Search

Abstract Aging is characterized by the accumulation of proteins that display amyloid-like behavior. However, the molecular mechanisms by which these proteins arise remain unclear. Here, we demonstrate that amyloid-like proteins are produced in a variety of human cell types, including stem cells, brain organoids and fully differentiated neurons by mistakes that occur in messenger RNA molecules. Some of these mistakes generate mutant proteins already known to cause disease, while others generate proteins that have not been observed before. Moreover, we show that these mistakes increase when cells are exposed to DNA damage, a major hallmark of human aging. When taken together, these experiments suggest a mechanistic link between the normal aging process and age-related diseases.

IntroductionIn mountainous tea plantations, which are the primary mode of tea cultivation in China, issues such as soil erosion and declining soil fertility are particularly severe. Although green manure cover is an effective agricultural measure for restoring soil fertility, its application in mountainous tea plantations has been relatively understudied.MethodsThis study investigated the effects of continuous green manure cover using the slope-protecting plant Ophiopogon japonicus on tea plant growth and soil microbial community structure. We implemented three treatments: 1 year of green manure coverage, 2 years of coverage, and a control, to study their effects on tea plant growth, soil physicochemical properties, and soil bacterial and fungal communities.ResultsResults demonstrate that green manure coverage significantly promote the growth of tea plants, enhanced organic matter and pH levels in soil, and various enzyme activities, including peroxidases and cellulases. Further functional prediction results indicate that green manure coverage markedly promoted several carbon cycling functions in soil microbes, including xylanolysis, cellulolysis, degradation of aromatic compounds, and saprotrophic processes. LEfSe analysis indicated that under green manure cover, the soil tends to enrich more beneficial microbial communities with degradation functions, such as Sphingomonas, Sinomonas, and Haliangium (bacteria), and Penicillium, Apiotrichum, and Talaromyce (fungi). In addition. Random forest and structural equation models indicated that carbon cycling, as a significant differentiating factor, has a significant promoting effect on tea plant growth.DiscussionIn the management practices of mountainous tea plantations, further utilizing slope-protecting plants as green manure can significantly influence the soil microbial community structure and function, enriching microbes involved in the degradation of organic matter and aromatic compounds, thereby positively impacting tea tree growth and soil nutrient levels.

Abstract 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. Graphical Abstract

Abstract Glioblastoma (GBM) is the most common malignant primary brain tumor. Despite comprehensive treatment with traditional surgery, radiotherapy, and chemotherapy, the median survival rate is

The tremendous improvement for satellite remote sensing has provided mankind with the feasibility of observing large-scale ocean phenomena from space. Passive remote sensing of ocean color has delivered continuous records of ocean surface information, although there are inherent limitations to this natural light-dependent measurement. The development of spaceborne lidars breaks through the restrictions of passive techniques that are unavailable for obtaining the vertical characteristics of water column, and provides a new approach to the detection of ocean subsurface. In this study, the active spaceborne lidar ICESat-2 and passive optical satellite are fused, to establish an inversion method of lidar equation and semi-analytical model. The diffuse attenuation coefficient (kd) was estimated in various areas of the global oceans, and vertical profiles of chlorophyll-a concentration (Chla) were further obtained. The results showed that Mean Absolute Errors (MAE) of Chla derived by the proposed method with BGC-Argo data were in the range of 0.003–0.14 mg/m3, and the Mean Absolute Percentage Error (MAPE) was between 12.5 % and 26.9 %; Comparison with ocean color products indicated that R2 of Chla was in the range of 0.84–0.98, and MAE of kd was in the range of 0.004–0.021 m−1. The multiple validation approaches have demonstrated the effectiveness of water column profile detection with ICESat-2 lidar and passive satellite, which contributes to the enhanced understanding of ocean subsurface phytoplankton and their spatial or temporal variations.

Abstract The metastasis of non-small cell lung cancer (NSCLC) is the leading death cause of NSCLC patients, which requires new biomarkers for precise diagnosis and treatment. Circular RNAs (circRNAs), the novel noncoding RNA, participate in the progression of various cancers as microRNA or protein sponges. We revealed the mechanism by which circEPB41L2 (hsa_circ_0077837) blocks the aerobic glycolysis, progression and metastasis of NSCLC through modulating protein metabolism of PTBP1 by the E3 ubiquitin ligase TRIP12. With ribosomal RNA-depleted RNA seq, 57 upregulated and 327 downregulated circRNAs were identified in LUAD tissues. circEPB41L2 was selected due to its dramatically reduced levels in NSCLC tissues and NSCLC cells. Interestingly, circEPB41L2 blocked glucose uptake, lactate production, NSCLC cell proliferation, migration and invasion in vitro and in vivo. Mechanistically, acting as a scaffold, circEPB41L2 bound to the RRM1 domain of the PTBP1 and the E3 ubiquitin ligase TRIP12 to promote TRIP12-mediated PTBP1 polyubiquitylation and degradation, which could be reversed by the HECT domain mutation of TRIP12 and circEPB41L2 depletion. As a result, circEPB41L2-induced PTBP1 inhibition led to PTBP1-induced PKM2 and Vimentin activation but PKM1 and E-cadherin inactivation. These findings highlight the circEPB41L2-dependent mechanism that modulates the “Warburg Effect” and EMT to inhibit NSCLC development and metastasis, offering an inhibitory target for NSCLC treatment.

Abstract Many types of cancer cells, including colorectal cancer cells (CRC), can simultaneously enhance glycolysis and repress the mitochondrial tricarboxylic acid (TCA) cycle, which is called the Warburg effect. However, the detailed mechanisms of abnormal activation of the glycolysis pathway in colorectal cancer are largely unknown. In this study, we reveal that the protein arginine methyltransferase 1 (PRMT1) promotes glycolysis, proliferation, and tumorigenesis in CRC cells. Mechanistically, PRMT1-mediated arginine asymmetric dimethylation modification of phosphoglycerate kinase 1 (PGK1, the first ATP-producing enzyme in glycolysis) at R206 (meR206-PGK1) enhances the phosphorylation level of PGK1 at S203 (pS203-PGK1), which inhibits mitochondrial function and promotes glycolysis. We found that PRMT1 and meR206-PGK1 expression were positively correlated with pS203-PGK1 expression in tissues from colorectal cancer patients. Furthermore, we also confirmed that meR206-PGK1 expression is positively correlated with the poor survival of patients with colorectal cancer. Our findings show that PRMT1 and meR206-PGK1 may become promising predictive biomarkers for the prognosis of patients with CRC and that arginine methyltransferase inhibitors have great potential in colorectal cancer treatment.

Elevation information from light detection and ranging (LiDAR) data relieve the phenomenon of “same spectrum with different object” in hyperspectral images (HSI) classification. Consequently, HSI and LiDAR joint classification is a significant research topic. However, existing methods encounter several challenges. Primarily, there exists a deficiency in intraclass information interaction and underutilization of discriminative feature. Furthermore, the process of labeling samples is time-consuming and laborious. To solve the aforementioned issues, a classification method based on pseudolabeled contrastive learning is proposed to exploit substantial amounts of unlabeled information in order to enhance intraclass information interaction. The proposed method is divided into two stages for semisupervised classification. In the first stage, an unsupervised feature extraction network is designed to improve the interaction of features from multimodal data. A multimodal data cross-attention module is proposed to enhance the interaction of multimodal information at corresponding locations. Exploiting pseudolabeling contrastive learning module facilitates the interaction of information between intraclass objects. In the second stage, supervised classification with a limited number of labeled samples is performed. The multisource discriminatively consolidate feature module is designed to generate discriminative features, which are used to guide the fusion feature enhancement process. Apart from this, this module leverages multiscale features to expand the receptive field. Tested on both self-constructed and public datasets, the proposed method provides higher classification accuracy than some existing methods with a limited amount of labeled samples.

With the wide application of IoT technologies in the power sector, power IoT faces serious security challenges, which can be severely affected by malicious attacks and unauthorised access. Meanwhile, devices in power IoT are usually resource-constrained and deployed in a decentralised manner, making them vulnerable to physical attacks. Therefore, a robust and reliable lightweight authentication scheme needs to be constructed to guarantee its information security. A lightweight authentication scheme for the power IoT based on Physical Unclonable Function (PUF) and Chebyshev chaotic map is proposed in this paper, which achieves two-way authentication and session key negotiation between gateways and terminal devices. Comparing with traditional authentication schemes, the PUF and Chebyshev chaotic map used in this scheme have high security and lower resource overhead. PUF is used to generate Challenge and Response Pairs (CRPs) for two-way authentication and key negotiation without storing any secret information about authentication in the device memory. At the same time, Chebyshev chaotic map is used to protect the transmission of secret information such as CRPs in insecure channels. The solution is therefore resistant to attacks such as physical, machine learning modelling and impersonation, ensuring the information security of the authentication process. The proposed scheme is analyzed and verified using the formal verification tool ProVerif and improved BAN logic along with informal methods. The verification results show that the scheme satisfies 12 security properties such as two-way authentication and user anonymity. Comparative analysis with existing related authentication schemes shows that the proposed scheme has low computation and communication costs while guaranteeing security, thus rendering it suitable for resource-constrained terminal devices in the power IoT.

Abstract 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.

At present, domain adaptation (DA) methods have made noteworthy advancements in cross-scene hyperspectral image (HSI) classification. Their success largely hinges on the alignment of distributions between source and target domains, which is a critical step in extracting domain-invariant features. However, this intense focus on domain-invariant feature extraction frequently leads to the neglect of class-discriminative features, limiting their utility in cross-scene coastal wetland classification, where the nuanced identification of different classes is crucial. In this paper, a feature disentanglement based domain adaptation network (FDDAN) is proposed to disentangle and exclude domain-specific features and class-invariant features, thereby obtaining class-specific domain-invariant features for classification tasks. Specifically, a transformer and convolution fusion-based feature extraction network is designed to capture global–local mixed features. To align domain distributions and learn shared features, two corresponding disentanglers separate domain-invariant features and domain-specific features from mixed features. Furthermore, to allow domain-invariant features containing purer category discriminative information, class-invariant features are also segregated. In addition, an adversarial learning strategy between three features is utilized to simultaneously enhance the transferability and discriminability of domain-invariant features. The effectiveness of FDDAN is demonstrated by the experimental results obtained from three cross-scene unmanned aerial vehicle datasets collected in coastal wetlands and a publicly available dataset. We will make our datasets and source code publicly available upon acceptance of the paper.

During the sinking process of ultra-large onshore caisson, the structural stress of caisson will change significantly due to the changes of earth pressure, end resistance, water level in caisson and attitude of caisson. Based on the south anchoring onshore caisson project of Nanjing Longtan Yangtze River Bridge, the structural stress analyses were carried out based on the automated caisson monitoring data. The results show that the automated monitoring system can acquire the key data of caisson in real time and efficiently, and the monitoring results reflect the structural stress and displacement characteristics in different construction stages. The vertical earth pressure at the bottom four corners of the caisson is significantly greater than the other positions, indicating that there is an obvious stress concentration phenomenon at the four corners. The stress at the bottom of the horizontal earth pressure monitoring is smaller than that at the middle and bottom of caisson, which verifies the existence of stress disturbance area around the cutting. Steel shell concrete at the bottom of partition wall is the weakest position of caisson structure, and the structural safety risk is higher from the first casting to the second sinking. If the risk of tensile cracking occurs in the process of casting, each diagonal can be carried out several times to make full use of the structure's own resistance to reduce the development of tensile stress. The conclusion can be used for reference in the design and construction of similar projects.

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.

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 B-cell acute lymphoblastic leukemia (B-ALL) is an aggressive hematological disorder with a dismal prognosis. The dysregulation of histone acetylation is of great significance in the pathogenesis and progression of B-ALL. Regarded as a fundamental acetyltransferase gene, the role of HBO1 (lysine acetyltransferase 7/KAT7) in B-ALL has not been investigated. Herein, we found that HBO1 expression was elevated in human B-ALL cells and associated with poor disease-free survival. Strikingly, HBO1 knockdown inhibited viability, proliferation, and G1-S cycle progression in B-ALL cells, while provoking apoptosis. In contrast, ectopic overexpression of HBO1 enhanced cell viability and proliferation but inhibited apoptotic activation. The results of in vivo experiments also certificated the inhibitory effect of HBO1 knockdown on tumor growth. Mechanistically, HBO1 acetylated histone H3K14, H4K8, and H4K12, followed by upregulating CTNNB1 expression, resulting in activation of the Wnt/β-catenin signaling pathway. Moreover, a novel small molecule inhibitor of HBO1, WM-3835, potently inhibited the progression of B-ALL. Our data identified HBO1 as an efficacious regulator of CTNNB1 with therapeutic potential in B-ALL.

The concepts of (digital) platform and (digital) infrastructure have been widely used and discussed in recent media research, and in neighbouring fields such as science and technology studies (STS). Yet there is considerable confusion about these concepts and the relations between them. This article seeks to bring these concepts together more coherently by showing how “platformisation” might be understood in terms of its impacts on information infrastructure, including on the principles of openness and generativity underlying early internet architecture, and potential further effects on media and culture deriving from those impacts. To develop this perspective, we draw on research from legal studies which: (a) articulates these principles more fully than in recent media studies and STS; (b) understands infrastructures as resources subject to political contestation; and (c) in the work of Julie Cohen, interprets digital platforms as strategies for disciplining infrastructures. We discuss how such a perspective might complement approaches to digital platforms and infrastructures to be found in political economy of media and internet governance research. We then apply the perspective to a case study: the transition of online music from chaotic experiments with alternative models of distribution in the early century to a thoroughly platformised environment in the 2020s.

Abstract With the increase in inverter‐based renewable energy resources, the complexity and uncertainty of low‐carbon power systems have increased significantly. Deep reinforcement learning (DRL)–based approaches have been extensively studied for frequency control to overcome the limitations of traditional model‐based approaches. The goal of DRL‐based methods for primary frequency control is to minimise load shedding while satisfying frequency safety requirements, thereby reducing control costs. However, the vulnerabilities of DRL models pose new security threats to power systems. These threats have not been identified and addressed in the existing literature. Therefore, in this paper, a series of vulnerability assessment methods are proposed for DRL‐based frequency control with a focus on the under‐frequency load shedding (UFLS) problem. Three adversarial sample production methods are designed with different optimisation directions: Q‐value‐based FGSM (Q‐FGSM), action‐based JSMA (A‐JSMA), and state‐action‐based CW (SA‐CW). Furthermore, combining the advantages of the above three attack methods, a hybrid adversarial attack algorithm is designed, Q‐value‐state‐action‐based mix (QSA‐MIX), to significantly affect the decision process of the DRL model. In case studies of the IEEE39 bus system, the proposed attack methods had a severe impact on system operation and control. In particular, the high attack transferability of the proposed attack algorithms in a black‐box setting provides further evidence that the vulnerability of current DRL‐based control schemes is prevalent.

Abstract Background Yin Yang‐1 (YY1) is identified as a transcription factor with multiple functions. However, the role of YY1 in tumorigenesis remains controversial and its regulatory effects may depend upon not only cancer types, but also its interacting partners, chromatin structure, and the context in which it acts. It has been detected that YY1 was highly expressed in colorectal cancer (CRC). Intriguingly, many YY1‐repressed genes exhibit tumor suppressive potential while YY1 silencing is related to chemotherapy resistance. Therefore, it is crucial to meticulously explore YY1 protein structure and the dynamic alteration of its interactome in each cancer type. This review attempts to describe the structure of YY1, summarize the mechanism that influence the expression level of YY1 and also highlight the recent advances in our understanding of regulatory insights of YY1 functions in CRC. Methods Related studies were identified through scoping search of PubMed, Web of science, Scopus and Emhase concerning the terms of “colorectal cancer”, colorectal carcinoma” or CRC with “YY1”. The retrieval strategy included title, abstract, and keywords with no language limitations. All the included articles were categorized depending on the mechanisms they explored. Results In total, 170 articles were identified for further screening. After removing the duplication, not relevant outcomes and review articles, 34 were finally included in the review. Among them, 10 articles revealed the reasons of YY1 high expression in CRC, 13 articles explored YY1 function in CRC, and 11 articles fell into both aspects. In addition, we also summarized 10 clinical trials concerning the expression and activity of YY1 in various diseases, which offers a hint for future application. Conclusions YY1 is highly expressed in CRC and broadly recognized as an oncogenic factor during the whole course of CRC. Sporadic controversial views are raised in term of CRC treatment, reminding us that future studies should take the influence of therapeutic regimens into concern.

Abstract Protein arginine methyltransferase 2 (PRMT2) is involved in several biological processes via histone methylation and transcriptional regulation. Although PRMT2 has been reported to affect breast cancer and glioblastoma progression, its role in renal cell cancer (RCC) remains unclear. Here, we found that PRMT2 was upregulated in primary RCC and RCC cell lines. We demonstrated that PRMT2 overexpression promoted RCC cell proliferation and motility both in vitro and in vivo. Moreover, we revealed that PRMT2-mediated H3R8 asymmetric dimethylation (H3R8me2a) was enriched in the WNT5A promoter region and enhanced WNT5A transcriptional expression, leading to activation of Wnt signaling and malignant progression of RCC. Finally, we confirmed that high PRMT2 and WNT5A expression was strongly correlated with poor clinicopathological characteristics and poor overall survival in RCC patient tissues. Our findings indicate that PRMT2 and WNT5A may be promising predictive diagnostic biomarkers for RCC metastasis. Our study also suggests that PRMT2 is a novel therapeutic target in patients with RCC.

Abstract 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.

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.

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.

Recently, graph convolutional networks (GCNs) has attracted wide attention on the wetland classification with limited samples. However, traditional approaches of superpixel generation rely on artificial experience and the spatial information is ignored during the construction of graph structure, which limits the classification performance. To address these problems, a feature-guided dynamic graph convolutional network (FG-DGCN) is proposed for wetland classification. First, a learnable superpixel generation module is proposed to generate adaptive superpixel boundaries, which composed of a pixel-wise feature enhancement block and a superpixel generation block. The former is utilized to improve the discrimination of features and the latter is applied to adjust the representation of superpixels by training. Second, a feature-guided adjacency matrix update mechanism is designed to dynamically capture and fuse the spectral and spatial correlations of graph nodes, promoting the aggregation of neighborhood information. Finally, the features are differentially projected back to the pixel space for wetland classification. Experiments on three wetland datasets demonstrate the superiority of FG-DGCN over state-of-the-art methods.

Reasonable fusion of multimodal data can increase the accuracy of remote sensing classification. In this article, an effective morphological convolution and attention calibration network is proposed for the joint classification of the hyperspectral image (HSI) and light detection and ranging (LiDAR). First, we devise a morphological convolution block, which combines the dilation and erosion operations in morphology with convolution to better capture the feature from the HSI and LiDAR. Next, we designed a dual attention module that uses self-attention to calibrate features and cross attention to combine multisource complementary information, respectively. Finally, considering the features of semantic inconsistency and different scales, the adaptive feature fusion module is introduced to dynamically fuse multimodal features. To verify the progressiveness of the proposed network, we experiment on three common datasets and one self-made dataset. The result shows that our network performs better than the state-of-the-art models.

Abstract 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 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 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.

Multi-source remote sensing monitoring plays a crucial part in the ecological protection and restoration of coastal wetlands. However, due to the inaccessible of wetlands environment, lacking of labeled samples is a challenge in wetland classification. In this article, an unsupervised cross-domain feature fusion and supervised classification network (UF2SCN) is proposed for coastal wetland classification, which fuses hyperspectral image (HSI) and light detection and ranging (LiDAR) data. First, an unsupervised single branch end to end network is developed to get HSI and LiDAR fusion feature, in which a feature extraction model with spectral attention is deployed to obtain the average distribution characteristics of all samples, and the HSI and LiDAR data is utilized to guide the whole process. Second, a supervised classification network with spatial attention is applied to used fusion feature for classification, which uses the limited samples. Finally, a two stages training strategy is proposed to improve the ability of feature fusion. Experiments conducted on two coastal wetland datasets created by ourselves prove the validity of the proposed method on HSI and LiDAR classification for coastal wetland.

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.

A new process for the development and utilization of titanium-iron sand ore from Yunnan province is proposed. We provide a detailed analysis of the chemical composition and mineral structure of the raw material, and describe a pretreatment involving low-temperature oxidation and reduction, hydrofluoric acidolysis, and hydrochloric acid leaching to remove impurities and generate titanium-rich materials. Hydrofluoric acid was used to break down the complex titanite, silica-titanium, and calcium-titanium mixture in the starting mineral into simple oxides. The results have indicated that the thermal reduction process should not be conducted at elevated temperatures. Optimal process conditions are established for the leaching process: leaching temperature of 80 °C; leaching time of 240 min; hydrofluoric acid concentration of 3%; liquid–solid ratio of 6 m l/g. Under these conditions, the extent of TFe leaching reached 96%, while the leaching of Ca and Mg reached 94%. The TiO _2 grade of the final titanium-rich material was 89.95%, with a CaO content of 0.39%, a MgO content of 0.15%, and a TFe content of 7.5%. This material can serve as raw material for subsequent chlorination.

Wiskott-Aldrich syndrome is caused by mutations in WASP, but the underlying mechanisms remain to be explored. Here the authors reveal that WASP deficiency results in aberrant RNA splicing, and that WASP regulates the transcription of splicing factor genes and co-transcriptional RNA splicing via a phase-separation process that involves splicing factors and nascent RNA.

In-situ layerwise imaging measurement of laser powder bed fusion (LPBF) provides a wealth of forming and defect data which enables monitoring of components quality and powder bed homogeneity. Using high-resolution camera layerwise imaging and image processing algorithms to monitor fusion area and powder bed geometric defects has been studied by many researchers, which successfully monitored the contours of components and evaluated their accuracy. However, research for the methods of in-situ 3D contour measurement or component edge warping identification is rare. In this study, a 3D contour measurement method combining gray intensity and phase difference is proposed, and its accuracy is verified by designed experiments. The results show that the high-precision of the 3D contours can be achieved by the constructed energy minimization function. This method can detect the deviations of common geometric features as well as warpage at LPBF component edges, and provides fundamental data for in-situ quality monitoring tools.

The types of marine oil spill pollution are closely related to source tracing and pollution disposal, which is an important basis for oil spill pollution punishment. The types of marine oil spill pollution generally include different types of oil products as well as crude oil and its emulsions in different states. This paper designed and implemented two outdoor oil spill simulation experiments, obtained the hyperspectral and thermal infrared remote sensing data of different oil spill pollution types, constructed a hyperspectral recognition algorithm of oil spill pollution type based on classical machine learning, ensemble learning and deep learning models, and explored to improve the identification ability of hyperspectral oil spill pollution type by adding thermal infrared features. The research shows that hyperspectral combined with thermal infrared remote sensing can effectively improve the recognition accuracy of different oils, but thermal infrared remote sensing cannot be used to distinguish crude oil and high concentration water-in-oil emulsion. On this basis, the recognition ability of hyperspectral combined with thermal infrared for different oil film thicknesses is also discussed. The combination of hyperspectral and thermal infrared remote sensing can provide important technical support for emergency response to maritime emergencies and oil spill monitoring business of relevant departments.

Controller area network (CAN) are widely used in smart vehicles to realize information interactions between electronic control units and other devices in vehicles. Owing to an increase in external communication interfaces, the cybersecurity of in-vehicle CAN bus networks is threatened. In-vehicle CAN intrusion detection systems with high detection rates and low false-negative rates have become important security protection measures for automotive networks. The boundary of the current machine learning-based in-vehicle CAN bus intrusion detection algorithm to determine the anomalous behavior triggered by CAN messages is unclear, and a validity check is required after the intrusion detection algorithm is designed. To solve the low coverage rate problem in the process of validating intrusion detection algorithms, an in-vehicle CAN fuzz-testing message generation model, the field-associative mutation generation adversarial network (FAMGAN), is proposed. To improve the defects of high randomness in generating messages in traditional fuzz-testing algorithms, FAMGAN adopts field division based on a conditional random field and the field association method based on the Apriori algorithm. Experiments were conducted on a real car using a code-built intrusion detection algorithm. The results demonstrate that FAMGAN can efficiently generate anomalous CAN messages and evaluate the performance of an in-vehicle CAN intrusion detection algorithm.

Abstract As the most commonly diagnosed lung cancer, non–small cell lung carcinoma (NSCLC) is regulated by many long noncoding RNAs (lncRNAs). In the present study, we found that SH3PXD2A-AS1 expression in NSCLC tissues was upregulated compared with that in normal lung tissues in The Cancer Genome Atlas (TCGA) database by using the GEPIA website. K-M analysis was performed to explore the effects of this molecule on the survival rate in NSCLC. The results demonstrated that SH3PXD2A-AS1 expression was increased in human NSCLC, and high SH3PXD2A-AS1 expression was correlated with poor overall survival. SH3PXD2A-AS1 promotes lung cancer cell proliferation and accelerates cell cycle progression in vitro. Animal studies validated that knockdown of SH3PXD2A-AS1 inhibits NSCLC cell proliferation in vivo. Mechanically, SH3PXD2A-AS1 interacted with DHX9 to enhance FOXM1 expression, promote tumour cell proliferation and accelerate cell cycle progression. Altogether, SH3PXD2A-AS1 promoted NSCLC growth by interacting with DHX9 to enhance FOXM1 expression. SH3PXD2A-AS1 may serve as a promising predictive biomarker for the diagnosis and prognosis of patients with NSCLC.

Abstract Schizophrenia is a complex polygenic disease that is affected by genetic, developmental, and environmental factors. Accumulating evidence indicates that environmental factors such as maternal infection and excessive prenatal neuroinflammation may contribute to the onset of schizophrenia by affecting epigenetic modification. We recently identified a schizophrenia-associated upregulated long noncoding RNA (lncRNA) RP5-998N21.4 by transcriptomic analysis of monozygotic twins discordant for schizophrenia. Importantly, we found that genes coexpressed with RP5-998N21.4 were enriched in immune defense-related biological processes in twin subjects and in RP5-998N21.4-overexpressing (OE) SK-N-SH cell lines. We then identified two genes encoding an interferon-induced protein with tetratricopeptide repeat (IFIT) 2 and 3, which play an important role in immune defense, as potential targets of RP5-998N21.4 by integrative analysis of RP5-998N21.4 OE-induced differentially expressed genes (DEGs) in SK-N-SH cells and RP5-998N21.4-coexpressed schizophrenia-associated DEGs from twin subjects. We further demonstrated that RP5-998N21.4 positively regulates the transcription of IFIT2 and IFIT3 by binding to their promoter regions and affecting their histone modifications. In addition, as a general nuclear coactivator, RMB14 (encoding RNA binding motif protein 14) was identified to facilitate the regulatory role of RP5-998N21.4 in IFIT2 and IFIT3 transcription. Finally, we observed that RP5-998N21.4 OE can enhance IFIT2- and IFIT3-mediated immune defense responses through activation of signal transducer and activator of transcription 1 (STAT1) signaling pathway in U251 astrocytoma cells under treatment with the viral mimetic polyinosinic: polycytidylic acid (poly I:C). Taken together, our findings suggest that lncRNA RP5-998N21.4 is a critical regulator of immune defense, providing etiological and therapeutic implications for schizophrenia.

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 Transketolase (TKT) which is an important metabolic enzyme in the pentose phosphate pathway (PPP) participates in maintaining ribose 5-phosphate levels. TKT is necessary for maintaining cell growth. However, we found that in addition to this, TKT can also affect tumor progression through other ways. Our previous study indicate that TKT could promote the development of liver cancer by affecting bile acid metabolism. And in this study, we discovered that TKT expression was remarkably upregulated in colorectal cancer, abnormal high expression of TKT is associated with poor prognosis of colorectal cancer. Additionally, TKT promoted colorectal cancer cell growth and metastasis. Further study demonstrated that TKT interacted with GRP78 and promoted colorectal cancer cell glycolysis through increasing AKT phosphorylation, thereby enhancing colorectal cancer cell metastasis. Thus, TKT is expected to become an indicator for judging the prognosis of colorectal cancer, and provide a theoretical basis for drug development of new treatment targets for colorectal cancer.

Abstract Circular RNAs (circRNAs) are classified as noncoding RNAs because they are devoid of a 5’ end cap and a 3’ end poly (A) tail necessary for cap-dependent translation. However, increasing numbers of translated circRNAs identified through high-throughput RNA sequencing overlapping with polysome profiling indicate that this rule is being broken. CircRNAs can be translated in cap-independent mechanism, including IRES (internal ribosome entry site)-initiated pattern, MIRES (m6A internal ribosome entry site) -initiated patterns, and rolling translation mechanism (RCA). CircRNA-encoded proteins harbour diverse functions similar to or different from host proteins. In addition, they are linked to the modulation of human disease including carcinomas and noncarcinomas. CircRNA-related translatomics and proteomics have attracted increasing attention. This review discusses the progress and exclusive characteristics of circRNA translation and highlights the latest mechanisms and regulation of circRNA translatomics. Furthermore, we summarize the extensive functions and mechanisms of circRNA-derived proteins in human diseases, which contribute to a better understanding of intricate noncanonical circRNA translatomics and proteomics and their therapeutic potential in human diseases.

Abstract Protein arginine methyltransferase 1 (PRMT1) is able to promote breast cancer cell proliferation. However, the detailed mechanisms of PRMT1-mediated breast cancer cell proliferation are largely unknown. In this study, we reveal that PRMT1-mediated methylation of EZH2 at the R342 site (meR342-EZH2) has a great effect on PRMT1-induced cell proliferation. We also demonstrate that meR342-EZH2 can accelerate breast cancer cell proliferation in vitro and in vivo. Further, we show that meR342-EZH2 promotes cell cycle progression by repressing P16 and P21 transcription expression. In terms of mechanism, we illustrate that meR342-EZH2 facilitates EZH2 binding with SUZ12 and PRC2 assembly by preventing AMPKα1-mediated phosphorylation of pT311-EZH2, which results in suppression of P16 and P21 transcription by enhancing EZH2 expression and H3K27me3 enrichment at P16 and P21 promoters. Finally, we validate that the expression of PRMT1 and meR342-EZH2 is negatively correlated with pT311-EZH2 expression. Our findings suggest that meR342-EZH2 may become a novel therapeutic target for the treatment of breast cancer.

Abstract Background Traditional Chinese medicine has been found effective for the therapy of knee osteoarthritis (KOA). This study was aimed at investigating the underlying mechanism of Bugan Rongjin decoction (BGRJ) in treating the postmenopausal KOA. Results Ovariectomized rat model of KOA and LPS-induced chondrocytes were successfully constructed for in vivo and in vitro model of postmenopausal KOA. X-ray and hematoxylin–eosin (H&E) staining showed that BGRJ alleviated pathological damage of articular cartilage in OVX rats with KOA. In addition, BGRJ inhibited inflammation and oxidative stress through decreasing the levels of serum IL-6, IL-1β, TNF-α and NO and regulated Wnt signaling pathway by downregulating the expression of Wnt5a and β-catenin and upregulating the expression of Sox9 and Collagen II in cartilage tissue, detected by immunohistochemistry (IHC) and western blot analysis. Furthermore, Wnt5a silencing reduced the apoptosis of LPS-induced ADTC5 cells, which was further suppressed by the combination of downregulation of Wnt5a and BGRJ. Conclusions In summary, BGRJ alleviates inflammation and oxidative stress to treat the postmenopausal KOA through Wnt signaling pathway.

The evaluation of the complex workpiece allowance and the scribing the datum line now extremely rely on manual operation. The manual operation is time-consuming and prone to misjudgment, and has a large blind area. To solve these problems, the robot-driven automatic scribing via three-dimensional (3D) measurement sensor method (RAS3DM) is proposed. In this study, we solve the key technology of calibrating the rigid transformation between the 3D measurement coordinate system and laser scribing coordinate system. And a fast and accurate multiple coordinate systems calibration and unification method is proposed, a fixed calibration target and a mobile calibration target are designed. Hence, a structured-light 3D shape measurement sensor driven by an industrial robot with six-degree of freedom (6-DOF) is utilized to obtain the complete 3D shape of the complex workpiece automatically, and then by comparing the 3D measurement result and the standard CAD model, the laser scribing equipment can automatically scribe the mechanical processing datum line to assist further processing. The experiment results illustrate that the proposed method is accurate and efficient: the complex workpiece allowance evaluation and scribing time is about 5 times faster than that of manual operation, and the scribing accuracy is 0.15mm, which can effectively solve various problems of manual scribing. The proposed RAS3DM can be widely applied in factories and laboratories.

Tripartite motif containing-21 (TRIM21), an E3 ubiquitin ligase, was initially found to be involved in antiviral responses and autoimmune diseases. Recently studies have reported that TRIM21 plays a dual role in cancer promoting and suppressing in the occurrence and development of various cancers. Despite the fact that TRIM21 has effects on multiple metabolic processes, inflammatory responses and the efficacy of tumor therapy, there has been no systematic review of these topics. Herein, we discuss the emerging role and function of TRIM21 in cancer metabolism, immunity, especially the immune response to inflammation associated with tumorigenesis, and also the cancer treatment, hoping to shine a light on the great potential of targeting TRIM21 as a therapeutic target.

Bathymetric information is critical for environmental monitoring and maintenance of marine safety. The signal received by passive optical remote sensors contains the reflectance information from the seabed, which then forms the informational basis of bathymetry; however, passive optical bathymetry models often require partial prior information. Although spaceborne active lidar has been shown to accurately detect bathymetry within narrow range of tracks, for some small islands or coastal sites, these tracks may not accurately traverse the shallow ocean area. To this end, a satellite derived bathymetry method based on ICESat-2 diffuse attenuation signal without prior information was proposed here. The diffuse attenuation coefficients of the water column were estimated according to the ICESat-2 lidar signal of deep areas near the target shallow water area, and the parameters for the bathymetry algorithm without prior depth information were obtained. Combined with the Sentinel-2 multispectral imagery, accurate large-scale bathymetric information of Zhongye Island and Reefs, Guam, and Ngau were obtained. Bathymetric data from ICESat-2 of multiple tracks were used for verification, and the results showed that the new model performed well, as mean absolute error (MAE) for the three study areas ranged 0.35–1.52 m, and root Mean square error (RMSE) fell between 0.74 and 1.89 m. In the absence of any bathymetric information, the new method can achieve higher or similar accuracy to the more commonly employed Stumpf model.

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.

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.

Here, the authors model the collecting duct system in kidneys by taking ureteric bud (UB) progenitor cells from both mouse and human primary tissues, as well as from hESC and hiPSC to generate organoids, which can model congenital anomalies of the kidney and urinary tract.