Evidence map›Paper›PMID 30253618›Full record

ArticlePhysical review. E2018

Using correlated motions to determine sufficient sampling times for molecular dynamics.

Ryan L Melvin, Jiajie Xiao, Kenneth S Berenhaut, Ryan C Godwin, Freddie R Salsbury

Abstract read
In one paragraph

Article in Physical review. E, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Ryan L MelvinDepartment of Physics and Department of Mathematics and Statistics, Wake Forest University, 1834 Wake Forest Road, Winston-Salem, North Carolina 27109, USA.
Jiajie XiaoDepartment of Physics and Department of Computer Science, Wake Forest University, 1834 Wake Forest Road, Winston-Salem, North Carolina 27109, USA.
Kenneth S BerenhautDepartment of Mathematics and Statistics, Wake Forest University, 1834 Wake Forest Road, Winston-Salem, North Carolina 27109, USA.
Ryan C GodwinDepartment of Physics, Wake Forest University, 1834 Wake Forest Road, Winston-Salem, North Carolina 27109, USA.
Freddie R SalsburyDepartment of Physics, Wake Forest University, 1834 Wake Forest Road, Winston-Salem, North Carolina 27109, USA.

Funding

Tumor Tissue CoreP30CA012197 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Ruben A. Mesa · 1985 to 2026
$55.4M
Structural and Computational Biophysics Training ProgramT32GM095440 · NIGMS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI HOLLIS, THOMAS J, LYLES, DOUGLAS S. · 2011 to 2020
$1.7M
NCI NIH HHS P30 CA012197NIGMS NIH HHS T32 GM095440
6 · The paper itself

Abstract

Here we present a time-dependent correlation method that provides insight into how long a system takes to grow into its equal-time (Pearson) correlation. We also show a usage of an extant time-lagged correlation method that indicates the time for parts of a system to become decorrelated, relative to equal-time correlation. Given a completed simulation (or set of simulations), these tools estimate (i) how long of a simulation of the same system would be sufficient to observe the same correlated motions, (ii) if patterns of observed correlated motions indicate events beyond the timescale of the simulation, and (iii) how long of a simulation is needed to observe these longer timescale events. We view this method as a decision-support tool that will aid researchers in determining necessary sampling times. In principle, this tool is extendable to any multidimensional time series data with a notion of correlated fluctuations; however, here we limit our discussion to data from molecular-dynamics simulations.

Identifiers

PMID30253618
PMCPMC6325644

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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.