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Recoverable Identifier

arXiv:2605.20103 · detector doi_compliance · incontrovertible · 2026-05-20 03:00:03.241669+00:00

advisory doi_compliance recoverable_identifier

DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.3389/fpsyt.2019.00843/full13) was visible in the surrounding text but could not be confirmed against doi.org as printed.

Paper page Integrity report arXiv Try DOI

Evidence text

Zhang Z, Song Y , Qi H. Age progression/regression by conditional adversarial autoencoder. InProceedings of the IEEE conference on computer vision and pattern recognition 2017; 5810 -5818. 9 Fig. 1. Top: Principal Component Analysis of world cancer risk data. Ethnic and cultural groups exhibit distinctive patterns. Bottom: Component variances and directions of maximal increase in the risk for different cancers. 10 Fig. 2. Top: Face landmarks coming from the DLIB software. Bottom: The selected landmarks, which capture osseous structure and are relatively independent of face expression (green points). 11 Fig. 3. Preliminary results for the face morphometry profile of the Cuban population. Two cohorts are compared, one from men born between 1940 and 1960, and the second from men born between 1961 and 1980. A schematics of face characteristics is shown on top. 12 Role in cancer and AD of genes involved in facial development Gene Alias Role in facial development (Frontiers in Genetics) Role in cancer (Genecards) Role in AD Ref. for AD ACAD9 Acyl-CoA Dehydrogenase Family Member 9 Philtrum Not reported Not reported ALX3 ALX Homeobox 3 Eye width Neuroblastoma Not reported ASPM Assembly Factor For Spindle Microtubules Chin prominence Cancer related Brain Size, AD protection https://www.sciencedirec t.com/science/article/pii/ S2352873719300642 CASC17 Cancer Susceptibility Candidate 17 (Non- Protein Coding) Nose prominence No? Not reported CHD8 Chromodomain Helicase DNA Binding Protein 

Evidence payload

{
  "printed_excerpt": "Zhang Z, Song Y , Qi H. Age progression/regression by conditional adversarial autoencoder. InProceedings of the IEEE conference on computer vision and pattern recognition 2017; 5810 -5818. 9 Fig. 1. Top: Principal Component Analysis of worl",
  "reconstructed_doi": "10.3389/fpsyt.2019.00843/full13",
  "ref_index": 21,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
}