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Abstract The presence of bacterial biofilms and the occurrence of excessive inflammatory response greatly imped the healing process of chronic wounds in diabetic patients. However, effective strategies to simultaneously address these issues are still lacking. Here, a microenvironment‐adaptive nanodecoy (GC@Pd) is constructed via the coordination and in situ reduction of palladium ions on gallic acid‐modified chitosan (GC) to promote wound healing by synergistic biofilm eradication, inflammation alleviation, and immunoregulation. During the weakly acidic conditions of the biofilm infection stage, GC@Pd serves as a nanodecoy to induce bacterial aggregation. Subsequently, through its oxidase‐like activity generating reactive oxygen species and the hyperthermia from photothermal effects, it effectively eliminates the biofilm. As the local microenvironment of diabetic wounds transitions to an alkaline inflammatory state, the enzyme‐like activity of GC@Pd adapts to catalase‐like activity, effectively eliminating reactive oxygen species at the site of inflammation. Additionally, GC@Pd could selectively capture pro‐inflammatory cytokines through Michael addition reactions. In vivo experiments and transcriptomic analysis confirmed that GC@Pd could accelerate the wound transition from inflammatory to proliferative phase by eliminating biofilm infection and reducing the inflammatory response, thus promoting diabetic chronic wound healing. The nanodecoy provides a potential therapeutic strategy for treating biofilm‐infected diabetic chronic wounds.

We employ an analytical model that incorporates both wavelength-dependent and wavelength independent depolarization to describe radio polarimetric observations of polarization at lambda lambda lambda 3.5, 6.2, 20.5 cm in M51 (NGC 5194). The aim is to constrain both the regular and turbulent magnetic field strengths in the disk and halo, modeled as a two- or three-layer magneto-ionic medium, via differential Faraday rotation and internal Faraday dispersion, along with wavelength independent depolarization arising from turbulent magnetic fields, A reduced chi-squared analysis is used for the statistical comparison of predicted to observed polarization maps to determine the best-fit magnetic field configuration at each of four radial rings spanning 2.4-7.2 kpc in 1.2 kpc increments. We find that a two layer modeling approach provides a better fit to the observations than a three layer model, where the near and far sides of the halo are taken to be identical, although the resulting hest-fit magnetic field strengths are comparable, This implies that all of the signal from the far halo is depolarized at these wavelengths. We find a total magnetic field in the disk of approximately 18 mu G and a total magnetic field strength in the halo of similar to 4-6 mu G Both turbulent and regular magnetic field strengths in the disk exceed those in the halo by a factor of a few. About half of the turbulent magnetic field in the disk is anisotropie, but in the halo all turbulence is only isotropic,

Dispersion in optical coherence tomography (OCT) poses a challenge that is exacerbated by the increased spectral bandwidth, which leads to image blur and feature loss. In this paper, we present a straightforward and cost-effective approach for dispersion compensation in OCT. To achieve this, we employed a pixel-to-pixel (Pix2Pix) generative adversarial network (GAN) architecture customized for image-to-image translation. Two data groups with varying amounts of training image data and epochs were used. The Pix2Pix GAN was trained to generate clear OCT images from the corresponding dispersion-affected OCT images in paired datasets. According to the experimental results, the Pix2Pix GAN technique demonstrated a substantial improvement over the basic GAN. Specifically, it increases the peak signal-to-noise ratio (PSNR) by 159%, structural similarity index (SSIM) by 370%, and Fréchet inception distance (FID) by 274%. These outcomes indicate that the proposed model can generate images with resilience and effectiveness, particularly when dealing with dispersion-affected OCT data.

Early in cancer development, tumour cells express vascular endothelial growth factor (VEGF), a secreted molecule that is important in all stages of angiogenesis, an essential process that provides nutrients and oxygen to the nascent tumor and thereby enhances tumor-cell survival and facilitates growth. Survivin, another protein involved in angiogenesis, is strongly expressed in most human cancers, where it promotes tumor survival by reducing apoptosis as well as favoring endothelial cell proliferation and migration. The mechanisms by which cancer cells induce VEGF expression and angiogenesis upon survivin up-regulation remain to be fully established. Since the PI3K/Akt signalling and beta-catenin-Tcf/Lef dependent transcription have been implicated in the expression of many cancer-related genes, including survivin and VEGF, we evaluated whether survivin may favor VEGF expression, release from tumor cells and induction of angiogenesis in a PI3K/Akt-beta-catenin-Tcf/Lef-dependent manner. Here, we provide evidence linking survivin expression in tumor cells to increased beta-catenin protein levels, beta-catenin-Tcf/Lef transcriptional activity and expression of several target genes of this pathway, including survivin and VEGF, which accumulates in the culture medium. Alternatively, survivin downregulation reduced beta-catenin protein levels and beta-catenin-Tcf/Lef transcriptional activity. Also, using inhibitors of PI3K and the expression of dominant negative Akt, we show that survivin acts upstream in an amplification loop to promote VEGF expression. Moreover, survivin knock-down in B16F10 murine melanoma cells diminished the number of blood vessels and reduced VEGF expression in tumors formed in C57BL/6 mice. Finally, in the chick chorioallantoid membrane assay, survivin expression in tumor cells enhanced VEGF liberation and blood vessel formation. Importantly, the presence of neutralizing anti-VEGF antibodies precluded survivin-enhanced angiogenesis in this assay. These findings provide evidence for the existance of a posititve feedback loop connecting survivin expression in tumor cells to PI3K/Akt enhanced beta-catenin-Tcf/Lef-dependent transcription followed by secretion of VEGF and angiogenesis.

With the increase in the availability of multitemporal hyperspectral images (HSIs), HSIs change detection (CD) methods, including pixel-level and subpixel-level based methods, have attracted great attention in recent years. However, the widespread presence of mixed pixels in HSIs may make it difficult for pixel-level methods to detect subtle changes; meanwhile, the less utilization of spatial information may also lead to limitations in some subpixel-level methods. Therefore, a joint framework, which aims to combine the advantages of pixel-level in spatial utilization and subpixel-level in temporal and spectral exploration, is proposed to enhance the performance of HSIs CD. Two models, convolutional sparse analysis and temporal spectral unmixing, are introduced and presented to characterize different spatial structures and overcome the effects of spectral variability under this framework, respectively. In addition, a multiple CD-based on subpixel analysis is discussed as well. Experiments conducted on three bitemporal HSIs datasets indicate that the proposed framework is robust in capturing effective features and has achieved great detection accuracy.

Abstract Adverse weather conditions, such as snow, heavy rain, fog, or limited illumination, pose significant challenges for autonomous vehicles (AVs) by degrading sensor reliability. This paper introduces E2ETrADS, an end-to-end transformer-based autonomous driving framework designed to operate robustly under such conditions. A comprehensive dataset was generated using the CARLA simulator, encompassing both nominal and adverse weather scenarios. The model is trained via imitation learning from an expert driver equipped with weather-adaptive MPC planner and PID controllers, enabling robust control under perception uncertainty. Experimental results demonstrate that E2ETrADS outperforms the TransFuser baseline in adverse conditions, exhibiting fewer infractions and improved lane adherence. The system dynamically adjusts vehicle speed to maintain control stability and adapts its control policies in response to sensor degradation, resulting in fewer missed turns and reduced lane invasions. Furthermore, E2ETrADS generalizes effectively to safety-critical long-tail scenarios, demonstrating human-like reasoning capabilities.

Offshore wind turbines are prone to structural damage over time due to environmental factors, which increases operational costs and the risk of accidents. Early detection of structural damage through monitoring systems can help reduce maintenance costs. However, under complex external conditions and varying structural parameters, existing methods struggle to accurately and quickly detect damage. Understanding the factors that influence structural health is critical for effective long-term monitoring, as these factors directly affect the accuracy and timeliness of damage identification. This study comprehensively analyzed 5 MW offshore wind turbine measurement data, including constructing a digital twin model, establishing a surrogate model, and performing a sensitivity analysis. For monopile-based turbines, sensors in x and y directions were installed at four heights on the pile foundation and tower. Via Bayesian optimization, the finite element model’s structural parameters were updated to align its modal parameters with sensor data analysis results. The update efficiencies of different objective functions and the impacts of neural network hyperparameters on the surrogate model were examined. The sensitivity of the turbine’s structural parameters to modal parameters was studied. The results showed that the modal flexibility matrix is more effective in iteration. A 128-neuron, double-hidden-layer neural network balanced computational efficiency and accuracy well in the surrogate model for modal analysis. Flange damage and soil degradation near the pile mainly impacted the turbine’s health.