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Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied first.
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Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than the dxy,...
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At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...

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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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Published on: January 30, 2018

Triplet markov fields for the classification of complex structure data.

Juliette Blanchet1, Florence Forbes

  • 1MISTIS team, INRIA Rhône-Alpes, ZIRST, Cedex, France. Juliette.Blanchet@inrialpes.fr

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 19, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces new Triplet Markov Field models for complex data classification, handling high dimensionality, dependencies, and general noise. The models offer a supervised learning framework and efficient parameter estimation via Bayesian clustering.

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Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Data Science

Background:

  • Classifying complex data presents challenges due to high dimensionality, observational dependencies, and intricate noise models.
  • Existing methods may struggle with the multifaceted nature of real-world datasets.

Purpose of the Study:

  • To propose novel Triplet Markov Field models capable of handling complex data characteristics.
  • To enable consistent integration of supervised learning within these generative models.
  • To provide a robust framework for parameter estimation and address identifiability issues.

Main Methods:

  • Investigation of existing Triplet Markov Fields.
  • Development of new Triplet Markov Field models accommodating general noise models.
  • Integration of a supervised learning framework.
  • Application of Bayesian clustering techniques for parameter estimation.
  • Analysis of identifiability in unsupervised settings.

Main Results:

  • The proposed models effectively handle high dimensionality, dependencies, and general noise in data.
  • Parameter estimation is feasible using Bayesian clustering, even with complex initial noise models.
  • The generative models serve as a supervised alternative to discriminative Conditional Random Fields.
  • Model performance is validated on both simulated and real-world complex datasets.

Conclusions:

  • The novel Triplet Markov Field models offer a powerful and flexible approach to classifying complex data.
  • These models provide a unified framework for both supervised and unsupervised learning scenarios.
  • The proposed methods demonstrate robust performance across diverse data complexities.