GigaScience最新文献

{"title":"stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multimodal feature representation.","authors":"Daoliang Zhang, Na Yu, Zhiyuan Yuan, Wenrui Li, Xue Sun, Qi Zou, Xiangyu Li, Zhiping Liu, Wei Zhang, Rui Gao","doi":"10.1093/gigascience/giae089","DOIUrl":"10.1093/gigascience/giae089","url":null,"abstract":"<p><strong>Background: </strong>Deciphering spatial domains using spatially resolved transcriptomics (SRT) is of great value for characterizing and understanding tissue architecture. However, the inherent heterogeneity and varying spatial resolutions present challenges in the joint analysis of multimodal SRT data.</p><p><strong>Results: </strong>We introduce a multimodal geometric deep learning method, named stMMR, to effectively integrate gene expression, spatial location, and histological information for accurate identifying spatial domains from SRT data. stMMR uses graph convolutional networks and a self-attention module for deep embedding of features within unimodality and incorporates similarity contrastive learning for integrating features across modalities.</p><p><strong>Conclusions: </strong>Comprehensive benchmark analysis on various types of spatial data shows superior performance of stMMR in multiple analyses, including spatial domain identification, pseudo-spatiotemporal analysis, and domain-specific gene discovery. In chicken heart development, stMMR reconstructed the spatiotemporal lineage structures, indicating an accurate developmental sequence. In breast cancer and lung cancer, stMMR clearly delineated the tumor microenvironment and identified marker genes associated with diagnosis and prognosis. Overall, stMMR is capable of effectively utilizing the multimodal information of various SRT data to explore and characterize tissue architectures of homeostasis, development, and tumor.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11604062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142750406","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":"PlasGO: enhancing GO-based function prediction for plasmid-encoded proteins based on genetic structure.","authors":"Yongxin Ji, Jiayu Shang, Jiaojiao Guan, Wei Zou, Herui Liao, Xubo Tang, Yanni Sun","doi":"10.1093/gigascience/giae104","DOIUrl":"10.1093/gigascience/giae104","url":null,"abstract":"<p><strong>Background: </strong>Plasmid, as a mobile genetic element, plays a pivotal role in facilitating the transfer of traits, such as antimicrobial resistance, among the bacterial community. Annotating plasmid-encoded proteins with the widely used Gene Ontology (GO) vocabulary is a fundamental step in various tasks, including plasmid mobility classification. However, GO prediction for plasmid-encoded proteins faces 2 major challenges: the high diversity of functions and the limited availability of high-quality GO annotations.</p><p><strong>Results: </strong>In this study, we introduce PlasGO, a tool that leverages a hierarchical architecture to predict GO terms for plasmid proteins. PlasGO utilizes a powerful protein language model to learn the local context within protein sentences and a BERT model to capture the global context within plasmid sentences. Additionally, PlasGO allows users to control the precision by incorporating a self-attention confidence weighting mechanism. We rigorously evaluated PlasGO and benchmarked it against 7 state-of-the-art tools in a series of experiments. The experimental results collectively demonstrate that PlasGO has achieved commendable performance. PlasGO significantly expanded the annotations of the plasmid-encoded protein database by assigning high-confidence GO terms to over 95% of previously unannotated proteins, showcasing impressive precision of 0.8229, 0.7941, and 0.8870 for the 3 GO categories, respectively, as measured on the novel protein test set.</p><p><strong>Conclusions: </strong>PlasGO, a hierarchical tool incorporating protein language models and BERT, significantly expanded plasmid protein annotations by predicting high-confidence GO terms. These annotations have been compiled into a database, which will serve as a valuable contribution to downstream plasmid analysis and research.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11659980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142863067","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":"Genomic decoding of Theobroma grandiflorum (cupuassu) at chromosomal scale: evolutionary insights for horticultural innovation.","authors":"Rafael Moysés Alves, Vinicius A C de Abreu, Rafaely Pantoja Oliveira, João Victor Dos Anjos Almeida, Mauro de Medeiros de Oliveira, Saura R Silva, Alexandre R Paschoal, Sintia S de Almeida, Pedro A F de Souza, Jesus A Ferro, Vitor F O Miranda, Antonio Figueira, Douglas S Domingues, Alessandro M Varani","doi":"10.1093/gigascience/giae027","DOIUrl":"10.1093/gigascience/giae027","url":null,"abstract":"<p><strong>Background: </strong>Theobroma grandiflorum (Malvaceae), known as cupuassu, is a tree indigenous to the Amazon basin, valued for its large fruits and seed pulp, contributing notably to the Amazonian bioeconomy. The seed pulp is utilized in desserts and beverages, and its seed butter is used in cosmetics. Here, we present the sequenced telomere-to-telomere genome of cupuassu, disclosing its genomic structure, evolutionary features, and phylogenetic relationships within the Malvaceae family.</p><p><strong>Findings: </strong>The cupuassu genome spans 423 Mb, encodes 31,381 genes distributed in 10 chromosomes, and exhibits approximately 65% gene synteny with the Theobroma cacao genome, reflecting a conserved evolutionary history, albeit punctuated with unique genomic variations. The main changes are pronounced by bursts of long-terminal repeat retrotransposons at postspecies divergence, retrocopied and singleton genes, and gene families displaying distinctive patterns of expansion and contraction. Furthermore, positively selected genes are evident, particularly among retained and dispersed tandem and proximal duplicated genes associated with general fruit and seed traits and defense mechanisms, supporting the hypothesis of potential episodes of subfunctionalization and neofunctionalization following duplication, as well as impact from distinct domestication process. These genomic variations may underpin the differences observed in fruit and seed morphology, ripening, and disease resistance between cupuassu and the other Malvaceae species.</p><p><strong>Conclusions: </strong>The cupuassu genome offers a foundational resource for both breeding improvement and conservation biology, yielding insights into the evolution and diversity within the genus Theobroma.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11152179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261605","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":"CAT: a computational anatomy toolbox for the analysis of structural MRI data.","authors":"Christian Gaser, Robert Dahnke, Paul M Thompson, Florian Kurth, Eileen Luders, The Alzheimer's Disease Neuroimaging Initiative","doi":"10.1093/gigascience/giae049","DOIUrl":"10.1093/gigascience/giae049","url":null,"abstract":"<p><p>A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893242","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":"Current status of global conservation and characterisation of wild and cultivated Brassicaceae genetic resources.","authors":"Elena Castillo-Lorenzo, Elinor Breman, Pablo Gómez Barreiro, Juan Viruel","doi":"10.1093/gigascience/giae050","DOIUrl":"10.1093/gigascience/giae050","url":null,"abstract":"<p><strong>Background: </strong>The economic importance of the globally distributed Brassicaceae family resides in the large diversity of crops within the family and the substantial variety of agronomic and functional traits they possess. We reviewed the current classifications of crop wild relatives (CWRs) in the Brassicaceae family with the aim of identifying new potential cross-compatible species from a total of 1,242 species using phylogenetic approaches.</p><p><strong>Results: </strong>In general, cross-compatibility data between wild species and crops, as well as phenotype and genotype characterisation data, were available for major crops but very limited for minor crops, restricting the identification of new potential CWRs. Around 70% of wild Brassicaceae did not have genetic sequence data available in public repositories, and only 40% had chromosome counts published. Using phylogenetic distances, we propose 103 new potential CWRs for this family, which we recommend as priorities for cross-compatibility tests with crops and for phenotypic characterisation, including 71 newly identified CWRs for 10 minor crops. From the total species used in this study, more than half had no records of being in ex situ conservation, and 80% were not assessed for their conservation status or were data deficient (IUCN Red List Assessments).</p><p><strong>Conclusions: </strong>Great efforts are needed on ex situ conservation to have accessible material for characterising and evaluating the species for future breeding programmes. We identified the Mediterranean region as one key conservation area for wild Brassicaceae species, with great numbers of endemic and threatened species. Conservation assessments are urgently needed to evaluate most of these wild Brassicaceae.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141901424","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":"Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data.","authors":"Zafran Hussain Shah, Marcel Müller, Wolfgang Hübner, Tung-Cheng Wang, Daniel Telman, Thomas Huser, Wolfram Schenck","doi":"10.1093/gigascience/giad109","DOIUrl":"10.1093/gigascience/giad109","url":null,"abstract":"<p><strong>Background: </strong>Convolutional neural network (CNN)-based methods have shown excellent performance in denoising and reconstruction of super-resolved structured illumination microscopy (SR-SIM) data. Therefore, CNN-based architectures have been the focus of existing studies. However, Swin Transformer, an alternative and recently proposed deep learning-based image restoration architecture, has not been fully investigated for denoising SR-SIM images. Furthermore, it has not been fully explored how well transfer learning strategies work for denoising SR-SIM images with different noise characteristics and recorded cell structures for these different types of deep learning-based methods. Currently, the scarcity of publicly available SR-SIM datasets limits the exploration of the performance and generalization capabilities of deep learning methods.</p><p><strong>Results: </strong>In this work, we present SwinT-fairSIM, a novel method based on the Swin Transformer for restoring SR-SIM images with a low signal-to-noise ratio. The experimental results show that SwinT-fairSIM outperforms previous CNN-based denoising methods. Furthermore, as a second contribution, two types of transfer learning-namely, direct transfer and fine-tuning-were benchmarked in combination with SwinT-fairSIM and CNN-based methods for denoising SR-SIM data. Direct transfer did not prove to be a viable strategy, but fine-tuning produced results comparable to conventional training from scratch while saving computational time and potentially reducing the amount of training data required. As a third contribution, we publish four datasets of raw SIM images and already reconstructed SR-SIM images. These datasets cover two different types of cell structures, tubulin filaments and vesicle structures. Different noise levels are available for the tubulin filaments.</p><p><strong>Conclusion: </strong>The SwinT-fairSIM method is well suited for denoising SR-SIM images. By fine-tuning, already trained models can be easily adapted to different noise characteristics and cell structures. Furthermore, the provided datasets are structured in a way that the research community can readily use them for research on denoising, super-resolution, and transfer learning strategies.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10787368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139466408","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":"Gapless genome assembly and epigenetic profiles reveal gene regulation of whole-genome triplication in lettuce.","authors":"Shuai Cao, Nunchanoke Sawettalake, Lisha Shen","doi":"10.1093/gigascience/giae043","DOIUrl":"10.1093/gigascience/giae043","url":null,"abstract":"<p><strong>Background: </strong>Lettuce, an important member of the Asteraceae family, is a globally cultivated cash vegetable crop. With a highly complex genome (∼2.5 Gb; 2n = 18) rich in repeat sequences, current lettuce reference genomes exhibit thousands of gaps, impeding a comprehensive understanding of the lettuce genome.</p><p><strong>Findings: </strong>Here, we present a near-complete gapless reference genome for cutting lettuce with high transformability, using long-read PacBio HiFi and Nanopore sequencing data. In comparison to stem lettuce genome, we identify 127,681 structural variations (SVs, present in 0.41 Gb of sequence), reflecting the divergence of leafy and stem lettuce. Interestingly, these SVs are related to transposons and DNA methylation states. Furthermore, we identify 4,612 whole-genome triplication genes exhibiting high expression levels associated with low DNA methylation levels and high N6-methyladenosine RNA modifications. DNA methylation changes are also associated with activation of genes involved in callus formation.</p><p><strong>Conclusions: </strong>Our gapless lettuce genome assembly, an unprecedented achievement in the Asteraceae family, establishes a solid foundation for functional genomics, epigenomics, and crop breeding and sheds new light on understanding the complexity of gene regulation associated with the dynamics of DNA and RNA epigenetics in genome evolution.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11238431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141590091","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":"PEPhub: a database, web interface, and API for editing, sharing, and validating biological sample metadata.","authors":"Nathan J LeRoy, Oleksandr Khoroshevskyi, Aaron O'Brien, Rafał Stępień, Alip Arslan, Nathan C Sheffield","doi":"10.1093/gigascience/giae033","DOIUrl":"10.1093/gigascience/giae033","url":null,"abstract":"<p><strong>Background: </strong>As biological data increase, we need additional infrastructure to share them and promote interoperability. While major effort has been put into sharing data, relatively less emphasis is placed on sharing metadata. Yet, sharing metadata is also important and in some ways has a wider scope than sharing data themselves.</p><p><strong>Results: </strong>Here, we present PEPhub, an approach to improve sharing and interoperability of biological metadata. PEPhub provides an API, natural-language search, and user-friendly web-based sharing and editing of sample metadata tables. We used PEPhub to process more than 100,000 published biological research projects and index them with fast semantic natural-language search. PEPhub thus provides a fast and user-friendly way to finding existing biological research data or to share new data.</p><p><strong>Availability: </strong>https://pephub.databio.org.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11238423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141590108","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":"Innovative approach for high-throughput exploiting sex-specific markers in Japanese parrotfish Oplegnathus fasciatus.","authors":"Yongshuang Xiao, Zhizhong Xiao, Lin Liu, Yuting Ma, Haixia Zhao, Yanduo Wu, Jinwei Huang, Pingrui Xu, Jing Liu, Jun Li","doi":"10.1093/gigascience/giae045","DOIUrl":"10.1093/gigascience/giae045","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The use of sex-specific molecular markers has become a prominent method in enhancing fish production and economic value, as well as providing a foundation for understanding the complex molecular mechanisms involved in fish sex determination. Over the past decades, research on male and female sex identification has predominantly employed molecular biology methodologies such as restriction fragment length polymorphism, random amplification of polymorphic DNA, simple sequence repeat, and amplified fragment length polymorphism. The emergence of high-throughput sequencing technologies, particularly Illumina, has led to the utilization of single nucleotide polymorphism and insertion/deletion variants as significant molecular markers for investigating sex identification in fish. The advancement of sex-controlled breeding encounters numerous challenges, including the inefficiency of current methods, intricate experimental protocols, high costs of development, elevated rates of false positives, marker instability, and cumbersome field-testing procedures. Nevertheless, the emergence and swift progress of PacBio high-throughput sequencing technology, characterized by its long-read output capabilities, offers novel opportunities to overcome these obstacles.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;Utilizing male/female assembled genome information in conjunction with short-read sequencing data survey and long-read PacBio sequencing data, a catalog of large-segment (&gt;100 bp) insertion/deletion genetic variants was generated through a genome-wide variant site-scanning approach with bidirectional comparisons. The sequence tagging sites were ranked based on the long-read depth of the insertion/deletion site, with markers exhibiting lower long-read depth being considered more effective for large-segment deletion variants. Subsequently, a catalog of bulk primers and simulated PCR for the male/female variant loci was developed, incorporating primer design for the target region and electronic PCR (e-PCR) technology. The Japanese parrotfish (Oplegnathus fasciatus), belonging to the Oplegnathidae family within the Centrarchiformes order, holds significant economic value as a rocky reef fish indigenous to East Asia. The criteria for rapid identification of male and female differences in Japanese parrotfish were established through agarose gel electrophoresis, which revealed 2 amplified bands for males and 1 amplified band for females. A high-throughput identification catalog of sex-specific markers was then constructed using this method, resulting in the identification of 3,639 (2,786 INS/853 DEL, ♀ as reference) and 3,672 (2,876 INS/833 DEL, ♂ as reference) markers in conjunction with 1,021 and 894 high-quality genetic sex identification markers, respectively. Sixteen differential loci were randomly chosen from the catalog for validation, with 11 of them meeting the criteria for male/female distinctions. The implementation of cost-effective and","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11258905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141727099","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":"Celebrating 30 years of access to NASA Space Life Sciences data.","authors":"Lauren M Sanders,Danielle K Lopez,Alan E Wood,Ryan T Scott,Samrawit G Gebre,Amanda M Saravia-Butler,Sylvain V Costes","doi":"10.1093/gigascience/giae066","DOIUrl":"https://doi.org/10.1093/gigascience/giae066","url":null,"abstract":"NASA's space life sciences research programs established a decades-long legacy of enhancing our ability to safely explore the cosmos. From Skylab and the Space Shuttle Program to the NASA Balloon Program and the International Space Station National Lab, these programs generated priceless data that continue to paint a vibrant picture of life in space. These data are available to the scientific community in various data repositories, including the NASA Ames Life Sciences Data Archive (ALSDA) and NASA GeneLab. Here we recognize the 30-year anniversary of data access through ALSDA and the 10-year anniversary of GeneLab.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"87 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}