dataesr/scientific-paragraphs-categorization huggingface dataset:
Integration with previous literature knowledge The two previous single-trait GWASs on qualitative ear morphology  [1, 2]  identified 58 autosomal SNPs at 50 loci mainly associated with earlobe features. We looked up these 58 SNPs in three of our cohorts (RS, TwinsUK, and NSPT) not considering CANDELA and TZL because these two cohorts were used in the previous GWASs. For 49 SNPs (nine SNPs were non-polymorphic in at least one dataset and thus got excluded from this analysis), both C-GWAS (P = 1. 1e-8) and MinGWAS (P = 2e-5) p-values highly significantly deviated from the null, while C-GWAS p-values obviously deviated further from the null than MinGWAS p-values did (Fig  1G ). Under the nominal significance level, C-GWAS re-identified a larger number of the previously established loci than MinGWAS did  (16 vs.
Functional annotations Several lines of evidence support the functional implications of the 16 discovered ear-associated loci, including 8 novel loci. A gene ontology enrichment analysis highlighted 6 ear development-related biological process terms (FDR < 0. 01, Tables  1  and S8 ) including ear development, inner ear development, development and morphogenesis of the skeletal system, and embryonic organ development and morphogenesis. The majority of the lead SNPs (10/16) showed evidence of regulatory activity in the 3DSNP database  [33]  (S9 Table ). Four out of the 16 loci showed positive enhancer activity in transgenic mice supported by the spatial pattern of expression located in ears/branchial arch/craniofacial  [34]. The majority (14/16) of nearby genes or 3D interaction genes has been associated with abnormal ear/craniofacial phenotypes in various databases  [35].
TwinsUK Study The TwinsUK Study (TwinsUK) included 1,153 phenotyped participants (all female and all of European ancestry) within the TwinsUK adult twin registry based at St. Thomas' Hospital in London. Volunteers gave written informed consent under a protocol reviewed by the St. Thomas' Hospital Local Research Ethics Committee. Participants were photographed using a Premier 3dMD face3-plus UHD camera (3dMD, Atlanta, Georgia, USA), and 3D avatars were rendered from two 2D photos taken from the left and right directions. Ears in the 2D photos were manually segmented and chopped for subsequent phenotyping. Genotyping of the TwinsUK cohort was done with a combination of Illumina HumanHap300 and Human-Hap610Q chips. Intensity data for each of the arrays were pooled separately and genotypes were called with the Illuminus32 calling algorithm, thresholding on a maximum posterior probability of 0.
95 as previously described  [50]. Imputation was performed using the IMPUTE 2. 0 software package using haplotype information from the 1000-Genomes Project (Phase 1, integrated variant set across 1,092 individuals, v2, March 2012). After all phenotype and genotype quality controls, the current study included a total of 9. 35 million autosomal SNPs (MAF > 0. 01, imputation R2 > 0. 3, SNP call rate > 0. 97, HWE > 1e-6) and 1,065 individuals of TwinsUK.
SNPs with more than 2% missing data, with a minor allele frequency smaller than 1%, and the ones that failed the Hardy-Weinberg deviation test (P < 1e-5) were also excluded. After applying these filters, we obtained a dataset of 2,600 samples with 776,213 SNPs. The chip genotype data were firstly phased using SHAPEIT  [52]. IMPUTE2  [53]  was then used to impute genotypes at non-genotyped SNPs using the 1000 Genomes Phase 3 data as the reference panel. After all phenotype and genotype quality controls, the current study included a total of 7,057,720 imputed and genotyped SNPs and 2,348 individuals of TZL.
Photos in each direction include one with eyes open and one with eyes closed. Ears were cropped from the left side facial photographs with open eyes. DNA was extracted from blood samples using the MagPure Blood DNA KF Kit. The DNA samples were genotyped on the Illumina Infinium Global Screening Array that investigates 707,180 variants which is a fully custom array designed by WeGene (https://www. wegene. com/). Individuals with more than 5% missing data, related individuals, and the ones that failed the X-chromosome sex concordance check or had ethnic information incompatible with their genetic information were excluded. In total, 2,487 individuals were enrolled (15HanTZ: N = 404; 17HanZZ: N = 644; 18HanNN: N = 1,119; 19HanTZ: N = 320). The genotype data were phased using SHAPEIT  [52]  and imputed to the 1000 Genomes Project Phase 3 reference panel using IMPUTE2  [53].
Deep learning based ear phenotyping Our computer pipeline for quantitative ear phenotyping, which includes automated ear detection, segmentation, landmarking, and phenotype acquisition is available at https://github. com/ Fun-Gene/EarPhenotyping. In all cohorts, we used the same pipeline to minimize potential variation caused by different methodology. Specifically, we used the previously established two-stage ear landmark detector consisting of two CNNs deep-learnt from ~15,000 labeled ear images  [3]. Both CNNs had the same architecture consisting of alternating 3 convolutional layers and 3 max-pooling layers followed by 3 fully connected layers. The 1 st CNN detects various orientations and sizes and rectifies all coordinates at a coarse-grained level. The 2 nd CNN was trained in a more controlled scenario for accurate landmarking of 55 ear landmarks.
C-GWAS In the application of C-GWAS analysis on 136 ear meta-analyses, we focused on SNPs with an observable frequency (MAF > 0. 01) in three different continental groups (European, East Asian, and Latin American). The C-GWAS is an R library that is freely available at https:// github. com/Fun-Gene/CGWAS. In the current application, default parameters were used. The summary statistics of the 136 meta-analyses were used as the input of C-GWAS. Details of C-GWAS method has been described previously  [4]. In brief, the null hypothesis (H0) for a SNP under testing is the absence of any allelic effect on all traits, and the alternative hypothesis (H1) is that its allelic effects deviate from 0 for at least one of the multiple traits. C-GWAS incorporate two different tests originated from either the effect based inversed covariance weighting or the truncated Wald test to maximize statistical power.
Post-GWAS analyses SNPs and nearby genes were annotated using ANNOVAR  [59]. Enrichment analysis of biological processes, molecular functions, and cellular components were conducted using Metascape  [60]  based on the Gene Ontology (GO) database. Regulatory activities of associated SNPs and 3D interacting genes were explored using the 3DSNP database  [33]. Enhancer activities and embryonic expression patterns at the associated loci were examined in transgenic mice in the Vista Enhancer Browser database  [34]. Potential functional links between nearby genes, 3D interacting genes and ear/craniofacial features were examined in the Harmonizome database  [35]  and gene expression patterns in the branchial arch and embryonic ectoderm were examined in the MGI database  [36, 37]. Gene expressions in cranial neural crest cells (CNCCs) were compared between genes of interest and background genes over the genome using RNA-seq data from Prescott et al.
[61] , GTEx  [44] , and ENCODE  [62]. We attained all annotated nearest genes of the 1e-4 sets of matched SNPs for 16 lead SNPs using SNPsnap  [63] , the 16 genes with the median expression value separately in 50 cell types were as the correspondent control genes. Partitioned heritability enrichments based on CNCCs  [61] , 4-stages of embryonic craniofacial tissues  [64]  and 97 cell types form Roadmap Epigenomics resource  [65, 66]. The annotations for CNCCs, embryonic craniofacial tissues and other cell types refer the previous study  [67]  using S-LDSC  [38]. For CNCCs, we downloaded and processed H3K27ac and ATAC data, and all data from different replicates. Peaks were called using MACS2  [68]. We iteratively obtained and combined the most reliable peaks based on a 50% peak overlap rate for all replicates from of H3K27ac and ATAC data using BEDtools.
[69]. Based on the combined peaks, we used ROSE  [70]  to infer enhancers including super-enhancers and annotated all enhancer regions (S14 Table ). For embryonic craniofacial tissues, we combined all regions with the following annotations from the 25-state chromHMM model: 'Enh', 'TxReg', 'PromD1', 'PromD2', 'PromU' and 'TssA'. For other cell types, we combined all regions with the following annotations from the 15-state chromHMM model: '1_TssA', '2_TssAFlnk', '7_Enh' and '6_EnhG'. Each annotation was individually added to the baseline LD model  [38]. Also, we use the C-GWAS summary data for 136 meta-analysis in European populations including RS and TwinsUK. Pleiotropic associations were looked up in GWAScatalog based on the region (basepair position of lead SNP +-500kb) and annotated gene  [71].
Mouse pinna phenotyping F2 sexually mature mice were sacrificed by cervical dislocation. Hair was removed with a razor and Weiting depilatory cream. A HandySCAN BLACK scanner was used to obtain 3D models (resolution 0. 3 mm, accuracy 0. 03 mm). The coordinates of 9 anatomical landmarks (3 facial and 6 ear landmarks) and 12 pseudo-landmarks were obtained using Geomagic Wrap  (Fig 2C and S13 Table). The 12 pseudo-landmarks were equally spaced from the four partial curves of the mouse auricle contour (including L1-L4, L4-L6, L6-L5, and L5-L3) for better covering the contour of the auricle. The R package 'geomorph' were used for series of analysis (https:// github. com/geomorphR/geomorph). GPA was used to remove the effects of translation, rotation and scaling. After superimposition of the GPA-adjusted coordinates, only the shape component remained in the aligned specimens.
Pediatric patients with recurrent and refractory cancers are in most need for new treatments. This study developed patient-derived-xenograft (PDX) models within the European MAPPYACTS cancer precision medicine trial (NCT02613962). To date, 131 PDX models were established following heterotopical and/or orthotopical implantation in immunocompromised mice: 76 sarcomas, 25 other solid tumors, 12 central nervous system tumors, 15 acute leukemias, and 3 lymphomas. PDX establishment rate was 43%. Histology, whole exome and RNA sequencing revealed a high concordance with the primary patient's tumor pro le, human leukocyteantigen characteristics and speci c metabolic pathway signatures. A detailed patient molecular characterization, including speci c mutations prioritized in the clinical molecular tumor boards are provided.
Introduction Internationally standardized multimodal treatments now achieve a 5-year overall survival above 85% in children, adolescents and young adults with cancer  1. This gure however varies greatly between tumor types and despite highly toxic therapy, nearly half of cancers escape current rst line treatment either by primary resistance or relapse after initial response. "MoleculAr Pro ling for Pediatric and Young Adult Cancer Treatment Strati cation" (MAPPYACTS; ClinicalTrials. gov identi er: NCT02613962) is an international, multicenter, prospective cancer precision medicine trial with the main objective to match relapsed or refractory pediatric patients to treatment with targeted agents based on individual advanced molecular tumor pro les  2. A central ancillary study of the trial was the establishment of relevant preclinical patient-derived xenograft (PDX) models with deep phenotype characterization provided here.
We have generated a new PDX tumor bank of 131 advanced pediatric tumors to implement at large scale modeling of pediatric relapsed and refractory tumors. These preclinical models not only provide a renewable source of biological material, but also allow to evaluate new anticancer drugs, therapeutic combinations and identify biomarkers in the most relevant pediatric cancer cohort (refractory/relapsed). This unique program sets new standards to develop PDX models within a pediatric precision medicine clinical trial. Our panel not only includes PDX models of various high-risk tumors but provides a wellannotated panel of matched PDX-clinical datasets. It therefore provides an unprecedented resource for the cancer community to study basic biology questions and evaluate novel treatment strategies at large scale.
Subcutaneous xenograft volume was calculated according to the equation: V (mm 3 ) = width 2 (mm Molecular abnormalities reporting. WES and RNA-Seq results of the PDX samples were analyzed and compared to the patient's tumor analysis. In order to identify mouse reads from the PDX, reads from WES and RNAseq were rst classi ed as of human (hg19) or mouse (mm10) origin with Xenome (v1. 0). All WES reads classi ed as human were then aligned using BWA (0. 7. 12; BWA, RRID:SCR_010910). The variant calling was performed using Varscan2 (2. 3. 9; VARSCAN, RRID:SCR_006849). The copy number calling was performed using Sequenza (2. 1. 2). Quanti cation of gene expression from the RNA-Seq human fraction was estimated using Salmon (0. 9. 0) on the GENCODE reference transcriptome (v27). Gene fusion calling was performed using the nf-core rnafusion pipeline running Arriba, Star-Fusion, EricScript and Squid.
The differential gene expression analysis was performed using the R package Deseq2 (DESeq2, RRID:SCR_015687). HLA I typing. Four-digit typing of classical HLA class I (HLA-A, -B, -C) alleles was performed for 34 PDX models of pediatric solid tumors established at Gustave Roussy with available WES and RNA-Seq, and from the corresponding NGS data of patient normal (WES) and primary tumor samples (PTS) (WES, RNA-Seq). For the other solid tumor models without currently available NGS data (n = 76), HLA class I genotypes were inferred from patient normal and PTS samples (Lim WC et al. in preparation). Brie y, HLA typing was performed using HLA-HD  54  from WES and RNA-Seq, and using both HLA-HD and HLAPro ler  55  from RNA-Seq. When both algorithm identi ed a discordant genotype, HLA typing was repeated using Optitype, xHLA and HISAT-genotype 56-58 , from patient normal and PDX WES.
Acknowledgments We are highly grateful to all patients and parents that participated in the trial and consented to the development of preclinical models. We thank  Noemie Assoun ,  Hélène Zakia Aid ,  Cécile Lopez ,  Karine Ser-Le Roux ,  Patrick Gonin  and the animal facilities at GR,  Gabriela Guillén ,  Ainara Magdaleno ,  Soledad Gallego  at  VHIR , pediatric surgeons, pathologists and oncologists from VH,  Régis Brion ,  Frédéric Jacquot  and  Sarah Renault  at the  UTE core facilities at Nantes University ;  Olivia Bawa  for technical assistance in histological and immunohistochemical techniques;  Irène Villa  for tissue section digitalization;  Windy Rondof ,  Gerome Jules-Clement ,  Thierno Abdoulaye Balde  for bioinformatics and data transfers;  Tiphaine Adam-De-Beaumais ,  Imene Hezam ,  Eve Lapouble  for operationals of MAPPYACTS.
This work was supported by grants from  Fondation Gustave Roussy ;  Fédération Enfants Cancers et Santé, Société (Equipe labellisée) ;  Fondation ARC  for the European projects  ERA-NET on Translational Cancer Research (TRANSCAN 2) Joint Transnational Call 2014  ( JTC 2014 ) ' Targeting Of Resistance in PEDiatric Oncology (TORPEDO) ',  ERA-NET TRANSCAN JTC 2014  ( TRAN201501238 ), and  TRANSCAN JTC 2017  ( TRANS201801292 );  Agence Nationale de la Recherche  ( ANR-10-EQPX-03 ,  Institut Curie Génomique d'Excellence (ICGex) ;  IMI   ITCC-P4  ; The  Child Cancer Research Foundation (CCRF) ,  Cancer Council Western Australia (CCWA) ;  PAIR-Pédiatrie/CONECT-AML  ( INCa-ARC-LIGUE_11905  and  Association Laurette Fugain) ,  Ligue contre le cancer  (Equipe labellisée, since 2016),  OPALE Carnot institute ;  Dell ;  Fondation Bristol-Myers Squibb ;  Association Imagine for Margo ;  Association Manon Hope ;  L'Etoile de Martin ;  La Course de l'Espoir ; M  la vie   avec Lisa ; ADAM;  Couleur Jade ;  Dans les pas du Géant ;  Courir pour Mathieu ;  Marabout de Ficelle ;  Olivier Chape ;  Les Bagouz à Manon ;  Association Hubert Gouin Enfance et Cancer ;  Les Amis de Claire ;  Kurt-und Senta Hermann Stiftung ;  Holcim Stiftung Wissen ;  Gertrud-Hagmann-Stiftung  für Malignom-Forschung;  Heidi Ras  Grant Forschungszentrum fürs Kind; Children's  Liver Tumour European Research Network  ( ChiLTERN )  EU H2020  projet ( 668596 );  Fundación FERO  and the  Rotary Clubs Barcelona Eixample ,  Barcelona Diagonal ,  Santa Coloma de Gramanet, München-Blutenburg ,  Deutschland Gemeindienst  e.
Scripts are available on demand and can be upload on a public GitHub server. Selected solid tumor PDX models from Gustave Roussy (n = 44), Institut Curie (n = 41), XenTech (n = 3) and Istituto Nazionale dei Tumori Milan (n = 2) were shared with the European Innovative Medicines Initiative 2 (IMI2) ITCC-P4 (Innovative Therapies for Children with Cancer-Paediatric Preclinical Proof-of- concept Platform) initiative (Project Number: 116064; IMI2/INT/2015-03842 v. 2020; https://www. itccp4. eu/), in which these centers participate. The models were established based on the MAPPYACTS program and consents, and will contribute also to the European IMI2 ITCC-P4 program. Statistical analysis design of study was descriptive with basic standard calculations, using Graphpad Prism® Software version 9. 00 (Graphpad Software Inc, La Jolia, CA, USA) and R for graphical presentations.
Enhancers control gene expression and play crucial roles in development and homeostasis  [1] [2] [3]. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here, we combine deep learning and transfer learning to design tissue-specific enhancers for five tissues in the Drosophila melanogaster embryo -the central nervous system (CNS), epidermis, gut, muscle, and brain. We first train convolutional neural networks (CNNs) using genome-wide scATAC-seq datasets and then fine-tune the CNNs with smaller-scale data from in vivo enhancer activity assays, yielding models with 25% to 75% positive predictive value according to cross-validation. We designed and experimentally assessed 40 synthetic enhancers (eight per tissue) in vivo, of which 31 (78%) were active and 27 (68%) functioned in the target tissue (100% for CNS and muscle).
Recently, we demonstrated that by utilizing genome-wide enhancer activity datasets in a model cell line in culture  6  , it is possible to train deep-learning convolutional neural networks (CNNs) to predict enhancer activity and strength directly from the DNA sequence and to design synthetic enhancers de novo. However, extending this achievement to in vivo systems has been challenging, presumably due to the limited number of functionally characterized enhancers, which has remained relatively low, typically falling below a few hundred per tissue in flies  4  and mice  5. Such quantities have been considered insufficient for effectively training deep learning models. A widely applicable approach to enhance prediction performance with limited data is through the utilization of transfer learning, which has been successfully employed in various fields  7  , including cell biology  8  , network biology  9  , and genomics  [10] [11] [12] [13].
Transfer learning involves pre-training models using large-scale datasets that share similarities with the target task, followed by target task-specific adjustment or fine-tuning on smaller datasets. Provided pre-training with datasets sufficiently similar to the target task, transfer learning yields improved prediction performance  7. To predict enhancer activity from the DNA sequence, leveraging genome-wide datasets of enhancer-associated chromatin features as a steppingstone seems particularly promising (e. g. refs. 3, 11, 13, 14  ). Single-cell ATAC-seq (scATAC-seq) datasets provide measurements of DNA accessibility at the single-cell level and thus allow the determination of cell type-specific accessibility profiles even within complex tissues comprising diverse cell populations  15. Given the association of enhancers with accessible chromatin, we decided to use a combination of scATAC-seq datasets and results from in vivo enhancer activity assays to develop a deep-learning model predictive of enhancer activity using transfer learning (Fig.
1A ; see Methods). We evaluated the models using cross-validation with left-out datasets containing active and inactive enhancers, with and without ATAC-seq signals. This revealed that the sequence-to-activity models obtained by transfer learning substantially improved the predictions for all five tissues as assessed by several performance measures compared to (1) models directly trained on the in vivo enhancer activity data starting from random initialization, (2) models pretrained on ATAC-seq data from a different tissue (salivary gland), and (3) the sequence-toaccessibility models without transfer learning (Fig. 1B , Extended Data Fig. 3  and 4 ). The transferlearned models also outperformed the other models in correctly discriminating accessible regions with and without enhancer activity, and the improvement was particularly strong for muscle and brain which had the fewest known enhancers for training (177 and 119, respectively) (Extended Data Fig.
g. GATA motifs in gut and other tissues). Overall, our work demonstrates the feasibility of targeted design of synthetic enhancers for selected tissues by deep and transfer learning. The framework proposed here should be applicable to any species and tissue provided a genome-wide dataset of enhancer-associated features (e. g. DNA accessibility, characteristic histone modifications, TF or cofactor binding, eRNAs, etc. ) and a reasonable number of functionally validated enhancers (here > 100). More traditional machine-learning approaches have been used successfully for the prediction of chromatin features, TF binding and enhancer sequences  4, [39] [40] [41] [42]  and for predicting genomic elements with highly constrained cis-regulatory codes and limited architectures (e. g. core-promoter elements  43  or highly defined enhancer-motif contexts  44  ).
However, the challenge of flexible enhancer design has only become possible with deep learning  6, 45  (and ref. 46  that was posted as a pre-print while our manuscript was in revision). A C C E L E R A T E D A R T I C L E P R E V I E W For the near future, we foresee great progress in deep-and transfer learning approaches for the prediction and design of enhancers and other genomic regulatory elements. These will likely include the application of large multi-task models trained simultaneously on many datasets comprising different tissues and cell types  47. As predictive sequence features such as TF motifs are often shared between tissues (e. g. Extended Data Fig. 2 , Supplementary Fig. 1 ), shared learning of large models might further improve model performance compared to the dedicated single-task models used here.
In conclusion, our work not only demonstrates the remarkable progress in enhancer design made possible by deep and transfer learning and the growing datasets on enhancers and chromatin, but also sets the stage for a future where the precise design and manipulation of gene expression patterns become a reality. A C C E L E R A T E D A R T I C L E P R E V I E W A C C E L E R A T E D A R T I C L E P R E V I E W A C C E L E R A T E D A R T I C L E P R E V I E W A C C E L E R A T E D A R T I C L E P R E V I E W 50. A C C E L E R A T E D A R T I C L E P R E V I E W Figure legends.