Collective Behavior Research Articles

The Semantic Web has transformed the way data is represented, shared, and integrated across multiple domains. However, as its size and interconnectedness continue to grow, it becomes increasingly exposed to cyber threats. Securing the Semantic Web is therefore a critical challenge, as traditional security methods often fail to protect the highly interlinked data, ontologies, and network structures on which it relies. This paper proposes a novel, nature-inspired cybersecurity approach that uses swarm optimization algorithm to improve the resilience of Semantic Web. These algorithms, modeled on the collective behavior of insects, can efficiently allocate limited security resources to detect and mitigate potential threats in real time. By applying a distributed and adaptive defense mechanism based on swarm optimization, Semantic Web nodes can autonomously respond to evolving attack patterns, reducing vulnerabilities and strengthening overall system security. The results demonstrate a significant improvement in the network's robustness against various attack scenarios, including those targeting ontologies and data relationships. The proposed nature-inspired strategy enables secure and reliable information exchange across distributed systems while adapting dynamically to new cyber threats.

This study investigates the blinking behavior of dye-loaded organic nanoparticles (NPs) and elucidates the role of energy transfer (EET) and dye aggregation. Using spectrally resolved single-molecule microscopy, we characterize the optical properties of NPs composed of poly(lactic-co-glycolic acid) (PLGA) and poly(methyl methacrylate) (PMMA) with varying dye loadings. At high dye concentrations, the NPs exhibit intensity fluctuations that are attributed to transient species quenching the emission. Temperature dependent studies evidence that the quenching disappears at 77 K, highlighting the role of dye diffusion in the polymer matrix. Spectral analysis confirms the formation of red-shifted emissive aggregates and nonemissive aggregates as major contributors to the blinking mechanism. Computational studies support these findings, identifying stable crossed J-dimers responsible for red-shifted emission and nonemissive H-dimers associated with fluorescence quenching. This work improves our understanding of the collective behavior of dyes embedded in polymeric NPs, paving the way for the development of brighter and more efficient fluorescent nanomaterials.

Environmental stressors such as hypoxia challenge the balance between individual physiological performance and the coordination required for collective behaviors like schooling. Here, we investigate how glass catfish (Kryptopterus vitreolus) modulate locomotor and group-level behavior across a gradient of oxygen saturation (95%-20%) while swimming steadily at a constant cruising speed. We found that tailbeat frequency decreased significantly with declining oxygen (p < 0.0001), alongside reductions in wave speed (p = 0.007). Tailbeat amplitude, by contrast, increased significantly under hypoxia (p < 0.0001), and posterior segment angles showed a slight, non-significant increase, consistent with modestly greater tail bending. Despite these changes, the Strouhal number remained fairly constant, and waveform topology was conserved. School structure, including nearest-neighbor distance and distance to the center of the school, remained stable across oxygen treatments, but with significant variation across individual schools. A clear behavioral threshold was observed below 25% oxygen saturation, beyond which coordinated schooling deteriorated. These findings demonstrate that glass catfish employ internally coordinated, energetically economical kinematic adjustments to preserve group cohesion under metabolic constraint. This strategy highlights a decentralized mechanism for sustaining collective behavior near physiological limits and offers biologically-grounded insights relevant to energy-aware coordination in bioinspired swarms.

Coupled many-body quantum systems exhibit rich emergent physics with diverse stationary and dynamical behaviours. By engineering platforms with tunable and distinct coupling mechanisms, new insights emerge into the collective behaviour of coupled many body systems. Particles can be exchanged via evanescent or ballistic coupling: the former, based on proximity, yields large spectral splitting, while the latter requires strict phase-matching, analogous to phase-coupled harmonic oscillators and has a smaller impact on the energy landscape. We demonstrate an all-optically tunable quantum fluid dimer based on exciton-polariton condensates in a photonic crystal waveguide with hyperbolic (saddle-like) dispersion. Varying the dimer's angle relative to the grating tunes the coupling from evanescent to ballistic. We directly observe spectral features and mass flow shaped by the saddle dispersion. This work highlights photonic crystals as powerful platforms to explore condensed matter phenomena lying at the interface between delay-coupled nonlinear oscillators and tight binding physics.

An inherent challenge in designing laboratory-grown, engineered living neuronal networks lies in predicting the dynamic repertoire of the resulting network and its sensitivity to experimental variables. To fill this gap, and inspired by recent experimental studies, we present a numerical model designed to replicate the anisotropies in connectivity introduced through engineering, characterize the emergent collective behavior of the neuronal network, and make predictions. The numerical model is developed to replicate experimental data, and subsequently used to quantify network dynamics in relation to tunable structural and dynamical parameters. These include the strength of imprinted anisotropies, synaptic noise, and average axon lengths. We show that the model successfully captures the behavior of engineered neuronal cultures, revealing a rich repertoire of activity patterns that are highly sensitive to connectivity architecture and noise levels. Specifically, the imprinted anisotropies promote modularity and high clustering coefficients, substantially reducing the pathological-like bursting of standard neuronal cultures, whereas noise and axonal length influence the variability in dynamical states and activity propagation velocities. Moreover, connectivity anisotropies significantly enhance the ability to reconstruct structural connectivity from activity data, an aspect that is important to understand the structure-function relationship in neuronal networks. Our work provides a robust in silico framework to assist experimentalists in the design of in vitro neuronal systems and in anticipating their outcomes. This predictive capability is particularly valuable in developing reliable brain-on-a-chip platforms and in exploring fundamental aspects of neural computation, including input-output relationships and information coding.

Matrix stiffness and the corresponding mechanosignalling play indispensable roles in cellular phenotypes and functions. How tissue stiffness influences the behaviour of monocytes, a major circulating leukocyte of the innate system, and how it may promote the emergence of collective cell behaviour is less understood. Here we show that human primary monocytes, uniquely among key immune cells, undergo a dynamic local phase separation to form highly regular, reversible, multicellular, multilayered domains on soft collagen-coated hydrogels of physiological stiffnesses. Local activation of the β2 integrin-ICAM-1 complex initiates intercellular adhesion, while global soluble inhibitory factors maintain the steady-state domain pattern over days. While inhibiting their phagocytic capability, domain formation promotes the survival of monocytes. A computational model incorporating the Cahn-Hilliard equation of phase separation with the Turing mechanism of local activation and global inhibition suggests that cell seeding density and chemotactic and random cell migration contribute to domain pattern formation, which is experimentally validated. This work reveals that cells can generate complex phases by exploiting their mechanosensing abilities and combined short-range interactions and long-range signals to enhance their survival.

Harnessing ambient low-density energy and converting it into macroscopic motion is a hallmark of living systems. However, artificial systems are often limited by high energy requirements and intricate control mechanisms. Inspired by Salmonella, which uses dynamic ion-binding coordination for continuous motion, we proposed a novel concept of self-oscillating motors leveraging molecular-level dynamic bonding to harvest trivial ambient energy and power macroscopic, self-sustained behavior. Specifically, we developed a coordination motorized oscillator (CoMO) based on novel supramolecular PDMS material with 25-fold thermo-inflation ability of normal PDMS and nearly 2000-fold that of the passive layer. CoMO can harvest ambient energy as low as body temperature to reversibly dissociate coordination crosslinks, transforming molecular transitions into sustained macroscopic oscillation. Its universality facilitates the amplification of macroscopic motion via the collective behaviors of CoMOs. Moreover, this principle empowers the development of ambient-driven coordination motored robots (CoMbot) with multi-modal locomotion and adaptability across diverse terrains. Such dynamic chemical transitions enabled chemo-mechanical coupling for self-sustained systems, paving new avenues for robust transition-mechanical transducing material systems and soft machineries with unprecedented capabilities.

Collective migration is a crucial mechanism guiding cell movement in developmental processes and disease progression. Understanding the migration behavior of cell clusters is key to advancing our knowledge of morphogenesis, wound healing, and collective cancer invasion. Despite the understanding of the response of single cells to environmental physical cues, the collective behavior of cells in response to different levels of extracellular matrix stiffness is yet to be fully understood. Here, we present a quantitative investigation into how substrate stiffness and cell cluster size modulate the collective behavior and migration dynamics of NIH 3T3 fibroblasts. With the variation of PDMS and curing agent concentrations, two contrasting soft and stiff substrates with different stiffness were developed. Using a combination of atomic force microscopy (AFM) to precisely characterize substrate elastic moduli and time-lapse microscopy for tracking migration parameters, we demonstrate that substrate mechanics and cluster geometry synergistically govern collective behavior. Fibroblast migratory characteristics were greatly improved with increased stiffness and cluster size. Large clusters on stiff substrates exhibited greater circularity (~ 0.8), migration distance, displacement (135.6 µm), directionality (0.81), and velocity (24 µm/h) compared to single cells and small clusters on soft and stiff substrates. Moreover, detailed analysis of cytoskeletal reorganization via actin staining revealed the mechanotransductive pathways that convert physical cues into migratory behavior. These findings provide important insights into how substrate stiffness influences collective cell migration, offering potential applications in elucidating the mechanisms of morphogenesis and the dynamics of collective cell invasion during tumor progression.

This paper delves into the largely unexplored terrain of how principles from maternal theory can be instrumental in cultivating an ethics of care within learning relationships. Traditional research in Human Resource Development (HRD) and Adult Learning has often emphasized identity-based developmental relationships, with a focus on race and gender. However, the maternal identity, despite its profound impact on individual and collective behaviors, has not been significantly addressed as a core identity within these relationships. This paper aims to bridge this gap by exploring the role of maternal practice as a pivotal element in fostering an ethics of care. Through an analysis of three key components of maternal practice—maternal thinking, motherwork, and othermothering—this paper explores how maternal practice can transform learning and development practices by enriching an ethics of care. We argue that embracing maternal practice in learning relationships not only deepens understanding but also enhances the efficacy of care strategies, ultimately contributing to more compassionate and effective practices within our discipline.

Semi-supervised Risk Assessment Research for Intelligent Vehicles Inspired by Collective Biological Risk-avoidance Behaviors

ConspectusTailored magnetic nanoparticles (MNPs) have emerged as powerful tools in biomedical imaging, offering enhanced sensitivity, specificity, spatial resolution, and multifunctionality. Their unique physicochemical properties also open promising avenues for therapeutic applications. Continued innovation in MNP design is critical to fully exploit advanced imaging platforms─including high-field magnetic resonance imaging (MRI), magnetic particle imaging (MPI), and multimodal imaging systems─for early diagnosis and precision therapy. However, conventional strategies centered on tuning particle size, shape, composition, and crystallinity offer only limited control over intrinsic microscopic parameters such as magnetic moment orientation, defect structure, and electronic activity, which fundamentally govern imaging performance. This limitation has created a persistent bottleneck in the development of high-performance MNPs. Assembly driven chemical design offers a multiscale design paradigm that spans atomic, interfacial, and nanoscale levels. By inducing emergent collective behaviors not present in individual building blocks, this strategy significantly broadens the design space for optimizing MNP functionality.In this Account, we summarize our recent advances in the assembly driven chemical design of MNPs and their biomedical applications. At the atomic scale, controlled atomic rearrangements, defect engineering, and surface atom segregation are harnessed to fine-tune magnetic moment alignment, magnetic susceptibility, water exchange kinetics, and catalytic activity. At the interfacial level, the assembly of core-shell and organic-inorganic hybrid structures modulates exchange coupling interactions, enabling integrated diagnostic and therapeutic capabilities. At the nanoscale, ligand-mediated MNP assembly imparts stimuli responsiveness and facilitates the integration of multimodal imaging functions. These multiscale design strategies collectively establish robust structure-activity relationships and allow precise tailoring of MNPs for specific biomedical imaging modalities and therapeutic outcomes.We then highlight key breakthroughs enabled by these MNP assemblies. In advanced magnetic imaging, they overcome longstanding limitations in sensitivity and resolution, achieving an ultralow transverse-to-longitudinal relaxivity ratio and enhanced T1-weighted contrast under high-field MRI, as well as submillimeter spatial resolution in MPI. These performance gains extend the imaging frontier to previously undetectable targets, such as isolated tumor cells as small as ∼0.16 mm, and enable real-time molecular imaging of neuronal signaling in vivo, paving the way for early diagnosis and imaging-guided therapy of malignancies and neurological diseases. Beyond imaging, atomic-scale reconfiguration enables MNPs to structurally mimic the active site architecture of metabolic enzymes such as xanthine oxidoreductase, thereby enabling tumor-selective metabolic therapy.Together, these findings underscore the transformative potential of assembly driven MNP design in next-generation biomedical imaging and precision medicine. We conclude by outlining future directions for constructing life-inspired, multiscale "transformative magnetic artificial molecules," to enable precise sensing and regulation of complex biological activities. Ultimately, assembly driven chemistry offers a robust and versatile framework for the rational development of high-performance MNPs, accelerating their clinical translation and inspiring new therapeutic innovations.

Collective cell migration is a fundamental process in development, wound healing, and cancer. The best-characterized modes of collective migration typically involve cells that retain an epithelial architecture. However, in this review, we explore less well-understood modes of migration driven by cells with a more mesenchymal phenotype. To better understand and compare contact-dependent collective cell behaviors, we propose envisioning each cell as a structure made up of smaller dynamic parts and inferring how these parts behave to understand the overall collective behavior. By examining how local cell shapes influence single-cell behaviors, we can gain insight into how swarm-like behaviors emerge through cell-cell contact. Through this lens, we compare key processes such as contact inhibition of locomotion, mesenchymal cell intercalation, and more complex heterotypic swarm behaviors. Finally, we discuss the emerging concept of contact-mediated rules that regulate motility and have the potential to encode blueprints for complex patterns and even organ shapes.

Living cells contain dynamic structures that constantly change shape, merge together, split apart, and travel in coordinated patterns, much like flocks of birds or schools of fish. Quantifying these complex, collective behaviours can be challenging, as most available tools are designed to follow discrete objects rather than distributed, shape-shifting pixel-level activity patterns. We developed ARCOS.px, a freely available software tool with a user-friendly graphical interface, to automatically identify and track these coordinated dynamic cellular events in time-lapse microscopy movies. The software works by taking semantically segmented binary images, in which pixels are classified as 'active' or 'inactive'. It then, uses spatial clustering to group pixels into distinct coordinated events, and tracks how these events evolve over time. We tested our method by tracking cellular structures involved in cell movement and signalling in REF52 cells. Our analyses revealed how different drugs affect the behaviour of these structures and uncovered the timing relationships between different cellular components during wave-like spreading events. ARCOS.px fills a gap in current image analysis tools by enabling researchers to quantify coordinated intracellular phenomena, which was previously difficult to achieve.

In animals, group living comes at the cost of increased pathogen exposure. In kin groups, social immune behaviours offset that cost and reach their most complex expression in eusocial insect societies. In the nests of these societies, collective social behaviours can modify the patterns of individual interactions across space, reducing the ability of pathogens to reach the reproductive core of the colony (organizational immunity). To be effective, these behaviours must separate infected and uninfected individuals; implying that the efficacy of social immune behaviours may depend upon nest structure. The role of nest space has received little attention, and most knowledge of social immune behaviour in social insects is based on the study of generalist entomopathogenic fungi. We examine the social immune behaviours involved in the interaction between the supercolonial, invasive tawny crazy ant (Nylanderia fulva) and its specialist, intracellular, microsporidian pathogen Myrmecomorba nylanderiae, to ask how nest structure influences social immunity. By manipulating nest structure, we demonstrate that preventing pathogen transmission to the colony core requires a multi-chambered nest. Without which, social immune function was lost, and disease transmission was universal. To understand how nest space enhances social immune efficacy, we first confirm that workers within tawny crazy ant nests form spatially and behaviourally segregated social sub-networks. We then find that infected ants introduced into the colony core migrate to the colony periphery, while uninfected ants do not. Behavioural tests indicate that, despite the infection being internal, uninfected ants can detect the infection status of a worker; thus, behaviours enforcing spatial segregation could be triggered by either party. Additionally, infected ants alter the behavioural tasks they perform, assuming more corpse removal tasks, particularly infected corpse removal, and reducing their efforts in foraging and brood care. With some exceptions, the social immune behaviours expressed by this supercolonial ant in response to microsporidian infection correspond to immune defence behaviours employed to defend against generalist entomopathogenic fungi. These behaviours appear to be conserved, generalized responses to pathogen infection among social insects.

Predicting the failure of carbon fiber composites remains a challenge due to the complex evolution of damage, where the collective behavior of defects like pores and microcracks dictates material strength. The objective of this work is to elucidate the transition from damage accumulation to final failure under cyclic loading by analyzing the integral structural characteristics of the material. A methodology combining microtomography and digital image correlation (DIC) was employed to monitor damage evolution in situ. The analysis of DIC-derived strain fields during block cyclic loading pinpointed the critical transition stage to failure. Furthermore, Bayesian Gaussian Mixture models were used for threshold segmentation and cluster analysis, revealing that mechanical loading induces distinct populations of small and large pores. The main results show that while the overall pore orientation distribution remains consistent, the clustering and ordering of pores evolve differently under cyclic loads compared to quasi-static conditions. Specifically, unloaded samples exhibit three distinct pore clusters based on orientation, a structure that is altered by cyclic loading through pore expansion and coalescence, which ultimately reduces specimen strength. These insights advance the understanding of damage criticality in composites and provide a foundation for developing more accurate predictive models.

Droplet-Based microfluidic production of soft alginate microrobots for magnetically targeted cargo delivery.

The study of flocking in biological systems has identified conditions for self-organized collective behavior, inspiring the development of decentralized strategies to coordinate the dynamics of swarms of drones and other autonomous vehicles. Previous research has focused primarily on the role of the time-varying interaction network among agents while assuming that the agents themselves are identical or nearly identical. Here, we depart from this conventional assumption to investigate how inter-individual differences between agents affect stability and convergence in flocking dynamics. We show that flocks of agents with optimally assigned heterogeneous parameters significantly outperform their homogeneous counterparts, achieving 20−40% faster convergence to desired formations across various control tasks. These tasks include target tracking, flock formation, and obstacle maneuvering. In systems with communication delays, heterogeneity can enable convergence even when flocking is unstable for identical agents. Our results challenge existing paradigms in multi-agent control and establish system disorder as an adaptive, distributed mechanism to promote collective behavior in flocking dynamics.

Self-organization underpins the emergence of complex structure in living systems but remains a major challenge for engineering synthetic multicellular materials. Here, we present intrinsically disordered protein display platform (iDP2), a generalizable platform for high-density display of intrinsically disordered proteins (IDPs) on the surface of Escherichia coli. iDP2 uses CsgF as a surface-tethered scaffold, enabling efficient fusion and presentation of protein domains that lack stable tertiary structure. Successful display selectively favors disordered sequences, which, when endowed with phase separation propensity, drive the formation of dynamic cellular condensates. Programming cells with orthogonal IDPs enables sequence-specific segregation of mixed populations, allowing the design of spatially organized living assemblies. The aggregation state of these condensates is dynamically tunable by environmental cues such as ionic strength, and temperature, with responses predictable from the known phase behavior of the displayed IDPs. Extrusion processing of condensates generates macroscale filaments that maintain structural integrity and population segregation. By linking protein sequence to emergent collective behaviors, iDP2 offers a programmable framework for rational control of cell-cell interactions. This approach establishes a foundation for engineering living materials with customizable viscoelastic properties, environmental responsiveness, and multicellular organization. More broadly, our work highlights the potential of disordered protein motifs as versatile tools for the design of adaptive, self-organizing biological systems.

Abstract We study the collective behavior in a stochastic agent-based model of active matter. Provided a critical take-up of energy, agents produce two types of goods x , y that follow a generalized Lotka–Volterra dynamics. For isolated agents, production would either reach a fixed point or diverge. Coupling agents’ production via a mean field of x , however, can lead to synchronized oscillations if agents cooperate in the production of x . The production of y supports the emergence of the synchronized dynamics by suppressing fluctuations and mitigating competition between agents, this way stabilizing the production of x . We find that in the synchronized state different groups of agents coexist, each following their own limit cycle. The Kuramoto order parameter is large within groups, and small across groups. The collective state is stable against shocks from agents temporarily switching between cooperation and competition. The model dynamics illustrates the principles of synergetics, i.e., the spontaneous emergence of order given a critical energy supply and cooperative interactions.

Multidrug resistance has also been accompanied by the prolonged use of antibiotics that makes complications in treatment. Biofilm in pathogenic bacteria is the most serious challenge linked with chronic illnesses and also contributes to virulence and drug resistance. Several bacterial pathogens employ the Quorum-sensing (QS) mechanism to coordinate their collective behaviors like bioluminescence, virulence, and biofilm formation. Therefore, agents that inhibit or interfere with bacterial QS and biofilm formation are emerging as a new class of next-generation antibacterial. Recently, nanoparticles have been employed to improve the efficacy of existing antibacterial agents. In the present study, gold nanocrystals were synthesized by using Koelreuteria paniculata (KP) leaf extract. Synthesized nanocrystals were characterized by a face-centered cubic structure of ~20 nm by XRD, FTIR, Zeta sizer, and TEM. Biogenic Gold nanocrystals (BGNCs) exhibited extended QS inhibition in bio-indicator strains Chromobacterium violaceum and Pseudomonas aeruginosa biosensor strains. BGNCs strongly suppressed QS-controlled violacein production in C. violaceum CV026, and elastase, protease, pyocyanin, alginate, and biofilm formation in P. aeruginosa (PA01). In addition, BGNCs notably suppressed the relative expression of PA01 quorum sensing, biofilm-forming, and virulence-regulating genes, as quantified by qRT-PCR. As a result of the broad-spectrum suppression of QS and biofilm by BGNCs, it is anticipated that these nontoxic bioactive nanocrystals can be employed as surface sterilization agents in nosocomial infections.