Evidence map›Paper›PMID 33313546›Full record

ArticlePlant phenomics (Washington, D.C.)2020

A Statistical Growth Property of Plant Root Architectures.

Sam Sultan, Joseph Snider, Adam Conn, Mao Li, Christopher N Topp, Saket Navlakha

Open access · goldAbstract read
In one paragraph

Article in Plant phenomics (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 21% of its field
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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. 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

6 authors at 3 institutions in 1 country.

Sam SultanCold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY, USA.
Joseph SniderUniversity of California San Diego, Institute for Neural Computation, La Jolla, CA, USA.ORCID https://orcid.org/0000-0002-0176-5107
Adam ConnCold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY, USA.
Mao LiDonald Danforth Plant Science Center, St. Louis, MO, USA.
Christopher N ToppDonald Danforth Plant Science Center, St. Louis, MO, USA.ORCID https://orcid.org/0000-0001-9228-6752
Saket NavlakhaCold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY, USA.ORCID https://orcid.org/0000-0002-5505-9718
Cold Spring Harbor Laboratory · USDonald Danforth Plant Science Center · USUniversity of California San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Numerous types of biological branching networks, with varying shapes and sizes, are used to acquire and distribute resources. Here, we show that plant root and shoot architectures share a fundamental design property. We studied the spatial density function of plant architectures, which specifies the probability of finding a branch at each location in the 3-dimensional volume occupied by the plant. We analyzed 1645 root architectures from four species and discovered that the spatial density functions of all architectures are population-similar. This means that despite their apparent visual diversity, all of the roots studied share the same basic shape, aside from stretching and compression along orthogonal directions. Moreover, the spatial density of all architectures can be described as variations on a single underlying function: a Gaussian density truncated at a boundary of roughly three standard deviations. Thus, the root density of any architecture requires only four parameters to specify: the total mass of the architecture and the standard deviations of the Gaussian in the three (

Identifiers

PMID33313546
PMCPMC7706341
OpenAlexW3101610118

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.