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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

717
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
717
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

6.0K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
6.0K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.9K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
2.9K
Cluster Sampling Method01:20

Cluster Sampling Method

12.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.8K
What are Estimates?01:06

What are Estimates?

5.4K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
5.4K
Stratified Sampling Method01:16

Stratified Sampling Method

12.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
12.9K

You might also read

Related Articles

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

Sort by
Same author

GTRspmix: Capturing Heterogeneity of Exchangeabilities Across Sites to Improve Protein Phylogenetics.

bioRxiv : the preprint server for biology·2026
Same author

IQ-TREE 3: phylogenomic inference software using complex evolutionary models.

Molecular biology and evolution·2026
Same author

Order-Dependent dissimilarity measures on phylogenetic trees.

Journal of mathematical biology·2026
Same author

How Does Transcription-Associated Mutagenesis Shape tRNA Microevolution?

Genome biology and evolution·2026
Same author

Allelic Variation at tRNA Genes in Three Nematode Species Indicates Mutation Load Despite Strong Purifying Selection.

Genome biology and evolution·2026
Same author

Correction: "Distinguishing Phylogenetic Level-2 Networks with Quartets and Inter-Taxon Quartet Distances".

Bulletin of mathematical biology·2026

Related Experiment Video

Updated: Sep 13, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

661

NANUQ+: A divide-and-conquer approach to network estimation.

Elizabeth S Allman1, Hector Baños2, John A Rhodes1

  • 1Department of Mathematics and Statistics, University of Alaska Fairbanks, Fairbanks, AK, USA.

Algorithms for Molecular Biology : AMB
|July 27, 2025
PubMed
Summary

This study introduces NANUQ+, a novel method for rapidly resolving level-1 species networks from genomic data. This advances phylogenetic network inference, enabling more detailed evolutionary structure analysis.

Keywords:
Level-1Multispecies coalescentPhylogenetic networkTree of blobs

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.2K
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.2K

Related Experiment Videos

Last Updated: Sep 13, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

661
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.2K
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.2K

Area of Science:

  • Computational Biology
  • Phylogenetics
  • Genomic Data Analysis

Background:

  • Inferring complex species networks from genomic data is challenging, with current methods often limited to simpler network structures.
  • Existing tools like TINNiK can infer a network's broad topology (Tree of Blobs), but detailed resolution of multifurcations remains difficult.

Purpose of the Study:

  • To develop a fast and efficient method, NANUQ+, for resolving level-1 phylogenetic networks.
  • To enhance the NANUQ pipeline for rapid inference of detailed species network structures.

Main Methods:

  • Development of the NANUQ+ algorithm for quick level-1 resolution of phylogenetic networks.
  • Integration of NANUQ+ into the existing NANUQ pipeline for comprehensive network inference.

Main Results:

  • NANUQ+ enables rapid and accurate level-1 resolution, improving the detail of phylogenetic networks.
  • The NANUQ pipeline with NANUQ+ provides tools to assess the validity of the level-1 assumption and explore network resolutions.

Conclusions:

  • NANUQ+ significantly advances the field of phylogenetic network inference by enabling detailed resolution of complex evolutionary histories.
  • This work offers a powerful divide-and-conquer approach to understanding species evolution from genomic data.