dataesr/scientific-paragraphs-categorization huggingface dataset:
The present clustering on behavioural profiles using smartphone app endpoint measures revealed three distinct social profiles each containing healthy controls, as well as Schizophrenia and Alzheimer's disease patients. This provisional finding demonstrates the potential for novel and/or alternative nosologies to emerge. Further, as these are quantitatively derived, they offer the opportunity for testable hypotheses to be formed and investigated rapidly. In a subsequent study, these behavioural profiles were coupled to other quantitative parameters collected in the study, such as the neuro-imaging data and revealed associations with these clusters. In this way, we are providing evidence for new ways of classifying cohorts and to develop a growing understanding of the associated phenotypes underlying these classifications.
Role of funders The PRISM project ( www. prism-project. eu ) leading to this application has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115916. This Joint Undertaking receives support from the European Union's Horizon 2020 Research and Innovation Programme and EFPIA. This publication reflects only the authors' views neither IMI JU nor EFPIA nor the European Commission are liable for any use that may be made of the information contained therein. from Acadia, Angelini, Gedeon Richter, Janssen Cilag, Lundbeck, Minerva, Otsuka, Roche, Sage, Servier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion and Takeda. BP has received (non-related) research funding from Jansen Research and Boehringer Ingelheim during the conduct of the study.
Acknowledgements The  PRISM  project ( www. prism-project. eu ) leading to this application has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  115916. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme  and  EFPIA. This publication reflects only the authors' views neither IMI JU nor  EFPIA  nor the  European Commission  are liable for any use that may be made of the information contained therein.
Results: We have generated the rst multi-omic dataset (whole-genome sequencing, WGS, and RNA-sequencing, RNA-seq) of PDTOs from the rare and understudied pulmonary neuroendocrine tumors (n = 12; 6 grade 1, 6 grade 2), and provide data from other rare neuroendocrine neoplasms: small intestine (ileal) neuroendocrine tumors (n = 6; 2 grade 1 and 4 grade 2) and large-cell neuroendocrine carcinoma (n = 5; 1 pancreatic and 4 pulmonary). This dataset includes a matched sample from the parental sample (primary tumor or metastasis) for a majority of samples (21/23) and longitudinal sampling of the PDTOs (1 to 2 time-points), for a total of n = 47 RNA-seq and n = 33 WGS. We here provide quality control for each technique, and provide the raw and processed data as well as all scripts for genomic analyses to ensure an optimal re-use of the data.
Key Points • Tumor-derived organoids are revolutionary experimental resources to test biological hypotheses and treatment options • We have generated the rst multi-omic dataset for neuroendocrine tumor organoids of the lung, and for the rare neuroendocrine tumors of the pancreas, and small intestine (ileum) ble 1). The dataset contains PDTOs of the lung (n = 12; 6 grade 1, 6 grade 2) and small intestine ileum (n = 6; 2 grade 1 and 4 grade 2), and LCNEC of the lung (n = 4) and pancreas (n = 1). This dataset includes longitudinal sampling of the organoids (2 to 3 time-points), and sequencing of the matched parental tumor for most samples (21/23, either primary tumors or metastases). Along with raw and processed data, we provide quality controls for each technique and scripts to run a complete molecular analysis.
Data processing All data processing was performed using the work ows developed by the rare cancers genomics team of the International Agency for Research on Cancer / World Health Organization (https://github. com/IARCbioinfo/), as detailed in  [14]  and  [15]. The work ows are written in the popular domain-speci c language next ow  [16]. All software dependencies are contained in conda environments and containerized with Docker and Singularity (containers available at https://hub. docker. com/ and https://singularity-hub. org/). WGS. Raw reads were mapped to reference genome GRCh38 using work ow alignment-nf (https://github. com/IARCbioinfo/ alignment-nf, v1. 2). This work ow rst maps reads (software bwa-mem2 v2. 0  [17, 18] ), then marks duplicates (software samblaster, v0. 1. 26  [19] ), and nally sorts reads (software sambamba, v0.
7. 1  [20] ). RNA-seq. Raw reads were mapped to reference genome GRCh38 with annotation gencode v33 using the work ow RNAseq-nf (https://github. com/IARCbioinfo/RNAseq-nf, v2. 4). This workow removes adapter sequences (wrapper Trim Galore v0. 6. 5  [21]  for software cutadapt  [22] ), maps reads (software STAR v2. 7. 3a  [23] ), marks duplicated reads (software samblaster, v0. 1. 25), and nally sorts reads (software sambamba, v0. 7. 1). Alignments were then post-processed using two work ows to improve their quality. Work ow abra-nf (https://github. com/IARCbioinfo/abra-nf, v3. 0) performs local realignment using software ABRA2 (v2. 22  [24] ), and BQSR-nf (https://github. com/IARCbioinfo/BQSR-nf, v1. 1) performs base quality score recalibration using gatk (v4. 0. 5. 1  [25] ). Variant calling from WGS.
Single nucleotide variants were called on all WGS samples using software Mutect2 from GATK4 (v4. 2. 0. 0  [26, 27] ) with work ow mutect-nf (https://github. com/IARCbioinfo/mutect-nf, v2. 2b), as described in  [6]. Resulting variant calling format (VCF) les were normalized using bcftools v1. 10. 2  [28]  (work ow https://github. com/ IARCbioinfo/vcf_normalization-nf, v1. 1) and annotated using ANNOVAR v2020Jun08 (work ow https://github. com/ IARCbioinfo/table_annovar-nf v1. 1. 1). Indels and multinucleotide variants were additionally ltered using the intersection of Mutect2 and strelka2  [29]  calls (work ow https:// github. com/IARCbioinfo/strelka2-nf v1. 2a), in order to reduce false positives that are more frequent in indel calls due to the di culty of detecting such variants with short reads sequencing.
Variant calling from RNA-seq. Variants were called on all RNA-seq samples using software Mutect2 from GATK4 (v4. 2. 0. 0  [26, 27] ) with work ow mutect-nf (https://github. com/IARCbioinfo/mutect-nf, branch RNAseq), in RNA-seq and tumor-only modes. The RNA-seq mode incorporates a preprocessing step to x CIGAR strings (removing NDN elements and ensuring that mapping quality 255 is not used as some mappers like STAR can do), and GATK4's Split-NCigarReads method that splits reads with Ns in their CIGAR string, in order to improve variant calling quality. Resulting variant calling format (VCF) les were normalized using bcftools v1. 10. 2  [28]  (work ow https://github. com/IARCbioinfo/vcf_normalization-nf, v1. 1) and annotated using ANNOVAR v2020Jun08 (work ow https://github. com/ IARCbioinfo/table_annovar-nf v1.
Sample matching We used software NGSCheckMate (cloned from the github repository https://github. com/parklab/NGSCheckMate revision 10799087bdfe4b990add5b5e536f87c47bbdb688) to check that samples from the same experiment indeed came from the same individual, in both WGS and RNA-seq simultaneously, IARCbioinfo/NGSCheckMate-nf, v1. 1). The sample matching algorithm correctly identi ed all experiments except one (Fig. 4 ). The WGS normal-derived organoid sample from experiment LCNEC3 (LCNEC3Np12_WGS in Fig. 4 ) was found not to match other LCNEC3 samples, suggesting a possible sample swap and was thus excluded from further analyses. Also, the RNA-seq tumor sample for the late-passage organoid of experiment LC-NEC3 (sample LCNEC3Tp17_RNA in Fig. 4 ) was found to better match experiment LNET2, and was thus excluded from the subsequent analyses.
Small variant calls from RNA-seq We classi ed small variants called from RNA-seq in 241 known neuroendocrine neoplasm driver genes (from Table  S4  in  [6] ) as somatic or germline, using a random forest (RF) algorithm  [35]  (R package randomForest v4. 7-1. 1  [36] ; Fig. 6 ), using a similar approach as we recently did to classify mutations in tumor-only WGS  [37]. After ltering out non-exonic, synonymous, and nonsynonymous mutations with a REVEL score  [38]  below 0. 5, and mutations not in the list of 241 drivers, we were left with 2430 variants. Among them, 1174 variants were in samples with WGS data available and their somatic status was thus known. We used 10 features in the RF model. One feature was directly informative about the potential germline status: the frequency of the allele in human populations from the ExAc database excluding cancers from the TCGA (feature ExAC_nontcga_ALL).
Four features were informative about the alignment: the median distance from the end of the read (feature MPOS), the likelihood ratio score of variant existence (feature TLOD), the coverage at the position (feature DP), and the allelic fraction of the alternative allele (RNA. AF). Finally, the other features were informative about the pathogenicity of the variant: the REVEL score of pathogenicity (feature REVEL), the presence in the COSMIC 92 database (feature cosmic92_coding_nonnull), the presence in the COSMIC 92 database in a lung tumor (feature cosmic92_coding_lung), and the InterVar annotation (feature InterVar_automated; with levels ". ", "Uncertain_signi cance", "likely_pathogenic", and "Pathogenic"), and the exonic function of the variant (missense, nonsense, inframe or frameshift insertion, etc).
10. 1). The most important features for the classi ca-tion were the REVEL score, the TLOD, and the cosmic annotation, while the frequency in the ExAC database was the least important, presumably because all these variants were very rare (Fig. 6C ). Indeed, the most representative tree from the RF, computed using the reprtree R package v0. 6 using the d2 distance metric between tree predictions  [40] , relied on these three variables, with all alterations present in a lung tumor from the COSMIC 92 database automatically classi ed as somatic (root of the tree), and TLOD and REVEL score being the most common features used for splitting (Fig. 6D ).
Re-use potential We describe here some of the very rst multi-omic datasets for patient-derived tumor organoids of pancreatic, small intestine (ileum), and pulmonary neuroendocrine neoplasms, in particular including the rst lung neuroendocrine tumor organoids. Because such low grade tumors are di cult to cultivate in vitro, there is currently a lack of adequate experimental sys- computed from RNA-seq data. In all panels, samples from each sex are encircled (red:male, blue:female), excluding LCNEC3Np12, which we report as not matching the other samples from the LCNEC3 experiment. tems for these tumors, and we expect the biobank associated with the data presented here to be the basis for future experimental studies-either fundamental or treatment oriented-on neuroendocrine neoplasms across body sites.
The multi-omic dataset we provide here constitutes the molecular ngerprints of these experimental models, and will be key to investigate oncogenic processes responsible for tumor initiation and progression, and to link drug responses to molecular features to design future personalized treatments. To facilitate future studies, we used the exact same data processing as in our previous studies of neuroendocrine neoplasms  [14, 15]  and other rare cancers  [41] , in particular using rigorous RNA-seq expression quanti cation with containerized software and operating systems (see methods section). In addition, we provide all R scripts to analyze the data at https://github. com/IARCbioinfo/MS_panNEN_organoids.
Conclusion We have shown that our multi-omic dataset is of high quality and can be easily re-used. Given the rarity of neuroendocrine tumors from the lung, pancreas, and small intestine, past genomic studies each only reported data for a handful of samples, limiting the potential discoveries. For example, for lung NETs, 29 WGS and 39 RNAseq were reported in  [42] , 3 WGS and 20 RNA-seq in  [14] , and 30 RNA-seq in  [43] ; for small intestine NETs, for example, 81 RNA-seq with no WGS were reported in  [44]  and 7 RNA-seq in  [45] ). As a result, the primary tumors and metastasis sequencing data we report here (10 samples with WGS, 21 with RNA-seq) alone are very valuable, and should be combined with other datasets in future studies to provide enough power to discover informative molecular features for diagnosis, prognosis, and treatment.
Funding The study was funded by the  NET Research Foundation  ( 2017   Petersen Accelerator Award  to H. C. ),  Worldwide Cancer Research  (2020 Grant Round to L. F-C),  NET Research Foundation  (2019 Investigator Award to L. F-C),  French National Cancer Institute  (INCa,  PRT-K 2017  to L. F-C. and M. F. ), and  Ligue Nationale contre le Cancer  (fellowship to L. Ma. ). T. L. D. was supported by an  EMBO long-term fellowship  ( ALTF-21-2017 ) and a  Marie Skłodowska-Curie IF  grant  797966 -PNECtumor. The  Oncode Institute  is supported by the  Dutch Cancer Society.
Availability of supporting data and materials The data set supporting the results of this article is available in the European Genome-Phenome archive repository, study EGAS00001005752. The study consists of seven datasets: EGAD00001009988, with WGS CRAM les for 2 experiments, EGAD00001009989 with WGS CRAM les for 6 experiments, EGAD00001009990, with WGS CRAM les for 2 experiments, EGAD00001009991 with RNA-seq fastq les from 4 experiments, EGAD00001009992, with RNAseq fastq les for 15 experiments, EGAD00001009993, with RNA-seq fastq les for 2 experiments, and EGAD00001009994, with gene expression in multiple formats (R data, tab-separated text les) and multiple units (raw counts, TPM, FPKM) for 21 samples. Because of the sensitivity of the data and the patient consent, to get access to the data, please contact the data access committee of the Division of Biomedical Genetics from UMC Utrecht at dacdbg@umcutrecht.
nl. Once a data access agreement has been signed and access granted, data can be downloaded using the EGA python client (see detailed instructions at https://github. com/EGA-archive/ega-download-client, and video tutorial at https://embl-ebi. cloud. panopto. eu/Panopto/ Pages/Viewer. aspx?id=be79bb93-1737-4f95-b80f-ab4300aa6f5a ) The multiQC report for WGS raw reads is available in Supplementary File S1; the multiQC report for RNA-seq raw reads is available in Supplementary File S2; the multiQC report for WGS alignments is available in Supplementary File S3; the multiQC report for RNA-seq alignments is available in Supplementary File S4.
9% 77. 6% 68. 0X 99. 9% LNET5Tp4 42% 93. 3% 87. 6% 75. 0X 99. 9% LNET6N 42% 86. 6% 22. 8% 43. 0X 99. 9% LNET6T 42% 93. 0% 83. 1% 72. 0X 99. 9% LNET6Tp1 42% 90. 2% 76. 8% 61. 0X 99. 9% SINET7N 42% 77. 2% 4. 9% 36. 0X 99. 9% SINET7M 41% 92. 8% 83. 5% 73. 0X 99. 9% SINET7Mp2 42% 92. 8% 85. 7% 69. 0X 99. 9% SINET8N 42% 93. 1% 91. 7% 75. 0X 99. 9% SINET8M 41% 92. 6% 81. 2% 64. 0X 99. 9% SINET8Mp2 42% 93. 0% 85. 5% 70. 0X 99. 9% SINET9N 42% 84. 4% 7. 2% 38. 0X 99. 9% SINET9M 41% 93. 0% 90. 0% 81. 0X 99. 9% SINET9Mp1 42% 90. 2% 49. 1% 49. 0X 99. 9% LNET10N 42% 86. 4% 9. 5% 39. 0X 99. 9% LNET10T 42% 93. 0% 90. 1% 71. 0X 99. 9% LNET10Tp4 42% 93. 0% 87. 1% 66. 0X 99. 9% using our work ow NGSCheckMate-nf (https://github. com/.
9825 SINET7Mp2_WGS SINET8M_WGS 0. 8623 SINET8Mp2_RNA 0. 8391 0. 8443 0. 8557 0. 8614 0. 9882 0. 8613 0. 8627 0. 8683 SINET9M_WGS 0. 9854 SINET9Mp1_WGS SINET12Mp1. 1_RNA 0. 8407 SINET12M_RNA 0. 9767 0. 9858 0. 9779 0. 8536 0. 9716 0. 9689 0. 8631 0. 8271 SINET7M_RNA 0. 8555 SINET7Mp2_RNA SINET8Mp2_WGS 0. 8613 SINET8M_RNA SINET9N_WGS SINET12Mp1. 3_RNA 0. 8471 0. 8622 0. 9799 0. 8511 SINET7M_WGS SINET8N_WGS correlation coefficient Match Figure 4. mance was assessed, we trained a RF model on the full 1174 variants and predicted the status of the remaining 1256 vari- ants. See github repository https://github. com/IARCbioinfo/ MS_panNEN_organoids for the complete R script.
Inclusion: We identi ed patients over 18 years of age who presented rhabdomyolysis and were admitted at the University Hospital of Rennes between June 2013 and December 2019, according to the medicoadministrative database of the medicalized information systems program. This database provides several pieces of information associated with each hospital stay (including the emergency visit), including the main diagnosis (which justi ed the hospitalization) and secondary diagnoses (i. e. which are not the main cause of the hospitalization, but explain at least part of it). We used the codes of the International Classi cation of Diseases (10th edition, ICD10): M62. 8 (Rhabdomyolysis) and T79. 6 (Traumatic muscle ischemia) to identify the primary and secondary diagnoses of rhabdomyolysis. We then excluded patients whose CPK levels were under ve times the normal concentration (i.
e. at the median of the Wongrakpanich study). This tends to validate the hypothesis that age in uences prognostic factors of rhabdomyolysis, notably through multimorbidity. One of the limitations of our study is the amount of missing data, due to the retrospective design of the study. Indeed, 124 patients with rhabdomyolysis were transferred to local hospitals and we were therefore not able to collect the biological evolution, complications and in-hospital mortality at 30 days. Similarly, we do not have data to ensure the exhaustivity of rhabdomyolysis coding in the database we used. However, as the database we analyzed is used by the health insurance for the pricing of hospital stays, its quality is regularly controlled. Since rhabdomyolysis pathology justi es high invoicing, it seems unlikely that a signi cant number of stays are missing.
TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGAAGCAGTGGTATCAACGCAG, adapter in italic) with the KAPA Real-Time Library Amplification Kit (Kapa Biosystems). Adapters were required for subsequent sequencing reactions. After purification with AMPure XP beads, an index PCR with Illumina sequencing adapters was performed using the Nextera XT Index Kit. This second PCR product was again purified with AMPure XP beads. Highthroughput sequencing of the generated amplicon products containing the TRG and TRD sequences was performed on an Illumina MiSeq platform using the V2 300 kit, with 150 base pairs (bp) at the 3′ end (read 2) and 150 bp at the 5′ end (read 1) [at the GIGA center, University of Liège, Belgium]. Raw sequencing reads from fastq files (read 1 and read 2) were aligned to reference V, D and J genes from GenBank database specifically for 'TRD' to build CDR3 sequences using the MiXCR software version 3.
ACKNOWLEDGMENTS Cell sorter analyses were performed at the  TBMCore facility (FACSility)  on  BD FACSAria™ III Sorter  and we thank  Atika Zouine  and  Vincent Pitard  for technical assistance, data acquisition and interpretation ( TBMCore CNRS  3427,  INSERM US005 ,  Université de Bordeaux ). We also thank the  CRISP' edit platform for their valuable advice during the setup of the CRISPR system. We thank  Keith Gull  ( University of Manchester ) for providing us the anti-PFR antibody. The Bringaud and Robinson teams are supported by the  Centre National de la Recherche Scientifique  (CNRS, https://www. cnrs. fr/), the  Université de Bordeaux  (https://www. u-bordeaux. fr/) and the  Agence Nationale de la Recherche  (ANR, https://anr. fr/) through the ParaFrap "Laboratoire d'Excellence" (LabEx, https://www.
enseignementsup-recherche. gouv. fr/cid51355/laboratoires-dexcellence. html) ( ANR-11-LABX-0024 ). The Bringaud team is also supported by the " Fondation pour la Recherche Médicale " (FRM, https://www. frm. org/) (" Equipe FRM ", grant  n°EQU201903007845 ) and the  ANR  grant  ADIPOTRYP  ( ANR19-CE15-0004-01 ) and the  Robinson  team by the  ANR  grant  Structu-Ring  ( ANR-20-CE91-0003 ). M. O is the holder of a  Canada research Chair  and the recipient of a  CIHR Foundation   Grant. He was the holder of a  University de Bordeaux IDEX   fellowship.
ACKNOWLEDGMENTS The authors acknowledge the  Natural History Museum in London  for providing a sample of the C. arabica lectotype. JS acknowledges funding from  Academy of Finland  (decisions  318288  and  329441 ) and  Nanyang Technological University  start-up grant. RG and S. O-A acknowledge funding from  Ecos-Nord   N°C21MA01  and  STICAMSUD 21-STIC-13. PR acknowledges  Academy of Finland  (grant  343656 ). ARP  acknowledges  NAPI Bioinformática  from  Fundação Araucária  and  TELearning  Project  2021-22  ( 21-STIC-13 ) from  STIC AmSud. YB acknowledges the funding from  Research Foundation -Flanders (FWO , No  G056517N ). YVdP acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (No. 833522 ) and from  Ghent University  ( Methusalem  funding, BOF.
Data availability Coffee genome assemblies are available at CoGe (https://genomevolution. org/): C. canephora: 50947, C. eugenioides: 60235, and C. arabica: 66663 (Pacbio HiFi) and 53628 (Pacbio). All genome information, including the VCF files with SNP information are available at ftp. solgenomics. net; the genome data is also available at ORCAE (https://bioinformatics. psb. ugent. be/orcae/overview/Coara and https://bioinformatics. psb. ugent. be/gdb/coffea_arabica/ ). The sequencing data have been deposited to NCBI under bioproject ID: PRJNA698600.
Study design and population The study was a post-hoc subgroup analysis of the SYNTAXES study (NCT03417050), which investigated 10-year mortality in the SYNTAX trial (NCT00114972) beyond its original follow-up of 5 years  [7, 8]. The design and the results of the SYNTAX study have been previously reported  [7, 11]. Briefly, the SYNTAX trial was an international, multicenter, randomized controlled trial performed in 85 hospitals across 18 North American and European countries. From March 2005 through April 2007, a total of 1800 patients with stable de novo three-vessel disease and/or LMCAD, who were considered eligible for both PCI and CABG based on clinical judgement and consensus of a heart team, were randomly enrolled in a 1:1 fashion to receive PCI (n = 903) with the uniform use of TAXUS Express paclitaxel-drug eluting stents (Boston Scientific Corporation, Marlborough, MA, USA) or CABG (n = 897).
LVEF subgroups In the SYNTAX trial, all the baseline characteristics were collected and reviewed during the Heart Team conference to assess whether the patient is eligible for inclusion  [12]. Pre-procedural LVEF was measured by transthoracic echocardiography or left ventriculogram. Out of 1800 patients, LVEF was available as a continuous variable in 1126 patients (62. 6 %), and a categorical variable in 1772 patients (98. 4 %) being defined as good (≥50 %), moderate (30-49 %), or poor (<30 %)  [13]. As described previously, multiple imputations of missing LVEF values were performed to effectively the available data  [10]. A total of 20 imputed datasets were generated. The continuous variables were calculated as an average value from the data of 20 imputations. Continuous values were adjusted to not deviate from the range of categorical values.
Study endpoint The primary endpoint was all-cause mortality at 10 years. All analyses were performed according to the intention-to-treat principle. Vital status and survival data were obtained by electronic medical records or by query of national death registries. An independent clinical events committee adjudicated major adverse cardiac and cerebrovascular events (MACCE) at five years, defined as the composite endpoint of all-cause death, MI, stroke, and any repeat revascularization  [8]. Finally, we applied the SYNTAX score 2020 (SS-2020) to patients with an LVEF≥50 % and LVEF<50 % to better refine their respective personalized vital prognosis and assess in a cross-validation the value of the SS-2020  [13] , specifically, among patients with reduced LVEF.