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The current challenges of numerical weather prediction (NWP) of rainfall mainly stem from the complex and multiscale nature of rainfall. In recent years, as observation capability improved worldwide, there has been an increased feasibility to use data‐driven models to enhance forecasting performance with rainfall observation. Compared to traditional statistical and machine learning models, deep‐learning models show considerable promise in capturing the spatial‐temporal features of weather processes from multiple predictors, but the convolution‐based feature extractor is suboptimal due to the linear nature of convolution kernels. In this study, a multilevel forecasting model is proposed to forecast each rainfall level, in which each submodel adopts a graph neural network for feature extraction. Spatial and temporal propagation functions based on grid structure are designed to explicitly represent feature fusion and propagation of multiple predictors across multiple scales. On model training, a weight setting strategy that balances the impact of samples with different rainfall values on the total training loss is proposed, and a soft classification label is designed to convert observed rainfall into the probability of rainfall above each threshold. The proposed model was trained and validated on NWP data provided by European Center for Medium‐Range Weather Forecast, and results show significant improvement over the NWP in terms of threat score (TS) and Heidke Skill Score (HSS) scores. Analysis of the forecast results for two typical rainfall processes also illustrates that the proposed method can predict rainfall with more reasonable location and intensity. Plain Language Summary: Rainfall forecast plays an important role in human's daily life, and numerical weather prediction (NWP) has helped to greatly improve the performance of rainfall forecast. However, accurate forecasting is still challenging due to the complex characteristics of rainfall and uncertainty of NWP models. In recent years, there has been accumulated large amount of high‐precision rainfall observation data around the world. It becomes possible to enhance the forecasting performance with these data. However, the traditional statistical models either ignore the weather background information or struggle to capture the complex interactions between different variables produced by the NWP model. To handle these shortages, a neural network model based on grid structure is proposed, which can extract multiscale features from the output variables of the NWP model trough an information propagation mechanism. Meanwhile, a multilevel forecasting strategy is proposed for better handling different rainfall levels along with different uncertainties, from light rain to heavy downpours. The proposed model was applied to a real NWP data for the central part of East China, and the results illustrate significant improvement in terms of common forecast skills and intuitive feeling. Key Points: A graph neural network for extracting features from the output variables of the numerical weather prediction (NWP) model for rainfall forecastingA level‐specific training label and a weighted loss function based on the relative frequency of rainfall amount to better training the modelThe proposed method improves the forecast skill at all rainfall intensity levels and can yields more reasonable location and intensity of rainfall [ABSTRACT FROM AUTHOR]

As the globe transitions to a low-carbon economy, China's renewable energy (RE) sector is crucial to attaining sustainable growth. Green financing and corporate social responsibility (CSR) strategies have become increasingly important in promoting the growth and sustainability of the RE sector. In order to evaluate CSR factors and green finance strategies for effective integration in supporting the RE industry, this research employs hybrid Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy VIekriterijumsko KOmpromisno Rangiranje (FVIKOR) approaches in the context of China. The FAHP method is used to determine the various CSR factors and subfactors, while the FVIKOR method is employed to prioritize the key green finance strategies. The findings of the FAHP method highlight the importance of socioeconomic development, environmental protection, and stakeholder engagement as critical CSR factors for RE sectors. Moreover, the results of the FVIKOR method reveal that innovative financing mechanisms, green bonds, and green banks emerged as significant green finance strategies for mobilizing capital and resources for RE projects. The study highlights the value of aspects of CSR and green financing techniques in fostering sustainable development in the RE industry. The results can aid in the promotion of sustainable practices and the assistance of China's transition to a more resource- and environment-wise economy by firms, investors, governments, and other stakeholders. [ABSTRACT FROM AUTHOR]

In recent years, children's and adolescents' growth and development issues have received increasing attention with the socioeconomic development. The etiology of child short stature involves heredity, race, sex, nutrition, and a variety of endocrine hormones, which is very complex. The age of 6∼14 is the key period of children's development. Understanding the height characteristics, the prevalence of short stature, and its influencing factors at this stage and formulating preventive measures as soon as possible are conducive to improving the average height of children and reducing the incidence of short stature. In this study, cluster sampling was used to select 56,865 children and adolescents aged 6∼14 years old from 40 primary and secondary schools in Furong District of Changsha City, and the height of each child and adolescent was measured. The results showed that the overall crude prevalence of short stature in children aged 6∼14 in Furong District of Changsha is 2.82%. Growth hormone level <10 μg/L, pubertal retardation, familial short stature, low egg intake, and intrauterine growth retardation are independent risk factors affecting the occurrence of short stature. In order to improve the status quo of short stature of children aged 6∼14 in Furong District, Changsha City, targeted intervention should be strengthened for people with combined high risk factors. [ABSTRACT FROM AUTHOR]

The invasion by smooth cordgrass (Spartina alterniflora) has a serious impact on ecosystem function and the ecological environment in the coastal region of subtropical China. However, little is known about the fluctuation of different forms of nitrogen in mangrove wetlands invaded by cordgrass. In this study, we compared the spatial and temporal distribution of different forms of nitrogen with or without invasion by S. alterniflora in a coastal wetland in the Jiulong River estuary, Fujian province, China. The results revealed that the variation of nitrogen concentration in the wetland was closely related to the main intertidal vegetation type, season and sampling location. Organic nitrogen accounted for the majority of the deposited nitrogen and exhibited the highest values in the mangrove zone. The content of ammonium nitrogen and nitrate nitrogen was higher in cordgrass zone and lower in the mangrove, reaching a maximum in summer. Total nitrogen concentration was significantly influenced by the tidal water level which was highest in high tide zone and lowest at low tide. Invasion of S. alterniflora significantly altered nitrogen stocks in the sediment, including the contents of total nitrogen, total organic nitrogen, ammonium nitrogen and nitrate nitrogen. [Display omitted] • Spartina alterniflora invasion promotes soil total nitrogen accumulation. • Spartina alterniflora invasion alters wetland nitrogen sinks. • Wetlands nitrogen content is influenced by tide level, vegetation and season. [ABSTRACT FROM AUTHOR]

ContextScutellarin (1) has been widely used in China to treat acute cerebral infarction and paralysis induced by cerebrovascular diseases. However, scutellarin (1) has unstable metabolic characteristics. ObjectiveThe metabolic profile of 6-O-scutellarein was studied to determine its metabolic stabilityin vivo. Materials and methodsIn this study, a method of UFLC/Q-TOF MS was used to study the 6-O-methyl-scutellarein metabolites in rat plasma, urine, bile and faeces after oral administration of 6-O-methyl-scutellarein (3). One hour after oral administration of 6-O-methyl-scutellarein (3) (34 mg/kg), approximately 1 mL blood samples were collected in EP tubes from all groups. Bile, urine and faeces samples were collected from eight SD rats during 0–24 h after oral administration. The mass defect filtering, dynamic background subtraction and information dependent acquisition techniques were also used to identify the 6-O-methyl-scutellarein metabolites. ResultsThe parent compound 6-O-methyl-scutellarein (3) was found in rat urine, plasma, bile and faeces. The glucuronide conjugate of 6-O-methyl-scutellarein (M1,M2), diglucuronide conjugate of 6-O-methyl-scutellarein (M3), sulphate conjugate of 6-O-methyl-scutellarein (M4), glucuronide and sulphate conjugate of 6-O-methyl-scutellarein (M5), methylated conjugate of 6-O-methyl-scutellarein (M6) were detected in rat urine.M1,M2andM3were detected in rat bile.M1was found in rat plasma andM7was detected in faeces. Discussion and conclusionBecause the parent compound 6-O-methyl-scutellarein (3) was found in rat urine, plasma, bile and faeces, we speculate that 6-O-methyl-scutellarein (3) had good metabolic stabilityin vivo.This warrants further study to develop it as a promising candidate for the treatment of ischemic cerebrovascular disease. [ABSTRACT FROM PUBLISHER]

Abstract: China is the largest fishery producer worldwide in term of its aquaculture output, and plays leading and decisive roles in international aquaculture development. To improve aquaculture output further and promote aquaculture business development, infectious diseases and immunity of fishes and other aquaculture species must be studied. In this regard, aquaculture proteomics has been widely carried out in China to get a better understanding of aquaculture host immunity and microbial pathogenesis as well as host–pathogen interactions, and to identify novel disease targets and vaccine candidates for therapeutic interventions. These proteomics studies include development of novel methods, assays, and advanced concepts in order to characterize proteomics mechanisms of host innate immune defense and microbial pathogenesis. This review article summarizes some recently published technical approaches and their applications to aquaculture proteomics with an emphasis on the responses of aquaculture animals to bacteria, viruses, and other aqua-environmental stresses, and development of broadly cross-protective vaccine candidates. The reviewed articles are those that have been published in international peer reviewed journals. [Copyright &y& Elsevier]

Weather radar echo is the data detected by the weather radar sensor and reflects the intensity of meteorological targets. Using the technique of radar echo extrapolation, which is the prediction of future echoes based on historical echo observations, the approaching short-term weather conditions can be forecasted, and warnings can be raised with regard to disastrous weather. Recently, deep learning based extrapolation methods have been proposed and show significant application potential. However, there are two limitations of existing extrapolation methods which should be considered. First, few methods have investigated the impact of the evolutionary process of weather systems on extrapolation accuracy. Second, current deep learning methods usually encounter the problem of blurry echo prediction as extrapolation goes deeper. In this paper, we aim to address the two problems by proposing a Multi-Level Correlation Long Short-Term Memory (MLC-LSTM) and integrate the adversarial training into our approach. The MLC-LSTM can exploit the spatiotemporal correlation between multi-level radar echoes and model their evolution, while the adversarial training can help the model extrapolate realistic and sharp echoes. To train and test our model, we build a real-life multi-level weather radar echoes dataset based on raw CINRAD/SA radar observations provided by the National Meteorological Information Center, China. Extrapolation experiments show that our model can accurately forecast the motion and evolution of an echo while keeping the predicted echo looking realistic and fine-grained. For quantitative evaluation on probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill score (HSS) metrics, our model can reach average scores of 0.6538 POD, 0.2818 FAR, 0.5348 CSI, and 0.6298 HSS, respectively when extrapolating 15 echoes into the future, which outperforms the current state-of-the-art extrapolation methods. Both the qualitative and quantitative experimental results demonstrate the effectiveness of our model, suggesting that it can be effectively applied to operational weather forecasting practice. [ABSTRACT FROM AUTHOR]

Introduction: Though neutrophil/lymphocyte ratio (NLR) level appears to be related with stroke events in general population, its relationship with stroke in peritoneal dialysis (PD) patients is still uncertain. This study aims to investigate the association between NLR and the first occurrence of stroke in PD patients.Methods: In this retrospective cohort study, 1507 PD patients were enrolled from four centers in China and stratified into tertiles of NLR levels. The incidence of the first occurrence of stroke was analyzed by Kaplan-Meier cumulative incidence curve among different NLR tertiles, competing risk analysis was used to calculate the incidence of the first occurrence of stroke in the presence of competing risk of other events, multivariable COX regression analysis was performed to estimate the hazard ratios (HRs) for the first occurrence of stroke, as well as forest plot was utilized to describe the relationship between NLR and the first occurrence of stroke in different subgroups.Results: During follow-up, 84 new-onset stroke events were recorded. Kaplan-Meier cumulative incidence curves showed significant differences in the incidence of the first occurrence of stroke among three groups (log-rank test: P < .001). In competing risk analysis, the cumulative incidence curves for tertiles of NLR levels were highly significant for the first occurrence of stroke (P < .001), but they were not statistically different for the occurrence of other events. Compared to the lowest tertile of NLR level, the highest tertile was associated with increased risk of the first occurrence of stroke in the adjusted Cox model (HR = 2.39, 95% CI: 1.37 to 4.15; P < .05). As for forest plot, there was no interaction in all subgroups.Conclusion: High NLR was an independent risk factor for the first occurrence of stroke in PD patients. [ABSTRACT FROM AUTHOR]

Cancer sera contain antibodies which react with a unique group of autologous cellular antigens called tumor-associated antigens (TAAs). This study determines whether a mini-array of multiple TAAs would enhance antibody detection and be a useful approach to cancer detection and diagnosis. The mini-array of TAAs comprised full-length recombinant proteins expressed from cDNAs encoding c-myc, p53, cyclin B1, p62, Koc, IMP1, and survivin. Enzyme immunoassay was used to detect antibodies in 527 sera from six different types of cancer. Antibody frequency to any individual TAA was variable but rarely exceeded 15-20%. With the successive addition of TAAs to a final total of seven antigens, there was a stepwise increase of positive antibody reactions up to a range of 44-68%. Breast, lung, and prostate cancer patients showed separate and distinct profiles of reactivity, suggesting that uniquely constituted antigen mini-arrays might be developed to distinguish between some types of cancer. Distinct antibody profiles were not observed in gastric, colorectal, and hepatocellular carcinomas with this set of seven TAAs. Detection of autoantibodies in cancer can be enhanced by using a mini-array of several TAAs as target antigens. Additional studies in early cancer patients and high-risk individuals and the design of unique antigen panels for different cancers would help to determine whether multiple antigen mini-arrays for the detection of autoantibodies might contribute a clinically useful noninvasive approach to cancer detection and diagnosis.

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and is particularly prevalent in Henan, China. The objective of this study was to analyze the frequency and specificity of autoantibodies associated with HCC in Henan. In the present study, 137 sera from HCC patients, 77 sera from other liver diseases, and 30 sera from normal human individuals were examined for autoantibodies using immunohistochemistry, Western blotting, and immunoprecipitation assays. Autoantibodies were detected in 80 of 137 (58.4%) HCC sera. Antinucleolar antibodies were seen more frequently in HCC compared to other liver diseases (9.5% vs. 1.3%, P < 0.05). Two nucleolar proteins-fibrillarin and NOR-90/ hUBF-were identified as autoantigens. The frequency of autoantibodies in HCC sera with known hepatitis C virus (HCV) infection was significantly higher than that in sera without HCV infection (84.2% vs. 57.7%, P < 0.01). Another interesting finding was that autoantibodies to a 90-kDa cytoplasmic antigen were found in 21% of HCC patients. This is the first report on the frequency and specificity of autoantibodies in sera from Chinese patients with HCC. The data support that autoimmune responses to certain cellular proteins may be a by-product in the transformation to HCC, and further studies of novel targeted autoantigens in Chinese HCC may provide insights into how these proteins might be involved in malignancy.