NUScon:一个社区驱动的NMR非均匀采样定量评估平台。

Q3 Physics and Astronomy
Magnetic resonance (Gottingen, Germany) Pub Date : 2021-11-25 eCollection Date: 2021-01-01 DOI:10.5194/mr-2-843-2021
Yulia Pustovalova, Frank Delaglio, D Levi Craft, Haribabu Arthanari, Ad Bax, Martin Billeter, Mark J Bostock, Hesam Dashti, D Flemming Hansen, Sven G Hyberts, Bruce A Johnson, Krzysztof Kazimierczuk, Hengfa Lu, Mark Maciejewski, Tomas M Miljenović, Mehdi Mobli, Daniel Nietlispach, Vladislav Orekhov, Robert Powers, Xiaobo Qu, Scott Anthony Robson, David Rovnyak, Gerhard Wagner, Jinfa Ying, Matthew Zambrello, Jeffrey C Hoch, David L Donoho, Adam D Schuyler
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引用次数: 4

摘要

尽管非均匀采样(NUS)的概念​​​​​​​) 而多维NMR中的非傅立叶谱重建在40年前开始出现,直到最近NUS才变得更加普遍。NUS的优点包括能够定制实验,以减少数据收集时间并提高光谱质量,无论是通过检测密集的峰值(即“分辨率”)还是弱强度的峰值(如“灵敏度”)。这些方法的广泛采用是由于计算性能的提高、软件的日益丰富和灵活性、NMR光谱仪供应商的支持以及更高磁场带来的数据采样需求的增加。然而,确定最佳做法仍然是一项重大挑战,尚未得到解决。与离散傅立叶变换不同,用于从NUS数据重建光谱的非傅立叶方法是非线性的,取决于信号的复杂性和性质,并且缺乏描述其性能的定量或形式化理论。看似微妙的算法差异可能导致光谱质量和伪影的显著变化。已经启动了一项基于社区的对新加坡国立大学挑战问题的批判性评估,称为“非均匀抽样竞赛”(NUScon),目的是确定处理和分析新加坡国立大学实验的最佳实践。我们通过从注入合成信号的NMR实验中构建挑战来解决这一目标,并使用社区提交的工作流程来处理这些挑战。在NUScon的最初几轮中,我们的目标是建立评估光谱重建质量的客观标准。我们在这里展示了一个用于进行定量分析的软件包,并展示了前两轮NUScon的结果。我们讨论了仍然存在的挑战,并提出了持续社区驱动发展的路线图,最终目的是在这个快速发展的领域提供最佳实践。NUScon软件包和评估挑战问题的所有数据都托管在NMRbox平台上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

NUScon: a community-driven platform for quantitative evaluation of nonuniform sampling in NMR.

NUScon: a community-driven platform for quantitative evaluation of nonuniform sampling in NMR.

NUScon: a community-driven platform for quantitative evaluation of nonuniform sampling in NMR.

NUScon: a community-driven platform for quantitative evaluation of nonuniform sampling in NMR.

Although the concepts of nonuniform sampling (NUS​​​​​​​) and non-Fourier spectral reconstruction in multidimensional NMR began to emerge 4 decades ago , it is only relatively recently that NUS has become more commonplace. Advantages of NUS include the ability to tailor experiments to reduce data collection time and to improve spectral quality, whether through detection of closely spaced peaks (i.e., "resolution") or peaks of weak intensity (i.e., "sensitivity"). Wider adoption of these methods is the result of improvements in computational performance, a growing abundance and flexibility of software, support from NMR spectrometer vendors, and the increased data sampling demands imposed by higher magnetic fields. However, the identification of best practices still remains a significant and unmet challenge. Unlike the discrete Fourier transform, non-Fourier methods used to reconstruct spectra from NUS data are nonlinear, depend on the complexity and nature of the signals, and lack quantitative or formal theory describing their performance. Seemingly subtle algorithmic differences may lead to significant variabilities in spectral qualities and artifacts. A community-based critical assessment of NUS challenge problems has been initiated, called the "Nonuniform Sampling Contest" (NUScon), with the objective of determining best practices for processing and analyzing NUS experiments. We address this objective by constructing challenges from NMR experiments that we inject with synthetic signals, and we process these challenges using workflows submitted by the community. In the initial rounds of NUScon our aim is to establish objective criteria for evaluating the quality of spectral reconstructions. We present here a software package for performing the quantitative analyses, and we present the results from the first two rounds of NUScon. We discuss the challenges that remain and present a roadmap for continued community-driven development with the ultimate aim of providing best practices in this rapidly evolving field. The NUScon software package and all data from evaluating the challenge problems are hosted on the NMRbox platform.

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