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

Arrhenius Plots02:34

Arrhenius Plots

46.7K
The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
The Arrhenius equation can be used...
46.7K
Residual Plots01:07

Residual Plots

6.2K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
6.2K
Microsoft Excel: Plotting Mean, SD, and SE01:18

Microsoft Excel: Plotting Mean, SD, and SE

1.1K
In Microsoft Excel, plotting the mean along with standard deviation (SD) and standard error (SE) helps visualize data variability and reliability. To plot these values, follow these steps:
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...
1.1K
Bode Plots01:26

Bode Plots

1.3K
Bode plots are graphical tools that use logarithmic scales for frequency on the x-axis and gain in decibels on the y-axis. This logarithmic method allows a wide range of frequencies to be compactly displayed, enabling the analysis of component effects on circuit behavior across a broad frequency spectrum.
A network function represents the ratio of a system's output to its input, with the magnitude and phase angle derived from the complex network function. The decibel logarithmic gain is...
1.3K
Scatter Plot01:15

Scatter Plot

10.7K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
10.7K
Bode Plots Construction01:24

Bode Plots Construction

1.1K
The Bode plot is an essential tool in control system analysis, mapping the frequency response of a system through a magnitude plot and a phase plot, both against a logarithmic frequency axis. To construct a Bode plot, consider the transfer function H(ω):
1.1K

You might also read

Related Articles

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

Sort by
Same author

Global approaches to infectious disease surveillance and modeling.

Nature medicine·2026
Same author

The emergence and molecular evolution of H5N1 influenza viruses in United States dairy cattle.

bioRxiv : the preprint server for biology·2026
Same author

Transmission lineage dynamics and the detection of viral importation in emerging epidemics.

Epidemics·2026
Same author

SERAPHIM 2.0: an extended toolbox for studying phylogenetically informed movements.

Bioinformatics (Oxford, England)·2026
Same author

Genomic characterization of Sabiá virus in Brazil, 2019-2020: Implications for diagnostics, virus evolution, and receptor binding.

PLoS neglected tropical diseases·2026
Same author

Can H5N1 avian influenza in dairy cattle be contained in the US?

Cell·2026
Same journal

Extending the temporal window of arbovirus evolutionary analysis through the recovery of a century-old bandavirus.

Virus evolution·2026
Same journal

Continuous <i>in silico</i> screening of fish, amphibians, reptiles, and birds expands the diversity of bornavirids and reveals unexpected genomic architectures.

Virus evolution·2026
Same journal

Genomic epidemiology and evolutionary analysis of Lassa virus from small mammals suggest bidirectional viral movement across humans and animals.

Virus evolution·2026
Same journal

Mapping the global distribution and spread of the <i>Plasmodium vivax</i>-associated virus MaRNAV-1.

Virus evolution·2026
Same journal

Extreme GC3 codon bias in a novel brown seaweed virus results in pseudoambigrammatic characteristics.

Virus evolution·2026
Same journal

The adaptive plasticity of temperate phage <i>λ</i>.

Virus evolution·2026
See all related articles

Related Experiment Video

Updated: Jan 19, 2026

Arrhenius Plot: Determining Activation Energy of a Reaction
02:34

Arrhenius Plot: Determining Activation Energy of a Reaction

46.7K

The multifurcating skyline plot.

Patrick Hoscheit1, Oliver G Pybus2

  • 1MaIAGE, INRA, Université Paris-Saclay, Domaine de Vilvert, Jouy-en-Josas 78350, France.

Virus Evolution
|September 19, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces the multifurcating skyline plot, a new method for analyzing demographic history using gene sequences. It accurately estimates population size changes over time, even with complex phylogenetic trees and superspreading events.

Keywords:
coalescentmaximum likelihoodmultifurcatingphylodynamicsphylogenetics

More Related Videos

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

35.8K
Residual Plots
01:07

Residual Plots

6.2K

Related Experiment Videos

Last Updated: Jan 19, 2026

Arrhenius Plot: Determining Activation Energy of a Reaction
02:34

Arrhenius Plot: Determining Activation Energy of a Reaction

46.7K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

35.8K
Residual Plots
01:07

Residual Plots

6.2K

Area of Science:

  • Population Genetics
  • Phylogenetics
  • Epidemiology

Background:

  • Coalescent theory methods infer demographic history from gene sequences.
  • Skyline plots are common Bayesian priors for phylogenetic trees in pathogen analysis.

Purpose of the Study:

  • Extend skyline plot methods to phylogenies with multifurcations (hard polytomies).
  • Develop the multifurcating skyline plot using Λ-coalescents to estimate effective population size through time.

Main Methods:

  • Utilized Λ-coalescents theory to develop the multifurcating skyline plot.
  • Implemented smoothing and extended the method for serially sampled (heterochronous) data.
  • Validated the estimator on simulated data using maximum likelihood.

Main Results:

  • Accurate estimation of Λ-coalescent process parameters on simulated data.
  • Application to superspreading models showed consistency with high-variance assumptions.
  • Estimated Λ-coalescent parameters provide insights into superspreading levels.

Conclusions:

  • The multifurcating skyline plot effectively estimates demographic history from complex phylogenies.
  • The method is robust and provides valuable information for epidemiological studies, particularly those involving superspreading.