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Protein allostery is a biological process facilitated by spatially long-range intra-protein communication, whereby ligand binding or amino acid change at a distant site affects the active site remotely. Molecular dynamics (MD) simulation provides a powerful computational approach to probe the allosteric effect. However, current MD simulations cannot reach the time scales of whole allosteric processes. The advent of deep learning made it possible to evaluate both spatially short and long-range communications for understanding allostery. For this purpose, we applied a neural relational inference model based on a graph neural network, which adopts an encoder-decoder architecture to simultaneously infer latent interactions for probing protein allosteric processes as dynamic networks of interacting residues. From the MD trajectories, this model successfully learned the long-range interactions and pathways that can mediate the allosteric communications between distant sites in the Pin1, SOD1, and MEK1 systems. Furthermore, the model can discover allostery-related interactions earlier in the MD simulation trajectories and predict relative free energy changes upon mutations more accurately than other methods. Here, the authors apply a neural relational inference model to infer dynamic networks of interacting residues in protein molecular dynamics simulations. The model can predict allosteric communication pathways and relative free energy changes upon mutations.

The accurate retrieval of the raindrop size distribution (DSD) is a longstanding objective in meteorology because it underpins reliable quantitative precipitation estimation. Among remote sensors, weather radars are the primary tool for mapping DSD over wide areas, and phased-array systems in particular have demonstrated unique advantages owing to their high temporal and spatial resolution together with agile beam steering. Exploiting the underused high-resolution capability of an X-band phased-array radar, this study induced a Rainfall Regression Model (RRM). The RRM assumes a normalized gamma DSD model and retrieves its three parameters. It was then applied to a rain event influenced by the remnant circulation of Typhoon Haikui that affected Guangzhou on 8 September 2023. First, collocated disdrometer observations and T-matrix scattering simulations are used to build polynomial regressions between DSD parameters (D0, Nw, μ) and the polarimetric variables. Validation against independent disdrometer samples yields Nash–Sutcliffe efficiencies of 0.93 for D0 and 0.91 for log10Nw. The RRM is then applied to the full volumetric radar data. Horizontal maps reveal that the surface elevation angle consistently exhibited the largest standard deviation for all three parameters. A vertical profile analysis shows that large-drop cores (D0 > 2 mm) can reside above 2 km and that iso-value contours tilt rather than align vertically, implying an appreciable horizontal drift of raindrops within the complex remnant typhoon–monsoon wind field. By demonstrating the ability of X-band phased-array radar to resolve the three-dimensional microphysical structure of remnant typhoon precipitation, this study advances our understanding of the vertical characteristics of raindrops and provides high-resolution DSD information that can be directly ingested into severe weather monitoring and nowcasting systems.

The timely detection of falls and alerting medical aid is critical for health monitoring in elderly individuals living alone. This paper mainly focuses on issues such as poor adaptability, privacy infringement, and low recognition accuracy associated with traditional visual sensor-based fall detection. We propose an infrared video-based fall detection method utilizing spatial-temporal graph convolutional networks (ST-GCNs) to address these challenges. Our method used fine-tuned AlphaPose to extract 2D human skeleton sequences from infrared videos. Subsequently, the skeleton data was represented in Cartesian and polar coordinates and processed through a two-stream ST-GCN to recognize fall behaviors promptly. To enhance the network’s recognition capability for fall actions, we improved the adjacency matrix of graph convolutional units and introduced multi-scale temporal graph convolution units. To facilitate practical deployment, we optimized time window and network depth of the ST-GCN, striking a balance between model accuracy and speed. The experimental results on a proprietary infrared human action recognition dataset demonstrated that our proposed algorithm accurately identifies fall behaviors with the highest accuracy of 96%. Moreover, our algorithm performed robustly, identifying falls in both near-infrared and thermal-infrared videos.

Terahertz (THz) frequency quantum cascade lasers emitting peak powers of >1 W from a single facet in the pulsed mode are demonstrated. The active region is based on a bound-to-continuum transition with a one-well injector, and is embedded into a surface-plasmon waveguide. The lasers emit at a frequency of ∼3.4 THz and have a maximum operating temperature of 123 K. The maximum measured emitted powers are ∼1.01 W at 10 K and ∼420 mW at 77 K, with no correction made to allow for the optical collection efficiency of the apparatus.

Translating the exceptional luminescent properties of AIEgens into efficient and practical sensing devices has long been a major challenge restricting their practical application. In this work, we demonstrate a novel strategy based on phase separation to fabricate stable, high-surface-area sensing films that address the fluorescence quenching typically associated with conventional nanospheres. Fluorescent polysiloxanes bearing tetraphenylphenyl (TPP) side groups were synthesized and processed into fibrous films via electrospinning. Leveraging the intrinsic incompatibility of the polymer, entropy-driven phase separation generated an \"sea-island\" morphology. This hierarchical structure significantly enlarged the specific surface area and facilitated analyte diffusion, thereby improving the accessibility of active sites. Molecular dynamics simulations not only predicted the formation of this architecture but also clarified the underlying entropy-driven mechanism. Overall, this work provides a solid foundation and conceptual framework for investigating how quantitative regulation of lumogenic unit density and spatial distribution governs sensing performance.

Global warming has increased the occurrence of extreme weather events, causing significant economic losses and casualties from rainfall-induced landslides. China, being highly prone to landslides, requires comprehensive predictions of future rainfall-induced landslide risks. By developing a landslide-prediction model integrated with the CMIP6 GCMs ensemble, we predict the spatiotemporal distribution of future rainfall-induced landslides in China, incorporating antecedent soil-wetness factors. In this study, antecedent soil wetness is represented by the antecedent effective rainfall index (ARI), which accounts for cumulative rainfall, evaporation, and runoff losses. Firstly, we calculated landslide susceptibility using seven geographic factors, such as slope and geology. Then, we constructed landslide threshold models with two antecedent soil-wetness indicators. Compared to the traditional recent cumulative rainfall thresholds, the landslide threshold model based on ARI demonstrated higher hit rates and lower false alarm rates. Ensemble predictions indicate that in the early 21st century, the risk of landslides decreases in the Qinghai–Tibet Plateau, Southwest, and Southeast regions but increases in other regions. Mid-century projections show a 10% to 40% increase in landslide risk across most regions. By the end of the century, the risk is expected to rise by more than 15% nationwide, displaying a spatial distribution pattern that intensifies from east to west.

Facial expression recognition has wide application prospects in many occasions. Due to the complexity and variability of facial expressions, facial expression recognition has become a very challenging research topic. This paper proposes a Vision Transformer expression recognition method based on hybrid local attention (HLA-ViT). The network adopts a dual-stream structure. One stream extracts the hybrid local features and the other stream extracts the global contextual features. These two streams constitute a global–local fusion attention. The hybrid local attention module is proposed to enhance the network’s robustness to face occlusion and head pose variations. The convolutional neural network is combined with the hybrid local attention module to obtain feature maps with local prominent information. Robust features are then captured by the ViT from the global perspective of the visual sequence context. Finally, the decision-level fusion mechanism fuses the expression features with local prominent information, adding complementary information to enhance the network’s recognition performance and robustness against interference factors such as occlusion and head posture changes in natural scenes. Extensive experiments demonstrate that our HLA-ViT network achieves an excellent performance with 90.45% on RAF-DB, 90.13% on FERPlus, and 65.07% on AffectNet.

The understanding of large‐scale rainfall microphysical characteristics plays a significant role in meteorology, hydrology and natural hazards managements. Traditional instruments for estimating raindrop size distribution (DSD), including disdrometers and ground dual‐polarization radars, are available only in limited areas. However, the development of space‐based radars and mesoscale numerical weather prediction models would allow for DSD estimation on a large scale. This study investigated the performance of the weather research and forecasting (WRF) model and the global precipitation measurement mission (GPM) dual‐frequency precipitation radar for DSD retrieval under different conditions. The DSD parameters (Dm and Nw), rain rate (R), rainfall kinetic energy (KE) and radar reflectivity (Z) were estimated in Chilbolton, United Kingdom, by using long‐term disdrometer observations for validation. The rainfall kinetic energy–rain rate (KE–R) and radar reflectivity–rain rate (Z–R) relationships were explored using a disdrometer, the WRF model and GPM. It was found that the DSD parameter distribution trends of the three approaches are similar although the WRF model has larger Dm and smaller Nw values. In terms of the rainfall microphysical relationship, GPM performs better when both Ku‐ and Ka‐band precipitation radars (KuPR and KaPR) observe precipitation simultaneously (R > 0.5 mm h−1), while the WRF model shows high accuracy in light rain (R < 0.5 mm h−1). The fusion of GPM and WRF model is recommended for the improved understanding of rainfall microphysical characteristics in ungauged areas. This study investigated the performance of the weather research and forecasting (WRF) model and global precipitation measurement mission (GPM) dual‐frequency precipitation radar (DPR) for raindrop size distribution (DSD) retrieval under different conditions. Our study makes a significant contribution to the literature because the understanding of large‐scale rainfall microphysical characteristics plays a significant role in various environmental and social disciplines; however, traditional ground‐based estimation methods are available only in limited areas. Our results show that the fusion of GPM and WRF model can improve understanding of rainfall microphysical characteristics in ungauged areas.

Triple-negative breast cancer (TNBC) is characterized by high rates of metastasis and recurrence, along with a low sensitivity to immunotherapy, resulting in a paucity of effective therapeutic strategies. Herein, we have developed polydopamine-coated zinc-copper bimetallic nanoplatforms (Cu-ZnO 2 @PDA nanoplatforms, abbreviated CZP NPs) that can efficiently induce photothermal amplified cuproptosis and cGAS-STING signaling pathway activation, thereby reversing the immunosuppressive tumor microenvironment of TNBC, upregulating PD-L1 expression, and boosting the efficacy of anti-programmed death-ligand 1 antibody (αPD-L1)-based immunotherapy. Within the acidic tumor microenvironment (TME), CZP NPs spontaneously release copper and zinc ions and hydrogen peroxide, generating highly oxidative hydroxyl radicals and downregulating iron-sulfur cluster proteins. These actions lead to the disruption of mitochondrial integrity, the release of mitochondrial DNA (mtDNA) and irreversible cuproptosis. The further synergy between mtDNA and zinc ions potentiates the activation of the cGAS-STING signaling pathway, triggering a robust antitumor immune response and sensitizing TNBC to αPD-L1 therapy. Additionally, using an 808 nm near-infrared laser for photothermal therapy significantly augments these effects, resulting in a cascade amplification of therapeutic efficacy against TNBC. The strategic combination of CZP NPs with αPD-L1 markedly bolsters antitumor immunity and suppresses tumor growth. Collectively, our findings present a promising synergistic strategy for TNBC treatment by linking cuproptosis, cGAS-STING activation, photothermal therapy, and immunotherapy. Graphical abstract

To reveal the mechanism by which exogenous dopamine (Da) regulates Shine Muscat grape (Vitis labrusca L. × Vitis vinifera L.) in response to low-temperature stress, annual Shine Muscat grape plants were used as material. Different concentrations of Da (0.2–1.0 mmol L−1) were set to investigate its synergistic regulatory effects on grape photosynthetic protection, osmotic adjustment, ion homeostasis, antioxidant defense, and cold-responsive gene expression and to identify the optimal concentration and core pathways through correlation analysis. The results showed that low-temperature stress significantly inhibited plant growth, reduced photosynthetic efficiency, disrupted ion balance, induced oxidative damage, and downregulated the expression of cold-responsive genes. Da exhibited a “low-concentration promotion and high-concentration inhibition” effect, with the 0.4 mmol L−1 treatment showing the best performance: growth indicators such as plant height and stem diameter increased by 22.4–52.2% compared with the low-temperature stress group; photosynthetic parameters and photosystem II (PSII) function were significantly improved; proline content increased by 40.3%; the Na+/K+ ratio decreased by 44.8%; activity of antioxidant enzymes such as superoxide dismutase (SOD) and peroxidase (POD) increased by 31.7–49.5%; and the expression of genes in the C-repeat binding factor (CBF) family was upregulated. Correlation analysis confirmed that the activity of SOD and catalase (CAT) showed a highly significant positive correlation with growth indicators (r > 0.8, p < 0.01) and a highly significant negative correlation with malondialdehyde (MDA) content (r < −0.8, p < 0.01), indicating that antioxidant defense is the core pathway. In conclusion, exogenous Da enhances the cold tolerance of Shine Muscat grape through multi-pathway synergy, with 0.4 mmol L−1 the optimal concentration, which can provide a theoretical basis for cold-resistant cultivation of grapes.