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Weak Base Solutions03:21

Weak Base Solutions

25.3K
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...
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Weak Acid Solutions04:02

Weak Acid Solutions

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

Titration of a Weak Acid with a Weak Base

5.0K
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...
5.0K
Titration Calculations: Weak Acid - Strong Base03:55

Titration Calculations: Weak Acid - Strong Base

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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
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Crossed Aldol Reaction Using Weak Bases01:14

Crossed Aldol Reaction Using Weak Bases

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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.
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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Weakly Supervised Biomedical Image Segmentation by Reiterative Learning.

Qiaokang Liang, Yang Nan, Gianmarc Coppola

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    |July 12, 2018
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    Summary
    This summary is machine-generated.

    This study introduces a novel deep learning approach for segmenting gastric cancer images, achieving high accuracy without extensive manual annotation. The overlapped region forecast algorithm improves segmentation quality and data accuracy.

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    Area of Science:

    • Biomedical imaging
    • Artificial intelligence
    • Deep learning

    Background:

    • Accurate biomedical image segmentation is crucial but often requires extensive manual annotation.
    • Deep learning shows promise for segmentation, yet its application to gastric cancer images is novel.
    • Existing methods face challenges with annotation dependency and patch boundary errors.

    Purpose of the Study:

    • To propose a deep learning architecture and algorithm for automatic gastric cancer image segmentation.
    • To develop a reiterative learning framework for training on weakly annotated data, reducing the need for manual annotation.
    • To improve segmentation accuracy and eliminate patch boundary errors in gastric cancer imaging.

    Main Methods:

    • A novel neural network architecture and the "overlapped region forecast" algorithm were developed.
    • A reiterative learning framework was implemented for training on weakly annotated images without pretraining.
    • A customized loss function was used to enhance model convergence and avoid local minima.
    • Iterative training integrated predictions and weak annotations to improve data quality.

    Main Results:

    • The proposed method achieved a mean Intersection over Union (IoU) coefficient of 0.883.
    • A mean accuracy of 91.09% was obtained on a partially labeled dataset.
    • The "overlapped region forecast" algorithm successfully eliminated patch boundary errors.
    • The reiterative learning framework demonstrated superior performance without manual pretraining or further annotation.

    Conclusions:

    • Deep learning, combined with the "overlapped region forecast" algorithm and reiterative learning, offers an effective solution for gastric cancer image segmentation.
    • The approach significantly reduces the reliance on comprehensive manual annotation, making it practical for biomedical applications.
    • This work represents the first application of deep learning to gastric cancer image segmentation and achieved competitive results.