Single-Atom-Based Nanoenzyme in Tissue Repair.

Since the discovery of ferromagnetic nanoparticles Fe3O4 that exhibit enzyme-like activity in 2007, the research on nanoenzymes has made significant progress. With the in-depth study of various nanoenzymes and the rapid development of related nanotechnology, nanoenzymes have emerged as a promising alternative to natural enzymes. Within nanozymes, there is a category of metal-based single-atom nanozymes that has been rapidly developed due to low cast, convenient preparation, long storage, less immunogenicity, and especially higher efficiency. More importantly, single-atom nanozymes possess the capacity to scavenge reactive oxygen species through various mechanisms, which is beneficial in the tissue repair process. Herein, this paper systemically highlights the types of metal single-atom nanozymes, their catalytic mechanisms, and their recent applications in tissue repair. The existing challenges are identified and the prospects of future research on nanozymes composed of metallic nanomaterials are proposed. We hope this review will illuminate the potential of single-atom nanozymes in tissue repair, encouraging their sequential clinical translation.

Stark Localization as a Resource for Weak-Field Sensing with Super-Heisenberg Precision.

Gradient fields can effectively suppress particle tunneling in a lattice and localize the wave function at all energy scales, a phenomenon known as Stark localization. Here, we show that Stark systems can be used as a probe for the precise measurement of gradient fields, particularly in the weak-field regime where most sensors do not operate optimally. In the extended phase, Stark probes achieve super-Heisenberg precision, which is well beyond most of the known quantum sensing schemes. In the localized phase, the precision drops in a universal way showing fast convergence to the thermodynamic limit. For single-particle probes, we show that quantum-enhanced sensitivity, with super-Heisenberg precision, can be achieved through a simple position measurement for all the eigenstates across the entire spectrum. For such probes, we have identified several critical exponents of the Stark localization transition and established their relationship. Thermal fluctuations, whose universal behavior is identified, reduce the precision from super-Heisenberg to Heisenberg, still outperforming classical sensors. Multiparticle interacting probes also achieve super-Heisenberg scaling in their extended phase, which shows even further enhancement near the transition point. Quantum-enhanced sensitivity is still achievable even when state preparation time is included in resource analysis.

An Efficient Sampling-Based Attention Network for Semantic Segmentation.

Self-attention is widely explored to model long-range dependencies in semantic segmentation. However, this operation computes pair-wise relationships between the query point and all other points, leading to prohibitive complexity. In this paper, we propose an efficient Sampling-based Attention Network which combines a novel sample method with an attention mechanism for semantic segmentation. Specifically, we design a Stochastic Sampling-based Attention Module (SSAM) to capture the relationships between the query point and a stochastic sampled representative subset from a global perspective, where the sampled subset is selected by a Stochastic Sampling Module. Compared to self-attention, our SSAM achieves comparable segmentation performance while significantly reducing computational redundancy. In addition, with the observation that not all pixels are interested in the contextual information, we design a Deterministic Sampling-based Attention Module (DSAM) to sample features from a local region for obtaining the detailed information. Extensive experiments demonstrate that our proposed method can compete or perform favorably against the state-of-the-art methods on the Cityscapes, ADE20K, COCO Stuff, and PASCAL Context datasets.