时  间：2017年3月21日(周二)上午9:00

地  点：理科楼A201

报告人：英国约翰·英纳斯中心  周济  博士

报告题目： From fields to cells – a multilayer crop phenomics approach to explore the dynamics between crop performance and environmental factors for bread wheat

摘要: Automated field phenotyping can provide continuous and precise measures of environmental adaptation and yield-related performance traits that are key to today’s crop research, breeding pipelines and agricultural practices. In the seminar, he will introduce an integrated field phenotyping approach established at Norwich Research Park (John Innes Centre, JIC and Earlham Institute, EI), including UAV (unmanned aerial vehicles), 3D scanning crop phenotyping platform (Phenospex), networked CropQuant workstations and other novel machine learning based software solutions that facilitate high-resolution and high-frequency crop phenomics. In particular, he will talk about how we are utilising novel phenotypic analysis to empower the assessment of genes controlling yield potential and environmental adaptation. Also, introduce CropQuant, a cost-effective IoT (Internet of Things) in agriculture platform that integrates networked sensors, single-board computers, in-field wireless communication and open high-throughput analysis algorithms to capture and process field experimental datasets. Besides trait analysis, he has also established predictive models to explore the dynamics between genotype, phenotype and environment (GxPxE). A case study based on Near-isogenic lines (NILs) of wheat including Ppd-1 (loss of function), Ppd-D1a (photoperiod insensitivity), Rht-D1b (semi dwarfing), stay green induced mutants, and Lr19 (hypersensitive response to the pathogen), and Paragon wild type will be discussed. 

Dr Ji Zhou

Education

• 2006-2011: PhD in Computer Science, University of East Anglia (UEA), Norwich UK

• 2003-2005: MSc in Information Systems, UEA, Norwich UK

• 1995-1999: BEng in Computer Controlling, Shanghai University of Engineering Science, China

Employment History

• 2014–present: Project leader, Earlham Institute (EI, previously known as The Genome Analysis Centre, TGAC), co-funded by John Innes centre (JIC), Norwich Research Park (NRP, UK)

• 2011–2014: Postdoctoral fellow in bioinformatics, The Sainsbury Laboratory (TSL), Norwich UK

• 2005–2009: Project consultant & lead systems analyst, IT Solutions, Norwich Union, Aviva UK

• 2002–2003: Bilingual ICT trainer, Singapore Informatics Group, Shanghai branch, China

• 1999–2002: Software engineer & ICT teacher, Shanghai Yucai Educational group, China

从领域到细胞–多层作物表型组学探讨作物表型和小麦生长环境因素之间的动态研究

报告摘要：自动化田间表型能够提供连续的、精准的环境适应性和产量相关性状，也是目前采取精确措施进行作物育种研究的关键，并已广泛应用于农业实践。此次研讨会，主要介绍英国约翰英纳斯中心的一个集成领域表型的方法，包括UAV（无人机），三维扫描作物表型平台（phenospex），网络工作站和其它新的基于机器学习的软件解决方案，有利于高分辨率和高频作物表型组学研究。同时，还特别讨论了该中心如何利用新的表型分析，控制产量潜力和环境适应的基因的评估。同时，介绍了集成网络传感器、单板计算机、现场无线通信和开放的高通量分析算法捕获和处理实验数据的高性价比的物联网（Internet of Things）农业平台。除了性状分析，还建立了预测模型，探讨基因型之间的动力学、表型和环境（gxpxe）。另外，还讨论了一种基于小麦近等基因系的案例研究（NILs），包括ppd-1（功能丧失），PPD-D1A（光周期不敏感）、Rht-D1b（半矮秆），持绿突变体，与Lr19（对病原体的过敏反应），野生型等。