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Mitigating global warming is a grand challenge for sustainable development in the increasingly urbanized world. How to build a low-carbon society and achieve economic growth at the same time remains less clear. In this paper, using Zhenjiang City in East China as a case study, we analyze the contribution of cultural and tourism industries (CTI) and important low-carbon industries to the sustainable development of the metropolitan area. We found that the CTI in Zhenjiang has accounted for 25% of its total gross domestic product (GDP); the forest recovery for the development of CTI sequestrates 150,000 Mt of carbon dioxide annually, which substantially decreases its carbon emission per GDP and promotes the development of a low-carbon city. With the development of CTI and the transformation of the traditional industrial structure, the tertiary industry has gradually emerged and expanded. CTI-related employment has also increased, contributing to poverty eradication and the achievement of global sustainable development goals. The low-carbon and sustainable development model in Zhenjiang will provide a successful example for other cities, not only within China, but also beyond.

The timely identification of individuals at high risk for peptic ulcers (PUs) is vital in preventing gastrointestinal bleeding after antiplatelet therapy. This study was designed to determine PU risk factors and develop a risk assessment model for PU detection in the general Chinese population. In a prospective dataset, clinical data from individuals undergoing gastroscopic evaluation between April 2019 and May 2022 were recorded. PUs were defined as mucosal defects exceeding 5 mm confirmed via gastroscopy. Participants were categorized into development (April 2019 to April 2021) and validation (May 2021 to May 2022) sets based on chronological order. LASSO-derived logistic regression analysis was employed to create a score, which was further validated via temporal validation. A total of 902 patients were ultimately enrolled, 204 (22.6%) of whom had PUs based on endoscopic findings. In the development cohort (n = 631), seven independent risk factors emerged: male sex (OR = 2.35, P  = 0.002), white blood cell (WBC) count (OR = 1.16, P  = 0.010), red blood cell (RBC) count (OR = 0.49, P  < 0.001), globulin level (OR = 0.92, P  = 0.004), albumin level (OR = 0.94, P  = 0.020), pepsinogen I (PGI) level (OR = 1.01, P  < 0.001), and positive Helicobacter pylori (HP) antibody (OR = 2.50, P  < 0.001). Using these factors, a nomogram (HAMPROW score [hazard ratio (HP) antibody, albumin, male, PGI, RBC, globulin, and WBC]) was developed for individual PU prediction. The ability of the HAMPROW score to predict survival was confirmed with AUCs of 0.854 (95% CI 0.816–0.891) and 0.833 (95% CI 0.771–0.895) in the development and validation sets, respectively. In conclusion, the HAMPROW score can be used to screen for PUs effectively in the general Chinese population, facilitating personalized early detection of high risk of gastrointestinal bleeding before antiplatelet therapy.

Forests provide vital ecosystem services, particularly as carbon sinks for nature-based climate solutions. However, the impact of elevated atmospheric carbon dioxide (CO2) levels on carbon and nitrogen interactions of forests remains poorly quantified. We integrate experimental observations and biogeochemical models to elucidate the synergies between enhanced nitrogen and carbon cycling in global forests under elevated CO2. Elevated CO2 alone increases net primary productivity (+27%; 95% CI: 23–31%) and leaf C/N ratio (+26%; 95% CI: 16–39%), while stimulating biological nitrogen fixation (+25%; 95% CI: 0–56%) and nitrogen use efficiency (+32%; 95% CI: 5–65%) according to a global meta-analysis. Under the elevated CO2 middle-road scenario for 2050, the forest carbon sink is projected to increase by 0.28 billion tonnes (PgC yr−1), with reactive nitrogen loss decreasing by 8 Tg yr−1 relative to the baseline. The monetary impact assessment of the elevated CO2 impact on forests represents a societal value of US$271 billion.Using a global meta-analysis approach, the authors show that elevated CO2 alone can increase primary productivity and leaf C/N ratio and stimulate nitrogen fixation and nitrogen use efficiency. They project increasing carbon sink and decreasing reactive nitrogen loss under climate change.

Software caches are an intrinsic component of almost every computer system. Consequently, caching algorithms, particularly eviction policies, are the topic of many papers. Almost all these prior papers evaluate the caching algorithm based on its hit ratio, namely the fraction of requests that are found in the cache, as opposed to disk. The hit ratio is viewed as a proxy for traditional performance metrics like system throughput or response time. Intuitively it makes sense that higher hit ratio should lead to higher throughput (and lower response time), since more requests are found in the cache (low access time) as opposed to the disk (high access time). This paper challenges this intuition. We show that increasing the hit ratio can actually hurt the throughput (and response time) for many caching algorithms. Our investigation follows a three-pronged approach involving (i) queueing modeling and analysis, (ii) implementation and measurement, and (iii) simulation to validate the accuracy of the queueing model. We also show that the phenomenon of throughput decreasing at higher hit ratios is likely to be more pronounced in future systems, where the trend is towards faster disks and higher numbers of cores per CPU.

With the exponential growth of data and evolving use cases, petabyte-scale OLAP data platforms are increasingly adopting a model that decouples compute from storage. This shift, evident in organizations like Uber and Meta, introduces operational challenges including massive, read-heavy I/O traffic with potential throttling, as well as skewed and fragmented data access patterns. Addressing these challenges, this paper introduces the Alluxio local (edge) cache, a highly effective architectural optimization tailored for such environments. This embeddable cache, optimized for petabyte-scale data analytics, leverages local SSD resources to alleviate network I/O and API call pressures, significantly improving data transfer efficiency. Integrated with OLAP systems like Presto and storage services like HDFS, the Alluxio local cache has demonstrated its effectiveness in handling large-scale, enterprise-grade workloads over three years of deployment at Uber and Meta. We share insights and operational experiences in implementing these optimizations, providing valuable perspectives on managing modern, massive-scale OLAP workloads.

This paper explores a prevailing trend in the industry: migrating data-intensive analytics applications from on-premises to cloud-native environments. We find that the unique cost models associated with cloud-based storage necessitate a more nuanced understanding of optimizing performance. Specifically, based on traces collected from Uber's Presto fleet in production, we argue that common I/O optimizations, such as table scan and filter, and broadcast join, may lead to unexpected costs when naively applied in the cloud. This is because traditional I/O optimizations mainly focus on improving throughput or latency in on-premises settings, without taking into account the monetary costs associated with storage API calls. In cloud environments, these costs can be significant, potentially involving billions of API calls per day just for Presto workloads at Uber scale. Presented as a case study, this paper serves as a starting point for further research to design efficient I/O strategies specifically tailored for data-intensive applications in cloud settings.

Humans and bacteria have always been closely related. However, pathogenic bacteria and drug‐resistant bacteria pose a certain threat to human life and health. On the other hand, probiotics, such as intestinal flora, also affect our daily life. Understanding the microstructure of microorganisms is important for effective treatment of bacterial infections. Luminogens with aggregation‐induced emission properties are now recognized as potent fluorescent agents for the diagnosis and treatment of microorganisms. In this review, we summarized the most recent developments of AIEgen‐based biomaterials and discussed the advantages of AIE fluorescent probes for imaging. Their applications for rapid imaging of bacteria, differentiation of Gram‐ negative and positive bacteria, and specific imaging of intracellular bacteria and fungi are presented. The monitoring of bacteria‐cell interaction was also introduced. Finally, the design strategy of engineered bacteria‐AIEgen hybrid system and their role in anti‐cancer applications was discussed. This review will highlight the achievements that AIEgens recently made in the field of microbe‐related visualization and therapy. First, they discuss AIEgens in discriminating bacteria. Next, visualization of phagocytosis processes and selective PDT toward intracellular bacteria will be introduced. In addition, engineered bacteria‐AIEgen hybrid systems for anticancer applications will be introduced.

Objective Evidence on the correlation between Oxidative Balance Score (OBS) and the risk of congestive heart failure (CHF) in hypertensive patients remains limited. This study aimed to explore the association between OBS and the risk of CHF in hypertensive patients. Methods We assessed the association between the Oxidative Balance Score (OBS) and congestive heart failure (CHF) risk in hypertensive patients using weighted, multivariable-adjusted logistic regression. The Weighted Quantile Sum (WQS) regression model quantified the relative contributions of individual components within the OBS. Nonlinear exposure-response relationships were evaluated with Restricted Cubic Spline (RCS) analysis. Model sensitivity and specificity were determined via Receiver Operating Characteristic (ROC) curves. Subgroup analyses examined heterogeneity across predefined clinical strata, and mediation analysis explored potential mechanistic pathways. Results A significant negative correlation was found between OBS and the risk of CHF in hypertensive patients (OR = 0.56 [0.40, 0.77], p  < .0001), and this relationship was linear (P for nonlinear > 0.05, P for overall < 0.001). There was an interaction between OBS components (0.377 [95% confidence interval (CI): 0.267–0.532], p  < .001). Among them, alcohol has the strongest impact on CHF risk (0.47%). The model demonstrated strong predictive performance, achieving an area under the ROC curve (AUC) of 0.725 (95% CI: 0.691–0.739). Subgroup analyses revealed no significant heterogeneity ( P  > .05). Serum creatinine (Scr) potentially mediate the relationship between OBS and CHF risk (Proportion = 17.7%). Conclusion A significant negative correlation was found between OBS and CHF risk in hypertensive patients, with Scr potentially playing a mediating role. Alcohol consumption had the greatest impact on the risk of CHF in hypertensive patients.

The Normalized Difference Vegetation Index (NDVI) is a fundamental metric for monitoring terrestrial ecosystem dynamics and assessing ecological responses to climate change. However, uncertainties persist across NDVI products, and a comprehensive assessment of their consistency is lacking. This study conducts a multi-faceted evaluation of three NDVI products, GIMMS V1.2 NDVI (NDVI3g+), PKU GIMMS NDVI (NDVIpku), and MODIS NDVI (NDVImod), to elucidate their performance across ecosystem applications. Our analysis encompasses a comparative analysis of NDVI values, trends, sensitivity to root-zone soil moisture (RSM), and performance in tracking photosynthesis benchmarked against solar-induced chlorophyll fluorescence (SIF). Our results reveal that NDVI3g+ deviates notably from NDVIpku and NDVImod over cold climates and Evergreen Broadleaf Forest (EBF). Additionally, NDVI3g+ exhibits significant global browning, in contrast to the significant greening observed for NDVIpku and NDVImod. Although their responses to RSM are generally uncertain, consistent positive responses appear in Drylands, with NDVImod showing the highest sensitivity. Additionally, the three NDVI products have high seasonality consistency with SIF, except over EBF, and NDVIpku and NDVI3g+ achieve the highest and lowest overall anomaly consistency with SIF, respectively. Furthermore, converting NDVI3g+, NDVIpku, and NDVImod to the corresponding kernel NDVIs improves seasonality consistency with SIF across 85% of the globe.

Pancreatic cancer (PC) is associated with dismal clinical outcomes, largely due to the high prevalence of liver metastasis (LM) at diagnosis or post‐resection. Despite its clinical significance, the molecular drivers of LM in PC remain poorly characterized, and few validated biomarkers or therapeutic targets are currently available. Our study identifies 7‐dehydrocholesterol reductase (DHCR7), a terminal enzyme in cholesterol biosynthesis, as significantly upregulated in PC tissues and closely correlated with LM progression. In vitro experiments demonstrated that DHCR7 enhances the proliferation, invasion, and migration ability in PC cells, and in vivo experiments demonstrated that DHCR7 promotes LM of PC. The mechanism of DHCR7 promoting LM may be mediated by elevating cholesterol synthesis of PC cells and then activating the PI3K‐Akt signaling pathway. Taken together, our findings uncover a novel molecular mechanism underlying LM in PC and highlight DHCR7 as a possible predictive biomarker or interventional target. In pancreatic cancer, elevated DHCR7 expression enhances cholesterol biosynthesis and then activates the PI3K‐Akt signaling pathway, promoting epithelial‐mesenchymal transition (EMT) and facilitating liver metastasis. Knockdown of DHCR7 suppresses tumor proliferation, invasion, and metastatic colonization in vivo and in vitro, highlighting its role as a therapeutic target.