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Full L1-regularized Traction Force Microscopy over whole cells

Overview of attention for article published in BMC Bioinformatics, August 2017
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Title
Full L1-regularized Traction Force Microscopy over whole cells
Published in
BMC Bioinformatics, August 2017
DOI 10.1186/s12859-017-1771-0
Pubmed ID
Authors

Alejandro Suñé-Auñón, Alvaro Jorge-Peñas, Rocío Aguilar-Cuenca, Miguel Vicente-Manzanares, Hans Van Oosterwyck, Arrate Muñoz-Barrutia

Abstract

Traction Force Microscopy (TFM) is a widespread technique to estimate the tractions that cells exert on the surrounding substrate. To recover the tractions, it is necessary to solve an inverse problem, which is ill-posed and needs regularization to make the solution stable. The typical regularization scheme is given by the minimization of a cost functional, which is divided in two terms: the error present in the data or data fidelity term; and the regularization or penalty term. The classical approach is to use zero-order Tikhonov or L2-regularization, which uses the L2-norm for both terms in the cost function. Recently, some studies have demonstrated an improved performance using L1-regularization (L1-norm in the penalty term) related to an increase in the spatial resolution and sensitivity of the recovered traction field. In this manuscript, we present a comparison between the previous two regularization schemes (relying in the L2-norm for the data fidelity term) and the full L1-regularization (using the L1-norm for both terms in the cost function) for synthetic and real data. Our results reveal that L1-regularizations give an improved spatial resolution (more important for full L1-regularization) and a reduction in the background noise with respect to the classical zero-order Tikhonov regularization. In addition, we present an approximation, which makes feasible the recovery of cellular tractions over whole cells on typical full-size microscope images when working in the spatial domain. The proposed full L1-regularization improves the sensitivity to recover small stress footprints. Moreover, the proposed method has been validated to work on full-field microscopy images of real cells, what certainly demonstrates it is a promising tool for biological applications.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Researcher 4 13%
Student > Master 4 13%
Student > Doctoral Student 3 10%
Student > Bachelor 2 7%
Other 3 10%
Unknown 7 23%
Readers by discipline Count As %
Engineering 11 37%
Biochemistry, Genetics and Molecular Biology 3 10%
Agricultural and Biological Sciences 3 10%
Physics and Astronomy 1 3%
Nursing and Health Professions 1 3%
Other 4 13%
Unknown 7 23%

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 10 August 2017.
All research outputs
#10,285,891
of 11,595,461 outputs
Outputs from BMC Bioinformatics
#3,929
of 4,273 outputs
Outputs of similar age
#224,079
of 265,312 outputs
Outputs of similar age from BMC Bioinformatics
#79
of 87 outputs
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We're also able to compare this research output to 87 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.