REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used in combination with many other predictive indicators. The purpose of this study was thus to identify a simplified class of blood biomarkers capable of predicting the risk of developing DKD. The Gene Expression Omnibus database was screened for DKD biomarkers, and differentially expressed genes (DEGs) in human blood and kidney were identified via gene expression analysis and the Least Absolute Shrinkage and Selection Operator regression. A comparison of the area under the curve (AUC) profiles on multiple receiver operating characteristic curves of the DEGs in DKD and other renal diseases revealed that REG1A and RUNX3 had the highest specificity for DKD diagnosis. The AUCs of the combined expression of REG1A and RUNX3 in kidney (AUC = 0.929) and blood samples (AUC = 0.917) of DKD patients were similar to each other. The AUC of blood samples from DKD patients and healthy individuals obtained for external validation further demonstrated that REG1A combined with RUNX3 had significant diagnostic efficacy (AUC=0.948). REG1A and RUNX3 expression levels were found to be positively and negatively correlated with urinary albumin creatinine ratio and estimated glomerular filtration rate, respectively. Kaplan-Meier curves also revealed the potential of REG1A and RUNX3 for predicting the risk of DKD. In conclusion, REG1A and RUNX3 may serve as biomarkers for predicting the risk of developing DKD

Measurement of the Cross Section for Electromagnetic Dissociation with Neutron Emission in Pb-Pb Collisions at √sNN = 2.76 TeV

The first measurement of neutron emission in electromagnetic dissociation of 208Pb nuclei at the LHC is presented. The measurement is performed using the neutron Zero Degree Calorimeters of the ALICE experiment, which detect neutral particles close to beam rapidity. The measured cross sections of single and mutual electromagnetic dissociation of Pb nuclei at √sNN = 2.76 TeV with neutron emission are σ_single EMD = 187.2±0.2 (stat.) +13.8−12.0 (syst.) b and σ_mutual EMD = 6.2 ± 0.1 (stat.) ±0.4 (syst.) b respectively. The experimental results are compared to the predictions from a relativistic electromagnetic dissociation model.publishedVersio

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

洪水災害による産業部門の操業停止損失計量化に関する方法論的研究

京都大学0048新制・課程博士博士(情報学)甲第19340号情博第592号新制||情||103(附属図書館)32342京都大学大学院情報学研究科社会情報学専攻(主査)教授 多々納 裕一, 教授 矢守 克也, 教授 守屋 和幸学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDGA

An Investigation of the Initial Recovery Time of Chinese Enterprises Affected by COVID-19 Using an Accelerated Failure Time Model

COVID-19 has had a great impact on the economy, society, and people’s lives in China and globally. The production and operations of Chinese enterprises have also faced tremendous challenges. To understand the economic impact of COVID-19 on enterprises and the key affecting factors, this study adds to the literature by investigating the business recovery process of enterprises from the micro perspective. Specific attention is paid to the initial stage of business recovery. A questionnaire survey of 750 enterprises explored the impact during the pandemic period from July to September 2020. An accelerated failure time model in survival analysis was adopted to analyze the data. The results show that the manufacturing industry is mainly faced by affecting factors such as enterprise ownership, employees’ panic and order cancellation on initial enterprise recovery. As for the non-manufacturing industry, more factors, including clients’ distribution, employees’ panic, raw material shortage, cash flow shortage and order cancellation, are found to be significant. Acceleration factors that estimate the effects of those covariates on acceleration/deceleration of the recovery time are presented. For instance, the acceleration factor of employees’ panic is 1.319 for non-manufacturing, which implies that, compared with enterprises where employees are less panicked, enterprises with employees obviously panicked will recover 1.319 times slower at any quantile of probability of recovery time. This study provides a scientific reference for the post-pandemic recovery of enterprises, and can support the formulation of government policies and enterprise decisions

Assessing Spatial Flood Risk from Multiple Flood Sources in a Small River Basin: A Method Based on Multivariate Design Rainfall

A key issue in assessing the spatial distribution of flood risk is considering risk information derived from multiple flood sources (river flooding, drainage inundation, etc.) that may affect the risk assessment area. This study proposes a method for assessing spatial flood risk that includes flooding and inundation in small-basin areas through multivariate design rainfall. The concept of critical rainfall duration, determined by the time of concentration of flooding, is used to represent the characteristics of flooding from different sources. A copula method is adopted to capture the correlation of rainfall amounts in different critical rainfall durations to reflect the correlation of potential flooding from multiple flood sources. Rainfalls for different return periods are designed based on the copula multivariate analysis. Using the design rainfalls as input, flood risk is assessed following the rainfall−runoff−inundation−loss estimation procedure. A case study of the Otsu River Basin, Osaka Prefecture, Japan, was conducted to demonstrate the feasibility and advantages of this method. Compared to conventional rainfall design, this method considers the response characteristics of multiple flood sources, and solves the problem of flood risk assessment from multiple flood sources. It can be applied to generate a precise flood risk assessment to support integrated flood risk management