Bioinformatics (Oxford, England)最新文献

{"title":"diffGEK: Differential Gene Expression Kinetics.","authors":"Melania Barile, Shirom Chabra, Tomoya Isobe, Berthold Gottgens","doi":"10.1093/bioinformatics/btaf316","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf316","url":null,"abstract":"<p><strong>Motivation: </strong>A defining characteristic of all metazoan organisms is the existence of different cell states or cell types, driven by changes in gene expression kinetics, principally transcription, splicing and degradation rates. The RNA velocity framework utilizes both spliced and unspliced reads in single cell mRNA preparations to predict future cellular states and estimate transcriptional kinetics. However, current models assume either constant kinetic rates, rates equal for all genes, or rates completely independent of progression through differentiation. Consequently, current models for rate estimation are either underparametrised or overparametrised.</p><p><strong>Results: </strong>Here we developed a new method (diffGEK) which overcomes this issue, and allows comparison of transcriptional rates across different biological conditions. diffGEK assumes that rates can vary over a trajectory, but are smooth functions of the differentiation process. Analysing Jak2 V617F mutant versus wild type mice for erythropoiesis, and Ezh2 KO versus wild type mice in myelopoiesis, revealed which genes show altered transcription, splicing or degradation rates between different conditions. Moreover, we observed that, for some genes, compensatory changes between different rates can result in comparable overall mRNA levels, thereby masking highly dynamic changes in gene expression kinetics in conventional expression analysis. Collectively, we report a robust pipeline for comparative expression analysis based on altered transcriptional kinetics to discover mechanistic differences missed by conventional approaches, with broad applicability across any biomedical research question where single cell expression data are available for both wild type and treatment/mutant conditions.</p><p><strong>Availability: </strong>This study does not include new data. All the codes are available on github: https://github.com/mebarile/transcriptional_kinetics.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268160","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":"Mapler: A pipeline for assessing assembly quality in taxonomically rich metagenomes sequenced with HiFi reads.","authors":"Maurice Nicolas, Claire Lemaitre, Riccardo Vicedomini, Clémence Frioux","doi":"10.1093/bioinformatics/btaf334","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf334","url":null,"abstract":"<p><strong>Summary: </strong>Metagenome assembly seeks to reconstruct the most high-quality genomes from sequencing data of microbial ecosystems. Despite technological advancements that facilitate assembly, such as Hi-Fi long reads, the process remains challenging in complex environmental samples consisting of hundreds to thousands of populations. Mapler is a metagenome assembly and evaluation pipeline with a focus on evaluating the quality of Hi-Fi long read metagenome assemblies. It incorporates several state-of-the-art metrics, as well as novel metrics assessing the diversity that remains uncaptured by the assembly process. Mapler facilitates the comparison of assembly strategies and helps identify methodological bottlenecks that hinder genome reconstruction.</p><p><strong>Availability and implementation: </strong>Mapler is open source and publicly available under the AGPL-3.0 licence at https://github.com/Nimauric/Mapler. Source code is implemented in Python and Bash as a Snakemake pipeline. A snapshot of the code is available on Software Heritage at swh:1:snp:df4f5f02e22ebbab285ec14af58d4d88436ee5d6.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251293","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":"Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data.","authors":"Vanessa E Kennedy, Ritu Roy, Cheryl A C Peretz, Andrew Koh, Elaine Tran, Catherine C Smith, Adam B Olshen","doi":"10.1093/bioinformatics/btaf265","DOIUrl":"10.1093/bioinformatics/btaf265","url":null,"abstract":"<p><strong>Motivation: </strong>Single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have recently provided key insights into cancer heterogeneity. Scaling these technologies across large patient cohorts, however, is cost and time prohibitive. Multiplexing, in which cells from unique patients are pooled into a single experiment, offers a possible solution. While multiplexing methods exist for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need.</p><p><strong>Results: </strong>Here, we introduce SNACS: Single-Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting. SNACS relies on a combination of patient-level cell-surface identifiers and natural variation in genetic polymorphisms to demultiplex scDNAseq data. We demonstrated the performance of SNACS on a dataset consisting of multi-sample experiments from patients with leukemia where we knew truth from single-sample experiments from the same patients. Using SNACS, accuracy ranged from 0.948-0.991 vs 0.552-0.934 using demultiplexing methods from the single-cell literature.</p><p><strong>Availability: </strong>SNACS is available at  https://github.com/olshena/SNACS  .</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236121","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":"Between Cluster Analysis: Supervised Dimensionality Reduction for Trajectory Inference.","authors":"Alexander Strzalkowski, Ron Zeira, Benjamin J Raphael","doi":"10.1093/bioinformatics/btaf306","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf306","url":null,"abstract":"<p><strong>Motivation: </strong>Single-cell RNA sequencing (scRNA-seq) measures the transcriptional state of individual cells, enabling more precise characterization of cell types, cell states, and developmental trajectories. Because of the high dimensionality of scRNA-seq data, a standard first step in scRNA-seq analysis is to perform dimensionality reduction. PCA and many other commonly used dimensionality reduction techniques are unsupervised, meaning that they do not incorporate any prior knowledge of the data being analyzed. On the other hand, nearly all trajectory inference methods are supervised, relying on information such as a clustering of cells into cell types/states.</p><p><strong>Results: </strong>We introduce Between Cluster Analysis (BCA), a supervised linear dimensionality reduction technique that uses cluster labels of cells as prior information and computes an embedding that maximizes the between cluster variance. We show on both simulated and real data that BCA improves trajectory inference compared to other dimensionality reduction methods, including Linear Discriminant Analysis (LDA), another supervised linear dimensionality reduction method. Additionally, we observe that many of the commonly used metrics to evaluate trajectory inference evaluate only the ordering of cell types and not the identification or ordering of intermediate cell states. We propose an alternative measure to evaluate trajectory inference methods in preserving intermediate cells, especially when the ordering of these intermediate cells is unknown.</p><p><strong>Availability: </strong>Code is available at https://github.com/raphael-group/BCA.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236120","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":"NEFFy: A Versatile Tool for Computing the Number of Effective Sequences.","authors":"Maryam Haghani, Debswapna Bhattacharya, T M Murali","doi":"10.1093/bioinformatics/btaf222","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf222","url":null,"abstract":"<p><strong>Motivation: </strong>A Multiple Sequence Alignment (MSA) contains fundamental evolutionary information that is useful in the prediction of structure and function of proteins and nucleic acids. The \"Number of Effective Sequences\" (NEFF) quantifies the diversity of sequences of an MSA. While several tools embed NEFF calculation with various options, none are standalone tools for this purpose, and they do not offer all the available options.</p><p><strong>Results: </strong>We developed NEFFy, the first software package to integrate all these options and calculate NEFF across diverse MSA formats for proteins, RNAs, and DNAs. It surpasses existing tools in functionality without compromising computational efficiency and scalability. NEFFy also offers per-residue NEFF calculation and supports NEFF computation for MSAs of multimeric proteins, with the capability to be extended to DNAs and RNAs.</p><p><strong>Availability and implementation: </strong>NEFFy is released as open-source software under the GNU Public License v3.0. The source code in C ++ and a Python wrapper are available at https://github.com/Maryam-Haghani/NEFFy. To ensure users can fully leverage these capabilities, comprehensive documentation and examples are provided at https://Maryam-Haghani.github.io/NEFFy.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210474","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":"Pool PaRTI: a PageRank-based pooling method for identifying critical residues and enhancing protein sequence representations.","authors":"Alp Tartici, Gowri Nayar, Russ B Altman","doi":"10.1093/bioinformatics/btaf330","DOIUrl":"10.1093/bioinformatics/btaf330","url":null,"abstract":"<p><strong>Motivation: </strong>Protein language models (PLMs) produce token-level embeddings for each residue, resulting in an output matrix with dimensions that vary based on sequence length. However, downstream machine learning models typically require fixed-length input vectors, necessitating a pooling method to compress the output matrix into a single vector representation of the entire protein. Traditional pooling methods often result in substantial information loss, impacting downstream task performance. We aim to develop a pooling method that produces more expressive general-purpose protein embedding vectors while offering biological interpretability.</p><p><strong>Results: </strong>We introduce Pool PaRTI, a novel pooling method that leverages internal transformer attention matrices and PageRank to assign token importance weights. Our unsupervised and parameter-free approach consistently prioritizes residues experimentally annotated as critical for function, assigning them higher importance scores. Across four diverse protein machine learning tasks, Pool PaRTI enables significant performance gains in predictive performance. Additionally, it enhances interpretability by identifying biologically relevant regions without relying on explicit structural data or annotated training. To assess generalizability, we evaluated Pool PaRTI with two encoder-only PLMs, confirming its robustness across different models.</p><p><strong>Availability and implementation: </strong>Pool PaRTI is implemented in Python with PyTorch and is available at github.com/Helix-Research-Lab/Pool_PaRTI.git. The Pool PaRTI sequence embeddings and residue importance values for all human proteins on UniProt are available at zenodo.org/records/15036725 for ESM2 and protBERT.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144201045","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":"Synergizing multimodal data and fingerprint space exploration for mechanism of action prediction.","authors":"Kaimiao Hu, Jianguo Wei, Changming Sun, Jie Geng, Leyi Wei, Qi Dai, Ran Su","doi":"10.1093/bioinformatics/btaf223","DOIUrl":"10.1093/bioinformatics/btaf223","url":null,"abstract":"<p><strong>Motivation: </strong>Effective computational methods for predicting the mechanism of action (MoA) of compounds are essential in drug discovery. Current MoA prediction models mainly utilize the structural information of compounds. However, high-throughput screening technologies have generated more targeted cell perturbation data for MoA prediction, a factor frequently disregarded by the majority of current approaches. Moreover, exploring the commonalities and specificities among different fingerprint representations remains challenging.</p><p><strong>Results: </strong>In this paper, we propose IFMoAP, a model integrating cell perturbation image and fingerprint data for MoA prediction. Firstly, we modify the Res-Net to accommodate the feature extraction of five-channel cell perturbation images and establish a granularity-level attention mechanism to combine coarse- and fine-grained features. To learn both common and specific fingerprint features, we introduce an FP-CS module, projecting four fingerprint embeddings into distinct spaces and incorporating two loss functions for effective learning. Finally, we construct two independent classifiers based on image and fingerprint features for prediction and for weighting the two prediction scores. Experimental results demonstrate that our model achieves highest accuracy of 0.941 when using multimodal data. The comparison with other methods and explorations further highlights the superiority of our proposed model and the complementary characteristics of multimodal data.</p><p><strong>Availability and implementation: </strong>The source code is available at https://github.com/ s1mplehu/IFMoAP. The raw image data of Cell Painting can be accessed from Figshare (https://doi.org/10.17044/scilifelab.21378906).</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210476","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":"epLSAP-Align: a non-sequential protein structural alignment solver with entropy-regularized partial linear sum assignment problem formulation.","authors":"Xuechen Zhang, Zhuoyang Chen, Junyu Li, Qiong Luo, Longjun Wu, Weichuan Yu","doi":"10.1093/bioinformatics/btaf309","DOIUrl":"10.1093/bioinformatics/btaf309","url":null,"abstract":"<p><strong>Motivation: </strong>The three-dimensional protein tertiary structure alignment is a fundamental problem that seeks insights into functions and evolution. Previous structure alignment algorithms have adopted the sequential assumption and used dynamic programming solvers. However, many distantly related structures exhibit non-sequential similarities, and non-sequential alignment tools are less efficient and accurate than sequential ones. In this paper, we formulate the non-sequential alignment as the Entropy-regularized Partial Linear Sum Assignment Problem (epLSAP) and propose a solver based on Sinkhorn algorithms, referred to as epLSAP-Align.</p><p><strong>Results: </strong>Compared with existing non-sequential alignment solvers, our epLSAP-Align can explicitly model the gap penalty, efficiently achieve global optimality and balance coverage and fidelity. We show that epLSAP-Align can be easily integrated into the existing frameworks, such as TM-align and MICAN, resulting in the non-sequential alignment tool epLSAP-TM and epLSAP-MICAN, respectively. Both epLSAP-TM and epLSAP-MICAN achieve better performance than the existing non-sequential alignment tools in terms of biologically meaningful structure overlaps on two sequential alignment test sets MALIDUP and MALISAM, and four non-sequential alignment test sets MALIDUP-ns, MALISAM-ns, 64-difficult-case and RIPC datasets. Also, compared with the most recent non-sequential alignment tool USalign2, our epLSAP-TM is at least 22% faster under the same setting.</p><p><strong>Availability and implementation: </strong>Our source code is available at https://github.com/xzhangem/epLSAP-align.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12137893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113031","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":"DiSC: a statistical tool for fast differential expression analysis of individual-level single-cell RNA-seq data.","authors":"Lujun Zhang, Lu Yang, Yingxue Ren, Shuwen Zhang, Weihua Guan, Jun Chen","doi":"10.1093/bioinformatics/btaf327","DOIUrl":"10.1093/bioinformatics/btaf327","url":null,"abstract":"<p><strong>Motivation: </strong>Single-cell RNA sequencing (scRNA-seq) has become an important method for characterizing cellular heterogeneity, revealing more biological insights than the bulk RNA-seq. The surge in scRNA-seq data across multiple individuals calls for efficient and statistically powerful methods for differential expression (DE) analysis that addresses individual-level biological variability.</p><p><strong>Results: </strong>We introduced DiSC, a method for conducting individual-level DE analysis by extracting multiple distributional characteristics, jointly testing their association with a variable of interest, and using a flexible permutation testing framework to control the false discovery rate (FDR). Our simulation studies demonstrated that DiSC effectively controlled the FDR across various settings and exhibited high statistical power in detecting different types of gene expression changes. Moreover, DiSC is computationally efficient and scalable to the rapidly increasing sample sizes in scRNA-seq studies. When applying DiSC to identify DE genes potentially associated with COVID-19 severity and Alzheimer's disease across various types of peripheral blood mononuclear cells and neural cells, we found that our method was approximately 100 times faster than other state-of-the-art methods and the results were consistent and supported by existing literature. While DiSC was developed for scRNA-seq data, its robust testing framework can also be applied to other types of single-cell data. We applied DiSC to cytometry by time-of-flight data, DiSC identified significantly more DE markers than traditional methods.</p><p><strong>Availability and implementation: </strong>The R software package \"SingleCellStat\" is freely available on CRAN (https://cran.r-project.org/web/packages/SingleCellStat/index.html) and GitHub (https://github.com/Lujun995/DiSC). The replication code for reproducing the analyses in this study is publicly accessible at https://github.com/Lujun995/DiSC_Replication_Code. The scRNA-seq expression matrix and metadata utilized in our simulations and analyses can be retrieved from https://cells.ucsc.edu/autism/rawMatrix.zip, https://cellxgene.cziscience.com/collections/1ca90a2d-2943-483d-b678-b809bf464c30, and https://covid19.cog.sanger.ac.uk/submissions/release1/haniffa21.processed.h5ad.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188675","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":"Pedixplorer: a Bioconductor package to streamline pedigree design and visualization.","authors":"Louis Le Nézet, Jason Sinnwell, Anna Letko, Catherine André, Pascale Quignon","doi":"10.1093/bioinformatics/btaf329","DOIUrl":"10.1093/bioinformatics/btaf329","url":null,"abstract":"<p><strong>Motivation: </strong>Understanding kinship relationships is fundamental to genetic research, particularly in the context of genetic linkage studies and population genetics. Pedigree design and analysis are a prerequisite for these investigations. The legacy kinship2 CRAN package has been a cornerstone in this area; however, the need for handling larger and more complex datasets necessitates an updated, flexible, and user-friendly toolset. To address this issue, we present Pedixplorer, a novel Bioconductor package designed to enhance kinship analyses with modern functionality and usability, especially in large multigeneration complex pedigrees with inbreeding loops, which are frequently seen in domestic animal breeding.</p><p><strong>Results: </strong>Pedixplorer builds upon the robust foundation of kinship2, integrating Bioconductor standards and most recent programming practices. Its core component is the S4 Pedigree object, facilitating efficient representation of complex pedigrees. The new functions enable automatic querying, filtering, and trimming of large pedigrees, while the graphical functions have been rewritten for better customization in pedigree visualizations. Additionally, Pedixplorer offers a comprehensive Shiny application, accessible both locally and via a dedicated website, allowing non-R users to easily create, filter, and customize pedigrees.</p><p><strong>Availability and implementation: </strong>The Pedixplorer package is freely available at: https://www.bioconductor.org/packages/release/bioc/html/Pedixplorer.html with additional documentation at https://louislenezet.github.io/Pedixplorer. A user-friendly web application is available at: https://pedixplorer.univ-rennes.fr.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210475","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}