Rosa Nicholas, Ristic Marko, Marshall Bevan, Newman Janet
Manufacturing (Biomedical), CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia.
Acta Crystallogr F Struct Biol Commun. 2018 Jul 1;74(Pt 7):410-418. doi: 10.1107/S2053230X18008038. Epub 2018 Jun 26.
The process of producing suitable crystals for X-ray diffraction analysis most often involves the setting up of hundreds (or thousands) of individual crystallization trials, each of which must be repeatedly examined for crystals or hints of crystallinity. Currently, the only real way to address this bottleneck is to use an automated imager to capture images of the trials. However, the images still need to be assessed for crystals or other outcomes. Ideally, there would exist some rapid and reliable machine-analysis tool to translate the images into a quantitative result. However, as yet no such tool exists in wide usage, despite this being a well recognized problem. One of the issues in creating robust automatic image-analysis software is the lack of reliable data for training machine-learning algorithms. Here, a mobile application, Cinder, has been developed which allows crystallization images to be scored quickly on a smartphone or tablet. The Cinder scores are inserted into the appropriate table in a crystallization database and are immediately available to the user through a more sophisticated web interface, allowing more detailed analyses. A sharp increase in the number of scored images was observed after Cinder was released, which in turn provides more data for training machine-learning tools.
制备适合X射线衍射分析的晶体的过程通常涉及进行成百(或上千)次独立的结晶试验,每次试验都必须反复检查是否有晶体或结晶迹象。目前,解决这一瓶颈的唯一真正方法是使用自动成像仪来捕捉试验图像。然而,这些图像仍需评估是否有晶体或其他结果。理想情况下,应该有一些快速可靠的机器分析工具将图像转化为定量结果。然而,尽管这是一个广为人知的问题,但目前还没有广泛使用的此类工具。创建强大的自动图像分析软件的问题之一是缺乏用于训练机器学习算法的可靠数据。在此,开发了一款移动应用程序Cinder,它可以在智能手机或平板电脑上快速对结晶图像进行评分。Cinder评分被插入到结晶数据库的相应表格中,用户可以通过更复杂的网络界面立即获取,从而进行更详细的分析。Cinder发布后,评分图像的数量急剧增加,这反过来又为训练机器学习工具提供了更多数据。