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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

133
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

99
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...
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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

587
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...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

66
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Observational Learning01:12

Observational Learning

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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...
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相关实验视频

Updated: Jul 26, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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隐藏马尔科夫模型参数学习的反应网络方案.

Carsten Wiuf1, Abhishek Behera2, Abhinav Singh3

  • 1Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.

Journal of the Royal Society, Interface
|June 21, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的反应网络,用于学习隐藏的马尔科夫模型参数,灵感来自生物的徒劳循环. 这种以人工细胞为灵感的系统展示了融合和准确的参数学习,反映了已建立的Baum-Welch算法.

关键词:
姆·韦尔奇算法 姆·韦尔奇算法隐藏的马尔科夫模型分子编程的分子编程.统计学学习的统计学学习合成生物学 合成生物学

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 人工智能的人工智能

背景情况:

  • 隐藏的马尔科夫模型 (HMM) 对于序列分析至关重要.
  • 像姆-韦尔赫 (BW) 算法这样的现有参数学习方法是计算密集的.
  • 开发生物启发的计算系统是一个日益增长的研究领域.

研究的目的:

  • 提出一种新的反应网络方案,用于学习HMM参数.
  • 为机器学习任务建立一个仿生方法.
  • 探索人工细胞和分子多剂体系统中的应用.

主要方法:

  • 设计了一个新的反应网络 (姆-韦尔赫反应网络),其中物种代表变量.
  • 反应涉及单个分子的转化,可逆的途径使用不同的酶.
  • 数学证明确定了网络的固定点和BW算法的固定点之间的等价性.

主要成果:

  • 反应网络方案被证明是数学上相当于HMMs的Baum-Welch算法.
  • 网络的"预期"和"最大化"步骤都以指数级的速度趋同.
  • 模拟证实,反应网络准确地学习HMM参数,并持续增加日志概率.

结论:

  • 拟议的姆-韦尔赫反应网络为学习HMM参数提供了一个可行的仿生替代方案.
  • 这项工作弥合了分子系统和机器学习的桥梁,为人工细胞和联合学习提供了潜力.
  • 反应网络展示了高效和准确的学习,反映了已建立的计算方法.