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相关概念视频

Olfaction01:25

Olfaction

44.3K
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...
44.3K
Olfactory Receptors: Location and Structure01:03

Olfactory Receptors: Location and Structure

9.2K
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...
9.2K
Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

8.3K
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...
8.3K
Molecular Models02:00

Molecular Models

38.2K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.2K
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.0K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Updated: Jun 20, 2025

Constructing an Olfactometer for Rodent Olfactory Behavior Studies Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
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Constructing an Olfactometer for Rodent Olfactory Behavior Studies Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

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一个深度位置编码模型,用于从分子结构和静电学中预测嗅觉感知.

Mengji Zhang1,2, Yusuke Hiki3, Akira Funahashi3

  • 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. mengji.zhang0809@gmail.com.

NPJ systems biology and applications
|July 17, 2024
PubMed
概括
此摘要是机器生成的。

从分子中预测气味是很难的. 一个新的深度学习模型,Mol-PECO,使用分子结构和静电学来准确预测嗅觉感知,优于其他方法.

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Olfactory Context Dependent Memory: Direct Presentation of Odorants
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A Lateralized Odor Learning Model in Neonatal Rats for Dissecting Neural Circuitry Underpinning Memory Formation
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科学领域:

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

背景情况:

  • 从分子结构来预测嗅觉感知是复杂的,因为嗅觉的不连续性质.
  • 现有的方法往往难以捕捉气味受体相互作用的细微差别.

研究的目的:

  • 介绍Mol-PECO,这是一个深度学习模型,用于从分子结构和静电学中预测嗅觉感知.
  • 为了证明Mol-PECO在传统的机器学习和图形神经网络方法上的优势.

主要方法:

  • 开发了Mol-PECO,这是一个使用库伦矩阵进行分子表示和位置编码的深度学习模型.
  • 在一个关于气味分子及其描述物的综合数据集上训练和评估Mol-PECO.
  • 将Mol-PECO的性能与传统的机器学习方法和图形神经网络进行比较.

主要成果:

  • 在预测嗅觉感知方面,Mol-PECO显著优于传统的机器学习方法和图形神经网络.
  • 通过Mol-PECO学习的分子嵌入,有效地捕获了气味空间.
  • Mol-PECO使气味描述物的全球聚类和类似的气味分子的局部检索成为可能.

结论:

  • Mol-PECO提供了一个有效的深度学习框架,用于预测嗅觉感知.
  • 库伦矩阵为嗅觉预测任务中的分子表示提供了一个强大的替代方案.
  • 这项研究促进了对嗅觉机制和分子相互作用的理解.