Diversity Supplement to R01 DC017757

项目摘要 现代技术使人们有可能捕捉一个视觉场景作为一张照片，改变它，把它发送到另一个 国家几乎瞬间，并存储它，而不必担心退化。这些目前都不可能实现 在嗅觉方面。尽管调香师和调味师擅长混合气味分子以产生所需的香味， 虽然这是一种感性的影响，但在定量层面上对这一过程的基本规则了解甚少。电流 用于向受试者显示气味的方法类似于需要感兴趣的每个视觉刺激的宝丽来。一 探测嗅觉系统的更有效的方法是使用一组“主要气味”--一些有限的 通过适当的混合物可以再现所有其他复杂气味的气味数量。两 听觉和视觉刺激已经被数字化，这最终也将在嗅觉中成为可能。 从化学结构预测气味从一开始就一直是该领域的一个问题，但最近的进展 在机器学习中，算法在类似的问题上取得了很大的进展，例如面部识别。 这项多样性补充提案旨在寻求资金，使美国黑人公民约书亚恩苏布加能够学习如何 设计实验，分析数据，并进行人类感官研究。我们制定了一个训练计划， 利用我们实验室的经验和莫内尔的科学学徒计划，以促进先生。 Nsubuga的技术，智力和职业发展。

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.