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Record W2081991776 · doi:10.1387/ijdb.072552cc

Skin, cornea and stem cells - an interview with Danielle Dhouailly

2009· article· en· W2081991776 on OpenAlexaboutno aff
Cheng‐Ming Chuong

Bibliographic record

VenueThe International Journal of Developmental Biology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsnot available
Fundersnot available
KeywordsBachelorBiologyStem cellDermisArt historyAnatomyCell biologyArtHistory

Abstract

fetched live from OpenAlex

Danielle Dhouailly received her Bachelor of Science degree (Biology) from Paris University. She then worked on a Ph.D. with Philippe Sengel at Grenoble University. After that, she went to Canada and the USA to work with Drs. M. Hardy, R. Sawyer and H. Sun before going back to Grenoble and starting her own laboratory. In the 1970s, she began a series of creative epithelial-mesenchymal recombination experiments among chicken feathers, mouse hairs and lizard scales, and later between rabbit cornea / mouse hairs. Through these original experiments, she elegantly demonstrated that the dermis initiates the formation of cutaneous appendages, while their type is specified by the class and regional origin of the epidermis. Subsequently she showed that the induction of an ectodermal organ, even in an adult epithelium, provokes the appearance of the related tissue stem cells. These works pioneered the concepts which are used in stem cell biology today. Her laboratory now works on the molecular mechanisms underlying these processes. Her papers are typically characterized by an initial insightful observation, followed by rigorous experiments and thoughtful discussions. They are rich with different shades of perspectives, almost like a piece of impressionist art. She loves gardening and her pets. She considers herself a good observer and hard worker driven by curiosity. Her best moments occur when she suddenly becomes enlightened as to an explanation of a basic concept when looking at experimental results or discussing ideas with colleagues. She believes that good results last forever, although interpretations can change. Her advice to young scientists is to be rigorous at the bench, to think hard, and not to be shy to speak up. The following is the story of how this young, female naturalist grew into a well-respected developmental biologist.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.049
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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 teacher head, 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

Citations3
Published2009
Admission routes1
Has abstractyes

Explore more

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