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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 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.

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.

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.

Abstract Background Genitourinary and/or brain malformation syndrome (GUBS) is a recently discovered syndrome involving abnormalities of the neurological or urogenital system. PPP1R12A may be the pathological gene causing this syndrome. Currently, to our knowledge, there is only one study related to GUBS in the world. Here, we report a clinical case of a Chinese newborn with congenital micropenis caused by a non‐coding sequence pathogenic variant of PPP1R12A, providing additional evidence on genetic causes of genital malformation. Methods The genetic cause of the patient's malformation was detected using trio‐whole exome sequencing and Sanger sequencing, and reverse transcription‐PCR analysis was performed by constructing the minigene mutant plasmid in vitro. Results Genetic testing revealed a novel heterozygous variant, c.2666+3A>G, of the PPP1R12A gene of the patient. The parents at this site were wild‐type, indicating that this might be a de novo variant. The minigene experiment showed that the c.2666+3A>G plasmid led to the deletion of 17 bp in exon 20, and a new mRNA product c.2650_2666del (p.Thr884IleTer2) with skipping of exon 20 was produced. This may lead to PPP1R12A haploinsufficiency and cause biological harm. Conclusions To our knowledge, this is the first clinical study on a rare variant of PPP1R12A in the Chinese population. The c.2666+3A>G may lead to external genitalia malformation, such as congenital micropenis in male neonates. The results of this study further verified the correlation between GUBS and PPP1R12A haploinsufficiency and revealed the important role of a non‐coding sequence variant in the pathogenesis of the disease.

In paleolimnology, ecological trajectories of biological communities are typically reconstructed using a sediment core from the deepest part of a lake. However, the representativeness of chironomid assemblages from the profundal zone and its influence on ecological response to environmental change in large, deep lakes remain poorly understood. To address this knowledge gap, we conducted a study on subfossil chironomids in Fuxian Lake, including thirty-five surface sediment samples and two sediment cores from different water depths. Our findings reveal a distinct bathymetric distribution pattern of subfossil chironomid assemblages attributed to limited homogenization in the large, deep lake. The profundal assemblages exhibited the lowest richness representativeness, while nearshore assemblages showed the least similarity to the entire-lake assemblage. Consequently, the response pattern of chironomid assemblage to the environmental change varied with depth. Two notable shifts in the mid-depth core, occurring in 1979 CE and 2005 CE, were primarily attributed to eutrophication processes. However, the chironomid assemblage from the deepest zone exhibited only one significant change in 2005 CE, likely indicating exacerbated hypoxia in the profundal zone. Our combined analysis of surface samples and sediment cores underscores that the profundal zone, where bottom water is characterized by low temperature and anoxia, is unsuitable as a coring site in large, deep lakes due to its extremely simplified species composition. Instead, mid-depth zones should be prioritized as candidate coring sites in large, deep lakes, given their relatively high richness representativeness, head capsule concentration, and stable sedimentary conditions. Specifically, we recommended retrieving sedimentary cores around the water depth of 50 m when conducting subfossil chironomid analysis in Fuxian Lake.

Measurement(s) speed Technology Type(s) Interpolation Imputation Technique Sample Characteristic - Location Xuancheng City Prefecture