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

Radioactive Decay and Radiometric Dating02:48

Radioactive Decay and Radiometric Dating

37.2K
Radioactivity is a spontaneous disintegration of an unstable nuclide and is a random process, as all the nuclei in the sample do not decay simultaneously. The number of disintegrations per unit time is called the activity (A), which is directly proportional to the number of nuclei in the sample. The decay constant (λ) is an average probability of decay per nucleus in unit time.
37.2K
Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

259
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
259
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

11.9K
The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
11.9K
Diffusion01:12

Diffusion

220.2K
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...
220.2K
Exponential and Sinusoidal Signals01:18

Exponential and Sinusoidal Signals

733
The exponential function is crucial for characterizing waveforms that rise and decay rapidly. This continuous-time exponential function is defined using exponential terms with constants α and A. When both constants are real, the function is represented as,
733
Exponential Functions with Base e01:30

Exponential Functions with Base e

259
Exponential functions with base e are essential for modeling continuous processes of growth and decay. The constant e, approximately 2.718, naturally arises in systems where change occurs proportionally to the current value. A positive exponent represents continuous growth, while a negative exponent represents continuous decay. These functions are especially useful for describing situations where change happens smoothly over time rather than in discrete steps.One clear example of exponential...
259

You might also read

Related Articles

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

Sort by
Same author

DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data.

BMC bioinformatics·2024
Same author

Returners and explorers dichotomy in the face of natural hazards.

Scientific reports·2024
Same author

Retraction: Tobacco-specific Carcinogens Induce Hypermethylation, DNA Adducts, and DNA Damage in Bladder Cancer.

Cancer prevention research (Philadelphia, Pa.)·2024
Same author

Role of adenosine deaminase in prostate cancer progression.

American journal of clinical and experimental urology·2023
Same author

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data.

Journal of visualized experiments : JoVE·2023
Same author

A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions.

Statistics and computing·2023

Related Experiment Video

Updated: Feb 8, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K

Exponentially time decaying susceptible-informed (SIT) model for information diffusion process on networks.

Wei Bao1, George Michailidis2

  • 1Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.

Chaos (Woodbury, N.Y.)
|July 2, 2018
PubMed
Summary

This study introduces a new model for information diffusion on social media that accounts for the time value of information. The Maximum Weight Tree approach accurately estimates diffusion size, improving upon existing epidemic models.

More Related Videos

Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer
07:54

Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer

Published on: October 15, 2015

8.5K
Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies
07:12

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Published on: November 19, 2020

2.6K

Related Experiment Videos

Last Updated: Feb 8, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K
Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer
07:54

Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer

Published on: October 15, 2015

8.5K
Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies
07:12

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Published on: November 19, 2020

2.6K

Area of Science:

  • Network Science
  • Information Diffusion
  • Computational Social Science

Background:

  • Information diffusion on social media is crucial but existing models like Susceptible-Infected-Removed and Susceptible-Infected-Susceptible (SIS) fail to account for the time value of information.
  • The diminishing attention to older content on social media necessitates models that incorporate information timeliness.

Purpose of the Study:

  • To develop and evaluate novel models for information diffusion that incorporate the intrinsic time value of information.
  • To accurately estimate diffusion size, phase transition epochs, and epidemic thresholds in online social networks.

Main Methods:

  • Utilized mean-field approximations to analyze diffusion size over time and at early stages.
  • Developed a Maximum Weight Tree (MWT) approximation to estimate the final diffusion fraction, weighting nodes by their probability of becoming informed.
  • Employed Monte Carlo simulations for extensive comparisons and validation.

Main Results:

  • Mean-field approximations provide accurate estimates for diffusion size, phase transition epoch, and epidemic threshold.
  • The Maximum Weight Tree (MWT) approximation offers an exact solution for tree-like networks and accurate estimations for sparse networks.
  • Both proposed methods demonstrate high accuracy when compared to Monte Carlo simulations.

Conclusions:

  • The novel approach, particularly the MWT approximation, significantly enhances the accuracy of information diffusion modeling on social media networks.
  • Accounting for the time value of information is essential for realistic modeling of content spread in online platforms.
  • The MWT method provides a fast and accurate tool for analyzing information diffusion dynamics.