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

Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.3K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
3.3K
Sampling Plans01:23

Sampling Plans

1.0K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.0K
Sample Handling01:02

Sample Handling

2.7K
Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
Samples should be transported carefully from collection points to the laboratory. They should be properly sealed and clearly labeled to prevent cross-contamination. To preserve the sample integrity, optimal temperature conditions during transport are essential. This could involve using...
2.7K
Sampling Theorem01:15

Sampling Theorem

1.4K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.4K
Bandpass Sampling01:17

Bandpass Sampling

551
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
551
Sampling Distribution01:12

Sampling Distribution

18.2K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
18.2K

You might also read

Related Articles

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

Sort by
Same author

IntelliCage protocols using incentive-disincentive dual motivation work with male as well as female mice and reveal sex differences in motivated behavior.

Frontiers in behavioral neuroscience·2026
Same author

Management of inguinal and femoral hernias in women: meta analysis of current practices and review of international guidelines.

Updates in surgery·2026
Same author

Error-prone translation as a driver of proteostasis collapse and neurodegeneration.

Neural regeneration research·2025
Same author

Delayed gastric conduit emptying after esophagectomy: attempt at a clinically relevant classification.

Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus·2025
Same author

Traces of phylogeny and ecology in hippocampal neuron numbers.

PNAS nexus·2025
Same author

Hippocampal structure, patterns of the calcium-binding proteins and neuron numbers in small echolocating bats.

Frontiers in neuroanatomy·2025

Related Experiment Video

Updated: Feb 15, 2026

Dissection of Hippocampal Dentate Gyrus from Adult Mouse
07:42

Dissection of Hippocampal Dentate Gyrus from Adult Mouse

Published on: November 17, 2009

84.2K

Sampling the Mouse Hippocampal Dentate Gyrus.

Lisa Basler1,2, Stephan Gerdes1, David P Wolfer1,3,4

  • 1Division of Functional Neuroanatomy, Institute of Anatomy, University of Zürich, Zürich, Switzerland.

Frontiers in Neuroanatomy
|January 10, 2018
PubMed
Summary

The Gundersen-Jensen coefficient of error (CE) estimator accurately assesses sampling precision for neuroscience morphology. Optimal CE estimates for mouse dentate gyrus layers are achieved with a smoothness factor of 0, particularly in frontal sections.

Keywords:
C57BL/6 miceCE estimatorsCavalieri estimatordentate gyrusstereologyvolume

More Related Videos

Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice
10:55

Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice

Published on: March 31, 2015

10.8K
Author Spotlight: Investigating Neural Activity of Dentate Gyrus Granule Cells with Miniature Microscope
07:00

Author Spotlight: Investigating Neural Activity of Dentate Gyrus Granule Cells with Miniature Microscope

Published on: August 2, 2024

2.4K

Related Experiment Videos

Last Updated: Feb 15, 2026

Dissection of Hippocampal Dentate Gyrus from Adult Mouse
07:42

Dissection of Hippocampal Dentate Gyrus from Adult Mouse

Published on: November 17, 2009

84.2K
Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice
10:55

Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice

Published on: March 31, 2015

10.8K
Author Spotlight: Investigating Neural Activity of Dentate Gyrus Granule Cells with Miniature Microscope
07:00

Author Spotlight: Investigating Neural Activity of Dentate Gyrus Granule Cells with Miniature Microscope

Published on: August 2, 2024

2.4K

Area of Science:

  • Neuroscience
  • Quantitative Morphology
  • Stereology

Background:

  • Accurate quantitative morphological data in neurosciences relies on representative sampling.
  • Sampling variability can obscure significant group differences in statistical analyses.
  • Coefficient of error (CE) estimators assess if sampling precision is adequate for meaningful statistical outcomes.

Purpose of the Study:

  • To evaluate the performance of the Gundersen-Jensen CE estimator.
  • To determine optimal sampling parameters for the mouse hippocampal dentate gyrus.
  • To provide guidance on achieving desired sampling precision.

Main Methods:

  • Applied the Gundersen-Jensen CE estimator to mouse hippocampal dentate gyrus layers (molecular layer, granule cell layer, hilus).
  • Investigated the impact of smoothness factor (m=0 vs. m=1) on CE estimates.
  • Analyzed the effect of section orientation (frontal, horizontal, sagittal) on CE values.
  • Utilized 3D reconstructions and intense sampling for detailed CE analysis.

Main Results:

  • The Gundersen-Jensen CE estimator provides reliable estimates of sampling precision.
  • A smoothness factor (m) of 0 generally yielded better CE estimates for individual layers.
  • A smoothness factor (m) of 1 improved CE estimates for the combined dentate gyrus.
  • Frontal (coronal) sections were most efficient, yielding the smallest CE values for equivalent sampling effort.
  • Observed expected CE transitions with varying sampling intensity in 3D reconstructions.

Conclusions:

  • The Gundersen-Jensen CE estimator is a valuable tool for optimizing sampling strategies in neuroanatomy.
  • Smoothness factor and section orientation significantly influence CE estimates.
  • The study provides data to guide the selection of sampling intervals for desired precision in the mouse dentate gyrus.