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Related Concept Videos

Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)01:22

Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)

Vicinal or three-bond coupling is commonly observed between protons attached to adjacent carbons. Here, nuclear spin information is primarily transferred via electron spin interactions between adjacent C‑H bond orbitals. This generally favors the antiparallel arrangement of spins, so 3J values are usually positive.
The extent of coupling depends on the C‑C bond length, the two H‑C‑C angles, any electron-withdrawing substituents, and the dihedral angle between the involved orbitals. The...
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)01:20

Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)

Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
The central atom need not be NMR-active because its electrons are affected by the electron polarization of the spin-active atoms. However, spin information is transmitted less effectively than in one-bond coupling, and 2J values are usually weaker than 1J values. The energy of...
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
¹H NMR: Long-Range Coupling01:27

¹H NMR: Long-Range Coupling

The coupling interactions of nuclei across four or more bonds are usually weak, with J values less than 1 Hz. While these are usually not observed in spectra, the presence of multiple bonds along the coupling pathway can result in observable long-range coupling.
In alkenes, spin information is communicated via σ–π overlap, as seen in allylic (four-bond) and homoallylic (five-bond) couplings. These coupling interactions are stronger when the σ bond is parallel to the alkene π orbitals.

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Related Experiment Video

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Partial mutual information for coupling analysis of multivariate time series.

Stefan Frenzel1, Bernd Pompe

  • 1Institute of Physics, University of Greifswald, D-17487 Greifswald, Germany.

Physical Review Letters
|February 1, 2008
PubMed
Summary

We introduce a new method using partial mutual information to find direct and indirect interactions in complex data. This approach helps build better graphical models for understanding multivariate time series relationships.

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13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Information theory
  • Time series analysis
  • Network inference

Background:

  • Understanding complex systems requires analyzing relationships within multivariate time series data.
  • Distinguishing direct from indirect interactions is crucial for accurate modeling.

Purpose of the Study:

  • To propose a novel method for discovering couplings in multivariate time series.
  • To leverage partial mutual information for enhanced interaction analysis.

Main Methods:

  • Utilizing partial mutual information, an information-theoretic measure.
  • Generalizing partial correlation to uncover conditional dependencies.
  • Developing an efficient estimator for partial mutual information.

Main Results:

  • The proposed method effectively differentiates between direct and indirect interactions.
  • The approach facilitates the derivation of appropriate graphical models.
  • An efficient estimator for partial mutual information is presented.

Conclusions:

  • Partial mutual information offers a powerful tool for network inference in multivariate time series.
  • The method provides a robust framework for analyzing complex system interactions.
  • Accurate identification of couplings is essential for scientific discovery.