Research Initiation Award: A Deeper Look at Transmembrane Permeability in Plants and its Implications to Food Security

历史上的黑人学院和大学本科课程（HBCU-UP）通过研究启动奖为历史上的黑人学院和大学的初级和中级职业教师提供支持，他们正在建立新的研究项目或重新定向和重建现有的研究项目。预计该奖项将有助于进一步提高教师的研究能力和效率，并改善家庭机构的研究和教学。这个奖项给萨凡纳州立大学支持教师和本科生的研究经验，以开发工具，将预测植物的能力，运输污染物从土壤和随后的易位整个工厂。具体而言，利用计算机模拟和生物验证技术，该项目可能会揭示植物生物学的机制见解。此外，植物根系吸收环境污染物并随后将其转移到可食用部分可能对粮食安全产生严重影响。植物是人类食物的基本组成部分，也是牲畜营养的主要来源。该研究项目通过考虑理化性质和基本植物材料的作用，推进了植物吸收和转运新兴污染物的基础知识。该项目提供了新的方法来评估根水分配和跨膜渗透性在分子和大尺度。在分子水平上，污染物与植物根系的相互作用将采用分子动力学模拟。在大的层面上，人工智能（AI）和机器学习（ML）技术将用于基于跨膜渗透性确定不同植物物种的吸收变异性。神经网络、模糊逻辑和聚类算法是将用于执行模拟的一些AI/ML技术。将使用这些分析来确定具有植物修复潜力的植物物种和在其可食用组织中具有较高污染可能性的植物物种。将开发一个微型机器学习（TinyML）系统，通过图像处理检测植物中出现的污染物。该系统使用计算机视觉模型来寻找受到新污染物影响的植物。该项目有可能提供一个易于使用的工具来分析作物污染和植物吸收的新兴污染物。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的知识价值和更广泛的影响审查标准的支持。

The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness and improve research and teaching at the home institution. This award to Savannah State University supports faculty and undergraduate research experiences to develop tools that will predict a plant’s ability to transport contaminants from the soil and subsequent translocation throughout the plant. Specifically, utilizing computer simulations and biological validation techniques, the project may reveal mechanistic insights in plant biology. Furthermore, uptake of environmental contaminants by plant roots and their subsequent translocation to the edible parts may have serious implications to the food security. Plants provide a fundamental fraction of the human food and are the primary source of nutrition for livestock. This research project advances the fundamental knowledge of plant uptake and translocation of emerging contaminants by considering the role of physicochemical properties and fundamental plant materials. The project offers new approaches for assessing root-water partitioning and transmembrane permeability at molecular and large scales. At the molecular level, the interactions of contaminants with plant roots will be examined using molecular dynamics simulation. At the large level, artificial intelligence (AI) and machine learning (ML) techniques will be used to determine the uptake variability in different plant species based on transmembrane permeability. Neural networks, fuzzy logic, and clustering algorithms are some of the AI/ML techniques that will be employed to perform the simulations. The plant species with potential for phytoremediation and plant species with higher possibility of contamination in their edible tissues will be determined using these analyses. A tiny machine learning (TinyML) system will be developed to detect emerging contaminants in plants through image processing. This system uses a computer vision model to find plants impacted by emerging contaminants. This project has the potential to provide an easy-to-use tool to analyze crop contamination and plant uptake of the emerging contaminants.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.