To mitigate severe cloud interference in optical remote sensing imagery and address the challenges of deploying complex cloud removal models on satellite platforms, this study proposes a lightweight gated parallel attention network, GCEPANet. By integrating optical and SAR data, the network fully exploits the penetration capability of SAR imagery and combines a Gated Convolution Module (GCONV) with an Enhanced Parallel Attention Module (EPA) to establish a "cloud perception-cloud refinement" cooperative mechanism. This mechanism enables the model to identify and filter features according to cloud intensity, effectively separating the feature flows of clear and cloudy regions, and adaptively compensating for cloud-induced degradation to reconstruct the true structural and radiative characteristics of surface objects. Furthermore, a joint spectral-structural loss is introduced to simultaneously constrain spectral consistency and structural fidelity. Extensive experiments on the SEN12MS-CR dataset demonstrate that the proposed GCEPANet consistently outperforms existing methods across multiple metrics, including PSNR, SSIM, MAE, RMSE, SAM, and ERGAS. Compared with the SCTCR model, GCEPANet achieves a 0.9306 dB improvement in PSNR, reduces the number of parameters by 85.5% (to 12.77M), and decreases FLOPs by 76.0% (to 9.71G). These results demonstrate that the proposed method achieves superior cloud removal performance while significantly reducing model complexity, providing an efficient and practical solution for real-time on-orbit cloud removal in optical-SAR fused remote sensing imagery.

Accurate sea ice forecasting is essential for safe navigation in the Arctic Ocean, but it remains particularly challenging during the summer melting season. This difficulty mainly arises from incomplete sea ice initialization due to the scarcity of sea ice thickness (SIT) observations from May to September. To address this issue, we develop a method to retrieve SIT using Deep Neural Network. The model integrates reflection data from Global Navigation Satellite System-Reflectometry (GNSS-R) with satellite-derived sea ice concentration (SIC) and relevant atmospheric and oceanic parameters. The retrieved SIT and observed SIC are assimilated into a mesoscale-permitting ice-ocean coupled model to generate a continuous sea ice "reanalysis" data set for the entire melting season. Evaluation shows that our reanalysis outperforms existing data sets, reducing errors in both SIC and SIT by over 10%. It provides a notably improved sea ice distribution in key marginal zones and serves as a superior initial field for synoptic-scale sea ice forecasts. SIT forecast errors are substantially reduced in regions with historically large prediction biases, such as the Beaufort Sea, where the 7th-day forecast error decreases by more than 80% compared to forecasts that do not use our retrieved SIT. This study confirms the value of combining GNSS-R technology with atmospheric and oceanic data for retrieving SIT, and highlights its importance for improving short-term Arctic sea ice forecasts.

The coherent radar utilizes the intrinsic relationship between the orbital velocity of the water particles and the wave height for the retrieval of the sea wave parameter. However, radar electromagnetic echoes frequently contain interference components such as target ships and broken waves, substantially constraining estimation accuracy. Although numerous existing methods suppress low-frequency energy to mitigate broken waves interference, they demonstrate limited efficacy in ship-target scenarios and lack a clear physical interpretation. To solve this problem, we propose a novel Laplacian-regularization-enhanced reduced-order variational mode decomposition (LR-RVMD) method for sea wave parameter inversion. Specifically, the proposed LR-RVMD decomposes the spatiotemporal radial velocity series of water particles obtained from radar echoes, yielding a set of low-order dynamic process components. Subsequently, the singular values hard threshold (SVHT) method estimates the sequence rank to guide the extraction of corresponding wind-wave components. Following energy compensation, the spatiotemporal radial velocity series is reconstructed to enable wave parameter estimation. By utilizing X-band coherent radar data, we extract significant wave height (SWH) Hs and average period Tav. The estimated results show a correlation coefficient (CC) of 0.935 and 0.913 with buoy data, and a mean square error (MSE) of 0.086 and 1.065, respectively. The experimental results indicate that the proposed LR-RVMD method can effectively achieve high-precision radar wave parameter inversion under complex sea conditions characterized by target interference and broken waves.

BACKGROUND:Pancreatic cancer is a highly aggressive neoplasm characterized by poor diagnosis. Amino acids play a prominent role in the occurrence and progression of pancreatic cancer as essential building blocks for protein synthesis and key regulators of cellular metabolism. Understanding the interplay between pancreatic cancer and amino acid metabolism offers potential avenues for improving patient clinical outcomes. METHODS:A comprehensive analysis integrating 10 machine learning algorithms was executed to pinpoint amino acid metabolic signature. The signature was validated across both internal and external cohorts. Subsequent GSEA was employed to unveil the enriched gene sets and signaling pathways within high- and low-risk subgroups. TMB and drug sensitivity analyses were carried out via Maftools and oncoPredict R packages. CIBERSORT and ssGSEA were harnessed to delve into the immune landscape disparities. Single-cell transcriptomics, qPCR, and Immunohistochemistry were performed to corroborate the expression levels and prognostic significance of this signature. RESULTS:A four gene based amino acid metabolic signature with superior prognostic capabilities was identified by the combination of 10 machine learning methods. It showed that the novel prognostic model could effectively distinguish patients into high- and low-risk groups in both internal and external cohorts. Notably, the risk score from this novel signature showed significant correlations with TMB, drug resistance, as well as a heightened likelihood of immune evasion and suboptimal responses to immunotherapeutic interventions. CONCLUSION:Our findings suggested that amino acid metabolism-related signature was closely related to the development, prognosis and immune microenvironment of pancreatic cancer.

Figure S1 shows basal centrosome amplification in cell lines. Figure S2 shows the effect of GF on cell viability. Figure S3 shows HSET expression in cell lines. Figure S4 shows the effect of GF on clonogenic potential after RT. Figure S5 shows the induction of micronuclei after GF and/or RT. Figure S6 shows the expression of IFN-β after GF and/or RT.

BackgroundProbiotics, as common regulators of the gut microbiota, have been used in research to alleviate clinical symptoms of atopic dermatitis (AD).ObjectiveOur research team has previously identified a potential relieving effect of Clostridium butyricum on the treatment of AD, but the specific mechanism of how Clostridium butyricum alleviates AD has not yet been confirmed.MethodsIn this study, we explored the relieving effect of Clostridium butyricum on AD through in vivo and in vitro experiments. AD mice induced by 2,4-dinitrofluorobenzene (DNFB) were orally administered with 1 × 108 CFU of Clostridium butyricum for three consecutive weeks.ResultsOral administration of Clostridium butyricum reduced ear swelling, alleviated back skin lesions, decreased mast cell and inflammatory cell infiltration, and regulated the levels of inflammation-related cytokines. Clostridium butyricum activated the intestinal immune system through the TLR4/MyD88/NF-κB signaling pathway, suppressed the expression of inflammatory factors IL-10 and IL-13, and protected the damaged intestinal mucosa.ConclusionClostridium butyricum administration improved the diversity and abundance of the gut microbiota, enhanced the functionality of the immune system, and protected the epidermal barrier.

Transcriptional dysregulation is a hallmark of cancer initiation and progression, driven by genetic and epigenetic alterations. Enhancer reprogramming has emerged as a pivotal driver of carcinogenesis, with cancer cells often relying on aberrant transcriptional programs. The advent of high-throughput sequencing technologies has provided critical insights into enhancer reprogramming events and their role in malignancy. While targeting enhancers presents a promising therapeutic strategy, significant challenges remain. These include the off-target effects of enhancer-targeting technologies, the complexity and redundancy of enhancer networks, and the dynamic nature of enhancer reprogramming, which may contribute to therapeutic resistance. This review comprehensively encapsulates the structural attributes of enhancers, delineates the mechanisms underlying their dysregulation in malignant transformation, and evaluates the therapeutic opportunities and limitations associated with targeting enhancers in cancer.

Cancer cells rewire metabolism to sculpt the immune tumor microenvironment (TME) and propel tumor advancement, which intricately tied to post-translational modifications. Histone lactylation has emerged as a novel player in modulating protein functions, whereas little is known about its pathological role in pancreatic ductal adenocarcinoma (PDAC) progression. Employing a multi-omics approach encompassing bulk and single-cell RNA sequencing, metabolomics, ATAC-seq, and CUT&Tag methodologies, we unveiled the potential of histone lactylation in prognostic prediction, patient stratification and TME characterization. Notably, "LDHA-H4K12la-immuno-genes" axis has introduced a novel node into the regulatory framework of "metabolism-epigenetics-immunity," shedding new light on the landscape of PDAC progression. Furthermore, the heightened interplay between cancer cells and immune counterparts via Nectin-2 in liver metastasis with elevated HLS unraveled a positive feedback loop in driving immune evasion. Simultaneously, immune cells exhibited altered HLS and autonomous functionality across the metastatic cascade. Consequently, the exploration of innovative combination strategies targeting the metabolism-epigenetics-immunity axis holds promise in curbing distant metastasis and improving survival prospects for individuals grappling with challenges of PDAC.

Dehazing individual remote sensing (RS) images is an effective approach to enhance the quality of hazy remote sensing imagery. However, current dehazing methods exhibit substantial systemic and computational complexity. Such complexity not only hampers the straightforward analysis and comparison of these methods but also undermines their practical effectiveness on actual data, attributed to the overtraining and overfitting of model parameters. To mitigate these issues, we introduce a novel dehazing network for non-uniformly hazy RS images: GLUENet, designed for both lightweightness and computational efficiency. Our approach commences with the implementation of the classical U-Net, integrated with both local and global residuals, establishing a robust base for the extraction of multi-scale information. Subsequently, we construct basic convolutional blocks using gated linear units and efficient channel attention, incorporating depth-separable convolutional layers to efficiently aggregate spatial information and transform features. Additionally, we introduce a fusion block based on efficient channel attention, facilitating the fusion of information from different stages in both encoding and decoding to enhance the recovery of texture details. GLUENet’s efficacy was evaluated using both synthetic and real remote sensing dehazing datasets, providing a comprehensive assessment of its performance. The experimental results demonstrate that GLUENet’s performance is on par with state-of-the-art (SOTA) methods and surpasses the SOTA methods on our proposed real remote sensing dataset. Our method on the real remote sensing dehazing dataset has an improvement of 0.31 dB for the PSNR metric and 0.13 for the SSIM metric, and the number of parameters and computations of the model are much lower than the optimal method.

Underwater image enhancement is critical for a variety of marine applications such as exploration, navigation, and biological research. However, underwater images often suffer from quality degradation due to factors such as light absorption, scattering, and color distortion. Although current deep learning methods have achieved better performance, it is difficult to balance the enhancement performance and computational efficiency in practical applications, and some methods tend to cause performance degradation on high-resolution large-size input images. To alleviate the above points, this paper proposes an efficient network GFRENet for underwater image enhancement utilizing gated linear units (GLUs) and fast Fourier convolution (FFC). GLUs help to selectively retain the most relevant features, thus improving the overall enhancement performance. FFC enables efficient and robust frequency domain processing to effectively address the unique challenges posed by the underwater environment. Extensive experiments on benchmark datasets show that our approach significantly outperforms existing state-of-the-art techniques in both qualitative and quantitative metrics. The proposed network provides a promising solution for real-time underwater image enhancement, making it suitable for practical deployment in various underwater applications.

Pancreatic cancer, predominantly pancreatic ductal adenocarcinoma (PDAC), is one of the most malignant tumors of the digestive system. Emerging evidence suggests the involvement of the microbiome and metabolic substances in the development of PDAC, yet the results remain contradictory. This study aims to identify the alterations and relationships in intratumoral microbiome and metabolites in PDAC. We collected matched tumor and normal adjacent tissue (NAT) samples from 105 PDAC patients and performed a 6-year follow-up. 2bRAD-M sequencing, untargeted liquid chromatography-tandem mass spectrometry, and untargeted gas chromatography-mass spectrometry were performed. Compared with NATs, microbial α-diversity decreased in PDAC tumors. The relative abundance of Staphylococcus aureus, Cutibacterium acnes, and Cutibacterium granulosum was higher in PDAC tumor after adjusting for confounding factors body mass index and M stage, and the presence of Ralstonia pickettii_B was found associated with a worse overall survival. Metabolomic analysis revealed distinctive differences in composition between PDAC and NAT, with 553 discriminative metabolites identified. Differential metabolites were revealed to originate from the microbiota and showed significant interactions with shifted bacterial species through KO (KEGG Orthology) genes. These findings suggest that the PDAC microenvironment harbors unique microbial-derived enzymatic reactions, potentially influencing the occurrence and development of PDAC by modulating the levels of glycerol-3-phosphate, succinate, carbonate, and beta-alanine. IMPORTANCE:We conducted a large sample-size pancreatic adenocarcinoma microbiome study using a novel microbiome sequencing method and two metabolomic assays. Two significant outcomes of our analysis are: (i) commensal opportunistic pathogens Staphylococcus aureus, Cutibacterium acnes, and Cutibacterium granulosum were enriched in pancreatic ductal adenocarcinoma (PDAC) tumors compared with normal adjacent tissues, and (ii) worse overall survival was found related to the presence of Ralstonia pickettii_B. Microbial species affect the tumorigenesis, metastasis, and prognosis of PDAC via unique microbe-enzyme-metabolite interaction. Thus, our study highlights the need for further investigation of the potential associations between pancreatic microbiota-derived omics signatures, which may drive the clinical transformation of microbiome-derived strategies toward therapy-targeted bacteria.

Glutamate dehydrogenase 1 (GLUD1) is implicated in oncogenesis. However, little is known about the relationship between GLUD1 and hepatocellular carcinoma (HCC). In the present study, we demonstrated that the expression levels of GLUD1 significantly decreased in tumors, which was relevant to the poor prognosis of HCC. Functionally, GLUD1 silencing enhanced the growth and migration of HCC cells. Mechanistically, the upregulation of interleukin-32 through AKT activation contributes to GLUD1 silencing-facilitated hepatocarcinogenesis. The interaction between GLUD1 and AKT, as well as alpha-ketoglutarate regulated by GLUD1, can suppress AKT activation. In addition, LIM and SH3 protein 1 (LASP1) interacts with GLUD1 and induces GLUD1 degradation via the ubiquitin-proteasome pathway, which relies on the E3 ubiquitin ligase synoviolin (SYVN1), whose interaction with GLUD1 is enhanced by LASP1. In hepatitis B virus (HBV)-related HCC, the HBV X protein (HBX) can suppress GLUD1 with the participation of LASP1 and SYVN1. Collectively, our data suggest that GLUD1 silencing is significantly associated with HCC development, and LASP1 and SYVN1 mediate the inhibition of GLUD1 in HCC, especially in HBV-related tumors.

Based on the high-resolution remote sensing images and deep learning models, more and more detailed spatial information around the coastlines can be extracted automatically nowadays. As so far, most of the sea-land segmentation networks choose batch normalization (BN) as the normalization layer. However, the accuracy of segmentation results is easily affected by the batch size, and the error increases markedly when the batch size is small. Aiming at this problem, this letter proposes a new sea-land segmentation network wider range of batch sizes (WRBSNet) using the XBNBlock(GN)_P2 module. We also construct a high-resolution sea-land segmentation dataset to evaluate the performance of the WRBSNet under different batch sizes. Experiments show that the WRBSNet has a wider available range of batch sizes and also has a better performance. When the batch size is 2, the aAcc, mIoU, and mAcc of WRBSNet's optimal segmentation results can reach more than 0.89, and when the batch size is 4, 8, and 16, the corresponding three metrics can reach 0.97.

Pancreatic cancer is a highly malignant solid tumor with a poor prognosis and a high mortality rate. Thus, exploring the mechanisms underlying the development and progression of pancreatic cancer is critical for identifying targets for diagnosis and treatment. Two important hallmarks of cancer—metabolic remodeling and epigenetic reprogramming—are interconnected and closely linked to regulate one another, creating a complex interaction landscape that is implicated in tumorigenesis, invasive metastasis, and immune escape. For example, metabolites can be involved in the regulation of epigenetic enzymes as substrates or cofactors, and alterations in epigenetic modifications can in turn regulate the expression of metabolic enzymes. The crosstalk between metabolic remodeling and epigenetic reprogramming in pancreatic cancer has gained considerable attention. Here, we review the emerging data with a focus on the reciprocal regulation of metabolic remodeling and epigenetic reprogramming. We aim to highlight how these mechanisms could be applied to develop better therapeutic strategies.

Background:Prospective studies on the association between Helicobacter pylori (H. pylori) infection and subclinical hyperthyroidism are limited. We, therefore, designed a large-scale cohort study to explore the association between H. pylori infection and the risk of subclinical hyperthyroidism in women.Methods:This prospective cohort study investigated 2,713 participants. H. pylori infection was diagnosed with the carbon 13 breath test. Subclinical hyperthyroidism was defined as serum thyroid-stimulating hormone levels are low or undetectable but free thyroxine and tri-iodothyronine concentrations are normal. Propensity score matching (PSM) analyses and Cox proportional hazards regression models were used to estimate the association between H. pylori infection and subclinical hyperthyroidism.Results:A total of 1,025 PS-matched pairs of H. pylori infection women were generated after PSM. During 6 years of follow-up, the incidence rate of subclinical hyperthyroidism was 7.35/1,000 person-years. After adjusting potential confounding factors (including iodine intake in food and three main dietary patterns score), the multivariable hazard ratio (HR; 95% confidence intervals) of subclinical hyperthyroidism by H. pylori infection was 2.49 (1.36, 4.56). Stratified analyses suggested a potential effect modification by age, the multivariable HR (95% confidence intervals) was 2.85 (1.45, 5.61) in participants aged ≥ 40 years and 0.70 (0.08, 6.00) in participants aged < 40 years (P for interaction = 0.048).Conclusion:Our prospective study first indicates that H. pylori infection is significantly associated with the risk of subclinical hyperthyroidism independent of dietary factors among Chinese women, especially in middle-aged and older individuals.Clinical Trial Registration:https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000031137, identifier UMIN000027174.

Cancer-associated fibroblasts (CAFs) are the predominant stromal cells in the microenvironment and play important roles in tumor progression, including chemoresistance. However, the response of CAFs to chemotherapeutics and their effects on chemotherapeutic outcomes are largely unknown. In this study, we showed that epirubicin (EPI) treatment triggered ROS which initiated autophagy in CAFs, TCF12 inhibited autophagy flux and further promoted exosome secretion. Inhibition of EPI-induced reactive oxygen species (ROS) production with N-acetyl-L-cysteine (NAC) or suppression of autophagic initiation with short interfering RNA (siRNA) against ATG5 blunted exosome release from CAFs. Furthermore, exosome secreted from EPI-treated CAFs not only prevented ROS accumulation in CAFs but also upregulated the CXCR4 and c-Myc protein levels in recipient ER+ breast cancer cells, thus promoting EPI resistance of tumor cells. Together, the current study provides novel insights into the role of stressed CAFs in promoting tumor chemoresistance and reveal a new function of TCF12 in regulating autophagy impairment and exosome release.

Purpose: Individual food items and nutrients are associated with the development of nephrolithiasis. Few studies have investigated the association between dietary patterns, particularly plant-based diets, and this disease. We aim to explore the associations between dietary patterns and incident nephrolithiasis risk. Materials and methods: This prospective cohort study included 26 490 participants. Factor analysis was applied to dietary information to identify three a posteriori dietary patterns, and six a priori plant-based dietary patterns (overall plant-based diet index [PDI], healthful plant-based diet index [hPDI], unhealthful plant-based diet index [uPDI], vegan diet, lacto-ovo-vegetarian diet, and fish-vegetarian diet) were defined. Nephrolithiasis was diagnosed using ultrasonography. Cox proportional hazard regression models were used to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) for incident nephrolithiasis related to dietary patterns. Results: After 101 094 person-years follow-up, we documented 806 incident nephrolithiasis cases. An a posteriori balanced dietary pattern characterized by a higher intake of vegetables, eggs, grains, legumes, legume products, and meat was associated with a lower risk of nephrolithiasis (P for trend = 0.02). Compared to the reference group in the lowest quartile of the balanced pattern, participants in the highest quartile had an adjusted HR (95% CI) of 0.72 (0.53-0.96) for incident nephrolithiasis. Adherence to the uPDI increased the risk of incident nephrolithiasis (P for trend < 0.01; adjusted HR4th quartile vs. 1st quartile, 1.46, 95% CI, 1.14-1.97). No significant association was found between other a posteriori or a priori dietary patterns and incident nephrolithiasis. Conclusions: Adherence to a balanced dietary pattern, but not a plant-based diet, was associated with a lower nephrolithiasis risk. Moreover, higher uPDI consumption increased incident nephrolithiasis risk.

Aiming at producing the road labels for deep neural networks (DNNs), this letter proposes a graph-cut-based method to make road annotations on very high-resolution (VHR) remote-sensing images. With the aid of OpenStreetMap (OSM), a superpixel method and the graph cut method are employed for road segmentation. After that, the road areas are refined by the OSM. In this process, the road annotations are made automatically. In experiments, two traditional methods, two deep learning methods, and the proposed method are utilized to segment the roads on two types of satellite images in Tianjin port area. The results show that the proposed method creates more accurate and integrated road labels compared with other methods.

This study aimed to evaluate the longitudinal association between sweet potato intake and risk of NAFLD in the general adult population. In total, the number of 15,787 participants (males, 42.4%) was included in this prospective cohort study. Sweet potato intake was assessed by using a validated food frequency questionnaire. NAFLD was diagnosed by transabdominal sonography during an annual health examination. Cox proportional hazards regression models were fitted to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) across categories of energy-adjusted sweet potato intake. Compared to participants with the lowest tertile of sweet potato intake, the finally adjusted HRs (95% CIs) of incident NAFLD for those with the highest tertile were 0.87 (0.78, 0.97) in males (p for trend = 0.009); and 1.05 (0.92, 1.21) in females (p for trend = 0.52). Our study revealed that sweet potato intake was inversely associated with the risk of NAFLD in males.

为了研究地震作用下施工过程对预应力钢筒混凝土管(PCCP)的影响,建立考虑PCCP施工过程影响的非线性有限元模型,利用生死单元技术实现PCCP从地应力平衡到施加预应力钢丝预应力、铺设管道,再到回填回填土过程的有限元分析,以压实度作为衡量回填土施工质量的指标,基于黏弹性人工边界理论、等效节点力地震动输入法以及静-动力边界转换法,分析考虑施工过程以及不同回填土压实质量对PCCP地震响应的影响.结果表明,管道应力受自身和回填土重力的影响,考虑施工过程影响时内外层混凝土管顶、管底的外侧单元和管腰内侧单元的环向压应力比未考虑施工过程大,管顶、管底的内侧单元和管腰的外侧单元环向压应力比未考虑施工过程小;地震作用下,回填土均匀压实时,增大回填土压实度可降低PCCP插口处的轴向拉应力,回填土压实度越不均匀,管道之间的水平和竖直转角最大值也越大,回填土压实质量对竖直方向转角的影响大于对其水平方向的影响.