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Related Concept Videos

Cohesion01:07

Cohesion

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Cohesion is the attraction between molecules of the same type, such as water molecules. Water molecules have an overall neutral charge but are polar molecule. An oxygen atom in one water molecule has a partial negative charge that can bind to a hydrogen atom with a partial positive charge in a second water molecule, forming a hydrogen bond. Each water molecule can form up to four hydrogen bonds with other water molecules. Hydrogen bonds are responsible for water's cohesive nature.
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Diversity of Archaea I01:30

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Archaea, a domain of single-celled microorganisms, are classified into five major phyla based on genetic and biochemical characteristics: Euryarchaeota, Crenarchaeota, Thaumarchaeota, Korarchaeota, and Nanoarchaeota. Among these, the phylum Euryarchaeota is notable for its remarkable diversity in morphology, metabolism, and ecological adaptations.Morphological and Metabolic DiversityMembers of Euryarchaeota exhibit a variety of cellular shapes, including rods and cocci. Their metabolic pathways...
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Cell Diversity01:13

Cell Diversity

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The concept of a cell started with microscopic observations of dead cork tissue by Robert Hooke in 1665. Hooke coined the term "cell" based on the resemblance of the small subdivisions in the cork to the rooms that monks inhabited, called cells. About ten years later, Antonie van Leeuwenhoek became the first person to observe the living and moving cells under a microscope. In the century that followed, the theory that cells represented the basic unit of life developed.
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Diversity of Archaea II01:24

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Archaea, one of the three domains of life, exhibit remarkable diversity and adaptability, thriving in both extreme and moderate environments. Historically, most identified archaea have been classified into two major phyla: Euryarchaeota and Crenarchaeota. However, recent molecular studies have expanded this classification to include three additional phyla: Thaumarchaeota, Nanoarchaeota, and Korarchaeota, each exhibiting unique characteristics and ecological roles.Thaumarchaeota: Mesophiles...
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Diversity of Protists I01:15

Diversity of Protists I

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Excavata is a diverse group of protists that includes both chemoorganotrophic and phototrophic species, with some thriving in anaerobic environments. Among the key groups within Excavata are diplomonads and parabasalids, which are flagellated protists that lack mitochondria and chloroplasts. These microorganisms typically inhabit anoxic environments, such as the intestines of animals, where they exist either symbiotically or as parasites, relying on fermentation for energy production. Some...
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Diversity of Protists II01:27

Diversity of Protists II

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Alveolates are a group of organisms recognized by the presence of alveoli, which are cytoplasmic sacs located beneath the cell membrane. While their function remains uncertain, alveoli may help regulate water balance by controlling how much water enters and leaves the cell. In dinoflagellates, these structures may serve as armor plates. There are three major types of alveolates: ciliates, which move using cilia; dinoflagellates, which use flagella for movement; and apicomplexans, which are...
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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

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Multiview Classification With Cohesion and Diversity.

Hong Tao, Chenping Hou, Dongyun Yi

    IEEE Transactions on Cybernetics
    |December 12, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new multiview classification method that leverages both common (consensus) and unique (complementarity) information across different data views. The approach enhances classification accuracy by maximizing agreement and diversity among classifiers.

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    Area of Science:

    • Machine Learning
    • Computer Vision
    • Data Science

    Background:

    • Multiview learning utilizes data from multiple sources, recognizing that different views contain both shared and unique information.
    • Effectively harnessing consensus and complementarity is crucial for improving classification performance in multiview settings.
    • Existing methods often struggle to explicitly model and exploit both commonalities and differences across views.

    Purpose of the Study:

    • To propose a novel framework for multiview classification that explicitly models and integrates consensus and complementarity.
    • To develop a method that enhances classification accuracy by maximizing agreement among view-specific classifiers while promoting diversity.
    • To provide an efficient and convergent optimization algorithm for training the proposed model.

    Main Methods:

    • A cohesion-increasing loss term with a learnable label-adjusting matrix was designed to promote consensus among multiview classifiers.
    • Diversity-promoting regularization using an independence measurement was employed to encourage complementary information capture.
    • An iterative optimization algorithm with proven convergence was developed for model training.

    Main Results:

    • The proposed method demonstrated improved classification performance by effectively utilizing both consensus and complementary information.
    • Experiments on various datasets confirmed the efficacy of the model in achieving more comprehensive and accurate classifications.
    • The integration of consensus and complementarity led to a more thorough exploration of multiview data.

    Conclusions:

    • The developed multiview classification approach successfully balances the exploitation of common and complementary information.
    • The method offers a robust strategy for enhancing classification accuracy in complex multiview learning scenarios.
    • The findings highlight the importance of explicitly modeling both consensus and complementarity for advanced multiview analysis.