Motion Transition Research Articles

Probing rotaxane dynamics with 19F NMR/MRI: Unveiling the roles of mechanical bond and steric hindrance

BackgroundDeciphering the molecular dynamics (MD) of rotaxanes is crucial for designing and refining their applications in molecular devices. This study employed fluorine-19 nuclear magnetic resonance (19F NMR) and magnetic resonance imaging (MRI) to unveil the interplay between mechanical bonds and steric hindrance in a series of fluorinated rotaxanes. Results1H/19F NMR revealed stable “Z”-shaped wheel conformations minimizing steric clashes and favoring π-π interactions with the axle. Utilizing fluorines and axle protons as reporters, 1H/19F relaxation rates and solid-state 19F NMR studies demonstrated that mechanical bond primarily governs wheel motion, while steric hindrance dictates axle movement. Intriguingly, mechanical bond mainly affects local axle groups, leaving distant ones minimally impacted. MD simulations corroborated these findings. Temperature-dependent 19F NMR indicated that energy input enhances rotational motion and wheel conformational transitions. Furthermore, the drastic increase in 19F relaxation rates upon mechanical bond formation and steric hindrance enables sensitive and selective 19F MRI visualization of MD changes. SignificanceThis study, by elucidating the roles of internal and external factors on rotaxane molecular dynamics using 19F NMR/MRI, offers valuable insights that can advance the field of rotaxane-based molecular devices.

Motion Control of a Hybrid Quadruped-Quadrotor Robot

Multimodal motion capability is an emerging topic in the robotics field, and this paper presents a hybrid robot system maneuvering in both terrestrial and aerial environments. Firstly, a micro quadruped–quadrotor robot with onboard sensing and computing is developed. This robot incorporates both the high mobility of unmanned aerial vehicles and the long endurance of mobile robots on the ground. A coordinated motion control scheme is then exploited for adaptive terrestrial–aerial motion transition. In this scheme, a bio-inspired terrestrial locomotion controller is proposed to generate various quadruped locomotions, and a model-based aerial locomotion controller is proposed to generate various quadrotor configurations. Then, an unified motion controller for the two subsystems which dynamically adjusts crawling and flying motion in a complicated environment is presented. Consequently, several practical trials are conducted to demonstrate the adaptability and the robustness of the proposed system.

Power Extraction Performance by a Hybrid Non-Sinusoidal Pitching Motion of an Oscillating Energy Harvester

This study proposes a hybrid pitching motion for oscillating flat plates aimed at augmenting the energy extraction efficiency of an energy harvester. The proposed hybrid pitching motion, within the first half cycle, integrates a non-sinusoidal movement starting at t/T = 0 and progressing to t/T = 0.25, with a sinusoidal movement initiating after t/T > 0.25 and continuing to t/T = 0.5. The second half of the cycle is symmetric to the first half but in the opposite direction. The calculated results show that the proposed hybrid pitching motion outperforms both the sinusoidal and the non-sinusoidal motions. The hybrid pitching motion merges the merits of both the sinusoidal and non-sinusoidal motions to optimize pitch angle variation. This integration is pivotal for enhancing the overall power output performance of an oscillating energy harvester characterized by momentum change that enhances the orientation of the heaving movement, smoother motion transitions, and consistent energy harvesting. The power generation is obtained at wing pitch angles of 55°, 65°, 70°, 75°, and 80° during a hybrid pitching motion. The proposed hybrid pitching motion, set at a pitch angle of 70°, achieves a maximum power output that exceeds the oscillating flat plate using a sinusoidal pitching motion by 16.0% at the same angle.

A hollow magnetic soft robot consisting of rod-shaped nanofiber actuators

Magnetic soft robots are prominent in biological environments due to the untethered movement and penetration under magnetic field. Moreover, in order to overcome biological barriers in microenvironment, the development of small size, biocompatible/biodegradable and precisely controlled mobility of magnetic soft robots is a promising. Thus, a hollow magnetic soft robot consisting of rod-shaped nanoactuators formed by the cleavage of photosensitive electrospun nanofibers is designed. The nanofiber actuators are composed of biodegradable polymer matrix and biocompatible superparamagnetic magnetic iron oxide nanoparticle fillers, and the uniform rod-shaped nanofibers are obtained from the molecule fracture of photosensitive copolymers under UV irradiation. Based on the bottom-up and top-down constructing concept, the hollow magnetic soft robots show unique property of the stabilized morphology, the programmed magnetization and the high order motion transition in an instant. This provides a strategy for designing and obtaining the adaptive magnetic soft robots, which can effectively improve the precise locomotion and fast movement efficiency in complex physiological environment.

Adaptive Locomotion Transition Recognition With Wearable Sensors for Lower Limb Robotic Prosthesis

Locomotion transition recognition with external disturbances is a key issue in lower limb robotic prostheses. Redonning the prosthetic socket and individual differences are encountered frequently in daily life. They change the feature distribution and further fail the previously trained recognition model. To bridge the technical gap between laboratory validation and practical demands, researchers need to construct an adaptive recognition method with high accuracy, quick response, time-efficient calibration, and minimal interference to the human body. Our study developed an adaptive recognition algorithm based on two miniaturized inertial sensors (anterior thigh and forefoot) and a foot pressure sensor, which can easily be integrated into the prosthesis. The algorithm fused probability-based fuzzy classifiers, which took heuristic-based features as inputs with a dynamic-time-warping-based automatic template generation block. It enables the recognition model to quickly fit the parameters for an untrained mode after external disturbances. We validated the proposed adaptive recognition method on 13 healthy subjects, performing 18 motion transitions with random interday intervals and across user evaluation, both offline and online. Even with the data of one subject as the prior knowledge, the other subjects produced an average accuracy of 98.04% and an average transition time latency of −180.99 ms without manual training in interday and intersubject uses. We also tested the method on two subjects using robotic prostheses, one person with a transfemoral amputation and the other person with a transtibial amputation. The average accuracy was still as high as 98.06% (transfemoral) and 100% (transtibial) without manual calibration after seven days of intervals. The results proved that the adaptive recognition method is robust to sensor redonning and user changes. Compared with the state-of-the-art methods, our study makes a further step toward the practical use of robotic prostheses. Future studies will be conducted to implement the algorithm on the robotic prosthesis for more extensive tests.

Dynamic response and wave motion of a periodically supported beam under an ultra-high-speed load: Wave dispersion and critical velocities

Vacuum tube transportation system is an attractive transportation mode due to the ultra-high-speed (over 1000 km/h) it can reach. Most vacuum tube transportation systems, e.g., the Hyperloop operate in elevated tube structure on periodical supports. When the speed of the vehicle reaches certain critical values, the periodical supporting structure experiences resonance-like response whose vibrational magnitude is significantly amplified. The amplification of the response has an adverse impact on the safety of the vehicle operation and severability of the supporting structures. This work aims to boost the fundamental understanding of the underlying physical mechanism of wave motions and critical velocities in the “moving load on periodical structure” problem utilizing an ultra-high-speed Hyperloop-type transportation system as an example. In this work, an ultra-high-speed moving load on a periodically supported beam is used to model the dynamic response of the supporting structure under an ultra-high-speed travelling vehicle. The steady-state response of the periodically supported beam is solved analytically and illustrated for sub- and trans-critical speeds. The dispersion relation is obtained while deriving the steady-state response. The relation between wave propagation in a periodical structure and critical speeds of the moving load on it is established. It is shown that the critical speeds could be within the operational speed range of ultra-high-speed transportation system. The number and magnitude of critical speeds are closely related to wave motions in the periodically supported structure. It is found that the primary critical speeds associated with largest beam responses may not exist, depending on the relative flexibility of the supports. The optimal structural parameters are associated with wave motion transition (between Local Resonance and Bragg Scattering) in the supporting structure. With this simplified model, wave motions in a periodical structure and critical speeds of the moving load on it is explained in a general physical sense. This work provides theoretical basis for the structural design and operation of the ultra-high-speed transportation system, as well as other engineering applications in which ultra-high-speed moving load is involved.

Study of sedimentation characteristics of an elliptical squirmer in a vertical channel

Abstract We used a two-dimensional lattice Boltzmann method to simulate the sedimentation motion of an elliptical squirmer in a vertical channel, taking into account the case of a circular squirmer, aiming to more realistically simulate the swimming of microorganisms in nature. The study in this was divided into two phases. The first phase comprised the numerical calculations of an elliptical squirmer with an aspect ratio of c = 2.0 and revealed three typical motion modes: steady inclined motion, wall-attraction oscillation, and large-amplitude oscillation. It was found that the formation of these three motion modes and transitions between modes are related to the pressure distribution formed between the elliptical squirmer and wall. In addition, significant differences exist between the motions of elliptical and circular squirmers. The force generated by the interaction between the elliptical squirmer and wall does not all point towards its center of mass, resulting in an additional torque on the elliptical squirmer; this is not the situation for the circular squirmer. The second phase of the study simulated squirmers with different aspect ratios (c = 1.0, c = 3.0). It was found that for an elliptical squirmer with an aspect ratio c = 3.0, the large-amplitude oscillation mode (among the above three motion modes) no longer exists. By combining the motion modes of a circular squirmer in the channel, it can be observed that as the aspect ratio c increases, the squirmer’s head direction tends to be more vertical, which may reduce the drag force during swimming.

Dynamic Motion Transition: A Hybrid Data-driven and Model-driven Method for Human Pose Transitions.

The rapid, accurate, and robust computation of virtual human figures' "in-between" pose transitions from available and sometimes sparse inputs is of fundamental significance to 3D interactive graphics and computer animation. Various methods have been proposed to produce natural lifelike transitions of human pose automatically in recent decades. Nevertheless, conventional pure model-driven methods require heuristic knowledge (e.g., least motion guided by physics laws) and ad-hoc clues (e.g., splines with non-uniform time warp) that are difficult to obtain, learn, and infer. With the fast emergence of large-scale datasets readily available to animators in the most recent years, deep models afford a powerful alternative to tackle the aforementioned challenges. However, pure data-driven methods still suffer from the remaining challenges such as unseen data in practice and less generative power in model/domain/data transfer, and the measurement of the generative power has always been omitted in these works. In essence, data-driven methods solely rely on the qualities and quantities of training datasets. In this paper, we propose a hybrid approach built upon the seamless integration of data-driven and model-driven methods, called Dynamic Motion Transition (DMT), with the following salient modeling advantages: (1) The data augmentation capability based on the limited human locomotion data capture and the concept of force-derived directly from physical laws; (2) Force learning by which skeleton joints are driven to move, and the Conditional Temporal Transformer (CTT) being trained to learn the force change in the local range, both at the fine level; and (3) At the coarse level, the effective and flexible creation of the subsequent step motion using Dynamic Movement Primitives (DMP) until the target is reached. Our extensive experiments have confirmed that our model can outperform the state-of-the-art methods under the newly devised metric by virtue of the least action loss function. In addition, our novel method and system are of immediate benefit to many other animation tasks such as motion synthesis and control, and motion tracking and prediction in this bigdata graphics era.

Motion of a viscous slug on heterogeneous surfaces: crossover from stick–slip to steady sliding

We present a theoretical study of viscous slug motion inside a microscopically rough capillary tube, where pronounced stick–slip motion can emerge at slow displacement rates. The mathematical description of this intermittent motion can be reduced to a system of ordinary differential equations, which also describe the motion of a pendulum inside a fluid-filled rotating drum. We use this analogy to show that the stick–slip motion transitions to steady sliding at high displacement rates. We characterize this crossover with a simple scaling relation and show that the crossover is accompanied by a shift in the dominant energy dissipation mechanisms within the system.

Simulation and Experiment Analysis of Driveshaft

A driveshaft is a small spring coil less than 1mm in diameter, composed of several stainless-steel wire filaments. In intervention, the driveshaft is used to transmit force and motion to the inside body through the existing micro channels (such as arteries, veins, and gastrointestinal tract). The performance of the driveshaft determines the efficiency, stability, and accuracy of force and motion transitions, the ability to pass through tortuous microchannels, and the damage to healthy tissues. To determine the influence of fabrication parameters (filament, wire diameter, and outer diameter) on the mechanical properties (such as bending stiffness and natural frequency) of the driveshaft, a simulation was established in ABAQUS to calculate the deformation displacement under 0.0098N and first-order natural frequency. Then, the bending stiffness is calculated. The results show that the bending stiffness and the first-order natural frequency of the driveshaft increase with the increase of the filament number and wire diameter, and with the outer diameter of the driveshaft increases, the bending stiffness increases, while the first-order natural frequency decreases. Finally, the simulation model is verified by measuring the deformation displacement in the experiment. This study provides a methodology for designing and selecting the driveshaft in Interventional therapy.

Biomechanical evaluation of different semi-rigid junctional fixation techniques using finite element analysis

BackgroundProximal junctional failure is a common complication attributed to the rigidity of long pedicle screw fixation constructs used for surgical correction of adult spinal deformity. Semi-rigid junctional fixation achieves a gradual transition in range of motion at the ends of spinal instrumentation, which could lead to reduced junctional stresses, and ultimately reduce the incidence of proximal junctional failure. This study investigates the biomechanical effect of different semi-rigid junctional fixation techniques in a T8-L3 finite element spine segment model. MethodsFirst, degeneration of the intervertebral disc was successfully implemented by altering the height. Second, transverse process hooks, one- and two-level clamped tapes, and one- and two-level knotted tapes instrumented proximally to three-level pedicle screw fixation were validated against ex vivo range of motion data of a previous study. Finally, the posterior ligament complex forces and nucleus pulposus stresses were quantified. FindingsSimulated range of motions demonstrated the fidelity of the general model and modelling of semi-rigid junctional fixation techniques. All semi-rigid junctional fixation techniques reduced the posterior ligament complex forces at the junctional zone compared to pedicle screw fixation. Transverse process hooks and knotted tapes reduced nucleus pulposus stresses, whereas clamped tapes increased nucleus pulposus stresses at the junctional zone. InterpretationThe relationship between the range of motion transition and the reductions in posterior ligament complex and nucleus pulposus stresses was complex and dependent on the fixation techniques. Clinical trials are required to compare the effectiveness of semi-rigid junctional fixation techniques in terms of reducing proximal junctional failure incidence rates.

Exploring the criticality hypothesis using programmable swarm robots with Vicsek-like interactions.

A widely mentioned but not experimentally confirmed view (known as the 'criticality hypothesis') argues that biological swarm systems gain optimal responsiveness to perturbations and information processing capabilities by operating near the critical state where an ordered-to-disordered state transition occurs. However, various factors can induce the ordered-disordered transition, and the explicit relationship between these factors and the criticality is still unclear. Here, we present an experimental validation for the criticality hypothesis by employing real programmable swarm-robotic systems (up to 50 robots) governed by Vicsek-like interactions, subject to time-varying stimulus-response and hazard avoidance. We find that (i) not all ordered-disordered motion transitions correspond to the functional advantages for groups; (ii) collective response of groups is maximized near the critical state induced by alignment weight or scale rather than noise and other non-alignment factors; and (iii) those non-alignment factors act to highlight the functional advantages of alignment-induced criticality. These results suggest that the adjustability of velocity or directional coupling between individuals plays an essential role in the acquisition of maximizing collective response by criticality. Our results contribute to understanding the adjustment strategies of animal interactions from a perspective of criticality and provide insights into the design and control of swarm robotics.

Multi-Scale Control Signal-Aware Transformer for Motion Synthesis without Phase

Synthesizing controllable motion for a character using deep learning has been a promising approach due to its potential to learn a compact model without laborious feature engineering. To produce dynamic motion from weak control signals such as desired paths, existing methods often require auxiliary information such as phases for alleviating motion ambiguity, which limits their generalisation capability. As past poses often contain useful auxiliary hints, in this paper, we propose a task-agnostic deep learning method, namely Multi-scale Control Signal-aware Transformer (MCS-T), with an attention based encoder-decoder architecture to discover the auxiliary information implicitly for synthesizing controllable motion without explicitly requiring auxiliary information such as phase. Specifically, an encoder is devised to adaptively formulate the motion patterns of a character's past poses with multi-scale skeletons, and a decoder driven by control signals to further synthesize and predict the character's state by paying context-specialised attention to the encoded past motion patterns. As a result, it helps alleviate the issues of low responsiveness and slow transition which often happen in conventional methods not using auxiliary information. Both qualitative and quantitative experimental results on an existing biped locomotion dataset, which involves diverse types of motion transitions, demonstrate the effectiveness of our method. In particular, MCS-T is able to successfully generate motions comparable to those generated by the methods using auxiliary information.

The Use of Regional Data Assimilation to Improve Numerical Simulations of Diurnal Characteristics of Precipitation during an Active Madden–Julian Oscillation Event over the Maritime Continent

This study examines the impact of regional data assimilation on diurnal characteristics of precipitation and winds over the Maritime Continent (MC) using a set of cloud-permitting-scale (~3 km) numerical simulations with the mesoscale community Weather Research and Forecasting (WRF) model and the NCEP Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid data assimilation system. Numerical experiments focus on January 2018, when a well-defined, active Madden–Julian Oscillation (MJO) propagated through the MC region. Available conventional and satellite data are assimilated. Results show that simulated precipitation with data assimilation generally agrees better with satellite-derived rainfall than the control simulation without data assimilation. Simulations with data assimilation also reproduce the diurnal cycle of precipitation better, especially for the timing of the precipitation peak. Data assimilation modulates the overstrong (overweak) diurnal forcing over the land (ocean) in the control simulation. The vertical phase shift of the thermodynamic environment, associated with the timing of vertical motion transition along with low-level water vapor supplies, results in maximum precipitation occurring later, especially over land. To further demonstrate the impact of data assimilation, an additional experiment assimilates NASA Cyclone Global Navigation Satellite System (CYGNSS)-derived ocean surface winds. The results indicate that the assimilation of CYGNSS data exhibits an evident impact on the diurnal variation of surface variables and a similar shift in the diurnal cycle of precipitation. Overall, this study highlights the importance of regional data assimilation in improving the representation of precipitation over the MC, paving the way for a better understanding of the interactions of local diurnal convective precipitation cycles with MJO.

Photoexcitation-Induced Assembly: A Bottom-Up Physical Strategy for Driving Molecular Motion and Phase Evolution

ConspectusIn the field of molecular assembly, photodriven self-assembly is a smart and crucial strategy to regulate the molecular orderliness, multiscale structure, and optoelectronic properties. Traditionally, photodriven self-assembly is based on photochemical processes, through molecular structural change induced by photoreactions. Despite great progress in the photochemical self-assembly, there still exists disadvantages (e.g., the photoconversion rate never reaches 100% with the possible side reactions). Therefore, the photoinduced nanostructure and morphology are often difficult to predict due to insufficient phase transition or defects. In contrast, the physical processes based on photoexcitation are straightforward and can fully utilize photons to avoid the drawbacks of photochemistry. The photoexcitation strategy excludes the change of molecular structure, only utilizing the molecular conformational change from the ground state to excited state. Then, the excited state conformation is employed to drive molecular movement and aggregation, further promoting the synergistic assembly or phase transition of the entire material system. The regulation and exploration of molecular assembly upon photoexcitation can open up a new paradigm to deal with the "bottom-up" behavior and develop unprecedented optoelectronic functional materials.This Account starts with a brief introduction to the problems faced by photocontrolled self-assembly and presents the photoexcitation-induced assembly (PEIA) strategy. Then, we focus on exploring PEIA strategy based on persulfurated arenes as the prototype. The molecular conformational transition of persulfurated arenes from the ground state to the excited state is conducive to the formation of intermolecular interactions, successively driving molecular motion, aggregation, and assembly. Next, we describe our progress in exploring PEIA of persulfurated arenes at the molecular level and then demonstrate that the PEIA of persulfurated arenes can synergistically drive molecular motion and phase transition in various block copolymer systems. Moreover, we provide the potential applications of PEIA in dynamic visual imaging, information encryption, and surface property regulation. Finally, an outlook on further development of PEIA is prospected.

Kinematic Analysis of Constrained General Planner Mechanisms Using MATLAB Graphical User Interface (GUI)

Mechanisms are significant in mechanical engineering as they are required for proper motion transition. This work studies the kinematic analysis of the planar mechanisms using constraint properties between links and joints. A joint library has been built by two types of joints (revolute and prismatic) and has been modeled to be extended in the future. The Kinematic Analysis of General Planer Mechanisms (KAGPM) has been described and implemented through the Graphical User Interface in MATLAB. The Newton-Raphson method was utilized as a numerically computational technique to resolve the kinematic constraint equations. The proposed KAGPM program has been in the kinematic analysis of a slider crank mechanism for the purpose of validation with Haug. For more validation of the program, the effects of some effective parameters have been investigated (the geometric of the mechanism, the initial conditions, and the transient of the mechanism).

Symmetry breaking of a closed flexible filament behind a rigid plate at low Reynolds numbers

In this paper, the motion modes transition and dynamic performance of the flow past a rigid–flexible coupling system were investigated at low Reynolds numbers. The coupling system consisted of a rigid plate and a trailing closed flexible filament and was simulated numerically using the immersed boundary method. According to whether the filament moves and the symmetry of its movement, six motion modes have been identified for different filament lengths and Reynolds numbers (Re), i.e., the symmetric and stationary mode, the asymmetric and stationary (AS) mode, the regular and unilateral flap (RUF) mode, the transition motion (TM) mode, the symmetric and bilateral flap mode, and the asymmetric and bilateral flap (ABF) mode. Moreover, symmetry breaking occurred in the AS mode, RUF mode, and ABF mode. Drag reduction was found at Re≥40, which was mainly influenced by the pressure behind the filament. However, drag reduction was weakened for the filament with Lr≥2.57 because of the large width suffered to the flow, where Lr is the ratio of the length of the filament to the width of the plate. The time-averaged lift was also found to be directly dependent on the symmetry of the filament. In addition, the motion modes were significantly affected by the bending coefficient of the filament. Symmetry breaking was prevented by the reduction of the bending coefficient before vortex shedding. Moreover, if the filament was symmetric before vortex shedding, the RUF mode and the TM mode did not appear with the increase in Re.

Analysis of super-harmonic resonance and periodic motion transition of fractional nonlinear vibration isolation system

The precision instruments and equipment are often utilized in low-frequency and micro-amplitude vibration systems, in which many vibration isolators of rubber materials are widely used. Ignoring the low-frequency amplitude will result in errors in the fatigue life design of the vibration isolators and predicting the dynamic response of each frequency band accurately becomes necessary. However, integer-order models cannot describe the frequency dependence of rubber materials, while the fractional-order models can describe it instead. On the other hand, the elastic restoring force is strongly nonlinear under large deformation, and the vibration of the nonlinear system contains multiple harmonic components. In order to solve those issues, the fractional nonlinear Nishimura model is used to characterize the constitutive relation of vibration isolators such as air springs, which are made of carbon black filled natural rubber. The high-order harmonic balance method is used to obtain the steady-state response of the vibration system, while the fourth-order Runge–Kutta method is applied to simulate the dynamic response of the system in the low-frequency region, and the Lyapunov exponent is used to determine the stability of the system. Furthermore, the influence of parameters on the amplitude–frequency characteristics of different frequency bands is also studied, and a method to solve the optimal damping coefficient is proposed based on the primary resonance amplitude–frequency curves. The results show that there is a diversity of periodic motions in the process of adjacent super-harmonic resonance transition. Numerical simulations also demonstrate that multi-periodic motions coexist in the system. The motion transition law between the polymorphic coexistence region and its adjacent regions is summarized.

Motion In-Betweening via Two-Stage Transformers

We present a deep learning-based framework to synthesize motion in-betweening in a two-stage manner. Given some context frames and a target frame, the system can generate plausible transitions with variable lengths in a non-autoregressive fashion. The framework consists of two Transformer Encoder-based networks operating in two stages: in the first stage a Context Transformer is designed to generate rough transitions based on the context and in the second stage a Detail Transformer is employed to refine motion details. Compared to existing Transformer-based methods which either use a complete Transformer Encoder-Decoder architecture or additional 1D convolutions to generate motion transitions, our framework achieves superior performance with less trainable parameters by only leveraging the Transformer Encoder and masked self-attention mechanism. To enhance the generalization of our transformer-based framework, we further introduce Keyframe Positional Encoding and Learned Relative Positional Encoding to make our method robust in synthesizing longer transitions exceeding the maximum transition length during training. Our framework is also artist-friendly by supporting full and partial pose constraints within the transition, giving artists fine control over the synthesized results. We benchmark our framework on the LAFAN1 dataset, and experiments show that our method outperforms the current state-of-the-art methods at a large margin (an average of 16% for normal-length sequences and 55% for excessive-length sequences). Our method trains faster than the RNN-based method and achieves a four-time speedup during inference. We implement our framework into a production-ready tool inside an animation authoring software and conduct a pilot study to validate the practical value of our method.

ZEB1-activated Notch1 promotes circulating tumor cell migration and invasion in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is recognized as the major subtypes of non-small cell lung cancer (NSCLC). Circulating tumor cells (CTCs) are critical players in tumor metastasis. A molecular profiling of CTCs has previously identified notch receptor 1 (Notch1) as an important mediator in NSCLC. Therefore, we investigate Notch1 roles in LUSC and its related mechanisms. The serum levels of Notch1 were measured by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The CTCs isolated from blood samples were characterized via an immunofluorescence method. Cell motion was determined using Transwell chambers. The regulatory relationship between Notch1 and zinc finger E-box-binding homeobox 1 (ZEB1) was verified by chromatin immunoprecipitation (ChIP) and luciferase reporter assays. The protein levels were detected by western blotting. Higher Notch1 expression in patients with LUSC than that in normal controls was observed. Notch1 knockdown inhibited cell motion and epithelial-mesenchymal transition (EMT). ZEB1 transcriptionally activated Notch1. ZEB1 upregulation exacerbated the malignant phenotypes of CTCs. ZEB1-activated Notch1 promotes malignant phenotypes of CTCs in LUSC and indicates poor prognosis.