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関連する概念動画

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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In 1928, bacteriologist Frederick Griffith worked on a vaccine for pneumonia, which is caused by Streptococcus pneumoniae bacteria. Griffith studied two pneumonia strains in mice: one pathogenic and one non-pathogenic. Only the pathogenic strain killed host mice.
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Human development is typically examined across three main domains: physical, cognitive, and socio-emotional. These domains represent the significant areas of change and continuity throughout the lifespan, from infancy to late adulthood.
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The membrane domains concentrate specific lipids and proteins at one place within the membrane, which helps in cell signaling, adhesion, and other critical cellular processes. These domains can differ in size, composition, function, and lifespan.
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Ribosomal RNA (rRNA) sequence analysis revealed three distinct groups of cells: eukaryotes, bacteria, and archaea. In 1978, Carl R. Woese proposed the concept of domains, a taxonomic level above kingdoms, to differentiate these groups. He suggested that archaea and bacteria, despite their similar appearance, represent separate domains. Domains differ in rRNA, membrane lipid structure, transfer RNA, and antibiotic sensitivity.In this classification, animals, plants, and fungi belong to the...
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ドメイン変換多様体学習による画像再構築

Bo Zhu1,2,3, Jeremiah Z Liu4, Stephen F Cauley1,2

  • 1A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.

Nature
|March 23, 2018
PubMed
まとめ
この要約は機械生成です。

マニフォールド・アプロシマーションによる自動変換 (AUTOMAP) は,画像の再構築,パフォーマンスの向上,アーティファクトの削減のためにディープラーニングを使用します. このデータベースのアプローチは,さまざまなイメージングアプリケーションのための統一された枠組みを提供し,騒音の耐性を高めます.

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科学分野:

  • * コンピュータ画像と機械学習
  • * 科学的イメージングのための新しいアルゴリズムの開発

背景:

  • * 画像再構築は医学画像から天文学まで様々な科学分野において重要です.
  • * 従来の方法は,しばしば専門的なチューニングを必要とする複雑なアドホック信号処理チェーンに依存しています.
  • * 未知の逆変換とノイズのようなセンサーの不完全さも問題です.

研究 の 目的:

  • AUTOMAPと呼ばれる画像再構築のための統一されたデータ主導のフレームワークを導入する.
  • * ディープラーニングによるAUTOMAPの柔軟性と効率性を実証する
  • * 再構築性能と騒音と人工物に対する頑丈さを向上させる.

主な方法:

  • * ディープニューラルネットワークアーキテクチャを使用してAUTOMAPを実装しました.
  • * センサから画像の領域マッピングを学習するためにデータ群でネットワークを訓練しました.
  • * 変数の散らばった表現を達成するために多様学習を使用した.

主要な成果:

  • * AUTOMAPは,一貫した超パラメータを持つ様々な磁気共鳴画像戦略のための再構築変換を成功裏に学習しました.
  • * 伝統的な方法と比較して,優れた騒音耐性と再構築アーティファクトの減少を示した.
  • * マニポリッド・ラーニングの役割が,稀少で低次元なデータ表現の作成に示された.

結論:

  • *AUTOMAPは,従来の画像再構築に柔軟で強力なデータ主導の代替手段を提供します.
  • *AUTOMAPのような学習された再構築アプローチは,既存のイメージング技術を強化することができます.
  • * この枠組みにより,新しい画像取得戦略の開発が加速される見込みです.