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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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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.
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Multimachine Stability01:25

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Structuralism01:26

Structuralism

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Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Nodal analysis is a fundamental method in electrical engineering used to simplify the process of circuit analysis. This method revolves around the concept of using node voltages as the primary variables for circuit analysis. The objective is to determine the voltage at each node in a circuit, which can then be used to find other quantities of interest, such as currents through specific components.
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Predictive structural dynamic network analysis.

Rong Chen1, Edward H Herskovits1,

  • 1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, School of Medicine, 100N. Greene St, 4th Floor, 22 S. Greene St., Baltimore, MD 21201, USA.

Journal of Neuroscience Methods
|February 25, 2015
PubMed
Summary
This summary is machine-generated.

We developed a novel network-based method to classify individuals using longitudinal magnetic resonance imaging data. This approach significantly improves classification accuracy for neurological conditions compared to existing methods.

Keywords:
Alzheimer's diseaseBrain networkDynamic Bayesian networkMagnetic resonance imagingPredictive modeling

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Classifying individuals using magnetic resonance (MR) data is crucial in neuroscience.
  • Current brain network methods are limited to cross-sectional studies and cannot analyze longitudinal data.
  • A new network-based predictive modeling method is proposed for longitudinal MR data.

Purpose of the Study:

  • To develop and validate a novel method for classifying subjects using longitudinal magnetic resonance data.
  • To improve upon existing methods that are limited to cross-sectional analyses.
  • To enhance the understanding of brain network dynamics in health and disease.

Main Methods:

  • Generated dynamic Bayesian network models representing spatiotemporal interactions among brain regions.
  • Calculated a network-derived score indicating deviation from expected network patterns.
  • Constructed predictive models using the network-derived score and other candidate predictors.

Main Results:

  • Validated the method using simulated data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.
  • A combined model using baseline biomarkers and the network-based score achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity in the ADNI study.
  • The proposed model showed significantly higher accuracy (p=0.002) than models based solely on baseline biomarkers (0.77 accuracy).

Conclusions:

  • Presented a method for subject classification based on structural dynamic network model scores.
  • The method is important for distinguishing subjects based on structural network dynamics.
  • Enhances understanding of network architecture in brain processes and disorders.