Evidence map›Paper›PMID 29477335›Full record

ReviewBiophysical journal2018

Out of the Randomness: Correlating Noise in Biological Systems.

Maddalena Collini, Margaux Bouzin, Giuseppe Chirico

Abstract readReview
In one paragraph

Review in Biophysical journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Maddalena ColliniDipartimento di Fisica e Centro di Nanomedicina, Università degli Studi di Milano-Bicocca, Milan, Italy; CNR-ISASI, Center for Complex Systems, Pozzuoli, Italy.
Margaux BouzinDipartimento di Fisica e Centro di Nanomedicina, Università degli Studi di Milano-Bicocca, Milan, Italy.
Giuseppe ChiricoDipartimento di Fisica e Centro di Nanomedicina, Università degli Studi di Milano-Bicocca, Milan, Italy; CNR-ISASI, Center for Complex Systems, Pozzuoli, Italy. Electronic address: giuseppe.chirico@unimib.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study of the dynamics of biological systems requires one to follow relaxation processes in time with micron-size spatial resolution. This need has led to the development of different fluorescence correlation techniques with high spatial resolution and a tremendous (from nanoseconds to seconds) temporal dynamic range. Spatiotemporal information can be obtained even on complex dynamic processes whose time evolution is not forecast by simple Brownian diffusion. Our discussion of the most recent applications of image correlation spectroscopy to the study of anomalous sub- or superdiffusion suggests that this field still requires the development of multidimensional image analyses based on analytical models or numerical simulations. We focus in particular on the framework of spatiotemporal image correlation spectroscopy and examine the critical steps in getting information on anomalous diffusive processes from the correlation maps. We point out how a dual space-time correlative analysis, in both the direct and the Fourier space, can provide quantitative information on superdiffusional processes when these are analyzed through an empirical model based on intermittent active dynamics. We believe that this dual space-time analysis, potentially amenable to mathematical treatment and to the exact fit of experimental data, could be extended to include the rich phenomenology of subdiffusive processes, thereby quantifying relevant parameters for the various motivating biological problems of interest.

Indexed as

DiffusionModels, BiologicalMolecular ImagingSpectrometry, FluorescenceStochastic Processes

Identifiers

PMID29477335
PMCPMC6129560

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.