Macroscopic Clusters Research Articles

Directional cues affect the collective behaviour of self-propelled particles in one dimension

This study explores the effect of quenched disorder on the characteristics of self-propelled particles in one dimension. Here, particles interact with disorder which serve as directional cues. The study investigates how the density of the directional cues influences the emergence of ordering and clustering in the collection of the self-propelled particles. We introduce the microscopic model as well as corresponding coarse-grained equations of motion for the local density and the orientation of particles. Directional cues affect the macroscopic ordering in the system, the size of the ordered clusters decays algebraically with density of cues. Further, in the presence of directional cues, a big macroscopic cluster breaks into small clusters, leading to the localization of particles around it and resulting in high density around the cues.

Ionization detail parameters and cluster dose: a mathematical model for selection of nanodosimetric quantities for use in treatment planning in charged particle radiotherapy

Objective. To propose a mathematical model for applying ionization detail (ID), the detailed spatial distribution of ionization along a particle track, to proton and ion beam radiotherapy treatment planning (RTP). Approach. Our model provides for selection of preferred ID parameters (I p ) for RTP, that associate closest to biological effects. Cluster dose is proposed to bridge the large gap between nanoscopic I p and macroscopic RTP. Selection of I p is demonstrated using published cell survival measurements for protons through argon, comparing results for nineteen I p : N k , k = 2, 3, …, 10, the number of ionizations in clusters of k or more per particle, and F k , k = 1, 2, …, 10, the number of clusters of k or more per particle. We then describe application of the model to ID-based RTP and propose a path to clinical translation. Main results. The preferred I p were N 4 and F 5 for aerobic cells, N 5 and F 7 for hypoxic cells. Significant differences were found in cell survival for beams having the same LET or the preferred N k . Conversely, there was no significant difference for F 5 for aerobic cells and F 7 for hypoxic cells, regardless of ion beam atomic number or energy. Further, cells irradiated with the same cluster dose for these I p had the same cell survival. Based on these preliminary results and other compelling results in nanodosimetry, it is reasonable to assert that I p exist that are more closely associated with biological effects than current LET-based approaches and microdosimetric RBE-based models used in particle RTP. However, more biological variables such as cell line and cycle phase, as well as ion beam pulse structure and rate still need investigation. Significance. Our model provides a practical means to select preferred I p from radiobiological data, and to convert I p to the macroscopic cluster dose for particle RTP.

Bacteria evolve macroscopic multicellularity by the genetic assimilation of phenotypically plastic cell clustering

The evolutionary transition from unicellularity to multicellularity was a key innovation in the history of life. Experimental evolution is an important tool to study the formation of undifferentiated cellular clusters, the likely first step of this transition. Although multicellularity first evolved in bacteria, previous experimental evolution research has primarily used eukaryotes. Moreover, it focuses on mutationally driven (and not environmentally induced) phenotypes. Here we show that both Gram-negative and Gram-positive bacteria exhibit phenotypically plastic (i.e., environmentally induced) cell clustering. Under high salinity, they form elongated clusters of ~ 2 cm. However, under habitual salinity, the clusters disintegrate and grow planktonically. We used experimental evolution with Escherichia coli to show that such clustering can be assimilated genetically: the evolved bacteria inherently grow as macroscopic multicellular clusters, even without environmental induction. Highly parallel mutations in genes linked to cell wall assembly formed the genomic basis of assimilated multicellularity. While the wildtype also showed cell shape plasticity across high versus low salinity, it was either assimilated or reversed after evolution. Interestingly, a single mutation could genetically assimilate multicellularity by modulating plasticity at multiple levels of organization. Taken together, we show that phenotypic plasticity can prime bacteria for evolving undifferentiated macroscopic multicellularity.

Counterflow-induced clustering: Exact results.

We analyze the cluster formation in a nonergodic stochastic system as a result of counterflow, with the aid of an exactly solvable model. To illustrate the clustering, a two species asymmetric simple exclusion process with impurities on a periodic lattice is considered, where the impurity can activate flips between the two nonconserved species. Exact analytical results, supported by Monte Carlo simulations, show two distinct phases, free-flowing phase and clustering phase. The clustering phase is characterized by constant density and vanishing current of the nonconserved species, whereas the free-flowing phase is identified with nonmonotonic density and nonmonotonic finite current of the same. The n-point spatial correlation between n consecutive vacancies grows with increasing n in the clustering phase, indicating the formation of two macroscopic clusters in this phase, one of the vacancies and the other consisting of all the particles. We define a rearrangement parameter that permutes the ordering of particles in the initial configuration, keeping all the input parameters fixed. This rearrangement parameter reveals the significant effect of nonergodicity on the onset of clustering. For a special choice of the microscopic dynamics, we connect the present model to a system of run-and-tumble particles used to model active matter, where the two species having opposite net bias manifest the two possible run directions of the run-and-tumble particles, and the impurities act as tumbling reagents that enable the tumbling process.

Stable pair liquid phase in fermionic systems

We predict the existence of a pair-liquid phase in lattice fermion systems with finite-range attractive interactions. This exotic state competes on one side with a normal Fermi liquid of unpaired fermions and on the other side with a phase-separated state where all fermions are coupled into macroscopic clusters. We show that such a phase is absent in bosonic systems and therefore is protected by the exclusion principle. In contrast with zero-range attractive systems where clustering of more than two fermions is directly prohibited, here quantum statistics acts dynamically and in a more subtle way. Since a many-fermion wave function must have nodes, the cluster formation threshold is larger than the pair formation threshold. By directly solving a four-body Schroedinger equation on one- and two-dimensional lattices, we map the boundaries of pair stability. The pair liquid phase should be observable in cold atom systems. We also discuss implications for the preformed-pair mechanism of high-temperature superconductivity.

Efficient and accurate multi-scale simulation for viscosity mechanism of konjac glucomannan colloids

The viscosity is a foundational parameter of biomacromolecule in the food industry. The viscosity of macroscopic colloids is closely related to the dynamical behaviors of mesoscopic biomacromolecule clusters, which are difficult to be investigated at molecular resolution by common methods. In this study, based on experimental data, multi-scale simulations combining microscopic molecular dynamics simulation, mesoscopic Brownian dynamics simulation, and macroscopic flow field construction were used to investigate the dynamical behaviors of mesoscopic clusters of konjac glucomannan (KGM) colloids (~500 nm) over a long time (~100 ms). Numerical statistical parameters of the mesoscopic simulation of macroscopic clusters were proposed and proved to represent the viscosity of colloids. Based on the intermolecular interaction and macromolecular conformation, the mechanism of the shear thinning effect was revealed as both the regular arrangement of macromolecules at low shear rates (<100 s−1) and structural collapse of macromolecules at high shear rates (>500 s−1). Then, the effect of molecular concentration, molecular weight, and temperature on the colloid viscosity and cluster structure of KGM colloids was investigated by experiments and simulations. This study provides a novel multi-scale numerical method and insight into the viscosity mechanism of biomacromolecule.

The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure

Humans differ from each other in a wide range of biometrics, but to what extent brain connectivity varies between individuals remains largely unknown. By combining diffusion-weighted imaging (DWI) and magnetoencephalography (MEG), this study characterizes the inter-subject variability (ISV) of multimodal brain connectivity. Structural connectivity is characterized by higher ISV in association cortices including the core multiple-demand network and lower ISV in the sensorimotor cortex. MEG ISV exhibits frequency-dependent signatures, and the extent of MEG ISV is consistent with that of structural connectivity ISV in selective macroscopic cortical clusters. Across the cortex, the ISVs of structural connectivity and beta-band MEG functional connectivity are negatively associated with cortical myelin content indexed by the quantitative T1 relaxation rate measured by high-resolution 7 T MRI. Furthermore, MEG ISV from alpha to gamma bands relates to the hindrance and restriction of the white-matter tissue estimated by DWI microstructural models. Our findings depict the inter-relationship between the ISV of brain connectivity from multiple modalities, and highlight the role of tissue microstructure underpinning the ISV.

Self-assembly of colloidal superballs under spherical confinement of a drying droplet

Understanding the relationship between colloidal building block shape and self-assembled material structure is important for the development of novel materials by self-assembly. In this regard, colloidal superballs are unique building blocks because their shape can smoothly transition between spherical and cubic. Assembly of colloidal superballs under spherical confinement results in macroscopic clusters with ordered internal structure. By utilizing Small Angle X-Ray Scattering (SAXS), we probe the internal structure of colloidal superball dispersion droplets during confinement. We observe and identify four distinct drying regimes that arise during compression via evaporating droplets, and we track the development of the assembled macrostructure. As the superballs assemble, we found that they arrange into the predicted paracrystalline, rhombohedral C1-lattice that varies by the constituent superballs’ shape. This provides insights in the behavior between confinement and particle shape that can be applied in the development of new functional materials.

Planar random-cluster model: fractal properties of the critical phase

This paper is studying the critical regime of the planar random-cluster model on {mathbb {Z}}^2 with cluster-weight qin [1,4). More precisely, we prove crossing estimates in quads which are uniform in their boundary conditions and depend only on their extremal lengths. They imply in particular that any fractal boundary is touched by macroscopic clusters, uniformly in its roughness or the configuration on the boundary. Additionally, they imply that any sub-sequential scaling limit of the collection of interfaces between primal and dual clusters is made of loops that are non-simple. We also obtain a number of properties of so-called arm-events: three universal critical exponents (two arms in the half-plane, three arms in the half-plane and five arms in the bulk), quasi-multiplicativity and well-separation properties (even when arms are not alternating between primal and dual), and the fact that the four-arm exponent is strictly smaller than 2. These results were previously known only for Bernoulli percolation (q = 1) and the FK-Ising model (q = 2). Finally, we prove new bounds on the one, two and four-arm exponents for qin [1,2], as well as the one-arm exponent in the half-plane. These improve the previously known bounds, even for Bernoulli percolation.

Many-body localization as a percolation phenomenon

We examine the standard model of many-body localization (MBL), i.e., the disordered chain of interacting spinless fermions, by representing it as the network in the many-body (MB) basis of noninteracting localized Anderson states. By studying eigenstates of the full Hamiltonian, for strong disorders we find that the dynamics is confined up to very long times to disconnected MB clusters in the Fock space. By keeping only resonant contributions and simplifying the quantum problem to rate equations (REs) for MB states, in analogy with percolation problems, the MBL transition is located via the universal cluster distribution and the emergence of the macroscopic cluster. On the ergodic side, our approximate RE approach to the relaxation processes captures well the diffusion transport, as found for the full quantum model. In a broad transient regime, we find an anomalous, i.e., subdiffusivelike, transport, emerging from weak links between MB states.

Features of the Electrical-Conductivity Mechanism in γ-Irradiated TlInSe2 Single Crystals under Hydrostatic Pressure

The effect of hydrostatic pressure to 10 kbar on the conductivity of γ-irradiated chain TlInSe2 single crystals with a resistivity of ~108 Ω cm is studied. It is found that the observed conductivity anomalies of TlInSe2 samples irradiated at doses of D 100 krad are associated with the ordered transformation of defects including interstitial cations and anions. The nature of the anomalous change and pressure dependences of the resistivity σ(P) in the irradiated samples suggest that pressure causes local energy levels due to macroscopic clusters of radiation-induced defects, with the result that the Fermi-level energy and carrier concentration change.

Liquid–liquid phase separation of type II diabetes-associated IAPP initiates hydrogelation and aggregation

Amyloidoses (misfolded polypeptide accumulation) are among the most debilitating diseases our aging societies face. Amyloidogenesis can be catalyzed by hydrophobic-hydrophilic interfaces (e.g., air-water interface in vitro [AWI]). We recently demonstrated hydrogelation of the amyloidogenic type II diabetes-associated islet amyloid polypeptide (IAPP), a hydrophobic-hydrophilic interface-dependent process with complex kinetics. We demonstrate that human IAPP undergoes AWI-catalyzed liquid-liquid phase separation (LLPS), which initiates hydrogelation and aggregation. Insulin modulates these processes but does not prevent them. Using nonamyloidogenic rat IAPP, we show that, whereas LLPS does not require the amyloidogenic sequence, hydrogelation and aggregation do. Interestingly, both insulin and rat sequence delayed IAPP LLPS, which may reflect physiology. By developing an experimental setup and analysis tools, we show that, within the whole system (beyond the droplet stage), macroscopic interconnected aggregate clusters form, grow, fuse, and evolve via internal rearrangement, leading to overall hydrogelation. As the AWI-adsorbed gelled layer matures, its microviscosity increases. LLPS-driven aggregation may be a common amyloid feature and integral to pathology.

Acceleration of Macroscopic Clusters in Crossed Magnetic Fields

In previous our investigations the acceleration of rotating plasma flow in crossed magnetic fields owing to the momentum transfer between the macroscopic degrees of freedom for the plasma flow was studied. Based on these results, we have discussed the acceleration of plasma flow containing the charged macroscopic particles. The estimates presented make it possible to draw a tentative conclusion about the axial acceleration of the dusty plasma flows.

Macroscopic Cluster Organizations Change the Complexity of Neural Activity.

In this study, simulations are conducted using a network model to examine how the macroscopic network in the brain is related to the complexity of activity for each region. The network model is composed of multiple neuron groups, each of which consists of spiking neurons with different topological properties of a macroscopic network based on the Watts and Strogatz model. The complexity of spontaneous activity is analyzed using multiscale entropy, and the structural properties of the network are analyzed using complex network theory. Experimental results show that a macroscopic structure with high clustering and high degree centrality increases the firing rates of neurons in a neuron group and enhances intraconnections from the excitatory neurons to inhibitory neurons in a neuron group. As a result, the intensity of the specific frequency components of neural activity increases. This decreases the complexity of neural activity. Finally, we discuss the research relevance of the complexity of the brain activity.

Emergence of a single cluster in Vicsek's model at very low noise

The classic Vicsek model [Phys.Rev.Lett. {\bf75},1226(1995)] is studied in the regime of very low noise intensities, which is shown to be characterized by a cluster (MC) that contains a macroscopic fraction of the system particles. It is shown that the well-known power-law behavior of the cluster size distribution loses its cutoff becoming bimodal at very low noise intensities: A peak develops for larger sizes to settle the emergence of the MC. The average cluster number m*, is introduced as a parameter that properly describes this change, i.e. a line in the noise-speed phase portrait can be identified to separates both regimes. The average largest cluster parameter also develops large fluctuations at a non zero critical noise. Finite size scaling analysis is performed to show that a phase transition to a macroscopic cluster is taking place. Consistency of the results with the literature is also checked and commented upon.

Hot Dense Matter: Deconfinement and Clustering of Color Sources in Nuclear Collisions

Within the first few microseconds from after the Big Bang, the hot dense matter was in the form of the Quark Gluon Plasm (QGP) consisting of free quarks and gluons. By colliding heavy nuclei at RHIC and LHC at a velocity close to the speed of light, we were able to create the primordial matter and observe the matter after expansion and cooling. In this report we present the thermodynamics and transport coefficients obtained in the framework of clustering of color sources in both hadron-hadron and nucleus-nucleus collisions at RHIC and LHC energies. Multiparticle production at high energies can be described in terms of color strings stretched between the projectile and target. At high string density single strings overlap and form color sources. This addition belongs to the non-perturbative domain of Quantum Chromo Dynamics (QGP) and manifests its most fundamental features. The Schwinger QED 2 mechanism produces color neutral q q ¯ pairs when color source strings break. Subsequent hardonization produces the observed hadrons. With growing energy and atomic number of the colliding nuclei the density of strings grows and more color sources form clusters in the transverse plane. At a certain critical density a macroscopic cluster appears, which marks the percolation phase transition. This is the Color String Percolation Model (CSPM). The critical density is identified as the deconfinement transition and happens at the hadronization temperature. The stochastic thermalization in p p and A-A is a consequence of the quantum tunneling through the event horizon introduced by the confining color fields, the Hawking-Unruh effect. The percolation approach within CSPM is successfully used to describe the crossover phase transition in the soft collision region. The same phenomenology when applied to both hadron-hadron and nucleus-nucleus collisions emphasizes the importance of color string density, creating a macroscopic cluster which identifies the connectivity required for a finite droplet of the QGP.

Quantum transport senses community structure in networks.

Quantum time evolution exhibits rich physics, attributable to the interplay between the density and phase of a wave function. However, unlike classical heat diffusion, the wave nature of quantum mechanics has not yet been extensively explored in modern data analysis. We propose that the Laplace transform of quantum transport (QT) can be used to construct an ensemble of maps from a given complex network to a circle S^{1}, such that closely related nodes on the network are grouped into sharply concentrated clusters on S^{1}. The resulting QT clustering (QTC) algorithm is as powerful as the state-of-the-art spectral clustering in discerning complex geometric patterns and more robust when clusters show strong density variations or heterogeneity in size. The observed phenomenon of QTC can be interpreted as a collective behavior of the microscopic nodes that evolve as macroscopic cluster "orbitals" in an effective tight-binding model recapitulating the network. python source code implementing the algorithm and examples are available at https://github.com/jssong-lab/QTC.

Observability transition in multiplex networks

We extend the observability model to multiplex networks composed of two network layers. We present mathematical frameworks, valid under the treelike ansatz, able to describe the emergence of the macroscopic cluster of mutually observable nodes in both synthetic and real-world multiplex networks. We show that the observability transition in synthetic multiplex networks is discontinuous. In real-world multiplex networks instead, edge overlap among layers is responsible for the disappearance of any sign of abruptness in the emergence of the macroscopic cluster of mutually observable nodes.

Direct evidence of amyloid precursor–like protein 1 trans interactions in cell–cell adhesion platforms investigated via fluorescence fluctuation spectroscopy

The amyloid precursor-like protein 1 (APLP1) is a type I transmembrane protein that plays a role in synaptic adhesion and synaptogenesis. Past investigations indicated that APLP1 is involved in the formation of protein-protein complexes that bridge the junctions between neighboring cells. Nevertheless, APLP1-APLP1 trans interactions have never been directly observed in higher eukaryotic cells. Here, we investigated APLP1 interactions and dynamics directly in living human embryonic kidney cells using fluorescence fluctuation spectroscopy techniques, namely cross-correlation scanning fluorescence correlation spectroscopy and number and brightness analysis. Our results show that APLP1 forms homotypic trans complexes at cell-cell contacts. In the presence of zinc ions, the protein forms macroscopic clusters, exhibiting an even higher degree of trans binding and strongly reduced dynamics. Further evidence from giant plasma membrane vesicles suggests that the presence of an intact cortical cytoskeleton is required for zinc-induced cis multimerization. Subsequently, large adhesion platforms bridging interacting cells are formed through APLP1-APLP1 trans interactions. Taken together, our results provide direct evidence that APLP1 functions as a neuronal zinc-dependent adhesion protein and allow a more detailed understanding of the molecular mechanisms driving the formation of APLP1 adhesion platforms.

Ordered phases in coupled nonequilibrium systems: Dynamic properties.

We study the dynamical properties of the ordered phases obtained in a coupled nonequilibrium system describing advection of two species of particles by a stochastically evolving landscape. The local dynamics of the landscape also gets affected by the particles. In a companion paper we have presented static properties of different phases that arise as the two-way coupling parameters are varied. In this paper we discuss the dynamics. We show that in the ordered phases macroscopic particle clusters move over an ergodic time scale growing exponentially with system size but the ordered landscape shows dynamics over a faster time scale growing as a power of system size. We present a scaling ansatz that describes several dynamical correlation functions of the landscape measured in steady state.