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

Deconvolution01:20

Deconvolution

162
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
162
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

70
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...
70
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

691
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
691
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.6K
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...
10.6K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
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...
7.4K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

522
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
522

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

Pediatric urethroscope versus flexible choledochoscope for removal of distal protein plugs and calculi in choledochal cyst: A prospective cohort study.

Journal of pediatric surgery·2026
Same author

Application and mechanistic research of novel therapeutic strategies in cisplatin-resistant small cell lung cancer.

Annals of medicine·2026
Same author

Timing of definitive surgery after external biliary drainage for completely perforated congenital choledochal cysts: a single-center retrospective cohort study.

Pediatric surgery international·2026
Same author

Nomogram for predicting anastomotic stricture after choledochal cyst excision in children: retrospective cohort study of 1700 patients.

Pediatric surgery international·2025
Same author

Construction and mechanistic exploration of a ferroptosis related gene based prognostic model for cisplatin resistance in bladder cancer.

Computer methods in biomechanics and biomedical engineering·2025
Same author

Risk factors for anastomotic stenosis after congenital choledochal cyst surgery and efficacy analysis of laparoscopic reoperation.

Journal of pediatric surgery·2025

相关实验视频

Updated: Jul 6, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

588

在采用深度生成模型的ptychography中进行噪声强的潜伏向量重建.

Jacob Seifert, Yifeng Shao, Allard P Mosk

    Optics express
    |January 4, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的计算成像方法,使用自动编码器进行图形重建. 它可以从杂的数据中进行强大的对象检索,并可视化优化景观以更好地理解.

    更多相关视频

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.4K
    Generating Strictly Controlled Stimuli for Figure Recognition Experiments
    05:39

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments

    Published on: March 18, 2019

    5.2K

    相关实验视频

    Last Updated: Jul 6, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    588
    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.4K
    Generating Strictly Controlled Stimuli for Figure Recognition Experiments
    05:39

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments

    Published on: March 18, 2019

    5.2K

    科学领域:

    • 计算机成像成像技术
    • 科学成像中的深度学习.

    背景情况:

    • 计算成像在科学学科中至关重要.
    • 深度生成模型可以在低维的潜空间中表示复杂的对象.
    • 传统的方法与稀疏或不良的成像问题作斗争.

    研究的目的:

    • 开发一种使用深度生成模型的新型图形图像重建方法.
    • 为了利用自动编码器在减少的潜空间中进行高效的对象搜索.
    • 为了提高噪声强度,并使重建过程的可视化.

    主要方法:

    • 将预先训练的自动编码器的深度生成模型集成到自动差异化图形 (ADP) 框架中.
    • 利用自动编码器的潜空间进行对象解决方案搜索.
    • 应用该方法从错位的衍射模式重建物体.

    主要成果:

    • 成功地从高度不合适的衍射模式中检索出物体.
    • 在图解学中证明了对噪声稳定的潜伏向量重建.
    • 启用了优化场景的可视化,提供了对反向问题融合的见解.

    结论:

    • 拟议的方法为图形图像重建提供了一个强大的新工具.
    • 这种方法提高了噪声的稳定性,并为优化过程提供了有价值的见解.
    • 促进了稀疏计算成像的新应用,特别是在低辐射或速度至关重要的地方.