serve as a precursor to synthesize Ag using ammonia as the solvent. The obtained Ag was fabricated to Ag paste by using mixed dispersion and solvent. The electrical resistivity of the Ag paste was recorded as 6.14 µΩ cm at 417 °C in a hydrogen atmosphere.

Human-machine-human interaction in motor control and rehabilitation: a review.

BACKGROUND: Human-human (HH) interaction mediated by machines (e.g., robots or passive sensorized devices), which we call human-machine-human (HMH) interaction, has been studied with increasing interest in the last decade. The use of machines allows the implementation of different forms of audiovisual and/or physical interaction in dyadic tasks. HMH interaction between two partners can improve the dyad's ability to accomplish a joint motor task (task performance) beyond either partner's ability to perform the task solo. It can also be used to more efficiently train an individual to improve their solo task performance (individual motor learning). We review recent research on the impact of HMH interaction on task performance and individual motor learning in the context of motor control and rehabilitation, and we propose future research directions in this area. METHODS: A systematic search was performed on the Scopus, IEEE Xplore, and PubMed databases. The search query was designed to find studies that involve HMH interaction in motor control and rehabilitation settings. Studies that do not investigate the effect of changing the interaction conditions were filtered out. Thirty-one studies met our inclusion criteria and were used in the qualitative synthesis. RESULTS: Studies are analyzed based on their results related to the effects of interaction type (e.g., audiovisual communication and/or physical interaction), interaction mode (collaborative, cooperative, co-active, and competitive), and partner characteristics. Visuo-physical interaction generally results in better dyadic task performance than visual interaction alone. In cases where the physical interaction between humans is described by a spring, there are conflicting results as to the effect of the stiffness of the spring. In terms of partner characteristics, having a more skilled partner improves dyadic task performance more than having a less skilled partner. However, conflicting results were observed in terms of individual motor learning. CONCLUSIONS: Although it is difficult to draw clear conclusions as to which interaction type, mode, or partner characteristic may lead to optimal task performance or individual motor learning, these results show the possibility for improved outcomes through HMH interaction. Future work that focuses on selecting the optimal personalized interaction conditions and exploring their impact on rehabilitation settings may facilitate the transition of HMH training protocols to clinical implementations.

Synergistic Integration of Chemo-Resistive and SERS Sensing for Label-Free Multiplex Gas Detection.

Practical sensing applications such as real-time safety alerts and clinical diagnoses require sensor devices to differentiate between various target molecules with high sensitivity and selectivity, yet conventional devices such as oxide-based chemo-resistive sensors and metal-based surface-enhanced Raman spectroscopy (SERS) sensors usually do not satisfy such requirements. Here, a label-free, chemo-resistive/SERS multimodal sensor based on a systematically assembled 3D cross-point multifunctional nanoarchitecture (3D-CMA), which has unusually strong enhancements in both "chemo-resistive" and "SERS" sensing characteristics is introduced. 3D-CMA combines several sensing mechanisms and sensing elements via 3D integration of semiconducting SnO2 nanowire frameworks and dual-functioning Au metallic nanoparticles. It is shown that the multimodal sensor can successfully estimate mixed-gas compositions selectively and quantitatively at the sub-100 ppm level, even for mixtures of gaseous aromatic compounds (nitrobenzene and toluene) with very similar molecular structures. This is enabled by combined chemo-resistive and SERS multimodal sensing providing complementary information.

Always-On Sub-Microwatt Spiking Neural Network Based on Spike-Driven Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device.

This paper presents a novel spiking neural network (SNN) classifier architecture for enabling always-on artificial intelligent (AI) functions, such as keyword spotting (KWS) and visual wake-up, in ultra-low-power internet-of-things (IoT) devices. Such always-on hardware tends to dominate the power efficiency of an IoT device and therefore it is paramount to minimize its power dissipation. A key observation is that the input signal to always-on hardware is typically sparse in time. This is a great opportunity that a SNN classifier can leverage because the switching activity and the power consumption of SNN hardware can scale with spike rate. To leverage this scalability, the proposed SNN classifier architecture employs event-driven architecture, especially fine-grained clock generation and gating and fine-grained power gating, to obtain very low static power dissipation. The prototype is fabricated in 65 nm CMOS and occupies an area of 1.99 mm2. At 0.52 V supply voltage, it consumes 75 nW at no input activity and less than 300 nW at 100% input activity. It still maintains competitive inference accuracy for KWS and other always-on classification workloads. The prototype achieved a power consumption reduction of over three orders of magnitude compared to the state-of-the-art for SNN hardware and of about 2.3X compared to the state-of-the-art KWS hardware.

High-Throughput NOx Removal by Two-Stage Plasma Honeycomb Monolith Catalyst.

A two-stage plasma catalyst system for high-throughput NOx removal was investigated. Herein, the plasma stage involved the large-volume plasma discharge of humidified gas and was carried out in a sandwich-type honeycomb monolith reactor consisting of a commercial honeycomb catalyst (50 mm high; 93 mm in diameter) located between two parallel perforated disks that formed the electrodes. The results demonstrated that, in the plasma stage, the reduction of NOx did not occur at room temperature; instead, NO was only oxidized to NO2 and n-heptane to oxygenated hydrocarbons. The oxidation of NO and n-heptane in the honeycomb plasma discharge state was largely affected by the humidity of the feed gas. Furthermore, the oxidation of NO to NO2 occurs preferably to that of n-heptane with a tendency of the NO oxidation to decrease with increasing feed gas humidity. The reason is that the generation of O3 decreases as the amount of water vapor in the feed gas increases. Compared to the catalyst alone, the two-stage plasma catalyst system increased NOx removal by 29% at a temperature of 200 °C and an energy density of 25 J/L.

Effective practical removal of acetaldehyde by a sandwich-type plasma-in-honeycomb reactor under surrounding ambient conditions.

The effective removal of acetaldehyde by humidified air plasma was investigated with a high throughput of contaminated gas in a sandwiched honeycomb catalyst reactor at surrounding ambient temperature. Here, acetaldehyde at the level of a few ppm was successfully oxidized by the honeycomb plasma discharge despite the harsh condition of large water content in the feed gas. The conversion rate of acetaldehyde increased significantly with the presence of catalysts coating on the surface channels. The increased conversion rate was also obtained with a high specific energy input (SEI) and total flow rate. Interestingly, the conversion changed negligibly under the acetaldehyde concentration range from 5 to 20 ppm. However, the conversion rate decreased toward increased water amount in the feed gas. Notably, about 60% of acetaldehyde in the feed was oxidized under SEI of 40 J/L at water amounts ≤ 2.5%, approximately 0.5 g/kWh for acetaldehyde removal. Also, the plasma-catalyst reaction was superior to the thermal reactive catalyst for acetaldehyde removal in airborne pollutants. In comparison with other plasma-catalyst sources for acetaldehyde removal, the energy efficiency under the condition is comparable. Moreover, the honeycomb plasma discharge features high throughput, avoiding pressure drop, and straightforward reactor configuration, suggesting potential practical applications.

A convolutional neural network to identify motor units from high-density surface electromyography signals in real time.

Objectives. This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signals in real time. Two distinct deep CNNs are compared with the convolution kernel compensation (CKC) algorithm using simulated and experimentally recorded signals. The effects of window size and step size of the input HD-EMG signals are also investigated.Approach. The MU spike trains were first identified with the CKC algorithm. The HD-EMG signals and spike trains were used to train the deep CNN. Then, the deep CNN decomposed the HD-EMG signals into MU discharge times in real time. Two CNN approaches are compared with the CKC: (a) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and (b) one multiple-output deep CNN (MO-DCNN) to decompose all MUs (up to 23) with one network.Main results. The MO-DCNN outperformed the SO-DCNN in terms of training time (3.2-21.4 s epoch-1vs 6.5-47.8 s epoch-1, respectively) and prediction time (0.04 vs 0.27 s sample-1, respectively). The optimal window size and step size for MO-DCNN were 120 and 20 data points, respectively. It results in sensitivity of 98% and 85% with simulated and experimentally recorded HD-EMG signals, respectively. There is a high cross-correlation coefficient between the neural drive estimated with CKC and that estimated with MO-DCNN (range ofr-value across conditions: 0.88-0.95).Significance. We demonstrate the feasibility and the validity of using deep CNN to accurately identify MU activity from HD-EMG with a latency lower than 80 ms, which falls within the lower bound of the human electromechanical delay. This method opens many opportunities for using the neural drive to interface humans with assistive devices.

Dependence of humidified air plasma discharge performance in commercial honeycomb monoliths on the configuration and key parameters of the reactor.

The effect of the reactor configuration and several key parameters such as the gas temperature, humidity, and flow rate on the corona discharge plasma in honeycomb monoliths was investigated. The AC corona discharge-based plasma reactor consisted of two parallel electrodes (perforated disk/wire-mesh) placed at both ends of the honeycomb monolith. Although the wire-mesh electrode offers increased sharpness, the perforated disk electrode, where the corona discharge started at the sharp edges of the holes, produced more discharge power because of the larger effective electrode area. Loading a small amount of metal onto the monolith was found to increase the discharge power significantly. Coating the monolith with a zeolite such as ZSM-5 (Si/Al: 23.9) led to a decrease in the discharge power because of its hydrophobic nature and large surface area. The result also revealed that the operating temperature, the humidity of the feed gas, and the gas velocity were key factors affecting the discharge performance. The discharge power was inversely proportional to the temperature. On the other hand, the use of a high-velocity feed gas with high water vapor content was found to be particularly advantageous for obtaining high discharge power.

Nonthermal plasma in practical-scale honeycomb catalysts for the removal of toluene.

Nonthermal plasma combined with a practical-scale honeycomb catalyst of 5.0 cm in height and 9.3 cm in diameter was investigated for the removal of toluene. The creation of plasma in the honeycomb catalyst greatly depended on the humidity of the feed gas and the presence of metals on the honeycomb surface. Compared to the bare ceramic honeycomb, the metal-loaded one gave higher toluene removal efficiency because the decomposition of toluene by the plasma-generated reactive species occurred not only homogeneously in the gas phase but also heterogeneously on the catalyst surface. The present plasma-catalytic reactor was able to successfully remove about 80% of dilute toluene (15 ppm in air) at a large flow rate of 60 L/min with a specific energy input of 58 J/L. The honeycomb-based plasma-catalytic reactor system is promising for practical applications since it can overcome such problems as high-pressure drop and difficulty in scale-up encountered in packed-bed reactors.

Free-Standing Carbon Nanofibers Protected by a Thin Metallic Iridium Layer for Extended Life-Cycle Li-Oxygen Batteries.

It is evident that the exhaustive use of fossil fuels for decades has significantly contributed to global warming and environmental pollution. To mitigate the harm on the environment, lithium-oxygen batteries (LOBs) with a high theoretical energy density (3458 Wh kg-1Li2O2) compared to that of Li-ion batteries (LIBs) have been considered as an attractive alternative to fossil fuels. For this purpose, porous carbon materials have been utilized as promising air cathodes owing to their low cost, lightness, easy fabrication process, and high performance. However, the challenge thus far lies in the uncontrollable formation of Li2CO3 at the interface between carbon and Li2O2, which is detrimental to the stable electrochemical performance of carbon-based cathodes in LOBs. In this work, we successfully protected the surface of the free-standing carbon nanofibers (CNFs) by coating it with a layer of iridium metal through direct sputtering (CNFs@Ir), which significantly improved the lifespan of LOBs. Moreover, the Ir would play a secondary role as an electrochemical catalyst. This all-in-one cathode was evaluated for the formation and decomposition of Li2O2 during (dis)charging processes. Compared with bare CNFs, the CNFs@Ir cathode showed two times longer lifespan with 0.2 VLi lower overpotentials for the oxygen evolution reaction. We quantitatively calculated the contents of CO32- in Li2CO3 formed on the different surfaces of the bare CNFs (63% reduced) and the protected CNFs@Ir (78% reduced) cathodes after charging. The protective effects and the reaction mechanism were elucidated by ex situ analyses, including scanning electron microscopy, transmission electron microscopy, and X-ray photoelectron spectroscopy.

Pneumatic AFO Powered by a Miniature Custom Compressor for Drop Foot Correction.

For active AFO applications, pneumatic remote transmission has advantages in minimizing the mass and complexity of the system due to the flexibility in placing pneumatic components and providing high back-drivability via simple valve control. However, pneumatic systems are generally tethered to large stationary air compressors, which greatly limit the practical daily usage. In this study, we implemented a wearable custom compressor that can be worn at the trunk of the body and can generate up to 1050 kPa of pressurized air to power an unilateral active AFO for dorsiflexion (DF) assistance of drop-foot patients. In order to minimize the size and weight of the custom compressor, the compression rate of the custom compressor was optimized to the rate of consumption required to power the active AFO. The finalized system can provide a maximum assistive torque of 9.8 Nm at a functional frequency of 1 Hz and the average resistive torque during free movement was 0.03 Nm. The system was tested for five hemiplegic drop-foot patients. The proposed system showed an average improvement of 12.3° of ankle peak dorsiflexion angle during the mid to late swing phase.

Dynamin regulates the dynamics and mechanical strength of the actin cytoskeleton as a multifilament actin-bundling protein.

The dynamin GTPase is known to bundle actin filaments, but the underlying molecular mechanism and physiological relevance remain unclear. Our genetic analyses revealed a function of dynamin in propelling invasive membrane protrusions during myoblast fusion in vivo. Using biochemistry, total internal reflection fluorescence microscopy, electron microscopy and cryo-electron tomography, we show that dynamin bundles actin while forming a helical structure. At its full capacity, each dynamin helix captures 12-16 actin filaments on the outer rim of the helix. GTP hydrolysis by dynamin triggers disassembly of fully assembled dynamin helices, releasing free dynamin dimers/tetramers and facilitating Arp2/3-mediated branched actin polymerization. The assembly/disassembly cycles of dynamin promote continuous actin bundling to generate mechanically stiff actin super-bundles. Super-resolution and immunogold platinum replica electron microscopy revealed dynamin along actin bundles at the fusogenic synapse. These findings implicate dynamin as a unique multifilament actin-bundling protein that regulates the dynamics and mechanical strength of the actin cytoskeletal network.