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

Introduction to Structures01:30

Introduction to Structures

1.1K
A structure is defined as a system of interconnected members designed to support or transfer forces and successfully withstand the loads acting on them. The internal forces of a structure can be determined by decomposing the structure and analyzing the free-body diagrams of the individual members or of a combination of members. This helps in understanding the structural elements' behavior and ensuring that the structure is stable and can withstand the subjected loads.
There are three main...
1.1K
Stability of structures01:14

Stability of structures

203
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
203
Indeterminate Structure01:18

Indeterminate Structure

869
Indeterminate structures refer to structures where internal forces and reactions cannot be determined using only the equations of static equilibrium.  Indeterminate structures have more unknown forces and reaction forces than equations of static equilibrium that can be used to determine them. Indeterminate structures are often used in engineering to create complex, efficient, and aesthetically pleasing structures. There are various types of indeterminate structures used in engineering and...
869
Structural Protein Function01:56

Structural Protein Function

2.8K
2.8K
Viral Structure00:56

Viral Structure

62.9K
Viruses are extraordinarily diverse in shape and size, but they all have several structural features in common. All viruses have a core that contains a DNA- or RNA-based genome. The core is surrounded by a protective coat of proteins called the capsid. The capsid is composed of subunits called capsomeres. The capsid and genome-containing core are together known as the nucleocapsid.
62.9K
Nucleic Acid Structure01:25

Nucleic Acid Structure

6.3K
The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...
6.3K

You might also read

Related Articles

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

Sort by
Same author

A Kolmogorov metric embedding for live cell microscopy signaling patterns.

Bioinformatics advances·2025
Same author

Automatic detection of spatio-temporal signaling patterns in cell collectives.

The Journal of cell biology·2023
Same author

The Cell Tracking Challenge: 10 years of objective benchmarking.

Nature methods·2023
Same author

LEVERSC: Cross-Platform Scriptable Multichannel 3-D Visualization for Fluorescence Microscopy Images.

Frontiers in bioinformatics·2022
Same author

Spatiotemporal control of ERK pulse frequency coordinates fate decisions during mammary acinar morphogenesis.

Developmental cell·2022
Same author

Fast Phylogeny of SARS-CoV-2 by Compression.

Entropy (Basel, Switzerland)·2022
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Aug 4, 2025

Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates
06:35

Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates

Published on: February 15, 2016

8.2K

The Cluster Structure Function.

Andrew R Cohen, Paul M B Vitanyi

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 5, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a cluster structure function based on algorithmic information theory to identify optimal data clustering. Analyzing this function helps find the best way to group data, improving model performance.

    More Related Videos

    Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
    09:58

    Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

    Published on: June 27, 2020

    2.8K
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.0K

    Related Experiment Videos

    Last Updated: Aug 4, 2025

    Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates
    06:35

    Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates

    Published on: February 15, 2016

    8.2K
    Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
    09:58

    Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

    Published on: June 27, 2020

    2.8K
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.0K

    Area of Science:

    • Data Science
    • Machine Learning
    • Algorithmic Information Theory

    Background:

    • Traditional clustering methods often struggle with determining the optimal number of clusters.
    • Algorithmic information theory provides a theoretical framework for data compression and pattern discovery.

    Purpose of the Study:

    • To develop a novel method for optimal data clustering using the cluster structure function.
    • To leverage algorithmic information theory (Kolmogorov complexity) for practical clustering applications.

    Main Methods:

    • Defined a cluster structure function mapping partition size to model deficiency.
    • Approximated Kolmogorov complexity using a concrete compressor for practical implementation.
    • Applied the method to real-world datasets, including MNIST digits and cell image segmentation.

    Main Results:

    • Demonstrated the cluster structure function effectively identifies optimal clustering solutions.
    • Showcased the practical applicability of the method on diverse datasets.
    • Validated the theoretical underpinnings with empirical results.

    Conclusions:

    • The cluster structure function offers a robust approach to optimal data clustering.
    • This method, grounded in algorithmic information theory, provides a powerful tool for data analysis in various scientific fields.