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Weak Base Solutions

Weak Base Solutions

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...

Titration of a Weak Acid with a Weak Base

Titration of a Weak Acid with a Weak Base

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...

Titration Calculations: Weak Acid - Strong Base

Titration Calculations: Weak Acid - Strong Base

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:

Weak Acid Solutions

Weak Acid Solutions

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...

Titration of a Weak Acid with a Strong Base

Titration of a Weak Acid with a Strong Base

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...

Titration of a Weak Base with a Strong Acid

Titration of a Weak Base with a Strong Acid

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...

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Weakly supervised colorectal gland segmentation through self-supervised learning and attention-based pseudo-labeling.

Huer Wen¹, Yan Wu², DeShuang Huang³

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

Scientific Reports

|January 19, 2026

Summary

This summary is machine-generated.

This study introduces a new method for segmenting colorectal cancer glands using image-level labels, overcoming the need for detailed pixel annotations. The approach achieves superior accuracy, aiding in better cancer diagnosis.

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Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Area of Science:

Computational pathology
Medical image analysis
Artificial intelligence in oncology

Background:

Accurate gland segmentation in colorectal cancer histopathology is essential for diagnosis.
Limited pixel-level annotations hinder the development of robust segmentation models.
Weakly supervised learning offers a potential solution to data scarcity.

Purpose of the Study:

To develop a highly accurate gland segmentation method for colorectal cancer histopathology.
To leverage image-level labels to overcome the scarcity of pixel-level annotations.
To propose a novel three-stage framework combining self-supervised learning, attention-based pseudo-labeling, and boundary-aware loss.

Main Methods:

Fine-tuning the DINOv2 vision transformer using self-supervised learning on unlabeled histopathology images.
Generating pseudo-labels via attention maps from a classification network trained on image-level data.
Refining pseudo-labels using blending, thresholding, and Conditional Random Field (CRF) post-processing.
Training a segmentation network with refined pseudo-labels and a boundary-aware loss function.

Main Results:

The fine-tuned encoder and post-processing steps significantly improved pseudo-label generation.
The boundary-aware loss function enhanced segmentation accuracy.
The proposed method outperformed state-of-the-art approaches on the GlaS dataset, achieving higher F1-score and Object Dice, and lower Object Hausdorff distance.
Demonstrated superior performance compared to both fully supervised and weakly supervised methods.

Conclusions:

The developed method effectively addresses the challenge of limited pixel-level annotations in histopathology.
Utilizing readily available image-level data provides a promising solution for improved colorectal cancer diagnosis.
The framework shows potential for generalization to other histopathology image analysis tasks.