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Summary: Background: Existing studies have not provided robust evidence about the CVD risk of non-smoking patients with restrictive spirometric pattern (RSP) or airflow obstruction (AFO), and how the risk is modified by body shape. We aimed to bridge the gap. Methods: We used never-smokers' data from the China Kadoorie Biobank (CKB) and performed Cox models by sex (278,953 females and 50,845 males). Spirometry was used to assess the baseline spirometric pattern. CVD outcomes were captured through multiple sources. Findings: Females’ results were presented primarily, though males' results were similar. During a median 12-year (maximum 14.5 years) follow-up, both RSP and AFO patients had increased risks of acute myocardial infarction (AMI), other ischaemic heart disease (other IHD), heart failure, pulmonary heart disease, arrhythmia, and intracerebral haemorrhage (ICH). RSP was also associated with ischaemic stroke (IS). The HRs (95% CIs) for AFO in females ranged from 1.29 (1.15–1.45) for ICH to 8.84 (7.79–10.03) for pulmonary heart disease, while those for RSP ranged from 1.11 (1.08–1.15) for IS to 3.17 (2.80–3.59) for pulmonary heart disease. These risks increased with the severity of AFO and reduced FVC. RSP/AFO was more strongly associated with other IHD, heart failure, and pulmonary heart disease in underweight females than in normal and obese counterparts, respectively. Interpretation: With the confounding of smoking fully controlled, both RSP and AFO were associated with higher risks of various CVD outcomes, which further increased with the severity of AFO and reduced FVC. These associations were even stronger in underweight individuals. Funding: National Natural Science Foundation of China, National Key Research and Development Program of China, Ministry of Science and Technology of the People's Republic of China, Kadoorie Charitable Foundation, UK Wellcome Trust, UK Medical Research Council, Cancer Research UK, and British Heart Foundation.

Summary: Background: In non-high-risk individuals, risk-category-based atherosclerotic cardiovascular disease (ASCVD) screening strategies may be more cost-effective than one-size-fits-all approaches. However, current decisions are constrained by a lack of research evidence. We aimed to explore appropriate risk-category-based screening interval strategies for non-high-risk individuals in ASCVD primary prevention in the Chinese population. Methods: We used data from 28,624 participants in the China Kadoorie Biobank (CKB) who had completed at least two field surveys. The risk assessment tools were the 10-year ASCVD risk prediction models developed based on the CKB cohort. We constructed multistate Markov models to model disease progression and estimate transition probabilities between different risk categories. The total person-years spent unidentified in the high-risk state over a 10-year period were calculated for each screening interval protocol. We also estimated the number of ASCVD events prevented, quality-adjusted life years (QALYs) gained, and costs saved when compared to the 3-yearly screening protocol. Findings: When compared to the uniform 3-yearly protocol, most risk-category-based screening interval protocols would identify more high-risk individuals timely, thus preventing more ASCVD events and gaining QALYs. A few of them would reduce total health-care costs. The protocol, which used 6-year, 3-year, and 2-year screening intervals for low-risk, intermediate-low-risk, and intermediate-high risk individuals, was optimal, and would reduce the person-years spent unidentified in the high-risk category by 17.9% (95% CI: 13.1%–21.9%), thus preventing an estimated 113 thousand (95% CI: 83–138) hard ASCVD events for Chinese adults aged 30–79 over a 10-year period. When using a lower cost of statin therapy, more screening protocols would gain QALYs while saving costs. Interpretation: For the primary prevention of ASCVD, risk-category-based screening protocols outperformed the one-size-fits-all approach in the Chinese population. Funding: This work was supported by National Natural Science Foundation of China (82192904, 82388102, 82192900) and grants (2023YFC2509400) from the National Key R&D Program of China. The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust (212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z), grants (2016YFC0900500) from the National Key R&D Program of China, National Natural Science Foundation of China (81390540, 91846303, 81941018), and Chinese Ministry of Science and Technology (2011BAI09B01).

With the continuous progress of remote sensing technology, the demand for means of detecting small targets in remote sensing images is escalating. The significance of detecting small targets in remote sensing images lies in enhancing the ability to identify small and elusive targets and the detection accuracy against complex backgrounds, holding significant application value in military reconnaissance, environmental monitoring, and disaster early-warning systems. Firstly, the minuteness of certain targets in relation to the entire image in which they occur, particularly when the camera is situated at a higher altitude, renders them difficult to detect. Secondly, the varying background and lighting conditions in remote sensing images further complicate the detection task. Conventional target detection methods are frequently incapable of addressing these complexities, resulting in a reduction in detection accuracy and an increase in false alarms. Hence, in this paper, we propose a lightweight remote-sensing image target detection network model, CBGS-YOLO, created by introducing the Ghost module to decrease the model parameters, applying the SPD-Conv module to optimize downsampling, and integrating the convolutional block attention module to enhance detection accuracy. The experimental outcomes demonstrate that CBGS-YOLO outperforms other models when applied to the DB_Licenta and USOD datasets, significantly enhancing detection performance for small targets. Compared with YOLOv9, this model can reduce the number of parameters from 7.10 M to 5.12 M, and the average precision (mAP) is effectively improved. The model strengthens the ability to identify small targets against complex backgrounds while maintaining lightweight properties and possesses remarkable application prospects and practical value.

Summary: Background: Previous observational studies established a positive relationship between snoring and stroke. We aimed to investigate the causal effect of snoring on stroke. Methods: Based on 82,339 unrelated individuals with qualified genotyping data of Asian descent from the China Kadoorie Biobank (CKB), we conducted a Mendelian randomization (MR) analysis of snoring and stroke. Genetic variants identified in the genome-wide association analysis (GWAS) of snoring in CKB and UK Biobank (UKB) were selected for constructing genetic risk scores (GRS). A two-stage method was applied to estimate the associations of the genetically predicted snoring with stroke and its subtypes. Besides, MR analysis among the non-obese group (body mass index, BMI

Abstract Background This study aimed to translate the French version of a perioperative satisfaction questionnaire (EVAN-G) scale, a validated questionnaire for assessing perioperative patient satisfaction, into a Chinese version and validate it in Chinese-speaking patients. Methods We developed the Chinese version of the EVAN-G (EVAN-GC) scale based on the original French version of the EVAN-G. The EVAN-GC scale, the Short version of the Spielberger State-Trait Anxiety Inventory (S-STAI), and the McGill pain questionnaire (MGPQ) were administered on the WeChat mini program. We invited patients to complete these questionnaires within 4 to 24 h after surgery. The psychometric validation of the EVAN-GC scale included validity, reliability, and acceptability. Results Among 220 patients, 217 (98.6%) completed the EVAN-GC scale after surgery. The item-internal consistency revealed good construct validity. Compared with the total scores of the S-STAI and MGPQ, the EVAN-GC scale showed excellent convergent validity (ρ = − 0.32, P

High-efficiency fertilizers suitable for mechanical spraying can improve the fertilizer application and utilization efficiency. In this study, two types of granular compound fertilizers suitable for unmanned air vehicle spraying were developed using urea, potassium chloride, humic acid, and an amino acid synergist according to the fertilization requirements of rice at the booting stage. The physical and chemical properties of the compound fertilizers were analyzed by Fourier transform infrared spectroscopy and scanning electron microscopy. The release kinetics of the fertilizer was studied via a soil column leaching experiment. The effect of fertilizer particles on unmanned air vehicle spraying uniformity was studied via an orthogonal simulation experiment. Results showed that the chemical interaction between compound-fertilizer raw materials formed complexes such as C-NH-C and -CO-NH-, which caused a change in fertilizer particle morphology. This led to chemical monolayer adsorption or internal fertilizer diffusion, which in turn delayed the fertilizer release by 5−10 d. A simulation bench test showed that the fertilizer particle hardness and roundness exceeded 20 N and 85%, respectively, while the particle size was in the range of 2.07−5.13 mm, which resulted in good unmanned air vehicle spraying uniformity with a lateral variation coefficient of less than 15%. Finally, the experimental field results demonstrated that the new compound fertilizers, when sprayed by an unmanned air vehicle, could improve the economic benefit by 916−2742 CNY ha−1 without reducing the nitrogen utilization rate and rice yield. The new compound fertilizers exhibit delayed release, good spraying uniformity, and improved economic benefit, rendering them suitable for large-scale promotion and application.

Accurate and rapid identification of the effective number of panicles per unit area is crucial for the assessment of rice yield. As part of agricultural development, manual observation of effective panicles in the paddy field is being replaced by unmanned aerial vehicle (UAV) imaging combined with target detection modeling. However, UAV images of panicles of curved hybrid Indica rice in complex field environments are characterized by overlapping, blocking, and dense distribution, imposing challenges on rice panicle detection models. This paper proposes a universal curved panicle detection method by combining UAV images of different types of hybrid Indica rice panicles (leaf-above-spike, spike-above-leaf, and middle type) from four ecological sites using an improved You Only Look Once version 4 (YOLOv4) model. MobileNetv2 is used as the backbone feature extraction network based on a lightweight model in addition to a focal loss and convolutional block attention module for improved detection of curved rice panicles of different varieties. Moreover, soft non-maximum suppression is used to address rice panicle occlusion in the dataset. This model yields a single image detection rate of 44.46 FPS, and mean average precision, recall, and F1 values of 90.32%, 82.36%, and 0.89%, respectively. This represents an increase of 6.2%, 0.12%, and 16.24% from those of the original YOLOv4 model, respectively. The model exhibits superior performance in identifying different strain types in mixed and independent datasets, indicating its feasibility as a general model for detection of different types of rice panicles in the heading stage.

In recent years, the development of deep learning has brought great convenience to the work of target detection, semantic segmentation, and object recognition. In the field of infrared weak small target detection (e.g., surveillance and reconnaissance), it is not only necessary to accurately detect targets but also to perform precise segmentation and sub-pixel-level centroid localization for infrared small targets with low signal-to-noise ratio and weak texture information. To address these issues, we propose UCDnet (Double U-shaped Segmentation Network Cascade Centroid Map Prediction for Infrared Weak Small Target Detection) in this paper, which completes “end-to-end” training and prediction by cascading the centroid localization subnet with the semantic segmentation subnet. We propose the novel double U-shaped feature extraction network for point target fine segmentation. We propose the concept and method of centroid map prediction for point target localization and design the corresponding Com loss function, together with a new centroid localization evaluation metrics. The experiments show that ours achieves target detection, semantic segmentation, and sub-pixel-level centroid localization. When the target signal-to-noise ratio is greater than 0.4, the IoU of our semantic segmentation results can reach 0.9186, and the average centroid localization precision can reach 0.3371 pixels. On our simulated dataset of infrared weak small targets, the algorithm we proposed performs better than existing state-of-the-art networks in terms of semantic segmentation and centroid localization.

Abstract Bone mesenchymal stem cells (BMSCs) are the most commonly investigated progenitor cells in bone defect repair and osteoarthritis subchondral bone regeneration; however, these studies are limited by complex inflammatory conditions. In this study, we investigated whether pro-autophagic γ-aminobutyric acid receptor-associated protein (GABARAP) promotes BMSCs proliferation and osteogenic differentiation by modulating autophagy in the presence or absence of interleukin-1 beta (IL-1β) in vitro. The expression levels of all relevant factors were evaluated by qRT-PCR or western blotting where appropriate. BMSCs differentiation were assessed by Alizarin Red, alkaline phosphatase, safranin O, and Oil Red O staining. Furthermore, the interactions between autophagy and osteogenic differentiation were investigated by co-treatment with the autophagy inhibitor 3-methyladenine (3-MA). As the results, we found that treatment with recombinant human His6-GABARAP protein promoted cell proliferation, inhibited apoptosis, and reduced ROS generation by increasing autophagic activity, particularly when co-cultured with IL-1β. Moreover, His6-GABARAP could effectively increase the osteogenic differentiation of BMSCs. The expression levels of inflammatory factors were significantly decreased by His6-GABARAP treatment, whereas its protective effects were attenuated by 3-MA. This study demonstrates that GABARAP maintains BMSCs survival and strengthens their osteogenic differentiation in an inflammatory environment by upregulating mediators of the autophagy pathway.

A multi-group full-spectrum k-distribution (MG-FSK) look-up table has been constructed for gas mixtures within a large range of thermodynamic states for two species, i.e., CO2 and H2O. The multi-group k-distribution of a mixture is assembled directly from the summation of the linear absorption coefficients of two species. A systematic approach is introduced in this paper to generate the MG-FSK look-up table. Gauss-Chebyshev quadrature is chosen to reduce the size of the look-up table. To efficiently obtain accurate a-values and k-values for arbitrary thermodynamic states from tabulated values of gas mixture, a 5-D linear interpolation method is employed. A large number of thermodynamic states have been carried out to test accuracy of the MG-FSK look-up table. Results show that, using the MG-FSK look-up table can provide excellent accuracy compared with the exact results. The relative error of the radiative heat flux using MG-FSK look-up table is generally no more than 1%, and the maximum relative error in all calculation examples is 6.59%. For non-isothermal gas radiation, MG-FSK look-up table shows higher accuracy than FSCK look-up table.

Background and purpose Previous studies, mostly focusing on the European population, have reported polygenic risk scores (PRSs) might achieve risk stratification of stroke. We aimed to examine the association strengths of PRSs with risks of stroke and its subtypes in the Chinese population.Methods Participants with genome-wide genotypic data in China Kadoorie Biobank were split into a potential training set (n=22 191) and a population-based testing set (n=72 150). Four previously developed PRSs were included, and new PRSs for stroke and its subtypes were developed. The PRSs showing the strongest association with risks of stroke or its subtypes in the training set were further evaluated in the testing set. Cox proportional hazards regression models were used to estimate the association strengths of different PRSs with risks of stroke and its subtypes (ischaemic stroke (IS), intracerebral haemorrhage (ICH) and subarachnoid haemorrhage (SAH)).Results In the testing set, during 872 919 person-years of follow-up, 8514 incident stroke events were documented. The PRSs of any stroke (AS) and IS were both positively associated with risks of AS, IS and ICH (p