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Related Concept Videos

Weak Base Solutions03:21

Weak Base Solutions

25.2K
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...
25.2K
Weak Acid Solutions04:02

Weak Acid Solutions

43.1K
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...
43.1K
Titration of a Weak Acid with a Weak Base01:08

Titration of a Weak Acid with a Weak Base

4.9K
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...
4.9K
Titration Calculations: Weak Acid - Strong Base03:55

Titration Calculations: Weak Acid - Strong Base

49.3K
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:
49.3K
Crossed Aldol Reaction Using Weak Bases01:14

Crossed Aldol Reaction Using Weak Bases

2.7K
This lesson deals with the crossed aldol reaction using weak bases. The self-condensation of an aldehyde having α hydrogen is prevented by adding it slowly to a mixture of formaldehyde and weak bases like hydroxide and alkoxide. Upon slow addition of the aldehyde, the base deprotonates the α carbon of the aldehyde to form the corresponding enolate. The enolate subsequently attacks the formaldehyde to form a single crossed product. Figure 1 depicts the aforementioned reaction.
2.7K
Titration of a Weak Base with a Strong Acid01:20

Titration of a Weak Base with a Strong Acid

9.0K
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...
9.0K

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Author Spotlight: Understanding Age-Related Macular Degeneration Pathophysiology with QAF Workflow
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Weakly Supervised Lesion Detection From Fundus Images.

Renzhen Wang, Benzhi Chen, Deyu Meng

    IEEE Transactions on Medical Imaging
    |December 12, 2018
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    Summary
    This summary is machine-generated.

    This study introduces a weakly supervised method for detecting diverse retinal lesions in fundus images. The approach effectively distinguishes lesions from normal structures without requiring specific annotations, improving computer-aided diagnosis.

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    Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Computer-Aided Detection

    Background:

    • Early diagnosis and monitoring of eye diseases are crucial.
    • Computer-aided detection of retinal lesions has advanced but struggles with diverse lesion types.
    • Automatic detection of unknown retinal lesion types remains challenging.

    Purpose of the Study:

    • To propose a weakly supervised method for automatic detection of diverse retinal lesions.
    • To develop a model that can distinguish lesions from normal structures without specific annotations.

    Main Methods:

    • A weakly supervised approach using normal and abnormal retinal images.
    • Fundus images modeled as background, blood vessels, and noise (lesions).
    • Background modeled as low-rank structure; noise modeled using Gaussian distributions.

    Main Results:

    • The proposed method accurately detects retinal lesions.
    • It outperforms previous related methods in lesion detection.
    • The model effectively distinguishes lesions from background noise.

    Conclusions:

    • The weakly supervised method offers a promising solution for detecting diverse retinal lesions.
    • This technique can aid in early diagnosis and continuous monitoring of eye diseases.
    • The approach advances computer-aided detection in retinal imaging.