Advanced imaging system for accelerating and automating arthropod bio-surveillance

当今生物多样性科学中最紧迫的挑战之一是需要收集关于生物多样性和丰富度的空间和时间上的定量数据。这些信息对于了解物种群落如何聚集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项目至关重要。在过去十年中，两个方法和技术的进步使得满足对全面生物监测的迫切需求成为可能。首先，基于短的标准化基因区域(即DNA条形码)的序列变异分析的鉴定系统在物种鉴别方面非常有效。其次，高通量测序仪可以廉价地获取数百万条DNA序列记录。这些发展使生物多样性调查以以前不可想象的速度和规模成为可能。该应用程序寻求支持，以获得一个数字显微镜系统，以便整合到当前的工作流程中，以促进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大多数步骤的自动化程度，从最初的数据输入到结果的上下文。进一步提高产量的最大剩余瓶颈来自于目前需要熟练的技术人员输入每个标本记录和相关的附属品数据，包括数字图像信息。新系统将有可能收集每个标本的高分辨率、聚焦的数字图像，它将生成非常适合开发机器学习方法的文件，机器学习方法将使用模式识别算法来自动执行每个标本的高级分类任务(目、族)。这样的自动化管道将把装载标本的板材转换为标本记录，以及与其相关的图像和分类任务。对研究项目和生物多样性科学的影响将是巨大的。

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.