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相关概念视频

Olfaction01:25

Olfaction

48.1K
The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
48.1K
Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

12.3K
Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...
12.3K
Olfactory Receptors: Location and Structure01:03

Olfactory Receptors: Location and Structure

11.2K
The process of olfaction, also known as the sense of smell, is a sophisticated chemical response system. The specialized sensory neurons that facilitate this process, known as olfactory receptor neurons, are situated in an upper segment of the nasal cavity, known as the olfactory epithelium. Olfactory sensory neurons are bipolar, with their dendrites extending from the epithelium's apex into the mucus that lines the nasal cavity. Airborne molecules, when inhaled, traverse the olfactory...
11.2K

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相关实验视频

Updated: Jan 14, 2026

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging
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Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

Published on: October 13, 2019

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可解释的多任务深度学习模型用于基于分子结构的气味感知.

Hiroaki Iwata1

  • 1Department of Biological Regulation, Faculty of Medicine, Tottori University, 86 Nishi-cho, Yonago, 683-8503, Japan.

Current research in food science
|October 27, 2025
PubMed
概括

本研究引入了一种多任务学习模型,用于从化学结构中预测气味类别,比传统方法提高准确性和稳定性. 该模型捕捉了化学上相关的特征,有助于合理的嗅觉设计.

科学领域:

  • 计算化学是一种计算化学.
  • 化学信息学是一种化学信息学.
  • 机器学习是机器学习.

背景情况:

  • 从分子结构预测气味对于香水和食品等行业至关重要.
  • 目前依赖于感官评估或手动特征工程的方法是低效的,不可扩展的.
  • 了解结构-气味关系有助于设计具有所需嗅觉特性的新分子.

研究的目的:

  • 开发一个多任务学习模型,同时从化学结构中预测多个气味类别.
  • 为了获取相关气味的共享表示,以改善预测.
  • 为合理的嗅觉设计提供一个可扩展和可解释的框架.

主要方法:

  • 开发了一个基于图形神经网络的多任务学习模型 (kMoL).
  • 在涵盖14个气味类别的实验数据上训练模型.
  • 使用集成梯度用于原子级贡献分析和UMAP/t-SNE用于结构可视化.

主要成果:

  • 多任务模型 (kMoL) 与单任务模型和随机森林相比,显示出更高的准确性和稳定性.
  • 标签共发生分析表明,化合物往往具有多个气味特征,有利于多任务学习.
  • 原子级分析确定了化学相关的亚结构,与已知的嗅觉受体相互作用部位保持一致.
关键词:
可解释的人工智能图表神经网络的神经网络多任务学习多任务学习气味预测 气味预测嗅觉受体是一种嗅觉受体.

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Real-time In Vitro Monitoring of Odorant Receptor Activation by an Odorant in the Vapor Phase
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Real-time In Vitro Monitoring of Odorant Receptor Activation by an Odorant in the Vapor Phase
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Real-time In Vitro Monitoring of Odorant Receptor Activation by an Odorant in the Vapor Phase

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结论:

  • 多任务学习方法有效地预测化学结构的气味类别,优于传统方法.
  • 该模型捕捉了化学和生物相关的特征,增强了可解释性和机制理解.
  • 该框架提供了一个可扩展和可解释的解决方案,用于设计具有特定嗅觉特征的分子.