STTR Phase I: CryoDiscovery? : An integrated cryo-EM intelligence solution

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响将是加速使用低温电子显微镜（“Cryo-EM”）发现新的分子结构。 Cryo-EM在微观层面上产生高分辨率的3D图像，并被许多领域的研究人员使用，包括生命科学，材料科学，纳米技术，半导体，能源，环境科学和食品科学。显微镜技术的进步使分子图像捕获的分辨率达到前所未有的水平，但产生的数据呈指数级增长，随后将这些图像处理成可见的3D结构既具有挑战性又耗时。每个项目可以生成超过100，000张图像，并需要数周时间才能完成一个可视的3D结构。当前的图像处理和数据分析解决方案没有很好地集成，在评估图像质量之前需要大量的手动用户参与和长时间的等待。我们将应用机器学习来自动化冷冻EM图像处理，以提高研究人员的生产力和准确性。 我们还将设计系统以减少用户培训时间。 这一结果将改善冷冻EM的使用，并加速许多科学领域的新突破。 这个小企业技术转让（STTR）第一阶段项目通过开发新的机器学习模型来自动化单颗粒分析的图像处理，这些模型可以识别具有可重复精度水平的颗粒，并将其集成到cryo-EM工作流程中，以便于部署。cryo-EM生成的图像噪声很大，目标是处理它们以构建可识别的3D分子结构。冷冻EM工作流程中的许多步骤需要人工干预和分析，这可能需要数周时间，并因用户偏见、时间等待和用户疲劳而导致错误。这项研究的目标是产生一个原型，一致和准确地预测粒子，并很容易集成到冷冻EM工作流程。该方法将增加来自广泛应用的训练和验证数据集，并利用现有的卷积神经网络框架。 我们将开发新的技术来运行实验以优化模型，将原型集成到已建立的低温EM工作流程中以进行端到端处理，并产生易于部署的交付方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Technology Transfer (STTR) Phase I project will be to accelerate discoveries of new molecular structures using cryogenic electron microscopy ("Cryo-EM"). Cryo-EM produces high-resolution 3D images at microscopic levels and is used by researchers in many fields including life sciences, materials science, nanotechnology, semiconductors, energy, environmental science, and food science. Microscopy advancements enable molecular image capture at unprecedented levels of resolution, but the data produced are growing exponentially and subsequent processing of those images into visible 3D structures is both challenging and time consuming. Each project can produce more than 100,000 images and take weeks to arrive at one viewable 3D structure. Current image processing and data analysis solutions are not well-integrated, requiring extensive manual user involvement and long wait times before assessing image quality. We will apply machine learning to automate cryo-EM image processing to improve researcher productivity and accuracy. We will also design the system to reduce user training time. The result will improve access to cryo-EM and accelerate new breakthroughs in many areas of science. This Small Business Technology Transfer (STTR) Phase I project automates image processing for single particle analysis by developing new machine learning models that recognize particles with repeatable accuracy levels and integrates them into the cryo-EM workflow for easy deployment. Images generated by cryo-EM are highly noisy, and the goal is to process them to build recognizable 3D molecular structures. Many steps in the cryo-EM workflow require manual intervention and analysis that can take several weeks and result in errors due to user bias, time waiting and user fatigue. The objectives of this research are to produce a prototype that consistently and accurately predicts particles and is easily integrated into the cryo-EM workflow. The approach will be to increase the training and validation datasets from a wide range of applications and utilize existing convolutional neural network frameworks. We will develop new techniques for running experiments to optimize the models, integrate the prototype into established cryo-EM workflows for end-to-end processing, and produce a delivery method for easy deployment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.