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

Amplifying Signals via Enzymatic Cascade01:22

Amplifying Signals via Enzymatic Cascade

19.2K
When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
19.2K
Time-Series Graph00:54

Time-Series Graph

5.6K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.6K
Probability Histograms01:17

Probability Histograms

13.8K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
13.8K
Protein Networks02:26

Protein Networks

2.9K
2.9K
Protein Networks02:26

Protein Networks

4.7K
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,...
4.7K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.5K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Documenting and analyzing pre-reflective self-consciousness underlying ongoing performance optimization in elite athletes: the theoretical and methodological approach of the course-of-experience framework.

Frontiers in psychology·2024
Same author

Biomechanical Characterization of Preparation for Airs above the Ground: A Mixed Approach.

Animals : an open access journal from MDPI·2024
Same author

Multi-task deep learning for glaucoma detection from color fundus images.

Scientific reports·2022
Same author

Correlating subword articulation with lip shapes for embedding aware audio-visual speech enhancement.

Neural networks : the official journal of the International Neural Network Society·2021
Same author

Coordination between Crew Members on Flying Multihulls: A Case Study on a Nacra 17.

Journal of sports science & medicine·2020
Same author

High-Resolution Light Field Capture With Coded Aperture.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2015
Same journal

A Matrix Block-Based Physics-Informed Probabilistic Quality-Relevant Monitoring Model.

IEEE transactions on cybernetics·2026
Same journal

A Knowledge-Guided Weight Optimization Method Based on Augmented Lagrangian for Active Suspension Preview Control.

IEEE transactions on cybernetics·2026
Same journal

A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths.

IEEE transactions on cybernetics·2026
Same journal

Robust Semiglobal and Global Stabilization for Nonlinear Normal Form Systems by Time-Varying Feedback.

IEEE transactions on cybernetics·2026
Same journal

Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization.

IEEE transactions on cybernetics·2026
Same journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Mar 31, 2026

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

6.2K

Event-Based Media Enrichment Using an Adaptive Probabilistic Hypergraph Model.

Xueliang Liu, Meng Wang, Bao-Cai Yin

    IEEE Transactions on Cybernetics
    |October 16, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel method for automatically finding relevant media content to illustrate events using an adaptive probabilistic hypergraph model. The approach effectively ranks media documents for event illustration, enhancing user experience during online event sharing.

    Related Experiment Videos

    Last Updated: Mar 31, 2026

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    6.2K

    Area of Science:

    • Computer Science
    • Information Science

    Background:

    • Digital capture technologies and social media generate vast event-related media.
    • Attendees share media online to document event experiences.

    Purpose of the Study:

    • To develop an automated method for identifying and ranking media content that effectively illustrates events.
    • To leverage semantic inference and multimodal analysis for enhanced media illustration.

    Main Methods:

    • An adaptive probabilistic hypergraph model where media items are vertices.
    • Hyperedges constructed using K-nearest neighbors, incorporating probabilistic vertex assignment.
    • Hypergraph weights optimized via regularization, solved as a second-order cone problem.
    • Transductive inference process initiated by seed media for ranking.

    Main Results:

    • The proposed method effectively ranks media documents for event illustration.
    • Validation on an event dataset demonstrated the approach's effectiveness.
    • Successful integration of semantic inference and multimodal analysis.

    Conclusions:

    • The adaptive probabilistic hypergraph model offers an effective solution for automatic media content illustration for events.
    • The method enhances the organization and retrieval of event-related media.
    • This approach contributes to better online event documentation and experience sharing.