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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Scanning Electron Microscopy01:07

Scanning Electron Microscopy

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A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
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相关实验视频

Updated: Jun 28, 2025

Author Spotlight: Introducing the Tile/SED/Array Interface for Rapid Field of View Positioning in Tissue Imaging
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Author Spotlight: Introducing the Tile/SED/Array Interface for Rapid Field of View Positioning in Tissue Imaging

Published on: September 15, 2023

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模拟实验描述标记语言 (SED-ML):对于1级版本5的语言规范.

Lucian P Smith1, Frank T Bergmann2, Alan Garny3

  • 17284 University of Washington , Seattle, USA.

Journal of integrative bioinformatics
|April 13, 2024
PubMed
概括
此摘要是机器生成的。

模拟实验描述标记语言 (SED-ML) 标准已更新到第1级版本5,增强了计算生物学研究. 这次更新通过扩展本体论的使用,改善了模拟实验的注释,共享和可重现性.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

背景情况:

  • 现代生物学研究严重依赖于计算机模拟.
  • 复制性和数据共享对于协作科学努力至关重要.
  • 关于模拟实验的最低信息 (MIASE) 为共享模拟数据提供了指导方针.

研究的目的:

  • 介绍并详细介绍模拟实验描述标记语言 (SED-ML) 一级5.5版本的进展.
  • 突出SED-ML如何促进计算模拟实验的注释,存档,共享和复制.
  • 以展示SED-ML在定义模拟参数和使用本体论的输出方面的扩展功能.

主要方法:

  • 该研究描述了SED-ML标准,这是一个基于MIASE指南的计算机可读格式.
  • 它侧重于在1级5版本中引入的增强功能,特别是与动力模拟算法本体学 (KiSAO) 的集成.
  • 文本概述了 KiSAO 的扩展使用,用于定义模拟任务,模型修改,数据范围和输出.

主要成果:

  • 在SED-ML Level 1 Version 5中,可以提供更全面的模拟描述.
  • 与KiSAO的集成使用户能够使用标准化的本体学来定义模拟组件.
  • 这个版本扩展了SED-ML在各种建模方法和模拟工具中的可用性.

结论:

  • SED-ML Level 1 Version 5显著提高了计算模拟实验的标准化和互操作性.
  • 增强的能力支持生物研究中的协作和可重复性.
  • 在日益增长的生物信息资源生态系统中,SED-ML是一个至关重要的工具.