Ultrafast spectroscopy of liquids using extreme-ultraviolet to soft-X-ray pulses
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Advancements in ultrafast photonics: confluence of nonlinear optics and intelligent strategies
Automatic mode-locking techniques, the integration of intelligent technologies with nonlinear optics offers the promise of on-demand intelligent control, potentially overcoming the inherent limitations of traditional ultrafast pulse generation that have predominantly suffered from the instability and suboptimality of open-loop manual tuning. The advancements in intelligent algorithm-driven automatic mode-locking techniques primarily are explored in this review, which also revisits the fundamental principles of nonlinear optical absorption, and examines the evolution and categorization of conventional mode-locking techniques. The convergence of ultrafast pulse nonlinear interactions with intelligent technologies has intricately expanded the scope of ultrafast photonics, unveiling considerable potential for innovation and catalyzing new waves of research breakthroughs in ultrafast photonics and nonlinear optics characters.
Experimental observation of gapped shear waves and liquid-like to gas-like dynamical crossover in active granular matter
Unlike crystalline solids, liquids lack long-range order, resulting in diffusive shear fluctuations rather than propagating waves. Simulations predict that liquids exhibit a k-gap in wave-vector space, where solid-like transverse waves reappear above this gap. Experimental evidence in classical liquids has been limited, observed only in 2D dusty plasmas. Here, we investigate this phenomenon using active Brownian vibrators and uncover distinct gas-like and liquid-like phases depending on the packing fraction. We measure key properties, including pair correlation functions, mean square displacements, velocity auto-correlation functions, and vibrational density of states. In the liquid-like phase, we confirm the k-gap in transverse excitations, whose size grows as the packing fraction decreases and eventually disappears in the gas phase. Our findings extend the concept of the k-gap to active granular systems and reveal striking parallels with supercritical fluids.
Legacies of temperature fluctuations promote stability in marine biofilm communities
The increasing frequency and intensity of extreme climate events are driving significant biodiversity shifts across ecosystems. Yet, the extent to which these climate legacies will shape the response of ecosystems to future perturbations remains poorly understood. Here, we tracked taxon and trait dynamics of rocky intertidal biofilm communities under contrasting regimes of warming (fixed vs. fluctuating) and assessed how they influenced stability dimensions in response to temperature extremes. Fixed warming enhanced the resistance of biofilm by promoting the functional redundancy of stress-tolerance traits. In contrast, fluctuating warming boosted recovery rate through the selection of fast-growing taxa at the expense of functional redundancy. This selection intensified a trade-off between stress tolerance and growth further limiting the ability of biofilm to cope with temperature extremes. Anticipating the challenges posed by future extreme events, our findings offer a forward-looking perspective on the stability of microbial communities in the face of ongoing climatic change.
A 50-spin surface acoustic wave Ising machine
Time-multiplexed spinwave Ising Machines have unveiled a route towards miniaturized and low-cost combinatorial optimization solvers but are constrained in the number of spins by nonlinear spinwave dispersion. In contrast, surface acoustic waves offer an intrinsically linear dispersion and high thermal stability. Here, we demonstrate an all-to-all, fully programmable, 50-spin Ising machine using a surface acoustic wave delay line and off-the-shelf microwave components. Our device solves random 50-spin MAX-CUT problems with a single run compute time of 10 ms and a figure of merit of 55 solutions s−1 W−1 reaching success probability of 84% for 99%-accurate solutions on 0.5-density matrices. Moreover, it demonstrates 4–5 orders of magnitude better thermal stability than optical Coherent Ising Machines while having similar scalability potential. Our results illustrate the general merits of wave-based time-multiplexed Ising machines operating in the microwave domain as compact, energy-efficient, and high-performance platforms for commercially feasible combinatorial optimization solvers.
Investigating dopaminergic abnormalities in schizophrenia and first-episode psychosis with normative modelling and multisite molecular neuroimaging
Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brain’s in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility, high costs, and high between-subject heterogeneity of neuroimaging measures. In this study, we explore the use of normative modelling (NM) to identify individual patient alterations by describing the physiological variability of molecular functions. NM potentially addresses challenges such as small sample sizes and diverse acquisition protocols typical of molecular neuroimaging studies. We applied NM to two PET radiotracers targeting the dopaminergic system ([11C]-(+)-PHNO and [18F]FDOPA) to create a reference-cohort model of healthy controls. The models were subsequently utilized on different independent cohorts of patients with psychosis in different disease stages and treatment outcomes. Our results showed that patients with psychosis exhibited a higher degree of extreme deviations (~3-fold increase) than controls, although this pattern was heterogeneous, with minimal overlap of extreme deviations topology (max 20%). We also confirmed that striatal [18F]FDOPA signal, when referenced to a normative distribution, can predict treatment response (striatal AUC ROC: 0.77–0.83). In conclusion, our results indicate that normative modelling can be effectively applied to molecular neuroimaging after proper harmonization, enabling insights into disease mechanisms and advancing precision medicine. In addition, the method is valuable in understanding the heterogeneity of patient populations and can contribute to maximising cost efficiency in studies aimed at comparing cases and controls.
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