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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Region of Convergence of Laplace Tarnsform01:20

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The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
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Extraction: Partition and Distribution Coefficients01:14

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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
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Structural Classification of Joints01:20

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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.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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Group-wise functional community detection through joint Laplacian diagonalization.

Luca Dodero, Alessandro Gozzi, Adam Liska

    Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    |December 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Researchers developed a new method to map brain networks across individuals using resting-state functional Magnetic Resonance Imaging (rs-fMRI). This approach identifies common functional sub-networks, overcoming challenges in cross-subject brain connectivity analysis.

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    Area of Science:

    • Neuroscience
    • Computational Biology
    • Medical Imaging

    Background:

    • Understanding brain function relies on mapping network connectivity between brain regions.
    • Resting state Functional Magnetic Resonance Imaging (rs-fMRI) is crucial for studying brain network functionality.
    • Individual variability poses a significant challenge in identifying common brain networks across subjects.

    Purpose of the Study:

    • To present a novel group-wise community detection method for identifying functional brain sub-networks across multiple subjects.
    • To address the challenge of mapping common brain networks despite individual variability in rs-fMRI data.

    Main Methods:

    • The proposed approach utilizes joint diagonalization of multiple graph Laplacians.
    • This technique aims to find a common eigenspace across individual brain graphs.
    • Dimensionality reduction via clustering is applied over the common eigenspace to identify shared sub-networks.

    Main Results:

    • The method successfully identified common functional sub-networks within a mouse brain rs-fMRI dataset.
    • The detected sub-networks correspond to important brain circuits previously described in scientific literature.

    Conclusions:

    • The novel group-wise community detection method effectively reveals consistent brain sub-networks across subjects.
    • This approach offers a robust solution for analyzing large-scale brain connectivity data from rs-fMRI.