dataesr/scientific-paragraphs-categorization huggingface dataset:
| Input data for analyses: Genotype likelihoods and single read sampling In order to take putative uncertainties in genotype calls due to the low sequencing depth into account, almost all of the population genetic analyses in this study relied on genotype likelihoods or single read sampling. Both were obtained using angsd limited to the quality filtered loci in the postQCloci set described above and using only reads with mapping scores of at least 30 and bases with a Phred quality score of at least 20. The genotype likelihoods were estimated using the SAMtools mode (-gl 1) in angsd. The single read sampling was performed using angsd -doIBS 1, which randomly samples a single base at each position for each sample (for details about the angsd commands used to generate the input files for each analysis, see Table  S6 ).
| PCA and admixture analysis We investigated the global population structure of all the harbour seals first by applying PCA, using pcangsd based on genotype likelihoods for all the loci in postQCloci that were polymorphic and had an MAF above 0. 05  (Meisner & Albrechtsen, 2018). We set the maximum number of iterations to 1,000 to ensure convergence. We also estimated admixture proportions for different groups of populations by applying ngsadmix  (Skotte et al. , 2013)  to the same data set with the number of assumed populations (K) ranging from 2 to 20. For each K, we performed the analysis independently 200 times, in order to assess convergence, which we defined as having the top five maximum likelihood results within 2 log-likelihood units. We subsequently evaluated the model fit of the admixture analysis using evaladmix (Garcia-Erill & Albrechtsen, 2020), which calculates the residuals under each model, and plotted the pairwise correlation of the residuals matrix between individuals.
| Historical relationships among populations To infer historical relationships among the sampled harbour seal localities, we built a population tree using treemix  (Pickrell & Pritchard, 2012). treemix uses allele frequency data to infer the order in which different populations split from each other historically, and its output is analogous to phylogenetic trees for representing the evolution of species. To prepare the allele frequency input, we first took the single read sampling data from all the loci in the postQCloci set that were polymorphic with an MAF above 0. 05 and had data missing from fewer than 30 samples. Then, within each locality we counted the number of major and minor alleles, which were determined for all samples at each of the loci, and used these counts to estimate allele frequencies for each locality.
To improve the rooting of the inferred tree, we also included data from three grey seal (Halichoerus grypus) samples that were extracted, sequenced and bioinformatically processed in the same manner as the harbour seal samples. We performed the treemix analysis based on the estimated allele frequency data with a window size of 500 SNPs to account for linkage disequilibrium (-k 500), specifying the grey seal samples as an outgroup. The harbour seal samples from Tromsø (TRO) were excluded from the treemix analyses due to insufficient sample size (n = 3).
| Genetic differentiation, nucleotide diversity and isolation by distance To quantify the levels of genetic differentiation and diversity among and within sampling localities, we estimated a number of SFSs using the function realSFS in angsd with the default settings. Specifically, we estimated the 1d-SFS for each sampling locality and a 2d-SFS for each pair of sampling localities. To prepare input for realSFS, we first used angsd to generate saf files using genotype likelihoods for all loci in the postQCloci set. Once the SFSs were estimated, we calculated a global estimate of Hudson's F ST  (Hudson et al. , 1992) , which accounts for imbalanced sample sizes among populations, directly from the 2d-SFS for each population pair using a custom python script. We also calculated the net nucleotide divergence (Da) for each population pair, following the formula: Da = Dxy -Πx + Πy.
2 , where Dxy denotes the pairwise nucleotide differences between the populations and π denotes the nucleotide diversity within the population  (Cruickshank & Hahn, 2014). The statistics of Dxy and π were directly calculated from estimated the 2d-SFS and 1d-SFS, respectively, with a custom Python script. We further tested for associations between the genetic distances (F ST ) and geographical distances for all population pairs using Mantel permutation tests with the R package vegan  (Oksanen et al. , 2020). A significant association would indicate isolation-by-distance between harbour seal populations. The geographical distances were calculated based on the shortest waterway distance between locations, following https://rpubs. com/ beare do/AOTTP -Seawa y-Dista nces.
| Estimation of effective migration surfaces (EEMS) To identify potential migration corridors and barriers, we conducted an Estimation of Effective Migration Surfaces (EEMS) analysis  (Petkova et al. , 2015). As an input to EEMS, we calculated the matrix of average genetic distances between all pairs of harbour seals based on the single-read sampling data from all the loci in the postQCloci set that were polymorphic with an MAF above 0. 05. The geographical limits for each EEMS analysis were drawn manually (http://www. birdt heme. org/usefu l/v3tool. html), and EEMS was deployed using the runeems_snps program with the settings of 18 million steps of the chain including 3 million burn-in iterations and 600 demes. To ensure convergence, we ran 10 independent Markov chain Monte Carlo (MCMC) chains and extended the chain by 15 million iterations if the initial chain had not converged.
| Implications for harbour seal taxonomy The taxonomy of harbour seals is a matter of debate. Up to five subspecies have been recognized: P. v. stejnegeri-or Kuril seal-in the Northwest Pacific; P. v. richardii in the Northeast Pacific; P. v. concolor in the Northwest Atlantic; P. v. vitulina in the Northeast Atlantic; and the freshwater Ungava seal (P. v. mellonae)  (Davies, 1958; Rice, 1998). However, based on unclear delineations and indications of paraphyly in mitochondrial DNA data  (Stanley et al. , 1996) , these subspecies have been questioned, and only three subspecies-P. v. richardii in the Pacific, P. v. vitulina in the Atlantic and the Ungava seal (P. v. mellonae)-are recognized by some researchers  (Berta & Churchill, 2012; Committee on Taxonomy, 2021). The genomic analyses presented herein serve to resolve some of these issues, while also raising new ones.
When microsatellite makers revealed clones, only one representative genotype was kept. Preliminary delimitation of Ceratonia siliqua evolutionary units using microsatellite data We selected localities with at least ten carob genotypes per population. We used 17 microsatellite markers for genotyping 1019 individuals collected in 56 localities across the Mediterranean basin as described in Table  S1. We also scored 15 SNPs present in the flanking regions of the 17 SSR loci following  Viruel et al. (2018). We calculated genetic differentiation between localities using the D index  (Winter, 2012; Mmod R package)  and converted it into Euclidean genetic distances based on the coordinates of an NMDS (Nonmetric Multidimensional Scaling; vegan R package). The Euclidean genetic distances and Euclidean geographic distances were then processed by ClustGeo  (Chavent et al.
94 (see Tab. S1 supplementary material). A reference file was generated by extracting the first sequence of each locus with pyrad2fasta script (available on https://github. com/pimbongaerts). Second, this file was used for a subsequent reference assembly for the 354 samples with the same parameters as previously except for the minimum number of samples per loci, which was fixed to 45. The dataset constructed using this reference assembly contained more than 64% missing data. The vcf file obtained from ipyrad was then processed to run a principal components analysis (PCA) using the glPca function of adegenet R package, and a neighborjoining tree, based on pairwise genetic differentiation (Gst, Mmod R package) among the seven CEUs, to check that the structure from RAD markers was congruent with previous analysis based on microsatellite markers.
We used Matrix condenser software (de  Medeiros and Farrell, 2018; de Medeiros, 2019)  to visualize missing data across samples aiming at maintaining a balance between reducing missing data and not significantly discarding SNPs. We filtered samples to optimize locus coverage and to keep the sampling equilibrium among CEUs and reconducted the reference assembly with ipyrad on a new set of 190 C. siliqua samples. The data was then reduced to one SNP per locus, based on two criteria: SNPs having the maximal minimum allelic frequency per locus and only keeping SNPs with an allelic frequency above 1. 05 % (i. e. , rarest allele present in at least two individuals). To examine the carob genetic diversity based on neutral processes, such as genetic drift and gene flow, we reduced the effect of outlier loci (i.
e. , having unexpectedly high differentiation among populations, which could have a great effect on the variance among populations). We used Outflank software  (Whitlock & Lotterhos, 2015) , which produces a lower false-positive rate compared to other methods (e. g. Silliman  2019 ), considering CEUs as populations. The false discovery rate (qvalue) was fixed to 0. 05. BLAST searches using the nucleotide NCBI database limited to flowering plant sequences were conducted for outliers, which were mostly assigned to plastid (pDNA) or mitochondrial (mtDNA) genomes. These outlier loci were removed to create a final data set of 3557 SNPs (one SNP/locus) for 190 C. siliqua and 4 C. oreothauma. Since a subsequent Outflank scan failed to detect further outliers (see results), we considered this set of SNPs as a genome-wide data set of neutral markers.
Recovery of plastid and mitochondrial data To obtain plastid and mitochondrial markers we performed ipyrad reference assembly with default parameters, and changed the maximum alleles per site to one. We used NCBI reference sequence NC-047061. 1, a plastid genome of C. siliqua produced by Zhang et al. (2020) , as a reference for plastid assembly, and for mitochondrial assembly, we used the Genbank sequence MW448447, the mitochondrial genome of C. siliqua produced by  Choi et al. (2021). In both cases, we produced data sets with and without missing data by using trimAl  (Capella-Gutierrez et al. , 2009).
Analysis of genetic differentiation and admixture The final data set of 3557 SNPs and 190 C. siliqua was converted from genind to genlight, vcf and treemix data formats using dartR  (Gruber et al. , 2018)  and radiator  (Gosselin, 2020)  R packages. Population structure analysis was performed using snmf (R LEA package,  Frichot and François, 2015) , which estimates admixture coefficients from the genotypic matrix assuming K ancestral populations. Snmf runs fast even on large data sets and without loss of accuracy compared to other Bayesian modelling software such as ADMIXTURE  (Alexander et al. , 2009; Frichot et al. , 2014). Snmf was run for K = 2 to 15, 100 repetitions, regularization parameter set to 250 and 25% of the genotypes masked to compute the crossentropy criterion. Barplots showing ancestry coefficients were obtained with the compoplot function in adegenet R package  (Jombart and Ahmed, 2011) , with genotypes sorted according to CEUs.
Screening for footprints of domestication in RADseq data Footprints of domestication were investigated by estimating genetic diversity and/or the presence of candidate loci under selection (outliers) for the CEUs. A stronger effect of domestication is expected in cultivated habitats compared to wild and seminatural habitats, and in the eastern CEUs compared to the western ones  (Zohary, 2002). Therefore, the analyses were organized according to combinations of these two factors. Observed heterozygosity (H obs ), Nei genetic diversity (H exp ), inbreeding coefficient (f) and the standardized association index (̅ d ), which indicates a lack of genetic mixing when it increases, were estimated using diveRsity and poppr R packages  (Keenan et al. , 2013; Kamvar et al. , 2014). Outliers were searched with the Outflank method with an FDR threshold of 0.
Assembly of RADseq loci and SNPs filtering The de novo assembly conducted on 36 samples, with a clustering threshold of 0. 94 and a minimum number of samples by loci of 30, produced a mean number of 48,828 loci by sample (Tab. S1), which were reduced to 13,371 loci retained after ipyrad filtering. The sequences of these loci were used as a reference for the assembly of 354 genotypes for 10,012 loci which revealed a pattern of genetic structure roughly congruent with microsatellite pattern of differentiation (Fig. S2 ). However, this data presented an overall missing data rate of 64%. To overcome the limitations of that 64% of missing data could have in our results, we searched for genotypes and loci having the highest missing rate as implemented in Matrix condenser, and their subsequent removal produced a matrix with 190 samples and 12,767 loci, with 14% missing data rate.
RADseq data further resolved these genetic structuring across the Mediterranean by identifying seven genetic clusters (Fig. 4 A, C ), which in some cases fully matched with a CEU (e. g. W1) or two CEUs (W3, W4), whereas, in other cases, a mixture of more than one genetic cluster was observed (e. g. C1). These data permit a better interpretation of the genetic diversity patterns between CEUs and are thus important for future designs of ex situ conservation actions. Our results suggest that a moderate genetic diversity is uniformly distributed across CEUs (Table  2 ). Only a slightly higher genetic diversity was estimated in western CEUs mostly based on SSR loci. Although W2 and C1 CEUs are highly admixed, these factors did not entail an increase in genetic diversity compared to nonadmixed clusters.
Acknowledgments This study is part of the  DYNAMIC  project supported by the  French national agency of research  ( ANR-14-CE02-0016 ) and benefited from equipment and services from the molecular biology facility (SCBM) at  IMBE (Marseille, France ). All bioinformatics and simulations were done on the High-Performance Computing Cluster from the  Pytheas informatic facility (OSU Institut Pytheas Aix Marseille Univ ,  INSU-CNRS UMS 3470 ). J. V. benefited from a  Postdoc Fellowship  funded by  DYNAMIC  and a  Marie Skłodowska-Curie Individual Fellowship (704464 -YAMNOMICS -MSCA-IF-EF-ST ). The authors thank for their help to complete our sampling:  Annette Patzelt  ( Oman Botanic Garden ),  Minas Papadopulos  ( Department of Forests of the Republic of Cyprus ),  Zahra Djabeur  ( Oran University ),  Nabil Benghanem  ( Tizi-Ouzou University ),  Gianluigi Bacchetta  ( Cagliari University ),  Sonja Yakovlev  ( Paris-Sud University ),  Errol Vela  ( Montpellier University ),  Maria Panitsa  ( Patras University ), and the services of  Junta de Andalucia.
Data Availability Statement Full information on population sampling and microsatellite data are available in  Viruel et al. (2020)  and deposited in DRYAD (https://doi. org/10. 5061/dryad. k7m020r). Raw RADseq reads are deposited at NCBI SRA database under Bioproject accession PRJNA793764. Assemblies from ipyRAD (vcf files), filtered R data files and R scripts of analyses are available at https://osf. io/t5m8y/.
Study design The NeoFit study is a prospective, national, multicenter, single-arm, open-label study promoted by the Institut Curie (Paris, France) (Fig. 1 ). It will enroll 300 participants from two comprehensive cancer care centers in France, beginning in September 2021 during a one-year period. This trial protocol conforms to the rules of the Declaration of Helsinki and was approved by the appropriate French ethics committee CPP Est I (ID RCB: 2021-A00268-33). The study database has been registered with the French national data protection authority (CNIL) (reference number: reference: 2,220,932 v 0). The study has been registered with http:// www. clini caltr ials. gov (NCT number: NCT05011721, registration date: 18/08/2021). Written informed consent will be obtained from each participant.
Setting up the activity tracker and explaining its use Participants will be asked to wear a Withings Steel HR activity tracker (Withings, Issy-les-Moulineaux, France) round the clock, throughout the study period. The Withings Health Mate mobile phone application will first be downloaded onto a smartphone or tablet at baseline, and the clinical research assistant will instruct the participant in the use of the activity tracker. The participant will then be asked to accept and activate the sharing of data collected with the dedicated "NeoFit" study server at Institut Curie. Participants will be asked to synchronize the activity tracker regularly (ideally daily) via Bluetooth with the Withings Health Mate application, for automatic data transfer to the secure "NeoFit" space. A clinical research assistant will optimize follow-up during the course of the study, via a platform checking the downloading of data from the activity tracker determining whether questionnaires have been completed within the time limit.
Clinical data The clinical research assistant will collect clinical data from the electronic medical records of the participants. The data collected will include information already analyzed in the care context and concerning disease status, neoadjuvant treatment, surgical data for biopsy and tumor specimens, adjuvant treatment and cancer progression. Toxicity data will be collected throughout the study. Toxicity will be defined according to the National Cancer Institute's Common Terminology Criteria for Adverse Events (NCI-CTCAE) v5. 0, as the occurrence of severe toxicity of at least grade 3.
REDCap eCRF The REDCap database is accessible via a protected Internet interface. User authentication is based on the of Institut Curie employees. On connection to the database, users are asked to confirm their identity using the credentials of their institutional accounts. REDCap has a strong authentication system, based on a "two-factor" authentication principle, with a validation code transmitted by SMS or e-mail at each connection. An audit trail system is used to trace any changes. Additions, deletions or modifications to the data are recorded, along with the identity of their authors. The REDCap database is hosted on the institutional MySQL database cluster managed by the IT department. Mirror databases are housed on two separate servers: a master server and a backup server in case of incidents.
Withings Application Programming Interface (API) A free guide to the use of the Withings API is available online (https:// devel oper. withi ngs. com). The data will be stored in a databased dedicated to the NeoFit project and will be accessible to only a few authorized members of the Data Office, for maintenance and automatic data feeding purposes, and to identified members of the project team for data analysis. Access to this database will be granted via dedicated user accounts. The Withings storage database is fed by an automatic routine, with users unable to perform manual interventions. None of the data are deleted or modified. Data can only be added, by the retrieval, at a predetermined frequency, of new measurements from the activity tracker. The Withings storage database is hosted on a dedicated instance within the data infrastructure, a virtualized infrastructure distributed over 12 physical servers.
Statistical analysis Sample size As the main objective of the study is descriptive, there is no need to define a minimum number of subjects. For model validation, we will divide our population into two groups: i) a training sample (n = 150) and ii) a validation sample (n = 100). The accuracy of the model for 150 subjects will therefore be about 8%. We hypothesize that 10% of the participants may be lost to follow-up. In the context of this study, that would mean not completing the questionnaires despite reminders, or not wearing the activity trackers. The inclusion of 300 participants is therefore expected to result in the collection of at least 250 completed questionnaires and 250 activity tracker datasets. CI = 2 × √ p × q n.
Statistical methods Qualitative variables, expressed as percentages, will be used for the descriptive analysis. The number of missing data will be recorded to check for dependence on study variables and to describe the individuals lost to followup during the study. Quantitative data will be described by means or medians, standard deviation, minimum and maximum values. Individual trajectories may also be plotted graphically. For the primary endpoint analysis, we will use mixed models with latent classes to identify different digital phenotypes. The use of mixed models will make it possible to analyze repeat data for the population, and to determine an average profile or trajectory for the whole population. The choice of shape for this trajectory will be determined by testing (linear, quadratic, cubic or spline shape).
Time series forecasting will be used to predict events from a series of previous observations. This will make it possible to predict physiological changes in the participant based on her care trajectory and a series of observations of her physiological state, a potentially valuable indicator of the patient's response to neoadjuvant treatment. No imputation procedure will be required for missing data as we will use a maximum likelihood approach. Such approaches are robust to noninformative missing data, and a joint model will be used to take informative missing data into account, if necessary. In terms of the development of algorithms and prediction models, we will: i) Compare the clinical data to a historical Institut Curie database containing data for patients receiving neoadjuvant treatment for BC collected between 2002 and 2011, ii) the characteristics of our population with those of worldwide users of Withings Steel HR activity trackers of the same age and with a similar BMI.
However, we will have no access to the medical histories of the other users, iii) Develop models for predicting treatment efficacy and toxicity. We will use the clinical data (personal information, pathological variables and treatmentrelated variables) and digital phenotype of the BC patient. Treatment toxicity will be evaluated with questionnaires based on the CTCAE criteria. Chemotherapy efficacy will be assessed by evaluating pathological complete response rates and determining the number of nodes sampled after NAC. We will train the model on 150 patients and validate it on the remaining 100 patients. iv) Develop models for predicting fatigue and changes in quality of life during treatment. We aim to develop a statistical model for detecting correlations between treatment-related variables (e.
Data monitoring The database for clinical data will be created with RED-Cap ® software. It will have secure access (personal ID and password required), with several security levels, depending on the role of the investigator. All questionnaires will be completed on an eCRF and merged with the clinical database and the activity tracker data at the end of the study. The trial steering committee will be responsible for data monitoring, including overall project supervision, the monitoring of progress, providing advice on scientific credibility and the appropriate running of the project, and ensuring its integrity. The clinical research assistant will check all consent forms, ensure compliance with the established protocol and procedures, and will verify the quality of the data in the eCRF monthly with the NeoFit professional dashboard.