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

Gene Duplication and Divergence02:37

Gene Duplication and Divergence

7.3K
The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...
7.3K
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

1.9K
In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
1.9K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.6K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.6K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

3.2K
3.2K
Long-term Potentiation01:35

Long-term Potentiation

56.6K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
56.6K
Long-term Potentiation01:25

Long-term Potentiation

3.0K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
3.0K

You might also read

Related Articles

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

Sort by
Same author

Self-Organized Criticality in Atmospheric Rivers.

Physical review letters·2026
Same author

Unveiling the importance of nonshortest paths in quantum networks.

Science advances·2025
Same author

Enhancing oocyte in vitro maturation and quality by melatonin/bilirubin cationic nanoparticles: A promising strategy for assisted reproduction techniques.

International journal of pharmaceutics: X·2024
Same author

Investigating the impact of weather on stroke in summer.

International journal of biometeorology·2024
Same author

Infiltration by monocytes of the central nervous system and its role in multiple sclerosis: reflections on therapeutic strategies.

Neural regeneration research·2024
Same author

<i>Gemmobacter denitrificans</i> sp. nov., a denitrifying bacterium, isolated from pond water for <i>Litopenaeus vannamei</i>.

International journal of systematic and evolutionary microbiology·2024
Same journal

Clinical crown height changes in mandibular anterior teeth retained with two types of fixed retainers over two years: findings from a randomized clinical trial.

Scientific reports·2026
Same journal

Rethinking water governance through indigenous systems: A comparative assessment of qanat and well irrigation productivity in Sabzevar County, Iran.

Scientific reports·2026
Same journal

Distributed Nash equilibrium seeking for second-order systems with finite/fixed-time convergence in the absence of velocity measurement.

Scientific reports·2026
Same journal

Determinants of pregnancy termination among ever-married women of reproductive age in Bangladesh.

Scientific reports·2026
Same journal

Occurrence and human health risk assessment of organochlorine pesticides in irrigated and non-irrigated agricultural soils of Wondogenet District, Ethiopia.

Scientific reports·2026
Same journal

High angular resolution diffusion imaging of neurodevelopment in children through data creation with deep learning.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Oct 26, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.1K

Higher-order temporal network effects through triplet evolution.

Qing Yao1,2, Bingsheng Chen3, Tim S Evans3

  • 1Blackett Laboratory and Centre for Complexity Science, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK. q.yao15@imperial.ac.uk.

Scientific Reports
|July 30, 2021
PubMed
Summary
This summary is machine-generated.

Higher-order interactions, not just pairwise ones, drive network evolution. Analyzing three-node graphlet dynamics reveals crucial patterns for understanding and predicting network changes in real-world systems.

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.3K

Related Experiment Videos

Last Updated: Oct 26, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.1K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.3K

Area of Science:

  • Network science
  • Graph theory
  • Complex systems

Background:

  • Network evolution is often modeled using pairwise interactions.
  • Higher-order interactions (beyond pairs) may significantly influence network dynamics.
  • Understanding these higher-order effects is key to accurate network modeling.

Purpose of the Study:

  • To develop a method for analyzing network evolution using three-node graphlets (triplets).
  • To quantify the importance of non-pairwise interactions in temporal networks.
  • To assess the utility of triplet dynamics for link prediction.

Main Methods:

  • Computed transition matrices to describe triplet evolution in temporal networks.
  • Compared triplet dynamics in real-world and artificial data against pairwise models.
  • Developed a link prediction algorithm based on triplet transition matrices.

Main Results:

  • Significant differences observed between computed and pairwise-based models, confirming non-pairwise interactions.
  • Identified distinct higher-order interaction patterns in different real-world systems.
  • The proposed link prediction method, incorporating higher-order interactions, outperformed most other methods.

Conclusions:

  • Non-pairwise interactions are essential for understanding real-world network evolution.
  • Triplet dynamics provide valuable insights into network structure and future changes.
  • Higher-order interaction patterns are critical for accurate network analysis and prediction.