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

Distance Problem01:29

Distance Problem

74
When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
74
Mean free path and Mean free time01:22

Mean free path and Mean free time

5.2K
Consider the gas molecules in a cylinder. They move in a random motion as they collide with each other and change speed and direction. The average of all the path lengths between collisions is known as the "mean free path."
5.2K
Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion

31.4K
Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
31.4K
Short-distance Transport of Resources02:12

Short-distance Transport of Resources

17.7K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
17.7K
Path Between Thermodynamics States01:21

Path Between Thermodynamics States

4.0K
Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:
4.0K
The Distance Formula01:20

The Distance Formula

652
In geometry, measuring the direct distance between two points on a plane is essential in various practical and theoretical applications. Whether in navigation, engineering, or computer graphics, determining the shortest path between two locations involves using the distance formula. This formula is derived from the Pythagorean Theorem, which relates the lengths of the sides of a right triangle. On a coordinate plane, the horizontal and vertical distances between two points serve as the legs of...
652

You might also read

Related Articles

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

Sort by
Same author

Response to commentary on "Paracentesis exceeding three liters increases risks of acute kidney injury even in cirrhotic patients with albumin infused refractory ascites".

Journal of the Formosan Medical Association = Taiwan yi zhi·2026
Same author

Outcome of cholecystectomy in octogenarian with concurrent cholecystitis and cholangitis receiving percutaneous cholecystostomy and subsequent interventive endoscopic retrograde cholangiopancreatography.

BMC geriatrics·2026
Same author

A Kernelization Algorithm for Finding a Perfect Phylogeny From Mixed Tumor Samples.

IEEE transactions on computational biology and bioinformatics·2025
Same author

Faster Algorithms for Constructing Frequency Difference Consensus Trees.

IEEE transactions on computational biology and bioinformatics·2025
Same author

Response to commentary on paracentesis exceeding three liters increases risks of acute kidney injury even in cirrhotic patients with albumin infused refractory ascites.

Journal of the Formosan Medical Association = Taiwan yi zhi·2025
Same author

Paracentesis exceeding three liters increases risks of acute kidney injury even in cirrhotic patients with albumin infused refractory ascites.

Journal of the Formosan Medical Association = Taiwan yi zhi·2025
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: Feb 8, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

6.7K

Fast Algorithms for Computing Path-Difference Distances.

Biing-Feng Wang, Chih-Yu Li

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |July 12, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces faster algorithms for comparing phylogenetic trees using path-difference distances. New methods significantly reduce computation time for various norms, improving phylogenetic analysis efficiency.

    More Related Videos

    Foraging Path-length Protocol for Drosophila melanogaster Larvae
    07:26

    Foraging Path-length Protocol for Drosophila melanogaster Larvae

    Published on: April 23, 2016

    9.9K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.9K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Quantifying Intermembrane Distances with Serial Image Dilations
    07:45

    Quantifying Intermembrane Distances with Serial Image Dilations

    Published on: September 28, 2018

    6.7K
    Foraging Path-length Protocol for Drosophila melanogaster Larvae
    07:26

    Foraging Path-length Protocol for Drosophila melanogaster Larvae

    Published on: April 23, 2016

    9.9K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.9K

    Area of Science:

    • Computational Biology
    • Phylogenetics
    • Algorithm Analysis

    Background:

    • Phylogenetic tree comparison is crucial for evolutionary studies.
    • Path-difference distances quantify dissimilarities between trees using path-length vectors.
    • Existing algorithms for these distances have a time complexity of O(n²).

    Purpose of the Study:

    • To develop more efficient algorithms for computing path-difference distances between phylogenetic trees.
    • To improve the computational performance for l1-, l2-, and l∞-norm distances.
    • To extend the findings for general lp-norm path-difference distances.

    Main Methods:

    • Development of novel algorithms based on efficient computation of path-length vectors.
    • Application of techniques to optimize calculations for specific norms (l1, l2, l∞).
    • Generalization of algorithms to handle lp-norm distances for arbitrary positive integers p.

    Main Results:

    • Achieved O(n log² n) time complexity for the l1-norm path-difference distance.
    • Achieved O(n log n) time complexity for the l2- and l∞-norm path-difference distances.
    • Demonstrated O(pn log² n) and O(p²n log n) time complexities for lp-norm distances.

    Conclusions:

    • The new algorithms offer substantial improvements over previous methods for phylogenetic tree comparison.
    • These advancements enable more scalable and efficient analysis of large phylogenetic datasets.
    • The generalized lp-norm algorithms provide flexibility for diverse evolutionary research needs.