dataesr/scientific-paragraphs-categorization huggingface dataset:
Study measures The MEDEA Study assessments include a baseline (M0) assessment (at time of enrollment), two "on-health    1. All patients in MEDEA are provided with Actigraph accelerometers  [60] , recording the duration and intensity of PA. Readings are obtained centrally through the use of the ACTILIFE software  [61]. Participants do not receive any feedback from the accelerometer unit, regardless of the study arm. Participants wear the accelerometer for 7 days at all four time points (M0, M6, M12, and M18). Accelerometers are mailed to participants by the study manager along with an instruction sheet regarding its use and a diary form to indicate the dates that the accelerometer was worn. Participants return the accelerometer to Gustave Roussy site along with a pre-addressed return mailer.
For the first visit, they wear the accelerometer before the start of the intervention if randomized to arm 2. For subsequent assessments, a pre-paid mailer is included in the packet so that participants can directly return materials to Gustave Roussy. Dietary assessment is performed using a 94-item food frequency questionnaire  [62]. Weight assessments are performed by health care professionals in the clinic during follow-up visits. Direct medical costs and indirect costs will be assessed from the perspective of the French national health insurance using data linkage to SNDS ("Système national des données de santé" -National Health data system [63]) database for each patient (indirect matching using the date of birth, zip code of the patient's residence, type/ date/hospital identification code for any breast cancer treatment, health insurance characteristics).
Data collection, management, and auditing The Biostatistics and Epidemiology Unit at Gustave Roussy implemented an electronic case report form (eCRF) to allow secure online-direct data collection, using the Trialmaster software suite. Each user has personal identifiers (user ID/password), and data access is strictly limited according to profiles: (1) hospital clinical research assistant (CRA), allowing data entry on the eCRF; (2) data manager, allowing the first data monitoring, perform consistency checks, and edit requests for clarification addressed to the investigator or hospital CRA; and (3) investigator profile, enabled to sign and validate the data electronically. An e-learning is mandatory to access the eCRF. The password is configured when the profile is activated and must be changed every 6 months.
For each patient included, the eCRF has to be completed by hospital CRA and signed by a study investigator. An audit trail within the system tracks all the changes made to the data. The data collected through the eCRF are the data source for the analysis. Data collected will be managed in the Biostatistics and Epidemiology unit at Gustave Roussy. Standard institutional practices will be followed to maintain the confidentiality and security of data collected in this study. A copy of the consent form and documentation of consent will be stored in a locked cabinet or an encrypted, password-protected computer drive. All protected health information collected from the study eCRF will be encrypted and password-protected. Questionnaires filled out on paper are stored in a locked cabinet in a secured office in Gustave Roussy.
e. , all analyses will retain each participant in the group he was randomly assigned regardless of missing data or drop-out status). The primary analysis of MEDEA will compare the primary endpoint of CRF scores at the M12 postrandomization time point between arms. This comparison between arms will employ a linear mixed model with random patient effect using time as a categorical variable, treatment arm, and a treatment by time interaction with patient-reported CRF measured by appropriate EORTC QLQ-C30 subscale scores. In a sensitivity analysis, baseline characteristics will be added as fixed covariates to the model. All other measures/time points/analyses will be considered secondary or exploratory analyses. Similar analyses as those specified for the primary endpoint will be performed for other scales.
Funding The MEDEA Study is supported by a grant from the French National Cancer Institute (Institut National du Cancer [InCA]; grant RISP 2018-13684. The Funding institution has no role in the study design, collection, management, analysis, and interpretation of the data; writing of the report; or the decision to submit the report for publication. ADM is supported by a Career Pathway Grant in Symptom Management from Conquer Cancer, the ASCO Foundation and Rising Tide Foundation for Clinical Cancer Research. MEDEA is sponsored by Gustave Roussy. Study responsible at sponsoring institutions: Delphine Vuillier-Le Goff, Responsable du Service Promotion des Etudes Cliniques [SPEC] Direction de la Recherche Clinique -Gustave Roussy, Villejuif, France-contact: +33 (0)1 42 11  65 42). The role of the sponsoring institution is limited to data collection and management.
Genotyping and haplotyping pipeline We followed the GATK best practice pipeline (Van der Auwera et al. 2013) including haplotype phasing-by-transmission, as applied in  Duranton et al. (2018). All scripts used in the pipeline are available in the Supplementary Scripts. We generated seven different datasets with various levels of filtering, and with or without haplome phasing, which are described in the Supplementary Data (see Table  S5  for details). All analyses were made using the newly available C. robusta assembly as the reference genome (GCA_009617815. 1; Satou et al. 2019). As a cautionary note, analyses in Le  Moan et al. (2021)  were made using the previous C. robusta reference genome published in 2011 (GCA_000224145. 1), therefore coordinates do not correspond between the two studies.
After quality control with FastQC v0. 11. 2, reads were aligned to the C. robusta reference genome using BWA-mem v0. 7. 5a  (Li and Durbin 2009) , and duplicates were marked using Picard v1. 119. The individual bam files of the introgression hotspot were used as dataset #7. The mean read depth was 21x across all samples (Table  S1 ). A series of steps were then performed using GATK v3. 4-0  (McKenna et al. 2010) , including: i) local realignment around indels, ii) individual variant calling in gVCF format using the HaplotypeCaller (options: dontUseSoftClippedBases, heterozygosity=0. 01, minimum base quality score=30), iii) joint genotyping using GenotypeGVCFs (heterozygosity=0. 01), iv) genotype refinement based on family priors. Hard-filtering was then applied to the SNPs and indels to produce a database of high-confidence variants.
The database was then used to recalibrate variant quality scores with the VQSR algorithm. After recalibration, a second round of genotype refinement based on family priors was applied. We then introduced a step of genotype verification (and correction where required) to check for reference bias and miscalling. First, we computed the individual variant allele fraction (VAF) at each site, i. e. the ratio of the alternate allele depth to the total (alternate+reference) depth. Then, a distribution across sites was plotted for the three possible genotypes (homozygous reference: 0/0, heterozygous: 0/1, homozygous alternate: 1/1). While the distributions for the homozygous genotypes were shaped as expected (i. e. 99% of the sites had a VAF < 0. 1 for 0/0 and VAF > 0. 9 for 1/1), the distribution of heterozygous genotypes was normally distributed around VAF=0.
5, but showed additional peaks near 0 (and near 1 to a lesser extent). Therefore, we corrected the miscalled 0/1 genotypes to 0/0 when the variant allele depth was below the 99th quantile of the 0/0 distribution and to 1/1 when it was above the 1st quantile of the 1/1 distribution. In addition, heterozygous genotypes with a VAF < ⅓ or > ⅔ were assigned as missing data (excluding the ones we corrected near VAF=0 or 1). We also considered as missing data the genotypes with a total depth below ten reads or above the 99th quantile of the depth distribution (to exclude repeated regions). Finally, low-quality variants were excluded from the VCF (QUAL<30), and we applied a stringent filter on individual genotype quality (GQ<30). Different missing data thresholds were then applied to produce datasets #2 (five missing genotypes), #5 (no missing genotypes allowed) and #6 (three missing genotypes).
Phased genomes were obtained using the tool PhaseByTransmission of GATK v3. 4-0. All trios were phased given parents and offspring genotype likelihoods, setting a de novo mutation prior to 1e-8 /bp/year (estimated for sea squirts in  Tsagkogeorga et al. 2012). Only sites where Mendelian transmission could be determined unambiguously were phased. The non-missing phased SNPs were then used as a reference panel for BEAGLE v4. 0  (Browning and Browning 2007). BEAGLE was run without imputing genotypes (impute=false) on the filtered VCF, with all variants being unphased. The parent-offspring relationships in the reference panel were specified to inform phasing-by-transmission with BEAGLE, except for the five C. roulei samples, which were not included in a trio and were statistically phased. Datasets #1, #3 and #4 are based on this phased VCF.
Analyses of population structure We used a Principal Component Analysis (PCA) to assess the partition of genetic variation in our sample of 45 individuals (i. e. all individuals except the three poorly sequenced parents, and the C. edwardsi individuals). SNPs were LD-pruned with PLINK v1. 9  (Purcell et al. 2007 ) using a window size (WD) of 20 SNPs, a window step size (CT) of 5 SNPs and a linkage threshold (r 2 ) of 0. 1. PLINK was then used to run a PCA on the unlinked SNPs. We recorded the amount of genotypic variance explained by each principal component (PC) and the SNP weights on each PC. Only the first two PCs were relevant to visualize population structure and were plotted using the R package tidyverse. We used VCFtools v0. 1. 15 (Danecek et al. 2011 ) on all SNPs to calculate the per-site nucleotide diversity (site-pi) in each population and the per-site F ST  (weir-fst-pop, Weir and Cockerham 1984)  between populations.
Detection of introgression with summary statistics To evaluate the extent of genome-wide admixture, we computed the D-statistic  (Green et al. 2010; Patterson et al. 2012 ) from a polarized set of SNPs using the outgroup species, C. edwardsi. The following topology was applied: ((( P1 = C. roulei ; P2 = C. intestinalis) ; P3 = C. robusta ) ; O = C. edwardsi ). Therefore, a positive value of D indicates an excess of ABBA sites, and so an excess of shared ancestry of C. robusta with C. intestinalis over that shared with C. roulei. We also estimated the fraction of the genome introgressed with the fd statistic  (Martin et al. 2015) , calculated in non-overlapping windows of 100 SNPs. The D and fd statistics were computed following Simon Martin's tutorial: https://github. com/simonhmartin/tutorials/blob/master/ABBA_BABA_whole_genome/README.
Detection of introgression with local ancestry inference We used Chromopainter (available in fineSTRUCTURE v2. 0. 7) to perform local ancestry inference based on the phased dataset. C. intestinalis was considered as the recipient population, while C. robusta and C. roulei (the latter being a non-introgressed population of C. intestinalis) were the donor populations. We used ten iterations of the expectation-maximization algorithm to estimate the probability of each position along each C. intestinalis haplotype to come from C. robusta or C. roulei. We then determined the boundaries of each ancestry tract. A given position was considered originating from C. robusta if this probability was >0. 95. To define the tracts, an extension from this focal position was then made as long as this probability was above 0.
Testing for selection Selection for an adaptive variant is expected to reduce haplotype variation in flanking regions, producing unusually long haplotypes  (Sabeti et al. 2002). To capture such a signal, we measured the extended haplotype homozygosity (EHH) score from the phased dataset using SelScan v2. 0. 0  (Szpiech 2021). Target SNPs were identified as the C. robusta alleles with the highest frequency to the left (959,519 bp) and right (1,061,854 bp) of the "missing data region" on chromosome 5. The maximum extension from the target SNP for a single EHH computation was 100 Kb. Then, we calculated with SelScan the (absolute) integrated haplotype score (iHS). Values were normalized using the norm v1. 3. 0 utility with 100 frequency bins over 50-Kb non-overlapping windows. Finally, we estimated the proportion of SNPs in each window associated with extreme iHS values (iHS>3, which refers to the 99th quantile of the iHS distribution).
We also tested for the footprint of selective sweeps using SweepFinder v2. 0 (DeGiorgio et al. 2016) and adaptive introgression using VolcanoFinder v1. 0  (Setter et al. 2020). These methods are based on polarized SNPs (using the outgroup species C. edwardsi) and do not use phase information. Chromosomes were scanned with the two methods by applying a log-ratio test for selection at test sites spaced by 1Kb. Finally, SplitsTree4 V4. 17. 0  (Huson and Bryant 2006)  was used on the phased dataset to produce neighbor-joining trees from 50-Kb windows framing the "missing data region" on chromosome 5.
Analyses of copy number variation To overcome the absence of genotyping in the "missing data region" (due to our filtering of repeated regions), we analyzed the read depth of the variants directly from the unfiltered bam files. Counts of the reference and alternate alleles were collected with GATK (CollectAllelicCounts) from the bam files, excluding duplicate reads and positions with a base quality (BQ) < 20. Candidate SNPs were defined based on their variant allele fraction (VAF = alternate read depth / total read depth). The following criteria were applied to identify variants differentiated between C. robusta and C. roulei (the latter is used as a non-introgressed C. intestinalis population), and introgressed into C. intestinalis: VAF <= 50% in C. intestinalis, VAF >= 85% (or 90%) in C.
Parameters were as follows: T = times in years (assuming two generations per year in European waters), Ne = effective population sizes in numbers of individuals, m = migration rates (independently estimated in both directions), %Barriers = fraction of the genome experiencing null migration (i. e. species barriers and their associated loci), %Ne reduced = fraction of the genome experiencing reduced Ne due to background selection, HRF = factor by which Ne is reduced. See Tables  S3  and S4  for details. The scripts used to define the demographic models and run the inferences are available in the Supplementary Scripts. Each demographic model was then fitted to the observed jSFS, with singletons masked. We ran five independent runs from randomized starting parameter values for each model. Likelihood optimization was performed using a "dual annealing" algorithm (optimize_dual_anneal).
The introgression hotspot on chromosome 5 is highlighted with an arrow. Dataset #1 "phased SNPs with offspring" was used. The intraspecific F1 individuals produced in the lab (Table  S1 ) fall within the genetic variance of their species, while the interspecific F1s fall halfway between the two species along the first axis (Figure  1B ), validating their F1 hybrid status. The intraspecific variance along the first axis was substantial within C. intestinalis. At the same time, this was not the case for C. robusta and C. roulei, suggesting that interspecific introgression affects specifically C. intestinalis individuals in the English Channel. The SNPs contributing to species divergence on the first axis are distributed genome-wide (Figure  1C ). Still, a decline of SNP contribution on the first axis at the start of chromosome 5 indicated a reduction of the divergence between C.
The maximum tract length is 156 Kb, the average is 2. 6 Kb, and the median is 0. 38 Kb. A blue dashed line depicts the 95th quantile of the length distribution (8. 3 Kb), and it was used as a threshold to delineate long tracts. A total of 38 of 57 long tracts were detected on chromosome 5 (red portion of each bar). C. Allele frequency of the SNPs lying on the C. robusta introgressed tracts (n=621,249 variants). The maximum frequency is 1, the average is 0. 14, and the median is 0. 08. On the right, the frequency of variants lying on short tracts (upper panel) or long tracts (lower panel) is depicted. The red portion of each bar indicates the tracts on chromosome 5. Allele frequency was calculated, excluding any position with missing data. Dataset #3a "phased SNPs" was used.
robusta in Europe (Figure  S1 ). Repeating the demographic analyses with chromosome 5 in the dataset led to very similar parameter estimates, except for the divergence times (Figure  S5  and Table  S4  for details). Indeed, the best model was now a secondary contact, where a long period of isolation (~2 My) was followed by a contemporary period of introgression (in the last 200 years), which may capture the signal left by the long introgressed tracts on chromosome 5. We used a neutral recombination clock to refine the time estimate since admixture at the introgression hotspot on chromosome 5. The average length of the introgressed tracts can be estimated using the formula L̄= [ (1f ) * r * (t -1) ] -1 , where r is the local recombination rate (crossovers per base pair per generation), f is the admixture proportion, and t is the time since the admixture event in generations  (Racimo et al.
But we found no evidence of introgressed tracts in the hotspot that have completely swept to fixation in C. intestinalis (Figure  4C ). To explicitly test if some sort of selection could explain this pattern on chromosome 5, we used various methods. We first sought the footprint of a classic selective sweep, where a de novo beneficial mutation arises on a C. robusta haplotype and quickly sweeps toward fixation, reducing diversity and   S3. Dataset #5 "all SNPs without missing data" was used, excluding chromosome 5. creating a signal of long-range LD around it. This signal can be captured by the extended haplotype homozygosity (EHH), which measures the decay of identity-by-descent between haplotypes as a function of the distance from a focal SNP. Taking as targets the SNPs with the highest C.
A blue dashed line depicts the 95th quantile of the length distribution (8. 3 Kb), and it was used as a threshold to delineate long tracts. A total of 38 of 57 long tracts were detected on chromosome 5 (red portion of each bar). C. Allele frequency of the SNPs lying on the C. robusta introgressed tracts (n=621,249 variants). The maximum frequency is 1, the average is 0. 14, and the median is 0. 08. On the right, the frequency of variants lying on short tracts (upper panel) or long tracts (lower panel) is depicted. The red portion of each bar indicates the tracts on chromosome 5. Allele frequency was calculated, excluding any position with missing data. Dataset #3a "phased SNPs" was used.
Five replicates were run for each model. Parameters are as follows: T = times in years, assuming two generations per year in European waters (the "GF" label refers to gene flow), Ne = effective population sizes in numbers of individuals, m = migration rates (direction given by the arrow), %Barriers = proportion of the genome with null migration, %Nereduced = fraction of the genome experiencing reduced Ne, HRF = factor by which Ne is reduced. Full details are provided in TableS3. Dataset #5 "all SNPs without missing data" was used, excluding chromosome 5.
robusta allele closest to the grey band as target SNP. Absolute iHS was calculated based on the EHH results and normalized in windows of 50 Kb. The threshold value of the normalized iHS was set to 3 (which refers to the 99th quantile). F. Neighbor-joining tree of a 50 Kb window to the left of the "missing data region" at the center of the chromosome 5 hotspot. Colored dots are red, dark blue and light blue for individuals of C. robusta, C. instestinalis from Brest and C. intestinalis from Aber, respectively. Dataset #2 "all SNPs with missing data" was used for the FST, #3a "phased SNPs" for Chromopainter haplotypes, #3c "FASTA version of phased SNPs" for the NJ tree and #4 "ancestry informative phased SNPs" for the LD triangle.
Acknowledgements The authors are grateful to the divers of the  Marine Operations department (Service Mer & Observation) at the Roscoff Biological Station  for the Ciona sampling in Brittany, and to  Marine Malfant ,  Sebastien Darras  and divers of the  laboratory Arago in Banyuls-Sur-Mer  for providing the Ciona edwardsi and C. roulei samples, and  Charlotte Roby  for DNA extractions. The authors are very thankful to  Jerome Coudret  and  Sarah Bouchemousse  for their help in carrying the laboratory crosses at Roscoff. We thank  Véronique Dhennin  from the  LIGAN genomics platform (Lille, France) , and  Nicolas González  from the  FASTERIS platform (Plan-les-Ouates, Switzerland). This work benefited from the  Montpellier Bioinformatics Biodiversity  platform supported by the  LabEx CeMEB , an  ANR  " Investissements d'avenir "  Christelle Fraïsse  et al.
Funding This work benefited from funding of the  French National Research Agency (ANR)  with regards the  ANR  Project  HYSEA  (no. ANR-12-BSV7-0011 ). It also benefited from the  MarEEE  project funded through the  French National Research Agency (ANR)  under the " Investissements d'Avenir " programme with the reference  ANR-16-IDEX-0006  (i-site MUSE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Supplementary Data Datasets used in the analyses (see Table  S5  for a detailed description). Dataset #1: phased SNPs with offspring. joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2. clean. biallelic. noindels_filter_phased_p hasedBeagle. vcf Dataset #2: all SNPs with missing data. joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2. clean. biallelic. noindels_filter_parents_o NA5. vcf Dataset #3a: phased SNPs. joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2. clean. biallelic. noindels_filter_phased_p hasedBeagle_parents. vcf Dataset #3b: CDS version of "phased SNPs". joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2. clean. biallelic. noindels_filter_phased_p hasedBeagle_parents_SNP_CHRall. orf. cds Dataset #3c: FASTA version of "phased SNPs".
joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2. clean. biallelic. noindels_filter_phased_p hasedBeagle_parents_SNP_chr5. sub_${START}-${END}. fasta Dataset #4: ancestry informative phased SNPs. joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2. clean. biallelic. noindels_filter_phased_p hasedBeagle_parents. frq. fixed. vcf Dataset #5: all SNPs without missing data. joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2. clean. biallelic. noindels_filter_parents_o NA. vcf Dataset #6: all polarized SNPs with missing data. joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2. clean. biallelic. noindels_filter_parents_r aw_refine_HQ_edwardsi_oNA3_polar. vcf Dataset #7: unfiltered mapping files. ciona_bwa-mapping_${IND}_bwa_mem_mdup_IR_chromosome5:700000-1500000_sorted_nodup.
38 Kb. A blue dashed line depicts the 95th quantile of the length distribution (8. 3 Kb), and it was used as a threshold to delineate long tracts. A total of 38 of 57 long tracts were detected on chromosome 5 (red portion of each bar). C. Allele frequency of the SNPs lying on the C. robusta introgressed tracts (n=621,249 variants). The maximum frequency is 1, the average is 0. 14, and the median is 0. 08. On the right, the frequency of variants lying on short tracts (upper panel) or long tracts (lower panel) is depicted. The red portion of each bar indicates the tracts on chromosome 5. Allele frequency was calculated, excluding any position with missing data. Dataset #3a "phased SNPs" was used.