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

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

2.0K
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
2.0K
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

1.9K
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
1.9K
Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences01:20

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences

1.5K
Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
1.5K
Mass Analyzers: Common Types01:19

Mass Analyzers: Common Types

1.7K
The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
1.7K
Atomic Absorption Spectroscopy: Interference01:25

Atomic Absorption Spectroscopy: Interference

2.2K
Interference leads to systematic error in atomic absorption (AA) measurements by enhancing or diminishing the analytical signal or the background. These interferences can be grouped into three main categories: spectral interference, chemical interference, and physical interference.
Spectral interference occurs when signals from other elements or molecules overlap with the analyte signal, falsely elevating or masking the analyte's absorbance. This interference can be corrected using Zeeman,...
2.2K
Law of Independent Assortment02:03

Law of Independent Assortment

63.8K
While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
63.8K

You might also read

Related Articles

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

Sort by
Same author

Effectiveness and tolerability of fenfluramine in pediatric and adult patients with developmental and epileptic encephalopathies: A multicenter, retrospective, real-world clinical-practice study.

Epilepsia·2026
Same author

Seizure-free days as a clinical outcome measure of reduced epilepsy burden: 5-year outcomes with cenobamate treatment.

Epilepsy & behavior : E&B·2026
Same author

Soticlestat as an adjunctive therapy in children and young adults with Dravet syndrome.

Epilepsia·2026
Same author

Affective and Cognitive Vulnerability Under Chronic Stress: Insights From Patients With Left Temporal Lobe Epilepsy and Caregivers.

Actas espanolas de psiquiatria·2026
Same author

Beyond seizure control: The impact of SEEG-guided radiofrequency thermocoagulation on quality of life.

Epileptic disorders : international epilepsy journal with videotape·2026
Same author

Freedon study: Real-life outcomes of cenobamate in different lines of treatment.

Epilepsia·2026
Same journal

ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion.

IEEE transactions on neural networks and learning systems·2026
Same journal

PIMPC-GNN: Physics-Informed Multiphase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Quantum Rényi α-Entropies for Graph Characterization.

IEEE transactions on neural networks and learning systems·2026
Same journal

LANet: A Lightweight and Accurate Balanced Network Based on State Space Models for Real-Time Semantic Segmentation.

IEEE transactions on neural networks and learning systems·2026
Same journal

MENDNet: Memory-Enhanced Dependency Network for Multistock Movement Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Temporal Mask-Embedding Learning and Query-Refined Head Network for Visual Tracking.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Mar 6, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Probabilistic Distance for Mixtures of Independent Component Analyzers.

Gonzalo Safont, Addisson Salazar, Luis Vergara

    IEEE Transactions on Neural Networks and Learning Systems
    |March 3, 2017
    PubMed
    Summary
    This summary is machine-generated.

    A new probabilistic distance (PDI) was developed for ICA mixture models, outperforming Kullback-Leibler divergence for change detection in signal processing and medical imaging applications.

    More Related Videos

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.4K
    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
    10:22

    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

    Published on: September 7, 2019

    8.9K

    Related Experiment Videos

    Last Updated: Mar 6, 2026

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.0K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.4K
    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
    10:22

    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

    Published on: September 7, 2019

    8.9K

    Area of Science:

    • Signal Processing
    • Machine Learning
    • Artificial Neural Networks

    Background:

    • Independent Component Analysis (ICA) is a blind source separation technique.
    • Existing ICA mixture models (ICAMMs) have limitations in modeling dependencies and require numerical integration for parameter comparison.
    • There is a need for improved methods for change detection in complex data.

    Purpose of the Study:

    • To propose a novel probabilistic distance (PDI) for comparing parameters learned by ICAMMs.
    • To develop a PDI that is computed explicitly, symmetric, and bounded between 0 and 1.
    • To evaluate the PDI's effectiveness in change detection applications.

    Main Methods:

    • Relaxing independence and linear restrictions in ICA using ICAMMs with a two-layer neural network structure.
    • Developing an explicit probabilistic distance (PDI) between learned ICAMM parameters, avoiding numerical integration.
    • Applying the PDI for change detection by measuring distances between ICAMMs from consecutive time windows.

    Main Results:

    • The proposed PDI is symmetric and bounded between 0 and 1, suitable for posterior probability in fusion.
    • PDI demonstrated superior change-detection capabilities compared to Kullback-Leibler divergence (KLD).
    • Effective detection of material flaws using ultrasound and changes in human electroencephalography signals during neuropsychological tests.

    Conclusions:

    • The developed PDI offers an explicit, robust, and effective metric for comparing ICAMMs.
    • PDI enhances change detection accuracy in diverse applications, including non-destructive testing and brain-computer interfaces.
    • This work provides a valuable tool for analyzing dynamic changes in complex systems modeled by ICAMMs.