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関連する概念動画

Cell Size01:22

Cell Size

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Cell sizes vary widely among and within organisms. Bacterial cells range between 1-10 micrometers (μm)and are considerably smaller than most eukaryotic cells. The smallest bacteria are 0.1 μm in diameter—about a thousand times smaller than eukaryotic cells, which typically range from 10-100 μm.
Surface Area
Cells can take in nutrients and water via diffusion through the plasma membrane itself or through specific channels in the membrane. The area of the membrane surrounding...
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Measurement: Standard Units03:38

Measurement: Standard Units

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Every measurement provides three kinds of information: the size or magnitude of the measurement (a number), a standard of comparison for the measurement (a unit), and an indication of the uncertainty of the measurement. While the number and unit are explicitly represented when a quantity is written, the uncertainty is an aspect of the errors in the measurement results.
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Dimensional Analysis03:40

Dimensional Analysis

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Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
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Units and Standards of Measurement01:10

Units and Standards of Measurement

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A physical quantity is defined either by specifying its measurement method or by stating how it is calculated from other measurements. For example, consider a metallic cube. We might define its mass and dimensions by specifying methods for measuring them, such as using a weighing machine and a meter scale. Then, we could define the volume by stating that it is the cube of its side, and we could calculate the density as the mass divided by the volume.
Measurements of physical quantities are...
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Dimensional Analysis02:19

Dimensional Analysis

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The concept of dimension is important because every mathematical equation linking physical quantities must be dimensionally consistent, implying that mathematical equations must meet the following two rules. The first rule is that, in an equation, the expressions on each side of the equal sign must have the same dimensions. This is fairly intuitive since we can only add or subtract quantities of the same type (dimension). The second rule states that, in an equation, the arguments of any of the...
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Dimensional Analysis01:23

Dimensional Analysis

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Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
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3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
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大きさの問題:人間のコネクトーム

Markus Axer1,2, Katrin Amunts1,3

  • 1Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.

Science (New York, N.Y.)
|November 15, 2022
PubMed
まとめ
この要約は機械生成です。

脳の繋がりを理解することは 脳の機能の鍵です 新しい神経イメージングと顕微鏡の手法と 機械学習を組み合わせて 全脳トラクトグラフィを進めて 詳細な脳アトラスを作成しています

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関連する実験動画

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科学分野:

  • 神経科学
  • コンピュータ生物学
  • 医療用イメージング

背景:

  • 神経の相互接続を理解することは 脳の機能や機能不全に 極めて重要です
  • 拡散磁気共鳴画像 (dMRI) とトラクトグラフィーは,人間の脳の接続性を研究する上で重要な役割を果たしています.
  • 顕微鏡の進歩により 高解像度な軸索とシナプス接続データが得られます

研究 の 目的:

  • 高解像度の地域接続データと全脳経路撮影を統合する新しい方法を開発する.
  • 機械学習とシミュレーションを活用して データの不足を予測する
  • 解像度と精度が向上した将来の相互運用可能な脳アトラスを構想する.

主な方法:

  • 拡散磁気共鳴画像 (dMRI) と トラクトグラフィーを利用する.
  • 高解像度データを用いて 偏光と光と電子顕微鏡を用いて
  • 機械学習とシミュレーションを 予測モデリングに応用する

主要な成果:

  • 空間的解像度と,アクソナル/シナプスレベルに対する感度が向上する.
  • 詳細な接続データでトラクトグラフィを制限する方法の必要性を特定しました.
  • データのギャップを埋めるための機械学習の可能性を強調した.

結論:

  • マルチスケールの接続データを統合することは 機械的な脳の理解に不可欠です
  • 将来の脳アトラスは高解像度のテンプレート,方向性,精度評価を必要とする.
  • 先進的な計算とイメージング技術が コネクトミクス研究に変化をもたらしています