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Background With the population aging, the number of patients with chronic diseases and disability is increasing, which brings a heavy burden on the medical system. While it is well established that comorbidity and disability are closely interconnected, there is a noticeable scarcity of studies addressing the relationship between different patterns of comorbidity and disability. Objective To explore the common comorbidity patterns and their associations with disability among the older adults in China taking Sichuan as an example. Methods A total of 501 older adults, aged 60 and above, were selected using quota sampling in Sichuan from August to Novermber 2022. Detailed data on chronic diseases, disability, and general demographics were collected. Comorbidity patterns among the older adults were identified using a two-step clustering method that combined self-organizing maps and K-Means. The association between patterns of comorbidity and disability was explored using a logistic regression model, employing the National Disability Assessment Scale for the Long-term Care issued by the National Healthcare Security Administration of China in 2021. Results In this survey, The prevalence of comorbidity 62.3% (312/501) and the prevalence of disability was 74.3% (372/501). We identified six comorbidity patterns: Arthritis/Rheumatism and Hypertension diseases, Cardiovascular and Metabolic diseases, Kidney and Arthritis/Rheumatism diseases, Cancer and Arthritis/Rheumatism diseases, Asthma, Hypertension, and Gastrointestinal diseases, Emotional/Mental and Memory-related diseases. The results of binomial logistic regression showed that the risk of disability was 6.3 times higher when people suffering from two or more chronic diseases at the same time (OR=6.3, 95%CI=3.9-10.3, P

Problem: China's declining birth rate presents significant challenges to obstetricians, impacting workload, career prospects, and the obstetric service system. Background: The reduction in birth rates alters the landscape for obstetricians, necessitating adaptation to new medical demands and technological environments. Aim: This paper aims to explore the future development and role transformations of Chinese obstetricians amidst declining birth rates. Key questions include: What is the impact of declining birth rates on China's obstetric services?What are the future development directions for obstetricians in this scenario?How will their roles evolve to meet new medical demands and technological environments?What international experiences can be drawn upon, and how applicable are they to China? Methods: A literature review method is employed, analyzing research papers, statistical data, and policy reports from domestic and international sources. Findings: The paper systematically summarizes and evaluates current and future trends in Chinese obstetrics in the context of declining birth rates. Discussion: It discusses practical implications for sustainable development, including optimizing medical resources, leveraging technological innovations, and enhancing talent development. Conclusion: This paper contributes a comprehensive analysis and practical recommendations for navigating the challenges posed by declining birth rates to Chinese obstetricians.

Ship tracking technology is crucial for emergency rescue in the event of a disaster. Quickly identifying the position and status of vessels is vital for rescue teams to be able to deploy efficiently in disaster areas. When responding to emergencies or natural disasters, ship tracking technology plays a critical role in supporting emergency rescue operations and resource allocation, improving the overall resilience of the maritime transportation system. However, the research on multi-object tracking (MOT) algorithms has primarily focused on optical image datasets. In contrast, image data from synthetic aperture radar (SAR) presents unique challenges, such as defocus interference, a high false alarm rate, and a lack of prior samples. To overcome these particular challenges, we propose a robust MOT algorithm developed for SAR images to achieve effective multi-vessel tracking under difficult imaging conditions. In particular, we optimize the YOLOv8 detection network by introducing a diffusion model-based training method for data augmentation. This method improves the robustness of the network to scaling, rotational and translational deformations. Moreover, an enhanced swin transformer is proposed as a feature extraction network, which strengthens the representation capability of the detection network. Furthermore, the state parameters within the KF technique are enhanced by directly capturing the details of the height and width of the tracking rectangle box. This refinement of the ByteTrack algorithm aims to achieve a more precise and accurate fit of the tracking rectangle to the ship, further improving the overall tracking performance. The experimental results from the ship detection and multiple objects tracking datasets show the impressive performance of the proposed model. With a precision of 97.60%, a recall of 96.36%, and an average precision of 96.72%, the model achieves exceptional detection accuracy with an 18% reduction in model parameters. Furthermore, significant improvements can be observed in key tracking metrics such as HOTA, MOTA and IDF1, with improvements of 4.8%, 8.5% and 6.8% respectively compared to the baseline algorithm, alongside a remarkable 37.5% reduction in IDS. It is noteworthy that the tracker works in real time, achieving an average analysis speed of 47 frames per second. The proposed MOT algorithm achieves state-of-art tracking performance on a SAR image dataset with short time sequences. Therefore, the proposed approach is a compelling solution for ship tracking in SAR imagery.

The Xuefeng black bone chicken (XFBC) represents an important poultry genetic resource. However, the darkness in breast muscle is heterogeneous. The molecular genetic mechanisms underlying melanogenesis of breast muscle in XFBC remains unclear. This study used RNA-seq to compare the difference in transcriptome between hyperpigmentation and hypopigmentation of breast muscle. Six cDNA libraries were constructed for hyperpigmentation and hypopigmentation groups in XFBC. We identified 395 differently expressed genes (DEGs) between hyperpigmentation and hypopigmentation group (P < 0.05, |[log.sub.2]FC|[greater than or equal to]1). Gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated several differentially enriched biological functions and pathways involved in melanogenesis of the breast muscle. Gene set enrichment analysis (GSEA) GO analysis identified two significant gene sets, including the pathways of pigment metabolic process and transmembrane receptor protein tyrosine kinase activity. GSEA-KEGG analysis identified the process of tyrosine metabolism and several genes related with melanogenesis in breast muscle of the XFBC. The protein-protein interaction network was constructed and eight genes were clustered in the module. We identified nine hub genes, including TYR, TYRP1, DCT, GPR143, MLANA, SLC24A5, GPNMB, MLPH, and EDNRB2. Taken together, the DEGs and hub genes identified in the study provide a solid basis for the study of the genetic regulatory mechanisms involved the melanogenesis in the breast muscle of the XFBC. Key words: Xuefeng black bone chicken, breast muscle, RNA-Seq, melanogenesis, 1. Introduction The black bone chicken is one of the most important poultry genetic resources in China (Li et al. 2020). The productions of black bone chicken are very popular [...]

The precise extraction of building footprints using remote sensing technology is increasingly critical for urban planning and development amid growing urbanization. However, considering the complexity of building backgrounds, diverse scales, and varied appearances, accurately and efficiently extracting building footprints from various remote sensing images remains a significant challenge. In this paper, we propose a novel network architecture called ME-FCN, specifically designed to perceive and optimize multi-scale features to effectively address the challenge of extracting building footprints from complex remote sensing images. We introduce a Squeeze-and-Excitation U-Block (SEUB), which cascades multi-scale semantic information exploration in shallow feature maps and incorporates channel attention to optimize features. In the network's deeper layers, we implement an Adaptive Multi-scale feature Enhancement Block (AMEB), which captures large receptive field information through concatenated atrous convolutions. Additionally, we develop a novel Dual Multi-scale Attention (DMSA) mechanism to further enhance the accuracy of cascaded features. DMSA captures multi-scale semantic features across both channel and spatial dimensions, suppresses redundant information, and realizes multi-scale feature interaction and fusion, thereby improving the overall accuracy and efficiency. Comprehensive experiments on three datasets demonstrate that ME-FCN outperforms mainstream segmentation methods. [ABSTRACT FROM AUTHOR]

The trust region method is an effective method for solving unconstrained optimization problems. Incorrectly updating the rules of the trust region radius will increase the number of iterations and affect the calculation efficiency. In order to obtain an effective radius for the trust region, an adaptive radius updating criterion is proposed based on the gradient of the current iteration point and the eigenvalue of the Hessian matrix which avoids calculating the inverse of the Hessian matrix during radius updating. This approach reduces the computation time and enhances the algorithm's performance. On this basis, we apply adaptive radius and non-monotonic techniques to the trust region algorithm and propose an improved non-monotonic adaptive trust region algorithm. Under proper assumptions, the convergence of the algorithm is analyzed. Numerical experiments confirm that the suggested algorithm is effective. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) imaging is used to identify ships, which is a vital task in the maritime industry for managing maritime fisheries, marine transit, and rescue operations. However, some problems, like complex background interferences, various size ship feature variations, and indistinct tiny ship characteristics, continue to be challenges that tend to defy accuracy improvements in SAR ship detection. This research study for multiscale SAR ships detection has developed an upgraded YOLOv5s technique to address these issues. Using the C3 and FPN + PAN structures and attention mechanism, the generic YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. The SAR ship detection datasets and AirSARship datasets, along with two SAR large scene images acquired from the Chinese GF-3 satellite, are utilized to determine the experimental results. This model’s applicability is assessed using a variety of validation metrics, including accuracy, different training and test sets, and TF values, as well as comparisons with other cutting-edge classification models (ARPN, DAPN, Quad-FPN, HR-SDNet, Grid R-CNN, Cascade R-CNN, Multi-Stage YOLOv4-LITE, EfficientDet, Free-Anchor, Lite-Yolov5). The performance values demonstrate that the suggested model performed superior to the benchmark model used in this study, with higher identification rates. Additionally, these excellent identification rates demonstrate the recommended model’s applicability for maritime surveillance.

Detailed management of the coastline is critical to the development of coastal states. However, the current classification of the coastline is relatively weak. This study proposed an automatic method to detect coastlines with category attributes based on multi-scale segmentation and multi-level inheritance classification. Fully integrating the advantages of multi-scale segmentation and multi-level classification, it solved the problems that traditional methods could not solve, such as extracting coastlines with categorical attributes, cultivation ponds that are easily affected by tidal flats, and complex coastal terrain. The Chinese GF-2 satellite images are used to extract various types of coastlines in Jiaozhou Bay and its surrounding areas such as the harbor-wharf coastline, silt coastline, pond coastline, rocky coastline, and sandy coastline. Compared with the human interpretation, it is found that the coastline extracted by our proposed method is different by 10.104 km in the harbor-wharf coastline, 0.099 km in the silt coastline, 2.677 km in the pond coastline, 8.831 km in the rocky coastline, and 0.218 km in the sandy coastline. Furthermore, compared to the object-based region growing integrating edge detection (OBRGIE) method, it is increased by 13.52%, 2.16%, 14.48%, 52.57%, and 22.97%, respectively. The results show that our proposed method is algorithmically more reasonable, accurate, and powerful. It can provide data support for refined coastline management.

【Objective】 Subsurface drip irrigation (SD1) is a water-saving technique but difficult to keep the surface soil moist to ensure seed germination in drought-prone areas. Alternate row planting (AP) combined with the raised bed has been developed as an effective cultivation to moisten the surface soil. The purpose of this paper is to experimentally investigate the suitable furrow depth to improve seedling emergence and sustain crop yield. 【Method】 We took spring maize as an example and used the variety Xuntian 1102 as the model plant. The field experiment was conducted from May 2018 to September 2018 in the National Water-saving Irrigation Engineering Technology Research Center at Daxing, Beijing. The depth of the furrows was 0 (CK), 10 cm (H10) or 15cm (H15). In all treatments, the seeds were drilled to 5 cm deep from the soil surface and the irrigation amount prior to seedling emergence was 30mm, same in all treatments. During the experiment, we measured the changes in soil moisture, seedling emergence and the ultimate yield in each treatment. 【Result】 Alternate row planting combined with the raised bed significantly increased soil moisture at the drilling points to facilitate seed germination. Compared to CK, irrigating 35 mm of water prior to seedling emergence in H10 and H15 increased soil moisture in the region proximal to the seeds by 18.2% and 25.7%, respectively. The improved soil moisture increased the seedling emergence rate to more than 88% in both treatments, equivalent to a14.6% (H10) or 16.3% (H15) increase over the CK (P

The study aimed to reveal the function of LXY30 peptide‐modified bone marrow mesenchymal stem cell‐derived exosomes (LXY30‐Exos) in NSCLC. LXY30 peptide is a peptide ligand targeting α3β1 integrin, and LXY30 specifically binds to Exos derived from different cells. We use transmission electron microscopy to identify LXY30‐Exos and tracking analysis for particles, and the LXY30‐Exos internalized by NSCLC cells in vitro and targeted NSCLC tumours in vivo were verified by multiple molecular technologies. The functions of LXY30‐Exos‐encapsulated miR‐30c, miR‐181b or miR‐613 were assessed using cell proliferation, migration and cell apoptosis assays. Meanwhile, the safety of the above engineered Exos was evaluated in vivo. After LXY30‐Exos were isolated and identified, LXY30‐Exos were confirmed to be internalized by NSCLC cells in vitro and specifically targeted NSCLC tumours in vivo. Functionally, LXY30‐Exos‐encapsulated miR‐30c, miR‐181b or miR‐613 weakened the proliferation, migration and cell cycle of NSCLC cells induced cellular apoptosis in vitro and restrained the tumour progression in vivo. Meanwhile, the safety of LXY30‐Exos‐encapsulated miR‐30c, miR‐181b or miR‐613 was confirmed in vivo. Overall, miR‐30c, miR‐181b and miR‐613 encapsulated in LXY30 peptide‐modified BMSC‐Exos relieved NSCLC. [ABSTRACT FROM AUTHOR]