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The rich feature information contained in the diverse remote sensing data has also exhibited growing potential in the field of image classification. However, the processing of multi-feature data still grapples with the challenges posed by the “curse of dimensionality” and the high computational costs. This paper proposes a remote sensing feature parameter selection method. This method is based on the Gini index of a Random Forest (RF) classifier and employs a 10% threshold decision to identify the optimal combination of remote sensing feature parameters. First, spectral features, texture features, temperature features, elevation features, and principal component features are selected to create a stack of remote sensing images. Second, multiple sets of decision trees are established to cross-validate the contributions of various feature parameters. The feature rankings are determined based on the normalized mean of feature importance. Subsequently, a threshold isset to filter out remote sensing feature parameters that meet the criteria. Finally, an iterative parameter optimization process is carried out to obtain the optimal combination of remote sensing feature parameters. The Landsat 8 image covering Hangzhou Bay in eastern China and the Sentinel-2 remote sensing image of Yancheng Nature Reserve in Jiangsu, China were selected for experiments. The results show that the feature parameters of the remote sensing image screened by the method in this paper are representative, and the proposed method demonstrates strong adaptability and generalization performance in complex environments. The study has important guiding significance for regional spatial planning and sustainable development.

Abstract Accurately and quickly estimating the soil organic carbon (SOC) content is crucial in the monitoring of global carbon. Environmental variables play a significant role in improving the accuracy of the SOC content estimation model. This study focuses on modeling methodologies and environmental variables, which significantly influence the SOC content estimation model. The modeling methods used in this research comprise multiple linear regression (MLR), partial least squares regression (PLSR), random forest, and support vector machines (SVM). The analyzed environmental variables include terrain, climate, soil, and vegetation cover factors. The original spectral reflectance (OSR) of Landsat 5 TM images and the spectral reflectivity after the derivative processing were combined with the above environmental variables to estimate SOC content. The results showed that: (1) The SOC content can be efficiently estimated using the OSR of Landsat 5 TM, however, the derived processing method cannot significantly improve the estimation accuracy. (2) Environmental variables can effectively improve the accuracy of SOC content estimation, with climate and soil factors producing the most significant improvements. (3) Machine learning modeling methods provide better estimation accuracy than MLR and PLSR, especially the SVM model which has the highest accuracy. According to our observations, the best estimation model in the study area was the “OSR + SVM” model (R2 = 0.9590, RMSE = 13.9887, MAE = 10.8075), which considered four environmental factors. This study highlights the significance of environmental variables in monitoring SOC content, offering insights for more precise future SOC assessments. It also provides crucial data support for soil health monitoring and sustainable agricultural development in the study area.

Abstract Visceral adiposity index (VAI) is a reliable indicator of visceral adiposity. However, no stu-dies have evaluated the association between VAI and DKD in US adults with diabetes. Theref-ore, this study aimed to explore the relationship between them and whether VAI is a good pr-edictor of DKD in US adults with diabetes. Our cross-sectional study included 2508 participan-ts with diabetes who were eligible for the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. Univariate and multivariate logistic regression were used to an-alyze the association between VAI level and DKD. Three models were used to control for pot-ential confounding factors, and subgroup analysis was performed for further verification. A tot-al of 2508 diabetic patients were enrolled, of whom 945 (37.68%) were diagnosed with DKD. Overall, the VAI was 3.36 ± 0.18 in the DKD group and 2.76 ± 0.11 in the control group. VAI was positively correlated with DKD (OR = 1.050, 95% CI 1.049, 1.050) after fully adjusting for co-nfounding factors. Compared with participants in the lowest tertile of VAI, participants in the highest tertile of VAI had a significantly increased risk of DKD by 35.9% (OR = 1.359, 95% CI 1.355, 1.362). Through subgroup analysis, we found that VAI was positively correlated with the occurrence of DKD in all age subgroups, male(OR = 1.043, 95% CI 1.010, 1.080), participants wit-hout cardiovascular disease(OR = 1.038, 95% CI 1.011, 1.069), hypertension (OR = 1.054, 95% CI 1.021, 1.090), unmarried participants (OR = 1.153, 95% CI 1.036, 1.294), PIR

Abstract In the realm of multiferroicity in 2D magnets, whether magnetic and polar skyrmions can coexist within a single topological entity has emerged as an important question. Here, we study Janus 2D magnets CrInX 3 (X=Se, Te) for a comprehensive investigation of the magnetic ground state, magnetic excited state, and corresponding ferroelectric polarization by first-principles electronic structure calculations and Monte Carlo simulations. Specifically, we have thoroughly elucidated the magnetic exchange mechanisms, and have fully exemplified the magnetic field dependence of the magnon spectrum. More importantly, our study reveals a previously unrecognized, remarkably large spin-spiral-induced ferroelectric polarization (up to 194.9 μC/m2) in both compounds. We propose an approach to identify polar skyrmions within magnetic skyrmions, based on the observed direct correlation between spin texture and polarization density. Elucidating this correlation not only deepens our understanding of magnetic skyrmions but also paves the way for innovative research in the realm of multiferroic skyrmions.

Highlights The interfacing between SnO2 and MXene alters electronic structures, shifting the d-band center in transition metals, enhancing catalytic efficiency by reducing electron filling in antibonding orbitals. A binder-free, ultrathin, laminar heterostructured interlayer on polypropylene separator is demonstrated. The ionic sieving mechanism and efficient adsorption–catalysis process enable deeper charge/discharge cycle and improved stability. The improved catalytic conversion and suppressed lithium dendrites formation enable a high loading of 7.5 mg cm−2 and an initial area capacity of 7.6 mAh cm−2.

Abstract Objective Evaluate efficacy, safety, and tolerability of adjunctive brivaracetam (BRV) in adult Asian patients with focal‐onset seizures (FOS). Methods Phase III, randomized, double‐blind, placebo‐controlled study (EP0083; NCT03083665) evaluating BRV 50 mg/day and 200 mg/day in patients (≥16–80 years) with FOS with/without secondary generalization (focal to bilateral tonic–clonic seizures) despite current treatment with 1 or 2 concomitant antiseizure medications. Following an 8‐week baseline, patients were randomized 1:1:1 to placebo, BRV 50 mg/day, or BRV 200 mg/day, and entered a 12‐week treatment period. Efficacy outcomes: percent reduction over placebo in 28‐day FOS frequency (primary); 50% responder rate in FOS frequency; median percent reduction in FOS frequency from baseline; seizure freedom during treatment period (secondary). Primary safety endpoints: incidences of treatment‐emergent adverse events (TEAEs); TEAEs leading to discontinuation; serious TEAEs. Results In this study, 448/449 randomized patients (mean age, 34.5 years; 53.8% female) received ≥1 dose of study medication (placebo/BRV 50 mg/BRV 200 mg/day: n = 149/151/148). Percent reduction over placebo in 28‐day adjusted FOS frequency was 24.5% (p = 0.0005) and 33.4% (p

Mangroves are one of the most important marine ecosystems globally, their spatial distribution is crucial for promoting mangrove ecosystems conservation, restoration, and sustainable managements. This study proposed a novel Unet-Multi-Scale High-Resolution Vision Transformer (UHRViT) model for classifying mangrove species using unmanned aerial vehicle (UAV-RGB), UAV-LiDAR, and Gaofen-3 Synthetic Aperture Radar (GF-3 SAR) images. The UHRViT utilized a multi-scale high-resolution visual Transformer as its backbone network and was designed to a multi-branch U-shaped network structure to extract features of different scales layer by layer, and to facilitate the interaction of high and low-level semantic information. We further verified the classification performance superiority of UHRViT model by comparing to HRViT and HRNetV2 algorithms. We also systematically investigated the effects of active–passive image combination ratios on mangrove communities mapping. The results revealed that: UAV-RGB images exhibited the better classification accuracy (mean F1-score＞95 %) for mangrove species than UAV-LiDAR and GF-3 SAR images; The classification performances and stability of UHRViT algorithm in the fifteen datasets outperformed the HRViT and HRNetV2 algorithms; Combining UAV-RGB with GF-3 SAR or UAV-LiDAR images respectively, both achieved better classifications than the single data source. Based on the UHRViT algorithm, the combination of UAV-RGB and UAV-LiDAR achieved the highest classification accuracy (Iou = 0.944, MIou = 50.2 %) for Avicennia corniculatum (AC). When the combination ratio of UAV-RGB with GF-3 SAR or UAV-LiDAR was 3:1, Avicennia marina and AC both obtained the optimal classification accuracy with average F1-scores of 98.19 % and 97.3 %, respectively. Our works revealed that the changes in the classification accuracies of mangrove communities under multi-sensor image combination ratios, and demonstrated that our model could effectively improve the classification accuracy of mangrove communities.

Mangroves face decline and degradation due to human activities and natural forces, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices that rely on multispectral image spectral characteristics suffer from limitations in terms of recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring.

China’s coastal mudflats, threatened by artificial reclamation and climate change, are undergoing drastic changes and their accurate mapping is important for their conservation and restoration. Traditional classification methods, which require large samples and complex classifiers, tend to have low computational efficiency and poor generalization ability; thus, they are unsuitable for the rapid and accurate extraction of coastal mudflats. This study proposes a Mudflat Index (MFI) based on hyperspectral images. MFI amplifies the difference in spectral characteristics between mudflats and other land cover types in intertidal environments, effectively improving the discrimination between coastal mudflats, salt marshes, mangroves, and muddy waters. Four typical coastal mudflat areas (i.e., the Yellow River Delta in Shandong, the Radial Sand Ridges of the South Yellow Sea in Jiangsu, Hangzhou Bay in Zhejiang, and the Qinzhou Bay-Nanliu River Estuary in Guangxi) based on ZY1-02D were selected as the study areas. The extraction accuracies in the four study areas are 97.60%, 96.88%, 97.16% and 96.97%, respectively. The further extraction experiments were calculated based on hyperspectral data from GF-5, PRISMA, and Hyperion. Sample datasets were produced using field surveys and Google Earth high-resolution imagery. Compared to the Hyperspectral Bare Soil Index (HBSI), Normalized Difference Bare Soil Index (NDBSI) and Microphytobenthos Index (MPBI), MFI demonstrates superior performance with average SDI value improvements of 0.82, 0.71 and 1.17, respectively, in distinguishing mudflats from other typical land cover types in the intertidal zone. The extraction results were also compared with those derived from Support Vector Machine (SVM) and Random Forest (RF) classifications, showing that MFI outperformed SVM and RF by an average of 1.52% and 0.58%. The results show that MFI can be applied to different hyperspectral remote sensing images and different areas of mudflat extraction. The MFI-based method is simple, fast and accurate at extracting the mudflat in the intertidal environment.

While the current research has achieved satisfactory results for the registration of single mode data, there has always been a significant challenge in the registration of multi-modal images due to the obvious nonlinear radiation differences caused by different imaging mechanisms and imaging time. For example, multi-temporal visible, visible-synthetic aperture radar, visible-near infrared, near infrared-short wave infrared, visible-MAP, etc. To address this problem, we propose a Robust and Accurate Feature Matching Method for Multi-modal Geographic Images Spatial Registration (RAMMR) to fully extract common key points between images, weaken the radiation difference between data, and finally accurately match more inliers to realize multi-modal image registration. Considering the influence of noise and edge information on key point extraction, RAMMR first constructs a new scale space by introducing the Side Window Filter (SWF); Then, we improve Harris algorithm to extract key points based on the SWF scale space; After that, we propose an enhanced log-polar descriptor based on the gradient angles and gradient amplitudes of the scale space, which effectively improves the quality of the descriptor and avoids the mismatch of key points; Based on the standard Euclidean distance, we design a re-match strategy to obtain the initial matching results, and Random Sample Consensus (RANSAC) is used to eliminate outliers. Finally, the affine transformation parameters are calculated based on inliers, and multi-modal image registration is realized. RAMMR is evaluated on different multi-modal datasets and compared with some state-of-art methods. The experimental results show that RAMMR accurately registers multi-modal geographic images and obtains comparative results compared with benchmark methods. Our source datasets are publicly available at https://github.com/RSmfmr/multimodal-dataset.

Abstract Rheumatoid arthritis (RA) is a chronic autoimmune disease with an unknown etiology. RA cannot be fully cured and requires lengthy treatment, imposing a significant burden on both individuals and society. Due to the lack of specific drugs available for treating RA, exploring a key new therapeutic target for RA is currently an important task. Activated fibroblast-like synoviocytes (FLSs) play a crucial role in the progression of RA, which release interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α resulting in abnormal inflammatory reaction in the synovium. A previous study has highlighted the correlation of m6A reader insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) with inflammation-related diseases in human. However, the role of IGF2BP2 in the inflammatory reaction of FLSs during RA progression has not been assessed. In this study, IGF2BP2 expression was decreased in the synovial tissues of RA patients and collagen-induced arthritis (CIA) rats. Intra-articular injection of an adeno-associated virus (AAV) vector overexpressing IGF2BP2 relieved paw swelling, synovial hyperplasia and cartilage destruction in CIA rats. IGF2BP2 overexpression also inhibited lipopolysaccharide (LPS)-mediated RA fibroblast-like synoviocytes (RA-FLSs) migration and invasion accompanied by a decreased level of inflammatory factors in vitro. Conversely, IGF2BP2 suppression promoted RA-FLSs migration and invasion with an elevated level of inflammatory factors in vitro. The sequencing result showed that glutathione S-transferase Mu 5 (GSTM5), a key antioxidant gene, was the target mRNA of IGF2BP2. Further experiments demonstrated that IGF2BP2 strengthened the stability of GSTM5 mRNA, leading to weakened inflammatory reaction and reduced expression of matrix metalloproteinase 9 and 13 (MMP9, MMP13). Therefore, IGF2BP2-GSTM5 axis may represent a potential therapeutic target for RA treatment.

ABSTRACTIn this study, foamed ceramics were successfully prepared using cyanide tailings (CTs) with a high sulfur content as the main raw material. The effects of sintering temperature, holding time, and heating rate on the bulk density, water absorption, porosity, pore structure, and compressive strength of foamed ceramics were systematically investigated. The results showed that the elevated sintering temperature promoted the generation of liquid phases, resulting in an obvious enlargement of the pore size and a decrease in bulk density. In addition, a similar evolution trend could be found in the pore size and bulk density with the extension of the holding time, which was attributed to the degrees of silicon carbide reaction. Foamed ceramics with small pore sizes could be prepared at a heating rate that did not exceed 4°C/min. Then, the higher heating rates could bring abnormally large pores and be detrimental to the mechanical properties of the foamed ceramics, which were proven to be related to the decomposition of FeS2 introduced by CTs during the heating process. In conclusion, the foamed ceramics prepared with CTs in this study provide effective solid waste utilization and have good application prospects in wall composite one-piece insulation panels.

Mangroves are vital coastal ecosystems that provide crucial links between land and sea. Tree height is a key indicator for assessing mangroves’ health status. Currently, there are still numerous challenges in estimating mangrove tree height. In this study, multiple deep learning and shallow machine learning regression models were developed to accurately estimate mangrove tree height using multi-dimensional Light Detection and Ranging (LiDAR) point clouds and their derivatives. We constructed a novel CNN_RepMLP model for mangrove tree height mapping. We also further verified the applicability of different types of regression models in estimating mangrove tree heights, and explored the influence of different LiDAR-derived features on the inversion accuracy for tree heights. The results indicated the following: (1) The CNN_RepMLP displayed satisfactory performance in the inversion of mangrove tree heights, and exhibited better robustness and generalization ability than the convolutional neural network (CNN) model. (2) Among the different LiDAR-derived feature combinations, combining height variables with intensity variables can not only mitigate the negative impact of height variables on inversion models, but also enhance the mangrove tree height inversion accuracy. (3) The ensemble learning framework with ExtraTrees as the meta-model can make better use of the differences and complementarities between different single base models, and exhibited better accuracy in estimating the height of mangrove trees compared with other ensemble learning models. (4) Multiple machine learning models based on multi-dimensional UAV-LiDAR point-cloud-derived features are suitable for the inversion of mangrove tree height. The CNN_RepMLP model outperformed the CNN and stacking ensemble learning models and had more detailed differentiation in terms of mangrove tree height. Its prediction results can more realistically reflect the spatial differentiation characteristics of mangrove tree height.

ABSTRACTMangroves are one of the important components of Earth's carbon sinks. The current problems of base-model composition strategy of ensemble learning and image features combination are still major challenges in mangrove species classification. This paper constructed two novel adaptive ensemble learning frameworks (AME-EL and AOS-EL) to explored the effect of combing different spatial-resolution optical and SAR images on classification performance, and evaluated the ability in mangrove species classification between dual-polarization and full-polarization SAR images. Finally, we used the SHAP method to explore the effects of different feature interactions on mangrove species classification. The results indicated that: (1) AME-EL and AOS-EL achieve the fine classification of mangrove species with overall accuracies between 77.50% and 94.77%. (2) Combination of Gaofen-7 multispectral and Gaofen-3 SAR improved the classification accuracy for Kandelia candel, with the F1 score increasing from 26.4% to 40.2%. (3) The VV/VH polarization performed better in the classification, with the F1 scores for Aegiceras corniculatum and Kandelia candel were higher than those of HH/HV and AHV polarization by 7%−16.1% and 5.9%−16.1%, respectively. (4) SAR features interacted well with other spectral features, which made a strong contribution to the classification accuracy of mangrove species, and effectively affect the prediction results.

ABSTRACTThe extraction of coastal aquaculture ponds from remote sensing images is affected by the effect of ‘same object with different spectrums’ and ‘different objects with same spectrums’. The U2-Net deep learning network is able to capture more contextual information from different scales to solve this problem and is suitable for salient object detection. This study proposed a remote sensing information extraction model of coastal ponds based on U2-Net deep learning network, completed the remote sensing information extraction of coastal aquaculture ponds in Zhoushan Archipelago from 1984 to 2022, analyzed the spatiotemporal evolution of coastal aquaculture ponds in Zhoushan Archipelago. The results showed that the developed model was more accurate with a precision rate of 96.12%, a recall rate of 95.43%, and an F1-measure of 0.96. During the study period, the area of the aquaculture ponds in the Zhoushan Archipelago demonstrated an increasing trend, expanding from 471.21 hm2 in 1984–3668.55 hm2 in 2022. In addition, over half of the aquaculture ponds had been active for more than 10 years. The method developed in this study is capable of rapidly and accurately mapping coastal aquaculture ponds, and thus is significant for the management of marine resources and promoting sustainable development.

ABSTRACTStudies on the retrieval of ocean surface current by Synthetic Aperture Radar (SAR) mainly concentrate on coastal regions with land coverage, allowing for continuous land to estimate non-geophysical Doppler shifts. As a strong western boundary in the Western Pacific, the Kuroshio flows through mostly islands or open sea, and lacks land coverage for range bias correction. We propose a non-geophysical Doppler shift correction algorithm suitable for Kuroshio observation. Three solutions matching different land coverage were adopted in the range direction, and the scalloping was removed by using average filtering in the azimuth direction. Comparison between SAR radial velocity and Global Surface Lagrangian Drifter (GLD) reveals that the correlation coefficient (R2) increase to 0.686, and root mean square error (RMSE) of 0.215 m/s after non-geophysical corrections, while the wind-wave bias corrections make R2 increase to 0.806 and RMSE decrease to 0.13 m/s. There is also a good consistency between SAR inversion and Regional Ocean Circulation Model (ROMS). Sensitivity analysis illustrates that ascending pass is more suitable for monitoring Kuroshio, and high incidence angles are less affected by ocean conditions and non-geophysical factors. The algorithm can be used in regions with discontinuous or absent land.

ABSTRACTAccurately mapping large-scale areas of aquaculture ponds along China’s coastal areas is challenging. This study proposes a multi-featured decision tree method that incorporates multi-spectral indices, water inundation frequency (WIF), and object-oriented rules to extract aquaculture ponds in coastal zones using 65332 Sentinel-2 images on the GEE platform. The proposed method was validated, with the average overall accuracy of the extraction results from 2017 to 2021 reaching 90.80%, and an average F1 Score of 0.91. The results of this study indicate that the overall area of aquaculture ponds in China's coast in 2021 was 7633.83 km2, mainly located in coastal bay estuaries in Shandong Province, Jiangsu Province and Guangdong Province. In recent years, the area of coastal ponds for aquaculture in China has shown a decreasing trend, with an average annual decrease of 235.65 km2. The analysis combined with land use data shows that the ponds that have been reduced in the past five years have mainly been converted to abandoned aquaculture ponds, wetlands, and other land use types.

During the service of asphalt pavement, it is affected by natural environmental factors for a long time, and the pavement performance declined. In order to simulate the cumulative effects of multiple aging components, the indoor ''ultraviolet radiation, high temperature and water'' multi-factor cyclic coupling aging test was designed. The properties of asphalt during cyclic coupled aging were measured by conventional physical properties tests, photoelectric colorimetric tests, and Fourier Transform Infrared Spectroscopy test. Then the surface morphology of asphalt was observed by camera and optical microscope. After asphalt aged, a large number of oxidation functional groups were formed, causing asphalt to become hard and brittle, adhesion to diminish, and surface roughness to increase. Ultraviolet light and high temperatures led to the exudation and dissolution of the liquid constituents within the asphalt, which were subsequently washed away by water. The thermal stress induced the shrinkage and wrinkle of the asphalt film, resulting in the formation of cracks. Under high temperatures, a limited number of cracks could potentially self-repair during the early stages of aging. Nevertheless, as the aging process progressed, the self-healing capacity of the asphalt diminished, leading to the widening and deepening of cracks. Concurrently, water infiltrated along the cracks into the inner layers of the asphalt, exacerbating the degradation of its properties.

Mangroves play a pivotal role in the attainment of United Nations Sustainable Development Goals. Leaf equivalent water thickness (EWT) and leaf mass per area (LMA) are vital functional trait parameters for monitoring heath status of mangroves. However, their inherent spectral response characteristics remain unclear, which presents a challenge for high-precision estimating EWT and LMA using remote sensing images. In this study, we attempted to synergistic retrieval of mangrove EWT and LMA using field hyperspectral and satellite data. Specifically, we proposed a novel Fractional-order assisted Spectral angle Analysis (FSA) approach to accurately identify sensitive spectral domains of EWT and LMA for three mangrove species based on in situ full-spectrum hyperspectral (350–2500 nm) data. We constructed a new spectral matching index (SMI) to screen the satellite-based sensitive spectral bands based on the field-based spectral domains, and further verified their feasibility of estimating EWT and LMA using three optical satellite sensors (Sentinel-2A, SDGSAT-1, and OHS-3D). Finally, we explored the effectiveness of combining SAR and thermal infrared (TIR) images with sensitive spectral bands on improving retrieval performance in estimating EWT and LMA, respectively. Results indicated that FSA approach could capture the sensitive spectral domains of EWT (1125–1868 nm) and LMA (1419–1999 nm) for three mangrove species. The SMI could effectively realize the spectral matching between field-based sensitive domains and satellite-based bands, and perform the synergistic retrieval of mangrove EWT (R2 = 0.65) and LMA (R2 = 0.70). Intriguing finding is that the additional TIR bands significantly enhanced the EWT and LMA inversion accuracy (R2 increased by 9.8 %–68.3 %) when compared to SAR images. We confirmed that EWT displayed an inverse trend of LMA with the flooding frequency increasing. Our works provide a scientific basis for protection and sustainable management of mangroves.

The magnetism-ductility contradictory relationship presents a significant challenge in the development of magnetic alloys. The impact of the BCC-B2 transition, along with Mn site occupancy, on magnetism and ductility have been investigated by using first-principles calculations. The calculations involved the evaluation of magnetic moments, density of states (DOS), phase stability and ductility of FeCoMn alloys. The results of binary alloys confirm the enhancement of magnetism due to the BCC-B2 transition. Furthermore, the ordering phase transition can strengthen the magnetic interaction between Fe and Mn atoms, which is associated with minimal variations in the density of states of Fe and Mn in the B2 structure. Regarding the ductility of FeCoMn alloys, two factors contribute to increased brittleness. Firstly, the increased covalent component in bonding, as a result of the strong hybridization between different elements, leads to an increased brittleness. Secondly, the increased Peierls stress provides a larger resistance to dislocation motion, which also contributes to the increased brittleness. Finally, the Pearson correlation coefficients and data analysis indicate that VEC, spin polarizations and Mn content provide major contributions to the contradictory relationship between magnetism and ductility.

Wetlands are the one of ecosystems with the highest biodiversity, ecological service functions and carbon storage. Affected by the synergistic impacts of human activities and climate change, the global wetland area has decreased by 35 % since 1970, with far-reaching implications on biodiversity loss. Compared with manual ground investigations, remote sensing is considered to be the most promising method for monitoring wetland biodiversity change in order to formulate the effective conservation strategies due to its characteristics of non-contact detection, low cost and timely. Here we used bibliometric method to analyze the study sites, methods, conclusions and shortcomings of published papers globally over the past 60 years for wetland biodiversity monitoring. We show that global distribution of wetlands monitoring was uneven, mostly concentrated in the United States, China and Northern Europe. Current researches mainly focused on coastal, marsh and estuarine wetlands, while other wetland (e.g., lake wetlands, riparian wetlands, artificial peatlands and high-altitude and high-latitude peatlands) monitoring were still lacking. Overall, 20 remote sensing platforms and sensors were used, and the near infrared shortwave length (780 ∼ 1100 nm) was the most reliable and sensitive spectral region. Among various estimation methods, the accuracy of nonlinear, multi-independent variables, and hyperspectral remote sensing models were generally higher than those of linear, single-factor and multispectral models, respectively. The estimation accuracy was affected by both ground sampling time and plant phenology. Most studies focused on the taxonomic and within-habitat diversity (α-diversity) of single-layer communities (grassland), while few paid attentions to the functional and phylogenetic diversity of inter-habitat (β-diversity) and region (γ-diversity) in the multi-layer communities (forest and shrubland), and biodiversity-ecosystem functioning (BEF) relationships. We suggest that prospective studies should strengthen wetland plant diversity monitoring globally. The multi-dimensional spectral data are mined and fused to provide new monitoring models with high accuracy. The monitoring should focus on the scale effects (α, β and γ), BEF relationships, and the plant diversity change with environmental gradients.

Urban functional zones (UFZ) identification with remote sensing imagery (RSI) is attracting increasing attention in urban planning and resource allocation in urban areas, etc. The UFZ is a comprehensive unit comprising geographical, how to effectively integrate the RSI and points of interest (POI) with different physical and socioeconomic characteristics is important and promising. However, there are two challenges for the UFZ identification. On one hand, the UFZ is closely related to buildings, and most current methods lack an in-depth understanding of building semantics. Therefore, an efficient integration of building footprint (FT) data deserves further investigation. On the other hand, these RSI, POI, and FT data are heterogeneous; how to effectively leverage complementary information among these highly heterogeneous modalities to enhance the comprehensive understanding of urban. To solve the above challenges, this article introduces an end-to-end deep learning-based multisource dynamic fusion network for UFZ identification on RSI, POI, and FT. In the proposed method, an adaptive weight interactive fusion module is designed to comprehensively integrate the complementary information among the heterogeneous RSI, POI, and FT data sources. In addition, a multiscale feature focus module is proposed to extract multiscale image features and emphasize critical characteristics. This method was applied to UFZ classification in Ningbo, Zhejiang Province, China, and the experimental results demonstrate the competitive performance.

Abstract Objective Rheumatoid arthritis (RA) is a chronic, progressive autoimmune disease with a complex pathogenesis that has not yet been fully elucidated, and T-cell pyroptosis is an important pathogenetic factor in RA. This study aimed to investigate the role of endoplasmic reticulum aminopeptidase 2 (ERAP2) in the pyroptosis of CD4+ T cells in RA and the specific molecular mechanism. Methods Peripheral venous blood was collected from human subjects, and CD4+ T cells were isolated and activated to measure the level of pyroptosis and ERAP2 expression. Pyroptosis levels were assessed using immunofluorescence, flow cytometry, qRT-PCR, and Western blotting. Changes in pyroptosis levels were observed upon knockdown or overexpression of ERAP2. To detect activated Caspase-1 in tissues, chimeric mice were engrafted with human synovial tissue and reconstituted with human CD4+ T cells. CD4 + T cells were treated with GLI1 antagonists and SMO receptor agonists to detect changes in pyroptosis levels. Results CD4+ T cell levels undergoing pyroptosis were found to be elevated in the blood and synovium of RA patients. The gene and protein expression of ERAP2 were significantly higher in CD4+ T cells from RA patients. Deletion of ERAP2 suppressed pyroptosis of these cells, attenuated the activation of Caspase-1 in tissue T cells, and reduced tissue inflammatory responses. Reciprocally, overexpression of ERAP2 triggered inflammasome assembly, activated Caspase-1, and induced pyroptosis in CD4+ T cells. Mechanistically, ERAP2 inhibits the Hedgehog signaling pathway and upregulates the expression of nucleotide-binding oligomerization segment-like receptor family 3(NLRP3), cleaved Caspase-1, and Gasdermin D to promote pyroptosis in CD4+ T cells. Conclusions Taken together, our results identify a novel mechanism by which ERAP2 regulates RA development and document the effect of the ERAP2/Hedgehog signaling axis on pyroptosis of CD4+ T cells from RA patients.

Abstract Exploring the cross-sensitivity between land use transformation and ecological service values in rare earth mining areas is of great significance for the development of ecological protection and restoration in rare earth mining areas. To study the impact of land use changes on ecosystem service functions in rare earth mining areas, firstly, the land use change trends in the study area from 2009 to 2019 were analyzed using the land transfer matrix; then the distribution of ecosystem service values and the flow direction of ecosystem service values in the study area were measured based on the ecosystem service value equivalents; a spatial autocorrelation analysis was done on the ecosystem service values to explore their spatial distribution patterns; and finally, the cross-sensitivity coefficient was used to quantitatively assess the extent and direction of the impact of land use change on ecosystem service values. The results show that the land use types in the study area are mainly forest land and farmland, with woodland accounting for the highest proportion of the study area. The ESV changes in the study area are consistent with the trend of land use transformation, with the overall increase and decrease being comparable, and the decrease in ESV is mainly concentrated in the areas with a large increase in mining land and construction land; during the study period, the study area was significantly reduced with low—low cluster areas and the ecological environment was improved; from 2009 to 2014, the ecological sensitivity coefficient is more variable, and is more sensitive to the net conversion between water and desert, from 2014 to 2019, the ecological sensitivity coefficient is less variable, and the most sensitive is the net conversion between cultivated land and water. The study area should be reasonably developed for rare earth resources and the ecological environment around the mining area should be reasonably protected to build an ecological security pattern.

Abstract Prophages, which enables bacterial hosts to acquire novel traits, and increase genetic variation and evolutionary innovation, are considered to be one of the greatest drivers of bacterial diversity and evolution. Stenotrophomonas maltophilia is widely distributed and one of the most important multidrug resistant bacteria in hospitals. However, the distribution and genetic diversity of S. maltophilia prophages have not been elucidated. In this study, putative prophages were predicted in S. maltophilia genomes by using virus prediction tools, and the genetic diversity and phylogeny of S. maltophilia and the prophages they harbor were further analyzed. A total of 356 prophage regions were predicted from 88 S. maltophilia genomes. Among them, 144 were intact prophages, but 77.09% of the intact prophages did not match any known phage sequences in the public database. The number of prophage carried by S. maltophilia is related to its host habitat and is an important factor affecting the size of the host genome, but it is not related to the genetic diversity of the prophage. The prediction of auxiliary genes encoded by prophage showed that antibiotic resistance genes was not predicted for any of the prophages except for one questionable prophage, while 53 virulence genes and 169 carbohydrate active enzymes were predicted from 11.24 and 44.1% prophages, respectively. Most of the prophages (72.29%) mediated horizontal gene transfer of S. maltophilia genome, but only involved in 6.25% of the horizontal gene transfer events. In addition, CRISPR prediction indicated 97.75% S. maltophilia strains contained the CRISPR-Cas system containing 818 spacer sequences. However, these spacer sequences did not match any known S. maltophilia phages, and only a few S. maltophilia prophages. Comparative genomic analysis revealed a highly conserved and syntenic organization with genomic rearrangement between the prophages and the known related S. maltophilia phages. Our results indicate a high prevalence and genetic diversity of prophages in the genome of S. maltophilia, as well as the presence of a large number of uncharacterized phages. It provides an important complement to understanding the diversity and biological characteristics of phages, as well as the interactions and evolution between bacteria and phages.

This paper presents a neuro-adaptive finite-time control strategy for uncertain nonstrict-feedback fractional-order nonlinear systems with multiple-objective constraints. To stabilize the uncertain nonlinear fractional-order systems, neural networks (NNs) are employed to identify the unknown nonlinear functions, and dynamic surface control is used to avoid the computational complexity of the backstepping design procedure. The effect caused by the algebraic loop problem can be solved via establishing fractional-order adaptive laws. Introducing a new barrier function, the system output is always limited to the predefined time-varying acceptable range while effectively solving the multi-objective constraint problem. Utilizing fractional-order finite-time stability theory, a finite-time control scheme is constructed to drive the system output to the reference signal in finite time, which ensures better tracking performance. Two examples are given to illustrate the availability and superiority of the presented control scheme.

Abstract Recently, transformer‐based networks have been introduced for the classification of hyperspectral image (HSI). Although transformer‐based methods can well capture spectral sequence information, their ability to fuse different types of information contained in HSI is still insufficient. To exploit rich spectral, spatial and semantic information in HSI, a novel semantic and spatial‐spectral feature fusion transformer (S3FFT) network is proposed in this study. In the proposed S3FFT method, spatial attention and efficient channel attention (ECA) modules are employed for the extraction of shallow spatial‐spectral features. Then, a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature extraction. Finally, the semantic, spatial and spectral features are combined by the transformer for classification. Compared with traditional deep learning methods and recently transformer‐based methods, the proposed S3FFT shows relatively better results on three HSI datasets.

Vegetation is crucial for wetland ecosystems. Human activities and climate changes are increasingly threatening wetland ecosystems. Combining satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral bands. This study aimed to propose a method to classify marsh vegetation using multi-resolution multispectral and hyperspectral images, combining super-resolution techniques and a novel self-constructing graph attention neural network (SGA-Net) algorithm. The SGA-Net algorithm includes a decoding layer (SCE-Net) to precisely fine marsh vegetation classification in Honghe National Nature Reserve, Northeast China. The results indicated that the hyperspectral reconstruction images based on the super-resolution convolutional neural network (SRCNN) obtained higher accuracy with a peak signal-to-noise ratio (PSNR) of 28.87 and structural similarity (SSIM) of 0.76 in spatial quality and root mean squared error (RMSE) of 0.11 and R2 of 0.63 in spectral quality. The improvement of classification accuracy (MIoU) by enhanced super-resolution generative adversarial network (ESRGAN) (6.19%) was greater than that of SRCNN (4.33%) and super-resolution generative adversarial network (SRGAN) (3.64%). In most classification schemes, the SGA-Net outperformed DeepLabV3 + and SegFormer algorithms for marsh vegetation and achieved the highest F1-score (78.47%). This study demonstrated that collaborative use of super-resolution reconstruction and deep learning is an effective approach for marsh vegetation mapping.

ObjectiveThis current study represents a novel endeavor to scrutinize the correlation between the temporal alteration in serum total bilirubin (TBIL) concentrations and the rate of estimated glomerular filtration rate (eGFR). Additionally, this study aims to probe the plausible molecular mechanism underpinning the renoprotective effects of bilirubin concerning its hormonal characteristics.Materials and methodsIn this study, a cohort of 103 patients diagnosed with DKD and receiving medical care at Dongzhimen Hospital were recruited and monitored over a period of 2-7 years. The progression of DKD was ascertained using a threshold of eGFR decline > -5.48%/year. To assess the relationship between the annual change in serum TBIL levels (%/year) and the slope of eGFR, multivariate binary logistic regression analysis was employed. Furthermore, the ROC curve analysis was employed to determine the cut-off value for TBIL levels (%/year).ResultsThe use of multivariate binary logistic regression models revealed that serum TBIL levels (%/year) exhibited a significant correlation with the slope of eGFR. Moreover, the ROC curve analysis indicated a cut-off value of -6.729%/year for TBIL levels (%/year) with a sensitivity of 0.75 and specificity of 0.603, in diagnosing eGFR decline >-5.48%/year.ConclusionsThe findings of this study suggest that the sustained elevation of serum bilirubin concentration within the physiological range can effectively retard the progression of Diabetic Kidney Disease (DKD). Furthermore, the hormonal attributes of bilirubin may underlie its renoprotective effects.

Monitoring and assessing wetland diversity is crucial for its accurate preservation. Hyperspectral satellites have been proven effective for detailed investigations of plant diversity in many places. However, it's unclear whether spectral diversity invert landscape diversity, and whether the inversion accuracy varies with spatial scale. In this study, the ZY1-02D hyperspectral remote sensing images of the Yellow River Estuary were supervised and classified by the support vector machine. Then, the landscape diversity indices (i.e., community richness, Shannon-Wiener index, Simpson index, and Pielou index) and spectral diversity indices (i.e., coefficient of variation, convex hull volume, and eight vegetation indices) were calculated. A random forest model was used to predict landscape diversity by using spectral diversity. The spatial scale relationship between spectral diversity and landscape diversity were explored lastly. Our results showed that the overall accuracy of plant community classification in the Yellow River Estuary was 91.53 %, with a Kappa coefficient of 0.90. Spectral diversity had the best inversion accuracy on the Shannon-Wiener index (14 ∼ 57 %, average = 38 %), while the intermediate on Pielou index (3 ∼ 56 %, average = 30 %) and community richness (2 ∼ 48 %, average = 30 %), but the lowest on the Simpson index (2 ∼ 43 %, average = 16 %). The inversion accuracy of landscape diversity index increased first and then stabilized with the increase of scales, reaching stability at a sampling size of 2880 m × 2880 m. Our results indicated that ZY1-02D hyperspectral data can be used to monitor spatial changes of landscape diversity in wetland systems. However, its accuracy is affected by diversity index type and spatial scaling effects. Our findings provide a new perspective for the conservation and management of large-scale wetland landscape diversity.

BackgroundSecretory leukocyte protease inhibitor (SLPI) is a multifunctional protein involved in the chronic inflammatory process, implicated in the pathogenesis of diabetic kidney disease (DKD). However, its potential as a diagnostic and prognostic biomarker of DKD has yet to be evaluated. This study explored the clinical utility of SLPI in the diagnosis and prognosis of renal endpoint events in patients with DKD.MethodsA multi-center cross-sectional study comprised of 266 patients with DKD and a predictive cohort study comprised of 120 patients with stage IV DKD conducted between December 2016 and January 2022. The clinical parameters were collected for statistical analysis, a multivariate Cox proportional hazards model was used to evaluate the independent risk factors for renal endpoints.ResultsSerum SLPI levels gradually increased with DKD progression (p

Abstract Tumor microparticles (T-MPs) are vesicles released from tumor cells when they receive apoptotic or stimuli signals. T-MPs, which contain some proteins, lipids and nucleic acids from tumor cells, contribute to the exchange of material, energy and information between cells. T-MPs contain both tumor antigens and innate immunostimulatory signals, making T-MPs as a new form of tumor vaccine. Meanwhile, T-MPs can be used as natural carriers to transport ‘‘cargoes’’, such as chemotherapy drugs, oncolytic viruses, nucleic acids, and metal nanoparticles to treat tumors. In addition, T-MPs enhance the effect of chemotherapy. This review introduces the application of T-MPs as vaccines, delivery systems and chemosensitizers in tumor prevention and treatment, with a focus on the mechanisms, clinical applications, and influencing factors of drug-loaded T-MPs in tumor treatment.

Abstract This paper proposes a triple-layer isolation device based on the single-story isolation and double-layer isolation. By establishing dynamic equilibrium equations and conducting earthquake response comparative analyses on the same super-tall building structure using three different forms of isolation: single-story isolation, double-layer isolation, and triple-layer isolation, it is found that the triple-layer isolation device, which adds an isolation layer on the basis of the double-layer isolation device, exhibits many different characteristics in terms of its damping mechanism, seismic response rules, etc., due to the differences in the position and quantity of the isolation layer. Rare earthquakes are equivalent to ASCE maximum considered earthquake or annual occurrence probability of 2%. The research shows that, under rare earthquake conditions, the isolation effect of the triple-layer isolation device in the super-tall frame-shear structure is better than that of the single-story isolation and double-layer isolation devices. Particularly, as the number of floors increases, the triple-layer isolation device can significantly reduce the seismic isolation support tension and compression stresses, inter-story displacement, single-story shear force, overturning moment, and floor acceleration of the high-rise building structure, and concentrate the lateral displacement of the structure on the three isolation layers, dissipating most of the seismic input energy.

Abstract Objective This retrospective study aimed to investigate the association between TNs and the systemic immune-inflammation index (SII) and the aggregate index of systemic inflammation (AISI) in patients with T2DM. Methods A total of 370 T2DM patients, who were admitted to Dongzhimen Hospital between January 2020 and March 2023, were included in this retrospective study. Binary logistic regression models with multivariable adjustment were employed to assess the relationship between SII, AISI quartiles, and TNs. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic accuracy of SII and AISI in identifying T2DM patients with TNs. Results Age, diabetes duration, diabetic nephropathy (DN), SII, and AISI demonstrated significant positive associations with TNs. Compared to the first quartile of SII, the second, third, and fourth quartiles showed increased risks of TNs with hazard ratios (HRs) of 1.578 (0.883–2.820), 2.279 (1.257–4.131), and 3.626 (1.931–6.810), respectively (P

Abstract The areas affected by the East and South Asian monsoons contain almost half the world’s population. Understanding natural variability in these monsoons under warmer climates is critical for managing future changes. Here we present a high-resolution record of Holocene drought events derived from lake level changes in the South Asian monsoon region. By combining the published storm events in the East Asian monsoon region, we demonstrate that extreme hydroclimate events frequently occurred within a ca. 500-year cycle during the cool early-late Holocene, exhibiting a fierce Asian monsoon. In contrast, there were fewer extreme hydroclimate events during the warm mid-Holocene period. We propose that tropical temperatures and air-sea interaction in the Indo-Pacific Oceans are responsible for the occurrences of centennial-scale extreme events. Our findings suggest that tropical influences can module climate responses in monsoon regions, and the Asian monsoon may be more peaceful than hitherto expected under future global warming scenarios.

Existing methods of hyperspectral anomaly detection still face several challenges: (1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; (2) the ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; and (3) current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind-spot network based on multi-scale blind-spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial–spectral joint module and a background feature attention mechanism to enhance the interaction between spatial–spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that combines pixel shuffle down-sampling (PD) with spatial spectral joint screening. This approach addresses anomalous identity mapping and enables finite-scale mask convolution for better detection of targets at various scales. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the-art methods.

Aboveground biomass (AGB) serves as a crucial indicator of the carbon sequestration capacity of coastal wetland ecosystems. Conducting extensive field surveys in coastal wetlands is both time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) and satellite remote sensing have been widely utilized to estimate regional AGB. However, the mixed pixel effects in satellite remote sensing hinder the precise estimation of AGB, while high-spatial resolution UAVs face challenges in estimating large-scale AGB. To fill this gap, this study proposed an integrated approach for estimating AGB using field sampling, a UAV, and Sentinel-2 satellite data. Firstly, based on multispectral data from the UAV, vegetation indices were computed and matched with field sampling data to develop the Field–UAV AGB estimation model, yielding AGB results at the UAV scale (1 m). Subsequently, these results were upscaled to the Sentinel-2 satellite scale (10 m). Vegetation indices from Sentinel-2 data were calculated and matched to establish the UAV–Satellite AGB model, enabling the estimation of AGB over large regional areas. Our findings revealed the AGB estimation model achieved an R2 value of 0.58 at the UAV scale and 0.74 at the satellite scale, significantly outperforming direct modeling from field data to satellite (R2 = −0.04). The AGB densities of the wetlands in Xieqian Bay, Meishan Bay, and Hangzhou Bay, Zhejiang Province, were 1440.27 g/m2, 1508.65 g/m2, and 1545.11 g/m2, respectively. The total AGB quantities were estimated to be 30,526.08 t, 34,219.97 t, and 296,382.91 t, respectively. This study underscores the potential of integrating UAV and satellite remote sensing for accurately assessing AGB in large coastal wetland regions, providing valuable support for the conservation and management of coastal wetland ecosystems.

This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward and inverse kinematic models of the robot. Subsequently, a 3-5-3 mixed polynomial interpolation trajectory planning approach is applied to the robot. Building upon the conventional sparrow algorithm, a two-dimensional Logistic chaotic system initializes the population. Additionally, a Levy flight strategy and nonlinear adaptive weighting are introduced to refine the discoverer position update operator, while an inverse learning strategy enhances the vigilante position update operator. These modifications boost both the local and global search capabilities of the algorithm. The improved sparrow algorithm, based on 3-5-3 hybrid polynomial trajectory planning, is then used for the time-optimal trajectory planning of the robot. This is compared with traditional sparrow search algorithm and particle swarm algorithm optimization results. The findings indicate that the proposed enhanced sparrow search algorithm outperforms both the standard sparrow algorithm and the particle swarm algorithm in terms of convergence speed and accuracy for robot trajectory optimization. This can lead to the increased work efficiency and performance of the robot.

Addressing the urgent global challenge of man-made greenhouse gas emissions and climate change necessitates collaborative action between shipping lines and government regulatory agencies. Aligning with the International Maritime Organization’s emissions reduction strategy, this paper presents a novel bi-level programming model that unifies these stakeholders. On the upper level of the proposed bi-level model, a number of shipping lines optimize retrofitting plans for their vessels to maximize economic benefits. On the lower level, the regulatory agency responds to the carbon reduction efforts by setting retrofitting subsidies and emission penalty rates. This framework represents a multi-leader–single-follower game involving shipping lines and the regulatory agency, and its equilibrium is determined through an equilibrium problem with equilibrium constraints (EPEC). The EPEC comprises multiple single-leader–follower problems, each of which can be formulated as a mathematical program with equilibrium constraints (MPEC). The diagonalization algorithm (DM) is employed for its solution. Simulation studies performed based on a ten-year planning period show that the proposed approach can effectively promote vessel retrofitting and the use of green fuels, which leads to an annual emission reduction of over 50%.

Abstract Objective Rheumatoid arthritis (RA) is a chronic, progressive autoimmune disease. Over-activation of fibroblast-like synoviocytes is responsible for the hyperplasia of synovium and destruction of cartilage and bone and pyroptosis of FLS plays a key role in those pathological processes during RA. This study investigated the detailed mechanisms that SMAD2 regulates the pyroptosis of FLS and secretion of inflammatory factors in rheumatoid arthritis. Methods We collected synovial tissues of RA patients and FLS-RA and cultured FLS for detection of expression of SMAD2. ASC, NLRP3, cleaved-caspase-1, and GSDMD-N were detected by Western blot after overexpression of SMAD2. Besides, flow cytometry, electron microscope, ELISA, HE staining, and Safranin O staining were performed to further demonstrate that SMAD2 can affect the pyroptosis of FLS-RA. Results The expression of SMAD2 was down-regulated in synovial tissues of RA patients and FLS-RA. Overexpression of SMAD2 can inhibit the expression of ASC, NLRP3, cleaved-caspase-1, and GSDMD-N. Flow cytometry and electron microscope further demonstrated that SMAD2 attenuated pyroptosis of FLS-RA. In addition, overexpression of SMAD2 also inhibited inflammatory factors such as IL-1β, IL-18, IL-6, and IL-8 secretion and release of LDH. Besides, overexpression of SMAD2 can reverse the decrease of p-SMAD2 and TGF-TGF-β induced by nigericin. In vivo experiments on CIA rats further demonstrated that overexpression of SMAD2 by local intra-articular injection of LV-SMAD2 can effectively alleviate joint redness, swelling, and destruction of cartilage and bones. Conclusion SMAD2 inhibited FLS-RA pyroptosis by down-regulating of NLRP3 inflammasomes (NLRP3, ASC, and caspase-1 complex) and eased the secretion of inflammatory factors via the TGF-β signaling pathway, thereby improving the symptom of RA. We hope that this study may provide a new research idea for RA and a potential target for the treatment of RA.

Collaboration between Light Detection and Ranging (LiDAR) point clouds and deep learning has been proven to be an effective approach for vegetation mapping. Current studies have predominantly focused on 2D vegetation mapping, whereas 3D mapping, which directly classifies point clouds at point level, offers a more comprehensive understanding of the stratified structural information of vegetation. However, there is a lack of research on 3D vegetation species mapping, and the disparities between 2D and 3D mapping in natural scenarios remain unclear. To resolve these issues, we compared the deep learning performance of 2D and 3D vegetation species mapping across three distinct natural scenes: karst wetland, mangrove forest, and hill forest. In addition, the 2D and 3D mapping in natural scenes are adversely affected by the elevated channel count of LiDAR-derived features and the extreme category imbalance in point cloud. To mitigate these challenges, we propose a novel Multi-resolution Feature Selection Network (MrFSNet) to select optimal feature combinations at different scales for better 2D mapping performance. Additionally, we introduce a novel Dynamic Weighted Sampling (DWS) strategy, which is combined with KPConv to address the extreme category imbalance present in 3D mapping. Results indicate that: (1) 3D vegetation species mapping exhibited the highest performance, achieving an mF1 of 89.78% for karst wetland, 92.25% for mangrove forest, and 92.05% for hill forest. (2) 3D mapping outperformed 2D mapping, improving mF1 by 3.43% to 27.08%. (3) MrFSNet adaptively extracted optimal features at various scales and performed well with the limited training data in 2D vegetation mapping, resulting in a 1.66%–18.46% higher mF1 than that of Swin Transformer. (4) DWS effectively resolved the extreme category imbalance problem and produced 1.28%–2.80% higher mF1 than the non-DWS version in 3D vegetation mapping.

Wetland vegetation is the basis for wetland ecosystems to regulate climate change, carbon sequestration and maintain biodiversity. Therefore, high-precision mapping and dynamic monitoring of wetland vegetation are essential for the effective management, restoration and sustainable development of wetland ecosystems. This study addressed to explore classification performance of backscattering coefficients and polarimetric decomposition parameters of multi-frequency SAR images, including single-polarimetric X-band TerraSAR (TS), full-polarimetric C-band Radarsat-2 (RS) and L-band ALOS PALSAR-2 (PS) for marsh vegetation mapping in Honghe National Nature Reserve, Northeast China. We proposed two transfer-learning strategies, and examined the feasibility of transfer-learning vegetation classifications between optical and SAR sensors, different frequencies SAR (X-, C- and L-band) and its derivative images, respectively. This paper further compared transfer-learning classification performance of marsh vegetation from polarimetric decomposition parameter images to backscattering coefficient images under the same SAR sensors. The results indicated that: (1) The three SAR images performed good classification ability in identifying marsh vegetation with the overall accuracies (OA) ranging from 0.74 to 0.88. For the same frequency SAR images, polarimetric decomposition parameters outperformed backscattering coefficients with an OA improvement ranging from 0.24 % to 2.41 %; (2) The longer wavelength SAR images produced better classification results, and L-band PS images realized the highest classification accuracy (OA = 0.871); (3) Full-polarimetric SAR images obtained the better transfer-learning classifications (OA > 0.8), and the transfer-learning classification ability of the same frequency SAR images outperformed the different frequencies; (4) The transfer learning classification results between SAR sensors outperformed that of between optical and SAR images. The results of this study provide a scientific basis for wetland change monitoring conservation and sustainable development.

Coastlines are important basic geographic elements and mapping their spatial and attribute changes can help monitor, model and manage coastal zones. Traditional studies focused on the accuracy of extraction methods and the evolution characteristics of coastlines. Thanks to the advances in remote sensing for earth observations, recent coastline extraction studies can reveal detailed ocean-land interaction changes. In this review, we aim to identify key milestones in coastline extraction using remote sensing by associating the emergence of major research topics with the occurrence of multiple application fields, multiple data sources, and multiple algorithms. Specifically, we define coastlines that can be applied to different application fields, summarize the characteristics of coastline products, and analyze the principles, advantages and disadvantages of methods. On this basis, we discussed the development direction and the challenges involved. This study provides practical insights that can be incorporated into the future development of coastline extraction approaches using remote sensing technologies.

Abstract Tumor microparticles (T-MPs) are considered as a tumor vaccine candidate. Although some studies have analyzed the mechanism of T-MPs as tumor vaccine, we still lack understanding of how T-MPs stimulate a strong anti-tumor immune response. Here, we show that T-MPs induce macrophages to release a key chemotactic factor CCL2, which attracts monocytes to the vaccine injection site and enhances endocytosis of antigen. Monocytes subsequently enter the draining lymph node, and differentiate into monocyte-derived DCs (moDCs), which present tumor antigens to T lymphocytes and deliver a potent anti-tumor immune response. Mechanically, T-MPs activate the cGAS-STING signaling through DNA fragments, and then induce monocytes to upregulate the expression of IRF4, which is a key factor for monocyte differentiation into moDCs. More importantly, monocytes that have endocytosed T-MPs acquire the ability to treat tumors. Collectively, this work might provide novel vaccination strategy for the development of tumor vaccines and facilitate the application of T-MPs for clinic oncotherapy.

Abstract Channel and spatial attention mechanisms have proven to provide an evident performance boost of deep convolution neural networks. Most existing methods focus on one or run them parallel (series), neglecting the collaboration between the two attentions. In order to better establish the feature interaction between the two types of attentions, a plug‐and‐play attention module is proposed, which is termed as ‘CAT’—activating the Collaboration between spatial and channel Attentions based on learned Traits. Specifically, traits are represented as trainable coefficients (i.e. colla‐factors) to adaptively combine contributions of different attention modules to fit different image hierarchies and tasks better. Moreover, the global entropy pooling is proposed apart from global average pooling and global maximum pooling (GMP) operators, which is an effective component in suppressing noise signals by measuring the information disorder of feature maps. A three‐way pooling operation is introduced into attention modules and the adaptive mechanism is applied to fuse their outcomes. Extensive experiments on MS COCO, Pascal‐VOC, Cifar‐100, and ImageNet show that our CAT outperforms the existing state‐of‐the‐art attention mechanisms in object detection, instance segmentation, and image classification. The model and code will be released soon.

Abstract A study on electrical conduction of carbon/epoxy laminates has so far been conducted in an ad hoc nature without a standardised method, involving many extrinsic factors. How these factors affect electrical conduction of carbon/epoxy laminates has not been well established. The objectives of this work are to ascertain the effects of electrical currents, temperatures, and clamping torques on the anisotropic electrical conduction of carbon/epoxy laminates. Two-probe method with solid electrodes was developed with machined carbon/epoxy laminate specimens of various dimensions. The contributions of elevated temperatures and clamping pressures to electrical conduction were investigated. Various contact conditions with or without conductive paint were examined. The relationship of electrical resistance correlating with temperature and clamping pressure was developed to aid an analysis of data trends. From the average test results of 18 groups, aided with qualitative predictions, the milliampere-to-ampere increases of current led to significant reductions in electrical conductivities in both in-plane and through-the-thickness directions. The rises of temperatures resulted in the similar reductions in electrical conductivity due to the increased resistance. The increase in clamping torque increased the electrical conductivity values in both directions. Applying conductive paint to the contact faces did not appear to affect the contact resistance. Thus, the enhanced values of electrical conductivity from the painted specimens were attributed to their lower body temperatures, as the conductive paint at the contact faces soaked up the substantial amount of the electrical energies.

Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands.

Automatic and accurate detection of clouds and cloud shadows is a critical aspect of optical remote sensing image preprocessing. This paper provides a time series maximum and minimum mask method (TSMM) for cloud and cloud shadow detection. Firstly, the Cloud Score+S2_HARMONIZED (CS+S2) is employed as a preliminary mask for clouds and cloud shadows. Secondly, we calculate the ratio of the maximum and sub-maximum values of the blue band in the time series, as well as the ratio of the minimum and sub-minimum values of the near-infrared band in the time series, to eliminate noise from the time series data. Finally, the maximum value of the clear blue band and the minimum value of the near-infrared band after noise removal are employed for cloud and cloud shadow detection, respectively. A national and a global dataset were used to validate the TSMM, and it was quantitatively compared against five other advanced methods or products. When clouds and cloud shadows are detected simultaneously, in the S2ccs dataset, the overall accuracy (OA) reaches 0.93 and the F1 score reaches 0.85. Compared with the most advanced CS+S2, there are increases of 3% and 9%, respectively. In the CloudSEN12 dataset, compared with CS+S2, the producer’s accuracy (PA) and F1 score show increases of 10% and 4%, respectively. Additionally, when applied to Landsat-8 images, TSMM outperforms Fmask, demonstrating its strong generalization capability.

HIV-1 reservoir cells that circulate in peripheral blood during suppressive antiretroviral therapy (ART) have been well characterized, but little is known about the dissemination of HIV-1-infected cells across multiple anatomical tissues, especially the CNS. Here, we performed single-genome, near full-length HIV-1 next-generation sequencing to evaluate the proviral landscape in distinct anatomical compartments, including multiple CNS tissues, from 3 ART-treated participants at autopsy. While lymph nodes and, to a lesser extent, gastrointestinal and genitourinary tissues represented tissue hotspots for the persistence of intact proviruses, we also observed intact proviruses in CNS tissue sections, particularly in the basal ganglia. Multi-compartment dissemination of clonal intact and defective proviral sequences occurred across multiple anatomical tissues, including the CNS, and evidence for the clonal proliferation of HIV-1-infected cells was found in the basal ganglia, in the frontal lobe, in the thalamus and in periventricular white matter. Deep analysis of HIV-1 reservoirs in distinct tissues will be informative for advancing HIV-1 cure strategies.