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Related Concept Videos

Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Causation01:27

Correlation and Causation

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Correlation01:09

Correlation

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Elimination Kinetics: First-Order and Zero-Order01:05

Elimination Kinetics: First-Order and Zero-Order

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Eliminating drugs from the body is a vital process that occurs through excretion or metabolism. Understanding the kinetics of drug elimination is crucial for drug development, dosage determination, and optimizing patient outcomes.
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Elimination Reactions02:25

Elimination Reactions

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A nucleophile can react with an alkyl halide to give the substitution product by displacing the halogen. Or it can function as a base to give the elimination product by deprotonation of the neighboring carbon to form an alkene. In an elimination reaction, the substrate loses two groups from adjacent carbons forming at least one π bond. The carbon attached to the halogen is called the α carbon, while the adjacent carbon is called the β carbon; hence, these reactions are called...
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Radical Formation: Elimination00:51

Radical Formation: Elimination

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Another method of radical formation is the elimination process. It is the opposite of the addition route and is driven by the instability of the radical. For example, as depicted in Figure 1, dibenzoyl peroxide yields a pair of unstable radicals upon homolysis. Given its instability, this radical spontaneously undergoes elimination via a C–C bond cleavage to form a relatively more stable phenyl radical. The mechanism involves cleavage of the bond between the α and β positions with respect...
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Related Experiment Video

Updated: Feb 2, 2026

A Multi-hole Cryovial Eliminates Freezing Artifacts when Muscle Tissues are Directly Immersed in Liquid Nitrogen
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Retrospective artifact elimination in MEGA-PRESS using a correlation approach.

Sofie Tapper1,2, Anders Tisell1,2, Gunther Helms3

  • 1Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.

Magnetic Resonance in Medicine
|November 13, 2018
PubMed
Summary

A new method called JKC (jackknife analyses with correlation of spectral windows) effectively removes artifacts from magnetic resonance spectroscopy (MRS) data. This technique improves data quality and can serve as a quality control measure for MRS datasets.

Keywords:
GABAMEGA-PRESSMRSartifact detectioncorrelation analysisjackknife

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Area of Science:

  • Medical Imaging
  • Spectroscopy
  • Data Analysis

Background:

  • Magnetic Resonance Spectroscopy (MRS) is crucial for in vivo metabolite quantification.
  • Artifacts in MRS data can significantly compromise the accuracy of metabolite concentration measurements.
  • Existing methods for artifact correction may be limited or require additional resources.

Purpose of the Study:

  • To develop and validate a novel retrospective method for artifact elimination in MRS data.
  • To introduce the Jackknife analyses with Correlation of spectral windows (JKC) method for artifact correction.
  • To assess the JKC method's effectiveness in improving the accuracy of metabolite quantification.

Main Methods:

  • The JKC method was developed, combining jackknife analyses with spectral window correlation.
  • Twelve healthy volunteers underwent 3T MR system measurements, including protocols with and without induced head movements.
  • Artifact-influenced datasets were split into training (1/3) and validation (2/3) sets for JKC implementation and testing.

Main Results:

  • The JKC method accurately identified artifacts in the majority of validation datasets.
  • Post-artifact elimination, metabolite concentrations derived from corrected data closely matched reference dataset values.
  • Application of JKC to artifact-free reference data did not alter estimated metabolite concentrations compared to standard averaging.

Conclusions:

  • The JKC method offers a cost-effective solution for retrospective artifact elimination in MRS data.
  • It is applicable to MRS datasets regardless of artifact presence.
  • The JKC method can function as a quality control tool, potentially indicating voxel placement shifts during measurements.