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Flexible food packaging in the market is increasingly favored, and its quality is essential and indispensable for safety and convenience. However, quality inspection still stays in the manual stage, or partially manual inspection remains, in production, leading low efficiency, lack and even false inspection, hardly meeting the requirements of the modern output. This paper proposes and optimizes the design of an automatic detection system with intelligence for flexible food packaging bag, which can effectively be adopted to check the quality of packaging trademark patterns, fillers, and sealing quality. The inspection system runs with two-stage structure, machine vision, pressure sensing and synchronization to improve efficiency and ensure the normal production beat. Simplex Method is adopted to determine the best synchronous speeds online to achieve the best expectation. Comparison has been made between the manual inspection and our automatic operation, the sample of 10000 was statistically analyzed and results have shown that two workers were saved and the correctness rate of inspection raised up to 999.8‰.

Lobular capillary hemangioma (LCH), previously known as pyogenic granuloma, is a benign vascular lesion commonly found within the oral and nasal cavities. However, it is rarely encountered within the trachea, especially in pediatric patients, where it manifests as hemoptysis, cough, and wheeze, and is frequently misdiagnosed as bronchitis or asthma. There is limited literature on the presentation, behavior, and management of tracheal LCH. Herein, we describe a rare case of tracheal LCH in an 11-year-old boy with a history of hemoptysis, which was successfully managed with arterial embolization followed by electrocautery loop snaring via flexible bronchoscopy. No complications occurred during and after the procedure. A review of the relevant literature is also provided. Our case is unique, given the therapeutic strategy utilized for pediatric tracheal LCH, and reminds physicians to be aware of tracheal LCH in the differential diagnosis for hemoptysis.

Background. Necroptosis, a recently identified type of programmed necrotic cell death, is closely related to the tumorigenesis and development of cancer. However, it remains unclear whether necroptosis-associated long noncoding RNAs (lncRNAs) can be used to predict the prognosis of kidney renal clear cell carcinoma (KIRC). This work was designed to probe the possible prognostic worth of necroptosis-associated lncRNAs along with their impact on the tumor microenvironment (TME) in KIRC. Methods. The Cancer Genome Atlas (TCGA) database was used to extract KIRC gene expression and clinicopathological data. Pearson correlation analysis was used to evaluate necroptosis-associated lncRNAs against 159 known necroptosis-associated genes. To define molecular subtypes, researchers used univariate Cox regression analysis and consensus clustering, as well as clinical significance, TME, and tumor immune cells in each molecular subtype. We develop the necroptosis-associated lncRNA prognostic model using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients were divided into high- and low-risk groups according to prognostic model. Moreover, comprehensive analyses, including prognostic value, gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint gene expression, were performed between the two risk groups. Finally, anticancer drug sensitivity analyses were employed for assessing associations for necroptosis-associated lncRNA expression profile and anticancer drug chemosensitivity. Results. Through univariate analysis, sixty-nine necroptosis-associated lncRNAs were found to have a significant relationship with KIRC prognosis. Two molecular clusters were identified, and significant differences were found with respect to clinicopathological features and prognosis. The segregation of patients into two risk groups was done by the constructed necroptosis-associated lncRNA model. The survival prognosis, clinical features, degree of immune cell infiltration, and expression of immune checkpoint genes of high-risk and low-risk groups were all shown to vary. Conclusions. Our study identified a model of necroptosis-associated lncRNA signature and revealed its prognostic role in KIRC. It is expected to provide a reference for the screening of KIRC prognostic markers and the evaluation of immune response.

We categorize phosphate-based cathodes in zinc-ion battery and highlight the relationship between structural properties and energy storage mechanisms. The major problems faced by each kind of materials and rational optimization strategies are summarized.

Objective: To explore the role of endothelial biomarkers in predicting damp-heat syndrome in diabetic kidney disease (DKD). Methods: A total of 183 patients with DKD were divided into 3 groups: the early DKD group, established DKD group, and advanced DKD group. All patients were classified according to traditional Chinese medicine (TCM) syndrome type, and clinical indexes were collected for statistical analysis. Results: A total of 183 DKD patients were included in this study. Fibroblast growth factor 23 (FGF23), chitinase-3-like protein 1 (CHI3L1), endocan, tumor necrosis factor receptor 1 (TNFR1), secretory leukocyte protease inhibitor (SLPI), and vascular endothelial growth factor A (VEGF-A) were increased in advanced DKD. FGF23, CHI3L1, endocan, SLPI, and TNFR1 showed a negative correlation with estimated glomerular filtration rate (eGFR), while they had a positive correlation with 24 h urine protein. After adjusting for age, gender, diabetes duration, body mass index (BMI), hemoglobin, glucose, uric acid, 24 h urine protein, cholesterol, triglyceride, low-density lipoprotein, and hemoglobin A1c (HbA1c), the multiple regression analysis showed that FGF23, endocan, TNFR1, and SLPI significantly correlated with eGFR. Conclusions: FGF23, endocan, TNFR1, and SLPI are elevated in advanced DKD compared with early stage, and they may take part in the pathogenesis and progression of DKD. Our study provides useful biomarkers for predicting the appearance of damp-heat syndrome, including FGF23, endocan, TNFR1, and SLPI.

Novel nonsteroidal mineralocorticoid receptor antagonists (MRAs) are noted for their potential cardiorenal benefits for patients with type 2 diabetes mellitus and chronic kidney diseases; however, the effect of this regimen on renal outcomes remains uncertain.We performed a systematic review and meta-analysis of nonsteroidal MRAs focusing primarily on renal outcomes and safety in randomized, controlled trials. The MEDLINE, Embase, and Cochrane databases were systemically searched for trials published through April 2022. We included randomized, controlled trials assessing the effects of nonsteroidal MRAs on renal outcomes, as well as cardiovascular disease (CVD) effects in patients with chronic kidney disease (CKD). Summary estimates of risk ratios (RRs) reductions were calculated with a random-effects model. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach was used to evaluate the certainty of evidence. This study is registered with PROSPERO under number CRD42022335464.In total, 11 trials and 1 pooled analysis including a total of 17,517 participants were enrolled. Nonsteroidal MRAs reduced renal composite endpoints by 17 % [HR = 0.83, 95 % (0.75, 0.91); low quality] with 16 % in kidney failure (high quality), 23 % in ESRD (high quality), 20 % in eGFR decreased to less than 15 mL/min/1.73 mNonsteroidal MRAs had a statistically beneficial effect on reducing the risk of the composite kidney outcome, the composite of cardiovascular outcomes, and all-cause mortality. Nonsteroidal MRAs were also associated with benefits of proteinuria remission and blood pressure lowering. Although these findings provided positive evidence for the use of nonsteroidal MRAs for cardiorenal protection in patients with or without CKD, the quality of this evidence is potentially uncertain.

The development of renewable energy is of great significance to relieve the pressure on the energy supply and promote the low-carbon operation of the power system. However, the volatility of renewable energy, especially wind and solar energy, has a great impact on the safe and reliable operation of the power system. If we want to introduce renewable energy and ensure the safe and reliable operation of the power system, it is necessary for the power system to provide enough flexibility. Northwest China is rich in natural resources and an important area of power supply in China, which also faces the problem of insufficient flexibility. Therefore, based on the power system development and natural conditions in northwest China, this paper studies the key factors affecting the flexibility of the power system when renewable energy accounts for a large proportion, and proposes measures to improve the flexibility of the power system by using the power system optimization tool Flextool developed by IRENA.

Tumor-infiltrating lymphocyte (TIL) is a class of cells with important immune functions and plays a crucial role in bladder cancer (BCa). Several studies have shown the clinical significance of TIL in predicting the prognosis and immunotherapy efficacy. TIL-related gene module was screened utilizing weighted gene coexpression network analysis. We screened eight TIL-related genes utilizing univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and multivariate Cox regression analysis. Then, we established a TIL-related signature model containing the eight selected genes and subsequently classified all patients into two groups, that is, the high-risk as well as low-risk groups. Gene mutation status, prognosis, immune cell infiltration, immune subtypes, TME, clinical features, and immunotherapy response were assessed among different risk subgroups. The results affirmed that the TIL-related signature model was a reliable predictor of overall survival (OS) for BCa and was determined as an independent risk factor for BCa patients in two cohorts. Moreover, the risk score was substantially linked to age, tumor staging, TNM stage, and pathological grade. And there were different mutational profiles, biological pathways, immune scores, stromal scores, and immune cell infiltration in the tumor microenvironment (TME) between the two risk groups. In particular, immune checkpoint genes’ expression was remarkably different between the two risk groups, with patients belonging to the low-risk group responding better to immune checkpoint inhibition (ICI) therapy. In conclusion, our study demonstrates that the TIL-related model was a reliable signature in anticipating prognosis, immune status, and immunotherapy response, which can help in screening patients who respond to immunotherapy.

Objective The aim of this study was to investigate the role of prophylactic use of statin in venous thromboembolism (VTE) in patients with primary membranous nephropathy (PMN). Methods A total of 734 patients with PMN were consecutively enrolled in this retrospective study. 564 patients had received statins prescription, while 170 patients did not. Kaplan–Meier methods were used for cumulative incidence plots of thromboembolic events and Cox proportional hazards regression models were used to assess risk factors. Finally, the effects of different potency of statins were evaluated. Results In the cohort, 37 patients (5.0%) experienced VTE. In a univariate Cox proportional hazard model, the hazard ratio (HR) for VTE in statin users versus statin non-users was 0.5 (95% CI 0.3–0.8, p = .03). Multivariable model proportional-hazards analysis corrected for co-medications and risk factors revealed that adjusted HR was 0.4 (95% CI 0.1–0.7, p = .03). According to the type and dose, statin users were assigned into 3 groups: high-intensity group (n = 278), moderate-intensity group (n = 186), and low-intensity group (n = 49). In comparison, incidences of VTEs in the three groups were similar (2.9% vs 4.8% vs 2.0%, p = .45). Conclusions The prophylactic use of statins could effectively decrease the occurrence of VTE in patients with PMN, and the benefits have no difference in different potency of statins.

Background and objectives Several clinical practice guidelines noted the potential benefits of urate-lowering therapy on cardiovascular disease and CKD progression; however, the effect of this regimen remains uncertain. In this systematic review, we aimed to evaluate the efficacy of urate-lowering therapy on major adverse cardiovascular events, all-cause mortality, kidney failure events, BP, and GFR. Design, setting, participants, & measurements We systematically searched MEDLINE, Embase, and the Cochrane databases for trials published through July 2020. We included prospective, randomized, controlled trials assessing the effects of urate-lowering therapy for at least 6 months on cardiovascular or kidney outcomes. Relevant information was extracted into a spreadsheet by two authors independently. Treatment effects were summarized using random effects meta-analysis. Results We identified 28 trials including a total of 6458 participants with 506 major adverse cardiovascular events and 266 kidney failure events. Overall urate-lowering therapy did not show benefits on major adverse cardiovascular events (risk ratio, 0.93; 95% confidence interval, 0.74 to 1.18) and all-cause mortality (risk ratio, 1.04; 95% confidence interval, 0.78 to 1.39) or kidney failure (risk ratio, 0.97; 95% confidence interval, 0.61 to 1.54). Nevertheless, urate-lowering therapy attenuated the decline in the slope of GFR (weighted mean difference, 1.18 ml/min per 1.73 m2 per year; 95% confidence interval, 0.44 to 1.91) and lowered the mean BP (systolic BP: weighted mean difference, −3.45 mm Hg; 95% confidence interval, −6.10 to −0.80; diastolic BP: weighted mean difference, −2.02 mm Hg; 95% confidence interval, −3.25 to −0.78). There was no significant difference (risk ratio, 1.01; 95% confidence interval, 0.94 to 1.08) in the risk of adverse events between the participants receiving urate-lowering therapy and the control group. Conclusions Urate-lowering therapy did not produce benefits on the clinical outcomes, including major adverse cardiovascular events, all-cause mortality, and kidney failure. Thus, there is insufficient evidence to support urate lowering in patients to improve kidney and cardiovascular outcomes.

Background: The aim of this study was to estimate the attributable risk for all-cause mortality in hypertensive adults living in Beijing, China. Methods: We conducted a prospective cohort study on the basis of the disease risk prediction model, which included 3006 hypertensive patients aged 50 and over who participated in the annual health examination from thirty-eight community health centers were randomly selected from all 53 community health centers in Dongcheng district of Beijing in China. This cohort study was conducted from January 1, 2013 to June 31, 2018 in these community health centers. Data included age, gender, education level, BMI, smoking and drinking status, renal function, diabetes mellitus (DM), coronary heart disease, levels of blood pressure, use of medications, and blood lipid levels. Results: the follow-up time was 4.90 ± 0.51 years. There were significant survival differences by gender, renal function (eGFR > 90 vs. 60–90 vs. 12 years), coronary heart disease (CHD) (CHD vs. NO CHD). In the multivariate Cox proportional hazard analysis, the prognostic factors of all-cause mortality in hypertensive patients were male [HR 1.662, 95% CI 1.110–2.489, p = 0.014], educational level

Land use/cover changes (LULC) are complicated and regionally diverse. When mining regional patterns, the use of a spatial relationship that is determined without considering the spatial correlation...

BACKGROUND: Sepsis has always been a challenge in pediatric intensive care unit (PICU) with poor prognosis. In order to evaluate the effect between routine continuous renal replacement therapy (CRRT) and high-volume hemofiltration (HVHF) in children with sepsis, we performed out this prospective, randomized, controlled, open-label trial. METHODS: Forty-seven children with sepsis were enrolled from January 2015 to December 2016. Twenty-two patients in Control group received routine CRRT and 25 patients in HVHF group received HVHF within 6 hours after the diagnosis of sepsis. The oxygenation index, serum creatinine, urea, lactate, inflammatory cytokines (IL-6, IL-10, and TNF-α), pediatric risk of mortality III (PRISM III) and 28-day mortality rate were collected and compared. RESULTS: The oxygenation index in HVHF group and Control group was significantly increased at 48 hours (P0.05). CONCLUSIONS: HVHF might be an effective treatment for children with sepsis.

In some cases, because of the complex internal structure of the machines, the positions of the vibration sensors are far away from the rolling bearings, such as in an aeroengine, causing the fault features to become extremely weak, which brings great challenge to the detection of rolling bearings. To address this problem, an integrated detection method is proposed. First, a method named MEDL is proposed to determine the optimal filter length in minimum entropy deconvolution (MED) to enhance the periodic fault impulse component in the weak signal, which accuracy is 1. After that, the MEDL is combined with variational mode decomposition (VMD) and autocorrelation to extract fault features from strong background noise. A series of fault simulation experiments for rolling bearings were conducted by using an aeroengine rotor experimental rig with casing. The results verify that the accuracy of the integrated detection method is 100 % in different measuring points, speeds and fault types. At the same time, it compared with spectral kurtosis (SK) and empirical wavelet transform (EWT). It proves that the integrated detection method is more robust in extracting the weak fault characteristic of rolling bearings from the casing signals effectively.

Assessing heat-related health risks is important for sustainable urban development. Although fine-scale information (e.g., at the community/neighborhood or city block level) is ideal for identifying and mitigating these risks, previous studies have preferred to work at the administrative unit level. High-resolution Local Climate Zone (LCZ) maps, i.e., maps of urban “zones” with different microclimates, could help to standardize the analyzing units. In this study, we proposed an LCZ-based risk assessment approach for this purpose. First, an LCZ map of the study site (Changzhou, China) was generated using multisource big data and machine-learning techniques. Next, Crichton's Risk Triangle framework, based on the hazard-exposure-vulnerability risk components, was employed to estimate heat-related health risks. Finally, the relationship between LCZ types and heat-related health risk levels was quantitatively analyzed in detail. The results indicated that at least 60% of LCZ1-5 (compact high-/mid-/low-rise, open high-/mid-rise areas) were designated as high-risk areas, while heat hazard mitigation and climate adaptation strategies in urban planning would benefit more from LCZ 6 (open low-rise). This study, based on the LCZ concept, shows the risk difference at the community level, and can be used for informing and implementing area-level urban planning strategies. It could contribute to global heat-related health risk analysis, since the LCZ is a globally consistent system for urban microclimate analysis.

The synthesis of nano-sized alloys of Pt and rare earth (RE) metal catalysts has been a huge challenge due to a significantly large standard reduction potential difference of Pt and RE metals and the high synthesis temperature. Pt

Studies have shown that circular RNAs (circRNAs) have significant potential as novel molecular markers for the diagnosis, treatment, and prognosis of prostate carcinoma (PCa). However, the role and mechanism of circRNA hsa_circ_0102485 in PCa remains unclear.The real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to quantify hsa_circ_0102485 expression in PCa. The potential mechanisms and roles of hsa_circ_0102485 in tumor growth were explored using dual-luciferase-reporter and subcutaneous-xenograft assays, rescue experiments, and immunohistochemical staining. Clinical correlations were assessed by tissue-on-a-chip in-situ hybridization and Fisher's exact test.Hsa_circ_0102485 expression was decreased in PCa, and overexpression of hsa_circ_0102485 suppressed the proliferation, metastasis, invasion, and antiapoptotic abilities of PCa cells. MicroRNA 188-3p (MiR-188-3p) is a direct target of hsa_circ_0102485, and cotransfection of hsa_circ_0102485 in PCa cells overexpressing miR-188-3p inhibited its promotive effects. Hsa_circ_0102485 indirectly promotes the expression of AT-rich interaction domain 5B (ARID5B) and androgen receptor (AR) by sponging miR-188-3p and inhibiting PCa cell growth. Correlation analysis showed that hsa_circ_0102485 expression in PCa was not significantly correlated with the age, International Society of Urologic Pathologists (ISUP) grade, Gleason score, or lymph node metastasis status of PCa patients.Hsa_circ_0102485 plays an inhibitory role in PCa by regulating the Mir-188-3p/ARID5B/AR axis and may be a potential diagnostic biomarker and therapeutic target for PCa that requires further study.

Introduction Diabetic kidney disease (DKD) is a major microvascular complication of diabetes mellitus (DM) and the primary cause of end-stage renal disease. Routine medication might result in adverse effects which limit their usage. Laboratory studies have shown that Xiao Zheng San Jie (XZSJ) granules is effective in reducing the levels of urinary protein and blood creatinine in rats with DKD. Hence, we aim to conduct a clinical trial to determine the efficacy and safety of XZSJ granules among patients with DKD Stage IV, who present with the syndromes of qi deficiency and blood stasis. The purpose of this paper is to report the methodology of this trial.Methods and analysisThis is a randomised, double-blind, placebo-controlled, multi-centre clinical trial. A total of 72 patients with DKD Stage IV who have the syndromes of qi deficiency and blood stasis will be randomly assigned to either intervention or placebo group. In addition to routine medication, the intervention group will be given XZSJ formula (in granules) and the control group will be given placebo for 12-weeks. Data collected at baseline as well as weeks 4, 8 and 12 will be recorded in case report forms and subsequently entered into EpiData 3.1 electronic database. Adverse events will also be documented. Primary outcomes – estimated glomerular filtration rate, urinary albumin-to-creatinine ratio, and serum creatinine – will be assessed after 12 weeks. Ethics and disseminationThis trial has been approved by the Research Ethics Committee of Dongzhimen Hospital (DZMEC-KY-2018-60). Informed consent will be obtained from all participants. Results of this study will be disseminated to the public through academic conferences and peer-reviewed journals.DiscussionThis trial might provide information on an effective method for the treatment of DKD, especially for patients who present with the syndromes of qi deficiency and blood stasis. We hope that this trial will serve as a reference for the conception of high-quality Traditional Chinese Medicine trials in the future, which incorporate syndrome diagnosis into the study design.Trial registration Chinese Clinical Trial Registry, ChiCTR1900021391. Registered 18 February 2018, http://www.chictr.org.cn/showproj.aspx?proj=36106.

The agent-based modelling (ABM) is commonly used to simulate urban land growth. A key challenge of ABM for the simulation of urban land-use dynamics in support of sustainable urban management is to understand and model how human individuals make and develop their location decisions that then shape urban land-use patterns. To investigate this issue, we focus on modelling the agent learning process in residential location decision-making process, to represent individuals' personal and interpersonal experience learning during their decision-making. We have constructed an extended reinforcement learning model to represent the human agents' learning when they make location decisions. Consequently, we propose and have developed a new agent-based procedure for residential land growth simulation that incorporates an agent learning model, an agent decision-making model, a land use conversion model, and the impacts of urban land zoning and the developers' desires. The proposed procedure was first tested by using hypothetical data. Then the model was used for a simulation of the urban residential land growth in the city of Nanjing, China. By validating the model against empirical data, the results showed that adding agent learning model contributed to the representation of the agent's adaptive location decision-making and the improvement of the model's simulation power to a certain extent. The agent-based procedure with the agent learning model embedded is applicable to studying the formulation of urban development policies and testing the responses of individuals to these policies.

Despite the importance of seasonal distribution and interannual variability of rainfall to the ecosystem and society, there is a lack of regional-level studies on rainfall seasonality, and teleconnections between global climate events and rainfall seasonality are not well understood. To address these issues, this study analyzed the spatial and temporal characteristics of rainfall seasonality across China from 1961 to 2012. A novel application of information theory-based rainfall seasonality indicators was conducted at the regional scale, and observed monthly precipitation data was used. The rainfall seasonality anomalies during negative and positive phases of the El Nino–Southern Oscillation, Indian Ocean Dipole, North Atlantic Oscillation, and Pacific Decadal Oscillation, and the possible physical mechanisms behind were also investigated. The results showed that rainfall seasonality increased, especially in Southeast China, which can be attributed to changes in magnitude (annual rainfall), timing, or duration of the wet season. Global climate events significantly affected rainfall seasonality indicators in Southeast China during negative and positive phases. The sea surface temperature (SST) or sea level pressure (SLP), and wind anomalies during the negative and positive phases might explain the spatial differences in the influences of global climate events on rainfall seasonality across China. These results may prove valuable for sustainable water resource management and agricultural production in China.

Objective: The aim of this study is to investigate the role of prophylactic anticoagulation regimens based on low molecular weight heparin (LMWH) or aspirin in thromboembolic events in patients with primary membranous nephropathy (PMN). Methods: A total of 717 patients with PMN were consecutively enrolled in this retrospective study. The propensity score matching method was utilized to adjust for the selection bias inherent in an analysis of outcomes, which was stratified by the anticoagulation prophylaxis regimen. Results: According to the anticoagulation prophylaxis regimen, patients were assigned into three groups: only LMWH therapy (L + A−, n = 53), only aspirin therapy (L − A+, n = 97), and no therapy of LMWH or aspirin (L − A−, n = 567). After performing 1:1 match, 37 patients were selected in the L + A − group and the L − A− group, respectively, and 94 patients were selected in the L − A+ group and the L − A− group, respectively. It showed that the prophylactic use of LMWH had no protective effects on arterial thromboembolic events (ATEs) (10.8% vs. 21.6%, p = .21) or venous thromboembolic events (VTEs) (8.1% vs. 10.8%, p = .69). The incidence of VTEs in the L − A+ group was lower than the L − A− group (2.1% vs. 10.6%, p = .02), while there were no significant differences in the incidences of ATEs between the L − A+ group and the L − A− group (5.3% vs. 7.4%, p = .55). Conclusions: The prophylactic use of LMWH showed no benefits on the incidence of ATEs or VTEs in patients with PMN. Aspirin effectively decreased the incidence of VTEs, without effects on the occurrence of ATEs.

Two-dimensional graphitic carbon nitride (g-C3N4, GCN) is considered as one of the promising visible light-responsive photocatalysts for energy storage and environmental remediation. However, the photocatalytic performance of pristine GCN is restricted by the inherent shortcomings of rapid charge carrier recombination and limited absorption of visible light. Vacancy engineering is widely accepted as the auspicious approach for boosting the photocatalytic activity of GCN-based photocatalysts. Herein, a magnesium thermal calcination method has been developed to reconstruct GCN, in which magnesium serves as a carbon etcher for introducing carbon vacancies and pores into GCN (Vc-GCN). The fabricated Vc-GCN demonstrates excellent photocatalytic performances of degrading hazardous 4-chlorophenol under visible light irradiation benefiting from the improved carrier separating and light absorption ability as well as rich reactive sites. The optimal Vc-GCN sample delivers 2.3-fold enhancement from the pristine GCN. The work provides a tactic to prepare GCN photocatalysts with controllable carbon vacancies and for a candidate for the degradation of organic pollutants from the environment.

Background IgA nephropathy is the most common glomerular disease and is a common cause of progression to end-stage renal disease in patients with kidney diseases. Proteinuria levels are critical for the prognosis of patients with IgA nephropathy, but many patients are still unable to effectively control their proteinuria levels after receiving RAAS blockers. Antimalarial drugs have shown good efficacy in the treatment of kidney disease in previous studies; however, there have been no strictly designed randomized controlled trials to confirm the clinical efficacy of artesunate for treating IgA nephropathy patients. Therefore, we designed this clinical trial to compare the effect of artesunate versus placebo in patients with IgA nephropathy. Methods This study is a randomized, double-blind, three-group-parallel, placebo-controlled clinical trial. One hundred and twenty eligible IgA nephropathy patients at risk of progression will be randomly divided into the artesunate 100-mg group, artesunate 50-mg group, and placebo group. Changes in proteinuria and renal function will be measured 6 months after the intervention. The levels of Gd-IgA1 and anti-Gd-IgA1 in the patient’s blood will also be tested to explore the possible immune mechanisms. Discussion Clinical evidence supporting artesunate treatment of IgA nephropathy is currently lacking, and we expect that the results of this trial will provide high-quality clinical evidence for artesunate as a treatment option for IgA nephropathy in the future. Trial registration Chinese Clinical Trial Registry ChiCTR2000038104. Registered on 10 September 2020

The monitoring and assessment of land use/land cover (LULC) change over large areas are significantly important in numerous research areas, such as natural resource protection, sustainable development, and climate change. However, accurately extracting LULC only using the spectral features of satellite images is difficult owing to landscape heterogeneities over large areas. To improve the accuracy of LULC classification, numerous studies have introduced other auxiliary features to the classification model. The Google Earth Engine (GEE) not only provides powerful computing capabilities, but also provides a large amount of remote sensing data and various auxiliary datasets. However, the different effects of various auxiliary datasets in the GEE on the improvement of the LULC classification accuracy need to be elucidated along with methods that can optimize combinations of auxiliary datasets for pixel- and object-based classification. Herein, we comprehensively analyze the performance of different auxiliary features in improving the accuracy of pixel- and object-based LULC classification models with medium resolution. We select the Yangtze River Delta in China as the study area and Landsat-8 OLI data as the main dataset. Six types of features, including spectral features, remote sensing multi-indices, topographic features, soil features, distance to the water source, and phenological features, are derived from auxiliary open-source datasets in GEE. We then examine the effect of auxiliary datasets on the improvement of the accuracy of seven pixels-based and seven object-based random forest classification models. The results show that regardless of the types of auxiliary features, the overall accuracy of the classification can be improved. The results further show that the object-based classification achieves higher overall accuracy compared to that obtained by the pixel-based classification. The best overall accuracy from the pixel-based (object-based) classification model is 94.20% (96.01%). The topographic features play the most important role in improving the overall accuracy of classification in the pixel- and object-based models comprising all features. Although a higher accuracy is achieved when the object-based method is used with only spectral data, small objects on the ground cannot be monitored. However, combined with many types of auxiliary features, the object-based method can identify small objects while also achieving greater accuracy. Thus, when applying object-based classification models to mid-resolution remote sensing images, different types of auxiliary features are required. Our research results improve the accuracy of LULC classification in the Yangtze River Delta and further provide a benchmark for other regions with large landscape heterogeneity.

Background: The mortality prediction scores were widely used in pediatric intensive care units. However, their performances were unclear in Chinese patients and there were also no reports based on large sample sizes in China. This study aims to evaluate the performances of three existing severity assessment scores in predicting PICU mortality and to identify important determinants. Methods: This prospective observational cohort study was carried out in eight multidisciplinary, tertiary-care PICUs of teaching hospitals in China. All eligible patients admitted to the PICUs between Aug 1, 2016, and Jul 31, 2017, were consecutively enrolled, among whom 3,957 were included for analysis. We calculated PCIS, PRISM IV, and PELOD-2 scores based on patient data collected in the first 24 h after PICU admission. The in-hospital mortality was defined as all-cause death within 3 months after admission. The discrimination of mortality was assessed using the area under the receiver-operating characteristics curve (AUC) and calibrated using the Hosmer-Lemeshow goodness-of-fit test. Results: A total of 4,770 eligible patients were recruited (median age 18.2 months, overall mortality rate 4.7%, median length of PICU stay 6 days), and 3,957 participants were included in the analysis. The AUC (95% confidence intervals, CI) were 0.74 (0.71-0.78), 0.76 (0.73-0.80), and 0.80 (0.77-0.83) for PCIS, PRISM IV, and PELOD-2, respectively. The Hosmer-Lemeshow test gave a chi-square of 3.16 for PCIS, 2.16 for PRISM IV and 4.81 for PELOD-2 (p ≥ 0.19). Cox regression identified five predictors from the items of scores better associated with higher death risk, with a C-index of 0.83 (95%CI 0.79-0.86), including higher platelet (HR = 1.85, 95% CI 1.59-2.16), invasive ventilation (HR = 1.40, 1.26-1.55), pupillary light reflex (HR = 1.31, 95% CI 1.22-1.42) scores, lower pH (HR 0.89, 0.84-0.94), and extreme PaO2 (HR 2.60, 95% CI 1.61-4.19 for the 1st quantile vs. 4th quantile) scores. Conclusions: Performances of the three scores in predicting PICU mortality are comparable, and five predictors were identified with better prediction to PICU mortality in Chinese patients.

Large-scale geospatial data have accumulated worldwide in the past decades. However, various data formats often result in a geospatial data sharing problem in the geographical information system community. Despite the various methodologies proposed in the past, geospatial data conversion has always served as a fundamental and efficient way of sharing geospatial data. However, these methodologies are beginning to fail as data increase. This study proposes a parallel spatial data conversion engine (PSCE) with a symmetric mechanism to achieve the efficient sharing of massive geodata by utilizing high-performance computing technology. This engine is designed in an extendable and flexible framework and can customize methods of reading and writing particular spatial data formats. A dynamic task scheduling strategy based on the feature computing index is introduced in the framework to improve load balancing and performance. An experiment is performed to validate the engine framework and performance. In this experiment, geospatial data are stored in the vector spatial data defined in the Chinese Geospatial Data Transfer Format Standard in a parallel file system (Lustre Cluster). Results show that the PSCE has a reliable architecture that can quickly cope with massive spatial datasets.

Impurity removal, the purification process from metallurgical grade silicon (MG-Si) required to obtain solar grade silicon (SoG-Si), is crucial to the preparation of silicon-based solar cells. Some...

Polygon intersection is an important spatial data-handling process, on which many spatial operations are based. However, this process is computationally intensive because it involves the detection ...

Few people have paid attention to community epidemic prevention workers in the postpandemic era of COVID-19. This study aimed to explore the prevalence and risk factors for mental health symptoms in community epidemic prevention workers during the postpandemic era. Mental health status was evaluated by the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Chinese Perceived Stress Scale, Insomnia Severity Index, and Maslach Burnout Inventory-General Survey. The results showed that a considerable proportion of community epidemic prevention workers reported symptoms of depression (39.7%), anxiety (29.5%), high stress (51.1%), insomnia (30.8%), and burnout (53.3%). The prevalence of depression and anxiety in community epidemic prevention workers was higher than in community residents. Among community epidemic prevention workers, short sleep duration was a risk factor for depression, anxiety, high stress and insomnia. Concurrent engagement in work unrelated to epidemic prevention and current use of hypnotics were risk factors for depression, anxiety and insomnia. Our study suggests that during the postpandemic era, the mental health problems of community epidemic prevention workers are more serious than those of community residents. Several variables, such as short sleep duration and concurrent engagement in work unrelated to epidemic prevention, are associated with mental health among community epidemic prevention workers.

Pulmonary hypertension (PH) is a vascular disease, and proinflammatory factors are strongly implicated in its pathogenesis, causing right ventricular (RV) hypertrophy and heart failure. Baicalin exhibits potent anti-inflammation activity. This study aimed to investigate the curative effects of baicalin in an infant rodent model of PH and to further explore the underlying mechanisms. A PH model in infant rats was induced by hypoxia and the resulting rats were administered baicalin in incremental dosages. Invasive hemodynamic methods were used to measure mean pulmonary arterial pressure (mPAP) and RV end-diastolic pressure (RVEDP). RV hypertrophy was assessed by mass pathology and histology. ELISAs were used to determine concentrations of high-mobility group box 1 (HMGB1), secretory receptor for advanced glycation end products (sRAGE), interleukin 6 (IL6) and transforming growth factor β (TGFβ1) in bronchoalveolar lavage fluid (BALF). Electrophoretic mobility shift and phosphorylation in nuclear extracts were used to evaluate the activation of peroxisome proliferator-activated receptor γ (PPARγ). Western blotting was used to detect the expression levels of heme oxygenase 1 (HO1), HMGB1, RAGE, IL6 and TGFβ1 in lung tissue. Baicalin administration significantly attenuated mPAP, RVEDP and RV hypertrophy in infant rats with PH. HMGB1, sRAGE, IL6 and TGFβ1 levels in BALF were also reduced by baicalin treatment. Baicalin activated PPARγ, which promoted expression of HO1. Furthermore, expression levels of HMGB1, RAGE, IL6 and TGFβ1 in lung tissue were dramatically decreased by baicalin in a dosage-dependent manner. Baicalin showed curative effects in infant rats with PH. Activation of PPARγ that inhibited HMGB1/RAGE inflammatory signaling was involved.

The periodic determination of land use changes over large areas is crucial for improving our understanding of land system dynamics. Jiangsu lies at the center of China&rsquo, s Yangtze Delta and has one of the fastest-developing economies in China. However, it is also a region where serious conflicts exist between the available land resources and the human demand for land. To address these conflicts, it is important to analyze the patterns of land use change in Jiangsu, as they can serve as a useful reference for other rapidly urbanizing regions in China as well as other developing countries. In this study, we propose a method of classification and regression tree-random forest (CART-RF) classification with a multifilter based on time-series Moderate Resolution Imaging Spectroradiometer (MODIS) imaging data. The proposed method integrates the CART decision tree and the random forest algorithms (CART-RF) to obtain accurate yearly land use data for large areas from multivariate time-series remote sensing data and employs a spatial-temporal-logical filter to exclude any abnormal changes in the multivariate time-series pixel data. The obtained results indicated that (1) the CART-RF classifier is effective for land use classification based on the multivariate time-series MODIS data, with the overall classification accuracy being greater than 90%, (2) the use of the proposed combinatorial spatial-temporal-logical filtering method effectively eliminates most anomalous changes and minimizes the effects of &ldquo, salt-and-pepper&rdquo, noise, and (3) from 2000 to 2015, land use in Jiangsu province underwent significant and spatiotemporally heterogeneous changes on a province-wide scale, owing to various factors, such as those related to the economy, location, and government policies. These changes were manifested as continuous expansions in the built-up land at the expense of farmland. While this expansion of built-up land has been very rapid in southern Jiangsu, especially in the region close to Yangtze River Delta, it has been relatively slower in northern Jiangsu.

This thesis discusses the decentralized iterative learning control problem for general interconnected discrete-time systems, which are interlinked by non-affine nonlinear dynamics. And each subsystem does not have direct transmission from input to output. That is to say, each system is irregular. According to the structural characteristics of the system, a D-type learning algorithm is constructed. Using the iterative converge principle, it is shown that the output tracking error of each subsystem can converge to zero along the iteration axis. The effectiveness of the algorithm is verified by simulation.

As urbanization has profound effects on global environmental changes, quick and accurate monitoring of the dynamic changes in impervious surfaces is of great significance for environmental protection. The increased spatiotemporal resolution of imagery makes it possible to construct time series to obtain long-time-period and high-accuracy information about impervious surface expansion. In this study, a three-step monitoring method based on time series trajectory segmentation was developed to extract impervious surface expansion using Landsat time series and was applied to the Xinbei District, Changzhou, China, from 2005 to 2017. Firstly, the original time series was segmented and fitted to remove the noise caused by clouds, shadows, and interannual differences, leaving only the trend information. Secondly, the time series trajectory features of impervious surface expansion were described using three phases and four types with nine parameters by analyzing the trajectory characteristics. Thirdly, a multi-level classification method was used to determine the scope of impervious surface expansion, and the expansion time was superimposed to obtain a spatiotemporal distribution map. The proposed method yielded an overall accuracy of 90.58% and a Kappa coefficient of 0.90, demonstrating that Landsat time series remote sensing images could be used effectively in this approach to monitor the spatiotemporal expansion of impervious surfaces.

Global warming and climate change have become a serious environmental problem and China's carbon emissions are currently the highest in the world. Cities are the main sources of carbon emissions and the key to solving these problems. Therefore, research on reducing carbon dioxide emissions is a matter of concern. In this study, a spatial autocorrelation analysis was performed to understand the spatial characteristics of carbon dioxide emissions in 171 Chinese cities. Then, stepwise and geographically weighted regressions were used to explore the processes that drive carbon dioxide emissions in Chinese cities. A two-step cluster was used to classify Chinese cities into different categories based on the degree of impact of each driver. The results showed that there is a spatial aggregation relationship between urban carbon dioxide emissions. High-high clusters mainly occur in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations, while low-low clusters occur in the central, western, and southwestern cities. Among all variables, freight volume, per capita gross domestic product, population density, and the proportion of secondary industries correlate positively with carbon dioxide emissions, whereas the number of buses per 10,000 people correlates negatively with carbon dioxide emissions. The geographically weighted regression model provided more detailed results and revealed the spatial heterogeneity of the effects of the different drivers. The impact of population, economic factors, and industrial factors in the eastern region is significantly greater than that in the central and western regions. Freight volume and public transport have the most significant impact in the northeast region. The clustering results showed that cities can be divided into four types. These findings provide a reference and policy suggestions for how cities in different regions should reduce carbon dioxide emissions.

Background: Several clinical practice guidelines noted the potential benefits of urate-lowering therapy (ULT) on cardiovascular disease (CVD) and chronic kidney disease (CKD) progression, however effect of this regimen remains much uncertainty. In this systematic review, we aimed to evaluate the efficacy of ULT on major adverse cardiovascular events(MACEs), all-cause mortality and kidney failure events. Methods: We systematically searched MEDLINE, Embase, and the Cochrane databases for trials published through April 2019. We included prospective, randomized, controlled trials assessing the effects of ULT on cardiovascular or renal outcomes. We extracted published data and did a meta-analysis of urate-lowering therapy on relative risks (RRs) of major adverse cardiovascular events, all-cause mortality, kidney failure events, and adverse events. Summary estimates of RR reductions were calculated with a random effects model. Findings: We identified 28 trials including a total of 9194 participants, of which 5070 participants received ULT, 4104 participants received placebo, usual-care or no treatment. Finally, 1279 MACEs, 377 deaths and 87 kidney failure events were recorded. Among all the population with 3103 (34·18%) participants having chronic kidney disease and 5718 (61·14%) having cardiovascular disease or stroke, ULT reduced the risk of MACEs [risk ratio (RR) 0·80, 95% confidence interval (CI) 0·67-0·95; I2 = 42·7%] and all-cause mortality (RR 0·80, 95% CI 0·66-0·97; I2 =0·0%), but had no effect on kidney failure (RR 1·04, 95%CI 0·43-2·42; I2 =64·4%). There was no significant difference (RR=0·99, 95% CI 0·95 to 1·03; I2 =55·7%) in the risk of adverse events between the participants receiving ULT and the control group. Interpretation: ULT has a beneficial effect on reducing the risk of MACEs and all-cause mortality. However, there is insufficient evidence to support an effect of ULT in reducing risk of kidney failure events. Funding Statement: This work was funded by National Key Research and Development Program of China (2018YFC1314004). JL was supported by the grant from the National Natural Science Foundation of China (no. 81270795), JZ was supported by the National Natural Science Foundation of China (81874401). Dr Badve is supported by a John Chalmers Clinical Research Fellowship with the support of Servier from The George Institute for Global Health. Declaration of Interests: Dr Badve reports grants from the National Health and Medical Research Council of Australia, personal fees from Bayer AG and Amgen Australia, and nonfinancial support from Bayer AG. Other authors listed in this paper declare no competing interests. Ethics Approval Statement: This study is registered with PROSPERO, number CRD42018104227.

The continuous reduction of farmland poses a serious threat to national food security. Delineating essential basic farmland for special protection is urgently needed in China and is an important measure to stabilize food production capacity and ensure regional food security. Currently, research on basic farmland delineation is mostly based on the evaluation of the characteristics of the farmland and lacks the integration of multiple factors such as spatial connectivity and scale constraints. Solving these issues will help improve the rationality and scientificity of basic farmland delineation. This study performed a case study of Changsha City based on land use, spatial planning, economic and social factors, natural geography, and other multi-source data. The index method and the food demand method were used to predict the scale of basic farmland protection to determine the basic farmland scale thresholds. Buffer analysis method is used to measure the spatial connectivity of farmland. This study implemented the LESA evaluation system to construct a comprehensive quality evaluation index system for farmland. Finally, based on the technical framework of basic farmland delimitation of “serial priority, quality screening, and scale constraint,” the basic farmland scale was determined to be 23,104,701 ha.

The electricity market reform in China promotes the marketization process of the electricity market, but in this process, there are still some behaviors that disturb the market order. The operation of electricity market needs to be evaluated in order to evaluate its operation efficiency, and the regulatory agency is an important part of it, so it is very necessary to construct the evaluation index and method of the operation efficiency of electricity market from the perspective of the regulatory agency. This paper constructed an evaluation system of power market operation efficiency based on hybrid BP neural network. In the construction of methods, appropriate evaluation methods become very necessary when considering the uncertainty and ambiguity of the assessed things. Based on the above analysis, this paper constructed an evaluation index system of power market operation efficiency based on SCP model, and constructed evaluation methods based on fuzzy Delphi, fuzzy AHP and neural network. Finally, this paper selects the actual data of a certain region for example verification, and conducts sensitivity analysis to analyze the factors that have a key impact on the efficiency of market operation, and draws the conclusion of this paper. This paper hopes that the research results can be applied to the decision-making of regulators in order to make the electricity market run efficiently.

Registration of remote sensing images of coral reefs is basis for detection and analysis of changes to coral reefs, which is difficult due to inadequate and unstable texture information. Affine invariant features matching (AIFM) method, combining maximally stable extremal region (MSER) detector and scale invariant feature transformation (SIFT) descriptor, followed by optimizing using RANdom SAmple Consensus (RANSAC), still cannot lead to satisfactory results of image registration. To address this problem, we propose a hierarchical filtering strategy for image registration, which is composed of two stages. Filter 1, constructed based on a geometric transformation model determined by the corresponding contours of coral reefs, is employed to remove the wrong matching pairs which obviously dissatisfy spatial distributions of matching features. Based on the filtering results of Filters 1 and 2, with an appropriate threshold value determined by overlapping ratio of the corresponding coral reefs, can lead to further optimized results for image registration. This threshold is used to filter all erroneous matching pairs, while retaining as many correct matching pairs as possible. To test the effect of these filters, we design two experiments to compare the four approaches: AIFM without filtering, AIFM with Filter 1, AIFM with Filter 2, and AIFM with hierarchical filtering ( ${\bf{Filter}}\;{\bf{1}} + {\bf{Filter}}\;{\bf{2}}$ ). The experimental results demonstrate that the approach with hierarchical filtering performs much better than the other approaches.

It is essential to adopt parallel computing technology to rapidly rasterize massive polygon data. In parallel rasterization, it is difficult to design an effective data decomposition method. Conventional methods ignore load balancing of polygon complexity in parallel rasterization and thus fail to achieve high parallel efficiency. In this paper, a novel data decomposition method based on polygon complexity (DMPC) is proposed. First, four factors that possibly affect the rasterization efficiency were investigated. Then, a metric represented by the boundary number and raster pixel number in the minimum bounding rectangle was developed to calculate the complexity of each polygon. Using this metric, polygons were rationally allocated according to the polygon complexity, and each process could achieve balanced loads of polygon complexity. To validate the efficiency of DMPC, it was used to parallelize different polygon rasterization algorithms and tested on different datasets. Experimental results showed that DMPC could effectively parallelize polygon rasterization algorithms. Furthermore, the implemented parallel algorithms with DMPC could achieve good speedup ratios of at least 15.69 and generally outperformed conventional decomposition methods in terms of parallel efficiency and load balancing. In addition, the results showed that DMPC exhibited consistently better performance for different spatial distributions of polygons. A novel data decomposition method based on polygon complexity (DMPC) is presented.DMPC can effectively parallelize different polygon rasterization algorithms.Large-scale vector polygon data can be favorably addressed with this method.DMPC can perform consistently well with different spatial distributions of polygons.

A forward-looking urban land use plan is crucial to a city’s sustainability, which requires a deep understanding of human-environment interactions between different domains, and modelling them soundly. One of the key challenges of modelling these interactions is to understand and model how human individuals make and develop their location decisions by learning that then shape urban land-use patterns. To investigate this issue, we have constructed an extended experience-weighted attraction learning model to represent the human agents’ learning when they make location decisions. Consequently, we propose and have developed an agent-based learning-embedded model (ABM-learning) for residential land growth simulation that incorporates a learning model, a decision-making model, a land use conversion model and the constraint of urban land use master plan. The proposed model was used for a simulation of the residential land growth in Shenzhen city, China. By validating the model against empirical data, the results showed that the site-specific accuracy of the model has been improved when embedding learning model. The analysis on the simulation accuracies has proved the argument that modelling individual-level learning matters in the agent’s decision model and the agent-based models. We also applied the model to predict residential land growth in Shenzhen from 2015 to 2035, and the result can be a reference for land-use allocation in detailed planning of Shenzhen. The ABM-learning is applicable to studying the past urban growth trajectory, aiding in the formulation of detailed residential land and public service facility planning and assessing the land use planning effectiveness.