Raw Signal Processing and Peak Cluster Geometry to Discover and Quantify Co-Indicative Associations between Target and Non-Target Environmental Contaminants

在化学系化学测量与成像项目的支持下，爱荷华大学的Ananya Sen Gupta和Keri Hornbuckle及其团队正在设计数据解释工具，以促进发现和量化已知和未知有毒污染物之间的关联。关键思想是将“大图景”（环境污染的主要原因和运输途径是什么？）与小规模细节联系起来，通过数学方法解决重叠的信号，发现用于分离和化学分析污染物的仪器数据的隐藏贡献者。对环境污染的研究通常集中在已知的污染物（“目标”）上，而忽略了未知成分可能发挥的重要作用。非目标化合物的数量可能大大超过已知的目标化合物，并且可能与目标污染物的存在密切相关，并强烈表明目标污染物的存在。实验数据通常包含关于非目标化合物的丰富信息，但这些信息很少在环境污染研究中被追踪或在监管政策中被考虑。Sen Gupta和Hornbuckle团队正在设计一套新颖的计算技术，以提高从复杂实验数据中恢复信息的能力，从而解决这一差距。他们的工作适用于广泛的化学污染物，如城市空气中的多氯联苯（PCBs）和其他工业污染物；农村井水中的硝酸盐和砷；以及海洋石油泄漏造成的碳氢化合物污染。除了污染研究的科学进步之外，这项工作还通过研究生和本科生的研究指导，积极吸引妇女和其他代表性不足的群体。Sen Gupta/Hornbuckle团队正在设计一套计算技术来检测原始仪器信号（主要来自气相色谱法和质谱法），并自主检测目标和非目标化合物的贡献，从而量化它们在环境污染中的相对关联。该方法结合了非线性优化、几何聚类和基于图的多尺度网络，其中目标分析物形成局部邻域中心，非目标分析物围绕目标中心聚类形成密集关联子图。在多个数据存储库和多个应用程序中验证技术，寻求对原始色谱和质谱信号的前所未有的全面解释。具体目标包括：(i)发现与目标化合物共洗脱的“隐藏峰”；（ii）识别目标和非目标化合物之间的联系，以加强对污染途径的识别；（iii）提供能够整合各种数据存储库的工具，可能会改变对环境污染途径的理解。该奖项反映了美国国家科学基金会的法定使命，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。

With support from the Chemical Measurement and Imaging program in the Division of Chemistry, Profs. Ananya Sen Gupta and Keri Hornbuckle and their groups at the University of Iowa are devising data interpretation tools to facilitate discovery and quantitation of associations between well-known and unknown toxic pollutants. The key idea is to connect the "big picture" (what are the major causes and transport pathways of environmental contamination?) with small-scale details by mathematically resolving overlapped signals to discover hidden contributors to data from instruments used for separation and chemical analysis of contaminants. Studies of environmental pollution typically focus on known pollutants ("targets") and ignore the potentially significant roles played by unknown constituents. Non-target compounds can vastly outnumber known target compounds, and may be closely associated with, and strongly indicative of, the presence of target pollutants. Experimental data often contains rich information about non-target compounds, but this information is rarely tracked in studies of environmental pollution or considered in regulatory policies. The Sen Gupta and Hornbuckle groups are addressing this gap by devising a novel suite of computational techniques to enhance recovery of information from complex experimental data. Their work is applicable to a wide range of chemical pollutants, such as polychlorinated biphenyls (PCBs) and other industrial pollutants in city air; nitrates and arsenic in rural well-water; and hydrocarbon pollution from marine oil spills. Beyond the scientific advances in pollution studies, the work is actively engaging women and other underrepresented groups through graduate and undergraduate research mentoring.The Sen Gupta/Hornbuckle team is devising a suite of computational techniques to examine raw instrument signal (primarily from gas chromatography and mass spectrometry) and to autonomously detect contributions from both target and non-target compounds, enabling quantitation of their relative associations in environmental contamination. The approach combines non-linear optimization, geometric clustering, and graph-based multi-scale networks, where the target analytes form the local neighborhood hub and non-targets cluster around the target hubs forming dense association sub-graphs. Techniques are validated across multiple data repositories and multiple applications, seeking unprecedented comprehensive interpretation of raw chromatographic and mass spectrometric signals. Specific objectives include: (i) discovery of "hidden peaks" that co-elute with target compounds; (ii) identification of associations between target and non-target compounds to enhance identification of contamination pathways; and (iii) provision of tools enabling integration of diverse data repositories, potentially transforming understanding of environmental contamination pathways.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.