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

Intermolecular Forces03:13

Intermolecular Forces

70.6K
Atoms and molecules interact through bonds (or forces): intramolecular and intermolecular. The forces are electrostatic as they arise from interactions (attractive or repulsive) between charged species (permanent, partial, or temporary charges) and exist with varying strengths between ions, polar, nonpolar, and neutral molecules. The different types of intermolecular forces are ion–dipole, dipole–dipole, hydrogen bonds, and dispersion; among these, dipole–dipole, hydrogen...
70.6K
Special Staining Techniques01:13

Special Staining Techniques

1.2K
Specialized staining techniques play a vital role in microbiology by enabling the visualization of specific bacterial structures that remain undetectable with standard microscopy methods. These techniques not only enhance the structural visualization of bacterial cells but also provide critical insights into their pathogenicity and classification. Additionally, they support diagnostic and research endeavors in microbiology by identifying key bacterial features.Capsule Staining for Virulence...
1.2K
Introduction to Special Senses01:26

Introduction to Special Senses

7.3K
Sensory receptors play an integral part in comprehending our external and internal environments. They receive diverse stimuli, converting them into the nervous system's electrochemical signals. This conversion occurs as the stimulus alters the sensory neuron's cell membrane potential, instigating the generation of an action potential. This action potential is subsequently transmitted to the central nervous system (CNS), which integrates with other sensory data or higher cognitive...
7.3K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.6K
VSEPR Theory for Determination of Electron Pair Geometries
45.6K
Machines01:19

Machines

563
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
563
Intermolecular vs Intramolecular Forces03:00

Intermolecular vs Intramolecular Forces

96.5K
Intermolecular forces (IMF) are electrostatic attractions arising from charge-charge interactions between molecules. The strength of the intermolecular force is influenced by the distance of separation between molecules. The forces significantly affect the interactions in solids and liquids, where the molecules are close together. In gases, IMFs become important only under high-pressure conditions (due to the proximity of gas molecules). Intermolecular forces dictate the physical properties of...
96.5K

You might also read

Related Articles

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

Sort by
Same author

Unsupervised machine learning derived bone phenotypes exhibit differential biomarker responses following acute ballistic loaded exercise.

Bone·2026
Same author

Artificial Intelligence Is a Useful Tool in Exercise Science and Sports Medicine: Response to Hando and Colleagues.

Medicine and science in sports and exercise·2026
Same author

Artificial Intelligence in Exercise Science and Sports Medicine.

Medicine and science in sports and exercise·2026
Same author

Author response to OSIN-D-26-00532: self-paced stair climb and handrail use may mask links to injurious falls.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·2026
Same author

Machine learning classifiers for automatic classification of foot strike patterns from 2D video.

Sports biomechanics·2026
Same author

Sex- and site-specific associations between hemoglobin levels and cortical bone structure and microarchitecture in older adults.

Bone·2026

Related Experiment Video

Updated: Jan 26, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K

Using Machine Learning to Predict Lower-Extremity Injury in US Special Forces.

Chris Connaboy1, Shawn R Eagle, Caleb D Johnson

  • 1Neuromuscular Research Laboratory/Warrior Human Performance Research Center, Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, PA.

Medicine and Science in Sports and Exercise
|April 16, 2019
PubMed
Summary

Military personnel with greater differences in single-leg landing mechanics and higher body mass face increased lower-extremity injury (LEI) risk. Machine learning models can identify these specific injury predictors.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

503
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

954

Related Experiment Videos

Last Updated: Jan 26, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

503
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

954

Area of Science:

  • Sports Medicine
  • Military Health
  • Biomechanics

Background:

  • Musculoskeletal injuries are a significant concern in military populations, impacting readiness and operational effectiveness.
  • Predictive modeling using machine learning offers a promising approach to identify individuals at high risk for lower-extremity injuries (LEI).

Purpose of the Study:

  • To investigate the predictive power of interacting risk factors for lower-extremity injury (LEI) in military special forces operators.
  • To develop a population-specific algorithm for LEI risk using a decision tree model.

Main Methods:

  • A prospective cohort study involving 140 Air Force Special Forces Operators.
  • Baseline assessments included body composition, strength, flexibility, and landing biomechanics, with unilateral landing evaluated.
  • Chi-squared automatic interaction detection (CHAID) was employed to analyze injury predictors and their interactions.

Main Results:

  • Twenty-seven percent of operators sustained an LEI within 365 days.
  • A knee flexion angle difference exceeding 25.1% was strongly associated with LEI (P = 0.006).
  • Operators with a >25.1% knee flexion difference and body mass >81.8 kg experienced a 100% LEI rate (P = 0.047).

Conclusions:

  • Greater asymmetry in single-leg landing strategies and higher body mass increase LEI risk in this cohort.
  • The CHAID decision tree model effectively identifies interacting risk factors for population-specific injury prediction.
  • This approach can enhance the development of targeted injury prevention strategies for military personnel.