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Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of mortality worldwide. Dysregulation of glucose metabolism and inflammation are key factors in the development of atherosclerosis. The glucose-to-lymphocyte ratio (GLR) is a comprehensive marker for assessing glucose metabolism and inflammation. This study aims to evaluate the association between GLR and all-cause as well as cardiovascular disease (CVD) mortality in patients with ASCVD within the U.S. population. This retrospective cohort study recruited 1,753 ASCVD patients from the 2003–2018 National Health and Nutrition Examination Survey (NHANES) with a median follow-up of 6.25 years. Mortality outcomes were determined by linkage to the National Death Index (NDI) records up to December 31, 2019. Weighted Cox proportional hazard models were used to assess the independent association between GLR and mortality risk. Restricted cubic spline (RCS) curves were used to display the relationship between GLR and all-cause mortality visually, and two-segment Cox proportional hazards models were constructed on either side of the inflection points. Kaplan-Meier survival curves were further used to assess the relationship between GLR and mortality, and further subgroup analyses were performed. Receiver operating characteristic curve (ROC) analysis was conducted to assess the predictive ability of GLR for survival. During a median follow-up of 6.25 years, 624 deaths from various causes were observed, with 254 deaths from CVD. Cox regression analysis revealed a positive association between GLR and both all-cause and CVD mortality. Based on RCS, a J-shaped nonlinear relationship was observed between GLR and all-cause mortality in ASCVD patients, with an inflection point at 3.13. When the GLR < 3.13, it showed a significant negative association with all-cause mortality (HR 0.65, 95% CI 0.47–0.89). When GLR ≥ 3.13 for all-cause mortality, there was a significant positive correlation with all-cause mortality (HR 1.13, 95% CI 1.09–1.17). Subgroup analysis revealed a positive association between GLR and CVD mortality across most subgroups, but the correlation between GLR and CVD mortality was weaker compared to its association with all-cause mortality. In addition, an interaction was detected between GLR and age in relation to all-cause mortality. Moreover, the predictive performance of GLR on all-cause and CVD mortality seemed superior to that of glucose or lymphocytes. Our findings indicate that elevated GLR was closely associated with an increased risk of all-cause mortality and CVD mortality in ASCVD patients. Notably, the relationship between GLR and all-cause mortality exhibited a J-shaped nonlinear pattern, with an inflection point at 3.13.

Stroke is the second leading cause of death globally. Oxidative stress plays a critical role in the development of stroke. The Oxidative Balance Score (OBS) is a tool used to assess the combined impact of diet and lifestyle on the body’s antioxidant capacity. The study included stroke survivors from the National Health and Nutrition Examination Survey (1999–2018), with a total of 1,781 participants and a median follow-up duration of 6.5 years, during which 786 participants (39.59%) died. The relationship between OBS and all-cause mortality was assessed using the Cox proportional hazards model. The results indicated that individuals in higher OBS quartiles had lower mortality rates. Specifically, patients in the fourth quartile had a 41% reduced risk of all-cause mortality compared to those in the first quartile (HR = 0.59, 95% CI = 0.42–0.84, p  = 0.003). Restricted cubic spline analysis revealed a linear inverse relationship between OBS and all-cause mortality. Subgroup analysis further demonstrated that the inverse association persisted across various population subgroups. Overall, our study suggests that higher levels of OBS can reduce the risk of all-cause mortality in stroke survivors and provides new evidence for their diet and lifestyle.

Clinical nurses' research capacity is essential for building a research-active nursing workforce and advancing evidence-based practice. Despite increasing global attention, few systematic reviews have examined the current state of nurses' research capacity or the factors influencing its development. This protocol outlines a systematic review designed to evaluate clinical nurses' research capacity and to identify the key barriers and facilitators to its improvement across diverse healthcare contexts. The review will follow PRISMA guidelines. Eight databases-PubMed, Cochrane Library, Embase, CINAHL, Web of Science, Scopus, CNKI, and Wanfang-will be searched for relevant quantitative, qualitative, and mixed-methods studies. Study selection, data extraction, and quality appraisal will be performed independently by two reviewers using the Mixed Methods Appraisal Tool (MMAT). Findings will be integrated through thematic synthesis based on Braun and Clarke's six-phase framework. Quantitative results will be transformed into narrative form and synthesized with qualitative data. Given the anticipated heterogeneity, a meta-analysis will not be conducted. The findings are expected to provide insights into clinical nurses' research capacity and its influencing factors to inform targeted strategies to support research engagement and integration into clinical practice.

Photo-thermal-coupling ammonia decomposition presents a promising strategy for utilizing the full-spectrum to address the H 2 storage and transportation issues. Herein, we exhibit a photo-thermal-catalytic architecture by assembling gallium nitride nanowires-supported ruthenium nanoparticles on a silicon for extracting hydrogen from ammonia aqueous solution in a batch reactor with only sunlight input. The photoexcited charge carriers make a predomination contribution on H 2 activity with the assistance of the photothermal effect. Upon concentrated light illumination, the architecture significantly reduces the activation energy barrier from 1.08 to 0.22 eV. As a result, a high turnover number of 3,400,750 is reported during 400 h of continuous light illumination, and the H 2 activity per hour  is nearly 1000 times higher than that under the pure thermo-catalytic conditions. The reaction mechanism is extensively studied by coordinating experiments, spectroscopic characterizations, and density functional theory calculation. Outdoor tests validate the viability of such a multifunctional architecture for ammonia decomposition toward H 2 under natural sunlight. The author report a Ru NPs/GaN NWs nanoarchitecture on Si for utilizing full spectrum to drive efficient and robust photothermal H 2 production from NH 3 with a high turnover number of >3,400,750 over 400 h.

With the development of deep learning and remote sensing technologies in recent years, many semantic segmentation methods based on convolutional neural networks (CNNs) have been applied to road extraction. However, previous deep learning-based road extraction methods primarily used RGB imagery as an input and did not take advantage of the spectral information contained in hyperspectral imagery. These methods can produce discontinuous outputs caused by objects with similar spectral signatures to roads. In addition, the images obtained from different Earth remote sensing sensors may have different spatial resolutions, enhancing the difficulty of the joint analysis. This work proposes the Multiscale Fusion Attention Network (MSFANet) to overcome these problems. Compared to traditional road extraction frameworks, the proposed MSFANet fuses information from different spectra at multiple scales. In MSFANet, multispectral remote sensing data is used as an additional input to the network, in addition to RGB remote sensing data, to obtain richer spectral information. The Cross-source Feature Fusion Module (CFFM) is used to calibrate and fuse spectral features at different scales, reducing the impact of noise and redundant features from different inputs. The Multiscale Semantic Aggregation Decoder (MSAD) fuses multiscale features and global context information from the upsampling process layer by layer, reducing information loss during the multiscale feature fusion. The proposed MSFANet network was applied to the SpaceNet dataset and self-annotated images from Chongzhou, a representative city in China. Our MSFANet performs better over the baseline HRNet by a large margin of +6.38 IoU and +5.11 F1-score on the SpaceNet dataset, +3.61 IoU and +2.32 F1-score on the self-annotated dataset (Chongzhou dataset). Moreover, the effectiveness of MSFANet was also proven by comparative experiments with other studies.

The occurrences, distributions, and risks of 55 target volatile organic compounds (VOCs) in water, sediment, sludge, and soil samples taken from a chemical industrial park and the adjacent area were investigated in this study. The Σ55-VOCs concentrations in the water, sediment, sludge, and soil samples were 1.22–5449.21 μg L−1, ND–52.20 ng g−1, 21.53 ng g−1, and ND–11.58 ng g−1, respectively. The main products in this park are medicines, pesticides, and novel materials. As for the species of VOCs, aromatic hydrocarbons were the dominant VOCs in the soil samples, whereas halogenated aliphatic hydrocarbons were the dominant VOCs in the water samples. The VOCs concentrations in water samples collected at different locations varied by 1–3 orders of magnitude, and the average concentration in river water inside the park was obviously higher than that in river water outside the park. However, the risk quotients for most of the VOCs indicated a low risk to the relevant, sensitive aquatic organisms in the river water. The average VOCs concentration in soil from the park was slightly higher than that from the adjacent area. This result showed that the chemical industrial park had a limited impact on the surrounding soil, while the use of pesticides, incomplete combustion of coal and biomass, and automobile exhaust emissions are all potential sources of the VOCs in the environmental soil. The results of this study could be used to evaluate the effects of VOCs emitted from chemical production and transportation in the park on the surrounding environment.

Cropland is a fundamental basis for agricultural development and a prerequisite for ensuring food security. The segmentation and extraction of croplands using remote sensing images are important measures and prerequisites for detecting and protecting farmland. This study addresses the challenges of diverse image sources, multi-scale representations of cropland, and the confusion of features between croplands and other land types in large-area remote sensing image information extraction. To this end, a multi-source self-annotated dataset was developed using satellite images from GaoFen-2, GaoFen-7, and WorldView, which was integrated with public datasets GID and LoveDA to create the CRMS dataset. A novel semantic segmentation network, the Global–Local Context Aggregation Network (GLCANet), was proposed. This method integrates the Bilateral Feature Encoder (BFE) of CNNs and Transformers with a global–local information mining module (GLM) to enhance global context extraction and improve cropland separability. It also employs a multi-scale progressive upsampling structure (MPUS) to refine the accuracy of diverse arable land representations from multi-source imagery. To tackle the issue of inconsistent features within the cropland class, a loss function based on hard sample mining and multi-scale features was constructed. The experimental results demonstrate that GLCANet improves OA and mIoU by 3.2% and 2.6%, respectively, compared to the existing advanced networks on the CRMS dataset. Additionally, the proposed method also demonstrated high precision and practicality in segmenting large-area croplands in Chongzhou City, Sichuan Province, China.

Supramolecular dynamic room temperature phosphorescence (RTP) is the focus of current research because of its wide application in biological imaging and information anti‐counterfeiting. Herein, a time‐dependent supramolecular lanthanide phosphorescent 4D assembly material with multicolor luminescence including white, which is composed of 4‐(4‐bromophenyl)‐pyridine salt derivative (G), inorganic clay (LP)/Eu complex and pyridine dicarboxylic acid (DPA) is reported. Compared with the self‐assembled nanoparticle G, the lamellar assembly G/LP showed the double emission of fluorescence at 380 nm and phosphorescence at 516 nm over time. Within 60 min, the phosphorescence lifetime and the quantum yield increases from none to 7.4 ms and 27.53% respectively, achieving the time‐dependent phosphorescence emission, due to the limitation of progressive stacking of LP electrostatically driven “domino effect.” Furthermore, the 4D assembly of DPA and G/LP/Eu leads to a time‐resolved multicolor emission from colorless to purple to white, which is successfully applied to information multi‐level logic anti‐counterfeiting and efficiently antibiotic selective sensor. Supramolecular dynamic room temperature phosphorescence is the focus of current research because of its wide application in biological imaging and information anti‐counterfeiting. Herein, a time‐dependent supramolecular lanthanide phosphorescent 4D assembly material with multicolor luminescence including white for encryption and visual sensing, is reported which is composed of 4‐(4‐bromophenyl)‐pyridine salt h derivative, inorganic clay/Eu complex and pyridine dicarboxylic acid.

Hepatocellular carcinomas (HCC) exhibit distinct promoter hypermethylation patterns, but the epigenetic regulation and function of transcriptional enhancers remain unclear. Here, our affinity- and bisulfite-based whole-genome sequencing analyses reveal global enhancer hypomethylation in human HCCs. Integrative epigenomic characterization further pinpoints a recurrent hypomethylated enhancer of CCAAT/enhancer-binding protein-beta (C/EBPβ) which correlates with C/EBPβ over-expression and poorer prognosis of patients. Demethylation of C/EBPβ enhancer reactivates a self-reinforcing enhancer-target loop via direct transcriptional up-regulation of enhancer RNA. Conversely, deletion of this enhancer via CRISPR/Cas9 reduces C/EBPβ expression and its genome-wide co-occupancy with BRD4 at H3K27ac-marked enhancers and super-enhancers, leading to drastic suppression of driver oncogenes and HCC tumorigenicity. Hepatitis B X protein transgenic mouse model of HCC recapitulates this paradigm, as C/ebpβ enhancer hypomethylation associates with oncogenic activation in early tumorigenesis. These results support a causal link between aberrant enhancer hypomethylation and C/EBPβ over-expression, thereby contributing to hepatocarcinogenesis through global transcriptional reprogramming. There are distinct hypermethylation patterns in gene promoters in hepatocellular carcinomas (HCCs). Here, the authors show that the enhancer of C/EBPβ is recurrently hypomethylated in human HCCs, recapitulating this in a transgenic murine model and linking aberrant enhancer hypomethylation to hepatocarcinogenesis.

With the development of Big Data, Industry 4.0, and other technologies, the concept of smart city has become a new goal, new concept, and new practice of many urban developments. It provides a method to solve the problem that public management cannot optimize resources in China’s urban development and puts forward a supporting scheme more in line with the optimization of public management resources. Effective use of relevant supporting schemes can improve urban public management capacity, optimize resources, and promote the city to embark on the road of scientific development. This paper starts with the multiobjective optimization algorithm to optimize the matching of public resources and realize the effective utilization of public management resources. Using particle swarm optimization algorithm, the optimal allocation management of 8 kinds of resources in this paper is carried out, and the optimization analysis is carried out from the performance indexes, such as resource allocation time and configuration complexity. Finally, the weights of the eight resources in importance, complexity, and resource demand are 0.4, 0.4, and 0.2, respectively. The proposed method realizes the classification of resources and the optimal matching of resources.