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两种算法的高通量和多参数定量<em>果蝇</em>神经肌肉接头形态

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신경과학

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JoVE 신문 신경과학

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

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两种算法的高通量和多参数定量<em>果蝇</em>神经肌肉接头形态

DOI:

10.3791/55395-v

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12:29 min

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May 03, 2017

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Anna Castells-Nobau*¹, Bonnie Nijhof*¹, Ilse Eidhof, Louis Wolf, Jolanda M. Scheffer-de Gooyert, Ignacio Monedero⁴, Laura Torroja, Jeroen A.W.M. van der Laak⁵, Annette Schenck*¹

¹Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour,Radboud University Medical Center, ²Microscopical Imaging Centre (MIC),Radboud University Medical Center, ³Department of Biology,Universidad Autónoma de Madrid, ⁴Department of Clinical and Experimental Medicine,Linköping University, ⁵Department of Pathology,Radboud University Medical Center

Chapters

00:05Title
01:03Creating NMJ Images with Defined Features
04:50Macro Settings: Rolling Ball Radius Value and Auto-threshold
07:26Macro Settings: Maxima Noise Tolerance Value, and Brp-puncta Thresholds
10:16Results: Validating the Marco by Recapitulating Known NMJ Defects
11:38Conclusion

Summary

자동 번역

两个图像分析算法，“ 果蝇 NMJ形态特征”和“ 果蝇 NMJ布顿形态特征” 被创建 ，以自动量化果蝇神经肌肉接头（NMJ）九个形态特征。

Tags

Drosophila Neuromuscular Junction Morphology Quantification High-throughput Multi-parametric Image Analysis Macros DLG1 BRP ROI Automated Analysis

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