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A harmonically trapped active Brownian particle exhibits two types of positional distributions—one has a single peak and the other has a single well—that signify steady-state dynamics with low and high activity, respectively. Adding inertia to the translational motion preserves this strict classification of either single-peak or single-well densities but shifts the dividing boundary between the states in the parameter space. We characterize this shift for the dynamics in one spatial dimension using the static Fokker-Planck equation for the full joint distribution of the state space. We derive local results analytically with a perturbation method for a small rotational velocity and then extend them globally with a numerical approach.
T-cell cytotoxic function relies on the cooperation between the highly specific but poorly adhesive T-cell receptor (TCR) and the integrin LFA-1. How LFA-1-mediated adhesion may scale with TCR stimulation strength is ill-defined. Here, we show that LFA-1 conformation activation scales with TCR stimulation to calibrate human T-cell cytotoxicity. Super-resolution microscopy analysis reveals that >1000 LFA-1 nanoclusters provide a discretized platform at the immunological synapse to translate TCR engagement and density of the LFA-1 ligand ICAM-1 into graded adhesion. Indeed, the number of high-affinity conformation LFA-1 nanoclusters increases as a function of TCR triggering strength. Blockade of LFA-1 conformational activation impairs adhesion to target cells and killing. However, it occurs at a lower TCR stimulation threshold than lytic granule exocytosis implying that it licenses, rather than directly controls, the killing decision. We conclude that the organization of LFA-1 into nanoclusters provides a calibrated system to adjust T-cell killing to the antigen stimulation strength.
Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus . We compare the results of our deep learning approach with experiments and with the results of a state-of-the-art analytical model. To that end, we propose a systematic methodology to assess the faithfulness of a collective motion model, exploiting a set of stringent individual and collective spatio-temporal observables. We demonstrate that machine learning (ML) models of social interactions can directly compete with their analytical counterparts in reproducing subtle experimental observables. Moreover, this work emphasizes the need for consistent validation across different timescales, and identifies key design aspects that enable our deep learning approach to capture both short- and long-term dynamics. We also show that our approach can be extended to larger groups without any retraining, and to other fish species, while retaining the same architecture of the deep learning network. Finally, we discuss the added value of ML in the context of the study of collective motion in animal groups and its potential as a complementary approach to analytical models.
We complete the kinetic theory of inhomogeneous systems with long-range interactions initiated in previous works. We use a simpler and more physical formalism. We consider a system of particles submitted to a small external stochastic perturbation and determine the response of the system to the perturbation. We derive the diffusion tensor and the friction by polarization of a test particle. We introduce a general Fokker–Planck equation involving a diffusion term and a friction term. When the friction by polarization can be neglected, we obtain a secular dressed diffusion equation sourced by the external noise. When the external perturbation is created by a discrete collection of N field particles, we obtain the inhomogeneous Lenard–Balescu kinetic equation reducing to the inhomogeneous Landau kinetic equation when collective effects are neglected. We consider a multi-species system of particles. When the field particles are at statistical equilibrium (thermal bath), we establish the proper expression of the fluctuation–dissipation theorem for systems with long-range interactions relating the power spectrum of the fluctuations to the response function of the system. In that case, the friction and diffusion coefficients satisfy the Einstein relation and the Fokker–Planck equation reduces to the inhomogeneous Kramers equation. We also consider a gas of Brownian particles with long-range interactions described by N coupled stochastic Langevin equations and determine its mean and mesoscopic evolution. We discuss the notion of stochastic kinetic equations and the role of fluctuations possibly triggering random transitions from one equilibrium state to the other. Our presentation parallels the one given for the kinetic theory of two-dimensional point vortices in a previous paper (Chavanis in Eur Phys J Plus 138:136, 2023).
Nanofluidics has a very promising future owing to its numerous applications in many domains. It remains, however, very difficult to understand the basic physico-chemical principles that control the behavior of solvents confined in nanometric channels. Here, water and ion transport in carbon nanotubes is investigated using classical force field molecular dynamics simulations. By combining one single walled carbon nanotube (uniformly charged or not) with two perforated graphene sheets, we mimic single nanopore devices similar to experimental ones. The graphitic edges delimit two reservoirs of water and ions in the simulation cell from which a voltage is imposed through the application of an external electric field. By analyzing the evolution of the electrolyte conductivity, the role of the carbon nanotube geometric parameters (radius and chirality) and of the functionalization of the carbon nanotube entrances with OH or COO− groups is investigated for different concentrations of group functions.
Sujets
Current fluctuations
Turbulence
Formation
Collective behaviour
Hydrodynamics
Evaporation
Critical phenomena
9862Gq
Statistical mechanics
Smoluchowski equation
Atmosphere
General relativity
Collapse
Entropy
TASEP
9880-k
Field theory scalar
Smoluchowski-Poisson
Mass density
Brownian motion
9536+x
DNA
Dark matter density
Dark matter
Physique statistique
Diffusion
Density
Transition vitreuse
Scattering length
Pressure
Fermi gas
Cosmological model
Wave function
Scalar field
Quantum mechanics
Random walker
Condensation Bose-Einstein
Marcheur aléatoire
Nanofiltration
Euler-Maclaurin
Effect relativistic
Denaturation
Thermodynamics
Gravitational collapse
Fermion
Stability
Chemotaxie
Chemotaxis
Computational modelling
Expansion acceleration
Black hole
Collisionless stellar-systems
Dark matter condensation
Gravitation self-force
Nonrelativistic
Fermion dark matter
Dark matter theory
Rotation
Bose–Einstein condensates
Axion star
Halo
Gas Chaplygin
Keller-Segel
Numerical calculations
Fokker-Planck
Quantum chromodynamics axion
Equation of state
Kinetic theory
Fermions
Energy high
9530Sf
Gravitation collapse
Collective behavior
Cosmology
Dark matter fuzzy
Energy internal
Field theory scalar complex
Distributed Control
Dark matter halo
Phase separation
Cosmological constant
9535+d
Dark energy
Galaxy
Dissipation
Catastrophe theory
Gravitation
Mouvement brownien
Structure
Bose-Einstein
Einstein
Collective motion
Bethe ansatz
Asymptotic behavior
Computational modeling
Energy density
Effondrement gravitationnel
Competition
Feedback
Axion