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，正在以压倒性研究人员以有意义的方式处理数据的能力的速度收集空前的信息。在过去的三十年中，将数字流转换为图形表示形式已被证明是有用的，可以识别复杂数据集的趋势，例如天气模式，股市波动和流感流行病。虽然可视化是一种有力的数据分析方法，但并非所有数据都适合可视化。 SONIFICATION是将信息映射到声音的映射，是从视觉混乱数据中提取有用信息的另一种方法。一个熟悉的数据超词性示例是盖策计数器，将无形的伽马辐射转换为可听到的点击频率。该项目在研究微生物如何游泳对生存至关重要的营养方面的研究中证明了索索的实用性。目的是促进更广泛地使用SONIFIENCE来分析生物学研究社区中的大数据。数据的索赔还可以提高公共科学素养和与科学技术的公众参与。正如吸引人的希格斯玻色曲调所证明的那样，Sonified数据使公众更容易发现亚原子颗粒。在该项目中，使用声音和音乐来提供一种媒介，通过欢迎科学兴奋的方式吸引小学学龄儿童。另一个值得注意的方面是，数据超索式提供了一个方便的平台，以吸引视力障碍的人参与研究。该项目汇集了生物系统工程和数字音乐组成方面的专业知识，这些专业知识为跨培训学生研究助理提供了多种观点。在此项目中，SONIFICATION用于检测暴露于化学刺激的微生物游泳模式的变化（即趋化性）。当通过显微镜检查游泳微生物的种群时，运动显得混乱，从而使单个生物体的路径有微妙的变化，无法实时辨别。通过将视觉图像实时映射到频域，一个人可以将混乱的视觉运动转换为听觉声音的可识别差异。特定的项目目标是：（1）确定在SONIFIED数据中检测到的细菌游泳运动的特征； （2）优化视频显微镜设置和视频过滤器，以增强收集数据的信噪比； （3）实时超声处理数据，以使观察者同时进行音频和视觉输入； （4）评估具有不同游泳行为的细菌数据超拟元算法的鲁棒性； （5）超出训练设置的趋化性微生物的筛选微生物，以评估超拟化过程的成功。这项工作的一个结果将是一个平台实时生成超声的数据，该平台与视觉观察同步，以允许对各种浓度范围内对不同的化学效果的各种物种的趋化响应进行高通量筛选。 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影响审查标准。