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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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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
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Sequence Networks of Rotating Machines01:24

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Normal Stress01:19

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Normal stress is a type of stress that occurs when forces act perpendicular, or normal, to a material's cross-sectional area. This stress often arises in structures when subjected to axial loading, which is the application of force along the axis of an object. A practical example of this can be found in bridge truss members.
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Related Experiment Video

Updated: Jan 21, 2026

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

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Information Transmission in Dynamical Networks: The Normal Network Case.

Giacomo Baggio1, Virginia Rutten2, Guillaume Hennequin3

  • 1Department of Mechanical Engineering, University of California at Riverside, Riverside, CA, USA.

Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control
|July 23, 2019
PubMed
Summary

This study introduces a new model for information transmission in linear networks, considering inter-symbol interference. Network structure, particularly for normal adjacency matrices, impacts the maximum information rate, with non-normal architectures showing potential benefits.

Keywords:
Linear dynamical networksShannon capacitydigital communicationinformation transmission over networksmatrix non-normalitynormal networks

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Area of Science:

  • Network science
  • Information theory
  • Dynamical systems

Background:

  • Networked systems in physics and biology rely on reliable information processing.
  • Understanding information transmission dynamics in these systems is crucial.

Purpose of the Study:

  • To propose a novel framework for modeling information transmission in linear dynamical networks.
  • To quantify information transmission using Shannon information rate.
  • To investigate the influence of network connectivity on information transmission.

Main Methods:

  • Modeling information propagation using a digital communication protocol incorporating inter-symbol interference.
  • Applying Shannon information rate to measure reliable information transfer over a fixed time window.
  • Analyzing the impact of network adjacency matrix properties, specifically normal matrices, on information rate.

Main Results:

  • The maximum achievable information rate in networks with a normal adjacency matrix is determined solely by the spectrum of the adjacency matrix.
  • Numerical results indicate that non-normal network architectures may enhance information transmission within the proposed framework.

Conclusions:

  • Network architecture significantly influences information transmission capacity.
  • Non-normal network structures present a promising avenue for improving information transmission efficiency in dynamical systems.