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Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

Extraction: Partition and Distribution Coefficients

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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
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Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Related Experiment Video

Updated: Jul 8, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Improving MEG source localizations: an automated method for complete artifact removal based on independent component

D Mantini¹, R Franciotti, G L Romani

¹Institute of Advanced Biomedical Technologies, G. D'Annunzio University Foundation, Università degli Studi di Chieti, Via dei Vestini 33, Chieti, Italy. d.mantini@unich.it

|December 25, 2007

Summary

This study introduces a novel automated method using approximate entropy (ApEn) to classify and remove artifacts from magnetoencephalography (MEG) recordings. This technique enhances signal clarity for improved brain source localization.

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Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

Neuroscience
Biophysics
Signal Processing

Background:

High-quality magnetoencephalography (MEG) recordings are often compromised by physiological and technical artifacts.
Artifacts such as eye movements, cardiac signals, muscular contractions, and environmental noise significantly hinder MEG signal analysis.
While independent component analysis (ICA) is effective for artifact removal, a standardized automated system is lacking due to difficulties in signal categorization.

Purpose of the Study:

To develop and validate an automated artifact rejection method for MEG data.
To utilize approximate entropy (ApEn) for reliable classification of independent components derived from ICA.
To improve the quality of MEG signals for more accurate brain source localization.

Main Methods:

Independent Component Analysis (ICA) was employed to decompose MEG signals.
Approximate Entropy (ApEn), a measure of signal regularity, was used to classify artifactual and neural components.
The proposed automated system was tested on MEG datasets from somatosensory, auditory, and visual stimulation paradigms.

Main Results:

The ApEn-based classification successfully identified and allowed for the rejection of artifactual components.
The method effectively attenuated both biological artifacts and environmental noise in MEG recordings.
Reconstructed MEG signals showed improved clarity, suitable for enhanced brain source localization.

Conclusions:

Approximate entropy provides a robust and automated approach for artifact rejection in MEG data.
This method overcomes the limitations of manual artifact identification and enhances the reliability of ICA-based noise reduction.
The developed system holds significant potential for improving the diagnostic and research applications of MEG.