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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.
We discuss formal analogies between a nonlinear Schrödinger equation derived by the author from the theory of scale relativity and the equations of Brownian theory. By using the Madelung transformation, the nonlinear Schrödinger equation takes the form of hydrodynamic equations involving a friction force, an effective thermal pressure, a pressure due to the self-interaction, and a quantum potential. These hydrodynamic equations have a form similar to the damped Euler equations obtained for self-interacting Brownian particles in the theory of simple liquids. In that case, the temperature is due to thermal motion and the pressure arises from spatial correlations between the particles. More generally, the correlations can be accounted for by using the dynamical density functional theory. We determine the excess free energy of Brownian particles that reproduces the standard quantum potential. We then consider a more general form of excess free energy functionals and propose a new class of generalized Schrödinger equations. For a certain form of excess free energy, we recover the generalized Schrödinger equation associated with the Tsallis entropy considered in a previous paper.
Sujets
Physique statistique
Asymptotic behavior
9862Gq
Transition vitreuse
Cosmological constant
Gravitation collapse
Bose-Einstein
Density
Mass density
Energy internal
Gas Chaplygin
General relativity
Dark energy
Competition
Brownian motion
Gravitational collapse
Collective behavior
Dark matter theory
Cosmology
Feedback
Energy density
Statistical mechanics
Dark matter density
Entropy
9880-k
Chemotaxis
9536+x
Cosmological model
Kinetic theory
Euler-Maclaurin
Structure
Condensation Bose-Einstein
Current fluctuations
Galaxy
Gravitation
Rotation
9535+d
Effondrement gravitationnel
Fermions
Field theory scalar
Catastrophe theory
Phase separation
Atmosphere
Mouvement brownien
Numerical calculations
Stability
Smoluchowski equation
Scalar field
Fokker-Planck
Collisionless stellar-systems
Einstein
Computational modeling
Collective motion
Random walker
Dissipation
Dark matter fuzzy
Turbulence
Effect relativistic
Expansion acceleration
Quantum mechanics
Thermodynamics
Diffusion
Fermion
Black hole
DNA
Distributed Control
TASEP
Nonrelativistic
Fermi gas
Equation of state
Critical phenomena
Keller-Segel
Pressure
Collapse
Collective behaviour
Chemotaxie
Computational modelling
Formation
Smoluchowski-Poisson
Axion star
Fermion dark matter
Axion
Hydrodynamics
Halo
Field theory scalar complex
Scattering length
Evaporation
Wave function
Marcheur aléatoire
Dark matter
Bose–Einstein condensates
Gravitation self-force
Quantum chromodynamics axion
Dark matter condensation
Energy high
Dark matter halo
Bethe ansatz
Nanofiltration
Denaturation
9530Sf