Browse Articles

Biomechanics-informed inertial tracking achieves the accuracy of marker-based kinematics

Intrinsically unidirectional stepwise chemically fueled rotary molecular motors

Enhanced and directional light emission from two-dimensional excitons using Mie voids

Abstract Controlling light emission at the nanoscale has important applications in solid-state lighting, displays, and quantum light sources. Achieving this control requires both enhanced local electromagnetic fields to boost emission intensity and engineered radiation patterns to direct photons efficiently. Mie voids, consisting of an air cavity surrounded by a high-index semiconductor, are particularly suited for this purpose because they expose their strongest fields in an accessible region for nearby emitters while supporting resonances that shape directional emission through interference. Here, we demonstrate an all-van der Waals nanophotonic platform that couples excitons in atomically thin WS 2 to Mie void resonators formed in WSe 2 . Guided by electromagnetic simulations, we identify void geometries that maximize photoluminescence through synergistic enhancement of excitation and emission processes. We also introduce a two-step fabrication approach that leverages van der Waals assembly to independently control the void diameter and depth. Experimentally, we observe up to a 600-fold increase in photoluminescence intensity from monolayer WS 2 placed on individual voids compared to unstructured WSe 2 , along with pronounced out-of-plane beaming of light that yields a forward-to-off-axis enhancement of 2.6 dB. These results establish Mie voids in van der Waals semiconductors as a versatile platform for controlling light-matter interactions at the nanoscale.

DMS-Vmunet: dynamic multi-scale vision Mamba Unet for non-small cell lung cancer CT image segmentation

Abstract The CT image segmentation task of non-small cell lung cancer (NSCLC) suffers from the challenges of small target and severe background noise interference, etc. Current research incorporates a variety of neural network architectures to improve the overall segmentation performance, but most of the frameworks still have difficulty in balancing local detail preservation and global background modeling, especially the lack of ability to perceive small nodules. Therefore, this paper proposes dynamic multi-scale vision Mamba UNet (DMS-Vmunet). HDVSS modules for enhancing the segmentation performance on small targets are proposed in this framework, including Multi-SS2D module and IDConv module. The Multi-SS2D module enhances the local sensing bias and perception of small targets through multi-scale parallel branching design, while the IDConv module focuses the convolution operation on the important regions of small targets through adaptive dynamic convolution. In addition, in order to ensure the effective fusion of local and global features in parallel branches, a dual-path feature fusion (DFC) module is introduced to fuse the multi-scale feature information from multiple branches. Experimental results show that the proposed framework can effectively improve the performance of CT image segmentation for non-small cell lung cancer, especially for small target segmentation.

Atomically dispersed Ti on MnOx-Fe2O3 tailors O 2p orbitals for CO oxidation and H2O/SO2 resistance

A physics-informed PointNet + + for hand gesture recognition using radar point cloud

Boosting genome editing in perennial plants by CRISPR-Combo mediated morphogenic gene activation

Prothrombin time predicts mortality in hypothermic sepsis and links hypothermia to impaired hepatic coagulation factor synthesis

In situ NIR-IIb imaging of endogenous H2S signaling for high-resolution abiotic stress visualization in plants

Development and external validation of a nomogram for overall survival in oral tongue squamous cell carcinoma using machine learning-assisted feature selection

Solvation-engineering-enabled quasi-solid positive electrode for four-electron aqueous Zn||Br batteries

The perception of realism is correlated with physical gamuts

Neutrophil NADPH oxidase breaks the inflammatory IL-1β/IL-17A circuit to enhance pathogen clearance during respiratory virus infections

Abstract Respiratory virus infections are invariably accompanied by an increase in oxidative stress through elevated production of Reactive Oxygen Species (ROS), which contribute to both host defence and pathogenesis. Using mouse models, we identify neutrophil NADPH Oxidase 2 (Nox2) as the major early source of ROS during Influenza A Virus (IAV) infection. Surprisingly, neutrophil Nox2-derived ROS display multifaceted effects, not only unleashing oxidative stress but also limiting pro-inflammatory IL-1β signalling. Absence of neutrophil Nox2 enhances IL-1β production, promoting the proliferation of IL-17-producing gamma delta (γδ) T cells. This early self-amplified augmentation of the IL-1β/IL-17 axis is associated with increased viral burden and reduced IFNα expression in the lung. We extend our findings to humans. Similar patterns of ROS production and cytokine regulation are observed in human neutrophils when exposed to IAV and the viral RNA analogue poly(I:C). Our discovery highlights that ROS, often associated with harm, play a dual role by regulating cytokine signalling and thus influencing the immune response against respiratory viruses.

Crisis and cephalalgia: screening for primary headache disorders in a syrian population—a cross-sectional study

Acoustic higher-order topological metals with bound corner states in the continuum

Mercury accumulation in tropical insects is structured by trophic gradients and contaminant risk landscapes

Abstract Mercury (Hg) contamination from artisanal and small-scale gold mining (ASGM) is widespread in the Amazon, yet its dynamics in terrestrial food webs remain poorly understood. Here, we quantified total mercury (THg) concentrations in 492 insects spanning nine taxa across three sites in the Peruvian Amazon representing contrasting mining influence. Taxa were selected to capture a gradient of resource use from plant-based to animal-derived organic matter. Using linear mixed-effects models, we evaluated the relative roles of trophic structure, spatial variation, environmental exposure, and biological traits in shaping THg accumulation. Trophic structure emerged as the dominant driver, with models incorporating feeding guilds explaining the majority of variation (R²ₘ ≈ 0.82) and outperforming all alternatives (ΔAIC > 60). THg concentrations increased monotonically along the trophic gradient, with an approximately 39-fold difference between herbivorous and necrophagous taxa. Spatial variation across sites reflected underlying contamination, with THg increasing ~ 7-fold from the least to most impacted site, but explained substantially less variation than trophic structure. Species-specific responses further modulated accumulation, partly associated with body weight. Environmental variables showed consistent but comparatively weak effects. To our knowledge, this study provides the first evidence from the Amazon Basin integrating Hg contamination in terrestrial insects within a trophic gradient framework.

Aryl radical addition–dehydrocyclization drives rapid polycyclic aromatic hydrocarbon growth

Credibility attacks do not enhance the impact of deepfake warnings

Abstract Synthetic deepfake videos are a realistic form of misinformation, which appear to show someone saying or doing something they never did. The societal risks posed by such fakeries have motivated legislation mandating the labelling of AI-generated content, but initial evidence suggests that even explicit warnings are insufficient to eliminate deepfake influences on viewer perceptions. Across two experiments (total N =  2,500), we examined the impact of a deepfake video in which a witness makes a criminal accusation, and we tested whether undermining the credibility of the witness could enhance the effect of a specific warning flagging the video as a deepfake. Two methods for reducing witness credibility were tested: presenting the witness with a foreign (vs. native) accent and explicitly introducing them as an untrustworthy (vs. trustworthy) character. Deepfake exposure significantly increased guilt judgements concerning the person being accused in the video; a deepfake warning only partially reduced this impact. When an explicit witness discreditation was added to the warning, effectiveness remained unchanged. Findings demonstrate the pervasive impact of known deepfakes, highlighting the need to combine or develop new interventions to combat their influence.

Wide field-of-view anisotropic optoelectronic resistive memory for monocular in-sensor 3D motion perception and localization system

Deep learning-based body length estimation in soil-dwelling arthropods

Abstract Body length is a fundamental functional trait in soil ecology used to estimate biomass and metabolic rates, but manual microscopic measurement is a major high-throughput bottleneck. Here, we introduce a device-independent Deep Learning (DL)-based regression framework for automated body length quantification of soil-dwelling arthropods from top-view digital images. Using a robust MaxViT-T backbone combined with image aspect-ratio metrics, the framework was validated across three distinct laboratory and field experiments without requiring manual taxonomic pre-sorting. In high-end laboratory stereomicroscopy (Test 1), the model achieved a global R 2 of 0.94 and a Mean Absolute Error (MAE) of 0.059 mm. To test cross-platform robustness, an independent external blind test was conducted on an unseen stereomicroscope-camera setup (Test 2), where the model maintained high predictive performance (R 2 = 0.96, MAE = 0.054 mm). For automated field extraction systems across 16 macro- and mesofauna groups (Test 3, N  = 1,807), the pipeline achieved an overall R 2 of 0.98 and a global MAE of 0.039 mm. Compared to conventional contour-based edge detection, which systematically introduced 3-fold higher errors due to organism curvature, the DL model maintained geometric precision across complex taxonomic body plans. These results demonstrate that deep learning computer vision provides an accurate, reproducible, and scalable framework for high-throughput trait-based ecological and biomass assessments.