GigaScience最新文献

{"title":"Open RGB imaging workflow for morphological and morphometric analysis of fruits using deep learning: a case study on almonds.","authors":"Jorge Mas-Gómez, Manuel Rubio, Federico Dicenta, Pedro José Martínez-García","doi":"10.1093/gigascience/giaf157","DOIUrl":"10.1093/gigascience/giaf157","url":null,"abstract":"<p><strong>Background: </strong>High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of artificial intelligence (AI) brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs.</p><p><strong>Results: </strong>The workflow was implemented in almond (Prunus dulcis (Mill.) D. A. Webb), a species where breeding efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals were phenotyped, making this the largest morphological study conducted in almond so far. The best segmentation and reconstruction approaches achieved error rates below 1%. Weight and area variables enabled accurate estimation of kernel thickness, with a root mean squared error of 0.47. Fifty-five heritable morphological, morphometric, and color traits were identified, highlighting their potential as target traits in breeding programs.</p><p><strong>Conclusion: </strong>The proposed workflow demonstrated robust performance across diverse datasets and was effective with limited training data for fine-tuning. Its compatibility with the output of AI-based labeling tools allows users to fully leverage the advantages of these technologies-reducing manual effort, accelerating dataset preparation, and streamlining the fine-tuning process of segmentation models. This flexibility enhances the scalability and practical applicability of the workflow in real-world phenotyping scenarios, especially in the context of breeding programs.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12970599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145793888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ScopeViewer: a browser-based solution for visualizing large biological images.","authors":"Danni Luo, Sophie Robertson, Yuanchun Zhan, Ruichen Rong, Shidan Wang, Xi Jiang, Sen Yang, Suzette Palmer, Peiran Quan, Hiroaki Kanzaki, Yujin Hoshida, Liwei Jia, Qiwei Li, Guanghua Xiao, Xiaowei Zhan","doi":"10.1093/gigascience/giag074","DOIUrl":"10.1093/gigascience/giag074","url":null,"abstract":"<p><strong>Background: </strong>Spatial transcriptomics (ST) enables a high-resolution interrogation of molecular characteristics within specific spatial contexts and tissue morphology. Despite its potential, visualization of ST data is a challenging task due to the complexities in handling, sharing, and visualizing large image datasets together with molecular information.</p><p><strong>Results: </strong>We introduce ScopeViewer, a browser-based software designed to overcome these challenges. ScopeViewer offers the following functionalities: (1) it visualizes large image data and associated annotations at various zoom levels, allowing for intricate exploration of the data; (2) it enables dual interactive viewing of the original images along with their annotations, providing a comprehensive understanding of the context; (3) it displays spatial molecular features with optimized bandwidth, ensuring a smooth user experience; and (4) it bolsters data security by circumventing data transfers.</p><p><strong>Conclusions and discussions: </strong>ScopeViewer offers the research community a convenient, powerful, and secure software for high-resolution images, including pathology images and ST. It serves as an open-source platform for imaging-based research. Future enhancements and new features will be shared on GitHub by the creators and are open for contributions from other researchers. ScopeViewer is freely available on the website at https://cdc.biohpc.swmed.edu/scopeviewer.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13369961/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148301581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A preregistered, open pipeline for early cerebral palsy risk assessment from infant videos.","authors":"Melanie Segado, Laura A Prosser, Andrea F Duncan, Michelle J Johnson, Konrad P Kording","doi":"10.1093/gigascience/giag003","DOIUrl":"10.1093/gigascience/giag003","url":null,"abstract":"<p><p>Cerebral palsy (CP), affecting approximately 1 in 500 children due to abnormal brain development, impacts movement control. Early risk assessment via the general movements assessment (GMA) at 3-4 months is highly predictive for CP but relies on trained clinicians. Machine-learning-based approaches for predicting GMA score from video have shown considerable promise, but typically rely on dataset-specific preprocessing, custom feature sets, and manually designed model pipelines, which make external benchmarking more difficult. This, combined with strict privacy constraints on sharing data, makes it challenging to train and evaluate models across datasets, which is important for assessing clinical utility. There is therefore a need to develop approaches that will work across different datasets to enable multi-site dataset aggregation and model training. To address this gap, we developed an end-to-end pipeline that uses off-the-shelf pose estimation, general-purpose feature extraction, and automated machine learning-none of which are tuned to a specific dataset. We applied this approach to a newly generated large dataset of 1053 infants (with approximately 10-12% positive class for adverse GMA outcome, drawn from a high-risk clinical cohort) within a preregistered study design. Model performance was evaluated on a strict \"lock-box\" test set, which remained untouched during any phase of model development or preprocessing optimization, and only used for evaluation once the final model and pipeline had been preregistered. The developed model achieved moderate predictive accuracy for clinician-assessed GMA scores (area under the receiver operating characteristic curve, ROC-AUC = 0.77; area under the precision-recall curve, PR-AUC = 0.41). The moderate accuracy is noteworthy given the 10-12% positive class prevalence, and power-law scaling of ROC-AUC as a function of increasing dataset size. By releasing de-identified feature data and open-source code, and simplifying the training pipeline using AutoML, our work establishes essential groundwork for future robust, globally relevant CP screening tools suitable for low-resource settings.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13152017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.","authors":"Indah Pakpahan, Mentari Sihombing, Haohan Liu, Mengyao Wang, Zheng Su, Mingyan Fang","doi":"10.1093/gigascience/giag004","DOIUrl":"10.1093/gigascience/giag004","url":null,"abstract":"<p><p>Accurate genetic variant interpretation is crucial for disease research and the development of targeted therapies. Artificial intelligence is transforming this field by integrating computational methodologies across structural biology, evolutionary analysis, and multimodal genomic data. This review examines the evolution from traditional rule-based systems and statistical models to contemporary machine learning, deep learning, and protein language models, while addressing critical challenges in variant classification. Key obstacles include data heterogeneity, interpretability, and the persistence of variants of uncertain significance, emphasizing the critical need for explainable artificial intelligence frameworks and more inclusive genomic databases to improve predictive accuracy across diverse populations. Based on the assessment of current variant impact predictors, we propose strategies for enhanced predictor selection, effective multi-omics data integration, and optimized computational workflows. These recommendations aim to enhance variant interpretation accuracy in both research settings and clinical practice, ultimately contributing to advances in personalized medicine.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12888390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145948740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GigaDB: a redesigned repository for data publishing and management.","authors":"Xiaoqiang Li, Cong Hua, Qian Yue, Zhiyong Li, Jiawei Tong, Ziheng Luo, Tao Yang, Lijin You, Hongfang Zhang, Dongni Ma, Xiaofeng Wei, Hongling Zhou","doi":"10.1093/gigascience/giag047","DOIUrl":"10.1093/gigascience/giag047","url":null,"abstract":"<p><p>GigaDB is a repository that links research articles with the underlying datasets, software, and metadata, helping to support open and reproducible research. As of February 2026, it contains 2,710 published datasets, covering 90.56 TB of data. Over the past year, the platform has been rebuilt to better support the growing scale of the repository and to improve data submission, management, discoverability, and reuse. The updated system is organized around four core modules: role-based permission control, dataset management, workflow management, and dataset retrieval. These improvements make submission and curation more efficient and transparent, strengthen access to published data and related resources, and provide a strong foundation for the future development of GigaDB services.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13201076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147698487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in motor imagery and execution.","authors":"Benjakarn Uengsawapak, Supavit Kongwudhikunakorn, Suktipol Kiatthaveephong, Wipamas Polpakdee, Rattanaphon Chaisaen, Chanitsada Chuenchit, Poramate Manoonpong, Gun Bhakdisongkhram, Theerawit Wilaiprasitporn","doi":"10.1093/gigascience/giag065","DOIUrl":"10.1093/gigascience/giag065","url":null,"abstract":"<p><p>This study presents the first publicly accessible electroencephalography (EEG) dataset explicitly targeting sit-to-stand and stand-to-sit transitions during both motor execution (ME) and motor imagery (MI) tasks. Twenty-two healthy participants performed sitting and standing transitions under well-controlled experimental conditions while 60-channel EEG, electrooculography (EOG), and electromyography (EMG) signals were synchronously recorded. The dataset enables the exploration of neural activation patterns associated with lower-limb movements and supports the development of EEG-based brain-computer interface (BCI) algorithms for mobility assistance and rehabilitation. To validate the dataset, benchmark classification was conducted on three baseline deep learning methods-CTNet, EEGNet, and TCANet. Given the high inter-subject variability inherent to EEG, leave-one-subject-out cross-validation is used to ensure no subject bias during evaluation. Results demonstrated consistent decoding performance with mean accuracies of approximately 81% for ME and 73% for MI, indicating the reliability and usability of the dataset. Additionally, analyses of movement-related cortical potentials (MRCPs) and event-related desynchronization/synchronization (ERD/ERS) patterns revealed distinct neural signatures across the transition phases. This dataset provides a comprehensive foundation for studying lower-limb motor control, neural dynamics, and the advancement of MI-based BCIs for rehabilitation and assistive technologies.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13270978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148053997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZNF331 modulates early embryonic transcription during zygotic genome activation in goat.","authors":"Yingnan Yang, Jinhao Zhang, Xiaowei Chen, Haonan Chen, Dongxu Li, Yongjie Wan, Mingtian Deng, Feng Wang","doi":"10.1093/gigascience/giag025","DOIUrl":"10.1093/gigascience/giag025","url":null,"abstract":"<p><p>Zygotic genome activation (ZGA) is a pivotal process during early embryogenesis, marking the maternal-to-zygotic transition. ZGA is regulated by a variety of epigenetic and transcriptional factors. However, the transcriptional regulatory mechanisms underlying ZGA in livestock species remain largely unclear. By integrating ATAC-seq and RNA-seq, we characterized chromatin accessibility and transcriptional dynamics in goat embryos. Transcriptional inhibition with α-amanitin markedly reduced promoter accessibility and disrupted RNA polymerase II (Pol II)-mediated transcription. Motif enrichment analysis identified ZNF331 as a potential regulator with specific upregulation at the 8-cell stage. Functional knockdown of ZNF331 resulted in impaired embryonic development, reduced blastocyst formation, and widespread transcriptome alterations. Mechanistically, ZNF331 depletion caused abnormal elevation of Pol II Ser5 phosphorylation, excessive transcriptional activity, maternal mRNA retention, and excessive activation of zygotic genes. Our study identifies ZNF331 as a critical regulator of goat ZGA, functioning through fine-tuning Pol II Ser5 phosphorylation to balance maternal transcript clearance and zygotic gene activation. These findings highlight the essential role of the ZNF331-Pol II axis in goat embryogenesis and suggest a potentially conserved mechanism across mammals.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13080935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MultiMS2: A curated multi-modal, multi-energy spectral library for metabolomics.","authors":"Adriano Rutz, Mario S P Correia, Nicola Zamboni","doi":"10.1093/gigascience/giag069","DOIUrl":"10.1093/gigascience/giag069","url":null,"abstract":"<p><strong>Background: </strong>Spectral libraries are essential for mass spectrometry-based metabolomics, enabling accurate metabolite annotation. Collision-induced dissociation (CID) dominates existing public libraries, but is rarely sufficient for structural elucidation. Electron-activated dissociation (EAD) provides complementary, radical-driven fragmentation, but remains sparsely represented. The lack of datasets spanning multiple dissociation mechanisms, energies, and ionization modes limits both analytical workflows and the development of robust machine learning models.</p><p><strong>Findings: </strong>We present MultiMS2, a curated metabolomics spectral library comprising 43,728 MS/MS spectra from 2,899 unique compounds. Spectra were acquired using both CID and EAD at three energies each, in positive and negative ionization modes. The dataset substantially expands publicly available EAD coverage while preserving matched acquisition conditions across energies and dissociation types.</p><p><strong>Conclusions: </strong>By systematically combining CID and EAD across multiple energies and polarities, MultiMS2 provides a unique resource for metabolite annotation, benchmarking, and machine learning. The library supports energy-aware and dissociation-aware analysis, enabling methodological innovation and improved generalization in computational metabolomics.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13312951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MIFE and MIFD: Minimum information for fermentation experiments and devices.","authors":"Georgios K Georgakilas, Brett Metcalfe, Ariane Bize, Matthew Crowther, Emilie Fernandez, Susana Maria Alonso Villela, Stuart Owen, Rudolf Wittner, David Camilo Corrales, Anselm von Gladiss, Peter Blomberg, Munazah Andrabi, Cesar Arturo Aceves Lara, Hans Mattila, Marily Wiebe, Theodore Dalamagas, Jasper J Koehorst","doi":"10.1093/gigascience/giag038","DOIUrl":"10.1093/gigascience/giag038","url":null,"abstract":"<p><strong>Background: </strong>As the technological advancements of the early 21st century are pushing industrial biotechnology (IB) into the realm of Big Data-driven innovation, the requirement for trustworthy data management, annotation, and standardization is emerging as a necessity. Minimum information models (MIMs) have long been used across disciplines as the backbone of good data management practices by providing the scaffold upon which standardized recording of metadata can adequately and succinctly describe an understudied phenomenon.</p><p><strong>Findings: </strong>Here we present a minimum set of metadata, named the minimum information for fermentation experiments (MIFE) and devices (MIFD), that has been specifically designed to accommodate the data management and annotation needs of IB-related fermentation experiments. Although the proposed schema is tailored to IB applications, MIFE and MIFD build upon well-established models and community standards to facilitate easier integration with existing infrastructure and easier adoption by the community, as well as aim to integrate Findable, Accessible, Interoperable, and Reproducible (FAIR) principles in the IB field. In addition, the integration with FAIR Data Station (FAIR DS), a tool that offers metadata validation and enables the automated uptake of (meta)data from data management repositories such as FAIRDOM-SEEK, is showcased. The proposed models are accompanied by a Python package that enables their programmatic use by creating a Linked Data Modeling Language (LinkML) schema that can fuel subsequent analyses.</p><p><strong>Conclusions: </strong>Through the promotion and simplification of knowledge discovery, we believe that MIFE and MIFD can accelerate the application of state-of-the-art artificial intelligence (AI) methods and the adoption of explainable AI to better understand bioprocesses at scale.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13184968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147672281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"4D single-cell spatial transcriptomics reveals dynamic morphogenetic gradients and regenerative domains in planarians.","authors":"Kai Han, Yuxiaofei Wang, Yao Li, Lidong Guo, Yue Chen, Xiawei Liu, Yaru Lin, Zhi Huang, Qun Liu, Wenjie Guo, Rui Zhang, Wandong Zhao, Langchao Liang, Xiaoyu Wei, Li Zhou, Xuebin Mao, Jiaqi Wang, Weijian Wu, Hongwei Pan, Tao Yang, He Zhang, Xiaoshan Su, Shanshan Liu, Wenwei Zhang, Longqi Liu, Søren Tvorup Christensen, Jifeng Fei, Xin Liu, Guangyi Fan, Hanbo Li, Ying Gu, Jian Wang, Huanming Yang, Gang Pei, Xun Xu, An Zeng, Mengyang Xu","doi":"10.1093/gigascience/giag064","DOIUrl":"10.1093/gigascience/giag064","url":null,"abstract":"<p><strong>Background: </strong>Understanding how organisms reconstruct complex tissue architectures following injury requires precise mapping of gene expression and cellular responses across space and time. Although planarians serve as a classic model for whole-body regeneration, capturing the continuous spatiotemporal dynamics of positional information and cell fate decisions at the organismal scale remains a significant challenge.</p><p><strong>Results: </strong>Using high-definition spatial transcriptomics, we generated a 4-dimensional atlas encompassing over 3.5 million cells from whole animals across 8 distinct regeneration timepoints. This comprehensive dataset enabled the definition of 36 spatial domains and the tracing of body axis restoration, revealing that positional control genes recover through self-organizing dynamics analogous to an underdamped control system. We identified an injury-induced spatial domain termed the anterior regenerative zone. This unique region is characterized by the convergence of epidermal, muscular, and neural lineages enriched with positional signals. Furthermore, we demonstrated that the transcriptional co-factor Mediator 8 is a critical regulator of this zone. Depletion of Mediator 8 impairs the formation of the anterior regenerative zone, disrupts polarity establishment, and prevents successful blastema formation.</p><p><strong>Conclusions: </strong>Our study provides a holistic molecular and cellular reconstruction of whole-body regeneration, directly linking dynamic gene expression gradients to morphological restoration. The discovery of the Mediator 8-regulated anterior regenerative zone highlights the importance of spatial domains in coordinating tissue repair. The resulting interactive atlas serves as a foundational resource for deciphering the logic of spatiotemporal patterning in regeneration.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13273413/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147987189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}