Synergy of multi-dimensional data chain-driven and intelligent decision-making： systematic construction of intelligent breeding technology systems and transition of genetic gain

ZHANG Zhiqi; WANG Xingtao; REN Duanyang; CAO Wenfu; WANG Jianjun

doi:10.13300/j.cnki.hnlkxb.2026.03.001

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Synergy of multi-dimensional data chain-driven and intelligent decision-making： systematic construction of intelligent breeding technology systems and transition of genetic gain

Agricultural artificial intelligence | 更新时间：2026-11-05

- Synergy of multi-dimensional data chain-driven and intelligent decision-making： systematic construction of intelligent breeding technology systems and transition of genetic gain
- Journal of Huazhong Agricultural University Vol. 45, Issue 3, Pages: 1-17(2026)
- 作者机构：
  
  山西农业大学生态农牧研究所，朔州 036001
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2026.03.001
  CLC： S337
- Received：10 June 2025，
  
  Published：25 May 2026
- 稿件说明：
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张之奇，王兴涛，任端阳，等.多维数据链驱动与智能决策协同：智能育种技术体系的系统构建与遗传增益跃迁［J］.华中农业大学学报，2026，45（3）：1‐17.

ZHANG Zhiqi,WANG Xingtao,REN Duanyang,et al.Synergy of multi-dimensional data chain-driven and intelligent decision-making： systematic construction of intelligent breeding technology systems and transition of genetic gain[J].Journal of Huazhong Agricultural University,2026,45(03):1-17.
张之奇，王兴涛，任端阳，等.多维数据链驱动与智能决策协同：智能育种技术体系的系统构建与遗传增益跃迁［J］.华中农业大学学报，2026，45（3）：1‐17. DOI： 10.13300/j.cnki.hnlkxb.2026.03.001.

ZHANG Zhiqi,WANG Xingtao,REN Duanyang,et al.Synergy of multi-dimensional data chain-driven and intelligent decision-making： systematic construction of intelligent breeding technology systems and transition of genetic gain[J].Journal of Huazhong Agricultural University,2026,45(03):1-17. DOI： 10.13300/j.cnki.hnlkxb.2026.03.001.

摘要

智能育种作为现代种业技术革新的核心驱动力，正推动作物遗传改良从经验依赖型向数据驱动型转变。本文系统梳理了智能育种技术体系的构建路径及其在作物遗传改良中的应用进展，旨在为我国种业智能化转型提供理论依据与实践参考。基于高通量表型与基因型数据采集、人工智能算法决策、多组学平台集成等核心技术模块，结合国内外典型案例，综述了智能育种在复杂性状设计、种质资源挖掘与遗传增益预测等方面的突破性进展。智能育种通过构建“基因型-表型-环境”多维数据驱动模型，显著提升了育种效率与精准性。未来应加强生物-信息-工程交叉融合，开发知识嵌入型算法，构建全球协作平台，推动种质资源数字化共享与商业化育种体系重构，建议通过政策引导建立产学研协同机制，加速技术成果向产业端转化。

Abstract

Intelligent breeding， as the core driving force of technological innovation in modern seed industry， is facilitating the transformation of crop genetic improvement from experience-based approaches to data-driven paradigms.This article systematically reviewed the path of constructing the intelligent breeding technology framework and the progress on its application in crop genetic improvement to provide theoretical foundations and practical references for the intelligent transformation of seed industry in China.The achieved breakthroughs of intelligent breeding in designing complex trait， mining germplasm resource， and predicting genetic gain were summarized based on combining key technical modules including the acquisition of data from high-throughput phenotyping and genotyping， the decision-making empowered by artificial intelligence （AI） algorithm and the integration of multi-omics platform with typical case studies at home and abroad.Intelligent breeding significantly improved the efficiency and precision of breeding by constructing multi-dimensional data-driven models that integrate genotype， phenotype， and environment （G×P×E）.In the future， we should strengthen the interdisciplinary integration of biology， information and engineering， develop knowledge-embeddedalgorithms， establish a global collaboration platform， promote the digital sharing of germplasm resources and the reconstruction of commercial breeding systems.It is recommended to establish policy-guided mechanisms to deepen industry-academia-research collaboration， thereby accelerating the translation of technological innovations into the industrial end.

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references

WALLACE J G，RODGERS-MELNICK E，BUCKLER E S．On the road to breeding 4.0：unraveling the good，the bad，and the boring of crop quantitative genomics［J］.Annual review of genetics，2018，52：421-444.

JING H C，TIAN Z X，CHONG K，et al.Progress and perspective of molecular design breeding［J］.Scientia sinica vitae，2021，51（10）：1356-1365.

ZHAO C J，ZHANG Y，DU J J，et al.Crop phenomics：current status and perspectives［J/OL］.Frontiers in plant science，2019，10：714［2025-06-10］. https：//doi.org/10.3389/fpls.2019.00714.

WANG B B，HOU M，SHI J P，et al. De novo genome assembly and analyses of 12 founder inbred lines provide insights into maize heterosis［J］.Nature genetics，2023，55（2）：312-323.

REYNOLDS D，BALL J，BAUER A，et al.CropSight：a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management［J/OL］.GigaScience，2019，8（3）：giz009［2025-06-10］. https：//doi.org/10.1093/gigascience/giz009.

ZHANG Y，HUANG G M，ZHAO Y X，et al.Revolutionizing crop breeding：next-generation artificial intelligence and big data-driven intelligent design［J］.Engineering，2025，44：245-255.

LANDE R，THOMPSON R.Efficiency of marker-assisted selection in the improvement of quantitative traits［J］.Genetics，1990，124（3）：743-756.

种康.作物育种进化史：从野生驯化到智能设计［J］.中国科技产业，2025（6）：4. ZHONG K.Crop breeding evolution： from domestication to intelligent design［J］.Science & technology industry of China，2025（6）：4 （in Chinese） .

简六梅，肖英杰，严建兵.从头驯化：作物品种设计与培育的新方向［J］.遗传，2023，45（9）：741-753. JIAN L M，XIAO Y J ，YAN J B.De novo domestication： a new way for crop design and breeding［J］.Hereditas，2023，45（9）：741-753 （in Chinese with English abstract） .

VAN VU T，DAS S，HENSEL G，et al.Genome editing and beyond：what does it mean for the future of plant breeding？［J/OL］.Planta，2022，255（6）：130［2025-06-10］. https：//doi.org/10.1007/s00425-022-03906-2.

BRESEGHELLO F，COELHO A S G.Traditional and modern plant breeding methods with examples in rice （ Oryza sativa L.）［J］.Journal of agricultural and food chemistry，2013，61（35）：8277-8286.

FAUX A M，GORJANC G，GAYNOR R C，et al.AlphaSim：software for breeding program simulation［J/OL］.The plant genome，2016，9（3）：27902803［2025-06-10］. https：//doi.org/10.3835/plantgenome2016.02.0013.

OUYANG W Z，XIONG D，LI G L，et al.Unraveling the 3D genome architecture in plants：present and future［J］.Molecular plant，2020，13（12）：1676-1693.

RUI Z，ZHANG Z，ZHANG M，et al.High-throughput proximal ground crop phenotyping systems：a comprehensive review［J/OL］.Computers and electronics in agriculture，2024，224：109108［2025-06-10］.https：//doi.org/10.1016/j.compag.2024.109108.

WANG W X，GUO W J，LE L，et al.Integration of high-throughput phenotyping，GWAS，and predictive models reveals the genetic architecture of plant height in maize［J］.Molecular plant，2023，16（2）：354-373.

CRAIN J，MONDAL S，RUTKOSKI J，et al.Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding［J/OL］.The plant genome，2018，11：170043［2025-06-10］.https：//doi.org/10.3835/plantgenome2017.05.0043.

ZHANG Y，WANG J L，DU J J，et al.Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high-throughput phenotypic analysis［J］.Plant biotechnology journal，2021，19（1）：35-50.

CLARK S A，VAN DER WERF J.Genomic best linear unbiased prediction （gBLUP） for the estimation of genomic breeding values［M］//Genome-Wide Association Studies and Genomic Prediction.Totowa：Humana Press，2013：321-330.

YU T X，ZHANG H，CHEN S K，et al．EXGEP：a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learng models［J/OL］．Briefings in bioinformatics，2025，26（4）：bbaf414［2025-06-10］．https：//doi.org/10.1093/bib/bbaf414.

王轲麟.基于深度学习的从基因型到表型预测模型研究［D］.北京：中国农业科学院， 2022.WANG K L.Research on deep learning-based genotype to phenotype prediction models［D］.Beijing： Chinese Academy of Agricultural Sciences，2022 （in Chinese with English abstract） .

COVARRUBIAS-PAZARAN G，MARTINI J W R，QUINN M，et al.Strengthening public breeding pipelines by emphasizing quantitative genetics principles and open source data management［J/OL］.Frontiers in plant science，2021，12：681624［2025-06-10］.https：//doi.org/10.3389/fpls.2021.681624.

JOSGARCPELVO F，QUEZ-INGELMO A，GARC-HOLGADO A.Explainable rules and heuristics in AI algorithm recommendation approaches：a systematic literature review and mapping study［J］.Computer modeling in engineering & sciences，2023，136（2）：1023-1051.

HANGLOO S，ARORA B.Multimodal fusion techniques：review，data representation，information fusion，and application areas［J/OL］.Neurocomputing，2025，649：130827［2025-06-10］.https：//doi.org/10.1016/j.neucom.2025.130827.

NICOLAS M， ALEX C O， BRYAN J E， et al.Breedbase： a digital ecosystem for modern plant breeding［J/OL］， Genes genomes genetics， 2022，12（7）：jkac078［2025-06-10］，https：//doi.org/10.1093/g3journal/jkac078.

TOFFETTI G，MILITANO L，THARAKA R，et al．ROS-based robotic applications orchestration in the compute continuum：challenges and approaches［C］//Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing．Taormina （Messina）， Italy：ACM，2023：1-6．

丁鹏钧.植物m6A调控网络数据库的构建及应用［D］.杨凌：西北农林科技大学，2025.DING P J.Construction and application of plant m6A regulatory network database［D］.Yangling：Northwest A & F University （in Chinese with English abstract） .

YANG F，KONG H J，YING J，et al.SeedLLM·Rice：a large language model integrated with rice biological knowledge graph［J］.Molecular plant，2025，18（7）：1118-1129.

XIE Y，ZHANG T H，YANG M H，et al.Engineering crop flower morphology facilitates robotization of cross-pollination and speed breeding［J］.Cell，2025，188（21）：5809-5830.

LI G T，AN L N，YANG W N，et al.Integrated biotechnological and AI innovations for crop improvement［J］.Nature，2025，643（8073）：925-937.

MENG L，MING B，LIU Y，et al.Maize biomass estimation by integrating spectral，structural，and textural features from unmanned aerial vehicle data［J/OL］.European journal of agronomy，2025，168：127647［2025-06-10］.https：//doi.org/10.1016/j.eja.2025.127647.

郭庆华，杨维才，吴芳芳，等.高通量作物表型监测：育种和精准农业发展的加速器［J］.中国科学院院刊，2018，33（9）：940-946.GUO Q H，YANG W C，WU F F，et al.High-throughput crop phenotyping： accelerators for development of breeding and precision agriculture［J］.Bulletin of Chinese Academy of Sciences，2018，33（9）：940-946 （in Chinese with English abstract） .

MAHLEIN A K.Plant disease detection by imaging sensors-paradigms and advances［J］.Plant disease， 2016，100（2）：2228-2242.

ZHOU H K，HUANG F D，LOU W D，et al．Yield prediction through UAV-based multispectral imaging and deep learing in rice breeding trials［J/OL］.Agricultural systems，2025，223：104214［2025-06-10］.https：//doi.org/10.1016/j.agsy.2024.104214.

AYAZ M，AMMAD-UDDIN M，SHARIF Z，et al.Internet-of-things （IoT）-based smart agriculture：toward making the fields talk［J］.IEEE access，2019，7：129551-129583.

AMIR A，BUTT M.Improved sap flow prediction：a comparative deep learning study based on LSTM，BiLSTM，LRCN，and GRU with stem diameter data［J/OL］.Smart agricultural technology，2025，12：101105［2025-06-10］.https：//doi.org/10.1016/j.atech.2025.101105.

ADAMO T，CAIVANO D，COLIZZI L，et al.Optimization of irrigation and fertigation in smart agriculture：an IoT-based micro-services framework［J/OL］.Smart agricultural technology，2025，11：100885［2025-06-10］.https：//doi.org/10.1016/j.atech.2025.100885.

BONGOMIN O，LAMO J，GUINA J M，et al.UAV image acquisition and processing for high-throughput phenotyping in agricultural research and breeding programs［J/OL］.The plant phenome journal，2024，7：e20096［2025-06-10］.https：//doi.org/10.1002/ppj2.20096.

PATEL A K，PARK E S，LEE H，et al.Deep learning-based plant organ segmentation and phenotyping of sorghum plants using LiDAR point cloud［J］.IEEE journal of selected topics in applied earth observations and remote sensing，2023，16：8492-8507.

NAKATO R，SAKATA T.Methods for ChIP-seq analysis：a practical workflow and advanced applications［J］.Methods，2021，187：44-53.

GUR E R，HUGHES J R.scATAC-seq generates more accurate and complete regulatory maps than bulk ATAC-seq［J/OL］.Scientific reports，2025，15：3665［2025-06-10］.https：//doi.org/10.1038/s41598-025-87351-7.

PENG Y，XIONG D，ZHAO L，et al.Chromatin interaction maps reveal genetic regulation for quantitative traits in maize［J/OL］.Nature communications，2019，10：2632［2025-06-10］.https：//doi.org/10.1038/s41467-019-10602-5.

LI T，HORSFALL D，BASURTO-LOZADA D，et al.WebAtlas pipeline for integrated single-cell and spatial transcriptomic data［J］.Nature methods，2025，22（1）：3-5.

NI L B，TIAN Z X.Toward cis -regulation in soybean：a 3D genome scope［J/OL］.Molecular breeding，2023，43（4）：28［2025-06-10］.https：//doi.org/10.1007/s11032-023-01374-4.

SUN L H，ZHOU J R，XU X，et al.Mapping nucleosome-resolution chromatin organization and enhancer-promoter loops in plants using Micro-C-XL［J/OL］.Nature communications，2024，15：35［2025-06-10］.https：//doi.org/10.1038/s41467-023-44347-z.

HUANG X H，WANG Y J，ZHANG S N，et al.Epigenomic and 3D genomic mapping reveals developmental dynamics and subgenomic asymmetry of transcriptional regulatory architecture in allotetraploid cotton［J/OL］.Nature communications，2024，15：10721［2025-06-10］.https：//doi.org/10.1038/s41467-024-55309-4.

ALBERT P S，YANG H，GAO Z，et al.Aneuploidy and ploidy variation conditioned by the B chromosome of maize［J］.Heredity，2025，12：00764-y［2025-06-10］.https：//doi.org/10.1038/s41437-025-00764-y.

SCHIESSL S V，KATCHE E，IHIEN E，et al.The role of genomic structural variation in the genetic improvement of polyploid crops［J］.The crop journal，2019，7（2）：127-140.

KOCHENDERFER M J.Algorithms for artificial intelligence［M］.California ：MIT Press， 2019.

COOPER M，TECHNOW F，MESSINA C，et al.Use of crop growth models with whole-genome prediction：application to a maize multienvironment trial［J］.Crop science，2016，56（5）：2141-2156.

WEI X，QIU J，YONG K C，et al.A quantitative genomics map of rice provides genetic insights and guides breeding［J］.Nature genetics，2021，53（2）：243-253.

SUBRAMANIAN I，VERMA S，KUMAR S，et al.Multi-omics data integration，interpretation，and its application［J］.Bioinformatics and biology insights，2020，14：1-24.

PAN L，TANG B F，ZHANG X，et al.Comprehensive analysis of multi-omics single-cell data using the single-cell analyst［J/OL］.iMeta，2025，4（3）：e70038［2025-06-10］.https：//doi.org/10.1002/imt2.70038..

BALLARD J L，WANG Z X，LI W R，et al.Deep learning-based approaches for multi-omics data integration and analysis［J/OL］.BioData mining，2024，17：38［2025-06-10］.https：//doi.org/10.1186/s13040-024-00391-z.

AHMED K T，SUN J，CHENG S，et al.Multi-omics data integration by generative adversarial network［J］.Bioinformatics，2021，38（1）：179-186.

HIRA M T，RAZZAQUE M A，ANGIONE C，et al.Integrated multi-omics analysis of ovarian cancer using variational autoencoders［J/OL］.Scientific reports，2021，11：6265［2025-06-10］.https：//doi.org/10.1038/s41598-021-85285-4.

GOODFELLOW I，POUGET-ABADIE J，MIRZA M，et al.Generative adversarial networks［J］.Communications of the acm，2020，63（11）：139-144.

LIU Y B，YUE L，XIAO S F，et al.Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages［J/OL］.Medical image analysis，2022，75：102266［2025-06-10］.https：//doi.org/10.1016/j.media.2021.102266.

YANG H，CHEN R，LI D D，et al.Subtype-GAN：a deep learning approach for integrative cancer subtyping of multi-omics data［J］.Bioinformatics，2021，37（16）：2231-2237.

BICA I， VELIČKOVIĆ P， XIAO H.Multi-omics data integration using cross-modal neural network［C］// ESANN 2018 proceedings，European Symposium on Artificial Neural Networks.Bruges：Computational Intelligence and Machine Learning.2018：385-390.

GUO H，LV X，LI Y Z，et al.Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification［J］.Briefings in functional genomics，2023，22（5）：463-474.

PENG W，CHEN T L，DAI W.Predicting drug response based on multi-omics fusion and graph convolution［J］.IEEE journal of biomedical and health informatics，2022，26（3）：1384-1393.

MOEINIZADE S，HAN Y，PHAM H，et al.A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression［J/OL］.Scientific reports，2021，11：3918［2025-06-10］.https：//doi.org/10.1038/s41598-021-83634-x.

RUTKOSKI J E.A practical guide to genetic gain［J］.Advances in agronomy，2019，157：217-249.

JONES D，FORNARELLI R，DERBYSHIRE M，et al.The pursuit of genetic gain in agricultural crops through the application of machine-learning to genomic prediction［J/OL］.Frontiers in genetics，2023，14：1186782［2025-06-10］.https：//doi.org/10.3389/fgene.2023.1186782.

刘海岚，夏超，兰海.全基因组选择技术在作物育种中的研究进展［J］.华北农学报，2022， 37（S1）： 51-58. LIU H L，XIA C ， LAN H.The research progress of genomic selection in breeding of crops［J］.Acta agriculturae boreali-sinica.2022， 37（S1）： 51-58 （in Chinese with English abstract）

SAKHA M M，DAIBER F，ENDERS M，et al.Enhancing plant variety discovery process with visual trait assessment in VR［C］//2025 IEEE Conference Virtual Reality and 3D User Interfaces （VR）.March 8-12，2025.Saint Malo：IEEE，2025：125-134.

KHAN B，DAS S，FAHIM N S，et al.Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification［J/OL］.Scientific reports，2024，14：21525［2025-06-10］.https：//doi.org/10.1038/s41598-024-72237-x.

YEWLE A D，MIRZAYEVA L，KARAKUŞ O.Multi-modal data fusion and deep ensemble learning for accurate crop yield prediction［J/OL］.Remote sensing applications：society and environment，2025，38：101613［2025-06-10］.https：//doi.org/10.1016/j.rsase.2025.101613.

MENA F，PATHAK D，NAJJAR H，et al.Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction［J/OL］.Remote sensing of environment，2025，318：114547［2025-06-10］.https：//doi.org/10.1016/j.rse.2024.114547.

HE Y，LIAO S Y，REN Q R，et al.CRISPR-Cas12i confers efficient genome editing and gene regulation in plants［J/OL］.Plant physiology，2025，198（1）：kiaf125［2025-06-10］.https：//doi.org/10.1093/plphys/kiaf125.

ABRAMSON J，ADLER J，DUNGER J，et al.Accurate structure prediction of biomolecular interactions with AlphaFold 3［J］.Nature，2024，630（8016）：493-500.

TIAN J G，WANG C L，CHEN F Y，et al.Maize smart-canopy architecture enhances yield at high densities［J］.Nature，2024，632（8025）：576-584.

ORDON J，BRESSAN M，KRETSCHMER C，et al.Optimized Cas9 expression systems for highly efficient Arabidopsis genome editing facilitate isolation of complex alleles in a single generation［J］.Functional & integrative genomics，2020，20（1）：151-162.

EARLES J M，BUCKLEY T N，BRODERSEN C R，et al.Embracing 3D complexity in leaf carbon-water exchange［J］.Trends in plant science，2019，24（1）：15-24.

SANG S F，SUN X H，MA T Y，et al.Efficient promoter editing of the SBEIIb gene enables fine-tuning of the resistant starch content in rice［J/OL］.International journal of biological macromolecules，2025，290：138904［2025-06-10］.https：//doi.org/10.1016/j.ijbiomac.2024.138904.

GUI S T，YANG L F，LI J B，et al.ZEAMAP，a comprehensive database adapted to the maize multi-omics era［J/OL］.iScience，2020，23（6）：101241［2025-06-10］.https：//doi.org/10.1016/j.isci.2020.101241.

GUO Z L，LI B，DU J J，et al.LettuceGDB：the community database for lettuce genetics and omics［J/OL］.Plant communications，2023，4（1）：100425［2025-06-10］.https：//doi.org/10.1016/j.xplc.2022.100425.

YANG Z Q，LUO C F，PEI X X，et al.SoyMD：a platform combining multi-omics data with various tools for soybean research and breeding［J］.Nucleic acids research，2024，52（D1）：D1639-D1650.

GUAN J T，MIAO H，ZHANG Z H，et al.A near-complete cucumber reference genome assembly and Cucumber-DB，a multi-omics database［J］.Molecular plant，2024，17（8）：1178-1182.

HE Q，WANG C C，HE Q，et al.A complete reference genome assembly for foxtail millet and Setaria -db，a comprehensive database for Setaria ［J］.Molecular plant，2024，17（2）：219-222.

WANG H Z，HOU J B，YE P，et al.A teosinte-derived allele of a MYB transcription repressor confers multiple disease resistance in maize［J］.Molecular plant，2021，14（11）：1846-1863.

COOPER L，JAISWAL P.The plant ontology：a tool for plant genomics［M］//EDWARDS D.Plant bioinformatics.New York：Springer，2016：89-114.

REAM M，PRATHER K L J.Engineered autonomous dynamic regulation of metabolic flux［J］.Nature reviews bioengineering，2024，2（3）：233-243.

GOMEZ-CANO F，RODRIGUEZ J，ZHOU P，et al.Prioritizing maize metabolic gene regulators through multi-omic network integration［PP/OL］.bioRxiv，2024，2：582075（2024-02-26）［2025-06-10］.https：//doi.org/10.1101/2024.02.26.582075.

SUN Y，GUO X D，YANG H.Win-former：window-based transformer for maize plant point cloud semantic segmentation［J/OL］.Agronomy，2023，13（11）：2723［2025-06-10］.https：//doi.org/10.3390/agronomy13112723.

QIU J，ZHOU Y J，MAO L F，et al.Genomic variation associated with local adaptation of weedy rice during de-domestication［J/OL］.Nature communications，2017，8：15323［2025-06-10］.https：//doi.org/10.1038/ncomms15323.

ZHAO T，GUAN X Y，HU Y，et al.Population-wide DNA methylation polymorphisms at single-nucleotide resolution in 207 cotton accessions reveal epigenomic contributions to complex traits［J］.Cell research，2024，34（12）：859-872.

QUIST D，CHAPELA I H.Transgenic DNA introgressed into traditional maize landraces in Oaxaca ，Mexico［J］.Nature，2001，414（6863）：541-543.

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