Diversity Supplement to R01 DC017757

R01 DC017757 的多样性补充

• 批准号: 10610599
• 负责人: Joel D Mainland
• 金额: $ 1.14万
• 依托单位: MONELL CHEMICAL SENSES CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10610599
• 关键词: Algorithms; Black race; Characteristics; Chemical Structure; Chemicals; Code; Collection; Color; Communities; Complex; Complex Mixtures; Country; Data; Data Set; Descriptor; Detection; Development; Eating; Enrollment; Face; Food; Food Preferences; Frequencies; Funding; Health; Human; Learning; Ligands; Machine Learning; Mass Fragmentography; Measurement; Measures; Methods; Modeling; Modernization; Molecular Structure; Neurosciences; Non-linear Models; Numerical value; Odors; Olfactory Pathways; Perception; Play; Process; Psychophysics; Research; Research Personnel; Role; Science; Sensory; Smell Perception; Stimulus; Techniques; Technology; Training; Translating; Vision; Visual; Vocabulary; Work; apprenticeship; auditory stimulus; base; career development; computer monitor; design; experience; experimental study; high dimensionality; improved; in silico; interest; laboratory experience; machine learning algorithm; member; novel; physical property; predictive modeling; predictive test; preference; programs; receptor; relating to nervous system; single molecule; visual stimulus

PROJECT SUMMARY Modern technology makes it possible to capture a visual scene as a photograph, alter it, send it to another country nearly instantaneously, and store it without concern for degradation. None of this is currently possible in olfaction. Although perfumers and flavorists are adept at mixing odorous molecules to produce a desired perceptual effect, the rules underlying this process are poorly understood at a quantitative level. Current methods for displaying odors to a subject are akin to requiring a Polaroid of every visual stimulus of interest. A more efficient method for probing the olfactory system would be to use a set of 'primary odors'—some limited number of odors from which all other complex odors could be reproduced by appropriate mixtures. Both auditory and visual stimuli have been digitized, and this will eventually be possible in olfaction as well. Predicting odor from chemical structure has been a problem in the field since its inception, but recent advances in machine learning algorithms have made great progress in analogous problems, such as facial recognition. This diversity supplement proposal seeks funds to enable Joshua Nsubuga, a Black U.S. citizen, to learn how to design experiments, analyze data, and conduct human sensory studies. We have crafted a training plan that draws on both our laboratory's experience and Monell's Science Apprenticeship Program to facilitate Mr. Nsubuga's technical, intellectual, and career development.

项目摘要 现代技术使捕获视觉场景作为照片，将其更改，将其发送到另一个 几乎是瞬间的国家，并不必担心退化。这些目前都不是 在嗅觉中。尽管调香师和香料师很擅长混合有气味的分子以产生所需的 感知效应，该过程的基础规则在定量层面上尚未理解。当前的 向受试者展示气味的方法类似于需要每种感兴趣的视觉刺激的宝丽来。一个 探测嗅觉系统的更有效的方法是使用一组“主要气味” - 一种有限 所有其他复杂气味的气味数量都可以通过适当的混合物复制。两个都 听觉和视觉刺激已被数字化，最终也可以在嗅觉中进行。 自成立以来，预测化学结构的气味一直是该领域的一个问题，但是最近的进步 在机器学习中，算法在类似问题（例如面部识别）方面取得了长足的进步。 这种多样性补充提案寻求资金使美国黑人公民约书亚·恩苏布加（Joshua Nsubuga）学习如何 设计实验，分析数据并进行人类感觉研究。我们制定了一个培训计划 借鉴了我们的实验室的经验和Monell的科学学徒计划，以促进先生 Nsubuga的技术，智力和职业发展。