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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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Functional Classification of Joints01:09

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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.
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Structural Joints: Synovial Joints01:16

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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Entropy and the Second Law of Thermodynamics01:20

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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
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Standard Entropy Change for a Reaction03:00

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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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Measuring Neuromuscular Junction Functionality
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Neural Joint Entropy Estimation.

Yuval Shalev, Amichai Painsky, Irad Ben-Gal

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    This study introduces a new method for estimating entropy and related information measures, especially effective for small sample sizes. The approach leverages deep neural networks for improved accuracy in machine learning and data compression tasks.

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    Area of Science:

    • Information Theory
    • Machine Learning
    • Statistics

    Background:

    • Estimating entropy for discrete random variables is crucial in information theory, machine learning, statistics, and data compression.
    • Existing methods often falter with small sample sizes relative to the variable's alphabet size.
    • Previous work by McAllester and Statos provides a foundation for entropy estimation.

    Purpose of the Study:

    • To develop a practical and accurate entropy estimation scheme for discrete random variables, particularly addressing limitations with small sample sizes.
    • To extend the proposed method for estimating related information-theoretic measures like conditional entropy and mutual information (MI).
    • To evaluate the performance and consistency of the new estimators across various applications.

    Main Methods:

    • Utilizing the generalization capabilities of cross-entropy estimation within deep neural networks (DNNs).
    • Extending existing work to create a family of novel estimators for entropy and related information-theoretic measures.
    • Applying the proposed estimators to large alphabet entropy estimation, MI estimation, conditional MI estimation, and transfer entropy (TE) estimation.

    Main Results:

    • The proposed estimators demonstrate strong consistency.
    • Improved performance was observed compared to existing methods across all tested scenarios, including large alphabet entropy, MI, conditional MI, and TE estimation.
    • The method effectively addresses the challenge of small sample sizes in entropy estimation.

    Conclusions:

    • The novel DNN-based approach offers a practical and accurate solution for entropy estimation, especially in low-sample regimes.
    • The developed estimators for conditional entropy and mutual information show significant promise for applications like independence testing.
    • This work advances the field of information-theoretic measure estimation with broad applicability in machine learning and data analysis.