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Due to its excellent biocompatibility and biodegradability, Mg and its alloys are considered to be promising materials for manufacturing of vascular sent. However, the manufacture of high-precision and high-performance Mg alloys minitubes is still a worldwide problem with a long manufacturing processing caused by the poor workability of Mg alloys. To solve this problem, the cyclic extrusion compression (CEC) was used to pretreat the billet by improving the workability of Mg alloys, finally shortening the manufacturing process. After CEC treatment, the size of grains and second phase particles of Mg alloys were dramatically refined to 3.2 µm and 0.3 µm, respectively. Only after three passes of cold drawing, the wall thickness of minitube was reduced from 0.200 mm to 0.135 mm and a length was more than 1000 mm. The error of wall thickness was measured to be less than 0.01 mm, implying a high dimensional accuracy. The yield strength (YS), ultimate tensile strength (UTS) and elongation of finished minitube were 220±10 MPa, 290±10 MPa and 22.0 ± 0.5%, respectively. In addition, annealing can improve mechanical property and corrosion resistance of minitubes by improving the homogeneity of the microstructure and enhancing the density of basal texture.

Abstract Remote sensing image object detection is widely used in civil and military fields. The important task is to detect objects such as ships, planes, airports, harbours and so on, and then it can obtain object category and position information. It is of great significance to use remote sensing images to observe the densely arranged and directional targets such as cars and ships parked in parking lots and harbours. The object detection task mainly includes object localization and classification. Remote sensing images contain large number of small objects and dense scenes due to the long shooting distance and wide coverage. Small objects occupy few pixels in the image, and they are easily miss‐detected. In dense scenes, the overlapping part of each object is large, so it is easy to detect objects repeatedly. The traditional small object detection methods deliver low accuracy and take long time. Therefore, object detection is very challenging. We put forward a novel deep learning‐based single shot multibox detector (SSD) model for object detection. First, we propose an improved inception network to optimize SSD to strengthen the small object feature extraction ability (FEA) in the shallow network. Second, the feature pyramid network is modified to enhance the fusion effect. Third, the deep feature fusion module is designed to improve the FEA of the deep network. Finally, the extracted image features are matched with candidate boxes with different aspect ratios to perform object detection and location with different scales. Experiments on DOTA show that the proposed algorithm can adapt to the remote sensing object detection in different backgrounds, and effectively improve the detection effect of remote sensing objects in complex scenes.

Magnesium (Mg) is abundant in humans. Studies have shown that Mg2+ significantly affects physiological processes such as osteogenesis, osteoblast adhesion and motility, immunomodulation, and angiogenesis. Mg-based alloy porous scaffolds have attracted increasing attention because of their degradability and mechanical properties. Hence, wire-cut EDM was used to fabricate porous scaffolds. In addition, fluoride treatment afforded an MgF2 coating, and racemic polylactic acid PDLLA was used as the carrier of rhBMP-2, which was evenly coated on the surface of the passivated porous alloy scaffold to slowly release growth factors and slow down the degradation rate. The rat femoral condyle defect model experiment was performed to study the in vivo bone regeneration capacity of porous scaffolds and compare the differences in the healing effect with or without rhBMP-2. The enhanced corrosion resistance of the porous scaffold was confirmed through in vitro immersion experiments. Micro-CT implied that porous scaffold with rhBMP-2 induced new bone formation and the new bone formation along the pores, as well as the histological examination in vivo. In summary, the porous scaffold promotes bone formation and has great potential for clinical translation.

Abstract The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

Here, we successfully synthesized Sr-doped perovskite-type oxides of La1−xSrxCo1−λO3−δ, “LSX” (x = 0, 0.1, 0.3, 0.5, 0.7), using the glycine-assisted solution combustion method. The effect of strontium doping on the catalyst structure, NO to NO2 conversion, NOx adsorption and storage, and NOx reduction performance were investigated. The physicochemical properties of the catalysts were studied by XRD, SEM-EDS, N2 adsorption–desorption, FTIR, H2-TPR, O2-TPD, and XPS techniques. The NSR performance of LaCoO3 perovskite was improved after Sr doping. Specifically, the perovskite with 50% of Sr doping (LS5 sample) exhibited excellent NOx storage capacity within a wide temperature range (200–400 °C), and excellent stability after hydrothermal and sulfur poisoning. It also displayed the highest NOx adsorption–storage capacity (NAC: 1889 μmol/g; NSC: 1048 μmol/g) at 300 °C. This superior performance of the LS5 catalyst can be attributed to its superior reducibility, better NO oxidation capacity, increased surface Co2+ concentration, and, in particular, its generation of more oxygen vacancies. FTIR results further revealed that the LSX catalysts primarily store NOx through the “nitrate route”. During the lean–rich cycle tests, we observed an average NOx conversion rate of over 50% in the temperature range of 200–300 °C, with a maximum conversion rate of 61% achieved at 250 °C.

The cardiovascular diseases (CVD) continue to be the major threat to global public health over the years, while one of the effective methods to treat CVD is stent intervention. Biomedical magnesium (Mg) alloys have great potential applications in cardiovascular stents benefit from their excellent biodegradability and absorbability. However, excessive degradation rate and the delayed surface endothelialization still limit their further application. In this study, we modified a Mg-Zn-Y-Nd alloy (ZE21B) by preparing MgF2 as the corrosion resistance layer, the dopamine polymer film (PDA) as the bonding layer, and hyaluronic acid (HA) loaded astaxanthin (ASTA) as an important layer to directing the cardiovascular cells fate. The electrochemical test results showed that the MgF2/PDA/HA-ASTA coating improved the corrosion resistance of ZE21B. The cytocompatibility experiments also demonstrated that this novel composite coating also selectively promoted endothelial cells proliferation, inhibited hyperproliferation of smooth muscle cells and adhesion of macrophages. Compared with the HA-loaded rapamycin (RAPA) coating, our MgF2/PDA/HA-ASTA coating showed better blood compatibility and cytocompatibility, indicating stronger multi-functions for the ZE21B alloy on cardiovascular application.

Abstract X-ray computed tomography (XCT) and X-ray fluorescence (XRF) imaging are two non-invasive imaging techniques to study cellular structures and chemical element distributions, respectively. However, correlative X-ray computed tomography and fluorescence imaging for the same cell have yet to be routinely realized due to challenges in sample preparation and X-ray radiation damage. Here we report an integrated experimental and computational workflow for achieving correlative multi-modality X-ray imaging of a single cell. The method consists of the preparation of radiation-resistant single-cell samples using live-cell imaging-assisted chemical fixation and freeze-drying procedures, targeting and labeling cells for correlative XCT and XRF measurement, and computational reconstruction of the correlative and multi-modality images. With XCT, cellular structures including the overall structure and intracellular organelles are visualized, while XRF imaging reveals the distribution of multiple chemical elements within the same cell. Our correlative method demonstrates the feasibility and broad applicability of using X-rays to understand cellular structures and the roles of chemical elements and related proteins in signaling and other biological processes.

The degradation of Mg alloys relates to the service performance of Mg alloy biodegradable implants. In order to investigate the degradation behavior of Mg alloys as vascular stent materials in the near service environment, the hot-extruded fine-grained Mg-Zn-Y-Nd alloy microtubes, which are employed to manufacture vascular stents, were tested under radial compressive stress in the dynamic Hanks’ Balanced Salt Solution (HBSS). The results revealed that the high flow rate accelerates the degradation of Mg alloy microtubes and its degradation is sensitive to radial compressive stress. These results contribute to understanding the service performance of Mg alloys as vascular stent materials.

Abstract The lattice reduction-aided algorithm has received broad attention from researchers since it operates as a maximum likelihood receiver with better system performance for multiple-input multiple-output orthogonal frequency division multiplexing systems and contains a full diversity. A novel iterative detection algorithm canceling parallel iterations that employ the lattice reduction-aided approach is proposed. Soft information is exchanged through the detector itself. Its iteration occurs inside the detector, which reduces much of the exchange cost between the multiple-input multiple-output orthogonal frequency division multiplexing detector and the turbo decoder. Since the parallel interference cancellation algorithm is constrained by the accuracy of the initial value of the detection, it is easy to form error propagation after several iterations. Due to the lattice reduction-aided algorithm, its performance is approximated with the maximum likelihood algorithm. Therefore, the lattice reduction-aided algorithm is introduced into the parallel interference cancellation algorithm to make its detection algorithm more accurate and overcome the effect of error propagation in the manuscript. Simulation results indicate that the proposed algorithm leads to an improvement of 0.8–2 dB when the bit error rate is set to 10–4 when compared to other algorithms.

When large-sized oil pipelines are subjected to skin effect heating under the constraint of limited current, the power supply converter must provide a higher current frequency. However, an inappropriate frequency can lead to increased energy losses and consequently reduce heating efficiency. To achieve higher heating efficiency of large-sized pipelines, this paper proposes a temperature control strategy based on optimal frequency forecast. First, a skin effect heating model is established to illustrate the necessity of frequency forecasting. Then, using the small sample data from the oil field, an optimal frequency forecasting method based on Gradient Boosting Decision Tree (GBDT) is proposed. At the forecasted frequency, a dual closed-loop control strategy for current and temperature, based on a designed three-phase converter, is employed to achieve temperature control under limited current conditions. The feasibility and effectiveness of this approach are demonstrated through a case study involving a 159 mm oil pipeline with a heating current of less than 80A. The experiment shows that with the forecasted frequency, the pipeline temperature reached 40 °C within 14 min, and the energy consumption per unit length of the pipeline is 394 W/m, complying with the standard Q/SY 06022-2016.

Abstract Natural products, while valuable for drug discovery, encounter limitations like uncertainty in targets and toxicity. As an important active ingredient in traditional Chinese medicine, celastrol exhibits a wide range of biological activities, yet its mechanism remains unclear. In this study, they introduced an innovative “Degradation‐based protein profiling (DBPP)” strategy, which combined PROteolysis TArgeting Chimeras (PROTAC) technology with quantitative proteomics and Immunoprecipitation‐Mass Spectrometry (IP‐MS) techniques, to identify multiple targets of natural products using a toolbox of degraders. Taking celastrol as an example, they successfully identified its known targets, including inhibitor of nuclear factor kappa B kinase subunit beta (IKKβ), phosphatidylinositol‐4,5‐bisphosphate 3‐kinase catalytic subunit alpha (PI3Kα), and cellular inhibitor of PP2A (CIP2A), as well as potential new targets such as checkpoint kinase 1 (CHK1), O‐GlcNAcase (OGA), and DNA excision repair protein ERCC‐6‐like (ERCC6L). Furthermore, the first glycosidase degrader is developed in this work. Finally, by employing a mixed PROTAC toolbox in quantitative proteomics, they also achieved multi‐target identification of celastrol, significantly reducing costs while improving efficiency. Taken together, they believe that the DBPP strategy can complement existing target identification strategies, thereby facilitating the rapid advancement of the pharmaceutical field.

Calcined clay and limestone powder composites offer a viable solution for reducing carbon emissions in building materials by allowing significant cement replacement. However, there is a need for further investigation into their impact on durability, especially for low-grade clays. In this study, different ratios of metakaolin and quartz powder are utilized to simulate various grades of calcined clay, while limestone powder is incorporated as a partial cement substitute. Initially, the influence of metakaolin content on rapid chloride migration was analyzed. Subsequently, various analytical techniques such as free water content, Mercury Intrusion Porosimetry (MIP), X-ray diffraction (XRD)/Rietveld method, and electrochemical impedance spectroscopy (EIS) test were employed to investigate the effects of metakaolin contents on the pore structure, phase assemblage, and electrochemical parameters of the blended cementitious system. The relationship between chloride migration coefficient and free water content, critical pore size, and impedance parameters was established to determine the key indicators for chloride migration. The study revealed that systems with partial metakaolin content demonstrate improved chloride resistance in the blended system, which could be predicted using Rct1 from the EIS test. Furthermore, the inclusion of metakaolin and limestone powder facilitated the formation of Monocarboaluminate (Mc) and Hemicarboaluminate (Hc), leading to the refinement of the pore structure and inhibition of chloride transport within the blended material. This study indicates the potential of low-grade calcined clay to enhance the overall durability of the blended cementitious system while contributing to carbon emissions reduction.

With the increasing demand for high temporal and spatial resolution multispectral image sequences, many studies have been carried out on fusion on Landsat-8 and Sentinel-2 images to obtain image sequences with a revisit cycle of 2 and 3 days and a spatial resolution of 10 m. However, current fusion methods suffer from complex computation and loss of spectral and spatial information. To address these issues, a Landsat-8 and Sentinel-2 image fusion based on multiscale smoothing-sharpening filter (MSSF) method is proposed. MSSF combines well the initial spatial prediction obtained from the Landsat-8 image at the target date and the detailed image extracted from the Sentinel-2 image at the reference date. Thin plate spline interpolation with morphological opening-closing algorithm is implemented on the Landsat-8 image at the target date, and the Laplacian of Gaussian enhancement algorithm is applied to the Sentinel-2 image at the reference date in the preprocessing stage. Smoothing-sharpening filter (SSIF) is employed to separate the high and low frequency components of the two preprocessed images. The multiscale SSIF is then utilized to migrate the details from the preprocessed Sentinel-2 image to the preprocessed Landsat-8 image. The performance of MSSF and five compared methods was evaluated qualitatively and quantitatively. Experiments on three remote sensing data sets gathered from different experimental sites confirm that the proposed MSSF method could efficiently generate Sentinel-2-like images with high spatial and spectral resolution.

In recent years, deep-learning-based classification methods have been widely used for hyperspectral images (HSIs). However, in the existing transformer-based HSI classification methods, how to effectively and comprehensively utilize the rich information still has room for improvement, for example, when utilizing multiple-image information, the comprehensive interaction between information has insufficient consideration. To address the above issues, cross-attention interaction, class token and patch token information, and multiscale spatial information are addressed in a unified framework, and a cross-attention-based multi-information fusion transformer (CAMFT) for HSI classification was proposed, which includes the multiscale patch embedding module, the residual connection-based DeepViT (RCD) module, and the double-branch cross-attention (DBCA) module. First, the multiscale patch embedding module is formed for multi-information preprocessing, accompanied by the built of different scale processing branches and the addition of learnable class tokens. Second, the RCD module is designed to utilize rich information from different layers; this module includes reattention and residual connection. Third, a DBCA module is constructed to obtain more representative multi-information fusion features; this module not only integrates multiscale patch information but also effectively utilizes complementary information between class tokens and patch tokens in the interaction of two branches. Moreover, numerous experiments demonstrate that, compared with other state-of-the-art classification methods, the proposed CAMFT method achieves the optimal classification performance, especially with a small training sample size, but it still has excellent performance.

In recent years, convolutional neural network (CNN) based methods have been widely used in remote sensing image compression tasks. However, CNN is commonly used to extract local information and does not fully utilize global contextual information. The transformer model can effectively extract the latent contextual information in remote sensing images due to its multihead self-attention mechanisms. Due to the multiscale local features and global low-frequency information of remote sensing images, existing deep-learning-based compression methods have not effectively combined CNN and transformer. In order to overcome the limitations of the above methods, a multihead global attention and spatial spectral information fusion network (MGSSNet) is proposed for remote sensing image compression. First, a spatial spectral information fusion attention module (SSIF-AM) is constructed to obtain multiscale local information. Second, a multihead global attention module (MHG-AM) is proposed to capture rich global context information. Third, a local global collaboration module is developed to explore the correlation between the multiscale local features obtained by SSIF-AM and the global visual features obtained by MHG-AM, and to efficiently model the intrinsic relationships between them to achieve effective feature fusion. Experimental results show that compared with advanced compression models, the proposed MGSSNet method achieves better compression performance. In addition, using reconstructed images obtained by different compression methods for classification tasks has proven that the proposed method can help achieve better classification performance, indicating that the proposed compression method can more fully preserve important information in the image.

Histone‐lysine N‐methyltransferase SETD2 (SETD2), the sole histone methyltransferase that catalyzes trimethylation of lysine 36 on histone H3 (H3K36me3), is often mutated in clear cell renal cell carcinoma (ccRCC). SETD2 mutation and/or loss of H3K36me3 is linked to metastasis and poor outcome in ccRCC patients. Epithelial‐to‐mesenchymal transition (EMT) is a major pathway that drives invasion and metastasis in various cancer types. Here, using novel kidney epithelial cell lines isogenic for SETD2, we discovered that SETD2 inactivation drives EMT and promotes migration, invasion, and stemness in a transforming growth factor‐beta‐independent manner. This newly identified EMT program is triggered in part through secreted factors, including cytokines and growth factors, and through transcriptional reprogramming. RNA‐seq and assay for transposase‐accessible chromatin sequencing uncovered key transcription factors upregulated upon SETD2 loss, including SOX2, POU2F2 (OCT2), and PRRX1, that could individually drive EMT and stemness phenotypes in SETD2 wild‐type (WT) cells. Public expression data from SETD2 WT/mutant ccRCC support the EMT transcriptional signatures derived from cell line models. In summary, our studies reveal that SETD2 is a key regulator of EMT phenotypes through cell‐intrinsic and cell‐extrinsic mechanisms that help explain the association between SETD2 loss and ccRCC metastasis.

Abstract In the past, convolutional neural network (CNN) has become one of the most popular deep learning frameworks, and has been widely used in Hyperspectral image classification tasks. Convolution (Conv) in CNN uses filter weights to extract features in local receiving domain, and the weight parameters are shared globally, which more focus on the high‐frequency information of the image. Different from Conv, Transformer can obtain the long‐term dependence between long‐distance features through modelling, and adaptively focus on different regions. In addition, Transformer is considered as a low‐pass filter, which more focuses on the low‐frequency information of the image. Considering the complementary characteristics of Conv and Transformer, the two modes can be integrated for full feature extraction. In addition, the most important image features correspond to the discrimination region, while the secondary image features represent important but easily ignored regions, which are also conducive to the classification of HSIs. In this study, a complementary integrated Transformer network (CITNet) for hyperspectral image classification is proposed. Firstly, three‐dimensional convolution (Conv3D) and two‐dimensional convolution (Conv2D) are utilised to extract the shallow semantic information of the image. In order to enhance the secondary features, a channel Gaussian modulation attention module is proposed, which is embedded between Conv3D and Conv2D. This module can not only enhance secondary features, but suppress the most important and least important features. Then, considering the different and complementary characteristics of Conv and Transformer, a complementary integrated Transformer module is designed. Finally, through a large number of experiments, this study evaluates the classification performance of CITNet and several state‐of‐the‐art networks on five common datasets. The experimental results show that compared with these classification networks, CITNet can provide better classification performance.

Abstract With the arrival of new data acquisition platforms derived from the Internet of Things (IoT), this paper goes beyond the understanding of traditional remote sensing technologies. Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation. However, due to the complex architecture of IoT and the lack of a unified security protection mechanism, devices in remote sensing are vulnerable to privacy leaks when sharing data. It is necessary to design a security scheme suitable for computation‐limited devices in IoT, since traditional encryption methods are based on computational complexity. Visual Cryptography (VC) is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images. The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT. In this study, the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved. By diffusing the error between the encryption block and the original block to adjacent blocks, the degradation of quality in recovery images is mitigated. By fine‐tuning the pre‐trained model from large‐scale datasets, we improve the recognition performance of small encryption datasets for remote sensing images. The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

ABSTRACTAchieving tourism carbon neutrality is essential for sustainable tourism development. This paper uses the coupled coordination distance model, modified gravity model and social network analysis to attempt to construct a nationwide tourism carbon neutral spatial network to clarify the role of each province in the process of achieving China’s tourism carbon neutrality. The results show that: (1) only seven provinces will achieve tourism carbon neutrality in the target year of carbon neutrality in China (2060). (2) From 2001 to 2060, most provinces are at the stage of coordinated development of tourism carbon emissions and tourism carbon sinks, but the degree of coordinated development is low. (3) The structure of China’s tourism carbon-neutral spatial network tends to be looser from 2001 to 2060. As the time series progresses, the role of each province in the spatial network will be gradually clarified. (4) In the process of achieving the goal of China’s tourism carbon neutrality, the number of tourism carbon sink input areas is much larger than that of tourism carbon sink output areas. Accordingly, this paper proposes countermeasures from three aspects: government-led, market system and voluntary mechanisms, in order to promote the achievement of China’s tourism carbon neutrality goal.

Improving strength while retaining good ductility is crucial for expanding the application of 2219 Al alloy. In this study, refined microstructure, excellent strength and ductility were obtained for the 2219 Al alloy under the combined effects of ultrasonic melt treatment (UMT), hot rolling and T6 heat treatment (HRT6). The mean grain size declined from 664.2 μm to 194.9 μm for the as-cast 2219 Al alloy after 240s UMT, with a refining efficiency of 70.7 %. Meanwhile, the Cu content in Al matrix was increased by 41.7 %, and the area fraction of reticular eutectic structure was accordingly lessened by 64.5 %. The nucleation of θʹʹ/θʹ-Al2Cu phase was actuated owing to the increased Cu content in Al matrix, resulting in more dispersive θʹʹ/θʹ-Al2Cu precipitates in the HRT6 alloy with UMT. Besides, the recrystallization was encouraged because the boundaries of refined as-cast grains provided more favorable nucleation sites, and the increased dispersive θʹʹ/θʹ-Al2Cu precipitates would inhibits the grain boundary merging during HRT6. Thus, the average dimension of the recrystallized grains was decreased to the lowest value of 71.3 μm in the HRT6 alloy with 240s UMT. Meanwhile, the ultimate tensile strength (UTS), yield strength (YS) and elongation (EL) were enhanced to 456.2 MPa, 307.0 MPa and 16.7 %, and precipitation strengthening contributed the most to the YS enhancement. The improved ductility was mainly due to the increased deformation capacity induced by the refined grains and reduced stress concentration caused by the dispersive θʹʹ/θʹ-Al2Cu particles.

To monitor temperature as a function of varying inductance and resistance, we propose a data-driven digital twin approach for the rapid and efficient real-time estimation of the rotor temperature in an induction motor. By integrating differential equations with online signal processing, the proposed data-driven digital twin approach is structured into three key stages: (1) transforming the nonlinear differential equations into discrete algebraic equations by substituting the differential operator with the difference quotient based on the sampled voltage and current; (2) deriving approximate analytical solutions for rotor resistance and stator inductance, which can be utilized to estimate the rotor temperature; and (3) developing a general procedure for obtaining approximate analytical solutions to nonlinear differential equations. The feasibility and validity of the proposed method were demonstrated by comparing the test results with a 1.5 kW AC motor. The experimental results indicate that our method achieves a minimum estimation error that falls within the standards set by IEC 60034-2-1. This work provides a valuable reference for the overheating protection of induction motors where direct temperature measurement is challenging.

Forests are an important part of natural resources and play an important role in carbon sinks. We measured carbon sinks in provincial forest parks using data from four forest inventory surveys in China and the forest stock expansion method. Carbon emissions from forest tourism were also estimated using energy statistics and forest park tourism data. On this basis, spatial analysis was used to summarize the spatial and temporal evolution of the carbon balance and the analysis of influencing factors. The results show the following: (1) With the passage of time, the carbon emissions from forest tourism in all provinces have increased to different degrees, and the national forest tourism carbon emissions have increased from 1,071,390.231 (million tons) in 2003 to 286,255,829.7 (million tons) in 2018; spatially, the distribution of carbon emissions from forest tourism is uneven, with an overall high in the south and low in the north, and a high in the east and a low in the west. (2) The carbon sink of forest parks showed a trend of gradual growth and spatially formed a spatial pattern of high in the northeast and low in the southwest, which is consistent with the distribution of forest resources in China. (3) For forest tourism carbon emissions, the total number of tourists, tourism income, and playing roads are significant influencing factors, and the baseline regression coefficients are 0.595, 0.433, and 0.799, respectively, while for forest park carbon sinks, the number of forest park employees can play a certain positive role in carbon sinks, with the regression coefficient being 1.533.

Pansharpening refers to the process of fusing multispectral (MS) images with panchromatic (PAN) images to obtain high-resolution multispectral (HRMS) images. However, due to the low correlation and similarity between MS and PAN images, as well as inaccuracies in spatial information injection, HRMS images often suffer from significant spectral and spatial distortions. To address these issues, a pansharpening method based on multimodal texture correction and adaptive edge detail fusion is proposed in this paper. To obtain a texture-corrected (TC) image that is highly correlated and similar to the MS image, the target-adaptive CNN-based pansharpening (A-PNN) method is introduced. By constructing a multimodal texture correction model, intensity, gradient, and A-PNN-based deep plug-and-play correction constraints are established between the TC and source images. Additionally, an adaptive degradation filter algorithm is proposed to ensure the accuracy of these constraints. Since the TC image obtained can effectively replace the PAN image and considering that the MS image contains valuable spatial information, an adaptive edge detail fusion algorithm is also proposed. This algorithm adaptively extracts detailed information from the TC and MS images to apply edge protection. Given the limited spatial information in the MS image, its spatial information is proportionally enhanced before the adaptive fusion. The fused spatial information is then injected into the upsampled multispectral (UPMS) image to produce the final HRMS image. Extensive experimental results demonstrated that compared with other methods, the proposed algorithm achieved superior results in terms of both subjective visual effects and objective evaluation metrics.

Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have made considerable advances in hyperspectral image (HSI) classification. However, most CNN-based methods learn features at a single-scale in HSI data, which may be insufficient for multi-scale feature extraction in complex data scenes. To learn the relations among samples in non-grid data, GCNs are employed and combined with CNNs to process HSIs. Nevertheless, most methods based on CNN-GCN may overlook the integration of pixel-wise spectral signatures. In this paper, we propose a pyramid cascaded convolutional neural network with graph convolution (PCCGC) for hyperspectral image classification. It mainly comprises CNN-based and GCN-based subnetworks. Specifically, in the CNN-based subnetwork, a pyramid residual cascaded module and a pyramid convolution cascaded module are employed to extract multiscale spectral and spatial features separately, which can enhance the robustness of the proposed model. Furthermore, an adaptive feature-weighted fusion strategy is utilized to adaptively fuse multiscale spectral and spatial features. In the GCN-based subnetwork, a band selection network (BSNet) is used to learn the spectral signatures in the HSI using nonlinear inter-band dependencies. Then, the spectral-enhanced GCN module is utilized to extract and enhance the important features in the spectral matrix. Subsequently, a mutual-cooperative attention mechanism is constructed to align the spectral signatures between BSNet-based matrix with the spectral-enhanced GCN-based matrix for spectral signature integration. Abundant experiments performed on four widely used real HSI datasets show that our model achieves higher classification accuracy than the fourteen other comparative methods, which shows the superior classification performance of PCCGC over the state-of-the-art methods.

Objective: This study aims to investigate the potential role of relaxin, a peptide hormone, in preventing cellulardeterioration and death in gastric carcinoma cells under hypoxic conditions. It explores the effects of recombinantrelaxin 2 (RLXH2) on growth, cell differentiation, invasive potential, and oxidative damage in these cells.Materials and Methods: In this experimental study, the NCI-N87 cell line was cultured under normal conditions andthen subjected to hypoxia using cobalt chloride (CoCl2). The cells were treated with RLXH2, and various assayswere performed to assess cellular deterioration, death, and oxidative stress. Western blot and quantitative real timepolymerase chain reaction (qRT-PCR) were used to measure the expression levels of nuclear factor erythroid 2-relatedfactor 2 (Nrf2) and HO-1, and the translocation of Nrf2 to the nucleus was confirmed through Western blot analysis.Results: This study demonstrates, for the first time, that RLXH2 significantly reduces the formation of reactive oxygenspecies (ROS) and the release of lactate dehydrogenase (LDH) in gastric cancer cells under hypoxic conditions.RLXH2 also enhances the activities of superoxide dismutase (SOD), glutathione peroxidase (GPX), and catalase(CAT), leading to a decrease in hypoxia-induced oxidative damage. RLXH2 promotes the translocation of Nrf2 to thenucleus, resulting in HO-1 expression.Conclusion: Our findings suggest that RLXH2 plays a significant protective role against hypoxia-induced oxidativedamage in gastric carcinoma cells through the Nrf2/HO-1 signalling pathway. This research contributes to a betterunderstanding of the potential therapeutic applications of RLXH2 in gastric cancer treatment.

Abstract Whether TMPRSS2-ERG fusion and TP53 gene alteration coordinately promote prostate cancer (PCa) remains unclear. Here we demonstrate that TMPRSS2-ERG fusion and TP53 mutation / deletion co-occur in PCa patient specimens and this co-occurrence accelerates prostatic oncogenesis. p53 gain-of-function (GOF) mutants are now shown to bind to a unique DNA sequence in the CTNNB1 gene promoter and transactivate its expression. ERG and β-Catenin co-occupy sites at pyrimidine synthesis gene (PSG) loci and promote PSG expression, pyrimidine synthesis and PCa growth. β-Catenin inhibition by small molecule inhibitors or oligonucleotide-based PROTAC suppresses TMPRSS2-ERG- and p53 mutant-positive PCa cell growth in vitro and in mice. Our study identifies a gene transactivation function of GOF mutant p53 and reveals β-Catenin as a transcriptional target gene of p53 GOF mutants and a driver and therapeutic target of TMPRSS2-ERG- and p53 GOF mutant-positive PCa.

Current materials comprising suture anchors used to reconstruct ligament-bone junctions still have limitation in biocompatibility, degradability or mechanical properties. Magnesium alloys are potential bone implant materials, and Mg2+ has been shown to promote ligament-bone healing. Here, we used Mg-2 wt.% Zn-0.5 wt.% Y-1 wt.% Nd-0.5 wt.% Zr (ZE21C) alloy and Ti6Al4V (TC4) alloy to prepare suture anchors to reconstruct the patellar ligament-tibia in SD rats. We studied the degradation behavior of the ZE21C suture anchor via in vitro and in vivo experiments and assessed its reparative effect on the ligament-bone junction. In vitro, the ZE21C suture anchor degraded gradually, and calcium and phosphorus products accumulated on its surface during degradation. In vivo, the ZE21C suture anchor could maintain its mechanical integrity within 12 weeks of implantation in rats. The tail of the ZE21C suture anchor in high stress concentration degraded rapidly during the early implantation stage (0–4weeks), while bone healing accelerated the degradation of the anchor head in the late implantation stage (4–12weeks). Radiological, histological, and biomechanical assays indicated that the ZE21C suture anchor promoted bone healing above the suture anchor and fibrocartilaginous interface regeneration in the ligament-bone junction, leading to better biomechanical strength than the TC4 group. Hence, this study provides a basis for further research on the clinical application of degradable magnesium alloy suture anchors.

To address the problems of noise interference and image blurring in hyperspectral imaging (HSI), this paper proposes a denoising method for HSI based on deep learning and a total variation (TV) prior. The method minimizes the first-order moment distance between the deep prior of a Fast and Flexible Denoising Convolutional Neural Network (FFDNet) and the Enhanced 3D TV (E3DTV) prior, obtaining dual priors that complement and reinforce each other’s advantages. Specifically, the original HSI is initially processed with a random binary sparse observation matrix to achieve a sparse representation. Subsequently, the plug-and-play (PnP) algorithm is employed within the framework of generalized alternating projection (GAP) to denoise the sparsely represented HSI. Experimental results demonstrate that, compared to existing methods, this method shows significant advantages in both quantitative and qualitative assessments, effectively enhancing the quality of HSIs.

In recent years, the use of deep neural network in effective network feature extraction and the design of efficient and high-precision hyperspectral image classification algorithms has gradually become a research hotspot for scholars. However, due to the difficulty of obtaining hyperspectral images and the high cost of annotation, the training samples are very limited. In order to cope with the small sample problem, researchers often deepen the network model and use the attention mechanism to extract features; however, as the network model continues to deepen, the gradient disappears, the feature extraction ability is insufficient, and the computational cost is high. Therefore, how to make full use of the spectral and spatial information in limited samples has gradually become a difficult problem. In order to cope with such problems, this paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model (FHDANet); the model constructs a dense two-branch pyramid structure, which can achieve the high efficiency extraction of joint spatial–spectral feature information and spectral feature information, reduce feature loss to a large extent, and strengthen the model’s ability to extract contextual information. A channel–space attention module, ECBAM, is proposed, which greatly improves the extraction ability of the model for salient features, and a spatial information extraction module based on the deep feature fusion strategy HLDFF is proposed, which fully strengthens feature reusability and mitigates the feature loss problem brought about by the deepening of the model. Compared with five hyperspectral image classification algorithms, SVM, SSRN, A2S2K-ResNet, HyBridSN, SSDGL, RSSGL and LANet, this method significantly improves the classification performance on four representative datasets. Experiments have demonstrated that FHDANet can better extract and utilise the spatial and spectral information in hyperspectral images with excellent classification performance under small sample conditions.

To optimize electron energy for in situ imaging of large biological samples up to 10 μm in thickness with nanoscale resolutions, we implemented an analytical model based on elastic and inelastic characteristic angles. This model has been benchmarked by Monte Carlo simulations and can be used to predict the transverse beam size broadening as a function of electron energy while the probe beam traverses through the sample. As a result, the optimal choice of the electron beam energy can be realized. In addition, the impact of the dose-limited resolution was analysed. While the sample thickness is less than 10 μm, there exists an optimal electron beam energy below 10 MeV regarding a specific sample thickness. However, for samples thicker than 10 μm, the optimal beam energy is 10 MeV or higher depending on the sample thickness, and the ultimate resolution could become worse with the increase in the sample thickness. Moreover, a MeV-STEM column based on a two-stage lens system can be applied to reduce the beam size from one micron at aperture to one nanometre at the sample with the energy tuning range from 3 to 10 MeV. In conjunction with the state-of-the-art ultralow emittance electron source that we recently implemented, the maximum size of an electron beam when it traverses through an up to 10 μm thick bio-sample can be kept less than 10 nm. This is a critical step toward the in situ imaging of large, thick biological samples with nanometer resolution.

There is a long history to cultivate the Pogostemon cablin in Guangdong Province, and it is a famous medicinal material. The bacterial wilt is the important disease that impacts the production and quality of P. cablin. Aiming to the control effects of metabolites of Alternaria sp. GHX-P17 strain that is belonging to an endophytic fungus isolated from the stems and leaves of P. cablin and the mechanism of disease resistance on bacterial wilt, a laboratory experiment had been conducted to investigate the incidence and severity of bacterial wilt in P. cablin at different time after artificial to inoculate the strain of Alternaria sp. GHX-P17 and to spray the crude extracts of the metabolites, and disease index (DI) was calculated. The activity changes of protective enzymes of phenylalanine ammonia lyase (PAL), peroxidase (POD）and superoxide dismutase (SOD) were determined in different time in P. cablin. The results were as follows: (1) The DI was significantly lower in the treatment groups with the crude extracts of Alternaria sp. GHX-P17 at different concentrations than that of control groups, and the DI decreased by 27.16% in the treatment groups at 204 h after inoculation. The variance analysis showed significant differences (P

Abstract Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.

Abstract Background Clear cell renal cell cancer (ccRCC), the 8th leading cause of cancer-related death in the US, is challenging to treat due to high level intratumoral heterogeneity (ITH) and the paucity of druggable driver mutations. CcRCC is unusual for its high frequency of epigenetic regulator mutations, such as the SETD2 histone H3 lysine 36 trimethylase (H3K36me3), and low frequency of traditional cancer driver mutations. In this work, we examined epigenetic level ITH and defined its relationships with pathologic features, aspects of tumor biology, and SETD2 mutations. Results A multi-region sampling approach coupled with EPIC DNA methylation arrays was conducted on a cohort of normal kidney and ccRCC. ITH was assessed using DNA methylation (5mC) and CNV-based entropy and Euclidian distances. We found elevated 5mC heterogeneity and entropy in ccRCC relative to normal kidney. Variable CpGs are highly enriched in enhancer regions. Using intra-class correlation coefficient analysis, we identified CpGs that segregate tumor regions according to clinical phenotypes related to tumor aggressiveness. SETD2 wild-type tumors overall possess greater 5mC and copy number ITH than SETD2 mutant tumor regions, suggesting SETD2 loss contributes to a distinct epigenome. Finally, coupling our regional data with TCGA, we identified a 5mC signature that links regions within a primary tumor with metastatic potential. Conclusion Taken together, our results reveal marked levels of epigenetic ITH in ccRCC that are linked to clinically relevant tumor phenotypes and could translate into novel epigenetic biomarkers.

As an environmentally friendly natural polymer, citric acid-modified chitosan (CAMC) can effectively regulate the hydration and exothermic processes of cement-based materials. However, the influence of CAMC on the macroscopic properties of concrete and the optimal dosage are still unclear. This work systematically investigates the effects of CAMC on the mixing performance, mechanical properties, shrinkage performance, and durability of concrete. The results indicated that CAMC has a thickening effect and prolongs the setting time of concrete. CAMC has a negative impact on the early strength of concrete, but it is beneficial for the development of the subsequent strength of concrete. With the increase in CAMC content, the self-shrinkage rate of concrete samples decreased from 86.82 to 14.52 με. However, the CAMC-0.6% sample eventually expanded, with an expansion value of 78.49 με. Moreover, the long-term drying shrinkage rate was decreased from 551.46 to 401.94 με. Furthermore, low-dose CAMC can significantly reduce the diffusion coefficient of chloride ions, improve the impermeability and density of concrete, and thereby enhance the freeze–thaw cycle resistance of concrete.

Driven by life-science applications, a mega-electron-volt Scanning Transmission Electron Microscope (MeV-STEM) has been proposed here to image thick frozen biological samples as a conventional Transmission Electron Microscope (TEM) may not be suitable to image samples thicker than 300–500 nm and various volume electron microscopy (EM) techniques either suffering from low resolution, or low speed. The high penetration of inelastic scattering signals of MeV electrons could make the MeV-STEM an appropriate microscope for biological samples as thick as 10 μm or more with a nanoscale resolution, considering the effect of electron energy, beam broadening, and low-dose limit on resolution. The best resolution is inversely related to the sample thickness and changes from 6 nm to 24 nm when the sample thickness increases from 1 μm to 10 μm. To achieve such a resolution in STEM, the imaging electrons must be focused on the specimen with a nm size and an mrad semi-convergence angle. This requires an electron beam emittance of a few picometers, which is ~1000 times smaller than the presently achieved nm emittance, in conjunction with less than 10−4 energy spread and 1 nA current. We numerically simulated two different approaches that are potentially applicable to build a compact MeV-STEM instrument: (1) DC (Direct Current) gun, aperture, superconducting radio-frequency (SRF) cavities, and STEM column; (2) SRF gun, aperture, SRF cavities, and STEM column. Beam dynamic simulations show promising results, which meet the needs of an MeV-STEM, a few-picometer emittance, less than 10−4 energy spread, and 0.1–1 nA current from both options. Also, we designed a compact STEM column based on permanent quadrupole quintuplet, not only to demagnify the beam size from 1 μm at the source point to 2 nm at the specimen but also to provide the freedom of changing the magnifications at the specimen and a scanning system to raster the electron beam across the sample with a step size of 2 nm and the repetition rate of 1 MHz. This makes it possible to build a compact MeV-STEM and use it to study thick, large-volume samples in cell biology.

In hyperspectral image (HSI) classification scenarios, deep learning-based methods have achieved excellent classification performance, but often rely on large-scale training datasets to ensure accuracy. However, in practical applications, the acquisition of hyperspectral labeled samples is time consuming, labor intensive and costly, which leads to a scarcity of obtained labeled samples. Suffering from insufficient training samples, few-shot sample conditions limit model training and ultimately affect HSI classification performance. To solve the above issues, an active learning (AL)-based multipath residual involution Siamese network for few-shot HSI classification (AL-MRIS) is proposed. First, an AL-based Siamese network framework is constructed. The Siamese network, which has relatively low demand for sample data, is adopted for classification, and the AL strategy is integrated to select more representative samples to improve the model’s discriminative ability and reduce the costs of labeling samples in practice. Then, the multipath residual involution (MRIN) module is designed for the Siamese subnetwork to obtain the comprehensive features of the HSI. The involution operation was used to capture the fine-grained features and effectively aggregate the contextual semantic information of the HSI through dynamic weights. The MRIN module comprehensively considers the local features, dynamic features and global features through multipath residual connections, which improves the representation ability of HSIs. Moreover, a cosine distance-based contrastive loss is proposed for the Siamese network. By utilizing the directional similarity of high-dimensional HSI data, the discriminability of the Siamese classification network is improved. A large number of experimental results show that the proposed AL-MRIS method can achieve excellent classification performance with few-shot training samples, and compared with several state-of-the-art classification methods, the AL-MRIS method obtains the highest classification accuracy.

Hyperspectral image classification plays a crucial role in remote sensing image analysis by classifying pixels. However, the existing methods require more spatial–global information interaction and feature extraction capabilities. To overcome these challenges, this paper proposes a novel model for hyperspectral image classification using an orthogonal self-attention ResNet and a two-step support vector machine (OSANet-TSSVM). The OSANet-TSSVM model comprises two essential components: a deep feature extraction network and an improved support vector machine (SVM) classification module. The deep feature extraction network incorporates an orthogonal self-attention module (OSM) and a channel attention module (CAM) to enhance the spatial–spectral feature extraction. The OSM focuses on computing 2D self-attention weights for the orthogonal dimensions of an image, resulting in a reduced number of parameters while capturing comprehensive global contextual information. In contrast, the CAM independently learns attention weights along the channel dimension. The CAM autonomously learns attention weights along the channel dimension, enabling the deep network to emphasise crucial channel information and enhance the spectral feature extraction capability. In addition to the feature extraction network, the OSANet-TSSVM model leverages an improved SVM classification module known as the two-step support vector machine (TSSVM) model. This module preserves the discriminative outcomes of the first-level SVM subclassifier and remaps them as new features for the TSSVM training. By integrating the results of the two classifiers, the deficiencies of the individual classifiers were effectively compensated, resulting in significantly enhanced classification accuracy. The performance of the proposed OSANet-TSSVM model was thoroughly evaluated using public datasets. The experimental results demonstrated that the model performed well in both subjective and objective evaluation metrics. The superiority of this model highlights its potential for advancing hyperspectral image classification in remote sensing applications.

A variety of volume electron microscopy techniques have been developed to visualize thick biological samples. However, the resolution is limited by the sliced section thickness (>30–60 nm). To preserve biological samples in a hydrated state, cryo-focused ion beam scanning electron microscopy has been developed, providing nm resolutions. However, this method is time-consuming, requiring 15–20 h to image a 10 μm thick sample with an 8 nm slice thickness. There is a pressing need for a method that allows the rapid and efficient study of thick biological samples while maintaining nanoscale resolution. The remarkable ability of mega-electron-volt (MeV) electrons to penetrate thick biological samples, even exceeding 10 μm in thickness, while maintaining nanoscale resolution, positions MeV-STEM as a suitable microscopy tool for such applications. Our research delves into understanding the interactions between MeV electrons and frozen biological specimens through Monte Carlo simulations. Single elastic scattering, plural elastic scattering, single inelastic scattering, and plural inelastic scattering events have been simulated. The electron trajectories, the beam profile, and the intensity change of electrons in each category have been investigated. Additionally, the effects of the detector collection angle and the focal position of the electron beam were investigated. As electrons penetrated deeper into the specimen, single and plural elastic scattered electrons diminished, and plural inelastic scattered electrons became dominant, and the beam profile became wider. Even after 10 μm of the specimen, 42% of the MeV electrons were collected within 10 mrad. This confirms that MeV-STEM can be employed to study thick biological samples.

The accuracy of hyperspectral target detection is often affected by the problems of spectral variation and complex background distribution. Inspired by the powerful representational ability of deep learning, we proposed a three-dimensional (3-D) convolution-based global spatial–spectral attention network (GS2A-Net) to deal with spectral variation in hyperspectral images (HSIs). GS2A-Net uses 3-D convolution kernels of different sizes to capture local spatial and spectral features to achieve multiscale information interaction. Different from the previous 2-D attention mechanisms, GS2A-Net simultaneously considers the information in the spatial and spectral dimensions, and creates a weight map consistent with the size of the original HSI. Furthermore, we proposed a new background suppression strategy based on the spectral angle mapping to achieve more accurate target detection, which can preserve the targets as much as possible when suppressing the background. The method was validated through experiments on five real-world HSI datasets. Compared with several classical and deep-learning-based methods, the proposed method exhibits greater detection accuracy.

Soil moisture (SM) plays a critical role in various fields such as agriculture, hydrology, and land-atmosphere interactions. This study aims to evaluate the performance of the categorical boosting algorithm (CatBoost) in comparison to other multiple-boosting algorithms for SM prediction. Appropriate feature selection is vital for achieving accurate predictions, and this study focuses on identifying relevant features and assessing CatBoost's suitability for the task. The study incorporates several boosting algorithms including Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and CatBoost to estimate SM. Results indicate that radar backscatter coefficient, soil roughness, and digital elevation model (DEM) are crucial features for SM retrieval. Comparatively, CatBoost outperforms GBDT, XGBoost, and LightGBM in various feature combinations. The most favorable results are obtained when utilizing all features as inputs for the algorithm. These optimal results yield a mean absolute error (MAE) of 2.40 vol.%, mean relative error (MRE) of 0.16 vol.%, root mean square error (RMSE) of 3.26 vol.%, and Pearson correlation coefficient of 0.73. Additionally, the study analyzes the inversion results for different ranges of SM and Normalized Difference Vegetation Index (NDVI). Within the range of SM from 0 to 25 vol.% and NDVI from 0 to 0.7, utilizing all features yields the most accurate results. Using CatBoost, this approach achieves an MAE of 1.52 vol.%, MRE of 0.12 vol.%, RMSE of 2.11 vol.%, and R of 0.81. The study suggests that applying boosting algorithms, especially CatBoost, holds promise in accurately estimating surface SM.

Remote sensing images (RSIs) contain important information, such as airports, ports, and ships. By extracting RSI features and learning the mapping relationship between image features and text semantic features, the interpretation and description of RSI content can be realized, which has a wide range of application value in military and civil fields, such as national defense security, land monitoring, urban planning, and disaster mitigation. Aiming at the complex background of RSIs and the lack of interpretability of existing target detection models, and the problems in feature extraction between different network structures, different layers, and the accuracy of target classification, we propose an object detection and interpretation model based on gradient-weighted class activation mapping and reinforcement learning. First, ResNet is used as the main backbone network to extract the features of RSIs and generate feature graphs. Then, we add the global average pooling layer to obtain the corresponding feature weight vector of the feature graph. The weighted vectors are superimposed to output class activation maps. The reinforcement learning method is used to optimize the generated region generation network. At the same time, we improve the reward function of reinforcement learning to improve the effectiveness of the region generation network. Finally, network dissecting analysis is used to obtain the interpretable semantic concept in the model. Through experiments, the average accuracy is more than 85%. Experimental results in the public RSI description dataset show that the proposed method has high detection accuracy and good description performance for RSIs in complex environments.

Soil moisture (SM) is valuable basic data in climate, hydrological models, and agricultural applications. The rapid development of remote sensing technology can be used to monitor changes in SM at multiple spatial and temporal scales. In this article, we unfolded an SM retrieval method using ensemble learning combined with the Water Cloud Model (WCM) by Sentinel-1 and Sentinel-2 with multisource datasets. First, using the WCM, the influence of vegetation cover on the backscattering coefficient was removed, where we use three vegetation index (enhanced vegetation index (EVI), normalized difference vegetation index, and normalized difference water index) for analysis and comparison. Then, combined with other multisource datasets, an SM retrieval model was established based on the ensemble learning algorithm. Here, we choose two familiar ensemble learning algorithms for analysis and comparison, using Pearson correlation significance analysis, which are the random forest (RF) and the adaptive boosting (AdaBoost). The results revealed that the RF model performed is slightly superior to the AdaBoost model. The optimal performance mean absolute error, root-mean-square error (RMSE), and the unbiased RMSE of RF model are 2.289 vol%, 2.934 vol%, 2.934 vol%, respectively, which are slightly better than the AdaBoost model. EVI is suitable for WCM model to remove vegetation scattering effect. It shows that it is attainable to utilize the ensemble learning method to inversion of SM using radar data. The proposed framework maximizes the potential of WCM, RF model, and multisource datasets in deriving spatiotemporally continuous SM estimates, which should be valuable for SM inversion development.

In this study, nanosheet g-C3N4-H2 was prepared by thermal exfoliation of bulk g-C3N4 under hydrogen. A series of Ru/g-C3N4-H2 catalysts with Ru species supported on the nanosheet g-C3N4-H2 were synthesized via ultrasonic assisted impregnation-deposition method. Ultrafine Ru nanoparticles (99% 4,4′-diaminodicyclohexylmethane selectivity, corresponding to a reaction activity of 35.7 molMDA molRu−1 h−1. Moreover, the reaction activity of catalyst in the fifth run was 36.5 molMDA molRu−1 h−1, which was comparable with that of the fresh one. The computational results showed that g-C3N4 as support was favorable for adsorption and dissociation of H2 molecules. Moreover, the substrate scope can be successfully expanded to a variety of other aromatic diamines. Therefore, this work provides an efficient and green catalyst system for selective hydrogenation of aromatic diamines.

Studying the mechanism by which organic admixtures affect the hydration and dissolution of tricalcium silicate (C3S) reveals the effect of organic admixtures on ordinary Portland cement and enables target regulation of cement-based materials. In this study, the effects of citric-acid-modified chitosan (CAMC) on the hydration exotherm, hydration products, and microscopic morphology of C3S were investigated. The interface structures, ion adsorptions, and dissolution properties of CAMC and C3S were analyzed using a molecular dynamics simulation method. The results showed the presence of an attraction between the C3S surface and CAMC due to the existence of Op-Hw, Op-Cas, and Hp-Ow ion pairs. CAMC adsorbed most of the Ca ions released upon dissolution of C3S in the aqueous solution and the resulting pairs exhibited low solubilities. The Ca ions were located on the surfaces of C3S particles, preventing the dissolution of the particles and proving the interference effect of additives on the hydration of the cement silicate phase.

Here the authors identify retinoblastoma (RB) protein as an intrinsic inhibitor of BRD4 and demonstrate that loss of RB induces BRD4 cistrome changes in the genome and enrichment of GPCR-cAMP signaling pathway, conferring resistance to small molecule BET inhibitor.

The synthesis of sustainable methanol and ethylene glycol (EG) via hydrogenation of ethylene carbonate (EC) has caught researchers’ growing interests on account of the indirect chemical utilization of CO2. Core-shell Cu@GO catalysts with random nanoporous network of graphite oxide (GO) were synthesized via a simple method of ultrasonic precipitation. Cu@GO catalysts were analyzed systematically by N2 physisorption, TGA measurement, XRD, FT-IR, Raman, TEM, SEM, and XPS (XAES). In particular, the mentioned method was confirmed to be effective to fabricate the high dispersity core-shell Cu@GO catalysts through promoting the specific surface area. The as-prepared Cu@GO catalyst was then successfully applied in the hydrogenation of CO2-derived EC to produce methanol and EG. A high TOF of 1526 mgEC gcat-1 h-1 could be attained in EC hydrogenation at the reaction temperature of 493 K. Accordingly, the correlation of catalytic structure and performance disclosed that the synergistic effect between Cu+ and Cu0 was responsible for achieving high activity of the catalyst. In addition, the reusability of Cu@GO catalyst suggested that graphite oxide shell structure could decrease the aggregation of Cu particles, thus enhance the stability of Cu-based catalysts. DFT calculation results suggested that the involvement of carbon film on Cu was favorable for the stabilization of the active sites. This study is helpful for developing new and stable catalytic system for indirect chemical utilization of CO2 to synthesize commodity methanol and EG.