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

Survival Tree01:19

Survival Tree

138
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
138
Phylogenetic Trees03:21

Phylogenetic Trees

46.0K
Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
46.0K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.1K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.1K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

556
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
556
Structural Classification of Joints01:20

Structural Classification of Joints

3.8K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.8K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

7.2K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
7.2K

You might also read

Related Articles

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

Sort by
Same author

Interannual changes in the association between land use, abundance of Culex quinquefasciatus and Culex tarsalis (Diptera: Culicidae), and occurrence of arboviruses in Maricopa County, Arizona.

Journal of medical entomology·2025
Same author

Moth caterpillar embryos and parasitoid egg infection as revealed in vivo and visualized by micro-CT scanning.

Journal of insect science (Online)·2025
Same author

Comparing Results from 2-D and 3-D Phenotyping Systems for Soybean Root System Architecture: A 'Comparison of Apples and Oranges'?

Plants (Basel, Switzerland)·2024
Same author

Long-Term Effects of Individual-Focused and Team-Based Training on Health Professionals' Intention to Have Serious Illness Conversations: A Cluster Randomised Trial.

Journal of CME·2024
Same author

Changes in intention to use an interprofessional approach to decision-making following training: a cluster before-and-after study.

BMC health services research·2024
Same author

Prescribing practices in opioid agonist treatment and changes in compliance to clinical dosing guidelines in British Columbia, Canada.

Addiction (Abingdon, England)·2024
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Aug 28, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Learning how a tree branches out: A statistical modeling approach.

Pierre Dutilleul1, Nishan Mudalige2, Louis-Paul Rivest2

  • 1Department of Plant Science, McGill University, Montréal, Québec, Canada.

Plos One
|September 21, 2022
PubMed
Summary
This summary is machine-generated.

Statistical models analyze tree branching patterns from computed tomography (CT) scans. Branch length significantly influences overall tree structure and complexity.

More Related Videos

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.2K
A Method for Quantifying Foliage-Dwelling Arthropods
08:20

A Method for Quantifying Foliage-Dwelling Arthropods

Published on: October 20, 2019

5.9K

Related Experiment Videos

Last Updated: Aug 28, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.2K
A Method for Quantifying Foliage-Dwelling Arthropods
08:20

A Method for Quantifying Foliage-Dwelling Arthropods

Published on: October 20, 2019

5.9K

Area of Science:

  • Botany
  • Computer Science
  • Statistics

Background:

  • Modern technologies generate large datasets, necessitating advanced statistical methods for analysis.
  • Plant computed tomography (CT) scanning generates complex data for studying tree leaf canopies and branching structures.

Purpose of the Study:

  • To develop and showcase a statistical modeling framework for analyzing tree branching structures using CT scan data.
  • To investigate how branch characteristics relate to their parent branches and overall tree complexity.

Main Methods:

  • Representing tree branching structures as hierarchical line segments in 3D.
  • Characterizing each branch by position, orientation, length, and number of offspring.
  • Applying descriptive statistics and statistical models to analyze branch variables.

Main Results:

  • The length of branches attached to the trunk is the most significant determinant of tree structure.
  • Branch length impacts the characteristics of all other branches within the tree.
  • Differences in structural complexity were observed among scanned miniature trees.

Conclusions:

  • The developed statistical framework effectively analyzes tree branching complexity from CT scan data.
  • Branch length is a critical factor influencing the development and structure of entire trees.
  • This approach provides insights into plant architecture and growth patterns.