Glasses are traditionally characterized by their rugged landscape of disordered low-energy states and their slow relaxation towards thermodynamic equilibrium. Far from equilibrium, dynamical forms of glassy behavior with anomalous algebraic relaxation have also been noted, for example, in networks of coupled oscillators. Due to their disordered and high-dimensional nature, such systems have been difficult to study theoretically, but data-driven methods are emerging as a promising alternative that may aid in their analysis. Here, we characterize glassy dynamics using the dynamic mode decomposition, a data-driven spectral computation that approximates the Koopman spectrum. We show that the gap between oscillatory and decaying modes in the Koopman spectrum vanishes in systems exhibiting algebraic relaxation, and thus, we propose a model-agnostic signature for robustly detecting and analyzing glassy dynamics. We demonstrate the utility of our approach through both a minimal example of a one-dimensional ODE and a high-dimensional example of coupled oscillators.
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Although light nuclear clusters are known to form abundantly in warm and dilute nuclear matter, their role in hot and dense nuclear matter remains unclear due to the lack of experimental indication for their modifications by the Mott effect under such conditions. To address this issue, we resort to intermediate-energy heavy-ion collisions, where light clusters are mainly produced in the transiently formed hot and dense matter. A kinetic approach, which includes dynamically the formation and dissociation of light clusters, is employed to deduce the strength of the Mott effect and the α-particle fraction in hot and dense nuclear matter from the light-nuclei yields measured by the FOPI Collaboration in central Au+Au collisions at energies of 0.25A to 0.6A  GeV. We find an unexpectedly abundant α clustering in this environment, which will have profound implications for modeling the nuclear equation of state and describing supernovae and neutron star mergers.
We review the key observations and theories relevant to the origin and evolution of the Galilean satellites. Key observations include: the potentially undifferentiated nature of Callisto; the increasing ice fraction with semi-major axis; the present-day existence of the Laplace resonance; the potential resurfacing of Ganymede mid-way through its evolution; and the metal-enriched nature of Jupiter's envelope. The most widely accepted theory for the formation of the satellites is the so-called "starved disk" model, although newer alternatives including decretion disks and pebble accretion have also been proposed. Models that allow slow satellite formation in a cold disk are preferred, based on the density progression and Callisto's apparent differentiation state. Major model uncertainties include the angular momentum distribution of the material infalling to the circumplanetary disk, the source of the solids, and the thermal and viscosity structure of the disk. We identify six outstanding questions, some of which will be answered by JUICE, Europa Clipper and Tianwen-4. A major difficulty in answering some questions is overprinting of primordial characteristics by later events.
Silk protein ionomer-based layer-by-layer (LBL) nanocoating strategy was utilized to preserve the viability, metabolic activity and differentiation potential of human mesenchymal stem cells (hMSCs) in an in vitro inflamed osteoarthritis (OA) tissue niche. These temporary ionomer coatings for cell protection were functionalized with cytokines to impart immune-modulatory behavior. The cytokine-functionalized ionomer nanocoatings polarized neutral M0 macrophages into M1 and M2 subtypes, depending on the specific cytokine used, thereby reducing synovial inflammation in an in vitro setup. This approach would potentially abrogate the need for ex vivo polarization before transplantation during immunotherapy. The modular nature of these silk ionomer nanocoatings in tailoring cell surface stability was further validated by extending the cell coatings to 3D spheroids and also by introducing tyramine to the cationic silk ionomer to facilitate covalent crosslinking in addition to the electrostatic interactions between the cell surface and the ionomers. These results demonstrate the versatile and modular nature of silk-based ionomer cell nanocoatings, allowing control at material, cellular, and spheroid levels to tune coating stability and inflammatory responses. These systems uniquely impart immunomodulatory function, providing a biomaterial platform for cell-based immune-regenerative therapy, such as for OA and other inflammatory diseases.
Nsp15 is an endoribonuclease, highly conserved among coronaviruses, that helps the virus evade detection in host cells by cleaving uridine-rich viral RNA sequences. Those sequences would otherwise trigger immune response pathways. Its essential role in pathogenesis and highly conserved nature make Nsp15 an attractive target for therapeutic intervention. While its crystal structure and uridine specificity are well-established, the influence of RNA structural dynamics and divalent cations on Nsp15 activity remains less understood. Leveraging single-molecule Pulsed Interleaved Excitation (PIE)-FRET and Fluorescence Correlation Spectroscopy (FCS) in combination, we developed an assay to track RNA cleavage by Nsp15 variants in real time and monitor the conformational dynamics of hybrid RNA/DNA substrates. Using our methodology and analysis strategies, we obtained clear indicators of RNA cleavage with both PIE-FRET and FCS data analysis. Our assay also revealed signatures of unique dynamic behavior in uridine-containing RNA substrates, indicating that divalent cations enhance substrate flexibility, which is associated with a faster observed reaction rate of RNA cleavage by Nsp15 in the presence of Mn2+. We propose that divalent cations induce a conformation that may be more favorable for cleavage in single-stranded RNA substrates that mediate the efficient nuclease activity of Nsp15.
Coronal hole boundaries are the interfaces between closed and open magnetic field regions in the solar atmosphere. Many fundamental processes take place at these regions, including magnetic reconnection that is responsible for solar wind release and restructuring of the solar magnetic field. In this paper, we present a case study in which we investigate the physical properties of the boundary of a large low-latitude coronal hole. Differential Emission Measure analysis is used to derive the plasma properties of these regions. We also apply correlation dimension mapping analysis to measure the irregularities of the coronal hole boundary. We find that the leading boundary of this coronal hole has a slightly higher average plasma temperature, is associated with a stronger and more unipolar magnetic field, and has a smoother boundary line than the trailing counterpart. These differences are hypothesised to be direct consequences of the local magnetic field configurations of the coronal hole boundary: the leading boundary corresponds to large, well-organised coronal loops, and the trailing boundary corresponds to more dispersed, randomly orientated small magnetic bipoles. Hence, we suggest that the surrounding magnetic field structure and the nature of magnetic reconnection influence the properties of coronal hole boundaries. The online version contains supplementary material available at 10.1007/s11207-026-02672-8.
Extracellular matrix (ECM) mechanics is pivotal regulators of tumor progression, yet how viscoelasticity and matrix architecture converge to shape metabolic and invasive adaptation remains insufficiently defined. We postulate that mechanical stimuli from the ECM induce coordinated changes in adhesive and metabolic pathways, and that the nature of this independent mechano-metabolic pathway is conserved across benign, low-invasive, and high-invasive bladder cancer phenotypes. Therefore, we engineered collagen-hyaluronan hydrogels with tunable stiffness to recapitulate soft and rigid tumor microenvironments and profiled bladder cancer spheroids representing benign, low-invasive, and highly invasive states. Integrating hydraulic force spectroscopy, rheology, and molecular phenotyping, we show that matrix stiffening differentially reprograms spheroid architecture, motility, and adhesion- and metabolism-related gene expression. Spheroid behavior emerged from the interplay between intrinsic mechanical properties, matrix rheology, and molecular adaptation. HCV29 spheroids formed rigid, compact structures, relying on cell-matrix adhesion rather than metabolic or proteolytic remodeling. HT1376 spheroids activated glycolysis (HK2) and MMP-2-dependent ECM remodeling in soft matrices, but remained largely nonmigratory, indicating decoupling of invasive priming from motility. T24 spheroids were soft, deformable, and highly migratory in compliant matrices, integrating metabolic reprogramming, adhesion remodeling (E-/N-cadherin, SDC4), and radial collagen fiber alignment to drive invasion. Notably, canonical FAK/AKT/mTOR signaling was absent across all spheroids, while pS6 ribosomal protein and ILK indicated noncanonical, SDC4/integrin-ILK-dependent mechanotransduction supporting cytoskeletal dynamics, metabolism, and ECM remodeling. Collagen organization further differed across spheroid types, with dense, radially aligned fibers in HT1376, intermediate architecture in HCV29, and loose, disorganized networks in T24, closely matching their distinct migratory behaviors and cell-ECM interactions. These findings reveal stage-specific mechanometabolic strategies in bladder cancer, demonstrating how ECM mechanics and architecture jointly guide invasion, metabolic adaptation, and local immune modulation, including the regulation of immune cell infiltration and tumor immune evasion.
Quasicrystals are ubiquitous in nature. They represent unique aperiodic materials featuring long-range order that occupy an intermediate niche between exactly periodic and disordered materials. These properties are reflected in the unusual evolutionary dynamics of excitations and unique localization properties of linear eigenmodes supported by quasicrystals. In particular, being the structures characterized by discrete rotational symmetryCνof orderν, photonic quasicrystals can support the stable propagation of linear vortex-carrying light beams and vortex solitons. However, the impact of discrete rotational symmetryνof quasicrystals on the properties of vortex light states has not been investigated so far, as only the systems constructed using the simplest Penrose tiling or corresponding optically induced Penrose lattices are considered in this context. Here, we propose a broad class of quasicrystals with global topological defects-disclinations-introduced into their structure that allows to produce novel quasicrystalline structures with any desired order of discrete rotational symmetry from the basic Penrose structure. Such global topological deformation substantially enriches the linear spectrum of quasicrystals, allowing them to support new types of linear vortex states and bifurcating from them families of stable thresholdless vortex solitons with unusual intensity and phase distributions. We find two different classes of stable vortex solitons comprising in-phase or out-of-phase pairs of closely located bright spots, with total intensity distribution reflecting a particular discrete rotational symmetry of the quasicrystal with disclination. Remarkably, even low-charge vortex solitons can be stable in quasicrystals with disclinations, whereas stability intervals for them broaden with the decrease of the discrete rotational symmetryCνof quasicrystals. Our results extend the theory of localization in quasicrystals to structures with global topological deformation, highlighting emerging prospects for the robust transmission of power or information arising in these systems.
Understanding the dynamic nature of developmental networks requires live imaging techniques capable of capturing real-time developmental processes in wild-type and mutant embryos as they are exposed to pharmacological perturbations. A peculiar developmental patterning process in early vertebrate embryos is the sequential segmentation of bilateral somites from the unsegmented tail tissue along their major axis. Earlier work discovered that segmentation is instructed by an oscillatory fibroblast growth factor (Fgf)/ERK signaling gradient sourced from the tailbud. Somite segmentation was recapitulated at will in the absence of the molecular oscillator, "the segmentation clock", via pulsatile drug inhibitions. Here, we present a live imaging setup for zebrafish embryos that incorporates a 3D-printed chamber and a programmed syringe pump for precise, automated, periodic drug delivery. The chamber secures the orientation of zebrafish embryos in agarose inserts and incorporates inflow and outflow ports to facilitate controlled drug perfusion. Servo motors controlled by an Arduino were integrated to automate valve switching, achieving fully automated exchange of two different fluids for alternating drug delivery and rinse cycles. Such periodic delivery of an inhibitor drug entrains the Fgf/ERK signaling gradient in the embryonic tail to oscillate in clock-deficient mutants, creating lab-reconstituted somites in otherwise defective embryos. Embryos expressing fluorescent markers can further be imaged at single-cell resolution during perturbations. Overall, this system provides a cost-effective, reproducible platform for investigating vertebrate development and interrogating cellular decision-making under controlled experimental conditions. We anticipate this setup will be broadly beneficial for the biomedical research community interested in controlled drug delivery and in vivo cellular dynamics.
The study of dense matter has been greatly advanced by progress in theoretical high-energy simulations and modern observational astronomy. Neutron stars serve as natural laboratories for probing matter under extreme density and strong gravitational fields. While polytropic equations of state are widely employed, conventional models based on a single polytropic index cannot adequately represent the stratified, layered nature of realistic compact-star interiors. To address this limitation, we develop a composite relativistic polytropic model in which the polytropic index varies smoothly with radius. By coupling the Einstein field equations with a generalized composite polytropic equation of state, we derive the composite Tolman-Oppenheimer-Volkoff (CTOV) system. The resulting nonlinear equations are solved using a Monte Carlo-based numerical integration method, which efficiently handles stiffness while enabling probabilistic exploration of the parameter space and natural uncertainty quantification. Our results demonstrate that increasing the relativistic parameter σ significantly reduces both the Emden function and the enclosed mass function, producing more compact stellar configurations. Sharper core-envelope transitions (ε = 0.01) yield systematically higher compactness than smoother transitions (ε = 0.03). The derived mass-radius relations reproduce the observed diversity of neutron stars, successfully matching both low-mass, large-radius systems such as PSR J0030 + 0451 and high-mass compact pulsars such as PSR J1614-2230. Importantly, the maximum-mass analysis shows that stiff composite configurations (nc = 1, ne = 2, xc = 0.7) can support gravitational masses up to Mmax ≈ 3.47 M[Formula: see text] for ε = 0.01 and Mmax ≈ 2.71 M[Formula: see text] for ε = 0.03, with corresponding minimum radii in the range Rmin ≈ 10.6-13.2 km, consistent with current observational constraints. These findings confirm that composite polytropes provide a flexible, physically motivated framework for modeling stratified compact stars and for constraining the dense-matter equation of state.
Synchronization transitions in oscillatory networks often manifest as the emergence of a periodic global mode. While perfect in the thermodynamic limit, this mode fluctuates for finite ensembles. We characterize the coherence of this mode in terms of the phase diffusion constant. In several examples, we always observed normal diffusion, but the dependence of the diffusion constant on the system size D∼N^{-μ} depends on the nature of coupled units. For coupled chaotic systems, we found μ=1, while for coupled periodic oscillators we observed, depending on the particular model, values between 2 and 2.7. These large values of the power index are attributed to the size dependence of collective chaos in the finite ensemble, which disappears in the thermodynamic limit. We also show that in the standard Kuramoto model for a symmetric set of frequencies, there is an additional transition to a symmetric chaotic state with vanishing diffusion of the global phase.
Bismuth antimonide (Bi1-x Sb x ) has emerged as a highly promising material for quantum applications due to its complex band structure. In this study, spherical Bi1-x Sb x quantum dots (QDs), with a diameter of around 8 ± 2 nm, were successfully synthesized by pulsed laser ablation in liquids. The energy bandgap was determined at 2.02 ± 0.27 eV, which is significantly higher than the bulk value (∼0.025 eV). The strong confinement nature of the dots was confirmed by the Raman peak shifts. The chemical composition of the Bi1-x Sb x QDs was measured to be around 77 ± 2 at. % of Bi and 23 ± 2 at. % of Sb. The colloid containing the Bi1-x Sb x QDs was classified as highly stable, displaying a zeta potential of -38 ± 18 mV. Finally, the Bi1-x Sb x QDs exhibited an electron spin resonance (ESR) signal at room temperature and at cryogenic temperature (4.2 K); consequently, revealing the presence of paramagnetic states.
The advent of van der Waals (vdW) heterostructures has enabled formation of bespoke materials with atomic precision, where numerous quantum and topological phenomena have already been discovered. This atomic-layer tunability, however, comes at a cost: individual 2D layers must be picked up, moved, and placed in a deterministic manner while keeping their interfaces atomically clean. Recent advances in machine learning and robotics place even stronger emphasis on the deterministic aspect of vdW assembly. Current polymer-based transfer methods satisfy neither the determinism nor cleanliness requirements. To this end, solutions are needed where adhesion can be dynamically and deterministically controlled without leaving organic contamination. Here, we present a polymer-free transfer technique employing thin muscovite (mica) crystals. Temperature control over mica adhesion enables deterministic pick-up, stacking, and release of 2D materials, while their crystalline, inorganic nature ensures pristine interfaces and suppresses strain. Fully compatible with existing fabrication workflows, this approach enables the assembly of demanding vdW heterostructures, including those with exposed conductive layers, moiré superlattices and suspended membranes. Our method represents a promising strategy for vdW heterostructure fabrication toward its automatisation.
Proteins perform diverse functions in biological systems, including catalysis, signaling, transport, and various mechanical roles. The growing efforts to integrate proteins' unique functionalities into synthetic environments have stimulated research on protein behaviors in non-native, synthetic environments, such as in organic solvents or plastics. This study bridges the understanding of protein mechanics in nature with their application in synthetic environments. We used all-atom molecular dynamics simulations to study the unfolding of de novo helix repeat proteins in organic solvents at different hydration ratios under mechanical force. The results show that proteins in organic solvents require higher unfolding forces than in aqueous media and display distinct unfolding pathways. Detailed analysis revealed that sufficient replenishment of hydrogen bonding between protein and water favors helix unraveling, whereas the reduced hydrophobic effect in organic solvents encourages the breakdown of tertiary structure. These findings contribute critical insights into the rational design of proteins as unique mechanosensitive elements in synthetic materials, highlighting the importance of considering environmental context when repurposing proteins for such environments.
Spin glasses are magnetic materials exhibiting numerous magnetization patterns, that randomly vary both in real space and in time. To date, it is still not well understood what the nature of these spatiotemporal dynamics is, namely if they are completely random or if there are relevant and correlated length and time scales. Here, we demonstrate dynamic heterogeneity in the aging dynamics of elemental neodymium. We used spin-polarized scanning tunneling microscopy in combination with atomistic spin dynamics simulations to image the locally ordered magnetic patterns in the self-induced spin glass state and tracked the induced spatiotemporal dynamics in response to external perturbations. We observed that the real-space magnetization exhibited a coexistence of slow and fast dynamics coinciding with particular length scales. We adapted a wavelet paradigm to identify and correlate local spatial order with its dynamical evolution. These results provide a platform to study dynamics in spin glasses beyond the mean-field limit, linking to a generalized picture of glasses.
Understanding tropical land temperature response to rising atmospheric CO2 in the past is crucial for better constraining future climate projections. However, the evolution of regional land temperatures on paleoclimate timescales remains uncertain due to the paucity of precise records. Here we reconstructed temperatures across the last deglaciation using nucleation-assisted microthermometry in a stalagmite from central-eastern South America. We show that cave temperatures increased by 5.8 ± 0.3 °C (2 standard errors of the mean, SEM) from the Last Glacial Maximum to the early Holocene, broadly tracking global atmospheric CO2 and Antarctic temperatures. Our results reveal an abrupt regional warming across the Antarctic Cold Reversal-Younger Dryas (ACR-YD) transition, linked to the weakening of the Atlantic Meridional overturning circulation (AMOC). Notably, the most rapid warming at our cave was still slower than projections of future long-term warming, highlighting the unprecedented nature of the current greenhouse gas forcing.
Plant cytochrome P450 enzymes are central to natural product biosynthesis, but remain difficult to express in microbial hosts due to their transmembrane nature. Lysate-based, cell-free expression systems allow supplementation with artificial membranes to support the expression and translocation of transmembrane proteins. We developed a framework to systematically test liposomal membrane compositions to enhance the plant P450 expression yield. Adjustments to common phospholipid ratios or the addition of plant galactolipids had minimal impact on expression. In contrast, blended liposomes containing Egg PC, sterol-conjugated phospholipids, and PEGylated lipids produced concentration-dependent increases in expression. Expression of an Escherichia coli mechanosensitive channel and three plant P450s improved more than 2-fold, with some P450s showing up to 14-fold enhancement. These findings highlight membrane composition as a key determinant of the P450 expression yield in cell-free expression systems. While P450 activity was not measured, these findings provide a framework for future workflows toward achieving functional plant transmembrane enzymes for the bioproduction of natural products.
Accurate simulation of fluid flow in porous media is a challenging task due to the complexity of pore-space geometries and the computational cost of solving the Navier-Stokes equations. Traditional numerical solvers rely on carefully constructed meshes, often requiring manual intervention, and typically exhibit slow convergence. This difficulty is particularly pronounced in porous media, where the diffusive nature of momentum transport is hindered by intricate solid boundaries. These challenges limit the efficiency of numerical simulations, particularly when repeated evaluations are required. We present a neural-network-based framework for predicting pore-scale velocity fields directly from sample geometry. The method is based on a convolutional encoder-decoder architecture with skip connections, designed to preserve fine-grained structural information. Physical consistency is encouraged through a custom loss function composed of multiple terms: incompressibility, no-flow conditions within solids, periodicity constraints, and agreement with the global tortuosity index. We systematically analyze the influence of weight selection for these loss terms, quantifying their individual contributions to prediction accuracy. Several architectural variants inspired by computer vision are evaluated to identify one providing the best performance and robustness. The generalization ability of the trained network is assessed on samples outside the training distribution, including variations in boundary conditions, obstacle geometry, and porosity. Finally, we demonstrate additional practical applications in which network predictions are used to initialize the Lattice-Boltzmann simulations, a standard fluid dynamics solver, often used in complex boundary problems like porous media and used by us to train the network. We have used network-generated velocity field as a starting point and found that this significantly accelerates LBM solver convergence, achieving improvements in over 90% of cases.
Bilayer graphene cavities where electrons are confined within finite graphene flakes provide an alluring platform not only for the future nanoelectronic devices owing to the tunable energy gap but also for investigating the quantum nature of chaos due to the trigonal warping of their Fermi surface. Here we demonstrate that rotating the cavity boundary relative to the underlying lattice structure drives a quantum transition from nearly integrable dynamics to chaotic regime, observed as a concomitant crossover of eigenvalue statistics and eigenstate profiles. Complementing the full quantum treatment, we examine the classical backbone of this onset of chaos by employing semiclassical ray dynamics. Our results position bilayer graphene cavities as a promising venue for investigating and engineering quantum-chaotic behavior in graphene-based devices.