血浆Big ET-1、SIRT1、CXCL12在ACS合并T2DM患者中的变化及与冠脉病变的相关性

目的探究血浆大内皮素(Big ET-1)、沉默信息调节因子1(Sirtuin-1,SIRT1)和CXC趋化因子配体12(CXCL12)在急性冠状动脉综合征(ACS)合并2型糖尿病(T2DM)患者中的变化及与冠脉病变的相关性。方法选取2021年3月至2023年3月太原钢铁(集团)有限公司总医院100例ACS合并T2DM患者作为研究组,另选同期100例ACS非糖尿病患者作为对照组。比较两组血浆Big ET-1、SIRT1和CXCL12水平,比较研究组不同冠脉病变程度患者临床资料及血浆Big ET-1、SIRT1和CXCL12水平,分析ACS合并T2DM患者冠脉病变程度加重的影响因素,分析血浆Big ET-1、SIRT1和CXCL12水平评估ACS合并T2DM患者冠脉病变程度加重的价值,比较方案A[冠心病家族史、血清同型半胱氨酸(Hcy)和前蛋白转化酶枯草溶菌素9(PCSK9)、左室射血分数(LVEF)及血浆Big ET-1、SIRT1和CXCL12水平联合评估]与方案B(冠心病家族史、血清Hcy、PCSK9和LVEF联合评估)的评估效果。结果研究组血浆Big ET-1、SIRT1和CXCL12水平高于对照组(P<0.05);冠脉病变重度患者有冠心病家族史占比、血清Hcy、PCSK9水平及血浆Big ET-1、SIRT1和CXCL12水平高于轻度患者,LVEF低于轻度患者(P<0.05);冠心病家族史、血清Hcy和PCSK9水平及血浆Big ET-1、SIRT1和CXCL12水平均为ACS合并T2DM患者冠脉病变程度加重的独立危险因素,LVEF是保护因素(P<0.05);血浆Big ET-1、SIRT1和CXCL12评估ACS合并T2DM患者冠脉病变程度加重的AUC分别为0.741、0.751和0.789;方案A评估的ACU为0.942(95%CI 0.876~0.979),大于方案B评估的ACU 0.859(95%CI 0.775~0.921)(P<0.05)。结论血浆Big ET-1、SIRT1和CXCL12升高与ACS合并T2DM具有紧密联系,为临床早期评估冠脉病变程度提供参考。

BEST WISHES TO THE JOURNAL OF THE CHINESE RARE EARTH SOCIETY

Since its initial issue in 1983,the Journal of theChinese Rare Earth Society has played positive rolein publicizing the achievements of Chinese rareearth science and technology and in promoting in-ternational academic exchange of rare earth scienceand technology as well.From now on,the Journalof the Chinese Rare Earth Society is published both

Investigation of Inhabitants' Wishes on Classified Collection of Waste in Wanghua District of Fushun

In order to disclose present situation and problem of classified collection of municipal solid waste in Wanghua District of Fushun and ana- lyze its practicability, questionnaire was designed in this paper, random research was adopted in Wanghua District, and statistic analysis of investi- gation result was conducted. This investigation could provide basis for popularizing classified collection of municipal solid waste in the whole nation.

Rural Development Program for State-Level New Area in Western China: An Empirical Analysis and Policy Suggestions Based on Villagers' Wishes

Rural development that has been raised to an unprecedented strategic position has become an important fulcrum for China to maintain its rapid economic growth in the future when China's economy has entered a period of new normal. As a quintessential example in the state-level new area of western China and mountainous city, rural areas around Chongqing's Liangjiang New Area have typical preferential policies, space structure, and industrial layout. This study is different from the traditional rural research which focuses on the spatial arrangement of settlements and facilities. From the perspective of villagers' participation and field investigation, this study objectively understands the characteristics, advantages and problems of rural development, and explores the development problems in rural areas, so as to provide a reference for rural planning.

Study of primordial deuterium abundance in Big Bang nucleosynthesis

Big Bang nucleosynthesis(BBN)theory predicts the primordial abundances of the light elements^(2) H(referred to as deuterium,or D for short),^(3)He,^(4)He,and^(7) Li produced in the early universe.Among these,deuterium,the first nuclide produced by BBN,is a key primordial material for subsequent reactions.To date,the uncertainty in predicted deuterium abundance(D/H)remains larger than the observational precision.In this study,the Monte Carlo simulation code PRIMAT was used to investigate the sensitivity of 11 important BBN reactions to deuterium abundance.We found that the reaction rate uncertainties of the four reactions d(d,n)^(3)He,d(d,p)t,d(p,γ)^(3)He,and p(n,γ)d had the largest influence on the calculated D/H uncertainty.Currently,the calculated D/H uncertainty cannot reach observational precision even with the recent LUNA precise d(p,γ)^(3) He rate.From the nuclear physics aspect,there is still room to largely reduce the reaction-rate uncertainties;hence,further measurements of the important reactions involved in BBN are still necessary.A photodisintegration experiment will be conducted at the Shanghai Laser Electron Gamma Source Facility to precisely study the deuterium production reaction of p(n,γ)d.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

Congratulary Wishes for Beijing Olympic Games

On the occasion of the XXIX Olympic Games,the ambassadors from foreign countries in Beijing sent their messages to China's For- eign Trade to express their best wishes for the exciting and grand event, so we open the special column'Wishes for Beijing Olympic Games'for expressing these sincere congratulations.One World One Dream. Let's wish a great success to Beijing Olympic Games and Paralympic Games and carry forward the spirit of Olympics together.

BEST WISHES FROM LEADERS TO CHINA AUTO INTERNATIONAL TOUR

国务院前副总理、全国人大常委会副委员长邹家华为“福田杯中国汽车国际(叙利亚)巡展”

BIG评分对接受去骨瓣减压术的中重度创伤性脑损伤儿童早期脑功能的预测价值

目的:探讨BIG评分(由格拉斯哥评分、国际标准化比值、碱剩余组成)对接受去骨瓣减压术(DC)的中重度创伤性脑损伤(TBI)患儿脑功能早期预后的预测价值。方法:回顾性分析2014年3月至2023年7月于我院接受DC治疗的所有中重度TBI患儿,以出院时儿童脑功能分类(PCPC)为结局,分为预后良好组(PCPC 1~2)和预后不良组(PCPC 3~6)。通过病历资料回顾,提取患儿的临床信息,并使用Logistic回归分析评估BIG评分的预测价值。结果:共纳入55例接受DC治疗的中重度TBI患儿,其中25例出院时脑功能良好,30例预后不良(包括9例死亡)。患儿入院时的高BIG评分(p < 0.001)、瞳孔对光反射差(p = 0.027),存在失血性休克(p = 0.042)及多发伤(p = 0.043)、脑水肿(p = 0.007),高血糖(p = 0.042)、高乳酸血症(p = 0.029)均与出院时脑功能不良相关。Logistic回归分析显示,入院时的高BIG评分是出院时脑功能不良的独立危险因素。ROC曲线分析确定的最佳BIG评分阈值为17.5,以此预测不良预后的敏感性为66.7%,特异性为88.0%。结论:接受DC的中重度TBI患儿出院时的总体脑功能不良比例为54.5%。入院时的BIG评分能够预测这些患儿出院时的早期脑功能预后,具有较高的敏感性和特异性。

Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering analysis.Firstly,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing efficiency.Then,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results.When new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in data.Finally,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic data.Through experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios.Therefore,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering techniques.It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments.This method shows great potential in real-world applications where dynamic data growth is common,and it is of significant importance for advancing the development of data analysis technology.

Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business constraints.The BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.

万分之一的心声100 Wishes动慢角色百人新年愿望大公开

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Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

Omics big data for crop improvement:Opportunities and challenges

The application of advanced omics technologies in plant science has generated an enormous dataset of sequences,expression profiles,and phenotypic traits,collectively termed“big data”for their significant volume,diversity,and rapid pace of accumulation.Despite extensive data generation,the process of analyzing and interpreting big data remains complex and challenging.Big data analyses will help identify genes and uncover different mechanisms controlling various agronomic traits in crop plants.The insights gained from big data will assist scientists in developing strategies for crop improvement.Although the big data generated from crop plants opens a world of possibilities,realizing its full potential requires enhancement in computational capacity and advances in machine learning(ML)or deep learning(DL)approaches.The present review discuss the applications of genomics,transcriptomics,proteomics,metabolomics,epigenetics,and phenomics“big data”in crop improvement.Furthermore,we discuss the potential application of artificial intelligence to genomic selection.Additionally,the article outlines the crucial role of big data in precise genetic engineering and understanding plant stress tolerance.Also we highlight the challenges associated with big data storage,analyses,visualization and sharing,and emphasize the need for robust solutions to harness these invaluable resources for crop improvement.

An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more prominent.The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big data.However,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability.In addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced.Based on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data usability.Specifically,we construct a new information loss function based on the information quantity theory.Considering that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss function.In addition,to reduce information loss,we improve K-anonymity in two ways.First,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering algorithm.In addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass.Meanwhile,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information loss.Finally,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.