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pV-Diagrams01:18

pV-Diagrams

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The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
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Multicompartment Models: Overview01:14

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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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Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

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The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
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Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

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The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Signal Flow Graphs01:18

Signal Flow Graphs

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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
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Related Experiment Video

Updated: Dec 28, 2025

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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Inference of compressed Potts graphical models.

Francesca Rizzato1, Alice Coucke1, Eleonora de Leonardis1

  • 1Laboratoire de Physique de l'Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France.

Physical Review. E
|February 20, 2020
PubMed
Summary

This study introduces a double regularization method for inferring graphical Potts models. Color compression reduces parameters and computation time without sacrificing reconstruction accuracy for high-frequency states.

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Area of Science:

  • Statistical physics
  • Computational biology
  • Machine learning

Background:

  • Inferring graphical Potts models is complex due to a large parameter space.
  • Regularization is essential for accurate parameter inference when data is limited.

Purpose of the Study:

  • To develop and evaluate a novel double regularization scheme for the inverse Potts problem.
  • To improve the efficiency and accuracy of graphical Potts model inference.

Main Methods:

  • Implemented a "color compression" strategy to reduce Potts states based on empirical frequency.
  • Applied adaptive cluster expansion (ACE) and pseudolikelihood maximization (PLM) for parameter inference.
  • Benchmarked performance on synthetic data from disordered Potts models on Erdős-Rényi graphs.

Main Results:

  • Color compression significantly reduces the number of parameters and computational time.
  • Reconstruction quality for high-frequency states remains high after color compression.
  • The method demonstrates effectiveness on protein family multisequence alignments.

Conclusions:

  • The proposed double regularization scheme offers an efficient approach to graphical Potts model inference.
  • Color compression is a viable technique for handling large state spaces in Potts models.
  • This method has practical applications in biological sequence analysis.