Evidence map›Paper›PMID 30826726›Full record

ArticleiScience2019

Olfactory Object Recognition Based on Fine-Scale Stimulus Timing in Drosophila.

Aarti Sehdev, Yunusa G Mohammed, Tilman Triphan, Paul Szyszka

Abstract read
In one paragraph

Article in iScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Coupling of Mouse Olfactory Bulb Projection Neurons to Fluctuating Odor Pulses.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022
    Article
  8. Article
  9. Article
  10. Article
  11. A spiking neural program for sensorimotor control during foraging in flying insects.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  12. 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

4 authors.

Aarti SehdevUniversity of Konstanz, Department of Biology, Neurobiology, Konstanz 78457, Germany.
Yunusa G MohammedUniversity of Konstanz, Department of Biology, Neurobiology, Konstanz 78457, Germany.
Tilman TriphanUniversity of Leipzig, Institute of Biology, Department of Genetics, Leipzig 04103, Germany.
Paul SzyszkaUniversity of Konstanz, Department of Biology, Neurobiology, Konstanz 78457, Germany. Electronic address: paul@szyszkalab.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Odorants of behaviorally relevant objects (e.g., food sources) intermingle with those from other sources. Therefore to determine whether an odor source is good or bad-without actually visiting it-animals first need to segregate the odorants from different sources. To do so, animals could use temporal stimulus cues, because odorants from one source exhibit correlated fluctuations, whereas odorants from different sources are less correlated. However, the behaviorally relevant timescales of temporal stimulus cues for odor source segregation remain unclear. Using behavioral experiments with free-flying flies, we show that (1) odorant onset asynchrony increases flies' attraction to a mixture of two odorants with opposing innate or learned valence and (2) attraction does not increase when the attractive odorant arrives first. These data suggest that flies can use stimulus onset asynchrony for odor source segregation and imply temporally precise neural mechanisms for encoding odors and for segregating them into distinct objects.

Indexed as

Behavioral NeuroscienceBiological SciencesEntomology

Identifiers

PMID30826726
PMCPMC6402261

What OpenQuestion holds

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Registered trials

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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.