Residents’ income distribution effect of business tax replaced with VAT reform—based on CGE model

Using the 2012 input-output table of China, this study constructs a computable general equilibrium model by embedding the value-added tax (VAT) deduction mechanism into the price model and analyses the effect of replacing the business tax with the VAT reform on residents’ income distribution. The study shows that the VAT reform is generally conducive to residents’ income distribution. Specifically, the VAT reform decreases the indirect tax burden of residents, increases their real income, and narrows down the relative income gap between urban and rural residents. From the perspective of differences between the before- and after-tax Gini coefficients (the MT index), both the pilot VAT reform and VAT reform improve the residents’ income distribution. The VAT reform also improves the welfare of households

Intraday volatility analysis of CSI 300 index futures: a dependent functional data method

This study introduces a new volatility model based on dependent functional data to investigate the intraday volatility characteristics of CSI 300 in the context of high-frequency data. The volatility curve is fitted and reconstructed using three methods: functional principal component analysis, Newey-West kernel, and truncationfree Bartlett kernel. We adopt a functional time series approach for short-term dynamic forecasting. The empirical results show that the proposed dependent functional volatility estimation model based on the long-term covariance of the truncated Bartlett kernel can accurately capture the intraday volatility trajectory and outperforms other models in terms of forecast accuracy and profitability. This study improves the volatility-related research methodology, which is conducive to discovering the price formation mechanism of the stock index futures market and improving risk management capabilities

How Long Non-Coding RNAs and MicroRNAs Mediate the Endogenous RNA Network of Head and Neck Squamous Cell Carcinoma: a Comprehensive Analysis

Background/Aims: Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) to compete for microRNAs (miRNAs) in cancer metastasis. Head and neck squamous cell carcinoma (HNSCC) is one of the most common human cancers and rare biomarkers could predict the clinical prognosis of this disease and its therapeutic effect. Methods: Weighted gene co-expression network analysis (WGCNA) was performed to identify differentially expressed mRNAs (DEmRNAs) that might be key genes. GO enrichment and protein–protein interaction (PPI) analyses were performed to identify the principal functions of the DEmRNAs. An lncRNA-miRNA-mRNA network was constructed to understand the regulatory mechanisms in HNSCC. The prognostic signatures of mRNAs, miRNAs, and lncRNAs were determined by Gene Expression Profiling Interactive Analysis (GEPIA) and using Kaplan–Meier survival curves for patients with lung squamous cell carcinoma. Results: We identified 2,023 DEmRNAs, 1,048 differentially expressed lncRNAs (DElncRNAs), and 82 differentially expressed miRNAs (DEmiRNAs). We found that eight DEmRNAs, 53 DElncRNAs, and 16 DEmiRNAs interacted in the ceRNA network. Three ceRNAs (HCG22, LINC00460 and STC2) were significantly correlated with survival. STC2 transcript levels were significantly higher in tumour tissues than in normal tissues, and the STC2 expression was slightly upregulated at different stages of HNSCC. Conclusion: LINC00460, HCG22 and STC2 exhibited aberrant levels of expression and may participate in the pathogenesis of HNSCC

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning

Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. Materials and methods: We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). We propose an echocardiography video-based atrial septal defect diagnosis system. In our model, we present a block random selection, maximal agreement decision and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. Results: We validate our model using our private dataset by five-cross validation. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score. Conclusion: The proposed model is multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors

Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery

Crop disease is widely considered as one of the most pressing challenges for food crops, and therefore an accurate crop disease detection algorithm is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust disease detection. However, current solutions on yellow rust detection are generally addressed by RGB images and the basic semantic segmentation algorithms (e.g., UNet), which do not consider the irregular and blurred boundary problems of yellow rust area therein, restricting the disease segmentation performance. Therefore, this work aims to develop an automatic yellow rust disease detection algorithm to cope with these boundary problems. An improved algorithm entitled Ir-UNet by embedding irregular encoder module (IEM), irregular decoder module (IDM) and content-aware channel re-weight module (CCRM) is proposed and compared against the basic UNet while with various input features. The recently collected dataset by DJI M100 UAV equipped with RedEdge multispectral camera is used to evaluate the algorithm performance. Comparative results show that the Ir-UNet with five raw bands outperforms the basic UNet, achieving the highest overall accuracy (OA) score (97.13%) among various inputs. Moreover, the use of three selected bands, Red-NIR-RE, in the proposed Ir-UNet can obtain a comparable result (OA: 96.83%) while with fewer spectral bands and less computation load. It is anticipated that this study by seamlessly integrating the Ir-UNet network and UAV multispectral images can pave the way for automated yellow rust detection at farmland scales

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Why Chinese central bank should focus on headline inflation

In this paper, we explore the change in short-term headline-core inflation dynamic relationship using threshold error correction model, and explain why the Chinese central bank should focus on headline inflation when conducting monetary policy. The results find that: (1) the deviation between core and headline inflation is eliminated mainly through reverting core inflation to headline inflation in high inflation period, indicating that headline inflation catches the long-term trend of inflation much better than core inflation does; (2) movements in food price have become a significant source of public’s inflation expectations and food inflation persistence is increasing, reflecting that the rising food price may not have been a transient phenomenon but has become a part of the long-term trend of inflation. The above conclusions imply Chinese central bank should not implement the monetary policy based on core inflation excluding food price but should make a certain response to the surging food price

Capital account liberalisation and systemic financial risk: evidence from 24 countries

AbstractCapital account liberalisation can give rise to uncertainty in capital flows, which may lead to an accumulation of financial risks. This study measures the systemic financial risk indices using the coefficient of variation method with data of 24 countries from 2003 to 2019 and the impact of capital account liberalisation on systemic financial risks using the panel threshold model. Evidence shows that systemic financial risk indices vary heterogeneously across countries. The systemic financial risk indices of high-income countries are lower than those of middle- and low-income countries. Second, capital account liberalisation has an asymmetric effect on systemic financial risk with a double threshold. Low-intensity and high-intensity capital account liberalisation increases systemic financial risk. However, medium-intensity capital account liberalisation is effective in reducing systemic financial risk. Third, the heterogeneity results suggest that capital account liberalisation is conducive to reducing the financial risk of high- and middle-income countries and has the opposite effect on low-income countries. Therefore, this study recommends that countries adjust the intensity of capital account liberalisation according to their national conditions. It is necessary to establish a regulatory system for cross-border capital flows and maximise the benefits of liberalisation while safeguarding financial market stability

Comparison of hyperdry amniotic membrane transplantation and conjunctival autografting for primary pterygium

Abstract Background The purpose of this study was to evaluate the safety and effectiveness of the hyperdry amniotic membrane transplantation compared with conjunctival autografting for the treatment of primary pterygium. Methods One hundred and forty-one eyes from 130 patients with primary pterygium were treated with excision followed by hyperdry amniotic membrane or conjunctival autografting after random selection. Seventy-nine eyes from 71 patients received hyperdry amniotic membrane transplantation (HD-AM group), and 62 eyes from 59 patients received conjunctival autografting (CG group). Patients were followed up at one week and one, three, six, and 12 months post-surgery. Recurrence rate, postoperative complications, and final follow-up patient visits were prospectively evaluated. Results The mean follow-up duration was 12.56 ± 4.35 months in the HD-AM group and 12.85 ± 3.90 months in the CG group. Recurrences were detected in four eyes (5.06%) in the HD-AM group and 13 eyes (20.97%) in the CG group. A statistically significant difference in frequency of recurrence between the two groups (P = 0.003) was observed. The cumulative non-recurrence rates at six and 12 months in all patients stratified by age and sex were not significantly different (P = 0.642 and P = 0.451, respectively, by log-rank test). Graft retraction and necrosis were not detected in the two groups during the follow-up period. Conclusion Hyperdry amniotic membrane transplantation was effective in preventing pterygium recurrence when compared with conjunctival autografting and can be considered a preferable and safe grafting procedure for primary pterygium. Trial registration Current Controlled Trials ISRCTN16900270, Retrospectively registered (Date of registration: 3 May 2018)