Jove
Visualize
Contact Us

Related Concept Videos

Spreading of Chromatin Modifications02:25

Spreading of Chromatin Modifications

9.5K
The histone proteins in the nucleosomes are post-translationally modified (PTM) to increase or decrease access to DNA. The commonly observed PTMs are methylation, acetylation, phosphorylation, and ubiquitination of lysine amino acids in the histone H3 tail region. These histone modifications have specific meaning for the cell. Hence, they are called "histone code". The protein complex involved in histone modification is termed as "reader-writer" complex.
Writers
The writer...
9.5K
Limiting Reactant02:27

Limiting Reactant

70.1K
The relative amounts of reactants and products represented in a balanced chemical equation are often referred to as stoichiometric amounts. However, in reality, the reactants are not always present in the stoichiometric amounts indicated by the balanced equation.
70.1K
Seed Structure and Early Development of the Sporophyte02:33

Seed Structure and Early Development of the Sporophyte

31.2K
Seed structures are composed of a protective seed coat surrounding a plant embryo, and a food store for the developing embryo. The embryo contains the precursor tissues for leaves, stem, and roots. The endosperm and cotyledons—seed leaves—act as the food reserves for the growing embryo.
31.2K
The Number e as a Limit01:29

The Number e as a Limit

91
The number e is a fundamental constant in calculus, playing a central role in describing continuous change, particularly exponential growth. It is most naturally defined through its relationship with the natural logarithm, which is the inverse of the exponential function with base e. This relationship allows e to be characterized using basic principles of differentiation rather than as an arbitrary numerical constant.A key property of the natural logarithm function, ln x, is that its derivative...
91
Types of Limits I01:23

Types of Limits I

190
Limits are a key mathematical concept for understanding how functions behave as their input approaches specific values, particularly when the function is undefined. They help reveal trends and discontinuities by examining the values a function approaches rather than its actual value.One-sided limits focus on the direction from which a value is approached. When a function behaves differently depending on whether the input approaches from the left or the right, the two one-sided limits may not...
190
Limit Laws I01:25

Limit Laws I

228
Limit laws provide essential tools for analyzing how functions behave as their input approaches a specific value. These laws are particularly useful when dealing with combinations of functions, provided the individual limits exist. The Sum and Difference Laws state that the limit of the sum or difference of two functions equals the sum or difference of their respective limits:The Product Law asserts that the limit of the product of two functions equals the product of their individual limits:A...
228

You might also read

Related Articles

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

Sort by
Same author

Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Microbloggers.

Entropy (Basel, Switzerland)·2025
Same author

Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach.

Entropy (Basel, Switzerland)·2024
Same author

Protect our environment from information overload.

Nature human behaviour·2024
Same author

Dynamics of crime activities in the network of city community areas.

Applied network science·2023
Same author

Network Analytics Enabled by Generating a Pool of Network Variants from Noisy Data.

Entropy (Basel, Switzerland)·2023
Same author

Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections.

Nature human behaviour·2023
Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026
Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026
Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026
Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026
Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026
Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·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: Feb 5, 2026

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.6K

Probing Limits of Information Spread with Sequential Seeding.

Jarosław Jankowski1, Boleslaw K Szymanski2,3, Przemysław Kazienko4

  • 1Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, 70-310, Szczecin, Poland. jjankowski@wi.zut.edu.pl.

Scientific Reports
|September 20, 2018
PubMed
Summary
This summary is machine-generated.

Sequential seeding, a new method for information spread, activates nodes in stages. This approach offers better or equal spread coverage compared to single-stage seeding, even outperforming complex greedy methods.

More Related Videos

Live Cell Imaging of Alphaherpes Virus Anterograde Transport and Spread
15:31

Live Cell Imaging of Alphaherpes Virus Anterograde Transport and Spread

Published on: August 16, 2013

11.5K
Quantitative Analysis of Cell Edge Dynamics during Cell Spreading
10:54

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Published on: May 22, 2021

6.0K

Related Experiment Videos

Last Updated: Feb 5, 2026

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.6K
Live Cell Imaging of Alphaherpes Virus Anterograde Transport and Spread
15:31

Live Cell Imaging of Alphaherpes Virus Anterograde Transport and Spread

Published on: August 16, 2013

11.5K
Quantitative Analysis of Cell Edge Dynamics during Cell Spreading
10:54

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Published on: May 22, 2021

6.0K

Area of Science:

  • Network science
  • Information diffusion modeling

Background:

  • Information spread occurs through diffusion cascades initiated by node activation.
  • Current methods often use single-stage seeding, activating nodes at once.
  • Optimizing spread coverage is crucial for various applications.

Purpose of the Study:

  • To introduce and analyze a novel sequential seeding approach for information spread.
  • To formally prove the efficacy of sequential seeding against single-stage methods.
  • To compare sequential seeding with existing algorithms, including the greedy approach.

Main Methods:

  • Development of a coordinated randomized execution for comparing algorithms.
  • Application of sequential seeding where newly activated nodes spread information in stages.
  • Formal proof of sequential seeding's coverage advantage over single-stage seeding.
  • Experimental evaluation on directed and undirected graphs.

Main Results:

  • Sequential seeding provides at least as good, and often provably better, spread coverage than single-stage seeding with the same number of seeds.
  • A simple degree-based sequential seeding strategy achieved higher coverage than the computationally intensive greedy approach.
  • Experimental results quantify the benefits of sequential seeding.

Conclusions:

  • Sequential seeding is a more effective strategy for maximizing information spread compared to traditional single-stage methods.
  • The proposed method offers a significant improvement over existing heuristics, including the greedy approach.
  • Sequential seeding presents a computationally efficient and highly effective alternative for network-based information dissemination.