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Record W3030462946 · doi:10.1038/s41467-020-16454-8

Genome and single-cell RNA-sequencing of the earthworm Eisenia andrei identifies cellular mechanisms underlying regeneration

2020· article· en· W3030462946 on OpenAlexaff
Yong Shao, Xiaobo Wang, Jin-Jin Zhang, Mingli Li, Shou-Song Wu, Xiyao Ma, Xue Wang, Huifang Zhao, Yuan Li, Helen He Zhu, David M. Irwin, Depeng Wang, Guojie Zhang, Jue Ruan, Dong‐Dong Wu

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of ChinaChinese Academy of SciencesVillum Fonden
KeywordsEisenia andreiEarthwormBiologyRegeneration (biology)GenomeRNAComputational biologyGeneticsEvolutionary biologyCell biologyGeneEcology

Abstract

fetched live from OpenAlex

The earthworm is particularly fascinating to biologists because of its strong regenerative capacity. However, many aspects of its regeneration in nature remain elusive. Here we report chromosome-level genome, large-scale transcriptome and single-cell RNA-sequencing data during earthworm (Eisenia andrei) regeneration. We observe expansion of LINE2 transposable elements and gene families functionally related to regeneration (for example, EGFR, epidermal growth factor receptor) particularly for genes exhibiting differential expression during earthworm regeneration. Temporal gene expression trajectories identify transcriptional regulatory factors that are potentially crucial for initiating cell proliferation and differentiation during regeneration. Furthermore, early growth response genes related to regeneration are transcriptionally activated in both the earthworm and planarian. Meanwhile, single-cell RNA-sequencing provides insight into the regenerative process at a cellular level and finds that the largest proportion of cells present during regeneration are stem cells.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.223
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations99
Published2020
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicInvertebrate Taxonomy and EcologyFrench-language works237,207