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Enhanced understanding of environmental restoration can be achieved by examining how urban park visitors’ perceptions of biodiversity contribute to their sense of environmental rejuvenation. In this study, a questionnaire survey was conducted among 554 visitors from five representative urban parks in Fuzhou, China, and a structural equation model was used to explore the interactions among perceived biodiversity, place attachment, and self-rated repair. The main findings were as follows: (1) Perceived biodiversity had significant positive and direct effects on place dependence and self-rated restoration, but not on place identity. It is worth noting that place dependence has a deep and direct impact on place identity. (2) Self-rated restoration could be directly influenced by perceived biodiversity and place dependence. The direct impact of perceived biodiversity showed more intensity than place dependence. (3) Place dependence can be the only intermediary or link in the chain between perceived biodiversity and self-rated restoration. Conversely, place identity may not act as an independent intermediary but can play a key role in the chain of intermediaries. The study not only advances our understanding of the complex relationship between perceived biodiversity, place attachment, and self-assessed restoration; it also provides practical implications for urban green eco-design initiatives, thereby contributing to the field of urban landscape planning and formulation.

Introduction: As the global urbanization process accelerates, the contradiction between economic development demands and ecological protection becomes increasingly prominent. Methods: In this study, we simulated the evolution of the ecological security pattern (ESP) of Pingtan Island from 2000 to 2020 by extracting the ecological sources using Remote Sensing Ecological Index (RSEI), and identifying the ecological corridors and key nodes by combining with Linkage Mapping (LM) and Circuit Theory. In addition, Geodetector was utilized to identify these major determinants affecting RSEI. Results: The results showed 1) From 2000 to 2020, the ecological environmental quality (EEQ) of Pingtan Island continued to improve, and the mean value of RSEI gradually increased from 0.47 to 0.51. 2) Univariate analysis showed that elevation and slope were the most significant factors affecting the spatial variability of the RSEI, with the interaction between slope and proportion of built-up area having a significant effect on EEQ. 3) The number and extent of ecological sources were expanded year by year with significant spatial variability. At the same time, the number and range of ecological corridors also underwent phase adjustment. 4) Further exploration of ESP of Pingtan Island in 2020 identified 32 ecological pinch points (EPPs) and 52 ecological barrier points (EBPs), which were mainly located within or near the ecological corridors, indicating key areas for future ecological restoration efforts. Discussion: These insights help to enhance urban spatial planning and ecosystem restoration on Pingtan Island and provide a blueprint for ESP development in comparable island urban environments.

Micro-supercapacitors are promising energy storage devices that can complement or even replace batteries in miniaturized portable electronics and microelectromechanical systems. Their main limitation, however, is the low volumetric energy density when compared with batteries. Here, we describe a hierarchically structured carbon microfibre made of an interconnected network of aligned single-walled carbon nanotubes with interposed nitrogen-doped reduced graphene oxide sheets. The nanomaterials form mesoporous structures of large specific surface area (396 m 2  g −1 ) and high electrical conductivity (102 S cm −1 ). We develop a scalable method to continuously produce the fibres using a silica capillary column functioning as a hydrothermal microreactor. The resultant fibres show a specific volumetric capacity as high as 305 F cm −3 in sulphuric acid (measured at 73.5 mA cm −3 in a three-electrode cell) or 300 F cm −3 in polyvinyl alcohol (PVA)/H 3 PO 4 electrolyte (measured at 26.7 mA cm −3 in a two-electrode cell). A full micro-supercapacitor with PVA/H 3 PO 4 gel electrolyte, free from binder, current collector and separator, has a volumetric energy density of ∼6.3 mWh cm −3 (a value comparable to that of 4 V–500 µAh thin-film lithium batteries) while maintaining a power density more than two orders of magnitude higher than that of batteries, as well as a long cycle life. To demonstrate that our fibre-based, all-solid-state micro-supercapacitors can be easily integrated into miniaturized flexible devices, we use them to power an ultraviolet photodetector and a light-emitting diode. Hierarchical hybrid carbon fibres consisting of a network of nitrogen-doped reduced graphene oxide and single-walled carbon nanotubes are synthesized and subsequently used to make a supercapacitor with high volumetric energy density.

The role of mast cells (MCs) in colorectal cancer (CRC) remains unclear, and a comprehensive single-cell study on CRC MCs has not been conducted. This study used a multi-omics approach, integrating single-cell sequencing, spatial transcriptomics, and bulk tissue sequencing data to investigate the heterogeneity and impact of MCs in CRC. Five MC signature genes ( TPSAB1 , TPSB2 , CPA3 , HPGDS , and MS4A2 ) were identified, and their average expression was used as a marker of MCs. The MC density was found to be lower in CRC compared to normal tissue, but MCs in CRC demonstrated distinct activation features. Activated MCs were defined by high expression of receptors and MC mediators, while resting MCs had low expression. Most genes, including the five MC signature genes, were expressed at higher levels in activated MCs. The MC signature was linked to a better prognosis in both CRC and pan-cancer patient cohorts. Elevated KITLG expression was observed in fibroblasts and endothelial cells in CRC samples compared to normal tissue, and co-localization of MCs with these cell types was revealed by spatial transcriptome analysis. In conclusion, this study finds decreased MC density in CRC compared to normal tissue, but highlights a shift in MC phenotype from CMA1 high resting cells to activated TPSAB1 high , CPA3 high , and KIT high cells. The elevated KITLG expression in the tumor microenvironment’s fibroblasts and endothelial cells may activate MCs through the KITLG - KIT axis, potentially suppressing tumor progression.

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This study aims to uncover the heterogeneity of endothelial cells (ECs) in colorectal cancer (CRC) and their crucial role in angiogenesis, with a special focus on tip cells. Using single-cell RNA sequencing to profile ECs, our data suggests that CRC ECs predominantly exhibit enhanced angiogenesis and decreased antigen presentation, a shift in phenotype largely steered by tip cells. We also observed that an increase in the density and proportion of tip cells correlates with CRC occurrence, progression, and poorer patient prognosis. Furthermore, we identified endothelial cell-specific molecule 1 ( ESM1 ), specifically expressed in tip cells, sustains a VEGFA-KDR-ESM1 positive feedback loop, promoting angiogenesis and CRC proliferation and migration. We also found the enrichment of KDR in tip cells and spotlight a unique long-tail effect in VEGFA expression: while VEGFA is primarily expressed by epithelial cells, the highest level of VEGFA expression is found in individual myeloid cells. Moreover, we observed that effective PD-1 blockade immunotherapy significantly reduced tip cells, disrupting the VEGFA-KDR-ESM1 positive feedback loop in the process. Our investigation into the heterogeneity of ECs in CRC at a single-cell level offers important insights that may contribute to the development of more effective immunotherapies targeting tip cells in CRC.

The growing prevalence of knowledge reasoning using knowledge graphs (KGs) has substantially improved the accuracy and efficiency of intelligent medical diagnosis. However, current models primarily integrate electronic medical records (EMRs) and KGs into the knowledge reasoning process, ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text. To better integrate EMR text information, we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning (GATiT), which comprises text representation, subgraph construction, knowledge reasoning, and diagnostic classification. In the text representation process, GATiT uses a pre-trained model to obtain text representations of the EMRs and additionally enhances embeddings by including chief complaint information and numerical information in the input. In the subgraph construction process, GATiT constructs text subgraphs and disease subgraphs from the KG, utilizing EMR text and the disease to be diagnosed. To differentiate the varying importance of nodes within the subgraphs features such as node categories, relevance scores, and other relevant factors are introduced into the text subgraph. The message-passing strategy and attention weight calculation of the graph attention network are adjusted to learn these features in the knowledge reasoning process. Finally, in the diagnostic classification process, the interactive attention-based fusion method integrates the results of knowledge reasoning with text representations to produce the final diagnosis results. Experimental results on multi-label and single-label EMR datasets demonstrate the model’s superiority over several state-of-the-art methods.

Few-shot knowledge graph completion (FKGC) refers to the task of inferring missing facts in a knowledge graph by utilizing a limited number of reference entities. Most FKGC methods assume a single similarity metric, which leads to a single feature space and makes it difficult to separate positive and negative samples effectively. Therefore, we propose a multi-scale relational metric network (MSRMN) specifically designed for FKGC, which integrates multiple scales of measurement methods to learn a more comprehensive and compact feature space. In this study, we design a complete neighbor random sampling algorithm to sample complete one-hop neighbor information, and aggregate both one-hop and multi-hop neighbor information to enhance entity representations. Then, MSRMN adaptively obtains prototype representations of relations and integrates three different scales of measurement methods to learn a more comprehensive feature space and a more discriminative feature mapping, enabling positive query entity pairs to obtain higher measurement scores. Evaluation of MSRMN on two public datasets for link prediction demonstrates that MSRMN attains top-performing outcomes across various few-shot sizes on the NELL dataset.