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Oral ulcer can be treated with diverse biomaterials loading drugs or cytokines. However, most patients do not benefit from these materials because of poor adhesion, short-time retention in oral cavity and low drug therapeutic efficacy. Here we report a self-stabilized and water-responsive deliverable coenzyme salt polymer poly(sodium α-lipoate) (PolyLA-Na)/coenzyme polymer poly(α-lipoic acid) (PolyLA) binary synergistic elastomer adhesive patch, where hydrogen bonding cross-links between PolyLA and PolyLA-Na prevents PolyLA depolymerization and slow down the dissociation of PolyLA-Na, thus allowing water-responsive sustainable delivery of bioactive LA-based small molecules and durable adhesion to oral mucosal wound due to the adhesive action of PolyLA. In the model of mice and mini-pig oral ulcer, the adhesive patch accelerates the healing of the ulcer by regulating the damaged tissue inflammatory environment, maintaining the stability of oral microbiota, and promoting faster re-epithelialization and angiogenesis. This binary synergistic patch provided a therapeutic strategy to treat oral ulcer. The therapeutic benefits of biomaterials-based treatments for oral ulcer have been limited by the materials’ poor adhesion and short-time retention in oral cavity. Here, the authors report a polymer binary elastomer adhesive patch that allows water-responsive sustainable delivery of bioactive small molecules and durable adhesion to oral mucosal wounds to achieve efficient therapy of oral ulcer.

We propose an advanced electronic nose based on SE-RelationNet for COPD diagnosis with limited breath samples. The model integrates residual blocks, BiGRU layers, and squeeze–excitation attention mechanisms to enhance feature-extraction efficiency. Experimental results demonstrate exceptional performance with minimal samples: in 4-way 1-shot tasks, the model achieves 85.8% mean accuracy (F1-score = 0.852), scaling to 93.3% accuracy (F1-score = 0.931) with four samples per class. Ablation studies confirm that the 5-layer residual structure and single-hidden-layer BiGRU optimize stability (h_F1-score ≤ 0.011). Compared to SiameseNet and ProtoNet, SE-RelationNet shows superior accuracy (>15% improvement in 1-shot tasks). This technology enables COPD detection with as few as one breath sample, facilitating early intervention to mitigate lung cancer risks in COPD patients.

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields) and unlabeled data (other fields) do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol); however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector θ. Then, using the basis vector θ, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA), and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy.

Graphene nanobubbles (GNBs) have attracted much attention due to the ability to generate large pseudo-magnetic fields unattainable by ordinary laboratory magnets. However, GNBs are always randomly produced by the reported protocols, therefore, their size and location are difficult to manipulate, which restricts their potential applications. Here, using the functional atomic force microscopy (AFM), we demonstrate the ability to form programmable GNBs. The precision of AFM facilitates the location definition of GNBs, and their size and shape are tuned by the stimulus bias of AFM tip. With tuning the tip voltage, the bubble contour can gradually transit from parabolic to Gaussian profile. Moreover, the unique three-fold symmetric pseudo-magnetic field pattern with monotonous regularity, which is only theoretically predicted previously, is directly observed in the GNB with an approximately parabolic profile. Our study may provide an opportunity to study high magnetic field regimes with the designed periodicity in two dimensional materials. Presence of nano-bubbles within an atomically thin material generates potentially large pseudo-magnetic fields. Here, the authors report an innovative technique to induce nano-bubbles in graphene with desirable features and high precision through energized AFM tips, and experimentally measure three-fold symmetric pseudo-magnetic fields up to 120 T.

The piezocone penetration test (CPTU) provides rapid, continuous measurements of in situ geotechnical parameters, making it a valuable tool for soil classification and stratigraphic identification. However, conventional classification methods frequently exhibit poor cross-regional generalizability and remain limited in achieving fine-grained stratigraphic identification. To address these limitations, this study constructs a cross-regional CPTU soil classification dataset by integrating data from three sources: the Premstaller Geotechnik database, the Global-CPT/3/1196 database, and a Chinese engineering project database. The compiled dataset was subsequently partitioned into a training set of 454,184 samples and three independent test sets. Three feature combinations and four machine learning algorithms—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost), were evaluated in terms of classification performance and cross-regional robustness. Results indicate that the XGBoost-based model, using Depth, corrected cone resistance (qt), friction ratio (Rf), pore pressure ratio (Bq), normalized friction ratio (Fr), and pore pressure (u2) as inputs, achieved the highest performance across the three independent test sets. Misclassifications primarily occurred between adjacent soil types with similar physical characteristics. SHapley Additive exPlanations (SHAP) analysis indicated that Fr and qt were the dominant contributors to model predictions; Rf played an important role in minority classes; Depth showed relatively balanced importance across classes, while Bq and u2 made minimal contributions. Applying the best-performing model to unseen CPTU data and comparing the predictions with borehole logs showed that the model not only preserves overall stratigraphic trends but also identifies finer-scale stratigraphic details.

NASA’s Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is a major source of precipitation data, having a larger coverage, higher precision, and a higher spatiotemporal resolution than previous products, such as the Tropical Rainfall Measuring Mission (TRMM). However, there rarely has been an application of IMERG products in flash flood warnings. Taking Yunnan Province as the typical study area, this study first evaluated the accuracy of the near-real-time IMERG Early run product (IMERG-E) and the post-real-time IMERG Final run product (IMERG-F) with a 6-hourly temporal resolution. Then the performance of the two products was analyzed with the improved Rainfall Triggering Index (RTI) in the flash flood warning. Results show that (1) IMERG-F presents acceptable accuracy over the study area, with a relatively high hourly correlation coefficient of 0.46 and relative bias of 23.33% on the grid, which performs better than IMERG-E; and (2) when the RTI model is calibrated with the gauge data, the IMERG-F results matched well with the gauge data, indicating that it is viable to use MERG-F in flash flood warnings. However, as the flash flood occurrence increases, both gauge and IMERG-F data capture fewer flash flood events, and IMERG-F overestimates actual precipitation. Nevertheless, IMERG-F can capture more flood events than IMERG-E and can contribute to improving the accuracy of the flash flood warnings in Yunnan Province and other flood-prone areas.

Inorganic nanoparticles (INPs) have been paid great attention in the field of oncology in recent past years since they have enormous potential in drug delivery, gene delivery, photodynamic therapy (PDT), photothermal therapy (PTT), bio-imaging, driven motion, etc. To overcome the innate limitations of the conventional INPs, such as fast elimination by the immune system, low accumulation in tumor sites, and severe toxicity to the organism, great efforts have recently been made to modify naked INPs, facilitating their clinical application. Taking inspiration from nature, considerable researchers have exploited cell membrane-camouflaged INPs (CMCINPs) by coating various cell membranes onto INPs. CMCINPs naturally inherit the surface adhesive molecules, receptors, and functional proteins from the original cell membrane, making them versatile as the natural cells. In order to give a timely and representative review on this rapidly developing research subject, we highlighted recent advances in CMCINPs with superior unique merits of various INPs and natural cell membranes for cancer therapy applications. The opportunity and obstacles of CMCINPs for clinical translation were also discussed. The review is expected to assist researchers in better eliciting the effect of CMCINPs for the management of tumors and may catalyze breakthroughs in this area. Graphical Abstract

Extreme weather events affect the development and survival of disease pathogens and vectors. Our aim was to investigate the potential effects of heat waves on the population dynamics of Asian tiger mosquito (Aedes albopictus), which is a major vector of dengue and Zika viruses. We modeled the population abundance of blood-fed mosquito adults based on a mechanistic population model of Ae. albopictus with the consideration of diapause. Using simulated heat wave events derived from a 35-year historical dataset, we assessed how the mosquito population responded to different heat wave characteristics, including the onset day, duration, and the average temperature. Two important observations are made: (1) a heat wave event facilitates the population growth in the early development phase but tends to have an overall inhibitive effect; and (2) two primary factors affecting the development are the unusual onset time of a heat wave and a relatively high temperature over an extended period. We also performed a sensitivity analysis using different heat wave definitions, justifying the robustness of the findings. The study suggests that particular attention should be paid to future heat wave events with an abnormal onset time or a lasting high temperature in order to develop effective strategies to prevent and control Ae. albopictus-borne diseases.

Power electronics is an important feature of the new power system. The high-speed development of the new power system has gradually increased the requirements for high-efficiency and high-reliability high-voltage transmission and substation equipment. Silicon carbide (SiC) devices, which have the advantages of fast switching speed, low power loss, and high-temperature resistance, are the key core technology for the upgrading of high-voltage transmission and substation equipment. The paper begins by examining the evolution of the demand for high efficiency, compact and reliable power electronic devices for high-voltage transmission and substation systems at different stages of energy development. SiC has emerged as a key technological path to break through the physical limits of silicon-based devices, due to its outstanding material properties. Silicon-based devices encounter significant bottlenecks in high-voltage and high-frequency applications, with high switching losses and a junction temperature tolerance that is typically limited to 150 °C. In contrast, SiC devices can reduce switching losses by 60–80% and operate stably at temperatures up to 200 °C or even higher, thereby significantly enhancing system efficiency and power density. Finally, the paper provides a systematic analysis of the application of SiC devices in high-voltage transmission and substation equipment, exploring and identifying the technical bottlenecks and future research directions for SiC-based high-voltage transmission equipment.

Flash flood, one of the most devastating weather-related hazards in the world, has become more and more frequent in past decades. For the purpose of flood mitigation, it is necessary to understand the distribution of flash flood risk. In this study, artificial intelligence (Least squares support vector machine: LSSVM) and classical canonical method (Logistic regression: LR) are used to assess the flash flood risk in the Yunnan Province based on historical flash flood records and 13 meteorological, topographical, hydrological and anthropological factors. Results indicate that: (1) the LSSVM with Radial basis function (RBF) Kernel works the best (Accuracy = 0.79) and the LR is the worst (Accuracy = 0.75) in testing; (2) flash flood risk distribution identified by the LSSVM in Yunnan province is near normal distribution; (3) the high-risk areas are mainly concentrated in the central and southeastern regions, where with a large curve number; and (4) the impact factors contributing the flash flood risk map from higher to low are: Curve number > Digital elevation > Slope > River density > Flash Flood preventions > Topographic Wetness Index > annual maximum 24 h precipitation > annual maximum 3 h precipitation.