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関連する概念動画

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Hybrid Zones

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Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
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The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
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hSNMF:画像由来空間トランスクリプトミクスのためのハイブリッド空間的に規則化されたNMF.

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    この要約は機械生成です。

    新しいノンネガティブ・マトリックス・ファクタライゼーション (NMF) 方法である空間的NMF (SNMF) とハイブリッド空間的NMF (hSNMF) は,腫瘍組織におけるクラスターのコンパクト性と生物学的一貫性を強化することによって,空間的トランスクリプトミクス分析を改善します.

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    科学分野:

    • コンピュータ生物学 コンピュータ生物学
    • ゲノミクスゲノミクスとは
    • バイオインフォマティックス

    背景:

    • 高解像度の空間トランスクリプトミックスプラットフォームは,複雑で高次元なデータを生成します.
    • 表現学習とクラスタリングのためにこのデータを分析することは,重要な計算上の課題を提示します.

    研究 の 目的:

    • 空間トランスクリプトミクスデータの非負行列因数分解 (NMF) をベンチマークし,拡張する.
    • データの分析を改善するために,新しい空間的に規則化されたNMF変数を導入する.

    主な方法:

    • 素因子ベクトル拡散による局所的な空間的平滑性のための開発された空間的NMF (SNMF).
    • 導入されたハイブリッド空間NMF (hSNMF) は,空間的に規則化されたNMFとライデンクラスタリングを組み合わせた.
    • 調整可能なミキシングパラメータ (alpha) を使用した統合された空間的近接性とトランスクリプトミックの類似性.

    主要な成果:

    • SNMFとhSNMFは,空間密度の改善を示した (CHAOS < 0.004,モランのI > 0.96).
    • より大きなクラスター分離性を達成した (シルエット>0.12,DBI <1.8).
    • 既存の方法と比較して,より高い生物学的一貫性 (CMCと濃縮) を示した.

    結論:

    • SNMFとhSNMFは,高次元空間トランスクリプトミクスのデータを分析するための効果的なソリューションを提供します.
    • これらの方法は,空間的な遺伝子発現パターンの生物学的解釈性を高めます.