Genomics, proteomics & bioinformatics最新文献

{"title":"Spatial Transcriptomics Unveils the Blueprint of Mammalian Lung Development.","authors":"Mingyue Chen 陈明月, Junjie Lv 吕俊杰, Qiao Zhang 张乔, Qian Gong 龚倩, Ting Zhao 赵婷, Zhenping Chen 陈振平, Haishen Xu 徐海深, Nan Zhou 周南, Shan Jiang 姜姗, Jian Du 都建, Xuepeng Chen 陈雪鹏, Yuwen Ke 柯玉文","doi":"10.1093/gpbjnl/qzaf053","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf053","url":null,"abstract":"<p><p>Mammalian lung development is a complex, highly orchestrated process involving the precise coordination of diverse cell types. Despite significant advances, the spatial gene expression patterns and regulatory mechanisms within the developmental niches of different lung structures remain incompletely understood. In this study, we present a comprehensive spatial transcriptomic atlas of mouse lung development, spanning from the early pseudoglandular to the alveolar stage. We further uncover transcription factor (TF) regulation landscapes by integrating spatial epigenome, including novel TF-enhancer-driven regulons (eRegulons) critical for epithelial progenitors during early lung development. Our analysis also identifies hundreds of spatiotemporally dynamic cell-cell communications, such as BMP8B-mediated ligand-receptor signaling enriched in airway branching. Notably, we delineate the distinct developmental trajectories of alveolar AT1 and AT2 cells and reveal that collagen pathways facilitate their spatial convergence, forming primary alveoli during the canalicular-saccular transition. Together, this spatial transcriptomic atlas provides a foundational resource for understanding the complex cellular and molecular orchestration underlying mammalian lung development.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268239","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}

{"title":"Proteome and Phosphoproteome of Tomato Fruit Identify REDUCED CHLOROPLAST COVERAGE 1a as A Ripening Regulator.","authors":"Jinjuan Tan, Zhongjing Zhou, Hanqian Feng, Jiateng Zhang, Ruikai Zhang, Zhongkai Chen, Yujie Niu, Fangyu Liu, Zhiping Deng","doi":"10.1093/gpbjnl/qzaf050","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf050","url":null,"abstract":"<p><p>Fruit ripening in tomato (Solanum lycopersicum) has been extensively studied at the transcriptomics level. However, comprehensive profiling of the tomato fruit proteome and phosphoproteome remains limited. In this study, we performed large-scale proteome and phosphoproteome profiling of tomato (Ailsa Craig) fruits across five ripening stages using tandem mass tags (TMT)-based quantitative proteomics. Our analysis quantified over 8800 proteins and 20,000 high-confidence phosphorylation sites. Ripening-associated phosphorylation and dephosphorylation events were identified in diverse ripening regulators, including transcription factors, ethylene biosynthesis and signaling proteins, and epigenetic modifiers. Weighted gene co-expression network analysis (WGCNA) revealed a tetratricopeptide repeat protein, REDUCED CHLOROPLAST COVERAGE 1a (REC1a), as a key regulator of fruit ripening. Parallel reaction monitoring (PRM)-based targeted proteomic analysis validated the expression profiles of REC1a and its three phosphorylation sites. Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9)-mediated knockout of REC1a resulted in reduced lycopene accumulation and slower chlorophyll degradation, highlighting its role in the chloroplast-to-chromoplast transition, which is critical for fruit pigmentation during ripening. Quantitative proteomic analyses of rec1a mutants demonstrated reduced levels of Clp proteases and chaperones, proteins known to regulate plastid transitions. Additionally, co-immunoprecipitation and split-luciferase complementation assays revealed that REC1a interacts with the eukaryotic translation initiation factor subunits eIF2α and eIF2Bβ, suggesting its role in regulating protein synthesis during ripening. This study provides the most comprehensive quantitative proteome and phosphoproteome atlas of tomato fruits to date and identifies REC1a as a novel regulator of plastid development, offering new insights into the molecular mechanisms underlying fruit ripening.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259707","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}

{"title":"YanHuang Paternal Genomic Resource Suggested A Weakly-Differentiated Multi-Source Admixture Model for the Formation of Han's Founding Ancestral Lineages.","authors":"Zhiyong Wang, Kaijun Liu, Haibing Yuan, Shuhan Duan, Yunhui Liu, Lintao Luo, Xiucheng Jiang, Shijia Chen, Lanhai Wei, Renkuan Tang, Liping Hu, Jing Chen, Xiangping Li, Qingxin Yang, Yuntao Sun, Qiuxia Sun, Yuguo Huang, Haoran Su, Jie Zhong, Hongbing Yao, Libing Yun, Jianbo Li, Junbao Yang, Yan Cai, Hong Deng, Jiangwei Yan, Bofeng Zhu, Kun Zhou, Shengjie Nie, Chao Liu, Mengge Wang, Guanglin He","doi":"10.1093/gpbjnl/qzaf049","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf049","url":null,"abstract":"<p><p>The revolution in large-scale human genomics and advancements in statistical methods have profoundly refined our understanding of genetic diversity and structure within human populations. Y-chromosome variations, with their distinct evolutionary characteristics, play crucial roles in reconstructing the origins and interactions of ancient East Asian paternal lineages. We launched the YanHuang cohort employing a high-resolution capture sequencing panel to explore the evolutionary trajectory of Han Chinese, one of the world's largest ethnic groups. We generated paternal genomic data for 5020 Han Chinese individuals across 29 Chinese administrative regions. We observed that multiple founding paternal lineages originating from ancient western Eurasia, Siberia, and East Asia contributed significantly to the Han Chinese gene pool. We identified fine-scale paternal genetic structures shaped by interactions among ancient populations and geographic barriers like the Qinling-Huaihe line and the Nanling Mountains. This structure reflects both isolation-enhanced and admixture-driven genetic differentiation, underscoring the complexity of Han Chinese genomic diversity. We observed a strong correlation between the frequency of multiple founding lineages and subsistence-related ancestral sources, including western pastoralists, Holocene Mongolian Plateau populations, and ancient East Asians. This relationship highlights the impact of ancient migrations and admixture on Chinese paternal genomic diversity. We introduce the Weakly-Differentiated Multi-Source Admixture model to clarify the intricate interactions among multiple ancestral sources influencing the Han Chinese paternal landscape. This study provides a comprehensive uniparental genomic resource from the YanHuang cohort, proposes a novel admixture model, and delineates the complex genomic landscape shaped by ancient herders, hunter-gatherers, and farmers integral to Han Chinese ancestry.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228061","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}

{"title":"Biomedical Big Data and Artificial Intelligence in Blood.","authors":"Fuhong He, Zhaojun Zhang, Xiangdong Fang, Qian-Fei Wang","doi":"10.1093/gpbjnl/qzaf043","DOIUrl":"10.1093/gpbjnl/qzaf043","url":null,"abstract":"","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12123044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma Proteomic Profiling Reveals ITGA2B as A Key Regulator of Heart Health in High-altitude Settlers.","authors":"Yihao Wang 王一豪, Pan Shen 沈磐, Zhenhui Wu 伍振辉, Bodan Tu 涂博丹, Cheng Zhang 张程, Yongqiang Zhou 周永强, Yisi Liu 刘溢思, Guibin Wang 王贵宾, Zhijie Bai 柏志杰, Xianglin Tang 汤响林, Chengcai Lai 赖成材, Haitao Lu 吕海涛, Wei Zhou 周维, Yue Gao 高月","doi":"10.1093/gpbjnl/qzaf030","DOIUrl":"10.1093/gpbjnl/qzaf030","url":null,"abstract":"<p><p>Myocardial injury is a common disease in the plateau, especially in the lowlanders who have migrated to the plateau, in which the pathogenesis is not well understood. Here, we established a cohort of lowlanders comprising individuals from both low-altitude and high-altitude areas and conducted plasma proteomic profiling. Proteomic data showed that there was a significant shift in energy metabolism and inflammatory response in individuals with myocardial abnormalities at high altitude. Notably, integrin alpha-Ⅱb (ITGA2B) emerged as a potential key player in this context. Functional studies demonstrated that ITGA2B upregulated the transcription and secretion of interleukin-6 (IL-6) through the integrin-linked kinase (ILK)/nuclear factor-κB (NF-κB) signaling axis under hypoxic conditions. Moreover, ITGA2B disrupted mitochondrial structure and function, increased glycolytic capacity, and aggravated energy reprogramming from oxidative phosphorylation to glycolysis. Leveraging the therapeutic potential of traditional Chinese medicine in cardiac diseases, we discovered that tanshinone ⅡA (TanⅡA) effectively alleviated the myocardial injury caused by the abnormally elevated expression of ITGA2B and hypobaric hypoxia exposure in mice, thus providing a novel candidate therapeutic strategy for the prevention and treatment of high-altitude myocardial injury.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.","authors":"Jin A 阿瑾, Ju Xiang 项炬, Xiangmao Meng 孟祥茂, Yue Sheng 盛岳, Hongling Peng 彭宏凌, Min Li 李敏","doi":"10.1093/gpbjnl/qzaf037","DOIUrl":"10.1093/gpbjnl/qzaf037","url":null,"abstract":"<p><p>Leukemia is a malignant disease characterized by progressive accumulation with high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed a novel Dynamic Network-based model integrating Differentially expressed Genes (DyNDG) to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background-temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background-temporal multilayer network. The results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at both https://ngdc.cncb.ac.cn/biocode/tool/BT7617 and https://github.com/CSUBioGroup/DyNDG.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biological Data Resources and Machine Learning Frameworks for Hematology Research.","authors":"Ying Yi 易莹, Yongfei Hu 胡永飞, Juanjuan Kang 康娟娟, Qifa Liu 刘启发, Yan Huang 黄燕, Dong Wang 王栋","doi":"10.1093/gpbjnl/qzaf021","DOIUrl":"10.1093/gpbjnl/qzaf021","url":null,"abstract":"<p><p>Hematology research has greatly benefited from the integration of diverse biological data resources and advanced machine learning (ML) frameworks. This integration has not only deepened our understanding of blood diseases such as leukemia and lymphoma, but also enhanced diagnostic accuracy and personalized treatment strategies. By applying ML algorithms to analyze large-scale biological data, researchers can more effectively identify disease patterns, predict treatment responses, and provide new perspectives for the diagnosis and treatment of hematologic disorders. Here, we provide an overview of the current landscape of biological data resources and the application of ML frameworks pertinent to hematology research.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12321297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HemaCisDB: An Interactive Database for Analyzing Cis-regulatory Elements Across Hematopoietic Malignancies.","authors":"Xinping Cai 蔡信平, Qianru Zhang 张倩茹, Bolin Liu 刘博琳, Lu Sun 孙露, Yuxuan Liu 刘宇璇","doi":"10.1093/gpbjnl/qzae088","DOIUrl":"10.1093/gpbjnl/qzae088","url":null,"abstract":"<p><p>Non-coding cis-regulatory elements (CREs), such as transcriptional enhancers, are key regulators of gene expression programs. Accessible chromatin and H3K27ac are well-recognized markers for CREs associated with their biological function. Deregulation of CREs is commonly found in hematopoietic malignancies, yet the extent to which CRE dysfunction contributes to pathophysiology remains incompletely understood. Here, we developed HemaCisDB, an interactive, comprehensive, and centralized online resource for CRE characterization across hematopoietic malignancies, serving as a useful resource for investigating the pathological roles of CREs in blood disorders. Currently, we collected 922 assay of transposase accessible chromatin with sequencing (ATAC-seq), 190 DNase I hypersensitive site sequencing (DNase-seq), and 531 H3K27ac chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets from patient samples and cell lines across different myeloid and lymphoid neoplasms. HemaCisDB provides comprehensive quality control metrics to assess ATAC-seq, DNase-seq, and H3K27ac ChIP-seq data quality. The analytic modules in HemaCisDB include transcription factor (TF) footprinting inference, super-enhancer identification, and core transcriptional regulatory circuitry analysis. Moreover, HemaCisDB also enables the study of TF binding dynamics by comparing TF footprints across different disease types or conditions via web-based interactive analysis. Together, HemaCisDB provides an interactive platform for CRE characterization to facilitate mechanistic studies of transcriptional regulation in hematopoietic malignancies. HemaCisDB is available at https://hemacisdb.chinablood.com.cn/.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12343011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glycolysis Induces Abnormal Transcription Through Histone Lactylation in T-cell Acute Lymphoblastic Leukemia.","authors":"Wenyan Wu, Jingyi Zhang, Huiying Sun, Xiaoyu Wu, Han Wang, Bowen Cui, Shuang Zhao, Kefei Wu, Yanjun Pan, Rongrong Fan, Ying Zhong, Xiang Wang, Ying Wang, Xiaoxiao Chen, Jianan Rao, Ronghua Wang, Kai Luo, Xinrong Liu, Liang Zheng, Shuhong Shen, Meng Yin, Yangyang Xie, Yu Liu","doi":"10.1093/gpbjnl/qzaf029","DOIUrl":"10.1093/gpbjnl/qzaf029","url":null,"abstract":"<p><p>The Warburg effect, characterized by excessive lactate production, and transcriptional dysregulation are two hallmarks of tumors. However, the precise influence of lactate on epigenetic modifications at a genome-wide level and its impact on gene transcription in tumor cells remain unclear. In this study, we conducted genome-wide profiling of histone H3 lysine 18 lactylation (H3K18la) in T-cell acute lymphoblastic leukemia (T-ALL). We observed elevated lactate and H3K18la levels in T-ALL cells compared to normal T cells, with H3K18la levels positively associated with cell proliferation. Accordingly, we observed a significant shift in genome-wide H3K18la modifications from T cell immunity in normal T cells to leukemogenesis in T-ALL, correlated with altered gene transcription profiles. We showed that H3K18la primarily functions in active transcriptional regulation and observed clusters of H3K18la modifications resembling super-enhancers. Disrupting H3K18la modification revealed both synergistic and divergent changes between H3K18la and histone H3 lysine 27 acetylation (H3K27ac) modifications. Finally, we found that the high transcription of H3K18la target genes, IGFBP2 and IARS, is associated with inferior prognosis of T-ALL. These findings enhance our understanding of how metabolic disruptions contribute to transcription dysregulation through epigenetic changes in T-ALL, underscoring the interplay of histone modifications in maintaining oncogenic epigenetic stability.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12402983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EryDB: A Transcriptomic Profile Database for Erythropoiesis and Erythroid-related Diseases.","authors":"Guangmin Zheng 郑光敏, Song Wu 吴松, Zhaojun Zhang, Zijuan Xin 辛子娟, Lijuan Zhang 张立娟, Siqi Zhao, Jing Wu 吴静, Yanxia Liu 刘彦霞, Meng Li 李蒙, Xiuyan Ruan, Nan Qiao, Yiming Bao 鲍一明, Hongzhu Qu 渠鸿竹, Xiangdong Fang 方向东","doi":"10.1093/gpbjnl/qzae029","DOIUrl":"10.1093/gpbjnl/qzae029","url":null,"abstract":"<p><p>Erythropoiesis is a finely regulated and complex process that involves multiple transformations from hematopoietic stem cells to mature red blood cells at hematopoietic sites from the embryonic to adult stages. Investigations into its molecular mechanisms have generated a wealth of expression data, including bulk and single-cell RNA sequencing data. A comprehensively integrated and well-curated erythropoiesis-specific database will greatly facilitate the mining of gene expression data and enable large-scale research of erythropoiesis and erythroid-related diseases. Here, we present EryDB, an open-access and comprehensive database dedicated to the collection, integration, analysis, and visualization of transcriptomic data for erythropoiesis and erythroid-related diseases. Currently, the database includes expertly curated quality-assured data of 3803 samples and 1,187,119 single cells derived from 107 public studies of three species (Homo sapiens, Mus musculus, and Danio rerio), nine tissue types, and five diseases. EryDB provides users with the ability to not only browse the molecular features of erythropoiesis between tissues and species, but also perform computational analyses of single-cell and bulk RNA sequencing data, thus serving as a convenient platform for customized queries and analyses. EryDB v1.0 is freely accessible at https://ngdc.cncb.ac.cn/EryDB/home.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484013","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}