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Signal Transmission of Biological Reaction-Diffusion System by Using Synchronization

Overview of attention for article published in Frontiers in Computational Neuroscience, October 2017
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Title
Signal Transmission of Biological Reaction-Diffusion System by Using Synchronization
Published in
Frontiers in Computational Neuroscience, October 2017
DOI 10.3389/fncom.2017.00092
Pubmed ID
Authors

Lingli Zhou, Jianwei Shen

Abstract

Molecular signal transmission in cell is very crucial for information exchange. How to understand its transmission mechanism has attracted many researchers. In this paper, we prove that signal transmission problem between neural tumor molecules and drug molecules can be achieved by synchronous control. To achieve our purpose, we derive the Fokker-Plank equation by using the Langevin equation and theory of random walk, this is a model which can express the concentration change of neural tumor molecules. Second, according to the biological character that vesicles in cell can be combined with cell membrane to release the cargo which plays a role of signal transmission, we preliminarily analyzed the mechanism of tumor-drug molecular interaction. Third, we propose the view of synchronous control which means the process of vesicle docking with their target membrane is a synchronization process, and we can achieve the precise treatment of disease by using synchronous control. We believe this synchronous control mechanism is reasonable and two examples are given to illustrate the correctness of our results obtained in this paper.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 29%
Student > Doctoral Student 1 14%
Student > Ph. D. Student 1 14%
Unknown 3 43%
Readers by discipline Count As %
Engineering 3 43%
Agricultural and Biological Sciences 1 14%
Unknown 3 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 October 2017.
All research outputs
#18,573,839
of 23,005,189 outputs
Outputs from Frontiers in Computational Neuroscience
#1,056
of 1,353 outputs
Outputs of similar age
#248,437
of 324,392 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#26
of 29 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,353 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 324,392 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.