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

Response Surface Methodology01:16

Response Surface Methodology

191
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
191
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

589
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...
589
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.4K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.7K
Transient and Steady-state Response01:24

Transient and Steady-state Response

221
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
221
Observational Learning01:12

Observational Learning

231
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
231

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

A generative artificial intelligence approach for peptide antibiotic optimization.

Nature machine intelligence·2026
Same author

Remote Sensory-Cognitive Assessment in Children with Autism: Evaluating Feasibility and Performance Outcomes.

Behavioral sciences (Basel, Switzerland)·2026
Same author

PLFest: A New Platform for Accessible, Reproducible, and Open Perceptual Learning Research.

Journal of cognitive enhancement : towards the integration of theory and practice·2026
Same author

The Efficacy and Safety of Minimally Invasive Glaucoma Surgery for Primary Open-Angle Glaucoma: A Systematic Review.

Healthcare (Basel, Switzerland)·2026
Same author

Examining oculomotor behavior in central vision loss with a gaze-contingent display.

bioRxiv : the preprint server for biology·2025
Same author

Effects of Gamification on Performance and Subjective Listening Effort on a Spatial Release From Masking Task.

Journal of speech, language, and hearing research : JSLHR·2025
Same journal

Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans.

medRxiv : the preprint server for health sciences·2026
Same journal

Nocturnal Respiratory Rate and Variability Predict Long-term Mortality in Stable Outpatients with Cardiovascular Disease.

medRxiv : the preprint server for health sciences·2026
Same journal

MOSAIC: Methylation-Oriented Site Analysis and Information Classifier for Robust Epigenomic Classification of Acute Leukemia in Clinical Cohorts with Variable Tumor Purity.

medRxiv : the preprint server for health sciences·2026
Same journal

Risk beliefs, intensive digital information and demand for a new preventative health product in public clinics: Evidence from an experiment in Zimbabwe.

medRxiv : the preprint server for health sciences·2026
Same journal

Development of an automated, imaging-based preoperative screening model for early identification of malnutrition in an abdominal surgery cohort.

medRxiv : the preprint server for health sciences·2026
Same journal

A Pilot Project Leveraging Large Language Models for Automated Screening and Variable Extraction in Observational Studies.

medRxiv : the preprint server for health sciences·2026
查看所有相关文章

相关实验视频

Updated: Jul 27, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.6K

与非参数贝叶斯主动学习对比响应函数估计.

Dom Cp Marticorena, Quinn Wai Wong, Jake Browning

    medRxiv : the preprint server for health sciences
    |June 9, 2023
    PubMed
    概括
    此摘要是机器生成的。

    机器学习通过平衡准确性和效率来提高对比敏感性函数 (CSF) 的估计. 这种新方法,MLCSF,提供了比传统技术更高的准确性,即使数据点较少.

    更多相关视频

    Topographical Estimation of Visual Population Receptive Fields by fMRI
    06:02

    Topographical Estimation of Visual Population Receptive Fields by fMRI

    Published on: February 3, 2015

    9.3K
    A Tactile Automated Passive-Finger Stimulator TAPS
    19:44

    A Tactile Automated Passive-Finger Stimulator TAPS

    Published on: June 3, 2009

    13.8K

    相关实验视频

    Last Updated: Jul 27, 2025

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.6K
    Topographical Estimation of Visual Population Receptive Fields by fMRI
    06:02

    Topographical Estimation of Visual Population Receptive Fields by fMRI

    Published on: February 3, 2015

    9.3K
    A Tactile Automated Passive-Finger Stimulator TAPS
    19:44

    A Tactile Automated Passive-Finger Stimulator TAPS

    Published on: June 3, 2009

    13.8K

    科学领域:

    • 眼科和视觉科学 眼科和视觉科学
    • 医疗保健中的机器学习

    背景情况:

    • 估计对比敏度函数 (CSFs) 对于理解视觉功能至关重要,但往往耗时.
    • 目前的临床方法损害了速度的准确性,或者依赖于有关骨形状的强有力的假设.
    • 在研究和临床环境中需要更准确和更有效的CSF估计方法.

    研究的目的:

    • 开发和评估基于机器学习的对比感应函数 (CSF) 估计器.
    • 评估机器学习CSF (MLCSF) 估计器的准确性和效率.
    • 探索MLCSF在研究和临床应用中的实用性.

    主要方法:

    • 开发了机器学习对比响应函数 (MLCRF) 估计器,量化任务成功概率.
    • 从MLCRF衍生出MLCSF,使得可调节的精度-效率权衡.
    • 使用模拟数据和人类对比反应数据评估MLCSF,采用贝叶斯主动学习来选择刺激.

    主要成果:

    • 使用贝叶斯主动学习的MLCSF实现了比随机刺激选择快近一个数量级的趋同.
    • MLCSF的效率与常规方法 (如快速CSF) 的效率相当,但系统性更高的准确性.
    • 该MLCSF方法允许可调节的精度效率,优于传统方法.

    结论:

    • 机器学习分类器提供了一个强大的方法来平衡CSF估计的准确性和效率.
    • 该MLCSF估计器显示了在视觉功能评估中改善研究和临床应用的巨大潜力.
    • 进一步探索MLCSF可调节的精度-效率平衡是有必要的.