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

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.5K
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...
2.5K
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

1.4K
In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
1.4K
Stratified Sampling Method01:16

Stratified Sampling Method

12.1K
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...
12.1K
Maximum Size of Aggregate01:12

Maximum Size of Aggregate

135
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
135
Construction of Frequency Distribution01:15

Construction of Frequency Distribution

7.7K
A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
7.7K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

77
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
77

You might also read

Related Articles

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

Sort by
Same author

A transcription termination mechanism for maintaining homogeneous protein expression.

Nucleic acids research·2025
Same author

Dynamic patterns of gene expression match extracellular signals through push-pull regulation.

PLoS genetics·2025
Same author

Modified paravaginal repair combined with sacrospinous ligament suspension for pelvic organ prolapse: a randomized controlled study.

International journal of surgery (London, England)·2025
Same author

Quantifying the nuclear localization of fluorescently tagged proteins.

Bioinformatics advances·2025
Same author

Effect of Intravenous Anesthesia With Remimazolam Besylate on Hemodynamics and Neuroprotection in Patients Undergoing Surgery for Craniocerebral Injury.

Annali italiani di chirurgia·2025
Same author

The type of carbon source not the growth rate it supports can determine diauxie in Saccharomyces cerevisiae.

Communications biology·2025
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jul 11, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

435

Nunchaku: optimally partitioning data into piece-wise contiguous segments.

Yu Huo1,2, Hongpei Li2, Xiao Wang2

  • 1Centre for Engineering Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom.

Bioinformatics (Oxford, England)
|November 15, 2023
PubMed
Summary
This summary is machine-generated.

Scientists developed a new Bayesian method to automatically identify linear relationships in 1D time series data. This approach improves reproducibility and enables high-throughput analysis for various scientific fields, including microbial growth studies.

More Related Videos

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K
A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

11.1K

Related Experiment Videos

Last Updated: Jul 11, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

435
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K
A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

11.1K

Area of Science:

  • Data analysis
  • Statistical modeling
  • Microbiology

Background:

  • Analyzing 1D time series often involves identifying linear relationships between variables.
  • Current methods for detecting these linear regions are typically ad hoc and subjective.

Purpose of the Study:

  • To develop a statistically rigorous, Bayesian approach for partitioning 1D time series data into segments with linear relationships.
  • To provide a general solution for identifying discontinuous change points in data.
  • To apply the method to microbial growth analysis.

Main Methods:

  • A Bayesian approach was developed to infer optimal partitioning of datasets.
  • The method identifies contiguous piece-wise linear segments and segments described by linear combinations of arbitrary basis functions.
  • The algorithm was applied to analyze microbial growth data, specifically optical density and cell counts.

Main Results:

  • The algorithm successfully identified regions of linear proportionality between optical density and cell number for microbial growth.
  • It automatically detected regions of exponential growth for Escherichia coli and Saccharomyces cerevisiae.
  • The Monod constant for budding yeast growth on fructose was inferred.

Conclusions:

  • The developed Bayesian algorithm offers a statistically rigorous and automated solution for identifying linear segments and change points in 1D time series data.
  • This method enhances reproducibility and facilitates high-throughput data analysis in diverse scientific disciplines.
  • The Nunchaku Python package is available for broader scientific use.