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Yeasts are single-celled organisms, but unlike bacteria, they are eukaryotes (cells with a nucleus). Cell signaling in yeast is similar to signaling in other eukaryotic cells. A ligand, such as a protein or a small molecule released from a yeast cell, attaches to a receptor on the cell surface. The binding stimulates second-messenger kinases to activate or inactivate transcription factors that further regulate gene expression. Many of the yeast intracellular signaling cascades have similar...
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BudFinder:一个蒙面的自动编码视觉转换器框架用于酵母芽检测.

Phuc Nguyen1, Zahra Mousavi Karimi2, Adrian Layer1

  • 1Department of Molecular Biology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.

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概括
此摘要是机器生成的。

在衰老研究中量化酵母细胞分裂是具有挑战性的. 这项研究引入了一种使用自主监督学习的新图像分析方法,大大减少了对准确发芽事件检测的数据注释需求.

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科学领域:

  • 衰老的研究研究.
  • 细胞生物学 细胞生物学
  • 图像分析 图像分析

背景情况:

  • 酵母复制寿命的量化对于衰老研究至关重要,但需要大量的劳动和时间.
  • 手动细胞分裂计数是低效的,容易产生偏差.
  • 现有的自动化方法需要大量的注释数据,并且缺乏跨微流体设置的适应性.

研究的目的:

  • 开发一种多功能且准确的图像分析方法,用于检测酵母细胞分裂事件.
  • 为了减少对大型注释数据集的依赖,用于训练自动化划分检测模型.
  • 提高用于酵母寿命研究的自动化工具的适应性.

主要方法:

  • 利用一个面具自动编码器预训练大规模细分酵母细胞图像进行自我监督学习.
  • 开发了一个直接训练在芽事件检测上的变压器模型.
  • 为了模型培训,利用了减少的注释数据 (少于50个母细胞).

主要成果:

  • 实现了对酵母细胞分裂事件的准确检测.
  • 显著减少了对注释训练数据的要求 (与以前的方法相比减少了5倍).
  • 尽管数据减少,但保持了与现有方法可比的准确性.

结论:

  • 提出的自我监督图像分析方法为量化酵母细胞分裂提供了更有效和更适应的解决方案.
  • 这种方法降低了酵母寿命自动化分析的进入障碍,特别是在微流体研究中.
  • 未来的应用可能包括在衰老研究和细胞生物学研究中更广泛地使用,这些研究需要精确的分裂事件跟踪.