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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

580
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
580
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

708
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
708
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
126
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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相关实验视频

Updated: Sep 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

635

记忆效率深度端到端后台网络 (深度) 逆向问题

Jyothi Rikhab Chand1, Mathews Jacob1

  • 1Department of Electrical and Computer Engineering, University of Iowa, IA, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|August 29, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了一种高效的深度学习方法来重建磁共振图像. 这种方法可以学习后部分布,改善图像恢复和提供不确定性地图.

关键词:
能源模式马普估计平行MRI重建不确定性估计

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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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相关实验视频

Last Updated: Sep 9, 2025

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Published on: December 15, 2023

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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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科学领域:

  • 医学成像
  • 计算神经科学
  • 机器学习

背景情况:

  • 端到端 (E2E) 解卷优化框架对磁共振 (MR) 图像恢复具有前景.
  • 这些确定性方法在训练过程中面临着高内存使用的挑战,并且缺乏后部分布采样能力.

研究的目的:

  • 在MR图像重建中引入后部分布的E2E学习的记忆效率方法.
  • 为了在图像恢复的同时实现不确定性量化.

主要方法:

  • 这是一个新的框架, 结合了数据一致性概率术语和CNN参数化的先前能量模型.
  • 通过最大概率优化对CNN权重进行E2E学习.
  • 从低采样MR数据中进行图像恢复的最大后期优化 (MAP).

主要成果:

  • 拟议的方法实现了与内存密集型E2E解卷算法相匹配的性能.
  • 它在MRI图像重建方面表现优于现有的存储效率高的同行.
  • 该框架成功生成了从后期分布抽样中得出的不确定性图.

结论:

  • 这种具有记忆效率的E2E学习框架有助于MR图像重建.
  • 它为高维 (3D+) 磁共振成像提供了可行的解决方案.
  • 能够采样后部分布提供了有价值的不确定性信息.