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Cancer is the leading cause of death in China and depicting the cancer pattern of China would provide basic knowhows on how to tackle it more effectively. In this study we have reviewed several reports of cancer burden, including the Global cancer statistics 2018 and Cancer statistics in China, 2015, along with the GLOBCAN 2018 online database, to investigate the differences of cancer patterns between China, the United States (USA) and the United Kingdom (UK). An estimated 4.3 million new cancer cases and 2.9 million new cancer deaths occurred in China in 2018. Compared to the USA and UK, China has lower cancer incidence but a 30% and 40% higher cancer mortality than the UK and USA, among which 36.4% of the cancer-related deaths were from the digestive tract cancers (stomach, liver, and esophagus cancer) and have relatively poorer prognoses. In comparison, the digestive cancer deaths only took up ≤ 5% of the total cancer deaths in either USA or UK. Other reasons for the higher mortality in China may be the low rate of early-stage cancers at diagnosis and non-uniformed clinical cancer treatment strategies performed by different regions. China is undergoing the cancer transition stage where the cancer spectrum is changing from developing country to developed country, with a rapidly increase cancer burden of colorectal, prostate, female breast cancers in addition to a high occurrence of infection-related and digestive cancers. The incidence of westernized lifestyle-related cancers in China (i.e. colorectal cancer, prostate, bladder cancer) has risen but the incidence of the digestive cancers has decreased from 2000 to 2011. An estimated 40% of the risk factors can be attributed to environmental and lifestyle factors either in China or other developed countries. Tobacco smoking is the single most important carcinogenic risk factor in China, contributing to ~ 24.5% of cancers in males. Chronic infection is another important preventable cancer contributor which is responsible for ~ 17% of cancers. Comprehensive prevention and control strategies in China should include effective tobacco-control policy, recommendations for healthier lifestyles, along with enlarging the coverage of effective screening, educating, and vaccination programs to better sensitize greater awareness control to the general public.

The potato is a key crop in addressing global hunger, and deep learning is at the core of smart agriculture. Applying deep learning (e.g., YOLO series, ResNet, CNN, LSTM, etc.) in potato production can enhance both yield and economic efficiency. Therefore, researching efficient deep learning models for potato production is of great importance. Common application areas for deep learning in the potato production chain, aimed at improving yield, include pest and disease detection and diagnosis, plant health status monitoring, yield prediction and product quality detection, irrigation strategies, fertilization management, and price forecasting. The main objective of this review is to compile the research progress of deep learning in various processes of potato production and to provide direction for future research. Specifically, this paper categorizes the applications of deep learning in potato production into four types, thereby discussing and introducing the advantages and disadvantages of deep learning in the aforementioned fields, and it discusses future research directions. This paper provides an overview of deep learning and describes its current applications in various stages of the potato production chain.

UAV-based small target detection is crucial in environmental monitoring, circuit detection, and related applications. However, UAV images often face challenges such as significant scale variation, dense small targets, high inter-class similarity, and intra-class diversity, which can lead to missed detections, thus reducing performance. To solve these problems, this study proposes a lightweight and high-precision model UAV-YOLO based on YOLOv8s. Firstly, a double separation convolution (DSC) module is designed to replace the Bottleneck structure in the C2f module with deep separable convolution and point-by-point convolution fusion, which can reduce the model parameters and calculation complexity while enhancing feature expression. Secondly, a new SPPL module is proposed, which combines spatial pyramid pooling rapid fusion (SPPF) with long-distance dependency modeling (LSKA) to improve the robustness of the model to multi-scale targets through cross-level feature association. Then, DyHead is used to replace the original detector head, and the discrimination ability of small targets in complex background is enhanced by adaptive weight allocation and cross-scale feature optimization fusion. Finally, the WIPIoU loss function is proposed, which integrates the advantages of Wise-IoU, MPDIoU and Inner-IoU, and incorporates the geometric center of bounding box, aspect ratio and overlap degree into a unified measure to improve the localization accuracy of small targets and accelerate the convergence. The experimental results on the VisDrone2019 dataset showed that compared to YOLOv8s, UAV-YOLO achieved an 8.9% improvement in the recall of mAP@0.5 and 6.8%, while the parameters and calculations were reduced by 23.4% and 40.7%, respectively. Additional evaluations of the DIOR, RSOD, and NWPU VHR-10 datasets demonstrate the generalization capability of the model.

MicroRNAs (miRNAs) are small non‐coding RNAs that regulate gene expression at a post‐transcriptional level via either the degradation or translational repression of a target mRNA. They play an irreplaceable role in angiogenesis by regulating the proliferation, differentiation, apoptosis, migration and tube formation of angiogenesis‐related cells, which are indispensable for multitudinous physiological and pathological processes, especially for the occurrence and development of vascular diseases. Imbalance between the regulation of miRNAs and angiogenesis may cause many diseases such as cancer, cardiovascular disease, aneurysm, Kawasaki disease, aortic dissection, phlebothrombosis and diabetic microvascular complication. Therefore, it is important to explore the essential role of miRNAs in angiogenesis, which might help to uncover new and effective therapeutic strategies for vascular diseases. This review focuses on the interactions between miRNAs and angiogenesis, and miRNA‐based biomarkers in the diagnosis, treatment and prognosis of angiogenesis‐related diseases, providing an update on the understanding of the clinical value of miRNAs in targeting angiogenesis.

Understanding music styles is essential for music information retrieval, personalized recommendation, and AI-assisted content creation. However, existing work typically addresses tasks such as emotion classification and singing style classification independently, thereby neglecting the intrinsic relationships between them. In this study, we introduce a multi-task learning framework that jointly models these two tasks to enable explicit knowledge sharing and mutual enhancement. Our results indicate that joint optimization consistently outperforms single-task counterparts, demonstrating the value of leveraging inter-task correlations for more robust singing style analysis. To assess the generality and adaptability of the proposed framework, we evaluate it across various backbone architectures, including Transformer, TextCNN, and BERT, and observe stable performance improvements in all cases. Experiments on a benchmark dataset, which were self-constructed and collected through professional recording devices, further show that the framework not only achieves the best accuracy on both tasks on our dataset under a singer-wise split, but also yields interpretable insights into the interplay between emotional expression and stylistic characteristics in vocal performance.

Background Emerging research has reported that circular RNAs (circRNAs) play important roles in cardiac cell death after myocardial ischemia and reperfusion (I/R). Ferroptosis, a new form of cell death discovered in recent years, has been proven to participate in the regulation of myocardial I/R. This study used circRNA sequencing to explore the key circRNA in the regulation of cardiac ferroptosis after I/R and study the mechanisms of potential circRNA function. Methods We performed circRNA sequencing to explore circRNAs differentially expressed after myocardial I/R. We used quantitative polymerase chain reactions to determine the circRNA expression in different tissues and detect the circRNA subcellular localization in the cardiomyocyte. Gain- and loss-of-function experiments were aimed to examine the function of circRNAs in cardiomyocyte ferroptosis and cardiac tissue damage after myocardial I/R. RNA pull-down was applied to explore proteins interacting with circRNA. Results Here, we identified a ferroptosis-associated circRNA (FEACR) that has an underlying regulatory role in cardiomyocyte ferroptosis. FEACR overexpression suppressed I/R-induced myocardial infarction and ameliorated cardiac function. FEACR inhibition induces ferroptosis in cardiomyocytes and FEACR overexpression inhibits hypoxia and reoxygenation-induced ferroptosis. Mechanistically, FEACR directly bound to nicotinamide phosphoribosyltransferase (NAMPT) and enhanced the protein stability of NAMPT, which increased NAMPT-dependent Sirtuin1 (Sirt1) expression, which promoted the transcriptional activity of forkhead box protein O1 (FOXO1) by reducing FOXO1 acetylation levels. FOXO1 further upregulated the transcription of ferritin heavy chain 1 ( Fth1 ), a ferroptosis suppressor, which resulted in the inhibition of cardiomyocyte ferroptosis. Conclusions Our finding reveals that the circRNA FEACR-mediated NAMPT-Sirt1-FOXO1-FTH1 signaling axis participates in the regulation of cardiomyocyte ferroptosis and protects the heart function against I/R injury. Thus, FEACR and its downstream factors could be novel targets for alleviating ferroptosis-related myocardial injury in ischemic heart diseases.

Metal oxide semiconductor gas sensors are widely used to detect toxic and inflammable gases in industrial production and daily life. The main research hotspot in this field is the synthesis of gas sensing materials. Previous studies have shown that incorporating two or more metal oxides to form a heterojunction interface can exhibit superior gas sensing performance in response and selectivity compared with single phase. This review focuses on mainly the synthesis methods and gas sensing mechanisms of metal oxide heterostructures. A significant number of heterostructures with different morphologies and shapes have been fabricated, which exhibit specific sensing performance toward a specific target gas. Among these synthesis methods, the hydrothermal method is noteworthy due to the fabrication of diverse structures, such as nanorod-like, nanoflower-like, and hollow sphere structures with enhanced sensing properties. In addition, it should be noted that the combination of different synthesis methods is also an efficient way to obtain metal oxide heterostructures with novel morphologies. Despite advanced methods in the metal oxide semiconductors and nanotechnology field, there are still some new issues which deserve further investigation, such as long-term chemical stability of sensing materials, reproducibility of the fabrication process, and selectivity toward homogeneous gases. Moreover, the gas sensing mechanism of metal oxide heterostructures is controversial. It should be clarified so as to further integrate laboratory theory research with practical exploitation.

Background Climate change, largely triggered by human-induced greenhouse gases (GHGs) emissions, seems unstoppable. There was a strong rebound of anthropogenic emissions of CO 2, the preponderant GHG in terms of contribution to global warming, around the world after the COVID-19 lockdown. Also, there is still no widely accepted international treaty on curbing the anthropogenic emissions of CH 4 and N 2O, the second and third predominant GHG, respectively, so far. Thereby, prima facie, in respect to mitigating climate change, currently, humans have no aces up their sleeves. It seems that current temperature rise is not high enough to take alarm until the occurrence of tipping point. Policy Climate-related international treaties, such as 2016 Paris agreement, are compromises among conflicting geopolitical pressures. However, currently, the climate treaties show little mandatory binding force on the signatories who are able to violate and then get off scot-free, thus may end up like a nostrum. Throughout the European history, I find that the only way, if at all, to achieve the peace or obedience of a treaty is via balancing powers, embodied in Bismarck's Realpolitik of Germany and Richelieu's Raison d'état of France. Similarly, the Chinese history in East Asia proved the significance of unadulterated ideological neutrality and Darwinian adaptability in the kaleidoscope of evolving circumstances in maintaining order and enforcement of international treaties through balancing the power of rivalries to constrain ever-recurring challengers for equilibrium. Recommendations A successful policy needs to make a thorough analysis of all relevant factors to form a long-term strategic notion. Then, statesmen need to distill an array of nebulous, always contradictory options into a tenacious, controllable direction. Thereby, I suggest that, for better curbing global warming, climate agreements or climate club be incorporated into an overall geopolitical framework among the international communities.

With the advancement of representation learning, graph representation learning has gained significant attention in the field of community detection for heterogeneous networks. A prominent approach in this domain involves the use of meta-paths to capture higher-order relationships between nodes, particularly when bidirectional or reciprocal relationships exist. However, defining effective meta-paths often requires substantial domain expertise. Moreover, these methods typically depend on additional clustering algorithms, which can limit their practical applicability. To address these challenges, context paths have been introduced as an alternative to meta-paths. When combined with a self-attention mechanism, models can dynamically assess the relative importance of different context paths. By leveraging the inherent symmetry within context paths, models enhance their ability to capture balanced relationships between nodes, thereby improving their representation of complex interactions. Building on this idea, we propose BP-GCN, a self-attention-based model for heterogeneous community detection. BP-GCN autonomously identifies node relationships within symmetric context paths, significantly improving community detection accuracy. Furthermore, the model integrates the Bernoulli–Poisson framework to establish an end-to-end detection system that eliminates the need for auxiliary clustering algorithms. Extensive experiments on multiple real-world datasets demonstrate that BP-GCN consistently outperforms existing benchmark methods.