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Climate transition zones are ecologically sensitive regions that respond to changes in complex natural conditions. Analyzing the spatiotemporal evolution trends and impact factors of landscape ecological risk is crucial for maintaining regional ecosystem security. However, research predominantly focused on the past analytical paradigm, which often needed more strategic predictions for future scenarios tailored to diverse developmental requirements. This study analyzed land use changes in the Huai River Basin during 2000, 2010, and 2020 and used the Future Land Use Simulation model to conduct a multi-scenario simulation for 2030. Subsequently, this study assessed the landscape ecological risk from 2000 to 2030 and analyzed the influencing mechanisms using the ridge regression model. The results showed that: (1) The primary transitions were concentrated between cropland and construction land. By 2030, the area of construction land was projected to continue to expand, with the greatest increase of 2906 km2 anticipated in the natural development scenario. (2) The overall spatial pattern of landscape ecological risk showed a “high in the east and low in the west” distribution, with the lowest risk areas predominating (accounting for over 43%). Over the past 20 years, the risk initially increased and then decreased, and by 2030, the risk was expected to decline further. (3) The risk exhibited significant positive spatial autocorrelation. By 2030, the constraint of spatial location on risk distribution would decrease. Local spatial clustering was mainly characterized by “Low-Low” regions (accounting for 20%). (4) Vegetation cover consistently correlated negatively with ecological risk and was the most influential factor, with relative contribution rates all exceeding 21%. The findings have provided a scientific reference for the ecological and environmental management of areas with intense human activity under complex climatic conditions.

In this study, E-nose, HS-GC-IMS, and HS-SPME-GC–MS technologies were used to evaluate the flavor characteristics of the pileus and stipe of Boletus edulis from eight origins. 23 key Volatile organic compounds (VOCs) with odor activity values (OAVs) > 1 were identified, and 19 aroma types have been identified in Boletus edulis at the same time. Vegetable and earthy were defined as the dominant aroma types for all pileus and stipe samples. Balsamic and musty were the main and characteristic aroma types for the pileus. The highest concentrations of VOCs in the pileus and stipe were originated from Chuxiong Prefecture and Aba Prefecture, respectively. 19 and 16 key VOCs were detected Chuxiong pileus and Aba stipe, respectively, and Methional was the decisive compound that influenced the vegetable aroma type. The results of this study could be helpful for flavor identification and application of pileus and stipe from Boletus edulis.

Aquifers supporting irrigated agriculture in Henan Plain in China (HNP) are under immense stress due to scarcity of surface water and over-pumping of groundwater. To assist in establishing a crop planting structure in this region that aligns more with the capacity of water resources, we have developed an enhanced methodology for estimating Groundwater Storage change (GWSC). This method integrates Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) inversion, Gravity Recovery and Climate Experiment (and Follow-on)/Global Land Data Assimilation System (GRACE/GLDAS) modelling, and hydraulic head measurements. The time-series comparisons based on well water levels and residual statistics of equivalent water thickness (EWT) validate the reliability of both MT-InSAR- and GRACE/GLDAS-derived GWSC, exhibiting a mean R-square of 0.8288 and an RMSE of 7.5 cm. The corrected and integrated GWSC significantly enhances the equal lon-lat tile of 1/4-degree (∼27 km) from CSR GRACE/GRACE-FO Mascon solution (CSR-M) to within 1 km in aquifers with known prior hydrogeological information. Furthermore, the geographic patterns analysis of GWSC in HNP using standard deviational ellipse (SDE) reveals that the central regions in HNP between the Yellow River and the Huaihe River suffer the most severe regional groundwater depletion. The depletion center has shifted to the northwest by 87 km, showing a trend of easing in the north-south direction and expanding in the east-west direction.

In response to environmental regulatory pressure from the government, enterprises – as the main employers of labor – adjust their production-related choices and may alter their demand for labor. Numerous researches have probed into how environmental regulations affect the labor demand, which has emerged as a significant concern in the discourse around the adoption of environmental regulation policies. Nonetheless, many researches predominantly concentrates on the evaluation on the effects of environmental regulations on economic and environmental levels. Moreover, rare attention has been paid to how environmental regulations affect social activities, especially in terms of labor demands. As a result, the Air Pollution Prevention and Control Action Plan (APPCAP) is adopted as a “quasi-natural experiment” and a difference-in-differences model is employed to analyze the effects of the APPCAP on labor demands of companies. Hence, the 2008–2020 panel data are considered for the 3,949 Chinese A-share listed enterprises. Furthermore, this paper probes deeply into the underlying mechanisms based on mediation models. The following findings are thus concluded: (1) The labor demands of enterprises can be remarkably increased by APPCAP; and the result is still persuasive even though the endogeneity issues are taken into account. (2) APPCAP improves enterprise labor demand through the output and factor substitution effects. (3) As evidently revealed by heterogeneity analysis, the APPCAP could significant positive affect the labor demand size in state-owned companies, large-scale companies, new companies, and companies in polluting industries. (4) The APPCAP strikingly boosts the demand for labor force with high skills. Nevertheless, it exerts little influence on the demand for labor force with low skills and company salary levels. Therefore, the government must continue to steadfastly implement environmental regulatory policies but adopt different policies based on enterprise characteristics. Overall, this study provides micro-level experimental evidence for more in-depth understanding of how environmental policies affect labor market, which is particularly important for actively resolving social employment problems and exploring new growth points in enterprise employment.

Temperature is a crucial indicator for studying climate, as well as the social and economic changes in a region. Temperature reanalysis products, such as ERA5-Land, have been widely used in studying temperature change. However, global-scale temperature reanalysis products have errors because they overlook the influence of multiple factors on temperature, and this issue is more obvious in smaller areas. During the cold months (January, February, March, November, and December) in the Yellow River Basin, ERA5-Land products exhibit significant errors compared to temperatures observed by meteorological stations, typically underestimating the temperature. This study proposes improving temperature reanalysis products using deep learning and multi-source remote sensing and geographic data fusion. Specifically, convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) capture the spatial and temporal relationships between temperature, DEM, land cover, and population density. A deep spatiotemporal model is established to enhance temperature reanalysis products, resulting in higher resolution and more accurate temperature data. A comparison with the measured temperatures at meteorological stations indicates that the accuracy of the improved ERA5-Land product has been significantly enhanced, with the mean absolute error (MAE) reduced by 28.7% and the root mean square error (RMSE) reduced by 25.8%. This method obtained a high-precision daily temperature dataset with a 0.05° resolution for cold months in the Yellow River Basin from 2015 to 2019. Based on this dataset, the annual trend of average temperature changes during the cold months in the Yellow River Basin was analyzed. This study provides a scientific basis for improving ERA5-Land temperature reanalysis products in the Yellow River Basin and offers theoretical support for climate change research in the region.

Background: Radiofrequency catheter ablation (RFCA) is a commonly used treatment for atrial fibrillation (AF), but the long-term recurrence rate remains relatively high. Given the inconsistent results regarding the role of left pulmonary vein (PV) ostial anatomy in post-ablative recurrence of RFCA in previous studies, we sought to investigate the role of left PV trunk length using an alternative methodology. Methods: A total of 369 AF patients undergoing catheter ablation were included. The left/right trunk length (LTL/RTL) of the PV was measured from pre-ablative computed tomography (CT) using three-dimensional reconstruction techniques. We constructed three multivariable Cox models, with the inclusion of the LTL, RTL, and no LTL/RTL, and used the Delong test, integrated discrimination index (IDI), and net reclassification index (NRI) to assess model improvement. We identified optimal cut-off values for LTL with the receiver operating characteristic (ROC) curve, and estimated outcomes using the Kaplan-Meier survival curve. We also used subgroup analysis to evaluate interactions. Results: The results of the Delong test, IDI, and NRI indicated that LTL had a favorable impact on the performance of the multivariate model. Subsequently, the multivariate Cox regression analysis identified LTL as a significant risk factor for post-ablative recurrence of AF (adjusted hazard ratio (HR) = 1.08, 95% CI: 1.05–1.12, p < 0.001). According to the ROC curve, the optimal cut-off value for LTL is 11.15 mm, and the Kaplan-Meier estimator revealed different outcomes (p < 0.001). We calculated p for interaction between LTL and other factors, and no significant interaction terms were observed. Conclusions: LTL is a robust prognostic indicator for post-ablative outcome in AF patients receiving RFCA, with a longer LTL indicating a higher risk of recurrence.

ABSTRACTAccurate and near-real-time crop mapping from satellite imagery is crucial for agricultural monitoring. However, the seasonal nature of crops makes it challenging to rely on traditional machine learning methods and previous samples generated within specific domains. In this study, we improved the histogram matching method for color correction of multi-temporal images and tested the performance and prediction classification accuracy of three semantic segmentation models based on weak samples. Classification experiments were conducted for nine categories in two cities in Henan province from 2019 to 2022 using 10 m resolution Sentinel-2 images with different feature selection schemes. We trained the models using classified and recorrected results in four selected sites in 2019 and 2020, and designed experiments to assess the performance of the improved histogram matching method and verify the transferability of semantic segmentation models across regions and years. The experimental results showed that the UNet++ model with feature selection and improved histogram matching methods outperformed other models, such as DeepLab V3+ and UNet, in crop classification transfer cases, with better model performance and higher classification accuracy. The UNet++ model without training samples achieved optimal overall accuracy, Kappa coefficient, and mean F1-score values from 2019 to 2022, exceeding 87%, 82%, and 65%, respectively. Moreover, the representative error of weak samples and prediction classification results were analyzed to improve the model robustness. As an application of transfer-learning in crop mapping, the proposed model effectively addressed the classification problem of multispectral satellite imagery with missing labels.

High-resolution deep-learning-based remote-sensing imagery analysis has been widely used in land-use and crop-classification mapping. However, the influence of composite feature bands, including complex feature indices arising from different sensors on the backbone, patch size, and predictions in transferable deep models require further testing. The experiments were conducted in six sites in Henan province from 2019 to 2021. This study sought to enable the transfer of classification models across regions and years for Sentinel-2A (10-m resolution) and Gaofen PMS (2-m resolution) imagery. With feature selection and up-sampling of small samples, the performance of UNet++ architecture on five backbones and four patch sizes was examined. Joint loss, mean Intersection over Union (mIoU), and epoch time were analyzed, and the optimal backbone and patch size for both sensors were Timm-RegNetY-320 and 256 × 256, respectively. The overall accuracy and F1 scores of the Sentinel-2A predictions ranged from 96.86% to 97.72% and 71.29% to 80.75%, respectively, compared to 75.34%–97.72% and 54.89%–73.25% for the Gaofen predictions. The accuracies of each site indicated that patch size exerted a greater influence on model performance than the backbone. The feature-selection-based predictions with UNet++ architecture and up-sampling of minor classes demonstrated the capabilities of deep-learning generalization for classifying complex ground objects, offering improved performance compared to the UNet, Deeplab V3+, Random Forest, and Object-Oriented Classification models. In addition to the overall accuracy, confusion matrices, precision, recall, and F1 scores should be evaluated for minor land-cover types. This study contributes to large-scale, dynamic, and near-real-time land-use and crop mapping by integrating deep learning and multi-source remote-sensing imagery

IntroductionThe Yellow River Basin (YRB) is not only a vital area for maintaining ecological security but also a key area for China’s economic and social development. Understanding its land-use change trends and habitat quality change patterns is essential for regional ecological conservation and effective resource allocation.MethodsThis study used the patch-generating land-use simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models to analyze and predict the spatial and temporal trends of habitat quality in the YRB from 2000 to 2030 under natural development (ND) and ecological conservation and high-quality development (ECD) scenarios. The PLUS model was used to predict land-use change in 2030 under different scenarios, after which the InVEST model was used to obtain the habitat quality distribution characteristics from the 2000–2030 period.Results(1) The mean values of habitat quality in the YRB in 2000, 2010, and 2020 were 0.6849, 0.6992, and 0.7001, respectively. The mean habitat quality values were moderately high. Spatial distribution characteristics were high in the west and low in the east and along the water. In 2030, habitat quality (0.6993) started to decline under ND, whereas under ECD, there was an indication of substantial improvement in habitat quality (0.7186). (2) The mean habitat degradation values in 2000, 2010, and 2020 were 0.0223, 0.0219, and 0.0231, respectively. The level of habitat degradation showed a decreasing trend, followed by an increasing trend with a stable spatial distribution pattern. The mean level of habitat degradation in 2030 (0.0241) continued to increase under ND, while a substantial decrease in the level of habitat degradation occurred under ECD (0.0214), suggesting that the level of habitat degradation could be effectively contained under the ECD scenario. (3) During the study period, the conversion of building land—both negative and positive—had the most pronounced impact on habitat quality per unit area. Further, the conversion of grassland was shown to be a key land transformation that may either lead to the deterioration or improvement of the ecological environment. The results provide scientifific theoretical support and a decision basis for ecological conservation and the high-quality development of the YRB.

Familial hypercholesterolemia is a hereditary disorder that causes severely elevated low-density lipoprotein levels, which can lead to an increased risk for premature cardiovascular disease. Mutations in the LDLR gene are the most common cause of familial hypercholesterolemia. In this study, we report the generation of ZZUNEUi029-A, a human induced pluripotent stem cell line (hiPSC) from a male patient with c. 622 G → A in LDLR gene using non-integrative Sendai viral reprogramming technology. This cell line expressed pluripotency markers, had a normal male karyotype (46XY) and maintained the ability to differentiate into the three germ layers in vitro.

Numerous studies have reported that tangeretin is a polymethoxylated flavone with a variety of biological activates, but little research has been done on the antioxidant mechanism of tangeretin. Hence, we investigated the effect of tangeretin on the nuclear factor erythroid 2-related factor 2 (Nrf2)/antioxidant response element (ARE) pathway and its potential molecular mechanisms by in vitro and in silico research. The results of molecular docking suggested that tangeretin bound at the top of the central pore of Kelch-like ECH-associated protein 1 (Keap1) Kelch domain, and the hydrophobic and hydrogen bond interactions contributed to their stable binding. Herein, the regulation of Nrf2-ARE pathway by tangeretin was explored in the human embryonic kidney cell line HEK293T, which is relatively easy to be transfected. Upon binding to tangeretin, Nrf2 translocated to the nucleus of HEK293T cells, which in turn activated the Nrf2-ARE pathway. Luciferase reporter gene analysis showed that tangeretin significantly induced ARE-mediated transcriptional activation. Real-time PCR and Western blot assays showed that tangeretin induced the gene and protein expressions of Nrf2-mediated targets, including heme oxygenase 1 (HO-1), nicotinamide adenine dinucleotide phosphate (NADPH) quinone dehydrogenase 1 (NQO1), and glutamate-cysteine ligase (GCLM). In addition, tangeretin could effectively scavenge 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radicals. In summary, tangeretin may be a potential antioxidant via activating the Nrf2-ARE pathway.

Typhoons are destructive weather events. News media reports contain large amounts of typhoon information. Transforming this information into useful knowledge to provide a basis for mining typhoon knowledge and supporting disaster prevention and relief is urgently required to solve this problem. Knowledge representation can be used to address this problem, although it presents several challenges. These challenges lie in expressing the static and dynamic characteristics of typhoons and formalizing the knowledge representation method and making it suitable for machine processing. Moreover, the general Chinese time and space representation method is overly cumbersome for use in ontologies. The present study proposes an ontology-based typhoon event representation method that solves the representation problems of the typhoon static concept and dynamic features. Furthermore, it summarizes the fixed patterns of time and space in Chinese news and designs a time and space model suitable for typhoon event ontologies. From the ontology population, typhoon event ontology instances are created, and the typhoon event ontology model is applied to the analysis of typhoon processes, verifying the effectiveness of the typhoon event ontology model.

The Yan River Basin of the Loess Plateau is a key region for ensuring the environmental protection and sustainable development of the Yellow River Basin. Therefore, it is essential to identify how vegetation cover has changed and determine the factors that have driven these changes. In this study, we applied a three-dimensional vegetation cover model to examine the spatiotemporal variation characteristics of vegetation cover at the watershed scale in the Yan River Basin from 2001 to 2020 and forecast future trends. Subsequently, the driving forces of fractional vegetation cover (FVC) change were quantified based on meteorological, surface, and anthropogenic factors to explore the common driving relationships among these factors. (1) The accuracy of 3DFVC is better than that of FVC in the Yanhe River Basin, where the terrain is complex. (2) The temporal change trends indicated that the vegetation cover in the Yan River Basin significantly recovered and the basin FVC increased rapidly from 2001 to 2013 (S = 0.0152/a, p < 0.01) and increased gradually from 2013 to 2020 (S = 0.0015/a). The main reason for the increase in vegetation cover was the enhanced growth of medium FVC. (3) The vegetation spatial distribution showed that the FVC values varied substantially from north to south, indicating spatial heterogeneity, and 83.9% of the area presented a trend of increasing vegetation. Furthermore, vegetation cover was predicted to improve in the future. (4) The spatial heterogeneity of FVC was mainly influenced by relative humidity and rainfall, and the spatial variations in FVC were mainly determined by climate factors. Land use and cover change variations, which are influenced by human activities, represent major factors underlying the observed spatial heterogeneity. Most interactions between driving factors showed two-way enhancement or non-linear enhancement, with relative humidity and land use patterns presenting the strongest explanatory power. This study provides a scientific basis for vegetation conservation in the Yan River Basin and contributes theoretical support for decision-making regarding ecological environmental protection in the Loess Plateau and sustainable development in the Yellow River Basin.

Land use simulations are critical in predicting the impact of land use change (LUC) on the Earth. Various assumptions and policies influence land use structure and are a key factor in decisions made by policymakers. Meanwhile, the spatial autocorrelation effect between land use types has rarely been considered in existing land use spatial simulation models, and the simulation accuracy needs to be further improved. Thus, in this study, the driving mechanisms of LUC are analyzed. The quantity demand and spatial distribution of land use are predicted under natural development (ND), economic development (ED), ecological protection (EP), and sustainability development (SD) scenarios in Zhengzhou based on the coupled Multi-Objective Programming (MOP) model and the Patch-generating Land Use Simulation model (PLUS) considering Spatial Autocorrelation (PLUS-SA). We conclude the following. (1) The land use type in Zhengzhou was mainly cultivated land, and 83.85% of the land for urban expansion was cultivated land from 2000 to 2020. The reduction in forest from 2010 to 2020 was less than that from 2000 to 2010 due to the implementation of the policy in which farmland is transformed back into forests. (2) The accuracy of PLUS-SA was better than that of the traditional PLUS and Future Land Use Simulation (FLUS) models, and its Kappa coefficient, overall accuracy, and FOM were 0.91, 0.95, and 0.29, respectively. (3) Natural factors (temperature, precipitation, and DEM) contributed significantly to the expansion of cultivated land, and the increase in forest, grass, and construction land was greatly affected by socioeconomic factors (population, GDP, and proximity to town). (4) The land use structure will be more in line with the current requirements for sustainable urban development in the SD scenario, and the economic and ecological benefits will increase by 0.75 × 104 billion CNY and 1.71 billion CNY, respectively, in 2035 compared with those in 2020. The PLUS-SA model we proposed had higher simulation accuracy in Zhengzhou Compared with the traditional PLUS and FLUS models, and our research framework can provide a basis for decision-makers to formulate sustainable land use development policies to achieve high-quality and sustainable urban development.

Blue–green space refers to blue space (rivers and lakes) and green space (lawns and trees), which have the cooling island effect and are increasingly acknowledged as a potential and effective way to help alleviate the urban heat island effect. Scientific and flexible blue–green space planning is required, especially for medium- and large-scale urban agglomerations in the face of climate change. However, the temporal evolution and spatial patterns of the cooling island effect in the blue–green space under different future scenarios of climate change have not been fully investigated. This would impede long-term urban strategies for climate change adaptation and resilience. Here we studied the relationship between future climate change and blue–green spatial layout with Weather Research and Forecasting (WRF), based on the numerical simulation data of 15 global climate models under different extreme Shared Socioeconomic Pathway (SSP) scenarios. As a result, future changes in urban cooling island (UCI) magnitudes were estimated between historical (2015–2020) and future timelines: 2030s (2021–2040), 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100). Our results showed different land use types in blue and green space across the study area were predicted to present various changes in the next 80 years, with forest, grassland, and arable land experiencing the most significant land use transfer. The future UCI intensity of cities under SPP5-8.5 (12) was found to be lower than that under SPP2-4.5 (15), indicating that cities may be expected to experience decreases in UCI magnitudes in the future under SSP5-8.5. When there is no expansion of urban development land, we found that the conversion of different land use types into blue and green space leads to little change in future UCI intensity. While the area growth of forests and water bodies is proportional to the increase in UCI, the increase of farmland was observed to have the most significant impact on reducing the amplitude of urban UCI. Given that Huai’an City, Yancheng City, and Yangzhou City have abundant blue–green space, the urban cooling island effect was projected to be more significant than that of other cities in the study area under different SSP scenarios. The simulation results of the WRF model indicate that optimizing the layout of urban blue–green space plays an important role in modulating the urban thermal environment.

BackgroundNumerous basic studies have demonstrated critical roles of metabolic and contractile remodeling in pathophysiological changes of atrial fibrillation (AF), but acetylation changes underlying atrial remodeling have not been fully elucidated. Quantitative acetylated proteomics enables researchers to identify a comprehensive map of protein alterations responsible for pathological development and progression of AF in the heart of patients.Materials and methodsIn this study, 18 samples (9 with chronic AF and 9 with sinus rhythm) of left atrial appendage (LAA) tissues were obtained during mitral valve replacement surgery. Changes in the quantitative acetylated proteome between the AF and sinus rhythm (SR) groups were studied by dimethyl labeling, acetylation affinity enrichment, and high-performance liquid chromatography-tandem mass spectrometry analysis.ResultsWe identified a total of 5,007 acetylated sites on 1,330 acetylated proteins, among which 352 acetylated sites on 193 acetylated proteins were differentially expressed between the AF and SR groups by setting a quantification ratio of 1.3 for threshold value and P < 0.05 for significant statistical difference. The bioinformatics analysis showed that the differentially expressed acetylated proteins were mainly involved in energy metabolism and cellular contraction and structure function-related biological processes and pathways. Among 87 differentially expressed energy metabolism acetylated proteins related to the processes of fatty acid, carbohydrate, ketone body metabolism, and oxidative phosphorylation, nearly 87.1% Kac sites were upregulated (148 Kac sites among 170) in the AF group. Besides, generally declining acetylation of cardiac muscle contraction-related proteins (88.9% Kac sites of myosin) was found in the LAA of patients with AF. Immune coprecipitation combined with Western blotting was conducted to validate the differential expression of acetylated proteins.ConclusionMany differentially expressed energy metabolism and cellular contraction acetylated proteins were found in the LAA tissues of patients with chronic AF, and may reflect the impaired ATP production capacity and decreased atrial muscle contractility in the atrium during AF. Thus, acetylation may play an important regulatory role in metabolic and contractile remodeling of the atrium during AF. Moreover, the identified new acetylated sites and proteins may become promising targets for prevention and treatment of AF.

BackgroundThe effect of primary site on cardiovascular mortality (CVM) post-radiotherapy (RT) in patients with limited-stage small cell lung cancer (LS-SCLC) remains unclear.MethodsWe screened the Surveillance, Epidemiology, and End Results (SEER) database between 1988 and 2013. We used cumulative incidence function (CIF) curves to compare CVM incidences, and performed Cox proportional hazards and Fine-Gray competing risk analyses to identify independent risk factors of CVM. Propensity score matching (PSM) analysis was conducted.ResultsAmong enrolled 4,824 patients (median age 57 years old, 49.2% were male), CVM accounts for 10.0% of all deaths after 5 years since cancer diagnosis. Hazard ratios (HRs) for CVM were 1.97 (95% CI: 1.23–3.16, P = 0.005) for main bronchus (MB) patients, 1.65 (95% CI: 1.04–2.63, P = 0.034) for lower lobe (LL) patients and 1.01 (95% CI: 0.40–2.59, P = 0.977) for middle lobe (ML) patients compared to upper lobe (UL) patients. CIF curves showed that the cumulative CVM incidence was greater in the re-categorized MB/LL group compared to UL/ML group both before PSM (P = 0.005) and after PSM (P = 0.012). Multivariate regression models indicated that MB/LL was independently associated with an increased CVM risk, before PSM (HRCox: 1.79, 95% CI: 1.23–2.61, P = 0.002; HRFine−Gray: 1.71, 95% CI: 1.18–2.48, P = 0.005) and after PSM (HRCox: 1.88, 95% CI: 1.20–2.95, P = 0.006; HRFine−Gray: 1.79, 95% CI: 1.15–2.79, P = 0.010).ConclusionsMB/LL as the primary site is independently associated with an increased CVM risk post-RT in patients with LS-SCLC.

Long QT syndrome is one of the most common hereditary arrhythmias. Mutations in KCNH2 can cause long QT syndrome type 2 (LQT2). In this study, we generated a human induced pluripotent stem cell line ZZUNEUi027-A from a LQT2 female patient with c. 128A → G in KCNH2 gene using non-integrative Sendai viral reprogramming technology. This cell line expresses pluripotency markers, exhibits a normal female karyotype (46, XX) and could differentiate into all three germ layers in vitro. ZZUNEUi027-A can serve as a cell disease model in the understanding of LQT2 pathogenesis.

Accurate acquisition of cultivated land area and location information is of great significance to agricultural management, agro-ecological environment monitoring, and national food security. The rapid development of deep learning technology provides a new way to extract cultivated land information. However, there are many parameters involved in deep learning, so it is time-consuming to find the optimal parameters. In order to simplify the complex parameter tuning process and explore the main parameters that affect the classification accuracy of deep learning, this study uses Gaofen-2 images as data support and based on U-Net semantic segmentation model to carry out an experiment of cultivated land extraction in Henan Province, China. This study designs model training in different scenarios based on four bands(near-infrared (Nir), red, green, and blue), three backbone models, and six patch sizes (pixel resolution), and analyzes the training model and classification results from multiple perspectives such as model training time, sample size, epoch, and classification categories and quantity. After analysis and evaluation, we conducted cultivated land extraction experiments in three years and different phenological periods, and finally tested the performance of U-Net, PSPNet, Deeplab, and Random Forest in cultivated land extraction. The results show that: (1) the patch size in the model has the greatest influence on the classification accuracy, which is affected by image resolution, geographic objects and batch size. When the patch size was smaller than 112 × 112 pixels, the model recall and F1 decreased substantially, with a decrease of 23.91% and 22.64%, respectively. (2) For U-Net model, after setting reasonable band combination, backbone model, patch size, epoch and sample category, the segmentation and classification accuracy of predicted results increased by 3.27% (Intersection over Union, IoU), 2.62%(overall accuracy, OA), 3.89%(F1) and 7.89%(Kappa) on average. (3) We also determined the optimal band combination (Nir, Red, and Green), patch size (224 × 224 pixels), and backbone (Resnet34) for cultivated land extraction from Gaofen-2 images. The appropriate patch size of cultivated land extraction in the study is between 224 × 224 and 256 × 256 pixels. (4) For cultivated land extraction, relying only on binary classification is not as effective as multi-classification.

The mitigation of the urban heat island effect is increasingly imperative in light of climate change. Blue–green space, integrating water bodies and green spaces, has been demonstrated to be an effective strategy for reducing the urban heat island effect and enhancing the urban environment. However, there is a lack of coupled analysis on the cooling island effect of blue–green space at the meso-micro scale, with previous studies predominantly focusing on the heat island effect. This study coupled the single urban canopy model (UCM) with the mesoscale Weather Research and Forecasting (WRF) numerical model to simulate the cooling island effect of blue–green space in the Eastern Sea-River-Stream-Lake Linkage Zone (ESLZ) within the northern subtropical zone. In particular, we comparatively investigated the cooling island effect of micro-scale blue–green space via three mitigation strategies of increasing vegetation, water bodies, and coupling blue–green space, using the temperature data at the block scale within 100 m square of the urban center on the hottest day in summer. Results showed that the longitudinally distributed lakes and rivers in the city had a significant cooling effect on the ambient air temperature (Ta) at the mesoscale, with the largest cooling range occurring during the daytime and ranging from 1.01 to 2.15 °C. In contrast, a 5~20% increase in vegetation coverage or 5~15% increase in water coverage at the micro-scale was observed to reduce day and night Ta by 0.71 °C. Additionally, the most significant decrease in physiologically equivalent temperature (PET) was found in the mid-rise building environment, with a reduction of 2.65–3.26 °C between 11:00 and 13:00 h, and an average decrease of 1.25°C during the day. This study aims to guide the optimization of blue–green space planning at the meso-micro scale for the fast-development and expansion of new urban agglomerations.

Abstract Background Previous studies demonstrated impaired lipid metabolism and augmented aerobic glycolysis in AF. The authors aimed to investigate whether the use of metformin, an AMPK activator, could reverse this metabolic remodeling in chronic AF and to explore the underlying mechanisms. Methods We conducted chronic AF animal models with 18 beagle dogs and divided them into SR (pacemaker implanted without pacing), AF (pacemaker implanted with sustained pacing at a frequency of 400 beats/min for 6 weeks), and metformin+AF group (daily oral administration of metformin was initiated 1 week before surgery and continued throughout the study period). After electrophysiological measurements, the left atrial appendage tissue samples were taken from the beating heart for further analysis. Protein expression, histological analysis, and biochemical measurements were conducted. Results The AF groups showed decreased expression of FAT/CD36, CPT-1, VLCAD, increased concentration of free fatty acid and triglyceride, and increased lipid deposition. The activation of AMPK/PGC-1α/PPARα pathway was decreased. The key factors of the Warburg effect, including HIF-1α, GLUT-1, PDK1, HK, and LDH, increased in AF group compared to SR group. The expression of PDH decreased significantly, accompanied by increased atrial lactate production. The extent of fibrosis increased significantly in the left atrial appendage of AF group. dERP, ∑WOV, and AF inducibility increased while ERP decreased in AF group compared to SR group. The use of metformin attenuated all these changes effectively. Conclusions Metformin improves lipid metabolism and reverses the Warburg effect in chronic AF via AMPK activation. It attenuates atrial electrical and structural remodeling.

In this paper, the definitions of the fractional derivative and integrals are given by the neutrix limit. Not only is it consistent with the classical results but also the representations of the fractional derivative and integrals are obtained for x−a−k, x−a−klnpx−a, x−a−kfx, and x−a−klnpx−afx, where k,p=1,2,…,fx is the analytic function.

Transformer models have achieved great results in the field of computer vision over the past 2 years, drawing attention from within the field of remote sensing. However, there are still relatively few studies on this model in the field of remote sensing. Which method is more suitable for remote-sensing segmentation? In particular, how do different transformer models perform in the face of high-spatial resolution and the multispectral resolution of remote-sensing images? To explore these questions, this paper presents a comprehensive comparative analysis of three mainstream transformer models, including the segmentation transformer (SETRnet), SwinUnet, and TransUnet, by evaluating three aspects: a visual analysis of feature-segmentation results, accuracy, and training time. The experimental results show that the transformer structure has obvious advantages for the feature-extraction ability of large-scale remote-sensing data sets and ground objects, but the segmentation performance of different transfer structures in different scales of remote-sensing data sets is also very different. SwinUnet exhibits better global semantic interaction and pixel-level segmentation prediction on the large-scale Potsdam data set, and the SwinUnet model has the highest accuracy metrics for KAPPA, MIoU, and OA in the Potsdam data set, at 76.47%, 63.62%, and 85.01%, respectively. TransUnet has better segmentation results in the small-scale Vaihingen data set, and the three accuracy metrics of KAPPA, MIoU, and OA are the highest, at 80.54%, 56.25%, and 85.55%, respectively. TransUnet is better able to handle the edges and details of feature segmentation thanks to the network structure together built by its transformer and convolutional neural networks (CNNs). Therefore, TransUnet segmentation accuracy is higher when using a small-scale Vaihingen data set. Compared with SwinUnet and TransUnet, the segmentation performance of SETRnet in different scales of remote-sensing data sets is not ideal, so SETRnet is not suitable for the research task of remote-sensing image segmentation. In addition, this paper discusses the reasons for the performance differences between transformer models and discusses the differences between transformer models and CNN. This study further promotes the application of transformer models in remote-sensing image segmentation, improves the understanding of transformer models, and helps relevant researchers to select a more appropriate transformer model or model improvement method for remote-sensing image segmentation.

Aim: The connection between revascularization for coronary artery disease (CAD) and the incidence of recurrent events of atrial fibrillation (AF) after ablation is unclear. This study aimed to explore the relationship between coronary revascularization and AF recurrence in patients who underwent radiofrequency catheter ablation (RFCA).Methods: Four hundred and nineteen patients who underwent performed coronary angiography at the same time as RFCA were enrolled in this study. Obstructive CAD was defined as at least one coronary artery vessel stenosis of ≥75% and percutaneous coronary intervention (PCI) was recommended. Non-obstructive CAD was defined as coronary artery vessel stenosis of

High-quality urban land-use maps are essential for grasping the dynamics and scale of urban land use, predicting future environmental trends and changes, and allocating national land resources. This paper proposes a multisample “voting mechanism” based on multisource data and random forests to achieve fine mapping of urban land use. First, Zhengzhou City was selected as the study area. Based on full integration of multisource features, random forests were used to perform the preliminary classification of multiple samples. Finally, the preliminary classification results were filtered according to the “voting mechanism” to achieve high-precision urban land-use classification mapping. The results showed that the overall classification accuracy of Level I features increased by 5.66% and 14.32% and that the overall classification accuracy of Level II features increased by 9.02% and 12.46%, respectively, compared with the classification results of other strategies. Therefore, this method can significantly reduce the influence of mixed distribution of land types and improve the accuracy of urban land-use classification at a fine scale.

The performance of deep neural networks depends on the accuracy of labeled samples, as they usually contain label noise. This study examines the semantic segmentation of remote sensing images that include label noise and proposes an anti-label-noise network framework, termed Labeled Noise Robust Network in Remote Sensing Image Semantic Segmentation (NRN-RSSEG), to combat label noise. The algorithm combines three main components: network, attention mechanism, and a noise-robust loss function. Three different noise rates (containing both symmetric and asymmetric noise) were simulated to test the noise resistance of the network. Validation was performed in the Vaihingen region of the ISPRS Vaihingen 2D semantic labeling dataset, and the performance of the network was evaluated by comparing the NRN-RSSEG with the original U-Net model. The results show that NRN-RSSEG maintains a high accuracy on both clean and noisy datasets. Specifically, NRN-RSSEG outperforms UNET in terms of PA, MPA, Kappa, Mean_F1, and FWIoU in the presence of noisy datasets, and as the noise rate increases, each performance of UNET shows a decreasing trend while the performance of NRN-RSSEG decreases slowly and some performances show an increasing trend. At a noise rate of 0.5, the PA (−6.14%), MPA (−4.27%) Kappa (−8.55%), Mean_F1 (−5.11%), and FWIOU (−9.75%) of UNET degrade faster; while the PA (−2.51%), Kappa (−3.33%), and FWIoU of NRN-RSSEG (−3.26) degraded more slowly, MPA (+1.41) and Mean_F1 (+2.69%) showed an increasing trend. Furthermore, comparing the proposed model with the baseline method, the results demonstrate that the proposed NRN-RSSEG anti-noise framework can effectively help the current segmentation model to overcome the adverse effects of noisy label training.

Frequent agricultural activities in farmland ecosystems bring challenges to crop information extraction from remote sensing (RS) imagery. The accurate spatiotemporal information of crops serves for regional decision support and ecological assessment, such as disaster monitoring and carbon sequestration. Most traditional machine learning algorithms are not appropriate for prediction classification due to the lack of historical ground samples and poor model transfer capabilities. Therefore, a transferable learning model including spatiotemporal capability was developed based on the UNet++ model by integrating feature fusion and upsampling of small samples for Sentinel-2A imagery. Classification experiments were conducted for 10 categories from 2019 to 2021 in Xinxiang City, Henan Province. The feature fusion and upsampling methods improved the performance of the UNet++ model, showing lower joint loss and higher mean intersection over union (mIoU) values. Compared with the UNet, DeepLab V3+, and the pyramid scene parsing network (PSPNet), the improved UNet++ model exhibits the best performance, with a joint loss of 0.432 and a mIoU of 0.871. Moreover, the overall accuracy and macro F1 values of prediction classification results based on the UNet++ model are higher than 83% and 58%, respectively. Based on the reclassification rules, about 3.48% of the farmland was damaged in 2021 due to continuous precipitation. The carbon sequestration of five crops (including corn, peanuts, soybean, rice, and other crops) is estimated, with a total carbon sequestration of 2460.56, 2549.16, and 1814.07 thousand tons in 2019, 2020, and 2021, respectively. The classification accuracy indicates that the improved model exhibits a better feature extraction and transferable learning capability in complex agricultural areas. This study provides a strategy for RS semantic segmentation and carbon sequestration estimation of crops based on a deep learning network.

Timely crop acreage and distribution information are the basic data which drive many agriculture related applications. For identifying crop types based on remote sensing, methods using only a single image type have significant limitations. Current research that integrates fine and coarser spatial resolution images, using techniques such as unmixing methods, regression models, and others, usually results in coarse resolution abundance without sufficient detail within pixels, and limited attention has been paid to the spatial relationship between the pixels from these two kinds of images. Here we propose a new solution to identify winter wheat by integrating spectral and temporal information derived from multi-resolution remote sensing data and determine the spatial distribution of sub-pixels within the coarse resolution pixels. Firstly, the membership of pixels which belong to winter wheat is calculated using a 25-m resolution resampled Landsat Thematic Mapper (TM) image based on the Bayesian equation. Then, the winter wheat abundance (acreage fraction in a pixel) is assessed by using a multiple regression model based on the unique temporal change features from moderate resolution imaging spectroradiometer (MODIS) time series data. Finally, winter wheat is identified by the proposed Abundance-Membership (AM) model based on the spatial relationship between the two types of pixels. Specifically, winter wheat is identified by comparing the spatially corresponding 10×10 membership pixels of each abundance pixel. In other words, this method takes advantage of the relative size of membership in a local space, rather than the absolute size in the entire study area. This method is tested in the major agricultural area of Yiluo Basin, China, and the results show that acreage accuracy (Aa) is 93.01% and sampling accuracy (As) is 91.40%. Confusion matrix shows that overall accuracy (OA) is 91.4% and the kappa coefficient (Kappa) is 0.755. These values are significantly improved compared to the traditional Maximum Likelihood classification (MLC) and Random Forest classification (RFC) which rely on spectral features. The results demonstrate that the identification accuracy can be improved by integrating spectral and temporal information. Since the identification of winter wheat is performed in the space corresponding to each MODIS pixel, the influence of differences of environmental conditions is greatly reduced. This advantage allows the proposed method to be effectively applied in other places.

Abstract Background Atrial lipid metabolic remodeling is critical for the process of atrial fibrillation (AF). Abnormal Fatty acid (FA) metabolism in cardiomyocytes is involved in the pathogenesis of AF. MET (Metformin), an AMPK (AMP-activated protein kinase) activator, has been found to be associated with a decreased risk of AF in patients with type 2 diabetes. However, the specific mechanism remains unknown. Methods Fifteen mongrel dogs were divided into three groups: SR, ARP (pacing with 800 beats/min for 6 h), ARP plus MET (treated with MET (100 mg/kg/day) for two weeks before pacing). We assessed metabolic factors, speed limiting enzymes circulating biochemical metabolites (substrates and products), atrial electrophysiology and accumulation of lipid droplets. Results The expression of AMPK increased in the ARP group and significantly increased in the MET+ARP group comparing to the SR group. In the ARP group, the expressions of PPARα、PGC-1α and VLCAD were down-regulated, while the concentration of free fatty acid and triglyceride and the lipid deposition in LAA (left atrial appendage) increased. Moreover, AERP and AERPd have also been found abnormally in this process. Pretreatment with MET before receiving ARP reversed the alterations aforementioned. Conclusions The FA metabolism in LAA is altered in the ARP group, mainly characterized by the abnormal expression of the rate-limiting enzyme. Metformin reduces lipid accumulation and promotes β-oxidation of FA in AF models partially through AMPK/PPAR-α/VLCAD pathway. Our study indicates that MET may inhibit the FA lipid metabolic remodeling in AF.

Abstract Background The effects of β blockers on the primary prevention of anthracycline-induced cardiotoxicity were controversial. Methods We searched PubMed, Embase and Cochrane Library for randomized controlled trials of the comparison of β blockers versus placebo in patients undergoing anthracycline chemotherapy. This meta-analysis was performed by using random-effect models. Results Nine hundred forty participants from 11 trials were included in this meta-analysis. β blockers led to a significant reduction in symptomatic heart failure (risk ratio [RR] 0.29, 95% CI 0.10 to 0.85). Compared with placebo, β blockers were associated with improved left ventricular ejection fraction (mean difference [MD] 4.46, 95% CI 1.77 to 7.15) and s’ (MD 0.78, 95% CI 0.01 to 1.55) in parallel with reduced left ventricular diameter (left ventricular end systolic diameter, MD -3.19, 95% CI -6.17 to − 0.21; left ventricular end diastolic diameter, MD -2.28, 95% CI 4.50 to − 0.05). β blockers also improved strain and strain rate when compared with placebo. There were no significant differences in diastolic function variables between β blockers and placebo except e’ (MD 2.33, 95% CI 0.16 to 4.51). In addition, β blockers compared with placebo reduced the risk of cardiac troponin I elevation > 0.04 ng/ml (RR 0.60, 95% CI 0.42 to 0.85). There was no marked difference in adverse events (RR 0.94, 95% CI 0.56 to 1.59) between β blockers and placebo. Conclusions In cancer patients with anthracycline therapy, prophylactic β blockers were associated with reduced risk of heart failure, decreased left ventricular diameter, improved left ventricular systolic function, and alleviative cardiomyocyte injury.

Accurate urban boundary data can directly reflect the expansion of urban space, help us accurately grasp the scale and form of urban space, and play a vital role in urban land development and policy-making. However, the lack of reliable multiscale and high-precision urban boundary data products and relevant training datasets has become one of the major factors hindering their application. The purpose of this study is to combine Sentinel-2 remote-sensing images and supplementary geographic data to generate a reliable high-precision urban boundary dataset for Henan Province (called HNUB2018). First, this study puts forward a clear definition of “urban boundary”. Using this concept as its basis, it proposes a set of operable urban boundary delimitation rules and technical processes. Then, based on Sentinel-2 remote-sensing images and supplementary geographic data, the urban boundaries of Henan Province are delimited by a visual interpretation method. Finally, the applicability of the dataset is verified by using a classical semantic segmentation deep learning model. The results show that (1) HNUB2018 has clear and rich detailed features as well as a detailed spatial structure of urban boundaries. The overall accuracy of HNUB2018 is 92.82% and the kappa coefficient reaches 0.8553, which is better than GUB (Henan) in overall accuracy. (2) HNUB2018 is well suited for deep learning, with excellent reliability and scientific validity. The research results of this paper can provide data support for studies of urban sprawl monitoring and territorial spatial planning, and will support the development of reliable datasets for fields such as intelligent mapping of urban boundaries, showing prospects and possibilities for wide application in urban research.

Ensuring the suitability of urban development land is essential for delineating spatial growth boundaries and urban spatial layouts. However, the significant impact of subjective uncertainty on the suitability evaluation process significantly reduces the reliability of the evaluation results. Thus, in this study, we developed a new method to address this issue and improve the accuracy of the evaluation results. Zhengzhou in China was considered as the research area and the data utilized were obtained from the following primary sources: Landsat TM/ETM/OLI image data, land use data, digital elevation model data, spatial primary geographical data, and digital map data. A new method for evaluating the suitability of urban development land was developed by combining logistic regression, principal component analysis, kriging interpolation, K-means, and the Geodetector method to evaluate and classify the suitability of urban development land in Zhengzhou City during 2013. By using logistic regression, we could accurately evaluate the effects of a single factor, thereby avoiding subjective assessments. The principal component can be used to reduce the dimensions of the evaluation results for a single factor where the weight of the principal component is determined by using the cumulative contribution rate in order to obtain the comprehensive evaluation result. Kriging interpolation can be used to predict the evaluation results for the grid surface by using the principal component to comprehensively evaluate the sample points. K-means can be used to automatically classify the evaluation results for the grid surface. Geodetector was used to detect the spatial differentiation of the results in order to confirm the validity of the spatial partition results. These methods can avoid interference due to human factors and yield more objective and accurate evaluation results. The results indicated that the proposed evaluation method can avoid the subjective influence of the evaluation index classification and the determination of the index weight to obtain extremely accurate evaluations and high effectiveness. The suitability grading and evaluation values were highly consistent with the spatial pattern, thereby demonstrating the applicability of the evaluation results. The method and evaluation results may provide a scientific reference to support decisions regarding land resource allocation during urban development.

Quantitative studies of the multiple factors influencing the mountain-mass effect, which causes higher temperatures in mountainous than non-mountainous regions, remain insufficient. This study estimated the air temperature in the Yellow River Basin, which spans three different elevation ranges, using multi-source data to address the uneven distribution of regional meteorological stations. The differences in mountain-mass effect for different geomorphic regions at the same altitude were then compared. The Manner–Kendall nonparametric test was used to analyse time series changes in temperature. Moreover, we employed the geographically weighted regression (GWR) model, with MODIS land-surface and air-temperature data, station-based meteorological data, vertical temperature gradients corresponding to the 2000–2015 period, and elevation data, to estimate the correlation between monthly mean surface temperature and air temperature in the Yellow River Basin. The following major results were obtained. (1) The GWR method and ground station-based observations enhanced the accuracy of air-temperature estimates with an error of only ± 0.74°C. (2) The estimated annual variations in the spatial distributions of 12-month average temperatures showed that the upper Tibetan Plateau is characterised by low annual air temperatures with a narrow spatial distribution, whereas north-eastern areas upstream of the Inner Mongolia Plateau are characterised by higher air temperatures. Changes in the average monthly air temperature were also high in the middle and lower reaches, with a narrow spatial distribution. (3) Considering the seasonal variation in the temperature lapse rate, the mountain-mass effect in the Yellow River Basin was very high. In the middle of each season, the variation of air temperature at a given altitude over the Tibetan Plateau was higher than that over the Loess Plateau and Jinji Mountain. The results of this study reveal the unique temperature characteristics of the Yellow River Basin according to its geomorphology. Furthermore, this research contributes to quantifying mountain-mass effects.

Land use is used to reflect the expression of human activities in space, and land use classification is a way to obtain accurate land use information. Obtaining high-precision land use classification from remote sensing images remains a significant challenge. Traditional machine learning methods and image semantic segmentation models are unable to make full use of the spatial and contextual information of images. This results in land use classification that does not meet high-precision requirements. In order to improve the accuracy of land use classification, we propose a land use classification model, called DADNet-CRFs, that integrates an attention mechanism and conditional random fields (CRFs). The model is divided into two modules: the Dual Attention Dense Network (DADNet) and CRFs. First, the convolution method in the UNet network is modified to Dense Convolution, and the band-hole pyramid pooling module, spatial location attention mechanism module, and channel attention mechanism module are fused at appropriate locations in the network, which together form DADNet. Second, the DADNet segmentation results are used as a priori conditions to guide the training of CRFs. The model is tested with the GID dataset, and the results show that the overall accuracy of land use classification obtained with this model is 7.36% and 1.61% higher than FCN-8s and BiSeNet in classification accuracy, 11.95% and 1.81% higher in MIoU accuracy, and with a 9.35% and 2.07% higher kappa coefficient, respectively. The proposed DADNet-CRFs model can fully use the spatial and contextual semantic information of high-resolution remote sensing images, and it effectively improves the accuracy of land use classification. The model can serve as a highly accurate automatic classification tool for land use classification and mapping high-resolution images.

Abstract Background There is a little evidence for the effects of catheter ablation (CA) on hard endpoints in patients with atrial fibrillation (AF) and heart failure (HF). Methods PubMed, Embase and Cochrane Library were searched for randomized controlled trials (RCTs) enrolling patients with AF and HF who were assigned to CA, rate control or medical rhythm control groups. This meta-analysis was performed by using random-effect models. Results Seven RCTs enrolling 856 participants were included in this meta-analysis. CA reduced the risks of all-cause mortality (risk ratio [RR] 0.52, 95% CI 0.35 to 0.76), HF readmission (RR 0.58, 95% CI 0.46 to 0.66) and the composite of all-cause mortality and HF readmission (RR 0.55, 95% CI 0.47 to 0.66) when compared with control. But there was no significant difference in cerebrovascular accident (RR 0.56, 95% CI 0.23 to 1.36) between two groups. Compared with control, CA was associated with improvement in left ventricular ejection fraction (mean difference [MD] 7.57, 95% CI 3.72 to 11.41), left ventricular end systolic volume (MD -14.51, 95% CI -26.84 to − 2.07), and left ventricular end diastolic volume (MD -3.78, 95% CI -18.51 to 10.96). Patients undergoing CA exhibited increased peak oxygen consumption (MD 3.16, 95% CI 1.09 to 5.23), longer 6-min walk test distance (MD 26.67, 95% CI 12.07 to 41.27), and reduced Minnesota Living with Heart Failure Questionnaire scores (MD -9.49, 95% CI -14.64 to − 4.34) than those in control group. Compared with control, CA was associated with improved New York Heart Association class (MD -0.74, 95% CI -0.83 to − 0.64) and lower B-type natriuretic peptide levels (MD -105.96, 95% CI -230.56 to 19.64). Conclusions CA was associated with improved survival, morphologic changes, functional capacity and quality of life relative to control. CA should be considered in patients with AF and HF.

In order to resist bubble loading, anisotropic composite materials are the development direction of the future. The objective of this paper was to experimentally investigate the hydrodynamic performance of anisotropic laminate composite plates, with a focus on the effect of its anisotropic characteristics on single bubble migration. In these experiments, the bubble was generated in a transparent water tank filled with sufficiently degassed water by Joule heating at the connecting point of the electrodes through the discharge of a 6600 μF charge to 800 V, and a high-speed camera system with a recording speed of 40,000 frames per second was used to record the temporal evolution of bubble patterns and the dynamic responses of the laminated composite plates. The results are presented for two anisotropic cantilever composite beams with different ply angles, namely, 0° and 30°. Several variables, such as the shapes of the bubble, the curved trail of motion of the bubble center, bubble collapse time, and bubble initial standoff distances were extracted from the photographic images. The results showed that bubble migration near the 30° plate presents a curved bubble trail with an evident tilted angle during the collapse and rebound stages, which is very different from bubbles that all move vertically above the 0° plate. Furthermore, a characterization method for bubble migration was proposed to quantitatively describe the curved bubble trails and the deformation of the composite beams in temporal and spatial scales. This method shows that the curved bubble trails near the 30° plate are closely related to the dynamic response of composite beams, with a focus on the bending-twisting coupling effect.

The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium- and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP- and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation.

Abstract Brown algae are photosynthetic multicellular marine organisms. They belong to the phylum of Stramenopiles, which are not closely related to land plants and green algae. Brown algae share common evolutionary features with other photosynthetic and multicellular organisms, including a carbohydrate-rich cell-wall. Brown algal cell walls are composed predominantly of the polyanionic polysaccharides alginates and fucose-containing sulfated polysaccharides. These polymers are prevalent over neutral and crystalline components, which are believed to be mostly, if not exclusively, cellulose. In an attempt to better understand brown algal cell walls, we performed an extensive glycan array analysis of a wide range of brown algal species. Here we provide the first demonstration that mixed-linkage (1 → 3), (1 → 4)-β-d-glucan (MLG) is common in brown algal cell walls. Ultra-Performance Liquid Chromatography analyses indicate that MLG in brown algae solely consists of trisaccharide units of contiguous (1 → 4)-β-linked glucose residues joined by (1 → 3)-β-linkages. This regular conformation may allow long stretches of the molecule to align and to form well-structured microfibrils. At the tissue level, immunofluorescence studies indicate that MLG epitopes in brown algae are unmasked by a pre-treatment with alginate lyases to remove alginates. These findings are further discussed in terms of the origin and evolution of MLG in the Stramenopile lineage.

Objective This study was conducted to investigate the level of microRNA-155 (miRNA-155) in patients with heart failure (HF) after myocardial infarction (MI) and its clinical importance. Methods Serum miRNA-155 levels were measured using quantitative reverse transcription (qRT)-PCR. The left ventricular ejection fraction (LVEF), left ventricular posterior wall thickness, and left ventricular end-diastolic diameter were measured by echocardiography. Serum amino-terminal pro-B-type natriuretic peptide (NT-proBNP) and other parameters were also analyzed. Results miRNA-155 levels in patients with HF were significantly higher than in control and MI groups. The area under the receiver operating characteristic curve of serum miRNA-155 in the diagnosis of HF after MI was 0.941, the cutoff value was 1.77, sensitivity was 92.73%, and specificity was 92.14%. NT-proBNP levels were significantly higher and LVEF was lower in patients with high versus low miRNA-155 expression. Conclusions Patients with HF after MI had elevated miRNA-155 levels and poor cardiac function, suggesting that determining miRNA-155 expression could be used to assess the severity of the disease.

Over the period 1982-2015, temperatures have exhibited an asymmetric warming pattern diurnally, as well as seasonally across the Loess Plateau. However, very limited research has studied the implications and effects of such seasonally heterogeneous warming across the Loess Plateau. In this study, we also analyzed the time series trends and seasonal spatial patterns of the maximum (Tmax) and minimum (Tmin) temperatures and evaluated how different vegetation responded to daytime and nighttime warming in the Loess Plateau from 1982 to 2015 based on the NDVI and meteorological parameters (precipitation or temperature). We found that Tmax and Tmin significantly increased throughout the years except for Tmax in autumn, and the diurnal asymmetric warming showed some striking seasonal differences. For example, the increasing rates of Tmin in spring, summer, autumn, and winter were 0.75, 1.20, 1.88, and 1.10 times larger than that of Tmax, respectively. NDVI showed significantly positive correlation with Tmax and Tmin in spring and winter, while NDVI presented significantly positive correlation with Tmin in summer and Tmax in autumn across entire Loess Plateau. Furthermore, we also discovered diverse seasonal responses in terms of vegetation types to daytime and nighttime warming. For instance, Spring NDVI showed significantly positive partial correlations with Tmax and Tmin. In summer, grasslands and wetlands merely displayed significantly positive partial correlations with Tmin. Cultivated land presented significantly positive partial correlation between the NDVI and Tmax (Tmin) in autumn. In winter, cultivated land, forest, and grassland exhibited significantly positive partial correlation with Tmax and Tmin, while only wetland showed a significantly positive partial correlation with Tmax. Our results demonstrated responses of vegetation to climate extremes and enhance a better understanding of the seasonally different responses of vegetation under global climate change at different scale.

Accurate temporal land use mapping provides important and timely information for decision making for large-scale management of land and crop production. At present, temporal land cover and crop classifications within a study area have neglected the differences between subregions. In this paper, we propose a classification rule by integrating the terrain, time series characteristics, priority, and seasonality (TTPSR) with Sentinel-2 satellite imagery. Based on the time series of Normalized Difference Water Index (NDWI) and Vegetation Index (NDVI), a dynamic decision tree for forests, cultivation, urban, and water was created in Google Earth Engine (GEE) for each subregion to extract cultivated land. Then, with or without this cultivated land mask data, the original classification results for each subregion were completed based on composite image acquisition with five vegetation indices using Random Forest. During the post-reclassification process, a 4-bit coding rule based on terrain, type, seasonal rhythm, and priority was generated by analyzing the characteristics of the original results. Finally, statistical results and temporal mapping were processed. The results showed that feature importance was dominated by B2, NDWI, RENDVI, B11, and B12 over winter, and B11, B12, NDBI, B2, and B8A over summer. Meanwhile, the cultivated land mask improved the overall accuracy for multicategories (seven to eight and nine to 13 during winter and summer, respectively) in each subregion, with average ranges in the overall accuracy for winter and summer of 0.857–0.935 and 0.873–0.963, respectively, and kappa coefficients of 0.803–0.902 and 0.835–0.950, respectively. The analysis of the above results and the comparison with resampling plots identified various sources of error for classification accuracy, including spectral differences, degree of field fragmentation, and planting complexity. The results demonstrated the capability of the TTPSR rule in temporal land use mapping, especially with regard to complex crops classification and automated post-processing, thereby providing a viable option for large-scale land use mapping.

The study of the temporal and spatial evolution of wetland landscapes and its driving factors is an important reference for wetland ecological restoration and protection. This article utilized seven periods of land use data in Henan Province from 1980 to 2015 to extract the spatial distribution characteristics of wetlands and analyze the temporal and spatial changes of wetlands in Henan Province. Transfer matrix, landscape metrics, correlation analysis, and redundancy analysis were applied to calculate and analyze the transformation types and area of wetland resources between all consecutive periods, and then the main driving factors of wetland expansion/contraction were explored. First, the total wetland area in Henan Province increased by 28% from 1980 to 2015, and the increased wetland area was mainly constructed wetlands, including paddy field, reservoir and pond, and canal. Natural wetlands such as marsh, lake, and floodplain decreased by 74%. Marsh area declined the most during 1990–1995, and was mainly transformed into floodplain and “Others” because of agricultural reclamation, low precipitation, and low Yellow River runoff. The floodplain area dropped the most from 2005 to 2010, mainly converted to canals and “Others” because of reclamation, exploitation of groundwater, the construction of the South–to–North Water Transfer Project, and recreational land development. Second, the results of correlation analysis and redundancy analysis indicated that economic factors were positively correlated with the area of some constructed wetlands and negatively correlated with the area of some natural wetlands. Socioeconomic development was the main driving factors for changes in wetland types. The proportion of wetland habitat in Henan Province in 2015 was only 0.3%, which is low compared to the Chinese average of 2.7%. The government should pay more attention to the restoration of natural wetlands in Henan Province.

Accurately identifying and delineating urban boundaries are the premise for and foundation of the control of disorderly urban sprawl, which is helpful for us to accurately grasp the scale and form of cities, optimize the internal spatial structure and pattern of cities, and guide the expansion of urban spaces in the future. At present, the concept and delineation of urban boundaries do not follow a unified method or standard. However, many scholars have made use of multi-source remote sensing images of various scales and social auxiliary data such as point of interest (POI) data to achieve large-scale, high-resolution, and high-precision land cover mapping and impermeable water surface mapping. The accuracy of small- and medium-scale urban boundary mapping has not been improved to an obvious extent. This study uses multi-temporal Sentinel-2 high-resolution images and POI data that can reflect detailed features of human activities to extract multi-dimensional features and use random forests and mathematical morphology to map the urban boundaries of the city of Zhengzhou. The research results show that: (1) the urban construction land extraction model established with multi-dimensional features has a great improvement in accuracy; (2) when the training sample accounts for 65% of the sample data set, the urban construction land extraction model has the highest accuracy, reaching 96.25%, and the Kappa coefficient is 0.93; (3) the optimized boundary of structural elements with a size of 13 × 13 is selected, which is in good agreement in terms of scope and location with the boundary of FROM-GLC10 (Zhengzhou) and visual interpretations. The results from the urban boundary delineation in this paper can be used as an important database for detailed basic land use mapping within cities. Moreover, the method in this paper has some reference value for other cities in terms of delineating urban boundaries.

Conflicting results identifying the association between tooth loss and cardiovascular disease and stroke have been reported. Therefore, a dose-response meta-analysis was performed to clarify and quantitatively assess the correlation between tooth loss and cardiovascular disease and stroke risk. Up to March 2017, seventeen cohort studies were included in current meta-analysis, involving a total of 879084 participants with 43750 incident cases. Our results showed statistically significant increment association between tooth loss and cardiovascular disease and stroke risk. Subgroups analysis indicated that tooth loss was associated with a significant risk of cardiovascular disease and stroke in Asia and Caucasian. Furthermore, tooth loss was associated with a significant risk of cardiovascular disease and stroke in fatal cases and nonfatal cases. Additionally, a significant dose-response relationship was observed between tooth loss and cardiovascular disease and stroke risk. Increasing per 2 of tooth loss was associated with a 3% increment of coronary heart disease risk; increasing per 2 of tooth loss was associated with a 3% increment of stroke risk. Subgroup meta-analyses in study design, study quality, number of participants and number of cases showed consistent findings. No publication bias was observed in this meta-analysis. Considering these promising results, tooth loss might provide harmful health benefits.

As tea is an important economic crop in many regions, efficient and accurate methods for remotely identifying tea plantations are essential for the implementation of sustainable tea practices and for periodic monitoring. In this study, we developed and tested a method for tea plantation identification based on multi-temporal Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. We used phenological patterns of tea cultivation in China’s Shihe District (such as the multiple annual growing, harvest, and pruning stages) to extracted multi-temporal Sentinel-2 MSI bands, their derived first spectral derivative, NDVI and textures, and topographic features. We then assessed feature importance using RF analysis; the optimal combination of features was used as the input variable for RF classification to extract tea plantations in the study area. A comparison of our results with those achieved using the Support Vector Machine method and statistical data from local government departments showed that our method had a higher producer’s accuracy (96.57%) and user’s accuracy (96.02%). These results demonstrate that: (1) multi-temporal and multi-feature classification can improve the accuracy of tea plantation recognition, (2) RF classification feature importance analysis can effectively reduce feature dimensions and improve classification efficiency, and (3) the combination of multi-temporal Sentinel-2 images and the RF algorithm improves our ability to identify and monitor tea plantations.

A soil erosion model is used to evaluate the conditions of soil erosion and guide agricultural production. Recently, high spatial resolution data have been collected in new ways, such as three-dimensional laser scanning, providing the foundation for refined soil erosion modelling. However, serial computing cannot fully meet the computational requirements of massive data sets. Therefore, it is necessary to perform soil erosion modelling under a parallel computing framework. This paper focuses on a parallel computing framework for soil erosion modelling based on the Hadoop platform. The framework includes three layers: the methodology, algorithm, and application layers. In the methodology layer, two types of parallel strategies for data splitting are defined as row-oriented and sub-basin-oriented methods. The algorithms for six parallel calculation operators for local, focal and zonal computing tasks are designed in detail. These operators can be called to calculate the model factors and perform model calculations. We defined the key-value data structure of GeoCSV format for vector, row-based and cell-based rasters as the inputs for the algorithms. A geoprocessing toolbox is developed and integrated with the geographic information system (GIS) platform in the application layer. The performance of the framework is examined by taking the Gushanchuan basin as an example. The results show that the framework can perform calculations involving large data sets with high computational efficiency and GIS integration. This approach is easy to extend and use and provides essential support for applying high-precision data to refine soil erosion modelling.

The Yellow River Basin has been affected by global climate change. Studying the spatial⁻temporal variability of the hydrothermal climate conditions in the Yellow River Basin is of vital importance for the development of technologies and policies related to ecological, environmental, and agricultural adaptation in this region. This study selected temperature and precipitation data observed from 118 meteorological stations distributed in the Yellow River Basin over the period of 1957⁻2015, and used the Mann⁻Kendall, Pettitt, and Hurst indices to investigate the spatial⁻temporal variability of the hydrothermal climate conditions in this area. The results indicated: (1) the annual maximum, minimum, and average temperatures have increased. The seasonal maximum, minimum, and average temperatures for the spring, summer, autumn, and winter have also increased, and this trend is statistically significant (p < 0.01) between 1957⁻2015. The rate of increase in the minimum temperature exceeded that of the maximum temperature, and diurnal warming was asymmetric. Annual precipitation and the total spring, summer, and autumn precipitations declined, while the total winter precipitation increased, although the trend was non-significant (p > 0.05). (2) Based on the very restrictive assumption that future changes will be similar to past changes, according to the Hurst index experiment, the future trends of temperature and precipitation in the Yellow River Basin are expected to stay the same as in the past. There will be a long-term correlation between the two trends: the temperature will continue to rise, while the precipitation will continue to decline (except in the winter). However, over the late stage of the study period, the trends slowed down to some extent.

It is demonstrated that an L band metallic photonic crystal TEM-TE11 mode converter is suitable for narrow band high power microwave application. The proposed mode converter is realized by partially filling metallic photonic crystals along azimuthal direction in a coaxial transmission line for phase-shifting. A three rows structure is designed and simulated by commercial software CST Microwave Studio. Simulation results show that its conversion efficiency is 99% at the center frequency 1.58 GHz. Over the frequency range of 1.56-1.625 GHz, the conversion efficiency exceeds 90 %, with a corresponding bandwidth of 4.1 %. This mode converter has a gigawatt level power handling capability which is suitable for narrow band high power microwave application. Using magnetically insulated transmission line oscillator(MILO) as a high power microwave source, particle-in-cell simulation is carried out to test the performance of the mode converter. The expected TE11 mode microwave output is obtained and the MILO works well. Mode conversion performance of the converter is tested by far-field measurement method. And the experimental result confirms the validity of our design. Then, high power microwave experiment is carried out on a Marx-driven Blumlein water line pulsed power accelerator. Microwave frequency, radiated pattern and power are measured in the far-field region and the results agree well with simulation results. The experiment also reveals that no microwave breakdown or pulse shortening took place in the experimental setup.

An idea of azimuthally dividing the slow wave structure (SWS) of a relativistic backward wave oscillator (RBWO) into two parts is introduced to realize a bifrequency oscillation. To enhance the stability of this device, two sectorial waveguides are inserted into the SWS specially. The synchronization condition that is necessary to get a sustainable microwave output is derived. In Particle in cell simulation, bifrequency microwave at frequencies of 9.7 GHz and 9.87 GHz is generated with average power of 0.66 GW, conversion efficiency is 15.8% when beam voltage is 520 kV and current 8 kA.