Quantitative characterization of cell physiological state based on dynamical cell mechanics for drug efficacy indication

Cell mechanics is essential to cell development and function,and its dynamics evolution reflects the physiological state of cells.Here,we investigate the dynamical mechanical properties of single cells under various drug conditions,and present two mathematical approaches to quantitatively characterizing the cell physiological state.It is demonstrated that the cellular mechanical properties upon the drug action increase over time and tend to saturate,and can be mathematically characterized by a linear timeinvariant dynamical model.It is shown that the transition matrices of dynamical cell systems significantly improve the classification accuracies of the cells under different drug actions.Furthermore,it is revealed that there exists a positive linear correlation between the cytoskeleton density and the cellular mechanical properties,and the physiological state of a cell in terms of its cytoskeleton density can be predicted from its mechanical properties by a linear regression model.This study builds a relationship between the cellular mechanical properties and the cellular physiological state,adding information for evaluating drug efficacy.

Correlation between Different Morus alba L. Leaf Nodes and the Change in Leaf Size or Mass

This paper aimed to explore the correlation between Morus alba L. leaf node and its size or mass change and provide a theoretical basis for the development of scientific Morus alba L. cultivation technology. During 2016-2017,the correlation between Morus alba L. leaf node and its size or mass change was investigated and researched. Results showed that through the correlation coefficient analysis,there was a weakly positive correlation between the leaf node and some indicators such as( leaf length ÷ leaf width) and petiole length,there was a weakly negative correlation between the leaf node and some indicators such as leaf width,( leaf length + leaf width),( leaf length × leaf width) and leaf mass,and there was a weakly negative correlation between the leaf node and the other indicators; there was a highly positive correlation between the leaf mass and some indicators such as Morus alba L. leaf length,leaf width,( leaf length + leaf width) and( leaf length × leaf width),there was a significantly positive correlation between the leaf mass and some indicators such as petiole transverse diameter in width and petiole mass,and there was a weakly positive correlation between the leaf mass and some indicators such as petiole transverse diameter in thickness. In terms of significant level of difference,it was found that there was no significant difference in the correlation coefficients between the leaf nodes on the Morus alba L. shoots and some indicators such as leaf length,leaf width,( leaf length + leaf width),( leaf length × leaf width) and leaf mass; there was an extremely significant difference in the correlation coefficients between the leaf mass and some indicators such as Morus alba L. leaf length,leaf width,( leaf length + leaf width),( leaf length × leaf width),petiole transverse diameter in width and petiole mass; there was no significant difference in the correlation coefficients between the leaf mass and the other indicators. The regression analysis was performed on the leaf node and leaf length,leaf width,( leaf length + leaf width),( leaf length × leaf width),( leaf length ÷ leaf width),petiole length,petiole diameter in width,petiole diameter in thickness,petiole mass and leaf mass. Significance F values were0. 4864,0. 1995,0. 2888,0. 3004,0. 2350,0. 2407,0. 8925,0. 5227,0. 7022 and 0. 2000,respectively,that is,there was an extremely significant difference between Morus alba L. leaf node and petiole diameter in width,there was a significant difference between Morus alba L.leaf node and petiole mass,and there was no significant difference between Morus alba L. leaf node and the other indicators. The comparative analysis of quadratic curve regression equation and linear regression equation was performed on some indicators such as leaf length,and it was found that the R value between the leaf node and petiole diameter in width was in line with the logarithmic curve regression equation,and the other R values of quadratic curve regression equation was larger than the other R values of linear regression equation,indicating that different Morus alba L. leaf nodes,leaf length,leaf mass and other indicators were in line with the quadratic curve regression equation. It was concluded that by investigating the leaf node on the Morus alba L. shoots and regression equation,we could predict the leaf size,leaf mass and other indicators,and we could take the leaf node on the Morus alba L. shoots as a main factor affecting the Morus alba L. leaf size and mass.

Development and Future Challenges of Bio-Syncretic Robots

Bio-syncretic robots consisting of both living biological materials and non-living systems possess desirable attributes such as high energy efficiency, intrinsic safety, high sensitivity, and self-repairing capabilities. Compared with living biological materials or non-living traditional robots based on elec- tromechanical systems, the combined system of a bio-syncretic robot holds many advantages. Therefore, developing bio-syncretic robots has been a topic of great interest, and significant progress has been achieved in this area over the past decade. This review systematically summarizes the development of bio-syncretic robots. First, potential trends in the development of bio-syncretic robots are discussed. Next, the current performance of bio-syncretic robots, including simple movement and controllability of velocity and direction, is reviewed. The living biological materials and non-living materials that are used in bio-syncretic robots, and the corresponding fabrication methods, are then discussed. In addition, recently developed control methods for bio-syncretic robots, including physical and chemical control methods, are described. Finally, challenges in the development of bio-syncretic robots are discussed from multiple viewpoints, including sensing and intelligence, living and non-living materials, control approaches, and information technology.

盘钻法经牙槽嵴顶上颌窦底提升术应用于上颌窦间隔的临床效果观察

目的探讨存在上颌窦间隔时应用盘钻法经牙槽嵴顶上颌窦底提升术的临床效果。方法收集2019年1月至2020年1月就诊于青岛大学附属医院口腔种植科,存在上颌窦间隔应用盘钻法经牙槽嵴顶上颌窦底提升术同期种植的病例(24例,女性14例,男性10例),年龄(39.3±11.7)岁(22~56岁)。术后6~8个月行永久修复,根据上颌窦间隔位置分为E类(颧牙槽嵴之前)、M类(颧牙槽嵴之后)和D类(近远中走向)。术前、术后即刻、6个月、1年、2年拍摄锥形束CT。测量术前上颌窦底剩余骨高度(residual bone height,RBH),术后即刻、6个月、1年、2年测量上颌窦底垂直骨高度(vertical bone height,VBH)。统计黏膜穿孔率、种植体存留率。结果24例患者均成功完成手术,共植入种植体24枚,患者均无头痛头晕等不适,黏膜穿孔率为0(0/24),种植体的愈合期内存留率为100%(24/24)。术前RBH为(5.26±1.90)mm;术后即刻、6个月、1年、2年的VBH分别为(11.82±1.09)、(10.98±0.52)、(10.66±0.44)和(10.40±0.33)mm,不同观测点的VBH差异有统计学意义(F=187.70,P<0.001)。但术后2年与术后1年的VBH比较,差异无统计学意义(P=0.071)。1例患者在术后18个月出现种植体周炎。结论在种植位点RBH>2 mm且存在上颌窦间隔时,使用盘钻经牙槽嵴顶上颌窦底提升术同期种植的患者均未发生黏膜穿孔,成功率高,可获得足够骨高度并完成同期种植,术后2年与术后1年比较未发生明显的骨吸收改建,骨结合形成良好,操作安全可控。

Bioinspired Musculoskeletal Model-based Soft Wrist Exoskeleton for Stroke Rehabilitation

Exoskeleton robots have demonstrated the potential to rehabilitate stroke dyskinesia.Unfortunately,poor human-machine physiological coupling causes unexpected damage to human of muscles and joints.Moreover,inferior humanoid kinematics control would restrict human natural kinematics.Failing to deal with these problems results in bottlenecks and hinders its application.In this paper,the simplified muscle model and muscle-liked kinematics model were proposed,based on which a soft wrist exoskeleton was established to realize natural human interaction.Firstly,we simplified the redundant muscular system related to the wrist joint from ten muscles to four,so as to realize the human-robot physiological coupling.Then,according to the above human-like musculoskeletal model,the humanoid distributed kinematics control was established to achieve the two DOFs coupling kinematics of the wrist.The results show that the wearer of an exoskeleton could reduce muscle activation and joint force by 43.3%and 35.6%,respectively.Additionally,the humanoid motion trajectories similarity of the robot reached 91.5%.Stroke patients could recover 90.3%of natural motion ability to satisfy for most daily activities.This work provides a fundamental understanding on human-machine physiological coupling and humanoid kinematics control of the exoskeleton robots for reducing the post-stroke complications.

A Manta Ray-Inspired Biosyncretic Robot with Stable Controllability by Dynamic Electric Stimulation

Biosyncretic robots,which are new nature-based robots in addition to bionic robots,that utilize biological materials to realize their core function,have been supposed to further promote the progress in robotics.Actuation as the main operation mechanism relates to the robotic overall performance.Therefore,biosyncretic robots actuated by living biological actuators have attracted increasing attention.However,innovative propelling modes and control methods are still necessary for the further development of controllable motion performance of biosyncretic robots.In this work,a muscle tissue-based biosyncretic swimmer with a manta ray.inspired propelling mode has been developed.What is more,to improve the stable controllability of the biosyncretic swimmer,a dynamic control method based on circularly distributed multiple electrodes(CDME)has been proposed.In this method,the direction of the electric field generated by the CDME could be real-time controlled to be parallel with the adtuation tissue of the dynamic swimmer.Therefore,the instability of the tissue actuation induced by the dynamic included angle between the tissue axis and electric field direction could be eliminated.Finally,the biosyncretic robot has demonstrated stable,controllable,and efective swimming,by adjusting the electric stimulation pulse direction,amplitude,and frequency.This work may be beneficial for not only the development of biosyncretic robots but also other related studies including bionic design of soft robots and muscle tissue engineering.

Designing Unpowered Shoulder Complex Exoskeleton via Contralateral Drive for Self-rehabilitation of Post-stroke Hemiparesis

Rehabilitation using exoskeleton robots can effectively remediate dysfunction and restore post-stroke survivors’ physical ability. However, low kinematic compatibility and poor self-participation of post-stroke patients in rehabilitation restrict the outcomes of exoskeleton-based therapy. The study presents an Unpowered Shoulder Complex Exoskeleton (USCE), consisting of Shoulder Girdle Mechanism (SGM), Ball-and-Socket Joint Mechanism (BSM), Gravity Compensating Mechanism (GCM) and Adjustable Alignment Design (AAD), to achieve self-rehabilitation of shoulder via energy transfer from the healthy upper limb to the affected counterpart of post-stroke hemiplegic patients. The SGM and AAD are designed to improve the kinematic compatibility by compensating for displacements of the glenohumeral joint with the adaptable size of USCE for different wearers. The BSM and GCM can transfer the body movement and energy from the healthy half of the body to the affected side without external energy input and enhance the self-participation with sick posture correction. The experimental results show that the USCE can provide high kinematic compatibility with 90.9% movement similarity between human and exoskeleton. Meanwhile, the motion ability of a post-stroke patient’s affected limb can be increased through energy transfer. It is expected that USCE can improve outcomes of home-based self-rehabilitation.

Quantum-enhanced reinforcement learning for control:a preliminary study

Reinforcement learning is one of the fastest growing areas in machine learning,and has obtained great achievements in biomedicine,Internet of Things(IoT),logistics,robotic control,etc.However,there are still many challenges for engineering applications,such as how to speed up the learning process,how to balance the trade-of between exploration and exploitation.Quantum technology,which can solve complex problems faster than classical methods,especially in supercomputers,provides us a new paradigm to overcome these challenges in reinforcement learning.In this paper,a quantum-enhanced reinforcement learning is pictured for optimal control.In this algorithm,the states and actions of reinforcement learning are quantized by quantum technology.And then,a probability amplifcation method,which can efectively avoid the trade-of between exploration and exploitation via quantized technology,is presented.Finally,the optimal control policy is learnt during the process of reinforcement learning.The performance of this quantized algorithm is demonstrated in both MountainCar reinforcement learning environment and CartPole reinforcement learning environment—one kind of classical control reinforcement learning environment in the OpenAI Gym.The preliminary study results validate that,compared with Q-learning,this quantized reinforcement learning method has better control performance without considering the trade-of between exploration and exploitation.The learning performance of this new algorithm is stable with diferent learning rates from 0.01 to 0.10,which means it is promising to be employed in unknown dynamics systems.

可定制多孔玻璃表面的溶解制造策略

玻璃表面微纳结构的设计和制造在实现所需功能方面具有重要意义.然而,玻璃固有的硬度和脆性给传统制造方法带来了不便.因此,开发一种简单且可控的制造策略对于制备具备功能应用的微纳结构玻璃至关重要.在本文中,我们提出了一种创新方法,利用可溶性氯化钠颗粒作为前驱体模板,在玻璃表面上创建可定制的多孔结构.通过我们的先导成型策略,成功利用可溶性氯化钠颗粒的潜力作为前驱体模板,从而便于制造量身定制的多孔玻璃表面.通过调节这些颗粒的大小和组合,我们实现了对所得多孔玻璃的连续调控,范围从超亲水(1°)到超疏水(142°).值得注意的是,制备的多孔玻璃表面表现出显著的亲油性,展示了在油水分离和自清洁等多种应用方面的巨大潜力.最重要的是,即使经过了10次制造迭代,玻璃表面的疏水-亲油功能依然完好,凸显了我们策略的耐久性和可重复性.这种方法为实现玻璃的特殊功能提供了方便且具有成本效益的途径,为不同领域的进展铺平了道路.

Special issue on cyber-physical systems and intelligent control in honor of the 65th birthday of Professor Bijoy K. Ghosh

It is our great pleasure to organize this special issue in Control Theory and Technology in honor of the 65th birthday of Professor Bijoy K. Ghosh, who has made many truly outstanding contributions to the field of systems and control through the years, which include robust and nonlinear control, robotics and machine vision in his earlier research period, and his recent focus on biology and biomedical modeling, learning control and multi-agent systems to name a few.