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相关概念视频

Weak Base Solutions03:21

Weak Base Solutions

24.8K
Some compounds produce hydroxide ions when dissolved by chemically reacting with water molecules. In all cases, these compounds react only partially and so are classified as weak bases. These types of compounds are also abundant in nature and important commodities in various technologies. For example, global production of the weak base ammonia is typically well over 100 metric tons annually, being widely used as an agricultural fertilizer, a raw material for chemical synthesis of other...
24.8K
Titration of a Weak Acid with a Weak Base01:08

Titration of a Weak Acid with a Weak Base

4.8K
Weak acids and bases do not undergo dissociation completely, and titrations between these two are rarely studied. When such studies are performed, say, for the titration of a weak acid with a weak base, the titration curve plots the change in pH as a function of the volume of base added. Take the titration of acetic acid with ammonia, for instance. During the titration, these two species form ammonium acetate and water, but the pH change is slow and gradual.
As a result, there is no simple...
4.8K
Titration Calculations: Weak Acid - Strong Base03:55

Titration Calculations: Weak Acid - Strong Base

49.1K
Calculating pH for Titration Solutions: Weak Acid/Strong Base
For the titration of 25.00 mL of 0.100 M CH3CO2H with 0.100 M NaOH, the reaction can be represented as:
49.1K
Weak Acid Solutions04:02

Weak Acid Solutions

42.3K
Few compounds act as strong acids. A far greater number of compounds behave as weak acids and only partially react with water, leaving a large majority of dissolved molecules in their original form and generating a relatively small amount of hydronium ions. Weak acids are commonly encountered in nature, being the substances partly responsible for the tangy taste of citrus fruits, the stinging sensation of insect bites, and the unpleasant smells associated with body odor. A familiar example of a...
42.3K
Titration of a Weak Acid with a Strong Base01:30

Titration of a Weak Acid with a Strong Base

4.3K
In titrating a weak acid with a strong base, different calculation methods are applied at various stages. Initially, the pH of a weak acid like acetic acid is calculated using its dissociation constant (Ka) and an ICE table. Upon addition of a strong base such as sodium hydroxide, a buffer forms, and its pH is determined using the Henderson-Hasselbalch equation. As more base is added and the titration reaches the halfway point, the pH becomes equal to the pKa of the acid, indicating equal...
4.3K
Titration of a Weak Base with a Strong Acid01:20

Titration of a Weak Base with a Strong Acid

8.6K
The titration curve of a weak base like ammonia with a strong acid like hydrochloric acid is the mirror image of the titration curve of a weak acid with a strong base.
Using the ICE table and substituting the Kb value, we calculate the initial pH of 50 mL of 0.1 M ammonia to be 11.11. Addition of 25 mL of 0.1 M hydrochloric acid to this solution of ammonia results in a buffer with an equal concentration of ammonia and ammonium ions. The pH of this buffer can be calculated by substituting these...
8.6K

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相关实验视频

Updated: Jan 21, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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通过自我监督的学习和基于注意力的伪标签,弱监督的结直肠腺细分通过自我监督的学习和基于注意力的伪标签.

Huer Wen1, Yan Wu2, DeShuang Huang3

  • 1School of Computer Science and Technology, Tongji University, Shanghai, 200092, China.

Scientific reports
|January 19, 2026
PubMed
概括

这项研究引入了一种新的方法,用于使用图像级标签对结直肠癌腺体进行细分,克服了对详细像素注释的需求. 这种方法实现了卓越的准确性,有助于更好地诊断癌症.

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科学领域:

  • 计算病理学计算病理学
  • 医疗图像分析 医学图像分析
  • 人工智能在瘤学中的应用

背景情况:

  • 在结直肠癌组织病理学中,精确的腺体细分对于诊断至关重要.
  • 有限的像素级注释阻碍了强大的细分模型的开发.
  • 弱监督的学习为数据稀缺提供了一个潜在的解决方案.

研究的目的:

  • 开发一种高精度的腺体细分方法,用于结直肠癌组织病理学.
  • 利用图像级标签来克服像素级注释的稀缺性.
  • 提出一个新的三阶段框架,结合自主监督学习,基于注意力的伪标签和边界意识损失.

主要方法:

  • 微调DINOv2视觉转换器,使用对未标记的基因病理图像进行自我监督学习.
  • 通过在图像级数据上训练的分类网络的注意力地图生成伪标签.
  • 使用混合,值和条件随机场 (CRF) 后处理精制伪标签.
  • 用精致的伪标签和边界感知损失函数训练一个细分网络.

主要成果:

  • 微调的编码器和后处理步骤显著改善了伪标签的生成.
  • 边界感知损失函数增强了细分精度.
  • 拟议的方法在Glass数据集上胜过了最先进的方法,实现了更高的F1分数和对象子,以及更低的对象豪斯多夫距离.
  • 与完全监督和弱监督方法相比,表现优越.

结论:

  • 开发的方法有效地解决了 histopathology 中有限的像素级注释的挑战.
  • 利用易于获得的图像级数据为改善结直肠癌诊断提供了一个有希望的解决方案.
  • 该框架显示了将其推广到其他组织病理学图像分析任务的潜力.