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Related Concept Videos

Cyclic Processes And Isolated Systems01:19

Cyclic Processes And Isolated Systems

A thermodynamic system with zero heat exchange and work is an isolated system. For these systems, the internal energy remains constant.
In the case of a non-isolated system, the change in the internal energy is zero only if the process is cyclic. A thermodynamic process is considered cyclic if the system undergoes a series of changes and returns to its initial state. 
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The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...
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Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...

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Framework to study dynamic dependencies in networks of interacting processes.

Daniel Chicharro1, Anders Ledberg

  • 1Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di Tecnologia, Via Bettini 31, 38068 Rovereto (TN), Italy. daniel.chicharro@iit.it

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We introduce a new information-theoretic framework to analyze dynamic dependencies in complex systems. This unified approach offers a more powerful way to understand emergent properties and system dynamics than isolated measures.

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Area of Science:

  • Computational neuroscience
  • Complex systems analysis
  • Information theory

Background:

  • Understanding emergent properties in complex systems requires analyzing dynamic dependencies.
  • Previous methods like bivariate mutual information, Granger causality, and transfer entropy have limitations in multivariate contexts.

Purpose of the Study:

  • To propose a unified, fully multivariate information-theoretic framework for analyzing dynamic dependencies in time-evolving systems.
  • To extend and unify existing bivariate and conditional approaches.

Main Methods:

  • Definition of multi-information measures to capture global, subsystem, and temporal statistical structures.
  • Development of stationary and nonstationary formulations.
  • Examination of decompositions, including transfer entropy, using causal graphs to assess measure specificity and sensitivity.

Main Results:

  • The framework reveals that transfer entropy is sensitive to internal subsystem changes and not solely a connectivity measure.
  • No straightforward relationship exists between connection strength and decomposition terms; causal and noncausal dependencies are not separable.
  • Transfer entropy can exhibit non-monotonic behavior concerning connectivity strength.

Conclusions:

  • The proposed multivariate framework provides a more powerful analysis of emergent properties and system dynamics compared to isolated measures.
  • It offers a nuanced understanding of interdependence, highlighting limitations in interpreting measures like transfer entropy as direct connectivity indicators.