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

NMR Spectroscopy: Spin–Spin Coupling01:08

NMR Spectroscopy: Spin–Spin Coupling

3.2K
The spin state of an NMR-active nucleus can have a slight effect on its immediate electronic environment. This effect propagates through the intervening bonds and affects the electronic environments of NMR-active nuclei up to three bonds away; occasionally, even farther. This phenomenon is called spin–spin coupling or J-coupling. Coupling interactions are mutual and result in small changes in the absorption frequencies of both nuclei involved. While nuclei of the same element are involved...
3.2K
Spin–Spin Coupling: One-Bond Coupling01:17

Spin–Spin Coupling: One-Bond Coupling

1.5K
Coupling interactions are strongest between NMR-active nuclei bonded to each other, where spin information can be transmitted directly through the pair of bonding electrons. While nuclei polarize their electrons to the opposite spins, the bonding electron pair has opposite spins. Configurations with antiparallel nuclear spins are expected to be lower in energy. When coupling makes antiparallel states more favorable, J is considered to have a positive value. The one-bond coupling constant, 1J,...
1.5K
Spin–Spin Coupling Constant: Overview01:08

Spin–Spin Coupling Constant: Overview

1.5K
In bromoethane, the three methyl protons are coupled to the two methylene protons that are three bonds away. In accordance with the n+1 rule, the signal from the methyl protons is split into three peaks with 1:2:1 relative intensities. The methylene protons appear as a quartet, with the relative intensities of 1:3:3:1.
Qualitatively, any spin plus-half nucleus polarizes the spins of its electrons to the minus-half state. Consequently, the paired electron in the hydrogen–carbon bond must...
1.5K
Self-Help Support Groups01:28

Self-Help Support Groups

346
Self-help support groups are voluntary, community-based organizations that provide a platform for individuals with shared concerns to exchange support, insights, and practical strategies for coping with life challenges. Typically led by group members or paraprofessionals, these groups form a cornerstone of mental health care, especially in reaching populations that are underserved by traditional healthcare systems.
Accessibility and Cost-Effectiveness
One of the primary strengths of self-help...
346
Frequency-dependent Selection01:21

Frequency-dependent Selection

23.9K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
23.9K
Machines01:19

Machines

577
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...
577

You might also read

Related Articles

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

Sort by
Same author

Tuna-Like Swimmers Experience a Fluid-Mediated Stable Side-by-Side Formation.

Bioinspiration & biomimetics·2026
Same author

Radiomics in thyroid nodule assessment.

BMC medical imaging·2026
Same author

Synthesis, characteristics, and applications of sustainable carbon quantum dots derived from biomass of recombinant bacterium.

Bioresource technology·2026
Same author

Loss of BATF3 impairs adipose-liver homeostasis and accelerates the transition from steatosis to fibrosis in high-fat diet-fed mice.

International journal of biological sciences·2026
Same author

BLyS/APRIL dual inhibition for systemic sclerosis: a single-centre, single-arm, open-label clinical trial of telitacicept.

Clinical and experimental rheumatology·2026
Same author

Single-cell technologies in deciphering drug resistance of multiple myeloma: mechanistic insights and clinical translation prospects.

Leukemia & lymphoma·2026
Same journal

Correction to "Learning Mechanisms in Alcohol Use Disorders".

Addiction biology·2026
Same journal

Abnormal Brain Structural Covariance Networks of Cortical Thickness in Cocaine Use Disorder.

Addiction biology·2026
Same journal

The Orexin System Modulates Stress-Induced Alcohol Preference and Reinstatement in Adolescents: Bioinformatics and Experimental Evidence.

Addiction biology·2026
Same journal

Learning Mechanisms in Alcohol Use Disorders.

Addiction biology·2026
Same journal

Predicting Outpatient Follow-Up Retention After Inpatient Treatment in Patients With Alcohol Use Disorder: A Data-Driven Random Forest Approach.

Addiction biology·2026
Same journal

Correction to "Virtual Reality-Based Cue Exposure and Aversion Therapy for Alcohol Dependence: A Randomized Controlled Trial".

Addiction biology·2026
See all related articles

Related Experiment Video

Updated: Jan 31, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Support vector machine-based multivariate pattern classification of methamphetamine dependence using arterial spin

Yadi Li1, Zaixu Cui2, Qi Liao3

  • 1Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, Ningbo, China.

Addiction Biology
|January 10, 2019
PubMed
Summary
This summary is machine-generated.

Arterial spin labeling (ASL) effectively detects methamphetamine (MA) dependence by identifying cerebral blood flow (CBF) abnormalities using a support vector machine (SVM) classifier. This method shows high accuracy in distinguishing MA-dependent individuals from controls.

Keywords:
arterial spin labelingcerebral blood flowmachine learningmethamphetamine

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

495

Related Experiment Videos

Last Updated: Jan 31, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

495

Area of Science:

  • Neuroimaging
  • Neurology
  • Radiology

Background:

  • Arterial spin labeling (ASL) is a non-invasive MRI technique used to assess cerebral blood flow (CBF).
  • Abnormalities in CBF are implicated in various brain disorders, including substance dependence.
  • Methamphetamine (MA) dependence is a significant public health concern with complex neuropathology.

Purpose of the Study:

  • To evaluate the efficacy of ASL magnetic resonance imaging (MRI) in detecting cerebral blood flow (CBF) abnormalities specific to methamphetamine (MA) dependence.
  • To develop and validate a multivariate pattern classification algorithm, specifically a support vector machine (SVM), for discriminating MA-dependent individuals from healthy controls.

Main Methods:

  • Utilized ASL-MRI to acquire CBF data from 45 MA-dependent subjects, 45 normal controls, and 36 heroin-dependent subjects.
  • Employed a support vector machine (SVM) algorithm to construct classifiers for differentiating subject groups based on ASL-CBF patterns.
  • Analyzed prediction performance using cross-validation, accuracy, sensitivity, specificity, kappa, and receiver operating characteristic (ROC) curve analysis (AUC).

Main Results:

  • A classifier trained on ASL-CBF data from all cerebral regions accurately differentiated MA-dependent subjects from controls with 89% accuracy, 94% sensitivity, 84% specificity, and an AUC of 0.95.
  • Significant hemispheric asymmetry was observed in classifier performance when trained on unilateral ASL-CBF data.
  • The most discriminative brain regions included the occipital lobe, insular cortex, postcentral gyrus, corpus callosum, and inferior frontal cortex.
  • The SVM classifier demonstrated specificity for MA dependence, with lower accuracy (55.56%) for heroin dependence.

Conclusions:

  • ASL-MRI combined with an SVM classifier shows high potential for the diagnosis of MA dependence.
  • The findings contribute to a better understanding of the neuropathological underpinnings of MA dependence.
  • This approach may aid in the clinical diagnosis and management of MA use disorder.