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

Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

112
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
112
Light Acquisition02:16

Light Acquisition

8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

68
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...
68
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

88
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
88
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

406
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
406

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

Association between maternal work environment and health-related quality of life during pregnancy using the 8-item short-form health survey (SF-8): the Japan Environment and Children's Study.

European journal of obstetrics, gynecology, and reproductive biology·2026
Same author

Impact of prenatal manganese and selenium exposure on childhood thyroid hormone parameters up to 4 years of age: Japan environment and children's study.

Environment international·2026
Same author

Relationship between pre-pregnancy body mass index and postpartum depression, and the mediation effects of cesarean delivery, low birth weight, child anomaly, and breastfeeding: a prospective birth cohort in the Japan environment and children's study.

BMC pregnancy and childbirth·2026
Same author

A high-resolution historical long-term gridded daily meteorological data set for agricultural climate change analysis in Japan.

Data in brief·2026
Same author

Impact of CD34<sup>+</sup> cell dose on outcomes of related allogeneic PBSCT in adult AML patients.

British journal of haematology·2026
Same author

ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms.

The plant genome·2026

相关实验视频

Updated: Jun 21, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

模拟大豆生长:一种混合模型方法.

Maud Delattre1, Yusuke Toda2, Jessica Tressou2,3

  • 1Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France.

PLoS computational biology
|July 11, 2024
PubMed
概括

这项研究引入了一种新的方法,结合非线性混合效应模型和SAEM算法来分析影响植物生长的遗传和环境因素. 该方法增强了对大豆生长模式的理解,并有助于基因组预测.

科学领域:

  • 植物生物学 植物生物学
  • 遗传学 遗传学 是一个
  • 生物信息学是一种生物信息学.

背景情况:

  • 了解对植物生长的遗传和环境影响对于遗传改进至关重要.
  • 分析生长模式的现有方法在植物生物学中很少被应用.

研究的目的:

  • 扩展非线性混合效应建模 (NLMEM) 和预期最大化算法 (SAEM) 的随机近似,用于分析对植物生长的遗传和环境影响.
  • 使用遗传关系矩阵整合遗传关系.
  • 为马尔科夫链蒙特卡洛 (MCMC) 方法提供一种高效的替代方案.

主要方法:

  • 使用非线性函数来建模生长曲线.
  • 随机效应被纳入,以考虑遗传和环境变异性.
  • 实施SAEM算法用于使用最大概率和最大后期方法进行参数估计.

主要成果:

  • 拟议的NLMEM-SAEM方法使用模拟数据和真实世界大豆生长数据进行了验证.
  • 分析了大约200种大豆品种的每日植物高度测量数据.
  • 该模型成功推断出人口增长模式和个别品种的增长曲线.

结论:

更多相关视频

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves
08:31

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves

Published on: December 2, 2016

10.9K
Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

8.0K

相关实验视频

Last Updated: Jun 21, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves
08:31

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves

Published on: December 2, 2016

10.9K
Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

8.0K
  • 通过NLMEM-SAEM方法,提高对大豆生长决定因素的理解.
  • 这种方法可以有效地用于对生长模式的基因组预测.
  • 这种方法为植物生物学研究和作物改进提供了一个强大的工具.