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We have experimentally investigated the spectral characteristics and spatial structure of femtosecond pulses from a titanium:sapphire laser during filamentation in an optical cell filled with argon at pressures up to 40 atm under pressure shock-drop conditions. This leads to the development of strong jet flows and vortex gas turbulence, which in turn triggers the early onset of multiple filamentation of the optical pulse and largescale broadening of its spectrum throughout the entire duration of the pressure drop. The magnitude of this spectrum broadening can reach 80 nm and is proportional to the initial gas pressure. Using computational fluid dynamics simulations, we studied the dynamics of the emergence, development, and relaxation of stimulated turbulence in compressed gas in the region of the cell outlet valve and assessed the effect it exerts on the propagating femtosecond pulse. The revealed regularities may serve as the basis for developing an effective method of controlling the spectrum of supercontinuum radiation via filamentation of highpower ultrashort laser pulses in gas cells under shock pressure release and rise conditions.
Plasma wakefield excitation driven by two color Laguerre Gaussian laser pulses carrying orbital angular momentum is investigated analytically and through quasi-cylindrical particle in cell simulations. Using a perturbative framework together with the quasistatic approximation, the influence of the transverse laser mode structure on the longitudinal and transverse wakefields in an underdense plasma is examined in the weakly relativistic regime. The results show that drivers with finite azimuthal index produce reduced and less regular on-axis longitudinal wakefields compared to conventional Gaussian drivers. However, radial longitudinal field distributions reveal that this reduction originates from a redistribution of the wakefield energy toward finite radii rather than a simple loss of wake excitation. Orbital angular momentum carrying modes generate hollow and ring shaped wake structures accompanied by strongly modified transverse electric fields and broader plasma density perturbations. Mixed Gaussian Laguerre Gaussian configurations exhibit intermediate behavior, combining weak on-axis acceleration with pronounced off axis wake excitation. The study demonstrates that structured two-color laser drivers fundamentally modify the topology of plasma wakefields and provide an additional mechanism for controlling transverse plasma dynamics, off-axis acceleration, and angular momentum mediated wakefield structures in plasma based accelerator schemes.
What determines whether an organism or collective will survive under particular conditions? This question is asked across the life sciences when determining adaptive fit, developing efficacious treatments for diseases, and assessing the risks posed by ecological shifts. To aid their investigations, researchers employ models of agents which must respect particular constraints to remain alive. By constraining the dynamics of these agents to bounded viability regions, these models form a class of extended dynamical systems where transient dynamics can lead to death, making traditional attractors and separatrices insufficient for characterizing the global space of possible behaviors. To remedy this, we develop viability space decomposition, an analysis framework for ordinary differential equation models of agents with viability constraints. We first introduce the general theory, revealing how several new classes of manifolds (mortality, ordering, and collapse) permit a complete decomposition of state space into regions of qualitatively similar survival outcomes: a viability portrait. We then demonstrate the method by completely analyzing the global behavior of three models: a subcellular network, a behaving cell with the same physiology, and two coupled cell networks. Finally, we finish by discussing how the framework scales and future directions for its development and application.
Single-cell trajectory inference from destructive time-course snapshots is fundamentally ill-posed: neither cross-time cell correspondences nor continuous trajectories are observed, so the snapshot distributions alone do not uniquely determine the underlying dynamics. Existing optimal transport and flow-based methods typically couple cells by Euclidean proximity at observed clock times, which can misalign trajectories when development is asynchronous and cells sampled at the same experimental time occupy different latent pseudotime stages. We propose PACE, a trajectory inference framework that recovers geometry-consistent continuous transport dynamics from destructive time-course snapshots through three coupled components. First, PACE constructs a state- and time-dependent anisotropic Riemannian metric that assigns low transport cost along locally supported tangent directions while penalizing normal velocity components. Second, it alternates between refining cross-time couplings under the induced path-action cost and fitting endpoint-preserving neural bridges between adjacent snapshots. Third, it distills the learned bridge dynamics into a global continuous-time velocity field over cellular states. Across seven controlled and biological datasets covering nine held-out reconstruction experiments, PACE achieves the strongest overall reconstruction performance, reducing MMD, Wasserstein-1 distance, and Wasserstein-2 distance by 23.7% on average relative to the strongest competing baseline. PACE also improves RNA-velocity alignment by 15.4% on an embryoid body differentiation benchmark, without requiring explicit cell pairing, lineage tracing, or RNA-velocity supervision during training. Code is available at https://github.com/AI4Science-WestlakeU/PACE.
Rxiv mechanobio
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