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Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently. Therefore, we propose a tool state recognition method based on the Pruned Optimized Graph Neural Network-Gated Recurrent Unit (POGNN-GRU) under unbalanced data. Firstly, design the Improved-Majority Weighted Minority Oversampling Technique (IMWMOTE) by introducing an adaptive noise removal strategy and improving the MWMOTE to alleviate the unbalanced problem of data. Subsequently, propose a POG graph data construction method based on a multi-scale multi-metric basis and a Gaussian kernel weight function to solve the problem of one-sided description of graph data under a single metric basis. Then, construct the POGNN-GRU model to deeply mine the spatial and temporal features of the data to better identify the state of the tool. Finally, validation and ablation experiments on the PHM 2010 and HMoTP datasets show that the proposed method outperforms the other models in terms of identification, and the highest accuracy improves by 1.62% and 1.86% compared with the corresponding optimal baseline model. [ABSTRACT FROM AUTHOR]

In symmetry‐adapted perturbation theory (SAPT), accurate calculations on non‐covalent interaction (NCI) for large complexes with more than 50 atoms are time‐consuming using large basis sets. More efficient ones with smaller basis sets usually result in poor prediction in terms of dispersion and overall energies. In this study, we propose two composite methods with baseline calculated at SAPT2/aug‐cc‐pVDZ and SAPT2/aug‐cc‐pVTZ with dispersion term corrected at SAPT2+ level using bond functions and smaller basis set with δMP2 corrections respectively. Benchmark results on representative NCI data sets, such as S22, S66, and so forth, show significant improvements on the accuracy compared to the original SAPT Silver standard and comparable to SAPT Gold standard in some cases with much less computational cost. [ABSTRACT FROM AUTHOR]

Optimizing ship energy efficiency is a crucial measure for reducing fuel use and emissions in the shipping industry. Accurate prediction models of ship energy consumption are essential for achieving this optimization. However, external factors affecting ship fuel consumption have not been comprehensively investigated, and many existing studies still face efficiency and accuracy challenges. In this study, we propose a neural network model called TCN-GRU-MHSA (TGMA), which incorporates the temporal convolutional network (TCN), the gated recurrent unit (GRU), and multi-head self-attention mechanisms to predict ship energy consumption. Firstly, the characteristics of ship operation data are analyzed, and appropriate input features are selected. Then, the prediction model is established and validated through application analysis. Using the proposed model, the prediction accuracy of ship energy consumption can reach up to 96.04%. Comparative analysis results show that the TGMA model outperforms existing models, including those based on LSTM, GRU, SVR, TCN-GRU, and BP neural networks, in terms of accuracy. Therefore, the developed model can effectively predict ship fuel usage under various conditions, making it essential for optimizing and improving ship energy efficiency. [ABSTRACT FROM AUTHOR]

Background: Myeloid-derived suppressor cells (MDSCs) promote tumor growth, metastasis, and lead to immunotherapy resistance. Studies revealed that miRNAs are also expressed in MDSCs and promote the immunosuppressive function of MDSCs. Currently, few studies have been reported on inducible cellular microvesicle delivery of nucleic acid drugs targeting miRNA in MDSCs for the treatment of malignant tumors. Results and conclusion: In this study, we designed an artificial DNA named G-quadruplex-enhanced circular single-stranded DNA-9 (G4-CSSD9), that specifically adsorbs the miR-9 sequence. Its advanced DNA folding structure, rich in tandem repeat guanine (G-quadruplex), also provides good stability. Mesenchymal stem cells (MSCs) were prepared into nanostructured vesicles by membrane extrusion. The MSC microvesicles-encapsulated G4-CSSD9 (MVs@G4-CSSD9) was delivered into MDSCs, which affected the downstream transcription and translation process, and reduced the immunosuppressive function of MDSCs, so as to achieve the purpose of treating melanoma. In particular, it provides an idea for the malignant tumor treatment. [ABSTRACT FROM AUTHOR]

With the rapid development of the information age, smart meters play an important role in the smart grid. However, there are more and more attacks on smart meters, which mainly focus on the identity authentication of smart meters and the security protection of electricity consumption data. In this paper, an efficient lightweight smart meter authentication scheme is proposed based on the Chinese Remainder Theorem (CRT), which can realize the revocation of a single smart meter user by publishing a secret random value bound to the smart meter identity. The proposed scheme not only protects the security of smart meter electricity consumption data by using encryption, but also resists identity attacks from both internal and external adversaries by using hash functions and timestamps. Experiment shows that the proposed scheme has lower computation overhead and communication overhead than other authentication schemes and is more suitable for smart meter authentication. [ABSTRACT FROM AUTHOR]

This study focused on examining the early stages of tea cultivation (1, 3, and 5 years) in mountainous tea plantations. It specifically aimed to investigate the changes in soil micro-ecology at different locations (inter-row, terrace surfaces, and terrace walls). It was revealed that as tea tree cultivation progressed over the years, bacterial diversity and co-occurrence networks annually decreased in different locations. The results of soil physicochemical index analysis showed that the soil's available nutrients and the activities of cellulase and protease increased. Furthermore, the amplitude of variation of these indexes in the inter-row soil was significantly higher than that on the terrace surfaces and the terrace walls (p < 0.05). Alterations occurred in the soil microbial community structure, with an enrichment of bacterial genera such as Sinomonas, Granulicella, and Sphingomonas, as well as fungal genera such as Trichoderma, Penicillium, and Talaromyces; an increase in the proportion of plant pathogenic fungi (Cladosporium, Fusarium, and Curvularia) was observed in the inter-row soil. The results of soil microbial function prediction showed that nitrification and nitrogen fixation decreased, but denitrification increased (p < 0.05). In conclusion, cultivating tea trees in mountainous terraced plantations significantly impacted the soil microbial community, accelerated the metabolism of soil organic matter, disrupted soil nitrogen cycling functions, and increased the presence of plant pathogenic fungal pathogens. Moreover, the changes in the structure and functions of the soil microbial community demonstrate a spatial distance effect across different terrace locations. [ABSTRACT FROM AUTHOR]

To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility. [ABSTRACT FROM AUTHOR]

BACKGROUND: Juxta-papillary duodenal diverticula (JPDD) are common but are usually asymptomatic, and they are often diagnosed by coincidence. OBJECTIVE: To analyse the anatomy and classification of JPDD and its relationship with biliary and pancreatic disorders, and to explore the diagnostic value of multi-slice spiral computed tomography (MSCT) in patients with JPDD. METHODS: The imaging data of patients with JPDD, which was obtained via abdominal computed tomography examination and confirmed via gastroscopy and/or upper gastrointestinal barium enema, in our hospital from 1 January 2019 to 31 December 2020 were retrospectively analysed. All patients were scanned using MSCT, and the imaging findings, classification and grading were analysed. RESULTS: A total of 119 duodenal diverticula were detected in 96 patients, including 73 single diverticula and 23 multiple diverticula. The imaging findings were mainly cystic lesions of the inner wall of the duodenum protruding to the outside of the cavity. The thin layer showed a narrow neck connected with the duodenal cavity, and the shape and size of the diverticula were different: 67 central-type cases and 29 peripheral-type cases. There were 50 cases of type I, 33 cases of type II, 19 cases of type III and six cases of type IV. Furthermore, there were seven small, 87 medium and 14 large diverticula. The differences in the location and size of the JPDD in MSCT grading were statistically significant (P < 0.05). CONCLUSION: The MSCT method has an important diagnostic value for the classification of JPDD, and MSCT images are helpful in the clinical evaluation of patients with JPDD and the selection of treatment options. [ABSTRACT FROM AUTHOR]

Background: Major psychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BPD) are complex genetic mental illnesses. Their non-Mendelian features, such as those observed in monozygotic twins discordant for SCZ or BPD, are likely complicated by environmental modifiers of genetic effects. 5-Hydroxymethylcytosine (5hmC) is an important epigenetic mark in gene regulation, and whether it is linked to genetic variants that contribute to non-Mendelian features remains largely unexplored. Methods: We combined the 5hmC-selective chemical labeling method (5hmC-seq) and whole-genome sequencing (WGS) analysis of peripheral blood DNA obtained from monozygotic (MZ) twins discordant for SCZ or BPD to identify allelic imbalances in hydroxymethylome maps, and examined association of allele-specific hydroxymethylation (AShM) transition with disease susceptibility based on Bayes factors (BF) derived from the Bayesian generalized additive linear mixed model. We then performed multi-omics integrative analysis to determine the molecular pathogenic basis of those AShM sites. We finally employed luciferase reporter, CRISPR/Cas9 technology, electrophoretic mobility shift assay (EMSA), chromatin immunoprecipitation (ChIP), PCR, FM4-64 imaging analysis, and RNA sequencing to validate the function of interested AShM sites in the human neuroblastoma SK-N-SH cells and human embryonic kidney 293T (HEK293T) cells. Results: We identified thousands of genetic variants associated with AShM imbalances that exhibited phenotypic variation-associated AShM changes at regulatory loci. These AShM marks showed plausible associations with SCZ or BPD based on their effects on interactions among transcription factors (TFs), DNA methylation levels, or other epigenomic marks and thus contributed to dysregulated gene expression, which ultimately increased disease susceptibility. We then validated that competitive binding of POU3F2 on the alternative allele at the AShM site rs4558409 (G/T) in PLLP-enhanced PLLP expression, while the hydroxymethylated alternative allele, which alleviated the POU3F2 binding activity at the rs4558409 site, might be associated with the downregulated PLLP expression observed in BPD or SCZ. Moreover, disruption of rs4558409 promoted neural development and vesicle trafficking. Conclusion: Our study provides a powerful strategy for prioritizing regulatory risk variants and contributes to our understanding of the interplay between genetic and epigenetic factors in mediating SCZ or BPD susceptibility. [ABSTRACT FROM AUTHOR]

We report the oxide, element geochemistry and Nd isotopic geochemical data of apatite in the middle Pleistocene medium‐ and fine‐grained trachyte in the Tianchi volcanic area (TVA) of Changbai Mountain, discussing the relationship between apatite and the composition of the whole rock. The purpose is to use the apatite geochemical data to constrain the evolutionary process of trachytic magma and the petrogenesis of trachyte in the cone‐forming period of the Tianchi volcano. Apatite (Ca5(PO4)3(OH, F, Cl)) is a common accessory mineral that occurs widely in volcanic rocks in the TVA. The apatites in the trachyte are mainly subhedral–anhedral, having the characteristics of magmatic apatite. In terms of oxide and element geochemistry, they have homogeneous Al2O3, SiO2, MgO, P2O5, K2O, CaO and heterogeneous TiO2, with high F content. They are generally enriched in Th, U and LREEs, depleted in Nb, Ta, Zr, Hf and HFSEs, showing negative Ba, Sr and Ti anomalies, similar to those of the whole‐rock host trachytes. The ratios of high (La/Yb)N, low δEu (Eu/Eu*), Sr/Y value and ΣREE content in apatite, and the F, Sr, Y, Th/U, La/Sm, and Nd/Tb with ΣREE and δEu anomalies showed a linear correlation, all of those indicating that the host magma has the characteristic of high differentiation. The apatite grains generally having 147Sm/144Nd, 143Nd/144Nd ratios and εNd(t) values of 0.1072–0.1195, 0.5123–0.5126 and –3.49 to –0.10, respectively, are similiar to those of the host rock. The Nd model ages TDM1 are 949–803 Ma in apatite. Combined with the εNd(t) value of the apatite core (–7.06 to –3.49), we conclude that the initial magma of the host trachyte was derived from the partial melting of Proterozoic crustal material and there was an assimilation of wall rocks during its evolution. [ABSTRACT FROM AUTHOR]

An outdoor experiment was conducted to measure the thickness of oil films (0~3000 μm) using an airborne hyperspectral imager and thermal infrared imager, and the spectral response and thermal response of oil films of different thicknesses were analyzed. The classic support vector regression (SVR) model was used to retrieve the oil film thickness. On this basis, the suitable range for retrieving oil film thickness using hyperspectral and thermal infrared remote sensing was explored, and the decision-level fusion algorithm was developed to fuse the retrieval capabilities of hyperspectral and thermal infrared remote sensing for oil film thickness. The following conclusions can be drawn: (1) Based on airborne hyperspectral data and thermal infrared data, the retrieval accuracy of oil films of different thicknesses reached 154.31 μm and 116.59 μm, respectively. (2) The S185 hyperspectral data were beneficial for retrieving thicknesses greater than or equal to 400 μm, and the H20T thermal infrared data were beneficial for retrieving thicknesses greater than 500 μm. (3) The result of the decision-level fusion model based on a fuzzy membership degree was superior to those obtained using a single sensor (hyperspectral or thermal infrared), indicating that it can better integrate the retrieval results of hyperspectral and thermal infrared remote sensing for oil film thickness. Furthermore, the feasibility of using hyperspectral and thermal infrared remote sensing to detect water-in-oil emulsions of different thicknesses was investigated through spectral response and thermal response analysis. [ABSTRACT FROM AUTHOR]

Convolutional Neural Network (CNN) has developed hyperspectral image (HSI) classification effectively. Although many CNN-based models can extract local features in HSI, it is difficult for them to extract global features. With its ability to capture long-range dependencies, Transformer is gradually gaining prominence in HSI classification, but it may overlook some local details when extracting features. To address these issues, we proposed a CNN and transformer interaction network (CTIN) for HSI classification. Firstly, A dual-branch structure was constructed in which CNN and Transformer are arranged in parallel to simultaneously extract global features and local features in HSI. Secondly, a feature interaction module has been imported between the two branches, thus facilitating a bi-directional flow of information between the global and local feature spaces. In this way, the network structure combines the advantages of CNN and Transformer in extracting features as much as possible. In addition, a token generation method is designed to harness abundant contextual information that is relevant to the centre pixel, and improve the accuracy of the final classification. Experiments were conducted on four hyperspectral datasets (two classical datasets – Indian Pines, Salinas Valley, a new satellite dataset – Yellow River, and an self-made UAV dataset-Yellow River Willow). Experimental results show that the proposed method outperforms the other state-of-the-art methods, with overall accuracies of 99.21%, 99.61%, 92.40%, and 98.17%, respectively. [ABSTRACT FROM AUTHOR]

Marine oil spills pose a crucial concern in the monitoring of marine environments, and optical remote sensing serves as a vital means for marine oil spill detection. However, optical remote sensing imagery is susceptible to interference from sunglints and shadows, leading to diminished spectral differences between oil films and seawater. This makes it challenging to accurately extract the boundaries of oil–water interfaces. To address these aforementioned issues, this paper proposes a model based on the graph convolutional architecture and spatial–spectral information fusion for the oil spill detection of real oil spill incidents. The model is experimentally evaluated using both spaceborne and airborne hyperspectral oil spill images. Research findings demonstrate the superior oil spill detection accuracy of the developed model when compared to Graph Convolutional Network (GCN) and CNN-Enhanced Graph Convolutional Network (CEGCN), across two hyperspectral datasets collected from the Bohai Sea. Moreover, the performance of the developed model in oil spill detection remains optimal, even with only 1% of the training samples. Similar conclusions are drawn from the oil spill hyperspectral data collected from the Yellow Sea. These results validate the efficacy and robustness of the proposed model for marine oil spill detection. [ABSTRACT FROM AUTHOR]

Sidewall roughness is a key factor influencing the shaft resistance of rock-socketed piles. Owing to the difficulties in onsite measuring and the inconsistency in quantitatively characterizing the roughness degree of sidewalls, existing approaches for estimating the shaft resistance of rock-socketed piles often cannot take this factor into account. Based on the measured surface curves of the 68 sockets in No. 6# and 7# group piles of the Chishi Bridge on the Ru-Chen Expressway in China, sidewall roughness is described by introducing the roughness factor (RF) based on the Horvath and Monash models, respectively, while a statistical analysis of the sidewall roughness in rock-socketed sections is also conducted. In addition, an analytical solution to the shaft resistance of rock-socketed piles with consideration of sidewall roughness and the relative settlement of the pile–rocks interface (∆s), is proposed and further compared with the field load tests. The results showed that: the RF obtained by the Horvath model is bigger than that obtained by the Monash model; the larger RF is, the bigger the mobilized shaft resistance; the analytical solution generally overestimates the mobilized shaft resistance of rock-socketed piles under the same ∆s, and the deviation is less than 15% if ∆s is larger than 3.00 mm. The Horvath model is recommended to quantitatively characterize the roughness degree of sidewalls for its good operability in practice. [ABSTRACT FROM AUTHOR]

The determination of soil parameters in geotechnical engineering and their variations during the construction process have long been a focal point for engineering designers. While the artificial neural network (ANN) has been employed for back analysis of soil parameters, its application to caisson sinking processes remains limited. This study focuses on the Nanjing Longtan Yangtze River Bridge project, specifically the south anchoring of an ultra-large rectangular caisson. A comprehensive analysis of the sinking process was conducted using 400 finite element method (FEM) models to obtain the structural stress and earth pressure at key locations. Multiple combinations of soil parameters were considered, resulting in a diverse set of simulation results. These results were then utilized as training samples to develop a back-propagating artificial neural network (BP ANN), which utilized the structural stress and earth pressure as input sets and the soil parameters as output sets. The BP ANN was individually trained for each stage of the sinking process. Subsequently, the trained ANN was employed to predict the soil parameters under different working conditions based on actual monitoring data from engineering projects. The obtained soil parameter variations were further analyzed, leading to the following conclusions: (1) The soil parameters estimated by the ANN exhibited strong agreement with the original values from the geological survey report, validating their reliability; (2) The surrounding soil during the caisson sinking exhibited three distinct states: a stable state prior to the arrival of the cutting edges, a strengthened state upon the arrival of the cutting edges, and a disturbed state after the passage of the cutting edges; (3) In the stable state, the soil parameters closely resembled the original values, whereas in the strengthened state, the soil strength and stiffness significantly increased, while the Poisson's ratio decreased. In the disturbed state, the soil strength and stiffness were slightly lower than the original values. This study represents a valuable exploration of back analysis for caisson engineering. The findings provide important insights for similar engineering design and construction projects. [ABSTRACT FROM AUTHOR]

At present, how to achieve high-precision hyperspectral image classification (HSIC) under the condition of few samples is a hot research issue. Metric-based meta-learning methods have proved to be very successful in this field. However, in terms of quantifying the dependencies between embedded features of hyperspectral samples, previous methods either only model marginal distribution and ignore joint distribution, limiting the expressive capability of feature representation, or bring large computational costs though considering joint distribution. In this letter, we propose a novel few-shot learning (FSL) method based on Brownian distance covariance (BDC) for HSIC, which learns hyperspectral images’ (HSIs) representations by measuring the discrepancy between joint characteristic functions of embedded features and product of the marginals. In addition, a lightweight feature extraction network based on tied block convolution is proposed to better model cross-channel correlation and aggregate global spectral–spatial features across channels. Extensive evaluations of several datasets show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Purpose: To develop a bidirectional slide guide to assist screw placement in the axial lamina and to preliminarily discuss the accuracy and feasibility of auxiliary screw placement. Methods: CT data from 40 randomly selected patients were imported into the software for modelling, and cross-pinning was used to simulate pinning. According to the different crossing methods of the upper and lower laminar screws, they are divided into two groups. In the software, the position of the needlepoint of each screw is accurately measured, and the needle point is kept unchanged to simulate the movable range of the screw tail under the condition that the body does not penetrate the cortical bone. The data were compared by grouping and gender. Finally, the guide was designed by combining the screw exit point and fine adjustment angle data of all patients with the centripetal principle of the slide rail. Results: The needle exit data L1/L2/L3/L4 were 6.44 ± 0.52 mm, 7.05 ± 0.48 mm, 3.55 ± 0.75 mm and 5.09 ± 0.74 mm, respectively, and the fine adjustment angle of the slide rail was 10.51° ± 0.87°. There was no significant difference between the two groups or between men and women (p > 0.05). Conclusion: In this experiment, using the data obtained from the simulation of screw insertion, a two-way slide guide was designed to assist the insertion of axial laminar screws. The guide locks the screw outlet point to position and guides the screw inlet point, which improves the accuracy and safety of screw placement. [ABSTRACT FROM AUTHOR]

As one of the most important wheat-producing areas in China, wheat is prone to heat stress during the grain filling period in the Huang-Huai-Hai Plain (3HP), which lowers yields and degrades the grain quality of wheat. To assess the effects of spraying potassium dihydrogen phosphate (KH2PO4) on the physiological traits in flag leaves and grain yield (GY) and quality under heat stress during the filling period, we conducted a two-year field experiment in the winter wheat growing seasons of 2020–2022. In this study, spraying water combined with heat stress (HT), 0.3% KH2PO4 (KDP), and 0.3% KH2PO4 combined with heat stress (PHT) were designed, and spraying water alone was used as a control (CK). The dates for the spraying were the third and eleventh day after anthesis, and a plastic film shed was used to impose heat stress on the wheat plants during the grain filling period. The results showed that spraying KH2PO4 significantly improved the chlorophyll content and net photosynthesis rate (Pn) in flag leaves compared with the non-sprayed treatments. Compared with CK, the Pn in HT decreased by 8.97% after heat stress, while Pn in PHT decreased by 7.44% compared to that of KDP. The activities of superoxide dismutase, catalase, and peroxidase in flag leaves were significantly reduced when the wheat was subjected to heat stress, while malonaldehyde content increased, and the enzyme activities were significantly enhanced when KH2PO4 was sprayed. Heat stress significantly decreased the contribution rate of dry matter accumulation (DM) after anthesis of wheat to grain (CRAA), whereas spraying KH2PO4 significantly increased the CRAA and harvest index. At maturity, the DM in CK was significantly higher than that in HT, KDP was significantly higher than PHT, and KDP had the highest DM. Compared with CK, the GY in KDP significantly increased by 9.85% over the two years, while the GY in HT decreased by 11.44% compared with that of CK, and the GY in PHT decreased by 6.31% compared to that of KDP. Spraying KH2PO4 after anthesis primarily helped GY by maintaining a high thousand grain weight to lessen the negative effects of heat stress on wheat. Moreover, heat stress significantly reduced protein concentration, wet gluten content, dough development time, and hardness index in grains of mature, while spraying KH2PO4 maintained a sufficient grain quality under the conditions of achieving higher yields. Overall, spraying KH2PO4 after anthesis could enhance the heat stress resistance of wheat and maintain the photosynthetic capacity of flag leaves, ensuring the dry matter production and reducing the negative effects on grain yield and quality in the 3HP. [ABSTRACT FROM AUTHOR]

Chlorophyll a concentration and suspended matter concentration, as typical water quality parameters related to spectral characteristics, are essential for characterizing the degree of eutrophication in water bodies. They have become crucial indicators for water quality assessment of inland water bodies. The support vector regression model (SVR) is suitable for small samples, has excellent generalization ability, and has high prediction accuracy. Still, it has the problem of difficult selection of model parameters and quickly falling into local extremes. To solve this problem, a hybrid Differential Evolution-Grey Wolf Optimizer (DE-GWO) algorithm is introduced into the parameter selection process of the support vector regression model, and an improved SVR algorithm (DE-GWO-SVR) is proposed for the remote sensing inversion of chlorophyll a concentration and suspended sediment concentration in water bodies. In this paper, the spectral reflectance of the water surface and the chlorophyll a and broken matter concentration values were obtained by field measurements in the Tangdao Bay waters of Qingdao, Shandong Province. The inverse model between the concentration values of the two water quality parameters and the corresponding sensitive factors was established by first determining the sensitive factors based on the response of the spectral reflectance to the two water quality parameters and introducing the DE-GWO optimization algorithm into the parameter selection process of the SVR model. Finally, the accuracy of the model was verified using Sentinel II satellite remote sensing spectral data, and then the inverse accuracy of the two water quality parameters was obtained. The mean relative error (MRE) of the chlorophyll a prediction model built by the DE-GWO algorithm optimizing the SVR is 25.1%, and the mean relative error (MRE) of the suspended matter prediction model is 32.5%. The inversion results were all better than the other models (linear regression, SVR, and GWO-SVR model). When the best model, built from the measured water surface spectral data, was applied to the Sentinel II satellite data, the improved SVR model outperformed the other models in terms of mean relative error. The experimental results confirm that the DE-GWO-SVR algorithm is an effective method for remote sensing inversion of chlorophyll a and suspended matter concentrations in water bodies, which can provide a reference for remote sensing inversion of chlorophyll a and suspended matter concentrations in Chinese offshore waters and subsequent scientific management of waters. [ABSTRACT FROM AUTHOR]

In recent years, Chinese offshore wind farms have experienced varying degrees of foundation scour since their completion. The maximum scour depth of pile foundations has far exceeded the design scour depth, which seriously threatens the safety of wind turbines. Among the current scour depth prediction formulas, the values calculated by the Chinese specification 65-1, 65-2 formula are small and the prediction results are on the dangerous side. The calculated value of the American Hec-18 formula is safe but conservative. The prediction formula of other specifications has a large deviation from the actual situation. Based on the available test data, the main factors influencing the local scour depth of pile foundations, the gauge analysis method, and the group method of data handling (GMDH), this paper proposes a prediction formula for the local scour depth of monopile foundation under the action of wave–current. In addition, monopile scour flume experiments were conducted. Combining the experimental data of the flume test and the scour monitoring data of the Rudong wind farm in Jiangsu, the calculated values of the depth prediction equation in this paper and the Chinese code equation, DNV code equation, HEC-18 equation, Rudolph equation, and Raaijmakers equation were compared and analyzed. The results show that the relative error, mean relative error, variance, and normalized variance between the predicted and measured values of this paper's formula are smaller than those of other prediction formulas. The formula in this paper has a high calculation accuracy and practical application value. [ABSTRACT FROM AUTHOR]

Marine oil spills can cause serious damage to marine ecosystems and biological species, and the pollution is difficult to repair in the short term. Accurate oil type identification and oil thickness quantification are of great significance for marine oil spill emergency response and damage assessment. In recent years, hyperspectral remote sensing technology has become an effective means to monitor marine oil spills. The spectral and spatial features of oil spill images at different levels are different. To accurately identify oil spill types and quantify oil film thickness, and perform better extraction of spectral and spatial features, a multilevel spatial and spectral feature extraction network is proposed in this study. First, the graph convolutional neural network and graph attentional neural network models were used to extract spectral and spatial features in non-Euclidean space, respectively, and then the designed modules based on 2D expansion convolution, depth convolution, and point convolution were applied to extract feature information in Euclidean space; after that, a multilevel feature fusion method was developed to fuse the obtained spatial and spectral features in Euclidean space in a complementary way to obtain multilevel features. Finally, the multilevel features were fused at the feature level to obtain the oil spill information. The experimental results show that compared with CGCNN, SSRN, and A2S2KResNet algorithms, the accuracy of oil type identification and oil film thickness classification of the proposed method in this paper is improved by 12.82%, 0.06%, and 0.08% and 2.23%, 0.69%, and 0.47%, respectively, which proves that the method in this paper can effectively extract oil spill information and identify different oil spill types and different oil film thicknesses. [ABSTRACT FROM AUTHOR]

Considering the flow‐independent viscosity of soil skeleton and the lateral inertia effect of pipe pile, the vertical dynamic behavior of a pipe pile embedded in unsaturated soil is investigated in this work when the pipe pile is subjected to the vertical dynamic loading. In order to model this problem, the fractional‐order standard linear solid (FSLS) model is introduced to characterize the flow‐independent viscosity of the soil skeleton, the Rayleigh‐Love rod model is employed to specify the lateral inertia effect of pipe pile, while both vertical and radial soil displacement are entirely considered. The analytical solution of the pipe pile complex impedance is obtained through the rigorous theoretical derivation. Numerical results with known analytical solution are finally presented to discuss the influence of the model parameters for the FSLS model, lateral inertia effect and geometrical characteristic for the pipe pile, and saturation degree and intrinsic permeability for the unsaturated soil on the vertical complex impedance of the pipe pile. The main results indicate that enlarging the fractional‐order index, raising the strain relaxation time, or shortening the stress relaxation time will reduce the oscillation amplitude and natural frequency for the dynamic stiffness and damping of the pipe pile but will improve their mean value. The lateral inertia effect of pipe pile has effect on its complex impedance only in the high‐frequency region. The outer and inner radii of pipe pile, pile length, soil saturation degree, and soil intrinsic permeability are highly correlated with the pipe pile complex impedance. [ABSTRACT FROM AUTHOR]

Background: Metabolic reprogramming is a hallmark of various cancers. Targeting metabolic processes is a very attractive treatment for cancer. Renal cell carcinoma (RCC) is a type of metabolic disease, and the lipidomic profile of RCC is significantly altered compared with that of healthy tissue. However, the molecular mechanism underlying lipid metabolism regulation in RCC is not clear. Methods: The XF long-chain fatty acid oxidative stress test kits were used to assess the dependence on long-chain fatty acids and mitochondrial function after knockdown TRIM21 in RCC cells. The effect of TRIM21 on the lipid content in RCC cells was determined by metabolomics analysis, Oil Red O staining, and cellular Nile red staining. qRT-PCR and western blot were used to explore the relationship between TRIM21 and lipogenesis, and then the key molecule sterol regulatory element binding transcription factor 1 (SREBF1) was identified to interact with TRIM21 by immunoprecipitation, which was also identified in an orthotopic model. Subsequently, the relevance and clinical significance of TRIM21 and SREBF1 were analyzed by The Cancer Genome Atlas (TCGA) database, and 239 tissues were collected from RCC patients. Results: TRIM21 silencing attenuated the dependence of RCC cells on fatty acids, and enhanced lipid accumulation in RCC cells. TRIM21 overexpression significantly decreased lipid contents by decreasing the expression of lipogenic enzymes via ubiquitination-mediated degradation of SREBF1. SREBF1 is critical for TRIM21-mediated lipogenesis inhibition in vitro and in vivo. Moreover, TRIM21 expression is negatively correlated with SREBF1 expression, and TRIM21-SREBF1 is a reliable combinational biomarker for RCC prognosis. Conclusion: The findings from this study reveal a novel pathway through which TRIM21 inhibits the lipid metabolism process of RCC and shed light on the development of targeted metabolic treatment and prognosis diagnosis of RCC. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) caused COVID‐19 epidemic is worsening. Binding of the Spike1 protein of SARS‐CoV‐2 with the angiotensin‐converting enzyme 2 (ACE2) receptor mediates entry of the virus into host cells. Many reports show that protein arginine methylation by protein arginine methyltransferases (PRMTs) is important for the functions of these proteins, but it remains unclear whether ACE2 is methylated by PRMTs. Here, we show that PRMT5 catalyses ACE2 symmetric dimethylation at residue R671 (meR671‐ACE2). We indicate that PRMT5‐mediated meR671‐ACE2 promotes SARS‐CoV‐2 receptor‐binding domain (RBD) binding with ACE2 probably by enhancing ACE2 N‐glycosylation modification. We also reveal that the PRMT5‐specific inhibitor GSK3326595 is able to dramatically reduce ACE2 binding with RBD. Moreover, we discovered that meR671‐ACE2 plays an important role in ACE2 binding with Spike1 of the SARS‐CoV‐2 Omicron, Delta, and Beta variants; and we found that GSK3326595 strongly attenuates ACE2 interaction with Spike1 of the SARS‐CoV‐2 Omicron, Delta, and Beta variants. Finally, SARS‐CoV‐2 pseudovirus infection assays uncovered that PRMT5‐mediated meR671‐ACE2 is essential for SARS‐CoV‐2 infection in human cells, and pseudovirus infection experiments confirmed that GSK3326595 can strongly suppress SARS‐CoV‐2 infection of host cells. Our findings suggest that as a clinical phase II drug for several kinds of cancers, GSK3326595 is a promising candidate to decrease SARS‐CoV‐2 infection by inhibiting ACE2 methylation and ACE2‐Spike1 interaction. [ABSTRACT FROM AUTHOR]

The magnetic anisotropy can stabilize long-range ferromagnetic order in pristine two-dimensional (2D) crystals, which inspires the research of fundamental physics and spintronic devices based on 2D ferromagnetic materials. 2D ferromagnetic materials with intriguing magnetic properties offer an excellent platform for studying magnetism at 2D limit and exploring advanced spintronic devices. As the dimensionality decreases to 2D scale, 2D ferromagnetic materials exhibit distinctive magnetic properties compared with their bulk counterparts. In this Perspective, the recent progress and prospects of 2D ferromagnetic materials in magnetism, manipulation, and device applications are highlighted. [ABSTRACT FROM AUTHOR]

Background: Multiple studies have investigated the use of mesenchymal stem cells (MSCs) for patients undergoing high tibial osteotomy (HTO), and the effectiveness thereof remains controversial. Purpose: To analyze the effectiveness of intra-articular MSC injection in patients who underwent HTO in terms of clinical outcomes, radiological outcomes, and cartilage repair by a meta-analysis of the available literature. Study Design: Systematic review; Level of evidence, 3. Methods: The electronic databases of PubMed, Embase, Web of Science, and the Cochrane Library were searched from their inception to October 30, 2021, for comparative studies between patients who underwent HTO with and without intra-articular injection of MSCs, according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Study quality was assessed by the Coleman Methodology Score (CMS). Data with comparable results were pooled for meta-analysis. The primary outcomes of interest were the Hospital for Special Surgery (HSS), International Knee Documentation Committee (IKDC), Knee injury and Osteoarthritis Outcome Score (KOOS), and Lysholm scores, as well as the International Cartilage Regeneration & Joint Preservation Society (ICRS) grade of cartilage repair. Radiological outcomes including femorotibial angle, posterior tibial slope, and hip-knee-ankle (HKA) angle were included as secondary outcomes. A fixed-model effect was used for meta-analyses with low heterogeneity between studies (I 2 < 25%), while the random-model effect was used for medium- to high-heterogeneity analyses (I 2 ≥ 25%). Results: A total of 843 studies were screened, of which 6 studies with 452 patients met the inclusion criteria and were included. The mean CMS was 81.17. Patients with MSC injection had significantly higher Lysholm scores (P =.007) and HSS scores (P =.01) and higher proportions of ICRS grade 1 (P =.03) and grade 2 (P =.02) cartilage repair in the medial femoral condyle and grade 2 cartilage repair in the tibial plateau (P =.04). There were no significant differences between groups in the IKDC score, KOOS Pain and Symptoms subscales, femorotibial angle, posterior tibial slope, or HKA angle. Conclusion: Intra-articular MSC injection may enhance the cartilage repair for patients who undergo HTO. However, evidence of improvement in knee functions remains limited. Registration: CRD42021291345 (PROSPERO). [ABSTRACT FROM AUTHOR]

Inspired by the formation of microspheres by hexachlorocyclotriphosphazene and 4, 4′‐sulfonyldiphenol, polyphosphazene‐functionalized microspheres were developed. Benefits from the supported supper basic phosphazene, the yield exceeded 99 % at room temperature in the manner of second‐order reaction kinetics toward Knoevenagel reaction and was still maintained at 99 % after 16 runs. In the experimental temperature from 0 °C to 90 °C, the yield increased from 92 % to 99 %, reflecting that the catalyst had strong applicability under mild conditions. This behavior was conducive to energy conservation. Meanwhile, simple separation and recovery further enhanced this advantage. In addition, the catalyst was also found to be insensitive to aqueous solution or organic solvents such as toluene, THF, EtOH and CH3CN. This property gave the Knoevenagel reaction a vast choice. All these features exhibit that this novel catalyst is an attractive and applicable alternative in organic synthesis. [ABSTRACT FROM AUTHOR]

The bone marrow microenvironment of acute myeloid leukemia (AML) consists of various cell types and signaling factors, which serve as a niche supporting leukemia cells in their invasion of the human body. However, a systematic landscape of metabolic heterogeneity and its relationship with immunity in the AML microenvironment at single-cell resolution has not yet been established. Herein, we addressed this issue by analyzing 208,543 bone marrow cells from 40 AML patients and 3 healthy donors obtained from GSE130756. We focused on the metabolic preference of AML progenitor cells and diverse immune cells, especially myeloid immune cells and T cells. Accordingly, the immune evasion mechanism of leukemia cells was proposed from the view of the allocation of energy and oxygen, providing a novel direction of treatment. Finally, we tentatively proposed potential targets for AML metabolic therapy, including ENO1, GSTP1, MT-ND4L and UQCR11. Collectively, our analysis facilitates the development of personalized therapies targeting unique immunometabolic profiles. [ABSTRACT FROM AUTHOR]

Background: DNMT1 has been shown to be highly expressed in a variety of cancers, including breast cancer. However, the mechanism is not very clear. Therefore, we aim to reveal the mechanism of DNMT1 highly express in breast cancer. And we also want to explore the role of DNMT1 in tumour microenvironment promoting breast cancer progression. Results: In this study, we demonstrate that DNMT1 is overexpressed in breast cancer and that DNMT1 promotes breast cancer tumorigenesis and metastasis. We discovered that ZEB1 activates DNMT1 expression in breast cancer cells by recruiting P300 binding to the DNMT1 promoter and increasing its acetylation. Moreover, we revealed that tumour-associated macrophages (TAMs) increase DNMT1 expression in breast cancer cells via the IL-6-pSTAT3-ZEB1-DNMT1 axis in the tumour microenvironment. DNMT1 is required for TAM-mediated breast cancer cell migration. In addition, we confirmed that there were positive correlations among CD163 (TAM marker) expression, ZEB1 expression and DNMT1 expression in breast cancer patient tissues. Conclusions: Our study indicates that DNMT1 is necessary for TAM-mediated breast cancer metastasis. Decitabine (DAC), as a specific DNA methylation inhibitor and FDA-approved drug, is a bona fide drug for breast cancer treatment. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) can record the backscattering information of ground objects by capturing the phase difference of echo signal in different polarization combinations. Due to the characteristics of coherent imaging, there is a lot of coherent speckle noise in PolSAR images. However, most of the current PolSAR image despeckling algorithms are difficult to balance the ability in aspects of speckle suppression, polarization information retention, and edge structure preservation. This article proposes an adaptive PolSAR trilateral filter (PTF) based on the mechanism of scattering consistency, which consists of the following three steps: 1) the marker map of ground object scattering category is first obtained based on the dominant scattering mechanism determined by Freeman decomposition of the polarization covariance matrix; 2) next, the neighborhood pixels with the same scattering mechanism as the center pixel are screened out within an adaptive filtering window; and 3) a PTF is established through integration of the spatial proximity, polarization similarity, and local structural characteristics of the center pixel and the screened pixels to effectively suppress the speckle noises. Experimental results upon the data of Radarsat-2 and airborne synthetic aperture radar (AirSAR) demonstrate that PTF can not only suppress coherent speckle noise effectively without sacrificing the edge structure information but also retain the unique polarization information of PolSAR image. In particular, compared with the traditional six widely used PolSAR despeckling algorithms, PTF has been significantly improved in terms of equivalent number of looks (ENLs) and degree of difference (Dod) indexes by 2.49 times and 6 orders of magnitude, respectively. [ABSTRACT FROM AUTHOR]

The flow-independent viscosity of the soil skeleton has significant influence on the elastic wave propagation in soils. This work studied the bulk and Rayleigh waves propagation in three-phase viscoelastic soil by considering the contribution of the flow-independent viscosity from the soil skeleton. Firstly, the viscoelastic dynamic equations of three-phase unsaturated soil are developed with theoretical derivation. Secondly, the explicit characteristic equations of bulk and Rayleigh waves in three-phase viscoelastic soil are yielded theoretically by implementing Helmholtz resolution for the displacement vectors. Finally, the variations of the motion behavior for bulk and Rayleigh waves with physical parameters such as relaxation time, saturation, frequency, and intrinsic permeability are discussed by utilizing calculation examples and parametric analysis. The results reveal that the influence of soil flow-independent viscosity on the wave speed and attenuation coefficient of bulk and Rayleigh waves is significantly related to physical parameters such as saturation, intrinsic permeability, and frequency. [ABSTRACT FROM AUTHOR]

Land desertification is a major challenge to global sustainable development. Therefore, the timely and accurate monitoring of the land desertification status can provide scientific decision support for desertification control. The existing automatic interpretation methods are affected by factors such as "same spectrum different matter", "different spectrum same object", staggered distribution of desertification areas, and wide ranges of ground objects. We propose an automatic interpretation method for the remote sensing of land desertification that incorporates multi-scale local binary pattern (MSLBP) and spectral features based on the above issues. First, a multi-scale convolutional LBP feature extraction network is designed to obtain the spatial texture features of remote sensing images and fuse them with spectral features to enhance the feature representation capability of the model. Then, considering the continuity of the distribution of the same kind of ground objects in local space, we designed an adaptive median filtering method to process the probability map of the extreme learning machine (ELM) classifier output to improve the classification accuracy. Four typical datasets were developed using GF-1 multispectral imagery with the Horqin Left Wing Rear Banner as the study area. Experimental results on four datasets show that the proposed method solves the problem of ill classification and omission in classifying the remote sensing images of desertification, effectively suppresses the effects of "homospectrum" and "heterospectrum", and significantly improves the accuracy of the remote sensing interpretation of land desertification. [ABSTRACT FROM AUTHOR]

Industrial robots are characterized by good flexibility and a large working space, and offer a new approach for the machining of large and complex parts with small machining allowances (extra material allowed for subsequent machining). Parts of this type (such as aircraft skin parts, wind turbine blades, etc.) are easily deformed due to their large scale and low stiffness. Therefore, these parts cannot be directly machined according to the designed model. A feasible method is to plan a robotic machining path by using the point clouds of parts after clamping from onsite measurement which contains inherent defects of measurement such as noise points and abrupt points. In this article, a novel method is proposed to plan and optimize a robotic machining path that meets the requirements of smoothness, dexterity, and stiffness based on the point cloud from onsite measurement. The dual nonuniform rational B-spline curves of the machining path points and tool axis points are generated at first. Next, an objective function of smoothness optimization is established to filter out the local mutation of the path by considering the constraints of both the deformation energy and the deviation. Then, the objective function of robot postures optimization is established to optimize dexterity and Cartesian stiffness of a robot during the machining process. To show the feasibility of the proposed method, simulation and experiments are carried out. It is proved that the proposed method can generate a smooth machining trajectory. The stability of joint rotation and the rigidity and dexterity of the robot are improved during the machining process. [ABSTRACT FROM AUTHOR]

The excavation of the foundation pit will cause changes in the soil stress field around the foundation pit, and that may have adverse effects on the adjacent subway tunnels. In this paper, a complex deep foundation pit excavated in different sections is taken as the research object, and the support structure of the complex foundation pit project is introduced, which accumulates experience in the selection of support structure for similar projects. The finite element models are established by MIDAS/GTS software to evaluate the influence of excavation in different sections of the foundation pit on the tunnel deformation, and the accuracy of the finite element calculation results is verified by comparing the monitoring data. The results show that: The horizontal deformation of the subway tunnel is generally smaller than the vertical deformation. Tunnel monitoring should focus more on the development of the vertical deformation of the tunnel. The maximum vertical deformation and horizontal deformation of the tunnel are both smaller than the local specification limits, and the excavation of the foundation pit in this project has little influence on the deformation of the subway tunnel. [ABSTRACT FROM AUTHOR]

The effect of large deep foundation excavations on the surrounding existing tunnels is a problem that cannot be avoided in the current construction background. This effect is difficult to assess precisely, especially when the geometry of the tunnels and pits is complex. In this paper, a three-dimensional (3D) finite element model has been developed based on a case containing four complex intersecting tunnels and a large deep foundation pit. The model used the hardening soil model with small-strain stiffness (HSS) and the Hoek–Brown (HB) model to describe the mechanical properties of the soil and rock, and various methods including the standard penetration test (SPT) and heavy dynamic penetration test (HDPT) were used to determine the model parameters. The results of the analysis are as follows: the excavation of the foundation pit caused the tunnels to heave and the heave deformation conforms to the normal distribution; the maximum heave of numerical simulation is 3.1 mm which is consistent with the field data; the horizontal displacement, horizontal convergence, and vertical convergence of the tunnels caused by the excavation of the pits are small, and all kinds of deformations meet the control requirements; the intersection of multiple tunnels shows obvious stress concentration when the tunnels were constructed, and the lining stress slightly decreases as excavation progresses. [ABSTRACT FROM AUTHOR]