UROL:ASC: AI-Supported Bionic Bivalves for Surface Water Monitoring based on Freshwater Mussel Response to Environmental Change

50多年来，生物学家一直在探索利用双壳类对水污染的反应，从它们作为哨兵生物中获益。最初的尝试是使用简单的传感器来测量有限的行为。在低功耗和分布式传感以及机器学习方面的最新发展，现在使新一代基于bivalve的生物传感器系统（BLiSS）能够进行多模式、更全面的评估，有助于减少公共水基础设施进水口和出水口存在的水污染物，这些污染物对人类和动物的健康构成威胁。该项目有望为双壳类与微环境的相互作用带来新的见解，并支持建模和数据分析，为检测环境对公共供水系统地表水的影响建立基线数据。虽然本地双壳类动物作为滤食性动物已经在水的清晰度和质量方面发挥了关键作用，但它们是地球上减少最快的分类群之一，对它们的保护是当务之急。该项目结合了利用双壳类进行生物修复和评估其生态影响的机会，以及提高对保护工作的认识和支持。将公民科学家和学生聚集在一起，特别是来自STEM中代表性不足的少数群体，这些团体将围绕一系列跨学科领域进行互动，包括嵌入式传感器系统，人工智能，物联网，数据分析，环境保护，双壳类生物和流行病学。该项目的目标是利用淡水贻贝对环境的生理和行为反应，建立仿生传感系统的基本物理和算法构建块，用于监测和监测公共供水系统入口和出口的地表水资源。我们的目标是提供a)新颖的多模态体上传感器，用于连续评估双壳类动物的行为和生理，并测量对环境条件的基线反应；B)便携、低成本和环保的嵌入式系统平台，将在未来的项目和领域范围内部署在受控的实验室条件下；C)在各种环境和污染物暴露因素下从单双壳类和双壳类群集收集的新数据库；d)基于模型和数据驱动的技术，用于在存在和不存在生物传感器响应的地面真值验证的情况下分析收集的数据。这将首先产生一个基于环境污染物异常检测的非特异性预警系统，以触发环境保护机构进一步的环境调查和化学测试。项目的下一阶段将专注于测量异常和基于学习的自适应数据分析策略，以提供相对更精确的特定暴露预测/识别。这些协同研究是由一个独特的、积极合作的跨学科团队实现的，他们拥有淡水贻贝生物学、水生生态系统流行病学、仿生动物-机器接口、无线嵌入式系统、传感器、新材料、机器学习和公民科学等方面的专业知识。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。

For over fifty years, biologists have explored using the response of bivalves to water pollution to benefit from them as sentinel organisms. Initial efforts used simple sensors to measure a limited set of behaviors. Recent developments in low power and distributed sensing, and machine learning, now enable a new generation of a Bivalve-based Living Sensor Systems (BLiSS) for multimodal, more comprehensive assessment that can help mitigate the presence of water contaminants at inlets & outlets of public water infrastructures, where they pose a risk to human and animal health. The project is expected to bring new insights into bivalve-microenvironment interactions and support modeling and data analytics that can establish baseline data for detecting environmental impacts to surface waters of public water systems. While native bivalves already play a key role in water clarity and quality as filter feeders, they are some of the fastest diminishing taxa on the planet and their conservation is a high priority. This project combines opportunities to use bivalves in bioremediation and assess their ecological impact as well as to raise awareness and support for conservation efforts. Bringing together citizen scientists and students, especially from underrepresented minority populations in STEM, these groups will interact around a range of interdisciplinary areas including embedded sensor systems, artificial intelligence, internet-of-things, data analytics, environmental conservation, bivalve biology and epidemiology.The goal of this project is to establish the fundamental physical and algorithmic building blocks of a bionic sensing system using the physiological and behavioral responses of freshwater mussels to their environment for the monitoring and surveillance of surface water resources in the inlet and outlet of public water systems. We aim to provide a) novel and multimodal on-body sensors for continuous assessment of bivalve behavior and physiology with measurements of the baseline responses to environmental conditions; b) portable, low-cost and environmentally robust embedded system platforms to be deployed in controlled laboratory conditions within the scope of this project and field in the future; c) a repository of novel data collected from single and cluster of bivalves under various environmental and pollutant exposure factors; and d) model-based and data-driven techniques to analyze the collected data in the presence and absence of ground truth validation of biosensor responses. This will initially result in an non-specific early warning system based on anomaly detection of environmental contaminants to trigger further environmental investigation and chemical testing by environmental protection agencies. The next stages of the project will focus on measured anomalies and a learning based adaptive data analytics strategy to provide a relatively more precise prediction/identification of particular exposure. These synergistic studies are enabled by a unique and actively collaborating interdisciplinary team with expertise in freshwater mussel biology, aquatic ecosystem epidemiology, bionic animal-machine interfaces, wireless embedded systems, sensors, novel materials, machine learning, and citizen science.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.