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相关概念视频

Classification of Neurotransmitters01:30

Classification of Neurotransmitters

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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
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Neuron Structure01:31

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Nervous Tissue: Neuron Types01:19

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Neurons, the fundamental units of the nervous system, can be classified based on both their structural and functional characteristics.
Structurally, neurons are categorized into three main types: multipolar, bipolar, and unipolar (or pseudounipolar). Multipolar neurons, which are the most common type in the brain and spinal cord, as well as all motor neurons, possess multiple dendrites and a single axon.
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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相关实验视频

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Cross-Modal Multivariate Pattern Analysis
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核子子类型的分类使用跨模式学习.

Lucas W Remedios1, Shunxing Bao2, Samuel W Remedios3,4

  • 1Vanderbilt University, Department of Computer Science, Nashville, USA.

Proceedings of SPIE--the International Society for Optical Engineering
|September 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了跨模式学习,以在虚拟的血素和素 (H&E) 染料中识别更多细胞类型,推进数字病理学注释,以更好地理解生理学.

关键词:
美国H&E公司在MxIFIF中使用MxIF.标注注释 标注注释这是分类分类的分类.核的分类核的分类.风格转移 风格转移 风格转移在虚拟的H&E中.整个幻灯片成像的成像.

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相关实验视频

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科学领域:

  • 数字病理学数字病理学
  • 计算生物学是一种计算生物学.
  • 组织学成像分析分析分析.

背景情况:

  • 细胞通信和空间关系对人类生理学至关重要.
  • 血素和欧 (H&E) 染色是临床和研究环境中常见的方法.
  • 目前的AI模型,如结肠核识别和分类 (CoNIC) 挑战,只能在H&E染色上标记有限数量的细胞类型.

研究的目的:

  • 开发一种新的方法,在虚拟的H&E图像上标记以前无法标记的细胞类型.
  • 通过将多重复合免疫光 (MxIF) 数据与H&E.数据相结合,利用跨模式学习.
  • 在数字病理学中增强细胞类型分类的细粒度.

主要方法:

  • 利用多重免疫光学 (MxIF) 组织学成像来识别14种不同的细胞子类.
  • 采用风格转移技术,从MxIF数据生成虚拟的H&E图像.
  • 将详细的细胞标签从MxIF转移到合成的虚拟H&E图像中进行分析.

主要成果:

  • 在虚拟的H&E图像上成功识别了辅助T细胞和原始细胞核.
  • 获得了0.34 ± 0.15的辅助T细胞和0.47 ± 0.1的祖先细胞的积极预测值.
  • 通过使用跨模式学习,证明了将高密度标签从MxIF转移到虚拟H&E的可行性.

结论:

  • 跨模式学习可以在虚拟的H&E图像上对更广泛的细胞类型进行注释.
  • 这种方法显著扩大了AI在自动化数字病理学的细胞分类方面的潜力.
  • 这些发现代表了在细胞病理学中详细的细胞分析的有希望的进步.