Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

New roles of research data infrastructure in research paradigm evolution

Research data infrastructures form the cornerstone in both cyber and physical spaces,driving the progression of the data-intensive scientific research paradigm.This opinion paper presents an overview of global research data infrastructure,drawing insights from national roadmaps and strategic documents related to research data infrastructure.It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem.The four new missions of research data infrastructures are:(1)as a pioneer,to transcend the disciplinary border and address complex,cutting-edge scientific and social challenges with problem-and data-oriented insights;(2)as an architect,to establish a digital,intelligent,flexible research and knowledge services environment;(3)as a platform,to foster the high-end academic communication;(4)as a coordinator,to balance scientific openness with ethics needs.

Utilization of Real-World Data in Drug Development

With the rapid development of modern science and technology, traditional randomized controlled trials have become insufficient to meet current scientific research needs, particularly in the field of clinical research. The emergence of real-world data studies, which align more closely with actual clinical evidence, has garnered significant attention in recent years. The following is a brief overview of the specific utilization of real-world data in drug development, which often involves large sample sizes and analyses covering a relatively diverse population without strict inclusion and exclusion criteria. Real-world data often reflects real clinical practice: treatment options are chosen according to the actual conditions and willingness of patients rather than through random assignment. Analysis based on real-world data also focuses on endpoints highly relevant to clinical benefits and the quality of life of patients. The booming big data technology supports the utilization of real-world data to accelerate new drug development, serving as an important supplement to traditional clinical trials.

Research Hotspots and Trends Analysis of Real-World Data Based on Social Network Analysis and Knowledge Graph

Objective To study the research status,research hotspots and development trends in the field of real-world data(RWD)through social network analysis and knowledge graph analysis.Methods RWD of the past 10 years were retrieved,and literature metrological analysis was made by using UCINET and CiteSpace from CNKI.Results and Conclusion The frequency and centrality of related keywords such as real-world study,hospital information system(HIS),drug combination,data mining and TCM are high.The clusters labeled as clinical medication and RWD contain more keywords.In recent 4 years,there are more articles involving the keywords of data specification,data authenticity,data security and information security.Among them,compound Kushen injection,HIS database and RWD are the top three keywords.It is a long-term research hotspot for Chinese and western medicine to use HIS to study clinical medication,clinical characteristics,diseases and injections.Besides,the research of RWD database has changed from construction to standardized collection and governance,which can make RWD effective.Data authenticity,data security and information security will become the new hotspots in the research of RWD.

Question classification in question answering based on real-world web data sets

To improve question answering （QA） performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,the question classifier draws both semantic and grammatical information into information retrieval and machine learning methods in the form of various training features,including the question word,the main verb of the question,the dependency structure,the position of the main auxiliary verb,the main noun of the question,the top hypernym of the main noun,etc.Then the QA query results are re-ranked by question class information.Experiments show that the questions in real-world web data sets can be accurately classified by the classifier,and the QA results after re-ranking can be obviously improved.It is proved that with both semantic and grammatical information,applications such as QA, built upon real-world web data sets, can be improved,thus showing better performance.

Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling

We examine the role of big data and machine learning in cancer research.We describe an example in cancer research where gene-level data from The Cancer Genome Atlas(TCGA) consortium is interpreted using a pathway-level model.As the complexity of computational models increases,their sample requirements grow exponentially.This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases.Thus,a large sample size is needed.The number of variables in a computational model can be reduced by incorporating biological knowledge.One particularly successful way of doing this is by using available gene regulatory,signaling,metabolic,or context-specific pathway information.We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.

Immunotherapy for advanced hepatocellular carcinoma:From clinical trials to real-world data and future advances

Hepatocellular carcinoma(HCC)is a leading cause of cancer-associated mortality worldwide.HCC is an inflammation-associated immunogenic cancer that frequently arises in chronically inflamed livers.Advanced HCC is managed with systemic therapies;the tyrosine kinase inhibitor(TKI)sorafenib has been used in 1st-line setting since 2007.Immunotherapies have emerged as promising treatments across solid tumors including HCC for which immune checkpoint inhibitors(ICIs)are licensed in 1st-and 2nd-line treatment setting.The treatment field of advanced HCC is continuously evolving.Several clinical trials are investigating novel ICI candidates as well as new ICI regimens in combination with other therapeutic modalities including systemic agents,such as other ICIs,TKIs,and anti-angiogenics.Novel immunotherapies including adoptive cell transfer,vaccine-based approaches,and virotherapy are also being brought to the fore.Yet,despite advances,several challenges persist.Lack of real-world data on the use of immunotherapy for advanced HCC in patients outside of clinical trials constitutes a main limitation hindering the breadth of application and generalizability of data to this larger and more diverse patient cohort.Consequently,issues encountered in real-world practice include patient ineligibly for immunotherapy because of contraindications,comorbidities,or poor performance status;lack of response,efficacy,and safety data;and cost-effectiveness.Further real-world data from high-quality large prospective cohort studies of immunotherapy in patients with advanced HCC is mandated to aid evidence-based clinical decision-making.This review provides a critical and comprehensive overview of clinical trials and real-world data of immunotherapy for HCC,with a focus on ICIs,as well as novel immunotherapy strategies underway.

基于re3data的中英科学数据仓储平台对比研究

以re3data为数据获取源,选取中英两国406个科学数据仓储为研究对象,从分布特征、责任类型、仓储许可、技术标准及质量标准等5个方面、11个指标对两国科学数据仓储的建设情况进行对比分析,试图为我国数据仓储的可持续发展提出建议:广泛联结国内外异质机构,推进多学科领域的交流与合作,有效扩充仓储许可权限与类型,优化技术标准的应用现况,提高元数据使用的灵活性。

Real-World Data for the Drug Development in the Digital Era

Randomized clinical trials(RCTs)have long been recognized the gold standard for regulatory approval in the drug development.However,RCTs may not be feasible in some diseases and/or under certain situations,and findings from RCTs may not be generalized to real-world patients in routine clinical practice.Real-world evidence(RWE),which is generated from various real-world data(RWD),has become more and more important for the drug development and clinical decision-making in the digital era.This paper described RWD and real-world data studies(RWDSs),followed by the characteristics and differences between RCTs and RWDSs.Furthermore,the challenges and limitations of RWD and RWE were discussed.Finally,this paper highlights that the efforts must be made during RWE generation from data collection/database selection,study design,statistical analysis,and interpretation of the results to minimize the biases and confounding effects.

A review on research of real estate big data

The research value and market potentials of "big data" in real estate industry are well acknowledged with the development of technologies. But research in this area is far away from systematic and thorough context. Aiming at this issue, we systematically examined the research outcomes related to real estate big data. It gives a comment to current research status and proposes the future directions in this area from the four aspects, i.e. the hierarchical structuring of real estate "Big Data", integrated implementation, exchange and pricing systems, and the market operation system, in order to assist the researchers for their future works.

Research on Audit Data Analysis under the Background of Big Data

With the arrival of the era of big data,the audit thinking mode has been promoted to change.Under the influence of big data,audit will become an activity of continuous behavio Through cloud data,the staff can control the operation status and risk assessment of the whole enterprise,timely analyze,control and respond to risks,and protect the enterprise to reduce risks.With the advent of the era of big data,audit data analysis is becoming more and more important.At the same time,a large amount of data analysis also brings challenges to auditors.Methods to deal and solve the challenges has become an urgent problem to be solved at present.This paper mainly studies the challenges and countermeasures brought by the changes of audit approaches and methods to audit data analysis under the background of big data,so as to continuously innovate and practice the improvement of audit technology and promote the healthy and rapid development of social economy.

Latest data on articles from Neural Regeneration Research (NRR) indexed by SCI,Chemical Abstracts (CA) and Excerpta Medica (EM)

NRR retrieved articles indexed by SCI, CA and EM at the beginning of March, 2008. The most recent data are as follows：

Database Research Method for Researches on International Chinese Language Teaching in an Era of Big Data

The database research method is a method that analyses, generalizes and deduces from the data of subject investigated with database techniques, quantitative statistics and mathematical models. As the big data age comes with the data explosion in modem society, the International Chinese Language Teaching （ICLT） shows signs of sizable data accumulation, remarkable economic property, strong modeling requirements and notable cross-research trends, which thus make this method necessary as a new and independent research method in the researches on this area. Theory bases, applicative areas, available software and data resources, research program designs, as well as their advantages and disadvantages will be figured out in this paper. In the near future, it will bring about a revolution to the international Chinese language teaching.

On the Combination of “The Textual Research on Historical Documents” and “The Comparative Study of Historical Data” —— and a Discussion on “The Law of Quan-ma and Gui-mei” in Chinese Language Studies

In Chinese language studies, both “The Textual Research on Historical Documents” and “The Comparative Study of Historical Data” are traditional in methodology and they both deserve being treasured, passed on, and further developed. It will certainly do harm to the development of academic research if any of the two methods is given unreasonable priority. The author claims that the best or one of the best methodologies of the historical study of Chinese language is the combination of the two, hence a new interpretation of “The Double-proof Method”. Meanwhile, this essay is also an attempt to put forward “The Law of Quan-ma and Gui-mei” in Chinese language studies, in which the author believes that it is not advisable to either treat Gui-mei as Quan-ma or vice versa in linguistic research. It is crucial for us to respect always the language facts first, which is considered the very soul of linguistics.

Application Research on Big Data in Material Bidding and Procurement Management

The 21st century is an era of rapid development of informatization.Under the background of the development of this era,people are increasingly researching the application of information technology.The application of big data technology is the development and application trend of an information technology formed after the rapid development of modem information technology.Big data technology has been applied to the development of many industries,including the material bidding and procurement management as well.With the aid of scientific analysis of big data,it is possible to help material procurement management to analyze the corresponding bidding procurement information,which is an important guarantee for improving its bidding procurement management capabilities.In view of this,this article has conducted special research on the application of big data in material bidding and procurement management,hoping to provide references for the application of big data technology in material bidding and procurement management with the help of the research in this article.

5-14 Research Activities of Nuclear Data Research Group in 2015

In 2015, the research activities of Nuclear Data Research Group were mainly focused on nuclear data measurements,calculations and fundamental research in heavy ion reactions. Some important results have been obtained.A Neutron Time-of-Flight (NTOF) spectrometer was developed for studies of neutron production of proton induced spallation reactions related to the ADS project[1].

2-32 Progress of Nuclear Data Research at IMP in 2014

In 2014, the researchers of Nuclear Data Research Group at IMP have carried out their research work on nucleardata measurements, calculations and fundamental research in heavy ion reactions. Some important results havebeen obtained.The experimental setup for ADS nuclear data measurement has been developed in PISA terminal of HIRLF-CSRat IMP. The first beam test experiments for the whole system of the experimental including detection system anddata acquisition system were done by using measurements of emission neutron, and charged particle identification,energy spectrum, yield and angular distribution for 400 MeV/u 16O bombardments on Pb andWtargets. At present,the experimental measurement of accelerator driven white neutron energy spectrum in the range of several MeVto hundreds of MeV was done for the first time in China. The experimental results show that the experimentalapparatus works well, and the experimental technique is reliable and correct[1].

Mapping Research Fronts of Big Data Study in 21st Century

In this paper, we aim at researching status and finding the research fronts in the field of Big Data in recent 15 years. This study applies CiteSpace software to the visualization analysis about the literature data from the year 2000 to 2014 in Web of Science, and to carry out preliminary discussion on the research hotspot and the fronts of Big Data. It concluded that Big Data have been exerting so great influence on the world that increasing researchers from different countries and institutes have continuously researched it in recent years.

Research on the Development of Ideological and Political Education Resources for College Students Based on the Data Mining Technology

With the rapid development of the computer technologies, the rapid spread and development of the network resources have a significant impact on people's thinking and lifestyle. The ideological and political education in colleges and universities should keep pace with the times, and examine the new ways and resources of the ideological and political education in colleges and universities from the perspective of the times. The high-tech has been applied to the development of the modem education, expanding the teaching space-time, improving the teaching methods of the ideological and political course, and enriching the teaching contents of the ideological and political theoretical courses. The development mechanism of the ideological and political education resources for college students based on the data mining technology is not only in line with the trend of the times, but can also play an ideal role in optimizing the ideological and political education mechanism for college students.

Research on the Accurate Computer Music Composition Mode under the Background of Big Data

In this paper, we conduct research on the accurate computer music composition mode under the basic background of big data. From the perspective of music theory, in terms of micro notes jump within a certain range, will not cause discomfort, notes there are upward at the same time, the downward makes the whole piece will not boring. From the macroscopic, the nature of the chord with internal connection between motivation and motivation as support, at the same time, the whole range can maintain in a certain range, making sound effects is can let a person feel cheerful. Computer science first occupied is music in the music sound production and the propagation of position as it is mainly used for digital audio clips and automated synthesis. But there is a certain distance away from the traditional concept of composition. Therefore, our research proposes the innovative perspective to deal with the issues for achieving the better performance.