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Related Concept Videos

Diffusion01:12

Diffusion

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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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Diffusion01:21

Diffusion

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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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Assessment of Diffusion and Perfusion01:17

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Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Optimal localization of diffusion sources in complex networks.

Zhao-Long Hu1, Xiao Han1, Ying-Cheng Lai2,3

  • 1School of Systems Science, Beijing Normal University, Beijing 100875, People's Republic of China.

Royal Society Open Science
|May 10, 2017
PubMed
Summary
This summary is machine-generated.

Identifying diffusion sources from minimal data is crucial. This study introduces a robust framework using network controllability and compressive sensing for optimal source localization in complex networks.

Keywords:
complex networkscompressive sensingdiffusion sourceslocatabilityoptimal localization

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Area of Science:

  • Network Science
  • Complex Systems
  • Information Theory

Background:

  • Locating diffusion sources from limited data is a significant challenge in network science.
  • A general theoretical framework for optimal source localization is currently lacking.

Purpose of the Study:

  • To develop an efficient and robust framework for optimal source localization in arbitrary weighted networks.
  • To combine network controllability theory with compressive sensing for source identification.

Main Methods:

  • Utilizing controllability theory for complex networks.
  • Applying compressive sensing techniques for sparse signal reconstruction.
  • Performing minimum output analysis to determine essential messenger nodes for source locatability.

Main Results:

  • Developed a general framework for optimal source localization in complex networks.
  • Demonstrated high efficiency and robustness in locating sources.
  • Identified that homogeneous and denser networks facilitate easier source localization.
  • Discovered that a single messenger node can suffice for locating multiple sources in certain network types.

Conclusions:

  • The proposed framework provides a significant advancement in understanding and solving the network source localization problem.
  • Offers efficient computational tools with broad applicability in various domains.
  • Highlights the potential for simplified source detection in specific network configurations.