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Abstract Aims Both hypercapnia and hypocapnia are common in patients with acute heart failure (AHF), but the association between partial pressure of arterial carbon dioxide (PaCO2) and AHF prognosis remains unclear. The objective of this study was to investigate the connection between PaCO2 within 24 h after admission to the intensive care unit (ICU) and mortality during hospitalization and at 1 year in AHF patients. Methods and results AHF patients were enrolled from the Medical Information Mart for Intensive Care IV database. The patients were divided into three groups by PaCO2 values of 45 mmHg. The primary outcome was to investigate the connection between PaCO2 and in‐hospital mortality and 1 year mortality in AHF patients. The secondary outcome was to assess the prediction value of PaCO2 in predicting in‐hospital mortality and 1 year mortality in AHF patients. A total of 2374 patients were included in this study, including 457 patients in the PaCO2 45 mmHg group. The in‐hospital mortality was 19.5%, and the 1 year mortality was 23.9% in the PaCO2 45 mmHg group was associated with an increased risk of in‐hospital mortality (HR 1.387, 95% CI 1.050–1.832, P = 0.021); the 1 year mortality showed no significant difference (HR 1.286, 95% CI 0.995–1.662, P = 0.055) compared with the PaCO2 = 35–45 mmHg group. The Kaplan–Meier survival curves showed that the PaCO2

Abstract Background In 2010, China launched a rural-oriented tuition-waived medical education (RTME) programme to train more general practitioners (GPs) to meet the needs of the rural health workforce. Motivating and maintaining GPs is an important consideration for the shortage in the rural health workforce. This study aimed to investigate job satisfaction and turnover among the first group of rural-oriented tuition-waived medical students (RTMSs) who had completed a three-year compulsory service in Guangxi, as well as the factors affecting RTMSs turnover. Methods This study adopted a mixed-method approach. A quantitative survey of 129 RTMSs was analysed (81.6% response rate), and qualitative interviews were conducted with 30 stakeholders, including 18 RTMSs, six administrators of the County Health Bureau, and six administrators of township health centers (THCs). A t-test, chi-square test, Fisher’s exact test, and logistic regression analysis were used to examine the quantitative data, and thematic analysis was used to analyse the qualitative data. Results Among the 129 participants, the turnover rate was high, with 103 RTMSs reporting turnover (79.84%). Interpersonal relationships scored the highest in job satisfaction (3.63 ± 0.64) among RTMSs, while working conditions were rated the lowest (2.61 ± 0.85). Marital status (odds ratio [OR] = 0.236, 95% confidence interval [95%CI] = 0.059–0.953, P = 0.043), only child status (OR = 8.660, 95%CI = 1.714–43.762, P = 0.009), and job return satisfaction (OR = 0.290, 95%CI = 0.090–0.942, P = 0.039) were significantly associated with turnover. Univariate analyses showed that income had a significant influence on turnover, but the relationship gone by multivariable; however it was deemed important in the qualitative study. Qualitative analysis revealed that turnover was influenced by the working atmosphere, effort-reward imbalance, professional competence, and opportunities for training and promotion. Conclusions This study provides insights for the policymakers about the priority areas for retaining GPs in rural locations and provides reference values for the retention of GPs in other regions with a shortage of rural health workers. For RTMSs to continue providing services to rural areas, the government should improve their salaries, balance their income and workload, provide more opportunities for training and career promotion, and managers should recognise their efforts and create an optimistic working atmosphere.

Abstract Road extraction from remote-sensing images is of great significance for vehicle navigation and emergency insurance. However, the road information extracted in the remote-sensing image is discontinuous because the road in the image is often obscured by the shadows of trees or buildings. Moreover, due to the scale difference of roads in remote-sensing images, it remains a computational challenge to extract small-size roads from remote-sensing images. To address those problems, we propose a road extraction method based on adaptive global feature fusion (AGF-Net). First, a dilated convolution strip attention (DCSA) module is designed from the encoder–decoder structure. It consists of the dilated convolution and the strip attention module, which adaptively emphasizes relevant features in vertical and horizontal directions. Then, multiple global feature fusion modules (GFFM) in the skip connection are designed to supplement the decoder with road detail features, and we design a multi-scale strip convolution module (MSCM) to implement the GFFM module to obtain multi-scale road information. We compare AGF-Net to state-of-the-art methods and report their performance using standard evaluation metrics, including Intersection over Union (IoU), F1-score, precision, and recall. Our proposed AGF-Net achieves higher accuracy compared to other existing methods on the Massachusetts Road Dataset, DeepGlobe Road Dataset, CHN6-CUG Road Dataset, and BJRoad Dataset. The IoU obtained on these datasets are 0.679, 0.673, 0.567, and 0.637, respectively.

Building pattern recognition is important for understanding urban forms, automating map generalization, and visualizing 3D city models. However, current approaches based on object-independent methods have limitations in capturing all visually aware patterns due to the part-based nature of human vision. Moreover, these approaches also suffer from inefficiencies when applying proximity graph models. To address these limitations, we propose a framework that leverages multi-scale data and a knowledge graph, focusing on recognizing C-shaped building patterns. We first employ a specialized knowledge graph to represent the relationships between buildings within and across various scales. Subsequently, we convert the rules for C-shaped pattern recognition and enhancement into query conditions, where the enhancement refers to using patterns recognized at one scale to enhance pattern recognition at other scales. Finally, rule-based reasoning is applied within the constructed knowledge graph to recognize and enrich C-shaped building patterns. We verify the effectiveness of our method using multi-scale data with three levels of detail (LODs) collected from AMap, and our method achieves a higher recall rate of 26.4% for LOD1, 20.0% for LOD2, and 9.1% for LOD3 compared to existing methods with similar precision rates. We also achieve recognition efficiency improvements of 0.91, 1.37, and 9.35 times, respectively.

Abstract Deep learning has recently been proven to deliver excellent performance in multi-view stereo (MVS). However, it is difficult for deep learning-based MVS approaches to balance their efficiency and effectiveness. Towards this end, we propose the DSC-MVSNet, a novel coarse-to-fine and end-to-end framework for more efficient and more accurate depth estimation in MVS. In particular, we propose an attention aware 3D UNet-shape network, which first uses the depthwise separable convolutions for cost volume regularization. This mechanism enables effective aggregation of information and significantly reduces the model parameters and computation by transforming the ordinary convolution on cost volume as depthwise convolution and pointwise convolution. Besides, a 3D-Attention module is proposed to alleviate the feature mismatching problem in cost volume regularization and aggregate the important information of cost volume in three dimensions (i.e. channel, space, and depth). Moreover, we propose an efficient Feature Transfer Module to upsample the low-resolution (LR) depth map to a high-resolution (HR) depth map to achieve higher accuracy. With extensive experiments on two benchmark datasets, i.e. DTU and Tanks & Temples, we demonstrate that the parameters of our model are significantly reduced to $$25\%$$ 25 % of the state-of-the-art model MVSNet. Besides, our method outperforms or maintains on par accuracy with the state-of-the-art models. Our source code is available at https://github.com/zs670980918/DSC-MVSNet .

Satellite remote sensing is an important means for humans to achieve high-resolution earth observation, and it has gradually become an important component supporting economic and social development as well as national defense and security.6G pursues deep integration of communication, perception, and computation, and communication and remote sensing integration has become an important feature of it.The development of satellite remote sensing at home and abroad were reviewed, the existing system architecture of satellite remote sensing systems and the integrated system architecture of communication and remote sensing in the 6G era were outlined, and the typical satellite remote sensing technologies and the latest research progress driven by artificial intelligence were elaborated on.In response to the trend of integrating satellite communication and remote sensing in the 6G era, the technical challenges and development trends of realizing the combination of “one satellite, multiple uses, multi-satellite networking, multi-network integration, and intelligent services” were discussed.

Hybrid nanostructures have garnered considerable interest because of their fascinating properties owing to the hybridization of materials and their structural varieties. In this study, we report the synthesis of [Au@Rh(OH)3]-Au island heterostructures using a seed-mediated sequential growth method. Through the thiol ligand-mediated interfacial energy, Au@Rh(OH)3 core-shell structures with varying shell thicknesses were successfully obtained. On these Au@Rh(OH)3 core-shell seeds, by modulating the diffusion of HAuCl4 in the porous Rh(OH)3 shell, site-specific growth of Au islands on the inner Au core or on the surface of the outer Rh(OH)3 shell was successfully achieved. Consequently, two types of distinct structures, the Au island-on-[Au@Rh(OH)3] dimer and Au island-Au bridge-[Au@Rh(OH)3] dumbbell structures with thin necks were obtained. Further modulations of the growth kinetics led to the formation of Au plate-Au bridge-[Au@Rh(OH)3] heterostructures with larger structural anisotropy. The flexible structural variations were demonstrated to be an effective means of modulating the plasmonic properties; the Au–Au heterostructures exhibited tunable localized surface plasmon resonance in the visible-near-infrared spectral region and can be used as surface-enhanced Raman scattering (SERS) substrates capable of emitting strong SERS signals. This diffusion-controlled growth of Au bridges in the Rh(OH)3 shells (penetrating growth) is an interesting new approach for structural control, which enriches the tool box for colloidal nanosynthesis. This advancement in structural control is expected to create new approaches for colloidal synthesis of sophisticated nanomaterials, and eventually enable their extensive applications in various fields.

Finding out about the ecosystem damaged by mining development and carrying out ecological risk diagnoses are important prerequisites for formulating mine ecological restoration strategies. This study established an integrated approach to quantitatively analyze mining ecological risks by combining water conservation and biodiversity conservation ecosystem service functions with natural ecological conditions, and based on these, proposes appropriate mine ecological restoration strategies. Results show that: (1) A total of 14,874.80 hm2 of ecosystems were damaged in the Qinghai section of the Yellow River Basin, caused by mining excavation, crushing and hollow collapse, and of which 52.10% were located in national important ecological function areas and National Nature Reserves, which caused a decrease of the important ecosystem service functions of water conservation and biodiversity conservation in the area, and aggravated the ecological risks of the river source area; (2) The areas of high ecological risk and comparatively high ecological risk in the research area are 1,093,800 hm2 and 902,100 hm2, which accounted for 7.27% and 6.00% of the land area, respectively. Ecological risk hotspot areas are mainly distributed in the Qilian Mountains, Hehuang Valley, Sanjiangyuan and other key water systems and water sources; (3) According to the principle of “one mine, one policy”, we propose five mine ecological restoration models: ecological reconstruction, artificial assistance and protection and conservation, artificial assistance, protection and conservation and natural restoration. This study provides a reliable basis for exploring the status of mining ecological risk at the source of the Yellow River and scientifically carrying out ecological restoration and risk management.

[This corrects the article DOI: 10.1371/journal.ppat.1007416.].

Abstract Background In flowering plants, proper seed development is achieved through the constant interplay of fertilization products, embryo and endosperm, and maternal tissues. Communication between these compartments is supposed to be tightly regulated at their interfaces. Here, we characterize the deposition pattern of an apoplastic lipid barrier between the maternal inner integument and fertilization products in Arabidopsis thaliana seeds. Results We demonstrate that an apoplastic lipid barrier is first deposited by the ovule inner integument and undergoes de novo cutin deposition following central cell fertilization and relief of the FERTILIZATION INDEPENDENT SEED Polycomb group repressive mechanism. In addition, we show that the WIP zinc-finger TRANSPARENT TESTA 1 and the MADS-Box TRANSPARENT TESTA 16 transcription factors act maternally to promote its deposition by regulating cuticle biosynthetic pathways. Finally, mutant analyses indicate that this apoplastic barrier allows correct embryo sliding along the seed coat. Conclusions Our results revealed that the deposition of a cutin apoplastic barrier between seed maternal and zygotic tissues is part of the seed coat developmental program.