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 David Rovnyak 教授和他的团队正在开发数据采样技术和原理，以加速发现复杂的分子结构。尺寸，例如科学家可以从中开发新的生物启发药物，发现这些分子的精确结构至关重要，满足这一需求的化学核心技术是核磁共振（NMR）。越复杂、越大分子越大，解析其结构就越困难，Rovnyak 小组正在开发和传播下一代数据采样方法，使先进的 NMR 分析更快、更容易为化学家所用。为本科生提供研究机会，包括代表性不足的群体的成员。该团队还参与外展活动，包括为高中生举办的夏季科学营以及在地区学校和博物馆进行的演示，这些实验通常用于检测长距离。范围原子连接性，需要探测日益复杂的贫质子天然产物和生物启发分子的结构，强大的二维核磁共振光谱的间接增量负担导致了非常长的采集时间和极低的灵敏度。通过一系列称为非均匀采样 (NUS) 的技术，“多维 NMR 的增量问题”已经被研究了大约三十年。在 2D-NMR 中，NUS 通常根本不使用或根本不使用。仅保守地，例如减少 50% 的数据，这无法利用 NUS 提供的优势。这项工作正在探索稀疏 NUS 的障碍，并为超出 50% 阈值的高级 2D-NMR 实验开发改进的 1D-NUS 计划。该工作包括具有超高增量值的“超分辨率”NMR 光谱策略。将制定直观且易于使用的 1D-NUS 计划。广泛使用，对实验化学科学界具有潜在广泛影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。