As of late 2020, much is still unknown about the novel SARS-CoV-2 virus, including what health risks could be present for current patients. We know that while being infected with the disease, patients are struck with many respiratory issues. However, little is known about the long-term effects COVID-19 survivors could be affected by. Using differential expression analysis, we identified several differentially expressed genes in COVID-19 positive patients that indicate an increase in the risk for heart disease in these patients - APOL3, KLF15, and CD163. These genes indicate an increase of risk for cardiovascular disease through increased apolipoprotein levels, decreased negative regulators for risk factors, and increased inflammation and infection.
{"title":"RNA-seq Reveals the Increased Risk of Heart and Cardiovascular Disease by SARS-CoV-2 Infection","authors":"YingQiao Wang, R. Wen, Mingcong Li","doi":"10.1145/3448748.3448753","DOIUrl":"https://doi.org/10.1145/3448748.3448753","url":null,"abstract":"As of late 2020, much is still unknown about the novel SARS-CoV-2 virus, including what health risks could be present for current patients. We know that while being infected with the disease, patients are struck with many respiratory issues. However, little is known about the long-term effects COVID-19 survivors could be affected by. Using differential expression analysis, we identified several differentially expressed genes in COVID-19 positive patients that indicate an increase in the risk for heart disease in these patients - APOL3, KLF15, and CD163. These genes indicate an increase of risk for cardiovascular disease through increased apolipoprotein levels, decreased negative regulators for risk factors, and increased inflammation and infection.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114643452","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}
Early diagnosis of melanoma can substantially increase patient survival rate. Currently, dermoscopy is the dominant approach for clinical detection, but this method requires interaction with a trained clinical professional resulting in a financial burden which is a major limiting factor for many patients, especially those in remote and rural locations. It has been proposed that deep convolutional neural networks (CNNs) could allow an automated approaches for diagnosis of melanoma. However, there has been limited work regarding the use of CNNs to diagnose melanoma due to a limited amount of labelled training data available, a major limiting factor for the implementation of CNNs. This study utilises data augmentation techniques to improve CNN performance for diagnosis of melanoma, resulting a 12.4% increase in validation accuracy despite the collection of no additional training data.
{"title":"Data Augmentation to Improve the diagnosis of Melanoma using Convolutional Neural Networks","authors":"Yifan Yang","doi":"10.1145/3448748.3448773","DOIUrl":"https://doi.org/10.1145/3448748.3448773","url":null,"abstract":"Early diagnosis of melanoma can substantially increase patient survival rate. Currently, dermoscopy is the dominant approach for clinical detection, but this method requires interaction with a trained clinical professional resulting in a financial burden which is a major limiting factor for many patients, especially those in remote and rural locations. It has been proposed that deep convolutional neural networks (CNNs) could allow an automated approaches for diagnosis of melanoma. However, there has been limited work regarding the use of CNNs to diagnose melanoma due to a limited amount of labelled training data available, a major limiting factor for the implementation of CNNs. This study utilises data augmentation techniques to improve CNN performance for diagnosis of melanoma, resulting a 12.4% increase in validation accuracy despite the collection of no additional training data.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117338086","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}
In order to effectively avoid the occurrence of traffic accidents, and ensure the safety of pedestrians, the paper proposes an intelligent method of identifying zebra crossings. First, we collect the road condition information of the automobile data recorder, analyze digital image and process identify zebra crossings. We can identify zebra crossing according to the edge detection of canny operator and Hough straight line detection algorithm, and design intelligent voice response reminders according to the recognition. Experiments have proved that under normal lighting conditions, the zebra crossing can be effectively identified and reminded in real time through the driving video, and the misrecognition rate is within 6.5%. This research provides an effective method for the detection of zebra crossings, which guarantees the bilateral safety of pedestrians and drivers to a certain extent, and has important practical significance for promoting harmonious road traffic safety.
{"title":"Polite Zebra Crossing Driver Reminding System Design","authors":"Nan Fang, Zhiyong Zhang, Bingcan Xia, Zichen Yao","doi":"10.1145/3448748.3448808","DOIUrl":"https://doi.org/10.1145/3448748.3448808","url":null,"abstract":"In order to effectively avoid the occurrence of traffic accidents, and ensure the safety of pedestrians, the paper proposes an intelligent method of identifying zebra crossings. First, we collect the road condition information of the automobile data recorder, analyze digital image and process identify zebra crossings. We can identify zebra crossing according to the edge detection of canny operator and Hough straight line detection algorithm, and design intelligent voice response reminders according to the recognition. Experiments have proved that under normal lighting conditions, the zebra crossing can be effectively identified and reminded in real time through the driving video, and the misrecognition rate is within 6.5%. This research provides an effective method for the detection of zebra crossings, which guarantees the bilateral safety of pedestrians and drivers to a certain extent, and has important practical significance for promoting harmonious road traffic safety.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125273552","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 histogram of oriented gradient(HOG) is a feature descriptor used for object detection in the computer vision and image processing, and it is widely used for pedestrian detection. The conspicuous image feature can improve the detective accuracy of the pedestrian detection. In order to improve the conspicuousness of extracted gradient, this paper modifies the gradient extraction operator based on the traditional HOG algorithm. By the tests of different operators, this paper chooses the Prewitt operator to extract the gradient information. Experimental results indicate that the mean and variance of extracted gradient are larger than the gradient of traditional HOG algorithm. The extracted gradient should be generated the conspicuous HOG feature that may improve the performance of pedestrian detection.
{"title":"A Modified HOG Algorithm based on the Prewitt Operator","authors":"Yu Li, Nanxi Huang, Kongling Liu, Hongguan Chen, Ziwei Wang, Juan Yu","doi":"10.1145/3448748.3448789","DOIUrl":"https://doi.org/10.1145/3448748.3448789","url":null,"abstract":"The histogram of oriented gradient(HOG) is a feature descriptor used for object detection in the computer vision and image processing, and it is widely used for pedestrian detection. The conspicuous image feature can improve the detective accuracy of the pedestrian detection. In order to improve the conspicuousness of extracted gradient, this paper modifies the gradient extraction operator based on the traditional HOG algorithm. By the tests of different operators, this paper chooses the Prewitt operator to extract the gradient information. Experimental results indicate that the mean and variance of extracted gradient are larger than the gradient of traditional HOG algorithm. The extracted gradient should be generated the conspicuous HOG feature that may improve the performance of pedestrian detection.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114407698","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}
During the current epidemic, most of the coleges and universities in China and abroad adopted online teaching method instead of traditional face-to-face class, to control the infections and protect the safety of teachers and students. Under current situation, this research applies quantitative research method by online bigdate analizin and inviting 230 college students to participate in a questionnaire survey, aiming to analyse the relationship between online hidden curriculum and the learning tendency of students. Through the research, it has been found that: (1) Online hidden curriculum significantly impact the learning tendency of students in a positive way. With the strengthening of students' understanding of hidden curriculum, their learning tendency will also increase accordingly; (2) The four dimensions, including learning rules and value of online hidden curriculum, learning about teachers, learning to restrain self, and gaining confidence in dialogue shows different effects on learner's learning tendency. In the process of online teaching, various colleges and universities can achieve the purpose of enhancing student's learning tendency by consciously designing hidden curriculum in online courses.
{"title":"Research of Online Hidden Curriculum Based on Bigdate","authors":"Zhen-zhi Meng","doi":"10.1145/3448748.3448800","DOIUrl":"https://doi.org/10.1145/3448748.3448800","url":null,"abstract":"During the current epidemic, most of the coleges and universities in China and abroad adopted online teaching method instead of traditional face-to-face class, to control the infections and protect the safety of teachers and students. Under current situation, this research applies quantitative research method by online bigdate analizin and inviting 230 college students to participate in a questionnaire survey, aiming to analyse the relationship between online hidden curriculum and the learning tendency of students. Through the research, it has been found that: (1) Online hidden curriculum significantly impact the learning tendency of students in a positive way. With the strengthening of students' understanding of hidden curriculum, their learning tendency will also increase accordingly; (2) The four dimensions, including learning rules and value of online hidden curriculum, learning about teachers, learning to restrain self, and gaining confidence in dialogue shows different effects on learner's learning tendency. In the process of online teaching, various colleges and universities can achieve the purpose of enhancing student's learning tendency by consciously designing hidden curriculum in online courses.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120956845","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}
In recent years, with the rapid development of deep learning technology and its wide application in the field of computer vision, various image understanding tasks including semantic segmentation and instance segmentation have made great progress, and people's further demand for image understanding has spawned a more comprehensive task image panoptic segmentation. Image panoptic segmentation can be seen as the combination of semantic segmentation and instance segmentation. For uncountable object categories (called stuff), the pixel category is distinguished. For countable object categories (called things), not only the semantic category of the target is recognized, but also each instance is distinguished. This task can provide more comprehensive scene information, and can be widely used in the understanding of various natural scenes. This paper investigate the commonly used panoptic segmentation methods, including the basic shared feature extraction method, the information combination method between semantic segmentation and instance segmentation sub-tasks, and the learnable method to remove the overlap between instances. This paper also summarize the commonly used panoptic segmentation datasets and the evaluation metrics, then the experimental performance evaluation results of various methods on commonly used datasets are showed. Finally, this paper summarize the general direction of panoptic segmentation, and predict the future research direction.
{"title":"Common Methods of Image Panoptic Segmentation Based on Deep Learning","authors":"Congcong Wang","doi":"10.1145/3448748.3448807","DOIUrl":"https://doi.org/10.1145/3448748.3448807","url":null,"abstract":"In recent years, with the rapid development of deep learning technology and its wide application in the field of computer vision, various image understanding tasks including semantic segmentation and instance segmentation have made great progress, and people's further demand for image understanding has spawned a more comprehensive task image panoptic segmentation. Image panoptic segmentation can be seen as the combination of semantic segmentation and instance segmentation. For uncountable object categories (called stuff), the pixel category is distinguished. For countable object categories (called things), not only the semantic category of the target is recognized, but also each instance is distinguished. This task can provide more comprehensive scene information, and can be widely used in the understanding of various natural scenes. This paper investigate the commonly used panoptic segmentation methods, including the basic shared feature extraction method, the information combination method between semantic segmentation and instance segmentation sub-tasks, and the learnable method to remove the overlap between instances. This paper also summarize the commonly used panoptic segmentation datasets and the evaluation metrics, then the experimental performance evaluation results of various methods on commonly used datasets are showed. Finally, this paper summarize the general direction of panoptic segmentation, and predict the future research direction.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124506527","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}
Growing number of enterprises nowadays are pursuing online marketing strategies, with their eyes focusing on the effectiveness of opinion leader value-creation on social media platform. Therefore, how to accurately identify opinion leaders on social media platforms is of great significance. The emotional value generated by communication between opinion leaders and fans will have a significant impact on the potential consumption behavior of fans. Most of the existing research on opinion leader identification is to establish models based on the existing indicator data of the platform, without taking the value of emotional communication into account. This paper proposes a social media opinion leader identification model based on online comment sentiment analysis. We first crawl online comments, then analyze the text data characteristics, establish emotional indicators of different attributes, calculate the sentiment value of the text data, and finally use artificial neural network technology to train to form an opinion leader recognition model. The experimental results show that emotional communication is a very important factor in opinion leader identification, and the proposed model can identify opinion leaders more accurately.
{"title":"Social Media Opinion Leader Identification Based on Sentiment Analysis","authors":"Yi Zhai, Zhijian Wang, Haoming Zeng, Zhensheng Hu","doi":"10.1145/3448748.3448816","DOIUrl":"https://doi.org/10.1145/3448748.3448816","url":null,"abstract":"Growing number of enterprises nowadays are pursuing online marketing strategies, with their eyes focusing on the effectiveness of opinion leader value-creation on social media platform. Therefore, how to accurately identify opinion leaders on social media platforms is of great significance. The emotional value generated by communication between opinion leaders and fans will have a significant impact on the potential consumption behavior of fans. Most of the existing research on opinion leader identification is to establish models based on the existing indicator data of the platform, without taking the value of emotional communication into account. This paper proposes a social media opinion leader identification model based on online comment sentiment analysis. We first crawl online comments, then analyze the text data characteristics, establish emotional indicators of different attributes, calculate the sentiment value of the text data, and finally use artificial neural network technology to train to form an opinion leader recognition model. The experimental results show that emotional communication is a very important factor in opinion leader identification, and the proposed model can identify opinion leaders more accurately.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132266569","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}
Analyzing the blood pressure signal of salt-sensitive rats can provide important information for the study of blood pressure changes caused by human salt sensitivity. The blood pressure signal usually contains noise. In order to extract a more pure blood pressure signal, this paper uses an improved EMD algorithm based on noise statistical features. First, emd is applied to the original signal, and the high frequency noise except the heart rate will be randomly sorted. Then this paper add this signal to the original noise and calculate the average value, use the result as the new noise signal to sum the original real signal, and then do EMD. This algorithm effectively reduces the power of noise. The simulation results show that this method can effectively extract the blood pressure signal of salt-sensitive (SS) rats. Under the high and low salt diet, the changes in blood pressure of the rats are in line with medical laws.
{"title":"Application of an improved empirical mode decomposition algorithm in the feature extraction of blood pressure signal in salt-sensitive rats","authors":"Haofan Wu, Jinbo Yang, Yili Zhu, Xinbao Wang, Zhaoqian Luo, Yating Xiao","doi":"10.1145/3448748.3448758","DOIUrl":"https://doi.org/10.1145/3448748.3448758","url":null,"abstract":"Analyzing the blood pressure signal of salt-sensitive rats can provide important information for the study of blood pressure changes caused by human salt sensitivity. The blood pressure signal usually contains noise. In order to extract a more pure blood pressure signal, this paper uses an improved EMD algorithm based on noise statistical features. First, emd is applied to the original signal, and the high frequency noise except the heart rate will be randomly sorted. Then this paper add this signal to the original noise and calculate the average value, use the result as the new noise signal to sum the original real signal, and then do EMD. This algorithm effectively reduces the power of noise. The simulation results show that this method can effectively extract the blood pressure signal of salt-sensitive (SS) rats. Under the high and low salt diet, the changes in blood pressure of the rats are in line with medical laws.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115614853","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}
C language programming which was an ordinary course for computer major was arranged both in higher vocational college and general university. The goals of teaching it should be different, because the points of higher vocational education and higher education were different. Many teachers in higher vocational college did not pay attention to different goals due to not distinguishing the difference. There are lots of papers on studying the patterns of teaching this course, but there is few paper on studying the goals of teaching it. This paper focused on explaining the goals of teaching C programming in higher vocational education, such as spirit of team work, communication with machine, good habit, base of major courses, training thinking.
{"title":"The Goals of Teaching C Language Programming in Higher Vocational Education","authors":"Jian Liu","doi":"10.1145/3448748.3448802","DOIUrl":"https://doi.org/10.1145/3448748.3448802","url":null,"abstract":"C language programming which was an ordinary course for computer major was arranged both in higher vocational college and general university. The goals of teaching it should be different, because the points of higher vocational education and higher education were different. Many teachers in higher vocational college did not pay attention to different goals due to not distinguishing the difference. There are lots of papers on studying the patterns of teaching this course, but there is few paper on studying the goals of teaching it. This paper focused on explaining the goals of teaching C programming in higher vocational education, such as spirit of team work, communication with machine, good habit, base of major courses, training thinking.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116243405","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}
With the development of computer technology, electronic engineering, statistics and other disciplines, artificial intelligence (AI) has made breakthroughs in the medical field, and intelligent diagnosis and treatment has become an important development trend. The core methodological research of AI focuses on machine learning, and machine learning on clinical medicine is the key technology for using medical big data. Lung cancer is the malignant tumor with the highest morbidity and mortality in the world. Early CT screening can reduce the mortality of lung cancer patients. However, there are currently a large number of screenings, a large workload of physicians, and a high rate of missed diagnosis. This article explores the use of artificial intelligence (AI) screening for early lung cancer, and discusses the clinical significance of this method in the diagnosis of lung nodules. In lung cancer diagnosis, a lot of work has been done in computer-aided diagnosis, including traditional image processing methods, traditional machine learning methods, deep learning methods, and convolutional neural networks. This article compares and analyzes the output results of the Tumar Deep-Dimensional Lung Nodule Intelligent Diagnosis System and the diagnosis results of the chest C-images of the two-person reading patient, and studies the important value of artificial intelligence in the early screening of lung cancer.
{"title":"Research on Early Screening of Lung Cancer Based on Artificial Intelligence","authors":"Liusheng Wu, Xiaoqiang Li","doi":"10.1145/3448748.3448814","DOIUrl":"https://doi.org/10.1145/3448748.3448814","url":null,"abstract":"With the development of computer technology, electronic engineering, statistics and other disciplines, artificial intelligence (AI) has made breakthroughs in the medical field, and intelligent diagnosis and treatment has become an important development trend. The core methodological research of AI focuses on machine learning, and machine learning on clinical medicine is the key technology for using medical big data. Lung cancer is the malignant tumor with the highest morbidity and mortality in the world. Early CT screening can reduce the mortality of lung cancer patients. However, there are currently a large number of screenings, a large workload of physicians, and a high rate of missed diagnosis. This article explores the use of artificial intelligence (AI) screening for early lung cancer, and discusses the clinical significance of this method in the diagnosis of lung nodules. In lung cancer diagnosis, a lot of work has been done in computer-aided diagnosis, including traditional image processing methods, traditional machine learning methods, deep learning methods, and convolutional neural networks. This article compares and analyzes the output results of the Tumar Deep-Dimensional Lung Nodule Intelligent Diagnosis System and the diagnosis results of the chest C-images of the two-person reading patient, and studies the important value of artificial intelligence in the early screening of lung cancer.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125800459","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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