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相关概念视频

The Physiology of Taste01:24

The Physiology of Taste

3.9K
The perception of a salty flavor is facilitated by sodium ions within the oral salivary fluid. Upon consumption of a salty substance, salt crystals disassemble, leading to the liberation of its constituents—Na+ and Cl- ions. These ions subsequently dissolve into the salivary fluid present in the oral cavity. The external environment of the gustatory cells experiences an elevation in Na+ concentration, thereby establishing a potent concentration gradient. This gradient propels the...
3.9K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

321
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
321
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.3K
VSEPR Theory for Determination of Electron Pair Geometries
34.3K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.3K
Gustation01:43

Gustation

47.8K
Gustation is a chemical sense that, along with olfaction (smell), contributes to our perception of taste. It starts with the activation of receptors by chemical compounds (tastants) dissolved in the saliva. The saliva and filiform papillae on the tongue distribute the tastants and increase their exposure to the taste receptors.
47.8K
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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相关实验视频

Updated: Jun 29, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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基于模块化设计的图形神经网络的苦味预测.

Yi He1, Kaifeng Liu1, Yuyang Liu1

  • 1Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, Changchun 130012, China.

Bioinformatics advances
|April 3, 2024
PubMed
概括

预测食物中的苦味对于食品安全至关重要. 这项研究介绍了BitterGNNs,一个图形神经网络 (GNN) 模型,在苦味预测中实现了高精度,超过了现有的方法.

科学领域:

  • 食品科学和感官分析
  • 计算化学和化学信息学
  • 机器学习和人工智能机器学习

背景情况:

  • 苦味是识别有害物质的关键味道.
  • 人体品尝用于味道分析是昂贵的,对于大规模选来说是不切实际的.
  • 在 silico 方法为苦味预测提供了一个实用的替代方案.

研究的目的:

  • 开发和评估先进的机器学习模型,用于准确的in silico苦味预测.
  • 引入图形神经网络 (GNN) 作为传统苦味预测方法的优越替代方案.
  • 创建可访问的工具和资源,用于食品科学中的苦味预测.

主要方法:

  • 开发一个混合图形神经网络 (HGNN) 模型用于苦味预测.
  • 实施BitterGNNs,这是一个基于GNN的预测器,包含HGNN和其他GNN架构.
  • 在公共数据集上对比特GNN与RDKFP-MLP等既有预测器的验证.

主要成果:

  • 苦GNNs预测器在苦/非苦和苦/甜的评估中实现了0.87的曲线下面面积 (AUC).
  • 比特GNNs的表现优于RDKFP-MLP预测器,该预测器的AUC值分别为0.86和0.85.
  • 创建了一个公开可访问的苦味预测网站和数据库,TastePD.

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Last Updated: Jun 29, 2025

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Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
09:12

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642
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结论:

  • 图形神经网络,特别是开发的BitterGNNs模型,提供高度准确和高效的苦味预测.
  • BitterGNNs预测器增强了食品测试方法,并有助于了解苦味的起源.
  • "TastePD"数据库和相关代码为研究人员和食品行业提供了宝贵的资源.