dataesr/scientific-paragraphs-categorization huggingface dataset:
Two rounds of bisulfite conversion were performed to ensure a conversion rate of over 99%. Enrichment for adaptor-ligated DNA was carried out through seven PCR cycles using KAPA HiFi Uracil + DNA Polymerase (Kapa Biosystems, https://www. kapabiosystems. com/). Library quality was monitored using the Agilent 2100 Bioanalyzer, and the library concentration was estimated using quantitative PCR with the library quantification kit from Roche Kapa Biosystems. Paired-end DNA sequencing (2 9 101 + 8 bp) was then performed using the HiSeq2500 (Illumina) following the manufacturer's protocol. Image analysis, base calling and quality scoring of the run were processed using the manufacturer's software Real Time Analysis (RTA 1. 18. 66. 3) and followed by generation of FASTQ sequence files. Raw reads were trimmed with TrimGalore! v.
Prunus dulcis phylome reconstruction The P. dulcis and P. persica phylomes, that is the complete collection of evolutionary histories of all encoded genes, were reconstructed using the PhylomeDB pipeline  (Huerta-Cepas et al. , 2011). In brief, for each protein-coding gene in the almond and peach genome we searched for homologs (Smith-Waterman Blast search, E-value cutoff < 1 9 10 -5 , minimum contiguous overlap over the query sequence cut-off ≥ 50%) in a database containing the proteomes of 17 species with sequenced genomes representing most of the important plant families (Table  S3 ). The most similar 150 homologs were aligned using three different programs, MUSCLE  (Edgar, 2004) , MAFFT  (Katoh et al. , 2009)  and KALIGN  (Lassmann and Sonnhammer, 2005) , in forward and reverse orientation.
The PE Illumina sequencing data from the almond cultivars were trimmed (length ≥ 35 bp, mean sliding window of 4 bp, phred quality score ≥ 20) using Trimmomatic  (Bolger et al. , 2014). and the output was quality checked using FastQC (https://www. bioinformatic s. babraham. ac. uk/projects/fastqc/). Trimmed data were aligned against the almond assembly using the BWA-MEM algorithm v. 0. 7. 16a-r1181 (http://bio-bwa. sourceforge. net/bwa. shtml) with default parameters. After removal of unmapped reads, PCR duplicates and reads with mapping quality < 10 we obtained the subset of 'clean' reads used for variant calling, which was performed with Samtools v. 1. 5 (Li, 2011)  with default parameters, except from the following: -q 10 -Q 20. Commands for trimming, alignment, bam filtering and variant calling can be found in the github repository (https://github.
Prunus dulcis contigs were aligned to the P. persica reference genome using Nucmer from the Mummer3 package  (Delcher et al. , 2003). Assemblytics  (Nattestad and Schatz, 2016)  was used to filter the alignment and detect genome-wide variants with the following cut-offs: unique sequence length required for considering an alignment = 10 000 bp, minimum variant size = 20 bp, maximum variant size = 25 000 bp. Structural variants were intersected with TE annotations of P. dulcis (insertions and repeat expansions) and P. persica (deletions and repeat contractions). A variant was considered to be TE-associated when at least 50% of its sequence was spanned by a TE. cultivars (A€ ı, Belle d'Aurons, Cristomorto, Desmayo largueta, Falsa Barese, Genco, Marcona, Nonpareil, Ripon, Vivot) and one P. webbii are available at the European Nucleotide Archive (ENA) under the study with a primary accession PRJEB32985 and corresponding experiment IDs ERX3390856-ERX3390868 (10 almond cultivars; there are two entries for Ai, Belle d'Aurons and Desmayo largueta due to double Illumina runs), ERX3391776-ERX3391779 (four peach cultivars) and ERX3391780 for P.
webbii. Raw Illumina data for the seven peach cultivars (Armking, Belbinette, Blanvio, Catherine, Flatmoon, Nectalady and Tiffany) were downloaded from SRA, corresponding to the SRA accessions ERS1801609-ERS1801614 and ERS1801617. Raw Illumina data for almond cultivars D05-187 and S3067 were downloaded from SRA with the corresponding accession IDs of SRX245830 and SRX245832. Sequencing reads and assembly data of P. dulcis cv. Texas are available via the ENA (PRJEB32994). The assembly and annotation are additionally accessible via the Genome Database for Rosaceae (https://www. rosaceae. org/analysis/295) and the CNAG-CRG (http://denovo. cnag. cat/almond). Table  S17. List of the 97 genes potentially involved in mesocarp development. Table  S18. Methylation status on genes potentially involved in mesocarp development and presenting transposable element insertions in peach or almond.
ACKNOWLEDGEMENTS This research was supported in part by grants from the  Spanish Ministry of Economy and Competitiveness (MINECO/FEDER  projects  AGL2012-40228-C02-01 ,  AGL2015-68329-R ,  AGL2016-78992-R  and  RTA2015-00050-00-00 ),  Severo Ochoa Program for Centres of Excellence in R&D   201-2019 SEV-2015-0533  and  CERCA Programme-Generalitat de Catalunya. MW acknowledges grant support from the  Waite Research Institute of the University of Adelaide  and MJR from the  Spanish Instituto Nacional de Investigaci on y Tecnolog ıa Agraria y Alimentaria (INIA)  project no. RTA-2014-00062.
INTRODUCTION Polydactyly is one of the most frequent inherited defects of the limb and has a prevalence of 0. 3-3. 6/1000 in live-births  (Castilla et al. , 1973; Malik, Ullah, Afzal, Lal, & Haque, 2014). It is characterized by supernumerary digits, ranging from extra additive soft tissue without bone structure to an integral and fully developed digit  (Umair, Ahmad, Bilal, Ahmad, & Alfadhel, 2018). According to the position of the extra digit, polydactyly has been classified into postaxial (PAP), mesoaxial and preaxial  (Malik, 2014). This defect is also associated with at least 221 syndromes according to the London Dysmorphology Database  (Phadke & Sankar, 2010)  covering 146 genes  (Umair et al. , 2018)  and can be a hallmark for a group of syndromes such as for ciliopathies. For this group of diseases, in addition to skeletal malformations such as PAP, the phenotypic spectrum can associate retinal degeneration, obesity, kidney dysfunction and sometimes intellectual disability as observed in the classical Bardet-Biedl syndrome (BBS; MIM# 209900) for which 22 genes have been identified.
Bioinformatics analysis The reads were mapped to the reference human genome (GRCh37/hg19) using BWA v0. 7. 12  (Etard, Roostalu, & Strahle, 2010). GATK UG v3. 4-46 was used to call SNV and indel variations  (DePristo et al. , 2011). Annotation and ranking of SNV and indel were performed by VaRank 1. 4. 3  (Geoffroy et al. , 2015)  in combination with the Alamut Batch software (1. 11, Interactive Biosoftware, Rouen, France). Very stringent filtering criteria were applied to filter out nonpathogenic variants (Supp. Table  1 ): (i) variants represented with an allele frequency of more than 1% in public variation databases including the 1000Genomes  (Altshuler et al. , 2015) , the gnomAD database  (Lek et al. , 2016) , the DGV  (MacDonald, Ziman, Yuen, Feuk, & Scherer, 2014)  or our internal exome database, (ii) variants in 5′ and 3′ UTR, downstream, upstream, intronic and synonymous locations without pathogenic prediction of local splice effect.
Reverse transcription of 1 µg total RNA to cDNA was performed using the BioRad iScript TM cDNA synthesis kit. Real-time quantitative polymerase chain reaction amplification was performed in a BioRad CFX96 TM Real-Time System using the iQ TM SYBR® Green Supermix and primer sets (Supp. Table  3 ) optimized for tested targets for SYBR Green-based real-time PCR for the real-time PCR. The normalized fold expression of the target gene was calculated using the comparative cycle threshold (C t ) method by normalizing target mRNA C t to those for both GAPDH and HPRT reference gene using the CFX Manager Software Version 1. 5 and excel calculation. Statistical analysis have been done using the "Graphpad prism" software.
1198C>T (p. Arg400Trp), also located in a homozygous region of 8. 5 Mb. This variant does not appear in public databases (gnomAD, 1000Genomes). The variants in both genes (IQCE and TULP1) segregated with the phenotypes within the family (Figure  1B ) and in combination explained most of the clinical features of this individual. In the non-consanguineous family B (Figure  1A ), WES revealed only 2 class-4 variations in IQCE at compound heterozygous state: c. 895_904del (p. Val301Serfs*8) in exon  12 and c. 1350_1353delAGAG, (p. Glu451Argfs*15)  in exon 16 (Figure  1B ). Both variations segregated in all affected members (B. II-1, B. II-2 and B. II-3). The second variation (c. 1350_1353delAGAG) was found in gnomAD with a frequency of 0. 0002381 with 66 heterozygous carriers. In family C, WES analysis of the most severe case, C.
IV-4 (Figure  1A ), revealed a known homozygous variant in ATP6V1B1 (NM_001692. 3), a gene that is associated with distal renal tubular acidosis with deafness  (Vargas-Poussou et al. , 2006)  (dRTA; MIM# 267300). This class-4 variant, c. 175-1G>C, is located in a canonical splice site and is predicted to cause the skipping of exon 3, this variation was not found in public databases (gnomAD, 1000Genomes). Interestingly, the c. 895_904del (p. Val301Serfs*8) variation in IQCE that has been already observed in family A and B (respectively at the homozygous and compound heterozygous state) was only found in C. IV-4 at the heterozygous state (whereas no second pathogenic allele could be identified by cDNA sequencing and quantification, data not shown). The WES did not reveal any other variations in known PAP genes for this individual.
DISCUSSION An IQCE homozygous variant affecting the splicing of the gene was recently identified by whole exome sequencing in a single Pakistani family presenting with postaxial polydactyly type A limited to lower limb  (Umair et al. , 2017). Here, we report 3 additional families with pathogenic variations in IQCE referred initially as ciliopathies with postaxial polydactyly involving the upper and lower limbs. Interestingly, the 3 families shared the same c. 895_904del (p. Val301Serfs*8) variation in IQCE exon 12 in a homozygous or in a heterozygous state with another truncating variant. These two IQCE variations have already been observed in the largest public databases. The c. 1350_1353del (p. Glu451Argfs*15) was described in Exome Variant Server (EVS)  (Tennessen et al. , 2012)  with an average allele frequency >1.
AVAILABILITY OF DATA AND MATERIAL Data generated or analysed during this study are included in the published article and the corresponding supplementary data. The raw sequencing data generated in the course of this study are not publicly available due to the protocol and the corresponding consents used that did not include such information. All variants have been submitted to ClinVar using the following range of accessions numbers SCV000929968 to SCV000929972 (https://www. ncbi. nlm. nih. gov/clinvar/). Anonymised NGS data and genomic variant data files will be made available upon request from qualified investigators studying the molecular basis of genomic disorders. Datasets can be obtained via the corresponding author on reasonable request. 1.
1198C>T (p. Arg400Trp), also located in a homozygous region of 8. 5 Mb. This variant does not appear in public databases (gnomAD, 1000Genomes). The variants in both genes (IQCE and TULP1) segregated with the phenotypes within the family (Figure  1B ) and in combination explained most of the clinical features of this individual. In the non-consanguineous family B (Figure  1A ), WES revealed only 2 class-4 variations in IQCE at compound heterozygous state: c. 895_904del (p. Val301Serfs*8) in exon 12 and c. 1350_1353delAGAG, (p. Glu451Argfs*15) in exon 16 (Figure  1B ). Both variations segregated in all affected members (B. II-1, B. II-2 and B. II-3). The second variation (c. 1350_1353delAGAG) was found in gnomAD with a frequency of 0. 0002381 with 66 heterozygous carriers. In family C, WES analysis of the most severe case, C.
DISCUSSION An IQCE homozygous variant affecting the splicing of the gene was recently identified by whole exome sequencing in a single Pakistani family presenting with postaxial polydactyly type A limited to lower limb  (Umair et al. , 2017). Here, we report 3 additional families with pathogenic variations in IQCE referred initially as ciliopathies with postaxial polydactyly involving the upper and lower limbs. Interestingly, the 3 families shared the same c. 895_904del (p. Val301Serfs*8) variation in 19 IQCE exon 12 in a homozygous or in a heterozygous state with another truncating variant. These two IQCE variations have already been observed in the largest public databases. The c. 1350_1353del (p. Glu451Argfs*15) was described in Exome Variant Server (EVS)  (Tennessen et al. , 2012)  with an average allele frequency >1.
AVAILABILITY OF DATA AND MATERIAL Data generated or analysed during this study are included in the published article and the corresponding supplementary data. The raw sequencing data generated in the course of this study are not publicly available due to the protocol and the corresponding consents used that did not include such information. All variants have been submitted to ClinVar using the following range of accessions numbers SCV000929968 to SCV000929972 (https://www. ncbi. nlm. nih. gov/clinvar/). Anonymised NGS data and genomic variant data files will be made available upon request from qualified investigators studying the molecular basis of genomic disorders. Datasets can be obtained via the corresponding author on reasonable request. Volcano plot of overrepresentation enrichment analysis of significantly misregulated transcripts in A.
Annotations are gathered using Alamut Batch v1. 11, especially for the variation databases including gnomAD (v2. 0. 2, Oct. 2017), 1000 Genomes Project phase3 release (version 20150813 v5b) and the following predictions including phastCons (UCSC, 44 vertebrates). Effect on the splice has been evaluated using the MaxEntScan  (Yeo & Burge, 2004) , NNSPLICE 0. 9  (Reese, Eeckman, Kulp, & Haussler, 1997)  and Splice Site Finder  (Shapiro & Senapathy, 1987)  by calculating score change between the wild type and the mutated sequences expressed as a percent differences. Missenses have been evaluated using default parameters from PolyPhen-2 (2. 2. 2)  (Adzhubei et al. , 2010)  and SIFT 4. 0. 3 (Kumar, Henikoff, & Ng, 2009). Default cut-offs used have been described in VaRank  (Geoffroy et al. , 2015)  for both type of predictions.
ACKNOWLEDGMENTS We would like to thank the patient's family for their participation and the patient's associations, Formicoeur,  Bardet-Biedl France  and  Unadev , for their constant and strong support. We also would like to acknowledge the members of the diagnostic laboratories (molecular and cytogenetic units, in particular  Manuela Antin ,  Anne-Sophie Leuvrey  and  Elsa Nourisson ) at the  University Hospital in Strasbourg  for technical assistance. We thank  Nadine Borel  and the zebrafish house team. We also thank our collaborators for screening their negative cohort (KM. Bujakowska, E. Pierce,  P. Beales ,  F. Hildebrandt ). We thank the  GenomEast platform (Strasbourg, France) especially Serge Vicaire  for library preparation,  Céline Keime  for supervising the transcriptome analysis as well as  Bernard Jost  and  Christelle Thibault.
ACKNOWLEDGMENTS We would like to thank the patient's family for their participation and the patient's associations, Formicoeur,  Bardet-Biedl France  and  Unadev , for their constant and strong support. We also would like to acknowledge the members of the diagnostic laboratories (molecular and cytogenetic units, in particular  Manuela Antin ,  Anne-Sophie Leuvrey  and  Elsa Nourisson ) at the  University Hospital in Strasbourg  for technical assistance. We thank  Nadine Borel  and the zebrafish house team. We also thank our collaborators for screening their negative cohort (KM. Bujakowska,  E. Pierce ,  P. Beales ,  F. Hildebrandt ). We thank the  GenomEast platform (Strasbourg, France) especially Serge Vicaire  for library preparation,  Céline Keime  for supervising the transcriptome analysis as well as  Bernard Jost  and  Christelle Thibault.
FUNDING INFORMATION Whole exome sequencing was funded by Retina France (100 exomes Program), by Hôpitaux Universitaires de Strasbourg  (API 2013 -2014 HUS N° 5885). RNASeq analysis performed in patients with IQCE pathogenic variant and other patients were funded by Fondation Maladie Rare and Fondation Jérome Lejeune. CE and US were supported by the HGF Biointerfaces Programme. AEC is supported by Fondation JED-Belgique. CD is supported by a FRM grant (ECO20170637509).
Control screening IQCE exon 12 was tested in 93 control DNA of matched origin (Algerian) and 93 multi ethnic. High resolution melting (HRM) assay was performed in a 20 µl reaction volume containing 50 ng of template DNA, 15 µl Precision Melt Supermix (Bio-Rad) and 10 µmol of each primer of the primer set. PCR was performed using a CFX96 (Bio-Rad) thermal cycler and consisted of predenaturation (98°C for 2 min) followed by 35 cycles of amplification involving denaturation (98°C for 5 s), annealing (60°C for 10 s) and a melting curve involving denaturation (95°C for 30 s), annealing (70°C for 30 s) and a melt curve (72-95°C with an increment of 0. 2°C per 10 s). Data generated were analyzed using Precision Melt Analysis software (Bio-Rad). In HRM analysis, differences in Tm and normalized curve shape were used together to discriminate even the most difficult-to-detect sequence variation.
Differential expression RNA-seq In order to identify variation in mRNA expression due to the absence of IQCE in the patient cells, comparisons of read counts were performed using R 3. S1. Summary of the whole exome sequencing results. SNV: Single nucleotide variation, indel: gain or loss of up to 50 nucleotides at a single locus, SV: Structural Variation. Annotations are gathered using Alamut Batch v1. 11, especially for the variation databases including gnomAD (v2. 0. 2, Oct. 2017), 1000 Genomes Project phase3 release (version 20150813 v5b) and the following predictions including phastCons (UCSC, 44 vertebrates). Effect on the splice has been evaluated using the MaxEntScan  (Yeo & Burge, 2004 ), NNSPLICE 0. 9  (Reese, Eeckman, Kulp, & Haussler, 1997)  and Splice Site Finder  (Shapiro & Senapathy, 1987)  by calculating score change between the wild type and the mutated sequences expressed as a percent differences.
89 61 SV SNV indel SV SNV Indel SV 63. 707 SNV Indel SV 4 rs10434219 rs344122 62598689 77637502 15. 04 175 85. 936 6 Total number of variants rs2076506 6_32485853_C_T 22570064 83129 32485853 11664 9. 92 31 646 81972 11319 17 82116 11195 16 15. 350 78906 11727 31 6 rs1059547 rs12189627 32557529 41059458 8. 50 309 27. 514 TULP1 7 7 After exclusion of variants with an allele frequency >1% (gnomAD, 1000G, internal exome database, DGV) rs4916935 rs798479 197267 rs7794563 rs80024169 5347295 9771 2691201 14775824 1125 2. 49 9. 43 41 78 16 7069 60. 828 120. 879 993 IQCE 6 6856 943 9 5323 982 17 7 8 8 rs4718101 After exclusion of SNV/indel in 5'UTR, 3'UTR, downstream, rs6460315 64152353 upstream, intron and synonymous locations without local splice effect prediction rs3989699 rs13271468 7218621 8_12273335_G_A rs66480340 12273335 66289040 788 12236202 24211885 2.
Some studies have shown moderate-to-vigorous physical activity (MVPA) to be associated with lower incidence of type 2 diabetes  [10] [11] [12] ; one study examined the shape of the association and showed that any duration of MVPA reduced the risk  [11]. In addition, type 2 diabetes diagnosis in these studies was obtained from interval-censored data (i. e. intermittent rather than continuous assessments over follow-up) but this feature was rarely addressed in the analyses. These studies may have produced biased results as they did not address the competing risk of death that occurred between two cycles of diabetes ascertainment  [13]. Finally, most previous studies had a mean follow-up of less than 10 years and thus were uninformative on the potential long-term protective effect of physical activity on type 2 diabetes incidence  [7, 14].
Physical activity assessment Over the follow-up from 1985-1988 to 2015-2016, physical activity was assessed seven times using a questionnaire; these measures of physical activity have been shown to be associated with cardiometabolic outcomes in the Whitehall II cohort study  [26] [27] [28] [29]. Data on physical activity were drawn from 1985-1988 for the first aim and from the first available wave after diabetes diagnosis for the second aim. During the 1985-1988 and 1991-1993 assessments, participants were asked about the duration and frequency of participation in mildly energetic (e. g. weeding, general housework, bicycle repair), moderately energetic (e. g. dancing, cycling, leisurely swimming) and vigorous physical activity (e. g. running, hard swimming, playing squash) by giving examples for each intensity of physical activity.
Ascertainment of type 2 diabetes We identified individuals with type 2 diabetes using data from clinical assessments between 1985 and 2016 and linkage to the national Hospital Episode Statistics (HES) database (until 31 March 2017) which was undertaken using the National Health Service (NHS) identification number. The following diagnostic criteria were used  [35] : fasting glucose ≥7. 0 mmol/l (126 mg/dl), use of diabetes medication, reported physician-diagnosed diabetes or HES record of diabetes (ICD-9 code 250 [www. icd9data. com/2007/Volume1] or ICD-10 code E11 [http://apps. who. int/classifications/icd10/browse/ 2016/en]). Participants with at least one of these criteria were considered to have type 2 diabetes.
Mortality outcome Mortality data were collected until 31 March 2017 from the British national mortality register (NHS Central Register). The NHS identification number of each participant was used to carry out the tracing exercise. Deaths related to CVD were identified using ICD codes for CHD (ICD-9 codes 410-414, ICD-10 codes I20-I25) and stroke (ICD-9 codes 430, 431, 434, 436, ICD-10 codes I60-I64).
Covariates For the first aim, covariate data were drawn from 1985-1988 and for the second aim from the first available wave after diabetes diagnosis. Sociodemographic variables included age, sex, ethnicity (white/non-white), marital status (married/ cohabitating vs others), education (less than primary school [up to age 11], lower secondary school [up to age 16], higher secondary school [up to age 18], university, higher university degree) and occupational position (administrative, professional/executive, clerical/support). Behavioural factors included smoking (never, past, current), alcohol consumption (none, 1-14 units/week, >14 units/week), and fruits and vegetables consumption (< once a day, once a day, ≥ twice daily). BMI was calculated using the measures of weight and height at the clinical examination and categorised as <20, 20-24.
9, 25-29. 9, and ≥30 kg/m 2. Starting from 1991-1993, the 36-Item Short Form Health Survey (SF-36) was used to assess general health and quality of life, from which the physical component summary (PCS) and mental component summary (MCS) were derived and included in the analysis  [36]. Cardiovascular health-related factors comprised hypertension (systolic/diastolic BP ≥140/90 mmHg, or use of antihypertensive medications) and total cholesterol assessed at the clinical examination, self-reported use of CVD drugs (antiplatelet, diuretic, antihypertensive, lipid-lowering and anticoagulant medication, and β-blockers), and prevalent CVD, including CHD identified by study-specific assessments (12lead resting ECG recording coded using the Minnesota system) and linkage to HES database (ICD-9 codes 410-414, ICD-10 codes I20-I25, or procedures K40-K49, K50, K75, U19) and stroke determined using linkage to HES database (ICD-9 codes 430, 431, 434, 436 and ICD-10 codes I60-I64).