Analysis Of Collective Motions Research Articles

Center manifold theory for the 1-dimensional collective motions of camphor disks with delta functions in the <inline-formula><tex-math id="M1">$ L^2 $</tex-math></inline-formula>-framework

Camphor boat and disk are types of interfacial self-propellers that rely on the modification of forces present at the air-liquid interface. In a one-dimensional circuit, self-organized congestion and clustering phenomena are observed experimentally and numerically [11,43]. Although the center manifold theories proposed in [5,7] are useful in the analysis of collective motions, the requirement in the reduction process is not fulfilled because the linearized operator involves Dirac delta functions. To overcome such mathematical difficulties, a theory in the $ (H^1)^* $-framework was developed in [17]. However, the reduced equation does not include any nonlinearity. Thus, information on the collective motions could not be obtained. The objectives of the present study are to propose a new approach to establish the existence and uniqueness of a center manifold in a system with delta functions in the $ L^2 $-framework and to derive a reduced system with suitable nonlinearity from the original model in [33].

Label-free cell tracking enables collective motion phenotyping in epithelial monolayers

SummaryCollective cell migration is an umbrella term for a rich variety of cell behaviors, whose distinct character is important for biological function, notably for cancer metastasis. One essential feature of collective behavior is the motion of cells relative to their immediate neighbors. We introduce an AI-based pipeline to segment and track cell nuclei from phase-contrast images. Nuclei segmentation is based on a U-Net convolutional neural network trained on images with nucleus staining. Tracking, based on the Crocker-Grier algorithm, quantifies nuclei movement and allows for robust downstream analysis of collective motion. Because the AI algorithm required no new training data, our approach promises to be applicable to and yield new insights for vast libraries of existing collective motion images. In a systematic analysis of a cell line panel with oncogenic mutations, we find that the collective rearrangement metric, D2min, which reflects non-affine motion, shows promise as an indicator of metastatic potential.

Factors underlying asymmetric pore dynamics of disaggregase and microtubule-severing AAA+ machines

Disaggregation and microtubule-severing nanomachines from the AAA+ (ATPases associated with various cellular activities) superfamily assemble into ring-shaped hexamers that enable protein remodeling by coupling large-scale conformational changes with application of mechanical forces within a central pore by loops protruding within the pore. We probed the asymmetric pore motions and intraring interactions that support them by performing extensive molecular dynamics simulations of single-ring severing proteins and the double-ring disaggregase ClpB. Simulations reveal that dynamic stability of hexameric pores of severing proteins and of the nucleotide-binding domain 1 (NBD1) ring of ClpB, which belong to the same clade, involves a network of salt bridges that connect conserved motifs of central pore loops. Clustering analysis of ClpB highlights correlated motions of domains of neighboring protomers supporting strong interprotomer collaboration. Severing proteins have weaker interprotomer coupling and stronger intraprotomer stabilization through salt bridges involving pore loops. Distinct mechanisms are identified in the NBD2 ring of ClpB involving weaker interprotomer coupling through salt bridges formed by noncanonical loops and stronger intraprotomer coupling. Analysis of collective motions of PL1 loops indicates that the largest amplitude motions in the spiral complex of spastin and ClpB involve axial excursions of the loops, whereas for katanin they involve opening and closing of the central pore. All three motors execute primarily axial excursions in the ring complex. These results suggest distinct substrate processing mechanisms of remodeling and translocation by ClpB and spastin compared to katanin, thus providing dynamic support for the differential action of the two severing proteins. Relaxation dynamics of the distance between the PL1 loops and the center of mass of protomers reveals observation-time-dependent dynamics, leading to predicted relaxation times of tens to hundreds of microseconds on millisecond experimental timescales. For ClpB, the predicted relaxation time is in excellent agreement with the extracted time from smFRET experiments.

Landscape-Based Mutational Sensitivity Cartography and Network Community Analysis of the SARS-CoV-2 Spike Protein Structures: Quantifying Functional Effects of the Circulating D614G Variant.

We developed and applied a computational approach to simulate functional effects of the global circulating mutation D614G of the SARS-CoV-2 spike protein. All-atom molecular dynamics simulations are combined with deep mutational scanning and analysis of the residue interaction networks to investigate conformational landscapes and energetics of the SARS-CoV-2 spike proteins in different functional states of the D614G mutant. The results of conformational dynamics and analysis of collective motions demonstrated that the D614 site plays a key regulatory role in governing functional transitions between open and closed states. Using mutational scanning and sensitivity analysis of protein residues, we identified the stability hotspots in the SARS-CoV-2 spike structures of the mutant trimers. The results suggest that the D614G mutation can induce the increased stability of the open form acting as a driver of conformational changes, which may result in the increased exposure to the host receptor and promote infectivity of the virus. The network community analysis of the SARS-CoV-2 spike proteins showed that the D614G mutation can enhance long-range couplings between domains and strengthen the interdomain interactions in the open form, supporting the reduced shedding mechanism. This study provides the landscape-based perspective and atomistic view of the allosteric interactions and stability hotspots in the SARS-CoV-2 spike proteins, offering a useful insight into the molecular mechanisms underpinning functional effects of the global circulating mutations.

Towards video based collective motion analysis through shape tracking and matching

The term ‘collective’ is used to refer to a group of animals such as a flock of birds or a herd of elephants. Aggregate motions of such collectives often give rise to visually pleasing shapes and patterns (e.g. V-shape formation of geese while they migrate from one place to another). While shapes of moving collectives are of great interest in many scientific studies, scant attention has been given to algorithmically extract and render these shapes via polygonal boundaries or graphs. In this Letter, the authors present a multi-stage, proof of concept framework for tracking geometric shapes and extracting video frames containing a user defined shape of moving collectives, by employing a deep-learning based object detection, well-known alpha shapes and a modified shape context. They demonstrate the usefulness of the proposed framework on a couple of test videos and discuss its potential applications in a wider area.

Quantifying and Detecting Collective Motion in Crowd Scenes.

People in crowd scenes always exhibit consistent behaviors and form collective motions. The analysis of collective motion has motivated a surge of interest in computer vision. Nevertheless, the effort is hampered by the complex nature of collective motions. Considering the fact that collective motions are formed by individuals, this paper proposes a new framework for both quantifying and detecting collective motion by investigating the spatio-temporal behavior of individuals. The main contributions of this work are threefold: 1) an intention-aware model is built to fully capture the intrinsic dynamics of individuals; 2) a structure-based collectiveness measurement is developed to accurately quantify the collective properties of crowds; 3) a multistage clustering strategy is formulated to detect both the local and global behavior consistency in crowd scenes. Experiments on real world data sets show that our method is able to handle crowds with various structures and time-varying dynamics. Especially, the proposed method shows nearly 10% improvement over the competitors in terms of NMI, Purity and RI. Its applicability is illustrated in the context of anomaly detection and semantic scene segmentation.

Automated collective motion analysis validates human keratinocyte stem cell cultures

Identification and quality assurance of stem cells cultured in heterogeneous cell populations are indispensable for successful stem cell therapy. Here we present an image-processing pipeline for automated identification and quality assessment of human keratinocyte stem cells. When cultivated under appropriate conditions, human epidermal keratinocyte stem cells give rise to colonies and exhibit higher locomotive capacity as well as significant proliferative potential. Image processing and kernel density estimation were used to automatically extract the area of keratinocyte colonies from phase-contrast images of cultures containing feeder cells. The DeepFlow algorithm was then used to calculate locomotion speed of the colony area by analyzing serial images. This image-processing pipeline successfully identified keratinocyte stem cell colonies by measuring cell locomotion speed, and also assessed the effect of oligotrophic culture conditions and chemical inhibitors on keratinocyte behavior. Therefore, this study provides automated procedures for image-based quality control of stem cell cultures and high-throughput screening of small molecules targeting stem cells.

Physical-barrier detection based collective motion analysis

Collective motion is one of the most fascinating phenomena and mainly caused by the interactions between individuals. Physical-barriers, as the particular facilities which divide the crowd into different lanes, greatly affect the measurement of such interactions. In this paper we propose the physical-barrier detection based collective motion analysis (PDCMA) approach. The main idea is that the interaction between spatially adjacent pedestrians actually does not exist if they are separated by the physical-barrier. Firstly, the physical-barriers are extracted by two-stage clustering. The scene is automatically divided into several motion regions. Secondly, local region collectiveness is calculated to represent the interactions between pedestrians in each region. Finally, extensive evaluations use the three typical methods, i.e., the PDCMA, the Collectiveness, and the average normalized Velocity, to show the efficiency and efficacy of our approach in the scenes with and without physical barriers. Moreover, several escalator scenes are selected as the typical physical-barrier test scenes to demonstrate the performance of our approach. Compared with the current collectivemotion analysis methods, our approach better adapts to the scenes with physical barriers.

Structure preserving clustering-object tracking via subgroup motion pattern segmentation

Tracking clustering objects with similar appearances simultaneously in collective scenes is a challenging task in the field of collective motion analysis. Recent work on clustering-object tracking often suffers from poor tracking accuracy and terrible real-time performance due to the neglect or the misjudgment of the motion differences among objects. To address this problem, we propose a subgroup motion pattern segmentation framework based on a multilayer clustering structure and establish spatial constraints only among objects in the same subgroup, which entails having consistent motion direction and close spatial position. In addition, the subgroup segmentation results are updated dynamically because crowd motion patterns are changeable and affected by objects’ destinations and scene structures. The spatial structure information combined with the appearance similarity information is used in the structure preserving object tracking framework to track objects. Extensive experiments conducted on several datasets containing multiple real-world crowd scenes validate the accuracy and the robustness of the presented algorithm for tracking objects in collective scenes.

Quantifying and Detecting Collective Motion by Manifold Learning

The analysis of collective motion has attracted many researchers in artificial intelligence. Though plenty of works have been done on this topic, the achieved performance isstill unsatisfying due to the complex nature of collective motions. By investigating the similarity of individuals, this paper proposes a novel framework for both quantifying and detecting collective motions. Our main contributions are threefold: (1) the time-varying dynamics of individuals are deeply investigated to better characterize the individual motion; (2) a structure-based collectiveness measurement is designed toprecisely quantify both individual-level and scene-level properties of collective motions; (3) a multi-stage clustering strategy is presented to discover a more comprehensive understanding of the crowd scenes, containing both local and global collective motions. Extensive experimental results on realworld data sets show that our method is capable of handling crowd scenes with complicated structures and various dynamics, and demonstrate its superior performance against state-of-the-art competitors.

Modelling and tracking the flight dynamics of flocking pigeons based on real GPS data (small flock)

Improvements in our ability to collect and process real-time data from animal behaviour have lead to increasing interest in the quantitative analysis of collective motion in various species. This motion can be characterised as movement of a group of animals that interact among themselves and with the surrounding environment to preserve the cohesive form of the group. Examples of this motion include a flock of birds, a fish school and a group of deer or sheep. The problem becomes particularly challenging as the group dynamics and the individual dynamics need to be disentangled for biological analysis. Within this framework we describe a new mathematical technique that may be applied in order to understand animals group movement and behaviour. In particular, we model and track the behaviour of a small flock of pigeons and verify using real data that the dynamics of each individual are a combination of two different mechanisms (local and global).

Local collective motion analysis for multi-probe dynamic imaging and microrheology

Dynamical artifacts, such as mechanical drift, advection, and hydrodynamic flow, can adversely affect multi-probe dynamic imaging and passive particle-tracking microrheology experiments. Alternatively, active driving by molecular motors can cause interesting non-Brownian motion of probes in local regions. Existing drift-correction techniques, which require large ensembles of probes or fast temporal sampling, are inadequate for handling complex spatio-temporal drifts and non-Brownian motion of localized domains containing relatively few probes. Here, we report an analytical method based on local collective motion (LCM) analysis of as few as two probes for detecting the presence of non-Brownian motion and for accurately eliminating it to reveal the underlying Brownian motion. By calculating an ensemble-average, time-dependent, LCM mean square displacement (MSD) of two or more localized probes and comparing this MSD to constituent single-probe MSDs, we can identify temporal regimes during which either thermal or athermal motion dominates. Single-probe motion, when referenced relative to the moving frame attached to the multi-probe LCM trajectory, provides a true Brownian MSD after scaling by an appropriate correction factor that depends on the number of probes used in LCM analysis. We show that LCM analysis can be used to correct many different dynamical artifacts, including spatially varying drifts, gradient flows, cell motion, time-dependent drift, and temporally varying oscillatory advection, thereby offering a significant improvement over existing approaches.

Evaluation of Protein Elastic Network Models Based on an Analysis of Collective Motions.

Elastic network models (ENMs) are valuable tools for investigating collective motions of proteins, and a rich variety of simple models have been proposed over the past decade. A good representation of the collective motions requires a good approximation of the covariances between the fluctuations of the individual atoms. Nevertheless, most studies have validated such models only by the magnitudes of the single-atom fluctuations they predict. In the present study, we have quantified the agreement between the covariance structure predicted by molecular dynamics (MD) simulations and those predicted by a representative selection of proposed coarse-grained ENMs. We then contrast this approach with the comparison to MD-predicted atomic fluctuations and comparison to crystallographic B-factors. While all the ENMs yield approximations to the MD-predicted covariance structure, we report large and consistent differences between proposed models. We also find that the ability of the ENMs to predict atomic fluctuations is correlated with their ability to capture the covariance structure. In contrast, we find that the models that agree best with B-factors model collective motions less reliably and recommend against using B-factors as a benchmark.

Continuum Mechanical Analysis of Collective Motion of Robots

Continuum Mechanical Analysis of Collective Motion of Robots

Low- and High-Frequency Raman Investigations on Caffeine: Polymorphism, Disorder and Phase Transformation

Raman investigations are carried out both in crystalline forms of caffeine and during the isothermal transformation of the orientationally disordered form I into the stable form II at 363 K. The time dependence of the Raman spectrum exhibits no significant change in the intramolecular regime (above 100 cm(-1)), resembling the spectrum of the liquid state. By contrast, significant changes are observed below 100 cm(-1), and the low-frequency spectra of forms I and II are observed to be different from that of the liquid. The temperature dependence of the 5-600 cm(-1) spectrum gives information on the static disorder through the analysis of collective motions, while information on dynamic disorder are obtained from the study of the 555 cm(-1) band corresponding to internal vibrations in the pyrimidine ring. This analysis indubitably reveals that form II is also orientationally disordered with a local molecular arrangement that mimics that in form I and the liquid state. The comparison of the low-frequency spectra recorded in theophylline and form II of caffeine allows one to describe the stable form of caffeine from the packing arrangement of anhydrous theophylline with the consideration of reorientational molecular disorder.

Multiscale analysis of collective motion and decision-making in swarms: An advection–diffusion equation with memory approach

We propose a (time) multiscale method for the coarse-grained analysis of collective motion and decision-making in self-propelled particle models of swarms comprising a mixture of ‘naïve’ and ‘informed’ individuals. The method is based on projecting the particle configuration onto a single ‘meta-particle’ that consists of the elongation of the flock together with the mean group velocity and position. We find that the collective states can be associated with the transient and asymptotic transport properties of the random walk followed by the meta-particle, which we assume follows a continuous time random walk (CTRW). These properties can be accurately predicted at the macroscopic level by an advection–diffusion equation with memory (ADEM) whose parameters are obtained from a mean group velocity time series obtained from a single simulation run of the individual-based model.

Coarse analysis of collective motion with different communication mechanisms

We study the effects of a signalling constraint on an individual-based model of self-organizing group formation using a coarse analysis framework. This involves using an automated data-driven technique which defines a diffusion process on the graph of a sample dataset formed from a representative stationary simulation. The eigenvectors of the graph Laplacian are used to construct ‘diffusion-map’ coordinates which provide a geometrically meaningful low-dimensional representation of the dataset. We show that, for the parameter regime studied, the second principal eigenvector provides a sufficient representation of the dataset and use it as a coarse observable. This allows the computation of coarse bifurcation diagrams, which are used to compare the effects of the signalling constraint on the population-level behavior of the model.

Time series analysis of collective motions in proteins.

The dynamics of alpha-amylase inhibitor tendamistat around its native state is investigated using time series analysis of the principal components of the C(alpha) atomic displacements obtained from molecular dynamics trajectories. Collective motion along a principal component is modeled as a homogeneous nonstationary process, which is the result of the damped oscillations in local minima superimposed on a random walk. The motion in local minima is described by a stationary autoregressive moving average model, consisting of the frequency, damping factor, moving average parameters and random shock terms. Frequencies for the first 50 principal components are found to be in the 3-25 cm(-1) range, which are well correlated with the principal component indices and also with atomistic normal mode analysis results. Damping factors, though their correlation is less pronounced, decrease as principal component indices increase, indicating that low frequency motions are less affected by friction. The existence of a positive moving average parameter indicates that the stochastic force term is likely to disturb the mode in opposite directions for two successive sampling times, showing the modes tendency to stay close to minimum. All these four parameters affect the mean square fluctuations of a principal mode within a single minimum. The inter-minima transitions are described by a random walk model, which is driven by a random shock term considerably smaller than that for the intra-minimum motion. The principal modes are classified into three subspaces based on their dynamics: essential, semiconstrained, and constrained, at least in partial consistency with previous studies. The Gaussian-type distributions of the intermediate modes, called "semiconstrained" modes, are explained by asserting that this random walk behavior is not completely free but between energy barriers.

Transverse momentum analysis of collective motion in relativistic nuclear collisions

Novel transverse-momentum technique is used to analyse charged-particle exclusive data for collective motion in the Ar+KCl reaction at 1.8 GeV/nucl. Previous analysis of this reaction, employing the standard sphericity tensor, revealed no significant effect. In the present analysis, collective effects are observed, and they are substantially stronger than in the Cugnon cascade model, but weaker than in the hydrodynamical model.

Cascade-model analysis of collective motion in relativistic nuclear reactions

A new cascade model has been constructed and applied to high-energy heavy-ion collisions, where the emphasis is placed on a systematic study of nuclear collective motion. The model provides a time-dependent microscopic description of nuclear cascade collisions which takes into account some important aspects of the colliding system. The calculated results agree consistently with the experimental data, which otherwise have been interpreted as evidence for collective motion.