Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Diffusion01:12

Diffusion

195.9K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
195.9K
Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

627
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...
627
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

863
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...
863
Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

4.5K
Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
4.5K
Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

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

29.3K
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...
29.3K
Facilitated Diffusion01:16

Facilitated Diffusion

547
The plasma membrane, a critical structure in cellular biology, houses an array of transporters, or carrier proteins, interspersed within its lipid bilayer. These proteins play a crucial role in solute transport through facilitated diffusion, a form of passive diffusion that uses transporters to move the molecules across the membrane.
In this process, substrates such as organic compounds and ions interact with a transporter on one side, triggering conformational changes in proteins that enable...
547

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Social contagion induced by uncertain information.

Physical review. E·2023
Same author

The switching mechanisms of social network densification.

Scientific reports·2021
Same author

Two types of densification scaling in the evolution of temporal networks.

Physical review. E·2020
Same author

The structured backbone of temporal social ties.

Nature communications·2019
Same author

Extracting the multi-timescale activity patterns of online financial markets.

Scientific reports·2018
Same author

Fragmenting networks by targeting collective influencers at a mesoscopic level.

Scientific reports·2016

Related Experiment Video

Updated: Aug 24, 2025

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

6.5K

Diffusion dynamics of competing information on networks.

Teruyoshi Kobayashi1

  • 1Department of Economics and Center for Computational Social Science, Kobe University, Kobe 657-8501, Japan.

Physical Review. E
|October 21, 2022
PubMed
Summary

This study introduces a new model for information diffusion on social networks, revealing that competing information spread is unpredictable. Individual optimization leads to random symmetry breaking or social polarization by chance.

Area of Science:

  • Social Network Analysis
  • Information Diffusion Dynamics
  • Computational Social Science

Background:

  • Existing threshold models for information diffusion on social networks do not account for individual optimization problems.
  • The dynamics of information propagation when individuals face multiple, potentially conflicting, information sources remain an open question.

Purpose of the Study:

  • To develop a microfounded general threshold model for analyzing collective dynamics in the propagation of multiple information sources.
  • To investigate how individual optimization problems influence information diffusion on social networks.

Main Methods:

  • Development of a microfounded general threshold model.
  • Analysis of collective dynamics under different individual decision-making assumptions (coordination with neighbors, irreversible choices).

More Related Videos

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells
05:56

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Published on: November 12, 2020

2.9K
Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
07:40

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

11.1K

Related Experiment Videos

Last Updated: Aug 24, 2025

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

6.5K
Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells
05:56

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Published on: November 12, 2020

2.9K
Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
07:40

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

11.1K

Main Results:

  • The virality of competing information sources is fundamentally indeterminate.
  • Maximizing coordination with neighbors leads to a saddle path diffusion process, resulting in unpredictable symmetry breaking.
  • Irreversible individual choices create a continuum of stable equilibria, leading to chance-driven social polarization.

Conclusions:

  • Individual optimization is critical for understanding complex information diffusion patterns on social networks.
  • The indeterminacy of competing information spread highlights the unpredictable nature of social dynamics.
  • Social polarization can emerge as a probabilistic outcome of individual decision-making in information consumption.