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In the context of ecological civilization construction, resource-based cities (RBCs) are tasked with the dual responsibility of national energy supply and transformation amidst complex social contradictions. These cities face the resource curse dilemma, characterized by resource depletion, conflicts in spatial production, in living, and in ecological spaces, and intertwined ecological–economic–social issues. Enhancing their ecological resilience is a crucial indicator of successful transformation strategies. This study focuses on Jinzhong City in Shanxi Province, employing Morphological Spatial Pattern Analysis (MSPA) to assist in the spatial analysis of urban ecological resilience. Additionally, Conefor Sensinode is utilized to identify key ecological sources that significantly contribute to urban resilience. A novel Risk-Connectivity-Potential (RCP) model is developed to construct a framework of indicators affecting the resilience of RBCs, which is used to assess the ecological resilience of Jinzhong City, particularly in relation to the spatial distribution of mining areas. The results indicate the following: (1) important ecological source areas within the city constitute approximately 39.47% of the total city area, predominantly located in woodland regions; (2) the overall assessment of ecological resilience reveals a heterogeneous pattern, increasing from west to east, with lower resilience observed in low-lying terrains and higher resilience in mountainous plateaus; (3) mines within significant ecological source areas are primarily situated in low-resilience zones near built land and agriculture land, while other mining areas are mainly found between high-resilience zones, posing risks of increased ecological resistance, reduced ecological connectivity, and potential ecological issues. This study explores the application of the ecological resilience framework in RBCs, providing a scientific basis and reference for the rational utilization of resources and urban transformation and development.The methodology and findings can be applied to similar cities globally, offering valuable insights for balancing resource management and ecological protection in the context of sustainable urban development.

Pressurized confined water below coal seams are serious threats to mining. The conventional water inrush coefficient method fails to accurately assess the risk of floor water inrush under some specific conditions, such as high water pressure and low water yield in the source aquifers. Large amounts of water inrush data including water inrush flow rate, water inrush coefficient ( T s ), floor aquiclude thickness ( M ), and water abundance, were collected and statistically analyzed. The results indicated that inrushes mostly occurred when M was less than 30 m and that the critical T s increased linearly with M . The occurrence of a water inrush and water inrush yield amount ( Q in L/s) were related to both the values of T s and the unit water inflow ( q in L/(s m)). In addition, 97.7% of the large- and medium-sized inrush events occurred when q  > 2 L/(s m) and only a small proportion (3.2%) of the small-sized inrushes happened when q  < 0.1 L (s m). T s ,  M and q were comprehensively analyzed and used to evaluate vulnerability to floor water inrush. By analyzing the distribution of water inrush points and the scale of water inrush events, the vulnerability was divided into four levels (safe, moderately safe, potentially dangerous, and highly risky) based on T s – M and T s – q models. Successful application of these models in the Huaibei mining area proved that they are feasible in practice. The T s – M and T s – q charts can be used independently or jointly. These new methods should improve the accuracy of predictions and evaluations during deep exploitation where the aquifers are often characterized with high pressure but low water abundance. The results could also help reduce the amount spent on mine water prevention and control.

BackgroundNeutrophil extracellular traps (NETs) were originally thought to be formed by neutrophils to trap invading microorganisms as a defense mechanism. Increasing studies have shown that NETs play a pivotal role in tumor progression and diffusion. In this case, transcriptome analysis provides an opportunity to unearth the association between NETs and clinical outcomes of patients with pan-cancer.MethodsThe transcriptome sequencing data of The Cancer Genome Atlas pan-cancer primary focus was obtained from UCSC Xena, and a 19-gene NETs score was then constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model based on the expression levels of 69 NETs initial biomarkers we collected from multistudies. In addition, multiple datasets covering multiple cancer types from other databases were collected and used to validate the signature. Gene ontology enrichment analyses were used to annotate the functions of NETs-related pathways. Immunohistochemistry (IHC) was implemented to evaluate the role of NETs-related genes in clinical patients across types of tumors, including lung adenocarcinoma (n=58), colorectal carcinoma (n=93), kidney renal clear cell carcinoma (n=90), and triple-negative breast cancer (n=80).ResultsThe NETs score was calculated based on 19-NETs related genes according to the LASSO Cox model. The NETs score was considered a hazardous factor in most cancer types, with a higher score indicating a more adverse outcome. In addition, we found that NETs were significantly correlated to various malignant biological processes, such as the epithelial to mesenchymal transition (R=0.7444, p<0.0001), angiogenesis (R=0.5369, p<0.0001), and tumor cell proliferation (R=0.3835, p<0.0001). Furthermore, in IHC cohorts of a variety of tumors, myeloperoxidase, a gene involved in the model and a classical delegate of NETs formation, was associated with poor clinical outcomes.ConclusionsCollectively, these constitutive and complementary biomarkers represented the ability of NETs formation to predict the development of patients’ progression. Integrative transcriptome analyses plus clinical sample validation may facilitate the biomarker discovery and clinical transformation.

In order to effectively combat the effects of global warming, all sectors must actively reduce greenhouse gas emissions in a sustainable and substantial manner. Sector coupling has emerged as a critical technology that can integrate energy systems and address the temporal imbalances created by intermittent renewable energy sources. Despite its potential, current sector coupling capabilities remain underutilized, and energy modeling approaches face challenges in understanding the intricacies of sector coupling and in selecting appropriate modeling tools. This paper presents a comprehensive review of sector coupling technologies and their role in the energy transition, with a specific focus on the integration of electricity, heat/cooling, and transportation, as well as the importance of hydrogen in sector coupling. Additionally, we conducted an analysis of 27 sector coupling models based on renewable energy sources, with the goal of aiding deciders in identifying the most appropriate model for their specific modeling needs. Finally, the paper highlights the importance of sector coupling in achieving climate protection goals, while emphasizing the need for technological openness and market-driven conditions to ensure economically efficient implementation.

The air pollution in China is a severe problem. The aim of our study was to investigate the impact of air pollutants on acute respiratory outcomes in outpatients. Outpatient data from 2 December 2013 to 1 December 2014 were collected, as well as air pollutant data including ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and particulate matter (PM2.5 and PM10). We screened six categories of acute respiratory outcomes and analyzed their associations with different air pollutant exposures, including upper respiratory tract infection (URTI), acute bronchitis (AB), community-acquired pneumonia (CAP), acute exacerbation of chronic obstructive pulmonary disease (AECOPD), acute exacerbation of asthma (AE-asthma), and acute exacerbation of bronchiectasis (AEBX). A case-crossover design with a bidirectional control sampling approach was used for statistical analysis. A total of 57,144 patients were enrolled for analysis. PM2.5, PM10, NO2, SO2, and CO exposures were positively associated with outpatient visits for URTI, AB, CAP, and AEBX. PM10, SO2, and CO exposures were positively associated with outpatient visits for AECOPD. Exposure to O3 was positively associated with outpatient visits for AE-asthma, but negatively associated with outpatient visits for URTI, CAP, and AEBX. In conclusion, air pollutants had acute effects on outpatient visits for acute respiratory outcomes, with specific outcomes associated with specific pollutants.

Biomolecular condensates play a significant role in chromatin activities, primarily by concentrating and compartmentalizing proteins and/or nucleic acids. However, their genomic landscapes and compositions remain largely unexplored due to a lack of dedicated computational tools for systematic identification in vivo. To address this, we develop CondSigDetector, a computational framework designed to detect condensate-like chromatin-associated protein co-occupancy signatures (CondSigs), to predict genomic loci and component proteins of distinct chromatin-associated biomolecular condensates. Applying this framework to mouse embryonic stem cells (mESC) and human K562 cells enable us to depict the high-resolution genomic landscape of chromatin-associated biomolecular condensates, and uncover both known and potentially unknown biomolecular condensates. Multi-omics analysis and experimental validation further verify the condensation properties of CondSigs. Additionally, our investigation sheds light on the impact of chromatin-associated biomolecular condensates on chromatin activities. Collectively, CondSigDetector provides an approach to decode the genomic landscape of chromatin-associated condensates, facilitating a deeper understanding of their biological functions and underlying mechanisms in cells. The authors develop CondSigDetector, a computational framework designed to detect condensate-like chromatin-associated protein co-occupancy signatures, to predict genomic loci and component proteins of distinct chromatin-associated biomolecular condensates.

Tumor fibrosis is recognized as a malignant hallmark in various solid tumors; however, the clinical importance and associated molecular characteristics of tumor fibrosis in liver metastases (LM) from colorectal cancer (CRLM) remain poorly understood. Here we show that patients with CRLM whose liver metastases (LM) exhibited tumor fibrosis (Fibrosis+ LM) had significantly worse progression-free survival ( P  = 0.025) and overall survival ( P  = 0.008). Single-cell RNA sequencing revealed that the tumor microenvironment of the Fibrosis+ LM was characterized by T cells with an exhausted phenotype, macrophages displaying a profibrotic and suppressive phenotype and fibrosis-promoting fibroblasts. Further investigation highlighted the pivotal role of VCAN_eCAF in remodeling the tumor fibrosis in the tumor microenvironment of Fibrosis+ LM, emphasizing potential targetable interactions such as FGF23 or FGF3 - FGFR1 . Validation through multiplex immunohistochemistry/immunofluorescence and spatial transcriptomics supported these findings. Here we present a comprehensive single-cell atlas of tumor fibrosis in LM, revealing the intricate multicellular environment and molecular features associated with it. These insights deepen our understanding of tumor fibrosis mechanisms and inform improved clinical diagnosis and treatment strategies.

IntroductionDietary intervention is fundamental for the management of type 2 diabetes (T2D), playing a crucial role in stabilising blood glucose levels and improving quality of life. As the major contributor to daily energy intake, the quality of carbohydrates can directly influence the glycaemic stability. Therefore, we aim to explore whether adjusting and optimising the composition of dietary carbohydrates, specifically starch, can provide multiple metabolic benefits for patients with T2D.Methods and analysisThis multicentre randomised crossover clinical trial will include 150 participants with T2D. Participants will be assigned to either a conventional diet (CD) following the guidelines for T2D or an optimised carbohydrate diet (OCD) focused on increasing resistant starch intake to 40 g/day and decreasing rapidly digestible starch intake for 12 weeks. This will be followed by a 6-week wash-out period, after which participants will crossover to the alternate diet with equal energy and consistent energy proportion of the three macronutrients for another 12 weeks. The primary outcome is the difference in the change of postprandial glycaemia (changes in the average incremental area under the blood glucose curve (iAUC)) induced by OCD and CD interventions. Secondary outcomes include changes in other glucose and lipid metabolism-related parameters and cognitive function, as well as psychological, behavioural and physiological factors. Exploratory outcomes include changes in the iAUCs for each of the three meals, appetite-related hormone levels, degree of hepatic steatosis, serum cytokines, immune functions and multiomics parameters.Ethics and disseminationThe protocol has received approval from the Ethics Committee of Shanghai Sixth People’s Hospital (Approval No. 2025–018; Protocol V.4.1, 20250112) and has been registered with the ClinicalTrials.gov Registry. The findings will be disseminated through peer-reviewed journal publications, conference presentations and media releases.Trial registration numberNCT06936657.

Understanding daily water consumption patterns is crucial for efficient management and distribution of water resources, as well as for promoting energy conservation and achieving carbon peaking and neutrality targets. It compares performance of three clustering algorithms, K-means Clustering (KC), Agglomerative Hierarchical Clustering (AHC), and Spectral Clustering (SC), using Silhouette Coefficient Index (SCI) and Calinski–Harabasz Index (CHI) as evaluation metrics. We conducted a case study using original hourly flow series of a water distribution division. It aims to identify typical daily water consumption patterns and explore factors that influence them. Findings are as follows: (1) among the three algorithms, KC demonstrates the best, with SCI of 0.6315, 0.5922, and 0.6272, and CHI of 305.9207, 274.1120, and 302.4738 for KC, AHC, and SC, respectively. (2) KC successfully identifies three distinct typical daily water consumption patterns. (3) Results indicate a significant impact of seasons on daily water consumption patterns. (4) Conversely, weekdays and holidays have minimal effect on daily water consumption patterns. It highlights the importance of comprehending daily water consumption patterns and underscores the effectiveness of KC in identifying such patterns. Furthermore, it emphasizes the significant influence of seasons while revealing limited impact of weekdays and holidays on daily water consumption patterns.