Collaborative Research: EAGER: Exploring beyond visualization: Data sonification of bacterial chemotaxis patterns

合作研究：EAGER：超越可视化的探索：细菌趋化模式的数据超声处理

• 批准号: 1950369
• 负责人: Roseanne Ford
• 金额: $ 24.85万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-15 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1950369&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Exploring; beyond

In this Era of Big Data an unprecedented amount of information is being collected at rates that are overwhelming researchers' capacity to process data in meaningful ways. Converting streams of numbers into graphical representations has proven to be useful over the past three decades to identify trends in complex data sets such as weather patterns, stock market fluctuations, and flu epidemics. While visualization is a powerful approach to data analysis, not all data are amenable to visualization. Sonification, the mapping of information to sound, is an alternative method for extracting useful information from visually chaotic data. One familiar example of data sonification is a Geiger counter that converts invisible gamma radiation to an audible frequency of clicks. This project demonstrates the utility of sonification in a study of how microbes swim toward nutrients that are critical for their survival. The goal is to promote more widespread use of sonification to analyze big data within the biological research community. Sonification of data can also increase public scientific literacy and public engagement with science and technology. As demonstrated by the catchy Higgs Boson tune, sonified data made the discovery of subatomic particles more accessible to the public. Sound and music are used in this project to provide a medium through which to engage elementary school-age children in a welcoming manner about the excitement of science. Another notable aspect is that data sonification provides a convenient platform to engage sight-impaired individuals in research. The project brings together expertise in biological systems engineering and digital music composition that provide diverse perspectives for cross-training student research assistants.In this project sonification is used to detect changes in the swimming patterns of microorganisms upon exposure to a chemical stimulus (i.e. chemotaxis). When examining a population of swimming microbes through a microscope the movement appears chaotic, making subtle changes in the paths of individual organisms impossible to discern in real time. By mapping visual images to the frequency domain in real-time one can transform the chaotic visual motion to discernible differences in auditory sounds. The specific project objectives are to: (1) identify the features of bacterial swimming motion that are detected in sonified data; (2) optimize video microscopy settings and video filters to enhance the signal-to-noise ratio of the data collected; (3) sonify data in real time to allow simultaneous audio and visual input to an observer; (4) evaluate the robustness of data sonification algorithms for bacteria that have different swimming behaviors; and (5) screen microbes for chemotaxis beyond the training set to evaluate the success of the sonification process. One outcome of this work will be a platform to generate sonified data in real-time that is synchronous with visual observations to allow high-throughput screening of chemotactic responses for various species to different chemoeffectors over a range of concentrations. Another, and perhaps more impactful outcome, will be to significantly expand the tools that biological scientists have at their disposal to identify patterns in complex data that they collect.This award is jointly funded by the Systems and Synthetic Biology Cluster and the Cellular and Dynamics Cluster in the Division of Molecular and Cellular Biology.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.

在这个大数据时代，收集的信息量前所未有，其速度超出了研究人员以有意义的方式处理数据的能力。过去三十年来，事实证明，将数字流转换为图形表示对于识别天气模式、股市波动和流感流行等复杂数据集中的趋势非常有用。虽然可视化是一种强大的数据分析方法，但并非所有数据都适合可视化。可听化（将信息映射到声音）是从视觉混乱的数据中提取有用信息的另一种方法。数据超声处理的一个熟悉的例子是盖革计数器，它将不可见的伽马辐射转换为可听的咔嗒声频率。该项目展示了超声波在微生物如何游向对其生存至关重要的营养物质的研究中的效用。目标是促进在生物研究界更广泛地使用可听化来分析大数据。数据的可听化还可以提高公众的科学素养和公众对科学技术的参与。正如朗朗上口的希格斯玻色子曲调所证明的那样，声音化数据使公众更容易发现亚原子粒子。该项目使用声音和音乐来提供一种媒介，让小学生以欢迎的方式了解科学的乐趣。另一个值得注意的方面是数据可听化提供了一个方便的平台来吸引视力障碍人士参与研究。该项目汇集了生物系统工程和数字音乐创作方面的专业知识，为交叉培训学生研究助理提供了不同的视角。在该项目中，声化用于检测微生物在接触化学刺激（即趋化性）后游泳模式的变化。当通过显微镜检查一群游动的微生物时，它们的运动显得混乱，使得个体生物体路径的微妙变化无法实时辨别。通过将视觉图像实时映射到频域，可以将混乱的视觉运动转换为可辨别的听觉差异。具体项目目标是：（1）识别在声化数据中检测到的细菌游动运动的特征； （2）优化视频显微镜设置和视频滤波器，以增强采集数据的信噪比； (3) 实时对数据进行声音处理，以便向观察者同时输入音频和视觉信息； （4）评估具有不同游动行为的细菌的数据超声处理算法的鲁棒性； (5) 筛选训练集之外的微生物趋化性，以评估声化过程的成功。这项工作的一个成果将是一个实时生成声音数据的平台，该数据与视觉观察同步，以允许高通量筛选不同物种在一定浓度范围内对不同化学效应物的趋化反应。另一个也许更有影响力的成果将是显着扩展生物科学家可以使用的工具，以识别他们收集的复杂数据中的模式。该奖项由系统和合成生物学集群以及细胞和动力学集群共同资助该奖项反映了 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。