Evidence map›Paper›PMID 37706011›Full record

ArticleFrontiers in neurorobotics2023

Taishi Mikami, Daiki Wakita, Ryo Kobayashi, Akio Ishiguro, Takeshi Kano

Abstract read
In one paragraph

Article in Frontiers in neurorobotics, 2023. 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.

Taishi MikamiResearch Institute of Electrical Communication, Tohoku University, Sendai, Japan.
Daiki WakitaResearch Institute of Electrical Communication, Tohoku University, Sendai, Japan.
Ryo KobayashiProgram of Mathematical and Life Sciences, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashihiroshima, Japan.
Akio IshiguroResearch Institute of Electrical Communication, Tohoku University, Sendai, Japan.
Takeshi KanoResearch Institute of Electrical Communication, Tohoku University, Sendai, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Worms often aggregate through physical connections and exhibit remarkable functions such as efficient migration, survival under environmental changes, and defense against predators. In particular, entangled blobs demonstrate versatile behaviors for their survival; they form spherical blobs and migrate collectively by flexibly changing their shape in response to the environment. In contrast to previous studies on the collective behavior of worm blobs that focused on locomotion in a flat environment, we investigated the mechanisms underlying their adaptive motion in confined environments, focusing on tubificine worm collectives. We first performed several behavioral experiments to observe the aggregation process, collective response to aversive stimuli, the motion of a few worms, and blob motion in confined spaces with and without pegs. We found the blob deformed and passed through a narrow passage using environmental heterogeneities. Based on these behavioral findings, we constructed a simple two-dimensional agent-based model wherein the flexible body of a worm was described as a cross-shaped agent that could deform, rotate, and translate. The simulations demonstrated that the behavioral findings were well-reproduced. Our findings aid in understanding how physical interactions contribute to generating adaptive collective behaviors in real-world environments as well as in designing novel swarm robotic systems consisting of soft agents.

Indexed as

active matteradaptive locomotionentangled blobsoft roboticsswarm

Identifiers

PMID37706011
PMCPMC10495593

What OpenQuestion holds

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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.