Collective Molecular Machines: Multidimensionality and Reconfigurability

Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work.During the last 60 years,designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research.Effective progress has been made,attributed to advances in various fields such as supramolecular chemistry,biology and nanotechnology,and informatics.However,individual molecular machines are only capable of producing nanometer work and generally have only a single functionality.In order to address these problems,collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm.In this review,we comprehensively discuss recent developments in the collective behaviors of molecular machines.In particular,collective behavior is divided into two paradigms.One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials.The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations.We discuss design strategies for both modes and focus on the modulation of features and properties.Subsequently,in order to address existing challenges,the idea of transferring experience gained in the field of micro/nano robotics is presented,offering prospects for future developments in the collective behavior of molecular machines.

Phase Transition in a Dense Swarm of Self-Propelled Bots

Swarms of self-organizing bots are becoming important elements in various technical systems,which include the control of bacterial cyborgs in biomedical applications,technologies for creating new metamaterials with internal structure,self-assembly processes of complex supramolecular structures in disordered media,etc.In this work,we theoretically study the effect of sudden fluidization of a dense group of bots,each of which is a source of heat and follows a simple algorithm to move in the direction of the gradient of the global temperature field.We show that,under certain conditions,an aggregate of self-propelled bots can fluidize,which leads to a second-order phase transition.The bots’program,which forces them to search for the temperature field maximum,acts as an effective buoyancy force.As a consequence,one can observe a sudden macroscopic circulation of bots from the edge of the group to its center and back again,which resembles classical Rayleigh-Benard thermal convection.In the continuum approximation,we have developed a mathematical model of the phenomenon,which reduces to the equation of a self-gravitating porous disk saturated with an incompressible fluid that generates heat.We derive governing equations in the Darcy-Boussinesq approximation and formulate a nonlinear boundary value problem.An exact solution to the linearized problem for infinitesimal perturbations of the base state is obtained,and the critical values of the control parameter for the onset of the bot circulation are calculated.Then we apply weakly nonlinear analysis using the method of multiple time scales.We found that as the number of bots increases,the swarm exhibits increasingly complex patterns of circulation.

Swarming Responsive Photonic Nanorobots for Motile-Targeting Microenvironmental Mapping and Mapping-Guided Photothermal Treatment

Micro/nanorobots can propel and navigate in many hard-to-reach biological environments,and thus may bring revolutionary changes to biomedical research and applications.However,current MNRs lack the capability to collectively perceive and report physicochemical changes in unknown microenvironments.Here we propose to develop swarming responsive photonic nanorobots that can map local physicochemical conditions on the fly and further guide localized photothermal treatment.The RPNRs consist of a photonic nanochain of periodically-assembled magnetic Fe_(3)O_(4)nanoparticles encapsulated in a responsive hydrogel shell,and show multiple integrated functions,including energetic magnetically-driven swarming motions,bright stimuli-responsive structural colors,and photothermal conversion.Thus,they can actively navigate in complex environments utilizing their controllable swarming motions,then visualize unknown targets(e.g.,tumor lesion)by collectively mapping out local abnormal physicochemical conditions(e.g.,pH,temperature,or glucose concentra-tion)via their responsive structural colors,and further guide external light irradiation to initiate localized photothermal treatment.This work facilitates the development of intelligent motile nanosensors and versatile multifunctional nanotheranostics for cancer and inflam-matory diseases.

Turing/Turing-like patterns:Products of random aggregation of spatial components

Turing patterns are typical spatiotemporal ordered structures in various systems driven far from thermodynamic equilibrium.Turing’s reaction-diffusion theory,containing a long-range inhibiting agent and a local catalytic agent,has provided an explanation for the formation of some patterns in nature.Numerical,experimental and theoretical studies about Turing/Turing-like patterns have been generally focused on systems driven far from thermodynamic equilibrium.The local dynamics of these systems are commonly very complex,which brings great difficulties to understanding of formation of patterns.Here,we investigate a type of Turing-like patterns in a near-equilibrium thermodynamic system experimentally and theoretically,and put forward a new formation mechanism and a quantitative method for Turing/Turing-like patterns.Specifically,we observe a type of Turing-like patterns in starch solutions,and study the effect of concentration on the structure of patterns.The experimental results show that,with the increase of concentration,patterns change from spots to inverse spots,and labyrinthine stripe patterns appear in the region of intermediate concentration.We analyze and model the formation mechanism of these patterns observed in experiments,and the simulation results agree with the experimental results.Our conclusion indicates that the random aggregation of spatial components leads to formation of these patterns,and the proportion of spatial components determines the structures.Our findings shed light on the formation mechanism for Turing/Turing-like patterns.

Enhanced sampling for lipid-protein interactions during membrane dynamics

The inflexible concept of membrane curvature as an independent property of lipid structures is today obsolete.Lipid bilayers behave as many-body entities with emergent properties that depend on their interactions with the environment.In particular,proteins exert crucial actions on lipid molecules that ultimately condition the collective properties of the membranes.In this review,the potential of enhanced molecular dynamics to address cell-biology problems is discussed.The cases of membrane deformation,membrane fusion,and the fusion pore are analyzed from the perspective of the dimensionality reduction by collective variables.Coupled lipid-protein interactions as fundamental determinants of large membrane remodeling events are also commented.Finally,novel strategies merging cell biology and physics are considered as future lines of research.

Flocking for swarm systems with fixed topology in a changing environment

This paper is mainly devoted to the flocking of a class of swarm with fixed topology in a changing environment. Firstly, the controller for each agent is proposed by employing the error terms between the state of the agent and the average state of its neighbors. Secondly, a sufficient condition for the swarm to achieve flocking is presented under assumptions that the gradient of the environment is bounded and the initial position graph is connected. Thirdly, as the environment is a plane, it is further proved that the velocity of each agent finally converges to the velocity of the swarm center although not one agent knows where the center of the group is. Finally, numerical examples are included to illustrate the obtained results.

Simulation of Crowd Motion Based on Boids Flocking Behavior and Social Force Model

In the process of crowd movement,pedestrians are often affected by their neighbors.In order to describe the consistency of adjacent individuals and collectivity of a group,this paper learns from the rules of the flocking behavior,such as segregation,alignment and cohesion,and proposes a method for crowd motion simulation based on the Boids model and social force model.Firstly,the perception area of individuals is divided into zone of segregation,alignment and cohesion.Secondly,the interactive force among individuals is calculated based upon the zone information,velocity vector and the group information.The interactive force among individuals is the synthesis of three forces:the repulsion force to avoid collisions,the alignment force to keep consistent with the velocity direction,and the attractive force to get close to the members of group.In segregation and alignment areas,the repulsion force and alignment force among pedestrians are limited by visual field factors.Finally,the interactive force among individuals,the driving force of destination and the repulsion force of obstacles work together to drive the behavior of crowd motion.The simulation results show that the proposed method can not only effectively simulate the interactive behavior between adjacent individuals but also the collective behavior of group.

Asymmetric and symmetric meta-correlations in financial markets

In financial markets, the relation between fluctuations of stock prices and trading behaviors is complex. It is intriguing to quantify this kind of meta-correlation between market fluctuations and the synchronous behaviors. We refine the theoretical index leverage model proposed by Reigneron et al., to exactly quantify the meta-correlation under various levels of price fluctuations [Reigneron P A, Allez R and Bouchaud J P 2011 Physica A 390 3026]. The characteristics of meta-correlations in times of market losses, are found to be significantly different in Chinese and American financial markets. In addition,unlike the asymmetric results at the daily scale, the correlation behaviors are found to be symmetric at the high-frequency scale.

Extremal Coalitions for Influence Games Through Swarm Intelligence-Based Methods

An influence game is a simple game represented over an influence graph(i.e.,a labeled,weighted graph)on which the influence spread phenomenon is exerted.Influence games allow applying different properties and parameters coming from cooperative game theory to the contexts of social network analysis,decision-systems,voting systems,and collective behavior.The exact calculation of several of these properties and parameters is computationally hard,even for a small number of players.Two examples of these parameters are the length and the width of a game.The length of a game is the size of its smaller winning coalition,while the width of a game is the size of its larger losing coalition.Both parameters are relevant to know the levels of difficulty in reaching agreements in collective decision-making systems.Despite the above,new bio-inspired metaheuristic algorithms have recently been developed to solve the NP-hard influence maximization problem in an efficient and approximate way,being able to find small winning coalitions that maximize the influence spread within an influence graph.In this article,we apply some variations of this solution to find extreme winning and losing coalitions,and thus efficient approximate solutions for the length and the width of influence games.As a case study,we consider two real social networks,one formed by the 58 members of the European Union Council under nice voting rules,and the other formed by the 705 members of the European Parliament,connected by political affinity.Results are promising and show that it is feasible to generate approximate solutions for the length and width parameters of influence games,in reduced solving time.

Diffusional inhomogeneity in cell cultures

Cell migrations in the cell cultures are found to follow non-Gaussian statistics. We recorded long-term cell migration patterns with more than six hundred cells located in 28 mm2. Our experimental data support the claim that an individual cell migration follows Gaussian statistics. Because the cell culture is inhomogeneous, the statistics of the cell culture exhibit a non-Gaussian distribution. We find that the normalized histogram of the diffusion velocity follows an exponential tail. A simple model is proposed based on the diffusional inhomogeneity to explain the exponential distribution of locomotion activity in this work. Using numerical calculation, we prove that our model is in great agreement with the experimental data.

Locust Behaved Particle Swarm Optimization Technique

The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density,which is controlled by a serotonin called 5-hydroxytryptamine( 5HT). In this paper,based on the mechanism of the locusts' collective behavior,a new particle swarm optimization technique called LBPSO is studied. The number of swarms is selfadaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT,which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO,social particle swarm optimization( SPSO), improved SPSO and the standard particle swarm optimization( StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark( MPB)show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.

Guest Editorial New and Interdisciplinary Approaches to Language and Cognition

This Special Section on Language and Cognition of Journal of Electronic Science and Technology（JEST） presents a collective of state-of-theart interdisciplinary research on language and cognition. It features empirical and theoretical studies on cognitive approaches to language, using a variety of methodological approaches, from behavioral measures to neuroimaging. The topics discussed are varied,ranging from language comprehension and acquisition to the language-emotion interactions, reflecting marked broadening of the research agenda in this field. We invite yet more integrated research to move the field forward.

Self organizing optimization and phase transition in reinforcement learning minority game system

Whether the complex game system composed of a large number of artificial intelligence(AI)agents empowered with reinforcement learning can produce extremely favorable collective behaviors just through the way of agent self-exploration is a matter of practical importance.In this paper,we address this question by combining the typical theoretical model of resource allocation system,the minority game model,with reinforcement learning.Each individual participating in the game is set to have a certain degree of intelligence based on reinforcement learning algorithm.In particular,we demonstrate that as AI agents gradually becomes familiar with the unknown environment and tries to provide optimal actions to maximize payoff,the whole system continues to approach the optimal state under certain parameter combinations,herding is effectively suppressed by an oscillating collective behavior which is a self-organizing pattern without any external interference.An interesting phenomenon is that a first-order phase transition is revealed based on some numerical results in our multi-agents system with reinforcement learning.In order to further understand the dynamic behavior of agent learning,we define and analyze the conversion path of belief mode,and find that the self-organizing condensation of belief modes appeared for the given trial and error rates in the AI system.Finally,we provide a detection method for period-two oscillation collective pattern emergence based on the Kullback–Leibler divergence and give the parameter position where the period-two appears.

SOFT CONTROL ON COLLECTIVE BEHAVIOR OF A GROUP OF AUTONOMOUS AGENTS BY A SHILL AGENT

This paper asks a new question： how can we control the collective behavior of self-organized multi-agent systems？ We try to answer the question by proposing a new notion called ＇Soft Control＇ which keeps the local rule of the existing agents in the system. We show the feasibility of soft control by a case study. Consider the simple but typical distributed multi-agent model proposed by Vicsek et al. for flocking of birds： each agent moves with the same speed but with different headings which are updated using a local rule based on the average of its own heading and the headings of its neighbors. Most studies of this model are about the self-organized collective behavior, such as synchronization of headings. We want to intervene in the collective behavior （headings） of the group by soft control. A specified method is to add a special agent, called a ＇Shill＇, which can be controlled by us but is treated as an ordinary agent by other agents. We construct a control law for the shill so that it can synchronize the whole group to an objective heading. This control law is proved to be effective analytically and numerieally. Note that soft control is different from the approach of distributed control. It is a natural way to intervene in the distributed systems. It may bring out many interesting issues and challenges on the control of complex systems.

A dynamic model of skeletal muscle based on collective behavior of myosin motors-Biomechanics of skeletal muscle based on working mechanism of myosin motors(I)

A dynalnic model of skeletal muscle is developed to describe its activation kinetics and contraction dynamics based on the collective working mechanism of myosin II motors with a statistical mechanics method. According to the structure of sar- comeres arranged in series and in parallel, the mechanical properties of skeletal muscle are studied. This model reveals the re- lations between action potential and muscle characteristics. It is shown that calcium concentration in sarcoplasmic （SP） in- creases linearly with the increasing stimulation frequency and gradually reaches saturation. Active force and contraction veloc- ity follow the trend of calcium concentration and reach a peak value at the maximum stimulation frequency. Contraction ve- locity is inversely proportional to the load while the contraction power increases to maximum and then reduces to zero with the increasing load. These properties are consistent with published physiological experimental results of skeletal muscle.

Quantifying the Influence of Websites Based on Online Collective Attention Flow

The availability of network big data, such as those from online users＇ surfing records, communication records, and e-commerce records, makes it possible for us to probe into and quantify the regular patterns of users＇ long-range and complex interactions between websites. If we see the Web as a virtual living organism, according to the metabolic theory, the websites must absorb ＂energy＂ to grow, reproduce, and develop. We are interested in the following two questions： 1） where does the ＂energy＂ come from？ 2） will the websites generate macro influence on the whole Web based on the ＂energy＂？ Our data consist of more than 30 000 online users＇ surfing log data from China Internet Network Information Center. We would consider the influence as metabolism and users＇ attention flow as the energy for the websites. We study how collective attention distributes and flows among different websites by the empirical attention flow network. Different from traditional studies which focused on information flow, we study users＇ attention flow, which is not only a ＂reversed＂ way to study Web structure and transmission mode, but also the first step to understand the underlying dynamics of the World Wide Web. We find that the macro influence of websites scales sub-linearly against the collective attention flow dwelling time, which is not consistent with the heuristics that the more users＇ dwelling time is, the greater influence a website will have. Further analysis finds a supper-linear scaling relationship between the influence of websites and the attention flow intensity. This is a websites version of Kleiber＇s law. We further notice that the development cycle of the websites can be split into three phases： the uncertain growth phase, the partially accelerating growth phase, and the fully accelerating growth phase. We also find that compared with the widespread hyperlinks analysis models, the attention flow network is an effective theoretical tool to estimate and rank websites.

Using multilayer network analysis to explore the temporal dynamics of collective behavior

Social organisms often show collective behaviors such as group foraging or movement.Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals.When social interactions change over time,collective behaviors may change because these behaviors emerge from interactions among individuals.Despite the importance of,and growing interest in,the temporal dynamics of social interactions,it is not clear how to quantify changes in interactions over time or measure their stability.Furthermore,the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent.Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors.We found that social interactions changed over time at a constant rate.Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed.Individuals that maintained a large and stable number of connections,despite changes in network structure,were the boldest individuals in the group.Therefore,social interactions and boldness are linked across time,but group collective behavior is not influenced by the stability of the social network.Our work demonstrates that dynamic social networks can be modeled in a multilayer framework.This approach may reveal biologically important temporal changes to social structure in other systems.

Chase,Pounce,and Escape Optimization Algorithm

While many metaheuristic optimization algorithms strive to address optimization challenges,they often grapple with the delicate balance between exploration and exploitation,leading to issues such as premature convergence,sensitivity to parameter settings,and difficulty in maintaining population diversity.In response to these challenges,this study introduces the Chase,Pounce,and Escape(CPE)algorithm,drawing inspiration from predator-prey dynamics.Unlike traditional optimization approaches,the CPE algorithm divides the population into two groups,each independently exploring the search space to efficiently navigate complex problem domains and avoid local optima.By incorporating a unique search mechanism that integrates both the average of the best solution and the current solution,the CPE algorithm demonstrates superior convergence properties.Additionally,the inclusion of a pouncing process facilitates rapid movement towards optimal solutions.Through comprehensive evaluations across various optimization scenarios,including standard test functions,Congress on Evolutionary Computation(CEC)-2017 benchmarks,and real-world engineering challenges,the effectiveness of the CPE algorithm is demonstrated.Results consistently highlight the algorithm’s performance,surpassing that of other well-known optimization techniques,and achieving remarkable outcomes in terms of mean,best,and standard deviation values across different problem domains,underscoring its robustness and versatility.

Swarming magnetic photonic-crystal microrobots withon-the-fly visual pH detection and self-regulated drug delivery

Swarming magnetic micro/nanorobots hold great promise for biomedical applications,but at present suffer from inferior capabilities to perceive and respond to chemical signals in local microenvironments.Here we demonstrate swarming magnetic photonic crystal microrobots(PC-bots)capable of sponta-neously performing on-the-fly visual pH detection and self regulated drug delivery by perceiving local pH changes.The magnetic PC-bots consist of pH-responsive hydrogel microspheres with encapsulated one-dimensional periodic assemblies of Fe3O4 nanoparticles.By programming extemnal rotating magnetic fields,they can self-organize into large swarms with much-enhanced collective velocity to actively find targets while shining bright“blinking”structural colors.When approaching the target with abnormal pH conditions(e.g.an ulcerated superficial tumor lesion),the PC-bots can visualize local pH changes on the fly via pH-responsive structural colors,and realize self-regulated release of the loaded drugs by recognizing local pH.This work facilita tes the develop-ment of intelligent micro/nanorobots for active“motile-targeting”tumor diag-nosis and treatment.

Persistent variation in spatial behavior affects the structure and function of interaction networks

The function of a network is affected by its structure. For example, the presence of highly interactive individuals, or hubs, influences the extent and rate of information spread across a network. In a network of interactions, the duration over which individual variation in interactions persists may affect how the network operates. Individuals may persist in their behavior over time and across situations, often referred to as personality. Colonies of social insects are an example of a biological system in which the structure of the coordinated networks of interacting workers may greatly influence information flow within the colony, and therefore its collective behavior. Here I investigate the effects of persistence in walking patterns on interaction networks us- ing computer simulations that are parameterized using observed behavior of harvester ants. I examine how the duration of persis- tence in spatial behavior influences network structure. Furthermore, I explore how spatial features of the environment affect the relationship between persistent behavior and network structure. I show that as persistence increases, the skewness of the weighted degree distribution of the interaction network increases. However, this relationship holds only when ants are confined in a space with boundaries, but not when physical barriers are absent. These findings suggest that the influence of animal personalities on network structure and function depends on the environment in which the animals reside [Current Zoology 61 （1）： 98-106, 2015].