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Synthetic-aperture radar (SAR) image target detection is widely used in military, civilian and other fields. However, existing detection methods have low accuracy due to the limitations presented by the strong scattering of SAR image targets, unclear edge contour information, multiple scales, strong sparseness, background interference, and other characteristics. In response, for SAR target detection tasks, this paper combines the global contextual information perception of transformers and the local feature representation capabilities of convolutional neural networks (CNNs) to innovatively propose a visual transformer framework based on contextual joint-representation learning, referred to as CRTransSar. First, this paper introduces the latest Swin Transformer as the basic architecture. Next, it introduces the CNN’s local information capture and presents the design of a backbone, called CRbackbone, based on contextual joint representation learning, to extract richer contextual feature information while strengthening SAR target feature attributes. Furthermore, the design of a new cross-resolution attention-enhancement neck, called CAENeck, is presented to enhance the characterizability of multiscale SAR targets. The mAP of our method on the SSDD dataset attains 97.0% accuracy, reaching state-of-the-art levels. In addition, based on the HISEA-1 commercial SAR satellite, which has been launched into orbit and in whose development our research group participated, we released a larger-scale SAR multiclass target detection dataset, called SMCDD, which verifies the effectiveness of our method.

On 20 July 2021, an extraordinary rainfall event occurred in Henan Province, China, resulting in heavy waterlogging, flooding, and hundreds of fatalities and causing considerable property damage. Because the damaged region was a major grain-producing region of China, assessing crop food production losses following this event is very important. Because the crop rotation production system is utilized in the region to accommodate two crops per year, it is very valuable to accurately identify the types of crops affected by the event and to assess the crop production losses separately; however, the results obtained using these methods are still inadequate. In this study, we used China’s first commercial synthetic aperture radar (SAR) data source, named Hisea-1, together with other open-source and widely used remote sensing data (Sentinel-1 and Sentinel 2), to monitor this catastrophic flood. Both the modified normalized difference water index (MNDWI) and Sentinel-1 dual-polarized water index (SDWI) were calculated, and an unsupervised classification (k-means) method was adopted for rapid water body extraction. Based on time-series datasets synthesized from multiple sources, we obtained four flooding characteristics, including the flooded area, flood duration, and start and end times of flooding. Then, according to these characteristics, we conducted a more precise analysis of the damages to flooded farmlands. We used the Google Earth Engine (GEE) platform to obtain normalized difference vegetation index (NDVI) time-series data for the disaster year and normal years and overlaid the flooded areas to extract the effects of flooding on crop species. According to the statistics from previous years, we calculated the areas and types of damaged crops and the yield reduction amounts. Our results showed that (1) the study area endured two floods in July and September of 2021; (2) the maximum areas affected by these two flooding events were 380.2 km2 and 215.6 km2, respectively; (3) the floods significantly affected winter wheat and summer grain (maize or soybean), affecting areas of 106.4 km2 and 263.3 km2, respectively; and (4) the crop production reductions in the affected area were 18,708 t for winter wheat and 160,000 t for maize or soybean. These findings indicate that the temporal-dimension information, as opposed to the traditional use of the affected area and the yield per unit area when estimating food losses, is very important for accurately estimating damaged crop types and yield reductions. Time-series remote sensing data, especially SAR remote sensing data, which have the advantage of penetrating clouds and rain, play an important role in remotely sensed disaster monitoring. Hisea-1 data, with a high spatial resolution and first flood-monitoring capabilities, show their value in this study and have the potential for increased usage in further studies, such as urban flooding research. As such, the approach proposed herein is worth expanding to other applications, such as studies of water resource management and lake/wetland hydrological changes.

Forest canopy height is a basic metric characterizing forest growth and carbon sink capacity. Based on full-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) data, this study used Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technology to estimate forest canopy height. In total the four methods of differential DEM (digital elevation model) algorithm, coherent amplitude algorithm, coherent phase-amplitude algorithm and three-stage random volume over ground algorithm (RVoG_3) were proposed to obtain canopy height and their accuracy was compared in consideration of the impacts of coherence coefficient and range slope levels. The influence of the statistical window size on the coherence coefficient was analyzed to improve the estimation accuracy. On the basis of traditional algorithms, time decoherence was performed on ALOS/PALSAR data by introducing the change rate of Landsat NDVI (Normalized Difference Vegetation Index). The slope in range direction was calculated based on SRTM (Shuttle Radar Topography Mission) DEM data and then introduced into the s-RVoG (sloped-Random Volume over Ground) model to optimize the canopy height estimation model and improve the accuracy. The results indicated that the differential DEM algorithm underestimated the canopy height significantly, while the coherent amplitude algorithm overestimated the canopy height. After removing the systematic coherence, the overestimation of the RVoG_3 model was restrained, and the absolute error decreased from 23.68 m to 4.86 m. With further time decoherence, the determination coefficient increased to 0.2439. With the introduction of range slope, the s-RVoG model shows improvement compared to the RVoG model. Our results will provide a reference for the appropriate algorithm selection and optimization for forest canopy height estimation using full-polarized L-band synthetic aperture radar (SAR) data for forest ecosystem monitoring and management.

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

Aiming at the problem of low change detection accuracy caused by the difference in the brightness of optical remote sensing images, an optical This method borrows the idea of convolution learning features in deep learning, performs convolution operations on two images, and obtains the convolutional map fusion. features in deep learning, performs convolution operations on two images, and obtains the convolutional difference map and logarithmic ratio map; and then performs multi-layer convolution on the two difference maps respectively. The feature maps after each layer of convolution are combined to obtain a The feature maps after each layer of convolution are combined to obtain a deep convolution difference map; two deep convolution difference maps are fused to obtain a deep convolution difference fusion map; finally the maximum This article uses LANDSAT satellite images to verify and compares the maximum variance automatic threshold between classes is used for segmentation to obtain change detection result graph. The experimental results show that the difference map fusion enhances the detailed information of the In terms of the detection effect, the anti-noise performance is enhanced. The detection accuracy is improved.

Iron‐based metal‐organic framework (MOF) nanozymes have garnered considerable attention owing to a large specific surface area, adjustable porosity, large Fe‐O clusters, and unsaturated Fe sites. However, the sluggish charge‐transfer rate and restricted active sites of the nanozymes lead to poor enzyme‐like activity and further impede their biomimetic catalysis. Herein, a three‐channel electron‐engineered Fe‐88A@CeO2/carbon dots (Fe‐88A@CeO2/CDs) nanozyme is proposed for efficient biomimetic catalysis. Fe‐88A@CeO2/CDs nanozyme is prepared by incorporation of CeO2 and CDs into the porosity of Fe‐88A. Specifically, the original Fe (II)/Fe (III) and the introduced Ce (III)/Ce (IV) redox couples of the nanozyme constitute a dual electron transfer channel. Furthermore, the presence of CDs produces another electron transfer channel. The three‐channel electron engineering strategy for nanozymes can accelerate the electron transfer process accompanied with more active sites, thereby greatly enhancing the oxidase‐like activity of Fe‐88A@CeO2/CDs for biomimetic catalysis. The nanozyme can efficiently convert oxygen to ·O2− ${\\mathrm{O}}_2^ - $ , oxidizing colorless 3,3′,5,5′‐tetramethylbenzidine (TMB) to blue ox‐TMB, and meanwhile the ox‐TMB effectively quenches the fluorescence of CDs. As proof of concept, the nanozyme is utilized to construct a colorimetric‐fluorescence bimodal immunosensor for monitoring Staphylococcal enterotoxin B with excellent performance. This work provides promising insight into designing excellent nanozymes for effective biomimetic catalysis in various fields.

Background: It is crucial to accurately predict the disease progression of systemic arterial hypertension in order to determine the most effective therapeutic strategy. To achieve this, we have employed a multimodal data-integration approach to predict the longitudinal progression of new-onset systemic arterial hypertension patients with suspected obstructive sleep apnea (OSA) at the individual level. Methods: We developed and validated a predictive nomogram model that utilizes multimodal data, consisting of clinical features, laboratory tests, and sleep monitoring data. We assessed the probabilities of major adverse cardiac and cerebrovascular events (MACCEs) as scores for participants in longitudinal cohorts who have systemic arterial hypertension and suspected OSA. In this cohort study, MACCEs were considered as a composite of cardiac mortality, acute coronary syndrome and nonfatal stroke. The least absolute shrinkage and selection operator (LASSO) regression and multiple Cox regression analyses were performed to identify independent risk factors for MACCEs among these patients. Results: 448 patients were randomly assigned to the training cohort while 189 were assigned to the verification cohort. Four clinical variables were enrolled in the constructed nomogram: age, diabetes mellitus, triglyceride, and apnea-hypopnea index (AHI). This model accurately predicted 2-year and 3-year MACCEs, achieving an impressive area under the receiver operating characteristic (ROC) curve of 0.885 and 0.784 in the training cohort, respectively. In the verification cohort, the performance of the nomogram model had good discriminatory power, with an area under the ROC curve of 0.847 and 0.729 for 2-year and 3-year MACCEs, respectively. The correlation between predicted and actual observed MACCEs was high, provided by a calibration plot, for training and verification cohorts. Conclusions: Our study yielded risk stratification for systemic arterial hypertension patients with suspected OSA, which can be quantified through the integration of multimodal data, thus highlighting OSA as a spectrum of disease. This prediction nomogram could be instrumental in defining the disease state and long-term clinical outcomes.

Purpose: To investigate the therapeutic effects of protease-activated receptor 2 (PAR-2) agonist SLIGRL-NH2 on loperamide-induced Sprague-Dawley (SD) rat constipation animal models. Materials and methods: Loperamide was injected subcutaneously to induce constipation twice a day for 3 days. SD rats (n = 30) were randomly divided into five groups: non-constipation group (control, n = 6), constipation group (constipation, n = 6), constipation + SLIGRL-NH2 low-dosage group (SLIGRL-NH2 low, n=6), constipation + SLIGRL-NH2 high-dosage group (SLIGRL-NH2 high, n = 6), and constipation + prucalopride (positive control, n = 6). The SLIGRL-NH2 low group and SLIGRL-NH2 high group were administered with 2.5 μmol/kg and 5 μmol/kg SLIGRL-NH2, respectively, and the prucalopride group received 2 mg/kg prucalopride. The control and constipation group received 1× PBS under the same pattern. SLIGRL-NH2 and prucalopride were orally administrated once daily for 7 days. On the final day of oral administration, food intake, water intake, the number of stool pellets, weight, and fecal water content was calculated; moreover, the colons of rats in different groups were collected and histological features were examined by hematoxylin and eosin staining; furthermore, the expression of anoctamin-1 was determined by Immunohistochemical methods, and the expressions of c-kit and PAR-2 were examined using real-time quantitative polymerase chain reaction and Western blot methods; finally, the expressions of neurotransmitter vasoactive intestinal peptide (VIP) and substance P (SP) were examined using enzyme-linked immunosorbent assay methods. Results: The feeding and excretion behaviors, intestinal transit ratio, and the histological feature of the colon in the constipated rats were all improved by SLIGRL-NH2 treatment; moreover, SLIGRL-NH2 treatment induced significant increase in the expression of PAR-2 and also increased number of interstitial Cajal cells. Furthermore, SLIGRL-NH2 also decreased the contents of the inhibitory neurotransmitter VIP and increased the expression of the excitatory neurotransmitter SP. High dose of SLIGRL-NH2 has shown similar anti-constipation effects as prucalopride. Conclusion: These results suggested that SLIGRL-NH2 can enhance gastrointestinal transit and alleviate in rats with loperamide-induced constipation.

As a well-known urban landscape concept to describe urban space quality, urban street vitality is a subjective human perception of the urban environment but difficult to evaluate directly from the physical space. The study utilized a modern machine learning computer vision algorithm in the urban build environment to simulate the process, which starts with the visual perception of the urban street landscape and ends with the human reaction to street vitality. By analyzing the optimized trained model, we tried to identify urban street vitality's visual features and evaluate their importance. A region around the Mochou Lake in Nanjing, China, was set as our study area. Seven investigators surveyed the area, recorded their evaluation score on each site's vitality level with a corresponding picture taken on site. A total of 370 pictures and recorded score pairs from 231 valid survey sites were used to train a convolutional neural network. After optimization, a deep neural network model with 43 layers, including 11 convolutional ones, was created. Heat maps were then used to identify the features which lead to high vitality score outputs. The spatial distributions of different types of feature entities were also analyzed to help identify the spatial effects. The study found that visual features, including human, construction site, shop front, and roadside/walking pavement, are vital ones that correspond to the vitality of the urban street. The consistency of these critical features with traditional urban vitality features indicates the model had learned useful knowledge from the training process. Applying the trained model in urban planning practices can help to improve the city environment for better attraction of residents' activities and communications.

The aim of this study is to characterize gut microbiome alterations in newly diagnosed subacute thyroiditis (SAT) patients, and identify potential microbial signatures associated with SAT and treatment response. Fecal samples collected from 20 newly diagnosed SAT patients and 20 healthy controls were analyzed using 16S ribosomal RNA gene sequencing. Bioinformatics analysis was performed to assess alpha and beta diversity, taxonomic composition, and differential abundance of gut microbiota between the groups. Correlations between gut microbiome and clinical parameters were also investigated. Newly diagnosed SAT patients exhibited significant alterations in gut microbiota composition. There was increased abundance of , , , , and , while the abundance of , , , , and were significantly decreased. Prednisolone treatment partially normalized the gut microbiota, with , , , and emerging as key biomarkers in post-treatment SAT. Significant correlations were found between specific gut microbiome and clinical markers. SAT is associated with distinct gut microbiome alterations, partially reversible with treatment, which suggest a potential role for the gut microbiome in SAT pathogenesis and treatment response.