Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
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...
38
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

79
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
79
Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

2.1K
The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
2.1K
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

327
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...
327
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

5.1K
The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
5.1K
Clearance Models: Compartment Models01:25

Clearance Models: Compartment Models

45
Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
45

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Peripheral CD4<sup>+</sup> naïve T cell remodeling and MMP1-associated inflammatory signatures in acute gouty arthritis.

Frontiers in immunology·2026
Same author

Two-phase collaborative model compression training for joint pruning and quantization.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

[Effect of <i>TBL1XR1</i> Mutation on Cell Biological Characteristics of Diffuse Large B-Cell Lymphoma].

Zhongguo shi yan xue ye xue za zhi·2025
Same author

MRAS: Master Regulator Analysis of Alternative Splicing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Recombinant Hydrophobic Polypeptide MBAY Loaded Into SPION-Exosome Realizes Sustained-Release to Improve Type 2 Diabetes Mellitus.

Drug design, development and therapy·2025
Same author

The value of intestinal fatty acid binding protein as a biomarker for the diagnosis of necrotizing enterocolitis in preterm infants: a meta-analysis.

BMC pediatrics·2025
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: May 24, 2025

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
14:14

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

11.5K

多目标凸量子化为高效的模型压缩.

Chunxiao Fan, Dan Guo, Ziqi Wang

    IEEE transactions on pattern analysis and machine intelligence
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了多目标凸量化,以实现高效的模型压缩. 这种新的方法优化了网络精度和量化误差,通过可微分函数和动态系数适应克服了训练挑战.

    更多相关视频

    Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain
    05:49

    Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain

    Published on: July 14, 2023

    1.3K
    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    24.5K

    相关实验视频

    Last Updated: May 24, 2025

    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
    14:14

    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

    Published on: April 16, 2017

    11.5K
    Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain
    05:49

    Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain

    Published on: July 14, 2023

    1.3K
    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    24.5K

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 计算机视觉 计算机视觉

    背景情况:

    • 模型压缩对于高效的深度学习部署至关重要.
    • 现有的量子化方法面临着挑战,因为量子化操作的非区分性.
    • 单一目标优化努力平衡精度和量化约束.

    研究的目的:

    • 为高效的模型压缩提出一种新的多目标凸量化方法.
    • 在培训期间解决网络量化中的非可区分性问题.
    • 为了实现高网络精度和低量子化误差之间的平衡.

    主要方法:

    • 模拟网络训练作为一个多目标优化问题.
    • 设计了一个可微分量的量化错误函数,以确保计算凸性.
    • 实施了一个时间序列自蒸培训计划.
    • 引入了动态拉格朗日系数调整以平衡损失.

    主要成果:

    • 成功地将量子化整合到网络培训中,避免了不可差异的反向传播.
    • 通过自蒸实现可控和稳定的性能趋同.
    • 在MNIST,CIFAR-10/100,ImageNet,Penn Treebank和微软COCO等基准指标上表现出色.
    • 超越了现有的模型压缩方法.

    结论:

    • 拟议的多目标凸量化有效地压缩模型,同时保持高性能.
    • 新的培训计划和适应系数使得稳定高效的优化成为可能.
    • 这种方法在深度学习模型压缩中提供了显著的进步.