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相关概念视频

Methods of Documentation III: PIE01:21

Methods of Documentation III: PIE

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Problem-intervention-evaluation (PIE) is a systematic approach to documentation used in healthcare settings for clinical decision-making and patient care planning. It is a structured approach to organizing patient data based on problems, interventions, and evaluations. Here's a breakdown of its key features and considerations:
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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Peptide Bonds02:43

Peptide Bonds

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A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
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Scanning Electron Microscopy01:07

Scanning Electron Microscopy

4.4K
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
Accelerated...
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PPE Use in Healthcare Settings II: Doffing01:10

PPE Use in Healthcare Settings II: Doffing

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The sequence of removing or doffing PPE starts with the gloves, as they are the most contaminated. Next is removal of the face shield or goggles, as they would interfere with removing other PPE. Then remove the gown, followed by the mask or respirator. Perform hand hygiene between steps if hands become contaminated and immediately after removing all PPE. Generally, the outside front and sleeves of the isolation gown, the goggles or the mask, the respirator, and the face shield are contaminated.
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Methods of Documentation II: POMR01:26

Methods of Documentation II: POMR

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The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
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Updated: Sep 18, 2025

gP2S, an Information Management System for CryoEM Experiments
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gP2S, an Information Management System for CryoEM Experiments

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PDF 实体注释工具 (PEAT) 的使用

Christopher G Stahl1, Kristan J Markey2, Brian C Jewell1

  • 1Oak Ridge National Laboratory, USA.

Journal of open source software
|June 23, 2025
PubMed
概括
此摘要是机器生成的。

研究人员开发了一个用于注释科学PDF的新工具,改进了机器学习模型的训练. 该系统保留了文档结构,并允许自定义方案用于域特定的数据提取,克服了现有方法的局限性.

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科学领域:

  • 计算语言学计算语言学
  • 生物信息学是一种生物信息学.
  • 数据科学是数据科学.

背景情况:

  • 文本挖掘和人工智能 (AI) 正在迅速发展,但为训练机器学习 (ML) 模型创建标记数据集的工具仍然是原始的.
  • 目前用于为科学文献开发标记文本数据的方法没有集成到专家工作流中,并且是劳动密集型的.
  • 从人类可读性优化的PDF等格式中提取信息,由于布局复杂性和结构损失,对机器处理具有挑战性.

研究的目的:

  • 开发一个改进的系统,直接在原始PDF格式中对科学出版物进行注释.
  • 为了使注释能够以机器可读的格式捕获,同时保持PDF固有的结构和来源.
  • 促进创建以领域为中心的方案,以定制相关的主题专家的数据注释.

主要方法:

  • 开发PDF实体注释工具 (PEAT),允许在没有纯文本提取的情况下直接在PDF文档结构上注释.
  • 整合了一个模式创建功能,使用户能够为特定的知识领域定义自定义注释标签.
  • 标注过程旨在保持原始文档的结构完整性和来源.

主要成果:

  • 与依赖于纯文本提取的传统方法相比,开发的系统允许更准确和更有效的注释.
  • 注释以机器可读的格式捕获,改善了ML/NLP模型开发的互操作性.
  • 该系统成功地支持创建特定领域的方案,满足各种科学领域的需求.

结论:

  • PEAT系统通过保留PDF结构并允许自定义模式开发来解决现有注释工具的局限性.
  • 这为科学文献提供了更简单,更准确的数据注释,这对于在研究中推进ML/NLP应用至关重要.
  • 该工具通过提供结构化,域相关和机器可读的注释数据集来增强机器学习模型的开发.