Wireless Capsule Endoscopy (WCE) is a popular diagnostic technology that enables gastroenterologists to view the human digestive tract and more particularly, the small bowel, searching for various abnormalities like blood-based abnormalities, ulcers and polyps. This technology captures videos that consist of approximately 50,000 frames making its examination a very tedious task. For power consumption reasons the rate at which the frames are taken is extremely low (3 frames / second). This has a negative effect on the smoothness of the captured video and the consistency of the observed objects. This paper proposes a sophisticated video interpolation methodology for creating smooth and visually pleasing intermediate images from consecutive frames, while preserving the structure of the observed objects. It utilizes techniques and concepts from various fields such as optical flow and elasticity. Illustrative results of the methodology are also given in this paper.
{"title":"An Elastic Video Interpolation Methodology for Wireless Capsule Endoscopy Videos","authors":"A. Karargyris, N. Bourbakis","doi":"10.1109/BIBE.2010.16","DOIUrl":"https://doi.org/10.1109/BIBE.2010.16","url":null,"abstract":"Wireless Capsule Endoscopy (WCE) is a popular diagnostic technology that enables gastroenterologists to view the human digestive tract and more particularly, the small bowel, searching for various abnormalities like blood-based abnormalities, ulcers and polyps. This technology captures videos that consist of approximately 50,000 frames making its examination a very tedious task. For power consumption reasons the rate at which the frames are taken is extremely low (3 frames / second). This has a negative effect on the smoothness of the captured video and the consistency of the observed objects. This paper proposes a sophisticated video interpolation methodology for creating smooth and visually pleasing intermediate images from consecutive frames, while preserving the structure of the observed objects. It utilizes techniques and concepts from various fields such as optical flow and elasticity. Illustrative results of the methodology are also given in this paper.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125520313","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}
Z. Gümüş, F. Siso-Nadal, Ada Gjrezi, P. McDonagh, I. Khalil, P. Giannakakou, H. Weinstein
We present an integrated experimental and computational approach designed to identify the key cellular components that either contribute to or drive therapeutic synergy of drug combinations with anticancer activity. The approach includes (i) quantification of drug synergy in high throughput transcriptome experiments, (ii) data-driven reverse engineering and forward simulation technology to develop an in silico model predictive of drug synergy, and (iii) utilization of databases of interaction and functional information in hypothesis generation that are validated experimentally in a final step (iv). The goal is to develop an integrated framework that aids in understanding the mechanistic details of drug synergy to design better combination drugs. We illustrate this approach with an application to the analysis of transcriptome changes in cells exposed to the synergistic anticancer drug combination of farnesyl transferase inhibitors (FTIs) combined with taxanes.
{"title":"Quantification and Analysis of Combination Drug Synergy in High-Throughput Transcriptome Studies","authors":"Z. Gümüş, F. Siso-Nadal, Ada Gjrezi, P. McDonagh, I. Khalil, P. Giannakakou, H. Weinstein","doi":"10.1109/BIBE.2010.46","DOIUrl":"https://doi.org/10.1109/BIBE.2010.46","url":null,"abstract":"We present an integrated experimental and computational approach designed to identify the key cellular components that either contribute to or drive therapeutic synergy of drug combinations with anticancer activity. The approach includes (i) quantification of drug synergy in high throughput transcriptome experiments, (ii) data-driven reverse engineering and forward simulation technology to develop an in silico model predictive of drug synergy, and (iii) utilization of databases of interaction and functional information in hypothesis generation that are validated experimentally in a final step (iv). The goal is to develop an integrated framework that aids in understanding the mechanistic details of drug synergy to design better combination drugs. We illustrate this approach with an application to the analysis of transcriptome changes in cells exposed to the synergistic anticancer drug combination of farnesyl transferase inhibitors (FTIs) combined with taxanes.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114550027","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 goal of the National Cancer Institute’s cancer Biomedical Informatics Grid initiative, or caBIG®, is the ability to share data and resources among cancer researchers. One means to achieving this goal is the development of semantically interoperable informatics tools based on common data models and controlled vocabularies. A tool for managing biospecimen repositories, caTissue, enables investigators to query for available tissues that are relevant to the needs of their research. For this functionality, the caTissue application data model must include annotation describing various specimen characteristics, and have this information accessible for query by the researcher end user. Having deployed caTissue over two years at Thomas Jefferson University, we report the lessons learned from our investigators’ use of this complex, semantically interoperable software application. Overall we have found that object model complexity and semantic completeness pose obstacles to end user accessibility that require effective strategies to overcome.
{"title":"Using the Semantically Interoperable Biospecimen Repository Application, caTissue: End User Deployment Lessons Learned","authors":"J. London, Devjani Chatterjee","doi":"10.1109/BIBE.2010.71","DOIUrl":"https://doi.org/10.1109/BIBE.2010.71","url":null,"abstract":"The goal of the National Cancer Institute’s cancer Biomedical Informatics Grid initiative, or caBIG®, is the ability to share data and resources among cancer researchers. One means to achieving this goal is the development of semantically interoperable informatics tools based on common data models and controlled vocabularies. A tool for managing biospecimen repositories, caTissue, enables investigators to query for available tissues that are relevant to the needs of their research. For this functionality, the caTissue application data model must include annotation describing various specimen characteristics, and have this information accessible for query by the researcher end user. Having deployed caTissue over two years at Thomas Jefferson University, we report the lessons learned from our investigators’ use of this complex, semantically interoperable software application. Overall we have found that object model complexity and semantic completeness pose obstacles to end user accessibility that require effective strategies to overcome.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126840030","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}
Tomoya Higashigaki, Kaname Kojima, R. Yamaguchi, Masato Inoue, S. Imoto, S. Miyano
In the estimation of gene networks from microarray gene expression data, we propose a statistical method for quantification of the hidden confounders in gene networks, which were possibly removed from the set of genes on the gene networks or are novel biological elements that are not measured by microarrays. Due to high computational cost of the structural learning of Bayesian networks and the limited source of the microarray data, it is usual to perform gene selection prior to the estimation of gene networks. Therefore, there exist missing genes that decrease accuracy and interpretability of the estimated gene networks. The proposed method can identify hidden confounders based on the conflicts of the estimated local Bayesian network structures and estimate their ideal profiles based on the proposed Bayesian networks with hidden variables with an EM algorithm. From the estimated ideal profiles, we can identify genes which are missing in the network or suggest the existence of the novel biological elements if the ideal profiles are not significantly correlated with any expression profiles of genes. To the best of our knowledge, this research is the first study to theoretically characterize missing genes in gene networks and practically utilize this information to refine network estimation.
{"title":"Identifying Hidden Confounders in Gene Networks by Bayesian Networks","authors":"Tomoya Higashigaki, Kaname Kojima, R. Yamaguchi, Masato Inoue, S. Imoto, S. Miyano","doi":"10.1109/BIBE.2010.35","DOIUrl":"https://doi.org/10.1109/BIBE.2010.35","url":null,"abstract":"In the estimation of gene networks from microarray gene expression data, we propose a statistical method for quantification of the hidden confounders in gene networks, which were possibly removed from the set of genes on the gene networks or are novel biological elements that are not measured by microarrays. Due to high computational cost of the structural learning of Bayesian networks and the limited source of the microarray data, it is usual to perform gene selection prior to the estimation of gene networks. Therefore, there exist missing genes that decrease accuracy and interpretability of the estimated gene networks. The proposed method can identify hidden confounders based on the conflicts of the estimated local Bayesian network structures and estimate their ideal profiles based on the proposed Bayesian networks with hidden variables with an EM algorithm. From the estimated ideal profiles, we can identify genes which are missing in the network or suggest the existence of the novel biological elements if the ideal profiles are not significantly correlated with any expression profiles of genes. To the best of our knowledge, this research is the first study to theoretically characterize missing genes in gene networks and practically utilize this information to refine network estimation.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127720800","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}
Counting phased small RNA cycles (PSRC) from mapped small RNA positions is a repeatedly invoked subproblem in the computation of identifying TRANS-ACTING siRNA (TAS) loci and loci of other small RNAs forming through mechanisms similar to that of trans-acting small interfering RNAs (ta-siRNAs). The efficiency of counting PSRC has a clear impact on the efficiency of the algorithms predicting these loci. There are two closely related variants on counting PSRC in real applications: WPSRC, which counts the number of distinct small RNAs falling onto the phased positions in a sliding window, and MPSRC, which counts the maximum consecutive PSRC from mapped small RNA positions. In this paper, we develop fast algorithms for both WPSRC and MPSRC. Our algorithms have O(max(S)) time complexity, while the existing algorithm and its variant have O(|S|·max(S)) and O(|S|·L) time complexity for MPSRC and WPSRC respectively, where S is a set of mapped small RNA positions and L the length of sliding window for WPSRC. Experimental results on two real-life datasets show that our algorithms are significantly faster than the existing algorithm and its variant. The proposed algorithms are applicable to TAS-like clusters with any PSRC length including 21-nt.
{"title":"Fast Phased Small RNA Cycle Counting Algorithms","authors":"F. S. Bao, Zhixin Xie, Yuanlin Zhang","doi":"10.1109/BIBE.2010.30","DOIUrl":"https://doi.org/10.1109/BIBE.2010.30","url":null,"abstract":"Counting phased small RNA cycles (PSRC) from mapped small RNA positions is a repeatedly invoked subproblem in the computation of identifying TRANS-ACTING siRNA (TAS) loci and loci of other small RNAs forming through mechanisms similar to that of trans-acting small interfering RNAs (ta-siRNAs). The efficiency of counting PSRC has a clear impact on the efficiency of the algorithms predicting these loci. There are two closely related variants on counting PSRC in real applications: WPSRC, which counts the number of distinct small RNAs falling onto the phased positions in a sliding window, and MPSRC, which counts the maximum consecutive PSRC from mapped small RNA positions. In this paper, we develop fast algorithms for both WPSRC and MPSRC. Our algorithms have O(max(S)) time complexity, while the existing algorithm and its variant have O(|S|·max(S)) and O(|S|·L) time complexity for MPSRC and WPSRC respectively, where S is a set of mapped small RNA positions and L the length of sliding window for WPSRC. Experimental results on two real-life datasets show that our algorithms are significantly faster than the existing algorithm and its variant. The proposed algorithms are applicable to TAS-like clusters with any PSRC length including 21-nt.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133528233","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}
mRNA-Seq is an emerging massive parallel sequencing based technology for identification and quantification of gene transcripts. Although gene level expression can easily be estimated from mRNA-Seq data, estimating the isoform level expression poses serious problems, and appropriate methods are required. In this paper, we introduce a mathematical method to estimate transcript isoform concentrations, i.e. transcript isoform expression estimation via nonnegative least squares (TIEE/NLS).
{"title":"Estimating the Expression of Transcript Isoforms from mRNA-Seq via Nonnegative Least Squares","authors":"Hyunsoo Kim, Y. Bi, R. Davuluri","doi":"10.1109/BIBE.2010.61","DOIUrl":"https://doi.org/10.1109/BIBE.2010.61","url":null,"abstract":"mRNA-Seq is an emerging massive parallel sequencing based technology for identification and quantification of gene transcripts. Although gene level expression can easily be estimated from mRNA-Seq data, estimating the isoform level expression poses serious problems, and appropriate methods are required. In this paper, we introduce a mathematical method to estimate transcript isoform concentrations, i.e. transcript isoform expression estimation via nonnegative least squares (TIEE/NLS).","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133427485","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}
Hiromi Arai, N. Tochio, Tsuyoshi Kato, T. Kigawa, M. Yamamura
The structural class information about a protein is important to understand its biological properties. NMR is one of the most powerful tools to obtain structural information of proteins in atomic resolution. However, an analysis of protein three-dimensional structure from NMR spectra usually requires laborious chemical shift assignment. We developed a new method for predicting the protein structural class directly from the NMR spectra without any chemical shift assignment. The results show that our method outperforms the methods using current secondary structure prediction.
{"title":"An Accurate Prediction Method for Protein Structural Class from Signal Patterns of NMR Spectra in the Absence of Chemical Shift Assignments","authors":"Hiromi Arai, N. Tochio, Tsuyoshi Kato, T. Kigawa, M. Yamamura","doi":"10.1109/BIBE.2010.15","DOIUrl":"https://doi.org/10.1109/BIBE.2010.15","url":null,"abstract":"The structural class information about a protein is important to understand its biological properties. NMR is one of the most powerful tools to obtain structural information of proteins in atomic resolution. However, an analysis of protein three-dimensional structure from NMR spectra usually requires laborious chemical shift assignment. We developed a new method for predicting the protein structural class directly from the NMR spectra without any chemical shift assignment. The results show that our method outperforms the methods using current secondary structure prediction.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132087186","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}
Proposed approach, Hilbert-Huang Transform (HHT), has already been successfully applied in many engineering fields. In this work, unique properties of the HHT approach, like (1) decomposing and expansion of data into components so-called Intrinsic Mode Functions (IMFs) (2) localizing events in time-frequency space by using temporal frequency energy distribution. An experiment conducted to show that it is possible to extract the contraction locations in the uterine MMG signal.
{"title":"Use of Hilbert Huang Transform in Uterine Contraction Analysis","authors":"Kemal Aydın, R. M. Demirer, Coskun Bayrak","doi":"10.1109/BIBE.2010.70","DOIUrl":"https://doi.org/10.1109/BIBE.2010.70","url":null,"abstract":"Proposed approach, Hilbert-Huang Transform (HHT), has already been successfully applied in many engineering fields. In this work, unique properties of the HHT approach, like (1) decomposing and expansion of data into components so-called Intrinsic Mode Functions (IMFs) (2) localizing events in time-frequency space by using temporal frequency energy distribution. An experiment conducted to show that it is possible to extract the contraction locations in the uterine MMG signal.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122115490","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}
Reentrant loops are an important structure motif in a-helical transmembrane proteins. A reentrant loop goes half way through the membrane and turns and exits the membrane in the same side it has entered. To better predict the topology of transmembrane proteins and understand their function, it is important to investigate why the reentrant loops form such a unique topology. Herein, we compare the reentrant loops with transmembrane helices and find that the two types of structure motifs differ significantly in residue composition and the difference in reside composition is sufficient to discriminate reentrant loops from transmembrane helices based on the amino acid sequence. Our study shows that the difference in residue composition makes the reentrant loops less hydrophobic than the transmembrane helices. Because of the hydrophobic environment of the lipid membrane, this reduced hydrphobicity in the reentrant loops will make them less stable inside the membrane. That may be one of the many factors that make the reentrant loops to form such a unique topology.
{"title":"A Comparison between Transmembrane Helices and Reentrant Loops","authors":"Changhui Yan, Jingru Luo","doi":"10.1109/BIBE.2010.54","DOIUrl":"https://doi.org/10.1109/BIBE.2010.54","url":null,"abstract":"Reentrant loops are an important structure motif in a-helical transmembrane proteins. A reentrant loop goes half way through the membrane and turns and exits the membrane in the same side it has entered. To better predict the topology of transmembrane proteins and understand their function, it is important to investigate why the reentrant loops form such a unique topology. Herein, we compare the reentrant loops with transmembrane helices and find that the two types of structure motifs differ significantly in residue composition and the difference in reside composition is sufficient to discriminate reentrant loops from transmembrane helices based on the amino acid sequence. Our study shows that the difference in residue composition makes the reentrant loops less hydrophobic than the transmembrane helices. Because of the hydrophobic environment of the lipid membrane, this reduced hydrphobicity in the reentrant loops will make them less stable inside the membrane. That may be one of the many factors that make the reentrant loops to form such a unique topology.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124081331","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}
Tung T. Nguyen, P. T. Foteinou, I. Androulakis, S. Calvano, S. Lowry
Human endotoxemia is a well-accepted surrogate model for studying the acute inflammatory responses. In order to discover the complex underlying dynamics, identifying biologically relevant transcriptional regulators as well as their putative regulatory interactions with target genes is an essential step. However, prediction of relevant transcriptional regulators in higher eukaryotes remains a challenge both in silico and in vivo. In this study, we analyzed gene expression data from human blood leukocytes to extract four significant patterns of highly coexpressed genes that capture the essence of inflammatory phases. Upon identification of these patterns, a number of inflammation-specific pathways are selected by evaluating the enrichment of the corresponding subsets. Subsequently, statistically significant cis-regulatory modules (CRMs) are selected and decomposed into a list of relevant transcription factors (34 TFs) which are further validated from prior experiments and computational studies in literature. Additionally, our analysis also allows for the construction of a putative dynamic representation of the transcriptional regulatory program, making it become a critical enabler for unraveling regulatory interactions which is an essential step towards a quantification of dynamic transcriptional regulatory networks.
{"title":"Dynamic Complexity of the Temporal Transcriptional Regulation Program in Human Endotoxemia","authors":"Tung T. Nguyen, P. T. Foteinou, I. Androulakis, S. Calvano, S. Lowry","doi":"10.1109/BIBE.2010.27","DOIUrl":"https://doi.org/10.1109/BIBE.2010.27","url":null,"abstract":"Human endotoxemia is a well-accepted surrogate model for studying the acute inflammatory responses. In order to discover the complex underlying dynamics, identifying biologically relevant transcriptional regulators as well as their putative regulatory interactions with target genes is an essential step. However, prediction of relevant transcriptional regulators in higher eukaryotes remains a challenge both in silico and in vivo. In this study, we analyzed gene expression data from human blood leukocytes to extract four significant patterns of highly coexpressed genes that capture the essence of inflammatory phases. Upon identification of these patterns, a number of inflammation-specific pathways are selected by evaluating the enrichment of the corresponding subsets. Subsequently, statistically significant cis-regulatory modules (CRMs) are selected and decomposed into a list of relevant transcription factors (34 TFs) which are further validated from prior experiments and computational studies in literature. Additionally, our analysis also allows for the construction of a putative dynamic representation of the transcriptional regulatory program, making it become a critical enabler for unraveling regulatory interactions which is an essential step towards a quantification of dynamic transcriptional regulatory networks.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130029229","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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