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Related Concept Videos

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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Clearance Models: Compartment Models01:25

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Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Three-Compartment Open Model01:06

Three-Compartment Open Model

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The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
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The bulk modulus is a scientific term used to describe a material's resistance to uniform compression. It is the proportionality constant that links a change in pressure to the resulting relative volume change.
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Updated: Jul 1, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
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CMaize: Simplifying inter-package modularity from the build up.

Zachery Crandall1,2, Theresa L Windus1,2, Ryan M Richard1,2

  • 1Chemical and Biological Sciences, Ames National Laboratory, Ames, Iowa 50011, USA.

The Journal of Chemical Physics
|March 6, 2024
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Summary
This summary is machine-generated.

CMaize simplifies creating robust CMake build systems for chemistry software modules. This tool enhances inter-package modularity by streamlining the build process for C/C++ scientific software development.

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Area of Science:

  • Computational Chemistry
  • Software Engineering

Background:

  • Growing demand for inter-package modularity in chemistry software.
  • Challenges in building and maintaining C/C++ scientific software build systems.
  • Need for binary interoperability across disparate modules.

Purpose of the Study:

  • To simplify the creation of robust CMake-based build systems.
  • To address complexities in inter-package builds for chemistry software.
  • To introduce CMaize as a solution for build system development.

Main Methods:

  • Development of CMaize, a tool to abstract CMake functionalities.
  • Focus on simplifying build system maintenance and robustness.
  • Demonstration of CMaize in production-level software.

Main Results:

  • CMaize significantly simplifies writing CMake build systems.
  • The tool facilitates inter-package modularity and binary artifact interoperability.
  • CMaize has been successfully applied in production environments.

Conclusions:

  • CMaize effectively addresses the challenges of CMake build system development.
  • The tool promotes code reuse and interoperability in chemistry software.
  • CMaize offers a practical solution for scientific software build automation.