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Fusion of Secretory Vesicles with the Plasma Membrane01:26

Fusion of Secretory Vesicles with the Plasma Membrane

16.6K
Proteins and neurotransmitters in secretory vesicles can be released from a cell upon vesicle docking, priming, and fusion with the plasma membrane. Vesicles are docked and primed in preparation for the quick exocytosis of their contents in response to a stimulus. The fusion process is mainly carried out by a SNAP Receptor or SNARE complex, consisting of synaptobrevin, syntaxin-1, and SNAP-25.
In 1993, Jim Rothman proposed that the antiparallel pairing of vesicular and transmembrane SNAREs, or...
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Nuclear Fusion02:45

Nuclear Fusion

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The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
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Microtubule Instability02:17

Microtubule Instability

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Microtubules are hollow cylindrical filaments having a diameter of approximately 25 nm and a length that varies from 200 nm to 25 μm. GTP-bound tubulin subunits form αβ-heterodimers for microtubule assembly. These core building blocks interact longitudinally, polymerizing into protofilaments. The protofilaments then interact with one another through lateral bonding forces to form stable cylindrical microtubules. These cylindrical filaments are dynamic as they undergo repeated...
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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関連する実験動画

Updated: Jan 26, 2026

Multilevel Oblique Lumbar Interbody Fusion in Degenerative Lumbar Disc Disease with Instability
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Multilevel Oblique Lumbar Interbody Fusion in Degenerative Lumbar Disc Disease with Instability

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ディープラーニングによる制御融合プラズマにおける破壊的不安定性の予測

Julian Kates-Harbeck1,2,3, Alexey Svyatkovskiy4,5, William Tang6,4

  • 1Department of Physics, Harvard University, Cambridge, MA, USA. jkatesharbeck@g.harvard.edu.

Nature
|April 19, 2019
PubMed
まとめ

核融合炉の故障を 異なるマシンでも正確に予測できる ディープラーニングの新手法です この進歩は将来の核融合発電所の 信頼性の高い運営に不可欠です

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科学分野:

  • 核融合エネルギー
  • プラズマ物理学
  • 機械学習アプリケーション

背景:

  • 磁気隔離トカマック炉は 持続可能でクリーンなエネルギーを 約束しています
  • プラズマの破壊は大きな課題です 発電を停止し部品を損傷します
  • ITERのような大規模なプロジェクトでは 破壊を正確に予測することが重要です

研究 の 目的:

  • トカマック炉の障害を予測するための高度なディープラーニング方法の開発.
  • 既存の第一原則と古典的な機械学習アプローチを改良する.
  • 異なる核融合装置の 破壊予測を確実に行えるように

主な方法:

  • 高次元の実験データで訓練された ディープラーニングのアプローチを使用しました
  • 精度と速度を高めるためのスーパーコンピューティングリソースを活用した.
  • DIII-DとJoint European Torus (JET) のトカマックからのデータでモデルを訓練した.

主要な成果:

  • ディープラーニングは 信頼性の高い 破壊予測能力を示しました
  • 将来の原子炉の重要な要件であるクロスマシン予測を成功させました.
  • 長い警告時間を備えた予測が可能で,アクティブな原子炉制御を容易にする.

結論:

  • ディープラーニングは 核融合エネルギー科学を 発展させるための強力なツールです
  • 開発された方法は,トカマックの障害予測を大幅に改善します.
  • このアプローチは 複雑な物理システムを予測する上で より広い意味を持つ.