Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

510
Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
510
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

790
Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
790
Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

992
Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...
992
Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion

29.0K
Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
29.0K
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

98
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
98
Protein Networks02:26

Protein Networks

4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Multi-scaling reservoir computing learns noise-induced transitions with Lévy noise.

Chaos (Woodbury, N.Y.)·2025
Same author

Early warnings are too late when parameters change rapidly.

Scientific reports·2025
Same author

Nonmonotonic emergence of order from chaos in turbulent thermoacoustic fluid systems.

Physical review. E·2025
Same author

Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

The circular movement of synchronous extreme precipitation preceding Kerala floods in 2018 and 2019.

Chaos (Woodbury, N.Y.)·2025
Same author

Extreme events in two coupled chaotic oscillators.

Physical review. E·2025
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Jul 13, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

基于网络透的大型复杂网络的扩散源推理.

Yang Liu, Xiaoqi Wang, Xi Wang

    IEEE transactions on neural networks and learning systems
    |October 13, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究优化了扩散源推理 (DSI) 的观察者集,以精确地准信息或疾病起源. 新的基于透的进化框架 (PrEF) 最小化了候选集,以更有效地识别源.

    更多相关视频

    In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging
    06:34

    In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging

    Published on: September 2, 2016

    6.4K
    Cortical Source Analysis of High-Density EEG Recordings in Children
    09:32

    Cortical Source Analysis of High-Density EEG Recordings in Children

    Published on: June 30, 2014

    21.4K

    相关实验视频

    Last Updated: Jul 13, 2025

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.2K
    In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging
    06:34

    In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging

    Published on: September 2, 2016

    6.4K
    Cortical Source Analysis of High-Density EEG Recordings in Children
    09:32

    Cortical Source Analysis of High-Density EEG Recordings in Children

    Published on: June 30, 2014

    21.4K

    科学领域:

    • 网络科学 网络科学
    • 信息科学 信息科学 信息科学
    • 计算社会科学 计算社会科学

    背景情况:

    • 扩散源推理 (DSI) 问题对于打击错误信息和控制疾病传播等应用至关重要.
    • 优化DSI需要精确的源定位,但在异质网络中,传统的指标失败了.

    研究的目的:

    • 开发一种优化观察者集配置以提高DSI准确性的方法.
    • 为了引入一个新的指标,候选人设置,用于评估DSI的有效性.
    • 提出一个框架来分析大规模网络中的DSI.

    主要方法:

    • 提出了基于透的进化框架 (PrEF),以优化观察者集.
    • 引入了候选集指标,以最大组件覆盖面为界限.
    • 利用网络透和进化算法,与网络免疫形成并行.

    主要成果:

    • 与各种网络中的最先进的方法相比,PrEF显著减少了候选集.
    • 该方法在26/27实证网络和155/162个临界值病例中表现出卓越的性能.
    • 该方法在各种感染概率,扩散模型和网络结构中是稳健的.

    结论:

    • 预测框架为DSI问题提供了更有效,更稳定的解决方案.
    • 这项工作为分析和改进复杂网络中源推理提供了有价值的工具.
    • 拟议的候选集指标提供了对DSI方法的更一般的评估.