ARNA is an interactive visualization system that supports comparison and alignment of RNA secondary structure. We present a new approach to RNA alignment that exploits the complex structure of the Smith-Waterman local distance matrix, allowing people to explore the space of possible partial alignments to discover a good global solution. The modular software architecture separates the user interface from computation, allowing the possibility of incorporating different alignment algorithms into the same framework.
{"title":"ARNA: Interactive Comparison and Alignment of RNA Secondary Structure","authors":"Gerald Gainant, David Auber","doi":"10.1109/INFVIS.2004.7","DOIUrl":"https://doi.org/10.1109/INFVIS.2004.7","url":null,"abstract":"ARNA is an interactive visualization system that supports comparison and alignment of RNA secondary structure. We present a new approach to RNA alignment that exploits the complex structure of the Smith-Waterman local distance matrix, allowing people to explore the space of possible partial alignments to discover a good global solution. The modular software architecture separates the user interface from computation, allowing the possibility of incorporating different alignment algorithms into the same framework.","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121090104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present Artifacts of the Presence Era, a digital installation that uses a geological metaphor to visualize the events in a physical space over time. The piece captures video and audio from a museum and constructs an impressionistic visualization of the evolving history in the space. Instead of creating a visualization tool for data analysis, we chose to produce a piece that functions as a souvenir of a particular time and place. We describe the design choices we made in creating this installation, the visualization techniques we developed, and the reactions we observed from users and the media. We suggest that the same approach can be applied to a more general set of visualization contexts, ranging from email archives to newsgroups conversations
{"title":"Artifacts of the Presence Era: Using Information Visualization to Create an Evocative Souvenir","authors":"F. Viégas, Ethan Perry, E. Howe, J. Donath","doi":"10.1109/INFVIS.2004.8","DOIUrl":"https://doi.org/10.1109/INFVIS.2004.8","url":null,"abstract":"We present Artifacts of the Presence Era, a digital installation that uses a geological metaphor to visualize the events in a physical space over time. The piece captures video and audio from a museum and constructs an impressionistic visualization of the evolving history in the space. Instead of creating a visualization tool for data analysis, we chose to produce a piece that functions as a souvenir of a particular time and place. We describe the design choices we made in creating this installation, the visualization techniques we developed, and the reactions we observed from users and the media. We suggest that the same approach can be applied to a more general set of visualization contexts, ranging from email archives to newsgroups conversations","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131984113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intelligence analysts receive thousands of facts from a variety of sources. In addition to the bare details of the fact — a particular person, for example — each fact may have provenance, reliability, weight, and other attributes. Each fact may also be associated with other facts, e.g. that one person met another at a particular location. The analyst’s task is to examine a huge collection of such loosely-structured facts, and try to "connect the dots" to perceive the underlying and unknown causes — and their possible future courses. We have designed and implemented a Java platform called VIM to support intelligence analysts in their work.
{"title":"VIM: A Framework for Intelligence Analysis","authors":"Alan Keahey, Kenneth C. Cox","doi":"10.1109/INFVIS.2004.72","DOIUrl":"https://doi.org/10.1109/INFVIS.2004.72","url":null,"abstract":"Intelligence analysts receive thousands of facts from a variety of sources. In addition to the bare details of the fact — a particular person, for example — each fact may have provenance, reliability, weight, and other attributes. Each fact may also be associated with other facts, e.g. that one person met another at a particular location. The analyst’s task is to examine a huge collection of such loosely-structured facts, and try to \"connect the dots\" to perceive the underlying and unknown causes — and their possible future courses. We have designed and implemented a Java platform called VIM to support intelligence analysts in their work.","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134267366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visualizing long-term acoustic data has been an important subject in the field of equipment surveillance and equipment diagnosis. This paper proposes a distortion-based visualization method of long-term acoustic data. We applied the method to 1 hour observation data of electric discharge sound, and our method could visualize the sound data more intelligibly as compared with conventional methods.
{"title":"Distortion-Based Visualization for Long-Term Continuous Acoustic Monitoring","authors":"F. Tsutsumi, N. Itoh, T. Onoda","doi":"10.1109/INFVIS.2004.17","DOIUrl":"https://doi.org/10.1109/INFVIS.2004.17","url":null,"abstract":"Visualizing long-term acoustic data has been an important subject in the field of equipment surveillance and equipment diagnosis. This paper proposes a distortion-based visualization method of long-term acoustic data. We applied the method to 1 hour observation data of electric discharge sound, and our method could visualize the sound data more intelligibly as compared with conventional methods.","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132102100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bongshin Lee, M. Czerwinski, G. Robertson, B. Bederson
We present PaperLens, a visualization that reveals connections, trends, and activity throughout the InfoVis conference community for the last 8 years. It tightly couples views across papers, authors, and references. This paper describes how we analyzed the data, the strengths and weaknesses of PaperLens, and interesting patterns and relationships we have discovered using PaperLens.
{"title":"Understanding Eight Years of InfoVis Conferences Using PaperLens","authors":"Bongshin Lee, M. Czerwinski, G. Robertson, B. Bederson","doi":"10.1109/INFVIS.2004.69","DOIUrl":"https://doi.org/10.1109/INFVIS.2004.69","url":null,"abstract":"We present PaperLens, a visualization that reveals connections, trends, and activity throughout the InfoVis conference community for the last 8 years. It tightly couples views across papers, authors, and references. This paper describes how we analyzed the data, the strengths and weaknesses of PaperLens, and interesting patterns and relationships we have discovered using PaperLens.","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134497685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual clutter denotes a disordered collection of graphical entities in information visualization. Clutter can obscure the structure present in the data. Even in a small dataset, clutter can make it hard for the viewer to find patterns, relationships and structure. In this paper, we define visual clutter as any aspect of the visualization that interferes with the viewer's understanding of the data, and present the concept of clutter-based dimension reordering. Dimension order is an attribute that can significantly affect a visualization's expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing information content or modifying the data in any way. Clutter reduction is a display-dependent task. In this paper, we follow a three-step procedure for four different visualization techniques. For each display technique, first, we determine what constitutes clutter in terms of display properties; then we design a metric to measure visual clutter in this display; finally we search for an order that minimizes the clutter in a display
{"title":"Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering","authors":"Wei Peng, M. Ward, Elke A. Rundensteiner","doi":"10.1109/INFVIS.2004.15","DOIUrl":"https://doi.org/10.1109/INFVIS.2004.15","url":null,"abstract":"Visual clutter denotes a disordered collection of graphical entities in information visualization. Clutter can obscure the structure present in the data. Even in a small dataset, clutter can make it hard for the viewer to find patterns, relationships and structure. In this paper, we define visual clutter as any aspect of the visualization that interferes with the viewer's understanding of the data, and present the concept of clutter-based dimension reordering. Dimension order is an attribute that can significantly affect a visualization's expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing information content or modifying the data in any way. Clutter reduction is a display-dependent task. In this paper, we follow a three-step procedure for four different visualization techniques. For each display technique, first, we determine what constitutes clutter in terms of display properties; then we design a metric to measure visual clutter in this display; finally we search for an order that minimizes the clutter in a display","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127197953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A standard method of visualizing high-dimensional data is reducing its dimensionality to two or three using some algorithm, and then creating a scatterplot with data represented by labelled and/or colored dots. Two problems with this approach are (1) dots do not represent data well, (2) reducing to just three dimensions does not make full use of several dimensionality-reduction algorithms. We demonstrate how Partiview can be used to solve these problems, in the context of handwriting recognition and image retrieval.
{"title":"Visualizing High Dimensional Datasets Using Partiview","authors":"Dinoj Surendran, Stuart Levy","doi":"10.1109/INFVIS.2004.76","DOIUrl":"https://doi.org/10.1109/INFVIS.2004.76","url":null,"abstract":"A standard method of visualizing high-dimensional data is reducing its dimensionality to two or three using some algorithm, and then creating a scatterplot with data represented by labelled and/or colored dots. Two problems with this approach are (1) dots do not represent data well, (2) reducing to just three dimensions does not make full use of several dimensionality-reduction algorithms. We demonstrate how Partiview can be used to solve these problems, in the context of handwriting recognition and image retrieval.","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129949337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many real world graphs have small world characteristics, that is, they have a small diameter compared to the number of nodes and exhibit a local cluster structure. Examples are social networks, software structures, bibliographic references and biological neural nets. Their high connectivity makes both finding a pleasing layout and a suitable clustering hard. In this paper we present a method to create scalable, interactive visualizations of small world graphs, allowing the user to inspect local clusters while maintaining a global overview of the entire structure. The visualization method uses a combination of both semantical and geometrical distortions, while the layout is generated by a spring embedder algorithm using recently developed force model. We use a cross referenced database of 500 artists as a running example
{"title":"Interactive Visualization of Small World Graphs","authors":"F. V. Ham, J. V. Wijk","doi":"10.1109/INFVIS.2004.43","DOIUrl":"https://doi.org/10.1109/INFVIS.2004.43","url":null,"abstract":"Many real world graphs have small world characteristics, that is, they have a small diameter compared to the number of nodes and exhibit a local cluster structure. Examples are social networks, software structures, bibliographic references and biological neural nets. Their high connectivity makes both finding a pleasing layout and a suitable clustering hard. In this paper we present a method to create scalable, interactive visualizations of small world graphs, allowing the user to inspect local clusters while maintaining a global overview of the entire structure. The visualization method uses a combination of both semantical and geometrical distortions, while the layout is generated by a spring embedder algorithm using recently developed force model. We use a cross referenced database of 500 artists as a running example","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116136011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The following item is made available as a courtesy to scholars by the author(s) and Drexel University Library and may contain materials and content, including computer code and tags, artwork, text, graphics, images, and illustrations (Material) which may be protected by copyright law. Unless otherwise noted, the Material is made available for non profit and educational purposes, such as research, teaching and private study. For these limited purposes, you may reproduce (print, download or make copies) the Material without prior permission. All copies must include any copyright notice originally included with the Material. You must seek permission from the authors or copyright owners for all uses that are not allowed by fair use and other provisions of the U.S. Copyright Law. The responsibility for making an independent legal assessment and securing any necessary permission rests with persons desiring to reproduce or use the Material.
{"title":"An Associative Information Visualizer","authors":"H. D. White, Xia Lin, J. Buzydlowski","doi":"10.1109/INFVIS.2004.4","DOIUrl":"https://doi.org/10.1109/INFVIS.2004.4","url":null,"abstract":"The following item is made available as a courtesy to scholars by the author(s) and Drexel University Library and may contain materials and content, including computer code and tags, artwork, text, graphics, images, and illustrations (Material) which may be protected by copyright law. Unless otherwise noted, the Material is made available for non profit and educational purposes, such as research, teaching and private study. For these limited purposes, you may reproduce (print, download or make copies) the Material without prior permission. All copies must include any copyright notice originally included with the Material. You must seek permission from the authors or copyright owners for all uses that are not allowed by fair use and other provisions of the U.S. Copyright Law. The responsibility for making an independent legal assessment and securing any necessary permission rests with persons desiring to reproduce or use the Material.","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115289063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We developed our own tool to visualize the history of the Infovis Symposiums. We call our tool “One-For-All”, because we want to generate one image to at once answer all four tasks of the contest effectively (though not necessarily optimally). Furthermore, we designed the tool to be intuitive, that is, we would like the user to immediately make sense of the visualization, even without a legend or a user guide. These are ambitious goals, but we believe that a good visualization should meet these criteria. Much preprocessing was needed before visualization. First, we have to coalesce the keywords, for example, we manually group “hierarchy” and “hierarchies” as the same keyword. Next, we find the most important keywords, with the most number of papers. We use these keywords as research areas within information visualization. We also notice that many of the 2002 papers do not have keywords, so we have to manually insert keywords according to their titles. We are not allowed to look up external information, that is, we cannot find their actual keywords from the publication. Next, we re-arrange the areas using MDS such that those areas with the most cross-references are placed near to one another. Since there is so much data, we find the important papers/authors, according to how much they are cited, and we emphasize them in our visualization. In the overview visualization of Figure 2, we show a lot of information, for example, (1) important keywords and all the papers under them, (2) related research areas (eg. graph is next to hierarchies), (3) citations, (4) important authors, (5) important papers, and (6) important external papers. A key design feature is that all these very different information can be visualized clearly in one single display. The way this is achieved is through color-coding, so that if any user is interested in any particular information, he/she just has to focus attention on that color. For example, if you focus on the cyan words, you see all the first authors, and everything else fades away (try it out yourself!). The important papers are represented by bigger circles according to how many citations they have within Infovis. Their titles are also labelled. The two most important papers also have their citations highlighted. In this way, the important papers are immediately visible. We can see that the top two papers are both in the earliest year (1995), and they are also in two very different topics, since they only have two common papers refer to them. The layout consists of vertical grey columns of research areas within Infovis. The areas that contain more papers are thicker and are labelled with darker text. We can clearly see that the most important area is “hierarchies” and this area contains many recent papers. “Graph drawing” particularly has many papers in 2001. “Information retrieval” has many papers early in 1995, but no papers in recent years. Adjactent to “Information retrieval” is “Information analysis”, which has
我们开发了自己的工具来可视化Infovis专题讨论会的历史。我们称我们的工具为“one - for - all”，因为我们想要生成一张图像来一次有效地回答比赛的所有四个任务(尽管不一定是最佳的)。此外，我们设计的工具是直观的，也就是说，我们希望用户立即理解可视化，即使没有图例或用户指南。这些都是雄心勃勃的目标，但我们认为，一个好的可视化应该满足这些标准。在可视化之前需要进行大量的预处理。首先，我们要合并关键字，例如，我们手动将“hierarchy”和“hierarchies”分组为同一个关键字。接下来，我们找到最重要的关键词，论文数量最多。我们使用这些关键词作为信息可视化的研究领域。我们还注意到，2002年的许多论文没有关键词，所以我们必须根据标题手动插入关键词。我们不允许查阅外部信息，也就是说，我们无法从出版物中找到他们的实际关键字。接下来，我们使用MDS重新排列区域，使交叉引用最多的区域彼此靠近。由于数据非常多，我们根据被引用的次数找到重要的论文/作者，并在我们的可视化中强调它们。在图2的概览可视化中，我们展示了大量的信息，例如(1)重要的关键词及其下的所有论文，(2)相关的研究领域(如;(3)引文，(4)重要作者，(5)重要论文，(6)重要外部论文。一个关键的设计特点是，所有这些非常不同的信息可以清晰地显示在一个显示器上。实现这一点的方法是通过颜色编码，所以如果任何用户对任何特定信息感兴趣，他/她只需要将注意力集中在颜色上。例如，如果你把注意力集中在青色的单词上，你会看到所有的第一作者，而其他的一切都消失了(你自己试试吧!)根据在infois中被引用的次数，重要的论文用更大的圆圈表示。他们的头衔也被贴上了标签。最重要的两篇论文的引用也有突出显示。这样一来，重要的文件就一目了然了。我们可以看到，前两篇论文都是在最早的年份(1995年)，而且它们也属于两个非常不同的主题，因为它们只有两篇共同的论文参考它们。该布局由Infovis中研究领域的垂直灰色列组成。含有较多纸张的区域较厚，并以较深的文字标记。我们可以清楚地看到，最重要的领域是“等级制度”，这个领域包含了许多最近的论文。《图形绘制》在2001年有多篇论文。《信息检索》早在1995年就有不少论文，但近年来一直没有论文。与“信息检索”相邻的是“信息分析”，从1996年到1999年有很多论文。这很有趣，因为分析逻辑上紧跟检索，所以它是有意义的。
{"title":"One-For-All: Visualization of the Information Visualization Symposia","authors":"S. Teoh, K. Ma","doi":"10.1109/INFVIS.2004.50","DOIUrl":"https://doi.org/10.1109/INFVIS.2004.50","url":null,"abstract":"We developed our own tool to visualize the history of the Infovis Symposiums. We call our tool “One-For-All”, because we want to generate one image to at once answer all four tasks of the contest effectively (though not necessarily optimally). Furthermore, we designed the tool to be intuitive, that is, we would like the user to immediately make sense of the visualization, even without a legend or a user guide. These are ambitious goals, but we believe that a good visualization should meet these criteria. Much preprocessing was needed before visualization. First, we have to coalesce the keywords, for example, we manually group “hierarchy” and “hierarchies” as the same keyword. Next, we find the most important keywords, with the most number of papers. We use these keywords as research areas within information visualization. We also notice that many of the 2002 papers do not have keywords, so we have to manually insert keywords according to their titles. We are not allowed to look up external information, that is, we cannot find their actual keywords from the publication. Next, we re-arrange the areas using MDS such that those areas with the most cross-references are placed near to one another. Since there is so much data, we find the important papers/authors, according to how much they are cited, and we emphasize them in our visualization. In the overview visualization of Figure 2, we show a lot of information, for example, (1) important keywords and all the papers under them, (2) related research areas (eg. graph is next to hierarchies), (3) citations, (4) important authors, (5) important papers, and (6) important external papers. A key design feature is that all these very different information can be visualized clearly in one single display. The way this is achieved is through color-coding, so that if any user is interested in any particular information, he/she just has to focus attention on that color. For example, if you focus on the cyan words, you see all the first authors, and everything else fades away (try it out yourself!). The important papers are represented by bigger circles according to how many citations they have within Infovis. Their titles are also labelled. The two most important papers also have their citations highlighted. In this way, the important papers are immediately visible. We can see that the top two papers are both in the earliest year (1995), and they are also in two very different topics, since they only have two common papers refer to them. The layout consists of vertical grey columns of research areas within Infovis. The areas that contain more papers are thicker and are labelled with darker text. We can clearly see that the most important area is “hierarchies” and this area contains many recent papers. “Graph drawing” particularly has many papers in 2001. “Information retrieval” has many papers early in 1995, but no papers in recent years. Adjactent to “Information retrieval” is “Information analysis”, which has ","PeriodicalId":109217,"journal":{"name":"IEEE Symposium on Information Visualization","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122443280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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