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The development of bone repair scaffolds has long been a research hotspot in tissue engineering. Owing to its unique capability for personalized customization of scaffold geometry and microstructure, 3D printing technology has been extensively adopted for fabricating bone repair scaffolds. Poly-L-lactic acid (PLLA), endowed with favorable biodegradability, excellent biocompatibility, and reliable in vivo safety, is widely used as a matrix material for 3D printed bone repair scaffolds. PLLA is a bioinert polymer characterized by inferior cell adhesion and osteogenic differentiation capabilities. To mitigate this bioinertness limitation, the present study employed N-methylpyrrolidone (NMP) etching to modify the surface of 3D-printed PLLA bone repair scaffolds. Following NMP etching for 1–24 h, the originally smooth scaffold surface evolved into a hierarchical, petal-like gradient microstructure, accompanied by a marked increase in surface roughness. Correspondingly, the hydrophilicity of the treated scaffolds was also enhanced. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) analyses further confirmed that the crystallinity of PLLA in the scaffolds was significantly enhanced. Concomitantly, the modified scaffolds exhibited a marked improvement in adsorption capacity for green fluorescent protein (GFP), while the adhesion and proliferation of MC3T3-E1 on their surface were also significantly promoted. In vivo animal experiments demonstrated that the NMP-etched scaffolds could accelerate the process of bone defect repair. Collectively, surface modification of 3D-printed PLLA bone scaffolds via NMP etching enables precise modulation of their physicochemical properties, thereby effectively mitigating the inherent bioinertness limitation of PLLA scaffolds. Graphical Abstract

Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.

Ground penetrating radar (GPR) has been extensively used in the routine inspection of reinforced concrete structures. However, the signatures in GPR images are reflected electromagnetic waves rather than their actual shapes. The interpretation of GPR data is a mandatory but time- and labor-consuming task. Furthermore, the rebars in the near-surface of concrete cause clutter in the GPR images, which hinders the interpretation of GPR data. This work presents a deep learning network to invert GPR B-scan images to permittivity maps of subsurface structures. The proposed network has a multi-path encoder which enables the network to leverage three kinds of GPR data: the original, migrated, and encoder–decoder-processed GPR data. Each type of processing method is designed to serve a different purpose: the original GPR images retain all the waveforms; the migration method intensifies the vertices of the subsurface anomalies; the encoder–decoder network suppresses rebar clutter and enhances the visibility of the defect echoes. The outputs of three processing methods are jointly used to interpret GPR B-scan images. We demonstrated the superiority of the proposed network by comparing it with a network with a single-path encoder. We also validated the proposed network with synthetic and experimental GPR data. The results indicate that the proposed network effectively reconstructs the defects in the reinforced concrete.

•An improved sub-voxel QSM separation method was applied to calculate WMH iron burden.•Multimodal neuroimaging was applied to explore the pathological mechanism of CSVD.•WMH iron overload mediates glymphatic dysfunction and cognitive impairment in CSVD. Glymphatic system function has been increasingly linked to cognition in cerebral small vessel disease (CSVD), although the underlying pathological mechanisms related to brain metabolism remain to be fully clarified. Iron overload within white matter hyperintensity (WMH), potentially reflecting metabolic abnormalities, may play a pivotal role in this process. This study investigated whether WMH iron burden mediates the association between glymphatic dysfunction and cognitive impairment in CSVD. A total of 102 patients with CSVD and 29 matched healthy controls (HCs) underwent brain MRI and cognitive assessments. WMH iron burden was quantified using a sub-voxel quantitative approach, while glymphatic function was assessed with the Diffusion Tensor Image Analysis aLong the Perivascular Space (DTI-ALPS) index. Correlation and mediation analyses were then conducted to evaluate relationships among WMH iron burden, DTI-ALPS index, and cognitive scores. Compared with HCs, CSVD patients exhibited significantly higher WMH iron burden, lower DTI-ALPS index, and poorer cognitive performances. Elevated WMH iron burden was associated with deficits in attention-executive (att-exe), memory, and visual-spatial domains, whereas reduced DTI-ALPS index correlated with impaired att-exe and memory function. Importantly, WMH iron burden fully mediated the link between DTI-ALPS index and both att-exe function (p < 0.001) and memory (p = 0.02) in the CSVD group. These findings noninvasively identify WMH iron overload, a probable representative of microglial activation, as a key mediator between glymphatic dysfunction and cognitive decline in CSVD, prompting a potential therapeutic target for disease management.

Marine resources in unique marine environments provide abundant, cost-effective natural biomaterials with distinct structures, compositions, and biological activities compared to terrestrial species. These marine-derived raw materials, including polysaccharides, natural protein components, fatty acids, and marine minerals, etc., have shown great potential in preparing, stabilizing, or modifying multifunctional nano-/micro-systems and are widely applied in drug delivery, theragnostic, tissue engineering, etc. This review provides a comprehensive summary of the most current marine biomaterial-based nano-/micro-systems developed over the past three years, primarily focusing on therapeutic delivery studies and highlighting their potential to cure a variety of diseases. Specifically, we first provided a detailed introduction to the physicochemical characteristics and biological activities of natural marine biocomponents in their raw state. Furthermore, the assembly processes, potential functionalities of each building block, and a thorough evaluation of the pharmacokinetics and pharmacodynamics of advanced marine biomaterial-based systems and their effects on molecular pathophysiological processes were fully elucidated. Finally, a list of unresolved issues and pivotal challenges of marine-derived biomaterials applications, such as standardized distinction of raw materials, long-term biosafety in vivo, the feasibility of scale-up, etc., was presented. This review is expected to serve as a roadmap for fundamental research and facilitate the rational design of marine biomaterials for diverse emerging applications. Graphical Abstract

Machine learning algorithms are crucial for crop identification and mapping. However, many works only focus on the identification results of these algorithms, but pay less attention to their classification performance and mechanism. In this paper, based on Google Earth Engine (GEE), Sentinel-2 10 m resolution images during a specific phenological period of winter wheat were obtained. Then, support vector machine (SVM), random forest (RF), and classification and regression tree (CART) machine learning algorithms were employed to identify and map winter wheat in a large-scale area. The hyperparameters of the three machine learning algorithms were tuned by grid search and the 5-fold cross-validation method. The classification performance of the three machine learning algorithms were compared, the results of which demonstrate that SVM achieves best performance in identifying winter wheat, and its overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient (Kappa) are 0.94, 0.95, 0.95, and 0.92, respectively. Moreover, 50 various combinations of training and validation sets were used to analyze the generalization ability of the algorithms, and the results show that the average OA of SVM, RF, and CART are 0.93, 0.92, and 0.88, respectively, thus indicating that SVM and RF are more robust than CART. To further explore the sensitivity of SVM, RF, and CART to variations of the algorithm parameters—namely, (C and gamma), (tree and split), and (maxD and minSP)—we employed the grid search method to iterate these parameters, respectively, and to analyze the effect of these parameters on the accuracy scores and classification residuals. It was found that with the change of (C and gamma) in (0.01~1000), SVM’s maximum variation of accuracy score is up to 0.63, and the maximum variation of residuals is 76,215 km2. We concluded that SVM is sensitive to the parameters (C and gamma) and presents a positive correlation. When the parameters (tree and split) change between (100~600) and (1~6), respectively, the RF’s maximum variation of accuracy score is 0.08, and the maximum variation of residuals is 1157 km2, indicating that RF is low in sensitivity toward the parameters (tree and split). When the parameters (maxD and minSP) are between (10~60), the maximum accuracy change value is 0.06, and the maximum variation of residuals is 6943 km2. Therefore, compared to RF, CART is sensitive to the parameters (maxD and minSP) and has poor robustness. In general, under the conditions of the hyperparameters, SVM and RF exhibit optimal classification performance, while CART has relatively inferior performance. Meanwhile, SVM, RF, and CART have different sensitivities toward the algorithm parameters; that is, SVM and CART are more sensitive to the algorithm parameters, while RF has low sensitivity toward changes in the algorithm parameters. The different parameters cause great changes in the accuracy scores and residuals, so it is necessary to determine the algorithm hyperparameters. Generally, default parameters can be used to achieve crop classification, but we recommend the enumeration method, similar to grid search, as a practical way to improve the classification performance of the algorithm if the best classification effect is expected.

High-spatiotemporal-resolution precipitation data are essential for studies on regional hydrology, meteorology, and ecological conservation. Because the Qaidam Basin is a data-scarce region with a few ground stations and coarse-resolution remote sensing products, its utility in regional research is constrained. Therefore, high-resolution precipitation data are urgently needed. Here, longitude, latitude, the normalized difference vegetation index (NDVI), the digital elevation model (DEM), daytime and nighttime land surface temperature, slope, and aspect were selected as environmental variables. Four machine learning methods, Artificial Neural Network (ANN), Cubist, Random Forest (RF), and Support Vector Machine (SVM), were used to downscale Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 to 1 km in the Qaidam Basin and validated using ground observation stations. For annual downscaling, the accuracy ranked as Cubist > ANN > RF > SVM, and residual correction further improved performance. The Cubist model produced the best results, generating finer spatial patterns and reducing outliers in both annual and monthly products. Longitude, latitude, the DEM, and the NDVI were important contributors to the Cubist model. The resulting high-resolution dataset provides valuable support for hydrological and climate change research in the Qaidam Basin.

The development of an effective scaffold for bone defect repair is an urgent clinical need. However, it is challenging to design a scaffold with efficient osteoinduction and antimicrobial activity for regeneration of bone defect. In this study, we successfully prepared a hydroxyapatite (HA) porous scaffold with a surface-specific binding of peptides during osteoinduction and antimicrobial activity using a three-dimensional (3D) printing technology. The HA binding domain (HABD) was introduced to the C-terminal of bone morphogenetic protein 2 mimetic peptide (BMP2-MP) and antimicrobial peptide of PSI10. The binding capability results showed that BMP2-MP and PSI10-containing HABD were firmly bound to the surface of HA scaffolds. After BMP2-MP and PSI10 were bound to the scaffold surface, no negative effect was observed on cell proliferation and adhesion. The gene expression and protein translation levels of type I collagen (COL-I), osteocalcin (OCN) and Runx2 have been significantly improved in the BMP2-MP/HABP group. The level of alkaline phosphatase significantly increased in the BMP2-MP/HABP group. The inhibition zone test against Staphylococcus aureus and Escherichia coli BL21 prove that the PSI10/HABP@HA scaffold has strong antibacterial ability than another group. These findings suggest that 3D-printed HA scaffolds with efficient osteoinduction and antimicrobial activity represent a promising biomaterial for bone defect reconstruction.

China’s coastal wetlands play a crucial role in maintaining biodiversity and providing essential ecosystem services. However, the absence of high-resolution wetland type maps poses substantial challenges for effective conservation and management. This study proposes a two-step classification framework that integrates pixel-based Random Forest algorithms with object-based hierarchical decision trees, utilizing Landsat-8 time-series imagery to generate a detailed wetland map comprising 10 wetland types and 5 non-wetland categories. The results reveal distinct spatial patterns along China’s coastline: freshwater wetlands and riverine systems dominate the northern regions, whereas southern coastal zones feature extensive tidal flats, aquaculture ponds, and mangrove ecosystems. The proposed method achieved an overall accuracy of 89.76% and a Kappa coefficient of 0.891, demonstrating its effectiveness for large-scale wetland mapping. This study provides robust technical support for the sustainable conservation and ecological management of coastal wetlands.

Background One of the greatest challenges for tissue-engineered bone is the low survival rate of locally grafted cells. The cell homing technology can effectively increase the number of these grafted cells, therefore, enhancing the repair of bone defects. Here we explore the effect of fucosylation modification on the directional homing of bone marrow mesenchymal stem cells (BMSCs) and their ability to repair bone defects. Results Glycosylated BMSCs expressed high levels of the Sialyl Lewis-X (sLe X ) antigen, which enabled the cells to efficiently bind to E- and P-selectins and to home to bone defect sites in vivo. Micro-CT and histological staining results confirmed that mice injected with FuT7-BMSCs showed an improved repair of bone defects compared to unmodified BMSCs. Conclusions The glycosylation modification of BMSCs has significantly enhanced their directional homing ability to bone defect sites, therefore, promoting bone repair. Our results suggest that glycosylation-modified BMSCs can be used as the source of the cells for the tissue-engineered bone and provide a new approach for the treatment of bone defects. Graphic Abstract