POLYCYCLIC AROMATIC HYDROCARBONS: ULTRASENSITIVE DETECTION, EARLY LIFE EXPOSURES-CLINICAL OUTCOMES (PRETERM BIRTHS, CHRONIC LUNG DISEASE, AND NEUROCOGNITIVE DEFICITS), PREVENTION AND REMEDIATION

多环芳烃：超灵敏检测、生命早期暴露-临床结果（早产、慢性肺病和神经认知缺陷）、预防和补救

• 批准号: 10401127
• 负责人: BHAGAVATULA MOORTHY
• 金额: --
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-28 至 2021-07-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10401127
• 关键词: Air; Area; Aromatic Compounds; Aromatic Polycyclic Hydrocarbons; Benchmarking; Biological; Chemicals; Chemistry; Chronic lung disease; Clinical; Clutterings; Complex; Data; Data Science; Detection; Development; Effectiveness; Environment; Family; Foundations; Geometry; Goals; Health; Human; Image; Investigation; Machine Learning; Mass Fragmentography; Methods; Molecular; Monitor; Neurocognitive Deficit; Outcome; Parents; Partition Coefficient; Patient Monitoring; Polymers; Premature Birth; Prevention; Research; Sampling; Soil; Spectrum Analysis; Surface; Testing; Training; Variant; Visual; Water; Work; base; design; detection sensitivity; early life exposure; effectiveness testing; exposed human population; hazard; improved; innovation; interest; learning classifier; machine learning algorithm; machine learning method; monolayer; nanoparticle; remediation; spectroscopic survey; superfund site; surface coating; synergism; tool; training opportunity; vibration

Project Summary The overarching goal of this project is the development of new and innovative approaches to ultrasensitive detection and identification of polycyclic aromatic hydrocarbon (PAH) molecules and their functionalized derivatives (polycyclic aromatic compounds, or PACs). Ms. Mary Bajomo, will pursue research central to these project goals, as outlined by both Specific Aims. The Specific Aims of this Diversity Supplement form a central, essential subset of the work required to achieve the project goals, and provide an essential foundation for the sensing methods to be developed over the course of this project. They also lay the groundwork for bringing Machine Learning methods into the subfield of spectroscopic chemical sensing. Pursuing research at the interface between Chemistry and Machine Learning presents an exceptional training opportunity for Ms. Bajomo, and will allow her to interact strongly with the three PIs and their research groups in three fields: the Halas group, for experimental chemistry in the area of surface-enhanced spectroscopic sensing, the Nordlander group, on nanoparticle-based substrate design, and the Patel group, experts in Machine Learning and Data Science. Our hypothesis is that Machine Learning classifiers can be developed and used to distinguish between specific PAH and PAC molecules found in environmental or biological samples through their vibrational spectroscopic signatures. An essential aspect of this approach is the identification of PAH and PAC molecules while embedded in a molecular or polymer capture layer that has been designed to extract PAH/PAC molecules from environmental and/or biological samples that has its own specific spectroscopic signature “background”. These investigations are foundational to the detection of multicomponent mixtures of PAH/PAC molecules from realistic environmental or biological samples, using a combination of surface- enhanced spectroscopies and Machine Learning algorithms analogous to image recognition in cluttered, complex background environments. The two Specific Aims that Ms Bajomo will pursue are: Specific Aim 1: The identification and quantitative characterization of a universal capture layer for the wide range of PAH and PAC compounds encountered in biological and environmental samples. This capture layer would be serve as a universal coating for surface-enhanced Raman and Infrared spectroscopic substrates, and allow for the extraction of PAH and PAC molecules from solution in concentrations suitable for detection, consistent with concentrations of these chemicals found in samples of interest. Specific Aim 2: To develop surface-enhanced chemical sensing data as input to Machine Learning classifiers, to benchmark their effectiveness in identifying specific PAH molecules and distinguishing PAH/PAC molecules from each other by ML methods. This close synergy between experimental spectroscopic studies and ML classifier testing presents an outstanding opportunity for graduate training at the interface between two extremely important and dynamic research field for Ms. Bajomo.

项目摘要 这个项目的首要目标是开发新的和创新的方法来处理超敏感 多环芳烃(PAH)分子及其功能化的检测与鉴定 衍生物(多环芳香化合物，或PAC)。Mary Bajomo女士将进行以这些为中心的研究 项目目标，由两个具体目标概述。这份多样性补充文件的具体目标形成了一个中心， 实现项目目标所需工作的基本子集，并为 将在该项目过程中开发的传感方法。它们还为带来 将机器学习方法引入光谱化学传感的子领域。在大学从事研究工作 化学和机器学习之间的接口为MS提供了一个特殊的培训机会。 Bajomo，并将使她能够在三个领域与三个PI及其研究小组进行强有力的互动： 哈拉斯小组，表面增强光谱传感领域的实验化学， Nordlander小组，基于纳米颗粒的基板设计，Patel小组，机器学习专家 和数据科学。我们的假设是，机器学习分类器可以被开发并用于 通过以下方法区分环境或生物样品中发现的特定PAH和PAC分子 它们的振动光谱特征。这种方法的一个基本方面是识别PAH和 当PAC分子嵌入分子或聚合物捕获层时，该捕获层被设计为提取 来自环境和/或生物样品的PAH/PAC分子，具有自己的特定光谱 签名“背景”。这些研究是检测多组分混合物的基础 PAH/PAC分子来自现实的环境或生物样品，使用表面- 增强的光谱和机器学习算法，类似于杂乱无章、 复杂的背景环境。巴约莫女士将追求的两个具体目标是：具体目标1： 广谱PAH和PAC通用捕获层的鉴定和定量表征 在生物和环境样品中遇到的化合物。该捕获层将被用作 用于表面增强拉曼和红外光谱衬底的通用涂层，并允许 从溶液中提取适合于检测的浓度的PAH和PAC分子，与 在感兴趣的样本中发现这些化学物质的浓度。具体目标2：开发表面增强技术 将化学传感数据作为机器学习分类器的输入，以衡量其在识别 并用ML方法对PAH/PAC分子进行了区分。如此接近 实验光谱研究和ML分类器测试之间的协同提供了一个突出的 在两个极其重要和充满活力的研究领域之间的交界处提供研究生培训的机会 给巴乔莫女士。