Jove
Visualize
Contact Us

Related Concept Videos

Vaccinations01:51

Vaccinations

51.5K
Overview
51.5K
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.5K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.5K
Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Partial Fractions01:28

Partial Fractions

215
A partial fraction is a component of a rational expression represented as the sum of simpler fractions. When a rational function is expressed as a ratio of two polynomials, it can often be decomposed into a sum of fractions whose denominators are simpler polynomials, typically linear or irreducible quadratic factors. This process is called partial fraction decomposition, and it is used to simplify complex expressions for integration, solving equations, or analysis.Partial fraction decomposition...
215
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K

You might also read

Related Articles

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

Sort by
Same author

Viscosity as the product of its ideal low-concentration value and a thermodynamic function.

Physical review. E·2025
Same author

Physical meaning of nonextensive term in Massieu functions.

Physical review. E·2025
Same author

Hybrid universality classes of systemic cascades.

Nature communications·2025
Same author

Epidemic control in networks with cliques.

Physical review. E·2023
Same author

Dependence on the thermodynamic state of self-diffusion of pseudo-hard-sphere and Lennard-Jones potentials.

Physical review. E·2023
Same author

Diffusion on a lattice: Transition rates, interactions, and memory effects.

Physical review. E·2022
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles
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 Experiment Video

Updated: Jan 29, 2026

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

9.1K

Dynamic vaccination in partially overlapped multiplex network.

L G Alvarez-Zuzek1, M A Di Muro1, S Havlin2

  • 1Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, and Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR-CONICET), Deán Funes 3350, 7600 Mar del Plata, Argentina.

Physical Review. E
|February 21, 2019
PubMed
Summary
This summary is machine-generated.

Dynamic vaccination, where individuals get vaccinated upon learning of infected contacts, significantly reduces disease spread compared to random or targeted immunization. This strategy proves highly effective, even preventing epidemics regardless of disease virulence.

More Related Videos

Author Spotlight: Expanding the Scope of Multiplex Immunoassays for Lyme Borreliosis Diagnostics and Pathogen Research
05:25

Author Spotlight: Expanding the Scope of Multiplex Immunoassays for Lyme Borreliosis Diagnostics and Pathogen Research

Published on: July 14, 2023

1.9K
Expression and Purification of Virus-like Particles for Vaccination
06:17

Expression and Purification of Virus-like Particles for Vaccination

Published on: June 2, 2016

22.8K

Related Experiment Videos

Last Updated: Jan 29, 2026

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

9.1K
Author Spotlight: Expanding the Scope of Multiplex Immunoassays for Lyme Borreliosis Diagnostics and Pathogen Research
05:25

Author Spotlight: Expanding the Scope of Multiplex Immunoassays for Lyme Borreliosis Diagnostics and Pathogen Research

Published on: July 14, 2023

1.9K
Expression and Purification of Virus-like Particles for Vaccination
06:17

Expression and Purification of Virus-like Particles for Vaccination

Published on: June 2, 2016

22.8K

Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Understanding epidemic dynamics in complex networks is crucial for public health.
  • Existing vaccination strategies like random or targeted immunization have limitations.
  • Multiplex networks, with overlapping nodes, present unique challenges for disease spread.

Purpose of the Study:

  • To introduce and analyze a novel "dynamic vaccination" strategy.
  • To evaluate the effectiveness of dynamic vaccination in a two-layer multiplex network.
  • To compare dynamic vaccination with traditional immunization methods.

Main Methods:

  • Modeling dynamic vaccination within a susceptible-infected-recovered (SIR) framework.
  • Utilizing a bond percolation model and generating functions for theoretical analysis.
  • Employing stochastic simulations to validate theoretical predictions.
  • Analyzing epidemic behavior across varying network overlap (q) and vaccination probabilities (ω).

Main Results:

  • A perfect agreement was found between theoretical predictions and simulation results.
  • A phase diagram revealed distinct epidemic and non-epidemic phases, with a critical threshold (βc) dependent on network overlap (q).
  • Decreasing network overlap (q) increased the disease virulence (β) required for an epidemic.
  • A region of highly effective vaccination was identified, preventing epidemics irrespective of disease virulence.
  • Dynamic vaccination demonstrated superior performance over random immunization and comparable or better results than targeted immunization.

Conclusions:

  • Dynamic vaccination is a highly effective strategy for controlling epidemics in multiplex networks.
  • The strategy's success is influenced by network structure and vaccination parameters.
  • Dynamic vaccination offers a promising alternative to traditional immunization methods, especially in interconnected populations.