Publication

Cross-correlation between a carbonyl C' chemical shift anisotropy and a long-range dipolar C'HA coupling in proteins using symmetrical reconversion

Geoffrey Bodenhausen
2003
Journal Articles

Abstract

A new sequence is described to measure the cross-correlation rates between the chem. shift anisotropy of the carbonyl carbon-13 nucleus and the dipole-dipole interaction between this carbonyl and the alpha-proton in proteins. The sequence is based on the sym. reconversion principle and is insensitive to exptl. errors and to violations of the secular approxn. The cross-correlation rate depends on the backbone angle y. The advantages and limitations of the sequence are discussed. [on SciFinder (R)]

Official source

https://infoscience.epfl.ch/entities/publication/be62d7e8-2504-4b98-8704-63b1c697b312

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Geoffrey Bodenhausen
2003
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/be62d7e8-2504-4b98-8704-63b1c697b312

About this result

Related concepts (32)

Intraclass correlation

In statistics, the intraclass correlation, or the intraclass correlation coefficient (ICC), is a descriptive statistic that can be used when quantitative measurements are made on units that are organized into groups. It describes how strongly units in the same group resemble each other. While it is viewed as a type of correlation, unlike most other correlation measures, it operates on data structured as groups rather than data structured as paired observations.

Correlation

In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.

Correlation does not imply causation

The phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them. The idea that "correlation implies causation" is an example of a questionable-cause logical fallacy, in which two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known by the Latin phrase cum hoc ergo propter hoc ('with this, therefore because of this').

Related publications (19)

Cross-correlation between a carbonyl C' chemical shift anisotropy and a long-range dipolar C'HA coupling in proteins using symmetrical reconversion

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