Bioinformatics advances最新文献_第7页

Unlocking the potential of PubMed Central supplementary data files. 释放PubMed Central补充数据文件的潜力。

IF 2.8

Bioinformatics advances Pub Date : 2025-06-27 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf155

Julien Gobeill, Déborah Caucheteur, Alexandre Flament, Pierre-André Michel, Anaïs Mottaz, Emilie Pasche, Patrick Ruch

{"title":"Unlocking the potential of PubMed Central supplementary data files.","authors":"Julien Gobeill, Déborah Caucheteur, Alexandre Flament, Pierre-André Michel, Anaïs Mottaz, Emilie Pasche, Patrick Ruch","doi":"10.1093/bioadv/vbaf155","DOIUrl":"10.1093/bioadv/vbaf155","url":null,"abstract":"Motivation: Biocuration workflows often rely on comprehensive literature searches for specific biological entities. However, standard search engines such as MEDLINE and PubMed Central provide an incomplete picture of the scientific literature because they do not index the increasing amount of valuable information published in supplementary data files. Over two years, we addressed this gap by systematically extracting text from a large proportion (85%) of these files, resulting in 35 million searchable documents. To assess the information gain provided by supplementary data files beyond the manuscripts, we searched both for mentions of dozens of Global Core Biodata Resources (GCBRs), which are fundamental biological databases essential for the life sciences. We searched for mentions of GCBR names and accession numbers, which uniquely identify biological entities within these resources.Results: The recall gain from using the supplementary data files to search for articles mentioning resource names is 6%. In addition, 97% of all accession numbers identified were published in the supplementary data files, highlighting their increasing importance for highly specific topics or curation pipelines. We show that the number of accession numbers published in the supplementary data files is increasing year on year, but that 87% of these are published in Excel files. This format facilitates human readability and accessibility, but severely limits machine reusability and interoperability. We therefore discuss alternative and complementary approaches to the publication of research data.Availability and implementation: All extracted data are accessible and searchable as a collection on the BiodiversityPMC platform (https://biodiversitypmc.sibils.org/).","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf155"},"PeriodicalIF":2.8,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12371329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980826","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}

引用次数: 0

Correction to: PIPLOM: prediction of exogenous peptide loading on major histocompatibility complex class I molecules. 修正：PIPLOM：预测外源肽在主要组织相容性复合体I类分子上的负载。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-27 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf138

引用次数: 0

ABCFold: easier running and comparison of AlphaFold 3, Boltz-1, and Chai-1. abfold：更容易运行和比较AlphaFold 3， Boltz-1和Chai-1。

IF 2.8

Bioinformatics advances Pub Date : 2025-06-27 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf153

Luc G Elliott, Adam J Simpkin, Daniel J Rigden

{"title":"ABCFold: easier running and comparison of AlphaFold 3, Boltz-1, and Chai-1.","authors":"Luc G Elliott, Adam J Simpkin, Daniel J Rigden","doi":"10.1093/bioadv/vbaf153","DOIUrl":"10.1093/bioadv/vbaf153","url":null,"abstract":"Motivation: The latest generation of deep learning-based structure prediction methods enable accurate modelling of most proteins and many complexes. However, preparing inputs for the locally installed software is not always straightforward, and the results of local runs are not always presented in an ideally accessible fashion. Furthermore, it is not yet clear whether the latest tools perform equivalently for all types of target.Results: ABCFold facilitates the use of AlphaFold 3, Boltz-1, and Chai-1 with a standardized input to predict atomic structures, with Boltz-1 and Chai-1 being installed on runtime (if required). MSAs can be generated internally using either the JackHMMER MSA search within AlphaFold 3, or with the MMseqs2 API. Alternatively, users can provide their own custom MSAs. This therefore allows AlphaFold 3 to be installed and run without downloading the large databases needed for JackHMMER. There are also straightforward options to use templates, including custom templates. Results from all packages are treated in a unified fashion, enabling easy comparison of results from different methods. A variety of visualization options are available which include information on steric clashes.Availability and implementation: ABCFold is coded in Python and JavaScript. All scripts and associated documentation are available from https://github.com/rigdenlab/ABCFold or https://pypi.org/project/ABCFold/.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf153"},"PeriodicalIF":2.8,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710027","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}

引用次数: 0

Genome Evaluation Pipeline (GEP): a fully automated quality control tool for parallel evaluation of genome assemblies. 基因组评估管道（GEP）：一个全自动化的质量控制工具，用于平行评估基因组组装。

IF 2.8

Bioinformatics advances Pub Date : 2025-06-26 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf147

James Sullivan, Diego De Panis, Valentina Galeone, Camila J Mazzoni

{"title":"Genome Evaluation Pipeline (GEP): a fully automated quality control tool for parallel evaluation of genome assemblies.","authors":"James Sullivan, Diego De Panis, Valentina Galeone, Camila J Mazzoni","doi":"10.1093/bioadv/vbaf147","DOIUrl":"10.1093/bioadv/vbaf147","url":null,"abstract":"Summary: The ability to generate high-quality genome assemblies is paramount for understanding global biodiversity. To streamline the quality control of the vast amount of assemblies currently being generated, we developed the Genome Evaluation Pipeline (GEP). Our Snakemake-based command-line tool is composed of two modes and was designed taking into consideration the recommendations of different international projects to standardize genome evaluation across the Tree of Life. With the Build Mode, GEP generates k-mer databases from high-accuracy sequencing reads, incorporating optional quality control and pre-processing steps. The Evaluate Mode leverages these databases to assess genome assembly quality using standard, gene content, and k-mer based metrics. Key features include the assessment of genome characteristics such as size, heterozygosity, and ploidy without reference sequences, and the comparison of k-mer profiles to evaluate assembly completeness and correctness. GEP also supports flexible input options for k-mer databases and the integration of Hi-C data for visual inspection of the assembly structure. Finally, our tool produces comprehensive reports summarizing contiguity, completeness, and correctness metrics of genome assemblies, facilitating their comparison and selection for downstream studies.Availability and implementation: GEP is publicly available as a Git repository at https://git.imp.fu-berlin.de/begendiv/gep.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf147"},"PeriodicalIF":2.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12296351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735826","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}

引用次数: 0

BugBuster: a novel automatic and reproducible workflow for metagenomic data analysis. BugBuster：一种用于宏基因组数据分析的新颖的自动和可重复的工作流程。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-26 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf152

Francisco Fuentes-Santander, Carolina Curiqueo, Rafael Araos, Juan A Ugalde

{"title":"BugBuster: a novel automatic and reproducible workflow for metagenomic data analysis.","authors":"Francisco Fuentes-Santander, Carolina Curiqueo, Rafael Araos, Juan A Ugalde","doi":"10.1093/bioadv/vbaf152","DOIUrl":"10.1093/bioadv/vbaf152","url":null,"abstract":"Summary: Metagenomic sequencing generates massive datasets that capture the complete genetic content of a sample, enabling detailed characterization of microbial communities. Yet the software and processes necessary to transform raw data into biologically meaningful results have become increasingly complex, limiting accessibility for researchers without specialist expertise. In this work, we present a novel modular a reproducible workflow developed to facilitate the analysis of metagenomic data. BugBuster is a fully containerized, modular, and reproducible workflow implemented in Nextflow. The pipeline streamlines analysis at level of reads, contigs, and metagenome-assembled genomes, offering dedicated modules for taxonomic profiling and resistome characterization. Thanks to the use of containers, BugBuster can be deployed with minimal configuration on workstations, high-performance clusters, or cloud platforms. Together, these features allow the robust, scalable, and reproducible analysis of metagenomic datasets.Availability and implementation: BugBuster was written in Nextflow-DSL2. The program applications, user manual, example data and code are freely available at https://github.com/gene2dis/BugBuster.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf152"},"PeriodicalIF":2.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12255886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627816","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}

引用次数: 0

EpipwR: efficient power analysis for EWAS with continuous outcomes. EpipwR: EWAS持续结果的有效功率分析。

IF 2.8

Bioinformatics advances Pub Date : 2025-06-25 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf150

Jackson Barth, Austin W Reynolds

{"title":"EpipwR: efficient power analysis for EWAS with continuous outcomes.","authors":"Jackson Barth, Austin W Reynolds","doi":"10.1093/bioadv/vbaf150","DOIUrl":"10.1093/bioadv/vbaf150","url":null,"abstract":"Motivation: Epigenome-wide association studies (EWAS) have emerged as a popular way to investigate the pathophysiology of complex diseases and to assist in bridging the gap between genotypes and phenotypes. Despite the increasing popularity of EWAS, very few tools exist to aid researchers in power estimation and those are limited to case-control studies. The existence of user-friendly tools, expanding power calculation functionality to additional study designs, would be a significant aid to researchers planning EWAS.Results: We introduce EpipwR, an open-source R-package that can efficiently estimate power for EWAS with continuous or binary outcomes. EpipwR uses a quasi-simulated approach, meaning that data is generated only for CpG sites with methylation associated with the outcome, while P-values are generated directly for those with no association (when necessary). Like existing EWAS power calculators, reference datasets of empirical EWAS are used to guide the data generation process. Two numerical studies show the effect of the selected empirical dataset on the generated correlations and power, while another explores the accuracy of EpipwR against case-control alternatives. EpipwR is shown to outperform existing alternatives on both simulated and real EWAS datasets.Availability and implementation: The EpipwR R-package is currently available on Bioconductor or at github.com/jbarth216/EpipwR.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf150"},"PeriodicalIF":2.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12303865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746313","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}

引用次数: 0

Survey and improvement strategies for gene prioritization with large language models. 基于大型语言模型的基因优先排序研究与改进策略。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-24 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf148

Matthew B Neeley, Guantong Qi, Guanchu Wang, Ruixiang Tang, Dongxue Mao, Chaozhong Liu, Sasidhar Pasupuleti, Bo Yuan, Fan Xia, Pengfei Liu, Zhandong Liu, Xia Hu

{"title":"Survey and improvement strategies for gene prioritization with large language models.","authors":"Matthew B Neeley, Guantong Qi, Guanchu Wang, Ruixiang Tang, Dongxue Mao, Chaozhong Liu, Sasidhar Pasupuleti, Bo Yuan, Fan Xia, Pengfei Liu, Zhandong Liu, Xia Hu","doi":"10.1093/bioadv/vbaf148","DOIUrl":"10.1093/bioadv/vbaf148","url":null,"abstract":"Motivation: Rare diseases remain difficult to diagnose due to limited patient data and genetic diversity, with many cases remaining undiagnosed despite advances in variant prioritization tools. While large language models have shown promise in medical applications, their optimal application for trustworthy and accurate gene prioritization downstream of modern prioritization tools has not been systematically evaluated.Results: We benchmarked various language models for gene prioritization using multi-agent and Human Phenotype Ontology classification approaches to categorize patient cases by phenotype-based solvability levels. To address language model limitations in ranking large gene sets, we implemented a divide-and-conquer strategy with mini-batching and token limiting for improved efficiency. GPT-4 outperformed other language models across all patient datasets, demonstrating superior accuracy in ranking causal genes. Multi-agent and Human Phenotype Ontology classification approaches effectively distinguished between confidently-solved and challenging cases. However, we observed bias toward well-studied genes and input order sensitivity as notable language model limitations. Our divide-and-conquer strategy enhanced accuracy, overcoming positional and gene frequency biases in literature. This framework optimized the overall process for identifying disease-causal genes compared to baseline evaluation, better enabling targeted diagnostic and therapeutic interventions and streamlining diagnosis of rare genetic disorders.Availability and implementation: Software and additional material is available at: https://github.com/LiuzLab/GPT-Diagnosis.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf148"},"PeriodicalIF":2.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12263109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144644232","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}

引用次数: 0

VueGen: automating the generation of scientific reports. VueGen：自动生成科学报告。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-24 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf149

Sebastián Ayala-Ruano, Henry Webel, Alberto Santos

{"title":"VueGen: automating the generation of scientific reports.","authors":"Sebastián Ayala-Ruano, Henry Webel, Alberto Santos","doi":"10.1093/bioadv/vbaf149","DOIUrl":"10.1093/bioadv/vbaf149","url":null,"abstract":"Motivation: The analysis of omics data typically involves multiple bioinformatics tools and methods, each producing distinct output files. However, compiling these results into comprehensive reports often requires additional effort and technical skills. This creates a barrier for non-bioinformaticians, limiting their ability to produce reports from their findings. Moreover, the lack of streamlined reporting workflows impacts reproducibility and transparency, making it difficult to communicate results and track analytical processes.Results: We present VueGen, a tool that automates the creation of reports from bioinformatics outputs, allowing researchers with minimal coding experience to communicate their results effectively. With VueGen, users can produce reports by simply specifying a directory containing output files, such as plots, tables, networks, Markdown text, and HTML components, along with the report format. Supported formats include documents (PDF, HTML, DOCX, ODT), presentations (PPTX, Reveal.js), Jupyter notebooks, and Streamlit web applications. To showcase VueGen's functionality, we present two case studies and provide detailed documentation to help users generate customized reports.Availability and implementation: VueGen is distributed as a Python package, Docker image, nf-core module, and desktop application. The source code is freely available on https://github.com/Multiomics-Analytics-Group/vuegen under the MIT license. Documentation is provided at https://vuegen.readthedocs.io/.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf149"},"PeriodicalIF":2.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12233086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585683","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}

引用次数: 0

The TEMPO integrator: accelerating molecular simulations by temporally multiscale force prediction. TEMPO积分器：通过时间多尺度力预测加速分子模拟。

IF 2.8

Bioinformatics advances Pub Date : 2025-06-20 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf142

Reshef Mintz, Barak Raveh

{"title":"The TEMPO integrator: accelerating molecular simulations by temporally multiscale force prediction.","authors":"Reshef Mintz, Barak Raveh","doi":"10.1093/bioadv/vbaf142","DOIUrl":"10.1093/bioadv/vbaf142","url":null,"abstract":"Motivation: Molecular dynamics (MD) simulations enable the study of complex biomolecular processes by integrating system forces over time, but their computational inefficiency limits application at relevant scales. Enhanced sampling methods often sacrifice kinetic detail and require prior knowledge of the energy landscape.Results: We developed the temporally multiscale prediction (TEMPO) Integrator, significantly reducing the number of force evaluations per simulated time unit by predicting forces at progressively larger intervals, thus boosting force-call efficiency. We incorporated the TEMPO integrator in a multiscale Brownian dynamics (MSBD) simulation tool. Compared with standard Brownian dynamics using the Euler-Maruyama integrator, our benchmarks of MSBD demonstrated 27- to 32-fold efficiency improvements for intrinsically disordered protein models and a seven-fold gain for nucleocytoplasmic transport through the nuclear pore complex (NPC), a critical cellular process in health and disease. Unlike conventional enhanced sampling, MSBD preserves kinetic properties, such as reaction rates, without relying on prior system knowledge or predefined reaction coordinates. By leveraging the inherently multiscale structure of energy landscapes, MSBD facilitates rapid molecular simulations while maintaining their accuracy. TEMPO's flexible framework is generalizable to various biomolecular systems and could complement existing enhanced sampling methods, facilitating efficient exploration of energy landscapes or complex dynamical processes.Availability and implementation: https://github.com/ravehlab/tempo.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf142"},"PeriodicalIF":2.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12377907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980829","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}

引用次数: 0

Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines. 使用可解释的Tsetlin机器预测急性腹膜炎腹膜透析患者的感染微生物。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-19 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf140

Olga Tarasyuk, Anatoliy Gorbenko, Matthias Eberl, Nicholas Topley, Jingjing Zhang, Rishad Shafik, Alex Yakovlev

{"title":"Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines.","authors":"Olga Tarasyuk, Anatoliy Gorbenko, Matthias Eberl, Nicholas Topley, Jingjing Zhang, Rishad Shafik, Alex Yakovlev","doi":"10.1093/bioadv/vbaf140","DOIUrl":"10.1093/bioadv/vbaf140","url":null,"abstract":"Motivation: The analysis of complex biomedical datasets is becoming central to understanding disease mechanisms, aiding risk stratification and guiding patient management. However, the utility of computational methods is often constrained by their lack of interpretability, which is particularly relevant in clinically critical areas where rapid initiation of targeted therapies is key.Results: To define diagnostically relevant immune signatures in peritoneal dialysis patients presenting with acute peritonitis, we analysed a comprehensive array of cellular and soluble parameters in cloudy peritoneal effluents. Utilizing Tsetlin Machines, a logic-based machine learning approach, we identified pathogen-specific immune fingerprints for different bacterial groups, each characterized by unique biomarker combinations. Unlike traditional 'black box' machine learning models, Tsetlin Machines identified clear, logical rules in the dataset that pointed towards distinctly nuanced immune responses to different types of bacterial infection. Importantly, these immune signatures could be easily visualized to facilitate their interpretation, thereby allowing for rapid, accurate and transparent decision-making. This unique diagnostic capacity of Tsetlin Machines could help deliver early patient risk stratification and support informed treatment choices in advance of conventional microbiological culture results, thus guiding antibiotic stewardship and contributing to improved patient outcomes.Availability and implementation: All underlying tools and the anonymized data underpinning this publication are available at https://github.com/anatoliy-gorbenko/biomarkers-visualization.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf140"},"PeriodicalIF":2.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144593045","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}

引用次数: 0