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Since the power generation structures and load characteristics in each province in China are quite different, the distribution of peak regulating resources and demands are extremely imbalance. Restricted by a low power marketization degree, peak regulating resource shortages, and transmission channel blocks, the efficient utilization of new energy is facing greater pressures. In order to improve the mutual aid in regional power grids and to obtain more precise simulation results, this paper proposes a two-layer optimization dispatching model, considering the mutual aid of peak regulation resources between each province. It determines the optimal startup mode and the units' power output in each province and obtains the power output arrangements for all the units and the technical and economic indicators. The model and the solution method are original and innovative. And it effectively solved the unequal distribution problem between the peak regulating demands and resources of each provincial power grid. Finally, taking an actual regional power grid in China as an example, the simulation results show that the proposed model can significantly improve the utilization rate of new energy, which verifies the effectiveness and feasibility of the proposed model and methods presented in this paper. [ABSTRACT FROM AUTHOR]

Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) P. K. Hsiao (MO) and Astragalus membranaceus (Fisch.) Bug. (ME) are two primary sources of the Astragalus herb, also known as "Huangqi" in China, which is widely applied to treat hypertension, glomerulonephritis, ischemic heart disease, and diabetes mellitus. As two different sources of the Astragalus herb, the chemical profiles of MO and ME may be different. Previous studies showed abundant differences in chemical composition between MO and ME. Therefore, the by-products of MO and ME, such as Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) P. K. Hsiao flower (MOF) and Astragalus membranaceus (Fisch.) Bug. flower (MEF), may have different phytochemical profiles. In this paper, a metabolomics method combined with ultra-high-performance liquid chromatography and electrospray ionization/quadrupole time-of-flight mass spectrometry (UHPLC−Q−TOF−MS/MS) was employed to analyze the components of MOF and MEF. Consequently, the results of principal component analysis (PCA) showed that MOF and MEF could be separated clearly. In total, 31 chemical markers differentiating MOF and MEF were successfully identified, including 22 flavonoids, 8 isoflavones and 1 benzopyran. Among them, the contents of 18 components, including Calycosin, Cyanidin-3-O-glucoside, Quercetin, Rutin, Kaempferol, Formononetin, Isomucronulatol and Prim-O-glucosylcimifugin in MEF, were significantly higher than in MOF. In turn, the contents of another 13 components, covering Biochanin A, Tectoridin, Isomucronulatol-7-O-glucoside, Liquiritin, Rhamnetin, etc., were lower in the MEF group than that in the MOF group. It is worth noting that flavonoids, especially flavonoid glycosides, were the primary active chemical ingredients in MOF and MEF. The 18 ingredients in MEF with a higher level carried out diverse activities, like anti-oxidant, anti-inflammatory, anti-bacterial and anti-tumor activities, which led us to speculate that MEF may have greater pharmacological effects and potential development prospects than MOF. The present results displayed that the contents of ingredients in the two different species of plants were radically different, and there was significant uniqueness to the components of MOF and MEF. Our study not only provides helpful chemical information for further quality assessment and active mechanism research of MOF and MEF but also offers scientific support for the resource utilization of MOF and MEF. [ABSTRACT FROM AUTHOR]

Clean energy has become one of the more broadly acceptable options for the future transformation of existing energy systems. The energy transformation also requires comprehensive preparatory actions. Organizing a multi-chain clean energy value chain into a complex clean energy network system is likely to be critical to the successful utilization of clean energy in the future. This article proposes a novel concept for a clean energy value co-creation system covering the entire clean energy value chain with division on power generation, energy storage, and end utilization sections. This article mainly analyzes the self-organizing evolution mechanism and value co-creation of the clean energy value co-creation system (CEVCS), which is therefore critical to the further development of the renewable energy industry. First, this article analyzes the connotation and basic structure of CEVCS and introduces the self-organizing characteristics of CEVCS. Second, the self-organizing evolution mechanism of CEVCS is discussed, which is analyzed from four aspects: evolutionary power, evolutionary path, evolutionary form, and evolutionary process. Third, this paper analyzes the information cooperative evolution process of CEVCS, defines the value order parameter, and studies how the constituent elements of CEVCS realize value creation through a synergy mechanism based on the theory of synergy. Then the dissipative structure model and information resource collaborative model of CEVCS are presented. Finally, the conclusions are given in the end. The proposed CEVCS concept would support the effective operation of the clean energy value chain within municipalities by establishing virtual energy cooperatives aimed at meeting energy needs and supporting community development. [ABSTRACT FROM AUTHOR]

After the rapid development of China's wind power industry, the lack of core technology and the mismatch between the expansion and development speed have become increasingly prominent. In order to correctly understand the current situation and problems, the whole research process is divided into the following four steps. The first step takes the profit rate of main business from 2017 to 2019 as sample variable to study the smile pattern of the wind power industry value chain. The second step takes the Principal Component Analysis as the research method, and the evaluation index system is constructed, so as to further analyze the value-added driving factors. The third step uses the Data Envelopment Analysis (DEA) and Malmquist model as evaluation methods, and the static and dynamic efficiencies of each value chain link are evaluated, respectively. In step four, the selected companies are effectively clustered with the Cluster Analysis. The results show that the value chain shows a gradual deepening smile, the overall value-added efficiency is not ideal, and there are specific problems in each link. This paper aims to provide reference for enterprises to make targeted improvement on the basis of analyzing the current situation. It also provides reference for the comparison and promotion of other countries. [ABSTRACT FROM AUTHOR]

With the rapid development of the wind power value chain, the selection of appropriate node companies has become the focus of wind power alliance, especially in the value chain of energy storage. Through the optimization of the selection of energy storage node enterprises, the role of energy storage in the wind power value chain can be better utilized. At the same time, the optimal selection of energy storage nodes can accelerate the realization of value increment in the wind power value chain. In this study, we combine Interval type-2 fuzzy number and Grey Theory the Interval type-2 fuzzy number with Cumulative Prospect Theory, which is called IGCPT, and select the optimal energy storage nodes in the value chain based on it. First, considering the sustainable development of the entire value chain, 10 decision criteria from the four dimensions of BOCR: benefits, opportunities, costs, and risks are determined. Based on this, the IGCPT method is used to select the optimal scheme, and an optimization model is proposed to determine the significance of the criteria. Then, through the comparative analysis of the multi-criteria decision-making method and the sensitivity analysis of the obtained results, it is shown that the method can be effectively applied to the selection of energy storage nodes in the wind power value chain. Finally, suggestions and conclusions for the selection of wind power value chain node companies are given. [ABSTRACT FROM AUTHOR]

Regional hybrid energy system (RHES) is an effective way to accommodate clean energy and reduce its uncertainty. It is also an important development direction for energy market reform, playing an important role in the field of energy supply and demand. Based on electricity price, thermal price, and the relationship between load supply and demand, the paper proposes an optimal scheduling model for regional energy systems to promote clean energy consumption. Firstly, the typical structure and system modules of the integrated regional energy system are introduced in detail, and the two subsystem operating models of electricity and heating are given. Secondly, a multi‐objective function that combines the economics and stability of the system is constructed. The model maximizes the economic benefits and minimizes the net load fluctuations on the basis of maximizing the consumption of clean energy. The multi‐objective singularity is realized by fuzzy membership function. The decision‐making risk caused by the uncertainty of clean energy output is analyzed by introducing robust optimization theory. Finally, the effectiveness of the proposed model is verified based on a region in northwestern China. The results show that the proposed multi‐objective model can combine different optimization demands together, maximize the economic characteristics of clean energy while properly controlling risks, and provide reasonable support for decision makers to develop optimal operation plans. [ABSTRACT FROM AUTHOR]

Energy is the foundation and driving force of modernization. Renewable energy has been developed with the help of global environmental awareness. In recent years, supported by the relevant national industrial policies, China's photovoltaic market has developed rapidly and its production capacity is huge. However, the core of supporting the sustainable development of an industry lies in the continuous appreciation of the value of itself. In order to correctly understand the status quo and problems of PV industry value chain in China, based on the "Smile Curve" theory, taking the gross profit rate of PV industry listed companies as sample variables from 2015 to 2017, the research finds that its value chain shows a gradually deepening smile. And to explore the extent to which gross profit margin is affected by various factors, 15 variables are selected from six aspects: internal innovation power, growth potential, profitability, operational efficiency, scale strength and external environment, the Stepwise Regression method is used to determine the optimal model first. Further use Principal Component Analysis to confirm the motivations affecting the value-added of listed companies in the PV industry in the upper, middle and down reaches. The results indicate that only 6 variables have significant effects on the gross profit margin of the PV industry value chain, and there are big diversities in the main influencing factors of the value-added capacity of PV companies in different links, so they should seek their own value-added breakthroughs. • China's PV industry value chain conforms to the "smile curve" form. • The profitability can significantly influence the value of the entire industry. • Innovation is the key factor to the upper and downstream PV companies' value added. • The key factor to midstream PV companies' value added is operational efficiency. • Company's scale and external environment have synergistic effects on the value added. [ABSTRACT FROM AUTHOR]

With the implementation of the electric power reform in China, an increasing number of state-owned power grid enterprises are encouraged to introduce social capital and develop mixed ownership in some businesses and therefore selecting suitable partners becomes a pressing issue. This study proposes an extended TODIM method to deal with the partner selection problem for state-owned power grid enterprises in the process of developing mixed ownership. First, 14 criteria are identified by Delphi method and classified into three categories including enterprise capacities, compatibility and risks. Then, specific decision-making steps of the proposed method are presented and in this method, intuitionistic fuzzy theory is introduced to handle the linguistic evaluation information and entropy weight method is applied to obtain the criteria weights. Next, the proposed method is implemented in a case study where Inner Mongolia Power Company plans to select the best new energy partner from four candidates. Finally, the influence of different attenuation factor of losses is discussed and a comparison analysis of different methods is performed to illustrate the robustness of the decision-making result and the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Land cover exerts considerable control over the exchange of energy, water, and carbon dioxide and other greenhouse gases between land surface and the atmosphere. In China, dramatic land-cover changes have occurred along with rapid economic development in the past 30 years. However, research specifically on whether such land-cover changes have any influence on root-zone soil moisture in the region has started only in very recent few years. In this study, the performance of selected land-surface models (Noah 2.7.1, Noah 3.2, Common Land Model (CLM version 2.0), and Mosaic) implemented in National Aeronautics and Space Administration (NASA)’s Land Information System (LIS version 6.1.6) is first tested using quality-controlled soil moisture observations from 108in situsites of the China Meteorological Administration. The best-performing model (CLM2.0) is selected to estimate the influence of land-cover changes on root-zone soil moisture, as well as drought occurrence in Yunnan Province in China. Both the 1992–1993 Advanced Very High Resolution Radiometer (AVHRR) and 2007–2010 Moderate Resolution Imaging Spectroradiometer Collection 5 (MODIS) land-cover products at 1 km resolution are employed to represent 1990 and 2010 land-cover status, respectively. These are verified using the local ground records of Yunnan Province over the two time periods. Their differences are considered roughly as land-cover changes occurring during the period 1990–2010. It is found that land-cover changes from primeval forest to grassland may increase root-zone soil moisture, thus reducing drought, while changes from grassland and primeval forest to cropland or reforested areas have increased the likelihood of drought. [ABSTRACT FROM PUBLISHER]

"Photovoltaic + energy storage" is considered as one of the effective means to improve the efficiency of clean energy utilization. In the era of energy sharing, the "photovoltaic - energy storage - utilization (PVESU)" model can create a more favorable market environment. However, the various uncertainties in the construction of the PVESU project have become the main obstacles to the development of the PVESU model. This paper aims to evaluate the risk level of China's PVESU projects through an improved Cloud-TODIM (Cloud-an acronym in Portuguese for Interative Multi-criteria Decision Making) method. First of all, 18 critical risk factors are identified using the constructed five-dimensional risk analysis model. Secondly, the improved method is used to calculate the weight of the comprehensive criteria and rank 14 PVESU alternatives. Finally, the accuracy and feasibility of the hybrid method are verified by sensitivity analysis tests concluding the loss decay coefficient adjustment and comparison analysis with the Cloud-TOPSIS(Cloud-Technique for Order of Preference by Similarity to Ideal Solution) method. Meanwhile, in terms of energy storage, some suggestions are made for the future development of China's PVESU project. This study can also provide insightful enlightenment for PVESU project investors, risk management professionals and decision makers. • The highlights stated are as follows: • Construct an evaluation system of Photovoltaic - Energy storage - Utilization (PVESU) project risk assessment. • Contribute to adding five-dimensional risk analysis method to select critical risk factors. • Propose an improved Cloud-TODIM method to analyze the risk level of PVESU projects. • Extend the research on integrated projects on the field of clean energy and energy storage. [ABSTRACT FROM AUTHOR]

The realization of carbon neutral goal is inseparable from the development of new energy industry, and scientific and effective policy support can accelerate the progress of the goal. In this paper, the policy driven ability of China's photovoltaic industry in the background of carbon neutral is evaluated. Firstly, the evaluation system is established by the improved diamond model. Then, the policy evaluation standard is formulated according to the Interval Type-2 Fuzzy sets (IT2FS). Finally, the weight of each index is determined by using the fuzzy OWA operator weighting method (F-OWA). Then, the policy driving ability of China's photovoltaic industry is evaluated by fuzzy matter-element extension method (F-MEEM), and the effectiveness of the evaluation results is further verified by weight sensitivity analysis. According to the evaluation results, policy support plays an important role in the development of photovoltaic industry. This paper also gives policy suggestions for the development of China's photovoltaic industry under the background of carbon neutral from the macro, meso and micro perspectives. • China's PV industry evaluation system is established by the improved diamond model. •IT2FS and F-OWA are used to process indicators. •The policy driving ability of China's PV industry is evaluated by F-MEEM. •The effectiveness of the evaluation results is further verified by weight sensitivity analysis. •The results are analyzed in detail, and corresponding suggestions are put forward. [ABSTRACT FROM AUTHOR]

Gene polymorphisms of the renin–angiotensin system are involved in the pathophysiology of hypertension. We genotyped 4 polymorphisms of angiotensinogen (AGT) gene A-20C (rs5050), A-6G (rs5051), C3889T (rs4762), and C4072T (rs699) by polymerase chain reaction-restriction fragment length polymorphism in 652 patients and 780 controls to examine the association of AGT and hypertension in a Northern Han Chinese population. There were significant differences in the distribution of genotypes and allele frequencies at C4072T between the patients and the controls (both P < .01); patients with CC genotype had a higher risk of hypertension (odds ratio = 1.7, 95% confidence interval 1.4–2.1). The distribution of genotypes at A-6G was significantly different between patients and controls (P < .05). No other significant differences in genotypes or frequencies were observed. No association was observed between the haplotypes of AGT and hypertension. The AGT-6A and 4072C alleles are associated with susceptibility to hypertension in this population. [ABSTRACT FROM PUBLISHER]

In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based on a large amount of historical data, and to prove that the prediction accuracy is related to both climate factors and load regularity. The results of load forecasting are affected by many climate factors, so firstly the climate variables affecting load forecasting are screened. Secondly, a load prediction model based on the IPSO-Elman network learning algorithm is constructed by taking the difference between the predicted value of the neural network and the actual value as the fitness function of particle swarm optimization. In view of the great influence of weights and thresholds on the prediction accuracy of the Elman neural network, the particle swarm optimization algorithm (PSO) is used to optimize parameters in order to improve the prediction accuracy of ELMAN neural network. Thirdly, prediction with and without climate factors is compared and analyzed, and the prediction accuracy of the model compared by using cosine distance and various error indicators. Finally, the stability discriminant index of historical load regularity is introduced to prove that the accuracy of the prediction model is related to the regularity of historical load in the forecast area. The prediction method proposed in this paper can provide reference for power system scheduling. [ABSTRACT FROM AUTHOR]

• Two novel concepts of UPIoTs and non-grid-connected wind power are explored in China. • ISM is improved with G1 method to overcome the shortcomings of traditional method. • 26 influence factors about how UPIoTs has impacts on wind power are identified. • The relationship between identified factors is researched by proposed model. • Suggestions about how to promote the consumption of wind power are proposed. In the last decade, the scale and speed of development for wind power in China presented an explosive growth and has been maintaining a leading position worldwide. However, rapid development, with addition of the nature of clean energy itself, is followed by the problem of energy consumption such as wind curtailment. To improve the energy efficiency, a new pattern of wind power generation, namely the non-grid-connected wind power, is gradually accessible to the public. At the same time, proposes a novel concept of ubiquitous power Internet of things (UPIoTs) to improve the energy-using environment and quality. In the context of above proposal, to research how UPIoTs promotes the consumption of wind power especially has become a crucial task. The purpose of this paper is to identify influence factors through literature review and expert interview on the basis of relationship between the referenced two. During the process of exploring effects among factors, the improved interpretative structural modeling (ISM) coupled with fuzzy order relation analysis method (fuzzy G1 method) is constructed and adopted to make up for the shortcomings of traditional method. According to the results, three factors are considered as the most influential and some suggestions are given from these three aspects, aiming at making a contribution to the solutions in theory and in practice. [ABSTRACT FROM AUTHOR]

Astragalus mongholicus (MG) and Astragalus membranaceus (MJ), both generally known as Huangqi in China, are two perennial herbals widely used in variety diseases. However, there were still some differences in the chemical ingredients between MG and MJ. In this paper, metabolomics combined with the ultra-high performance liquid chromatography coupled with electrospray ionization/quadrupole time-of-flight mass spectrometry (UHPLC-ESI-Q-TOF-MS/MS) was employed to contrastively analyze the chemical constituents between MG and MJ. As a result, principal component analysis showed that MG and MJ were separated clearly. A total of 53 chemical markers were successfully identified for the discrimination of MG and MJ. Of them, the contents of 36 components including Astragaloside I~III, Astragaloside IV, Agroastragaloside I, etc. in MJ were significantly higher than those in MG. On the contrary, the contents of 17 other components including coumaric acid, formononetin, sophoricoside, etc. in MG were obviously higher than those in MJ. The results showed that the distinctive constituents in MG and MJ were remarkable, and MJ may own stronger pharmacological activities than MG. In a word, MG and MJ may be treated as two different herbs. This paper demonstrated that metabolomics was a vitally credible technology to rapidly screen the characteristic chemical composition of traditional Chinese medicine. [ABSTRACT FROM AUTHOR]

• Construct an evaluation system of energy storing node optimization selection. • Contribute to adding four subnetworks using BOCR with this study's concern. • Extend the research on energy storage enterprises selection. • Extend the research on overall influential factor evaluation and ranking analysis. Energy storage has developed into a key solution to the problem of energy abandonment, which has been paid close attention. In the era of energy sharing, there is a general trend of cooperation between energy storage companies and power suppliers. This paper evaluates energy storage node enterprises to make their alliance more valuable with power generation node enterprises. First, this paper expounds the importance of sustainable energy storing node optimization selection and identifies 23 criteria from the perspective of benefits, opportunities, costs and risks to establish the evaluation index system. Then a hybrid ANP-BOCR -TOPSIS method is proposed to evaluate the index system including determining the integrated criteria weights and ranking four alternatives by TOPSIS method. Next, a case study is used to verify the accuracy and feasibility of the hybrid method. Through the validation with DEMATEL and comparative analysis of three integrated methods, it is concluded that the hybrid ANP-BOCR-TOPSIS method can be applied to the evaluation of clean energy further research. Finally, the suggestions and conclusions are given in the end. [ABSTRACT FROM AUTHOR]

With the continuous attention on clean energy and energy abandonment, clean energy power generation – energy storage-energy using virtual enterprise (PGSU VE) centered on energy storage has been highly valued. The alliance can not only effectively integrate enterprise resources, but also efficiently adapt to the change of market environment. However, the various uncertainties in the alliance become a major obstacle to PGSU development. To ensure the healthy development of the alliance, risk evaluation has become a crucial step. This paper aims to identify risk factors through questionnaire survey and literature review and calculate the overall risk levels of PGSU VEs with an integrated approach with fuzzy Analytic Hierarchy Process (fuzzy AHP). Results of factors identification consisted of market, social politics and environmental, capacity, cooperation and coordination, alliance formation and risk commitment and profit distribution risk categories. Overall risk levels of three VEs were calculated and ranked with proposed approach whose feasibility and consistency were verified through a comparative analysis with Best Worst Method (BWM) and Stochastic multi-criteria acceptability analysis (SMAA). Moreover, considering existing risks, some suggestions were put forward for future development of clean PGSU VE. The implications and limitations were presented in the conclusions. [ABSTRACT FROM AUTHOR]

Pyrite cinder is a typical hazardous waste produced in sulfuric acid industry, which, however, contains a significant level of valuable metals, such as iron, copper, cobalt, etc. About 12 million tons of pyrite cinder are generated in China annually, while the utilization rate is less than 50% due to the complex relationship of various components. In this study, a process of phosphoric acid leaching was used to selectively separate and recover Cu, Co and Fe from the pyrite cinder. The effects of different leaching parameters on the recovery rate of valuable metals in phosphoric acid leaching systems were investigated. Under the optimal conditions, the results indicated that the leaching efficiency of copper and cobalt was above 82% and 99%, respectively. More than 98% of iron was transferred into insoluble FePO 4 ·2H 2 O, which could be used as precursor of battery-grade electrode material after a hydrofluoric acid purification process. Cu and Co in the leaching solutions were extracted efficiently by P204. • A novel method for high-value-added utilization of pyrite cinder was proposed. • Valuable metals of Cu and Co were selectively recovered efficiently. • Battery grade iron phosphate with uniform particle size was prepared. • No solid waste was produced during the comprehensive utilization process. [ABSTRACT FROM AUTHOR]

Ischemic stroke, one of the prevalent causes of death and disability worldwide, is linked to environmental and genetic factors, including polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene involved in homocysteine metabolism. The present study aimed to explore the relationship between the MTHFR C677T variant, plasma homocysteine, and risk of developing large-artery atherosclerotic ischemic stroke (LAAIS) among Han Chinese. A population-based case-control study, which included 1810 patients with LAAIS and 1765 unrelated control subjects, was conducted. Compared to the controls, LAAIS patients had a significantly higher prevalence of hypertension, diabetes mellitus, smoking, and alcohol consumption (P < .001), as well as significantly higher mean fasting blood glucose, triglyceride, total cholesterol, and plasma homocysteine levels (P < .001). The TT homozygous genotype correlated with increased risk of developing LAAIS, as indicated by a significantly higher odds ratio (OR) compared to the CT and CC genotypes, in both additive (OR = 3.215, P = .01) and recessive models (OR = 3.265, P = .01). The plasma homocysteine level was genotype-dependent according to the following trend: TT > CT > CC. In conclusion, our data demonstrate that, in spite of its low prevalence in both patients and controls (1.5% vs 0.8%), the MTHFR C677T variant could, at least in part, affect homocysteine levels and this, either alone or in combination with other factors, increases the risk of LAAIS., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.)

Abstract: Voltage-gated Ca2+ channels play a key role in the regulation of arterial tone and blood pressure. The aim of this study was to determine whether the association of calcium voltage-gated channel subunit alpha1 C (CACNA1C) rs1006737 with essential hypertension (EH) exists in both Chinese Han and ethnic Russian populations of Northeast Asia. We used a case-control study of 2 ethnic groups in the same latitude geographical area to investigate the association between the susceptibility of EH and rs1006737 polymorphism. A total of 1512 EH patients and 1690 controls in Chinese Han people (Heilongjiang Provence, China), 250 EH patients, and 250 controls in ethnic Russian people (Chita, Russia), participated in this study. All participants were genotyped using the TaqMan SNP genotyping assay (Agena Company). Baseline characteristics and the minor allele frequencies of rs1006737 vary substantially among common Chinese Han and ethnic Russian people. Allele A was found to be a risk factor for EH in Chinese Han [(odds ratio) OR 1.705, (confidence interval) 95% CI: 1.332-2.182, P < .001] and ethnic Russian (OR 1.437; 95% CI: 1.110-1.860, P = .006). The GA genotype was significantly associated with an increased risk of hypertension (OR 1.538, 95% CI: 1.188-1.991, P = .001) for Chinese Han people, and the AA genotype (OR 2.412, 95% CI: 1.348-4.318, P = .003) for ethnic Russian people. The results of this study indicate that the A allele of the variant rs1006737 in the CACNA1C gene may be a useful genetic marker for EH risk prediction in Chinese Han and ethnic Russian populations., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)