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相关概念视频

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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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,...
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Per-Unit Sequence Models01:26

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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

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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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When an object is in equilibrium, it is either at rest or moving with a constant velocity. There are two types of equilibrium: static and dynamic. Static equilibrium occurs when an object is at rest, while dynamic equilibrium occurs when an object is moving with a constant velocity. In both cases, there must be a balance of forces acting on the object.
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马尔科夫状态模型与加权合奏模拟:如何消除轨迹合并偏差

Samik Bose1,2, Ceren Kilinc1, Alex Dickson1,2

  • 1Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States.

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概括

权重组合 (WE) 算法可以在罕见事件模拟中得到改进. 一种新方法纠正了从WE数据构建的马尔科夫状态模型 (MSM) 中的"合并偏差",提高了长时间过程的准确性.

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科学领域:

  • 计算化学是一种计算化学.
  • 生物物理学的生物物理.
  • 统计力学就是统计力学.

背景情况:

  • 权重组合 (WE) 算法是分子动力学模拟的流行罕见事件方法.
  • WE对于计算动力性质,如蛋白质折叠率和连接体结合率,是有效的.
  • 马尔科夫状态模型 (MSM) 汇总来自多个WE模拟的数据,以提高准确性.

研究的目的:

  • 在与加权集团 (WE) 算法相结合时,识别和解决马尔科夫状态模型 (MSM) 中的偏差.
  • 开发一种方法来纠正这种偏差,特别是当MSM滞后时间 (τ) 超过WE重新采样时间 (τWE) 时.

主要方法:

  • 标识的识别方式
  • 合并偏见 合并偏见
  • 在基于WE的MSM中发生时 τ > τWE.
  • 开发一个合并偏差校正 (MBC) 算法以消除这种偏差.
  • 使用简单的模型系统和复杂的生物分子示例进行验证.

主要成果:

  • 拟议的MBC-MSM算法成功地纠正了合并偏差.
  • 与标准MSM相比,MBC-MSM在较长的滞后时间中显示出显著提高的准确性.
  • 该方法提高了WE模拟的动力速率计算的可靠性.

结论:

  • 合并偏差是从WE数据构建MSM时的一个关键问题,其中 τ > τWE.
  • MBC-MSM提供了一个强大的解决方案,产生更准确的过渡率.
  • 这一进步改善了WE和MSM用于研究复杂分子动态的应用.