Advanced imaging system for accelerating and automating arthropod bio-surveillance

用于加速和自动化节肢动物生物监测的先进成像系统

• 批准号: RTI-2020-00414
• 负责人: Hebert, Paul
• 金额: $ 10.46万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=693574
• 关键词: Advanced; imaging; system; accelerating; automating

One of the most pressing challenges in today's biodiversity science is the need to gather quantitative data on organismal diversity and abundance over space and time. This information is essential to understand how species community assemble and change as well as to support monitoring programs that assess how biodiversity responds to natural and anthropogenic disturbance. Two methodological and technological advances over the past decade have made it possible to satisfy this urgent need for comprehensive biomonitoring. Firstly, identification systems based on the analysis of sequence variation in short, standardized gene regions (i.e. DNA barcodes) are very effective in species discrimination. Secondly, high-throughput sequencers permit the inexpensive acquisition of millions of DNA sequence records. These developments are enabling biodiversity surveys at speeds and scales that were previously inconceivable. This application seeks support to acquire a digital microscopy system for integration into current workflows to advance the production of DNA barcode records. Recent technological developments have increased automation of most steps in the barcode workflow from initial data entry to contextualization of results. The greatest remaining bottleneck to further increases in production derive from the current need for skilled technicians to enter each specimen record and associated collateral data including digital image information. The new system will make it possible to gather high resolution, in-focus digital images of every specimen and it will generate files that are ideally suited for the development of machine learning approaches that will employ pattern recognition algorithms to automate a high-level taxonomic assignment (order, family) for each specimen.Such an automated pipeline that will transform specimen-loaded plates into specimen records complete with their associated image and taxonomic assignment. The impact on research programs and on biodiversity science will be dramatic.

当今生物多样性科学最紧迫的挑战之一是需要收集有关空间和时间上生物多样性和丰度的定量数据。这些信息对于了解物种群落如何组装和变化以及支持评估生物多样性如何应对自然和人为干扰的监测计划至关重要。过去十年的两项方法和技术进步使得满足全面生物监测的迫切需求成为可能。首先，基于短标准化基因区域（即 DNA 条形码）序列变异分析的识别系统在物种区分方面非常有效。其次，高通量测序仪可以廉价地获取数百万条 DNA 序列记录。这些发展使得生物多样性调查能够以以前难以想象的速度和规模进行。该申请寻求支持以获得数字显微镜系统，以便集成到当前工作流程中，以推进 DNA 条形码记录的生产。最近的技术发展提高了条形码工作流程中从初始数据输入到结果情境化的大多数步骤的自动化程度。进一步提高产量的最大瓶颈来自于当前对熟练技术人员输入每个样本记录和包括数字图像信息在内的相关附属数据的需求。新系统将能够收集每个标本的高分辨率、清晰的数字图像，并将生成非常适合开发机器学习方法的文件，该方法将采用模式识别算法来自动执行高级分类任务每个标本的（顺序，家族）。这样的自动化管道将把装载标本的板转换成标本记录，并附有相关图像和分类分配。对研究计划和生物多样性科学的影响将是巨大的。