RUI: Towards Robust Sparsity in Nonuniform Sampling Multidimensional NMR

RUI：非均匀采样多维 NMR 中的鲁棒稀疏性

• 批准号: 2305086
• 负责人: David Rovnyak
• 金额: $ 15.38万
• 依托单位: Bucknell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2305086&HistoricalAwards=false
• 关键词: RUI; Towards; Robust; Sparsity; Nonuniform

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor David Rovnyak and his group at Bucknell University are developing data sampling technology and principles to accelerate the discovery of complex molecular structures. Broadly, chemists are studying molecules of increasing complexity and size, such as complex natural products from which scientists can develop new bio-inspired drugs, where it is vital to discover the exact structures of these molecules. A central technique in chemistry that meets this need is nuclear magnetic resonance (NMR). However, the more complex and large a molecule is, the more difficult it is to solve its structure. Using statistical and related data science methods, the Rovnyak group is developing and disseminating next-generation data sampling methods that make advanced NMR analysis much faster and more accessible to chemists. The work is providing research opportunities to undergraduate students, including members of underrepresented groups. The team is also involved in outreach activities, including summer science camps for high school students and presentations at regional schools and museums. Advanced 2D-NMR experiments, often used for detecting long range atomic connectivities, are required to probe the structure of increasingly complex proton-poor natural products and bio-inspired molecules. The burden of indirect incrementation in powerful 2D-NMR spectroscopies leads to very long acquisition times and extremely low sensitivity. Solutions to the ‘incrementation problem’ of multidimensional NMR have been pursued for about three decades, through a broad family of techniques known as nonuniform sampling (NUS). In 2D-NMR, NUS is often not used at all or only conservatively, such as 50% data reduction. This fails to take advantage of the benefits NUS offers. This work is probing the barriers to sparser NUS and developing improved 1D-NUS schedules for advanced 2D-NMR experiments beyond the 50% threshold while ensuring high spectral fidelity. The work includes strategies for ‘super resolution’ NMR spectroscopy with ultra-high incrementation values. Intuitive and easy-to-use 1D-NUS schedules will be made widely available with potentially wide impact on the experimental chemical science community.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.

在化学系化学测量和成像项目的支持下，巴克内尔大学的大卫罗夫尼亚克教授和他的团队正在开发数据采样技术和原理，以加速复杂分子结构的发现。 从广义上讲，化学家正在研究越来越复杂和大小的分子，例如科学家可以从中开发新的生物启发药物的复杂天然产物，其中发现这些分子的确切结构至关重要。 满足这一需求的化学核心技术是核磁共振（NMR）。 然而，分子越复杂，越大，解决其结构就越困难。 Rovnyak集团正在使用统计和相关数据科学方法开发和推广下一代数据采样方法，使化学家更快更容易获得高级NMR分析。 这项工作为本科生提供了研究机会，包括代表性不足的群体的成员。 该小组还参与了外联活动，包括高中生科学夏令营以及在地区学校和博物馆的演讲。 先进的2D-NMR实验通常用于检测长程原子连接性，需要探测日益复杂的质子贫乏的天然产物和生物分子的结构。在强大的2D-NMR光谱中间接增加的负担导致非常长的采集时间和极低的灵敏度。 多维NMR的“增量问题”的解决方案已经追求了大约三十年，通过一个广泛的技术家族被称为非均匀采样（NUS）。 在2D-NMR中，NUS通常根本不使用或仅保守地使用，例如50%的数据减少。 这无法利用NUS提供的好处。这项工作正在探索稀疏NUS的障碍，并为超过50%阈值的高级2D-NMR实验开发改进的1D-NUS计划，同时确保高光谱保真度。 这项工作包括具有超高增量值的“超分辨率”NMR光谱学的策略。 直观和易于使用的1D-NUS时间表将被广泛使用，对实验化学科学界具有潜在的广泛影响。该奖项反映了NSF的法定使命，并被认为值得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来支持。