dataesr/scientific-paragraphs-categorization huggingface dataset:
As the literature is very poor on the subject and no study reference is available, no formal sample size calculations were performed. Changes in outcome measurements between Day 0 and weeks 4 and 12 were assessed using analysis of variance (ANOVA) using a mixed-model for normally distributed data, and the Friedman test for non-parametric distributions. The resultant p-values were not adjusted for multiplicity and are reported primarily for illustrative purposes. All data analyses were performed using SAS version 9. 4 (SAS Institute Inc. North Carolina, USA).
2 ). Improvements in lymphedema-speci c QoL and global QoL scores were observed. At 12 weeks, scores decreased (indicating improved QoL) in each of the LyQLI physical (-0. 219, p = 0. 1099), psychosocial (-0. 221, p = 0. 05), and practical domains (-0. 143, p = 0. 6842), while global QoL assessed via EQ-5D VAS showed a 9. 5-point increase (indicating improved QoL) from baseline (p = 0. 18). When evaluated using the EQ-5D-5L health state questionnaire, 60. 7 % of patients in the ITT population reported a better QoL at 12 weeks. Sleep quality remained relatively similar across the study period (with mean scores of 8. 25 and 8. 11 at baseline and week 12 respectively). There was little change on self-reported impact of lymphedema on discomfort during sexual activity (data not shown).
Sampling, sequencing and environmental parameters Sampling, size fractionation, measurement of environmental parameters and associated metadata, DNA extraction and metagenomic sequencing were conducted as described previously  30, 31. Samples were collected at 113 Tara Oceans stations for six size fractions (0-0. 2, 0. 22-1. 6/3, 0. 8-5, 5-20, 20-180, 180-2000 µm; Supplementary Fig. 1b ; Supplementary Table  1 ) and two depths (subsurface and deep chlorophyll maximum (DCM)). The prokaryote-enriched size fraction was collected either a 0. 22-1. 6 µm or 0. 22-3 µm filter  18, 30. We used physico-chemical data measured in situ during the Tara Oceans expedition (depth of sampling, temperature, chlorophyll, phosphate, nitrate and nitrite concentrations), supplemented with simulated values for iron and ammonium (using the MITgcm Darwin model described below in "Ocean circulation simulations"), day length, and 8-day averages calculated for photosynthetically active radiation (PAR) in surface waters (AMODIS, https://modis.
Nearly all samples in our data set had at least 100 million reads (or at least 30 million for the 0-0. 2 µm fraction; Supplementary Table  1 ). We estimated b-diversity for metagenomic reads with the following equation within Simka: Metagenomic b-diversity = (b + c) / (2a + b + c) Where a is the number of distinct k-mers shared between two samples, and b and c are the number of distinct k-mers specific to each sample. We represented the distance between each pair of samples on a heatmap using the heatmap. 2 function of the R-package 34 gplots_2. 17. 0  35. The dissimilarity matrices we produced for each plankton size fraction (on a scale of 0 = identical to 100 = completely dissimilar) are available as Supplementary Tables  3 4 5 6 7 8.
Calculation of OTU-based community dissimilarity Within the 0-0. 2 µm size fraction, we used previously published viral populations (equivalent to OTUs)  36  and viral clusters (analogous to higher taxonomic levels)  5  based on clustering of protein content. For the 0. 22-1. 6/3 µm size fraction, we used previously derived miTAGs based on metagenomic matches to 16S ribosomal DNA loci and processed them as described  18. For the four eukaryotic size fractions, we added additional samples to a previously published Tara Oceans metabarcoding data set and processed them using the same methods  19  (also described at DOI: 10. 5281/zenodo. 15600). We calculated OTU-based community dissimilarity for all size fractions as the Jaccard index based on presence/absence data using the vegdist function implemented in vegan 2.
Definition of genomic provinces We used a hierarchical clustering method on the metagenomic pairwise dissimilarities produced by Simka for all surface and DCM samples, and multiscale bootstrap resampling for assessing the uncertainty in hierarchical cluster analysis. We focused on metagenomic dissimilarity due to its higher resolution, and confirmed that the patterns found in metagenomic data were consistent when using OTU data (Supplementary Fig. 5 ). We used UPGMA (Unweighted Pair-Group Method using Arithmetic averages) clustering, as it has been shown to have the best performance to describe clustering of regions for organismal biogeography  39. The R-package pvclust_1. 3-2  40 , with average linkage clustering and 1,000 bootstrap replications, was used to construct dendrograms with the approximately unbiased p-value for each cluster (Supplementary Fig.
6 ). Because the number of genomic provinces by size fraction was not known a priori, we applied a combination of visualization and statistical methods to compare and determine the consistency within clusters of samples. First, the silhouette method  41  was used to measure how similar a sample was within its own cluster compared to other clusters using the R package cluster_2. 0. 1  42. The Silhouette Coefficient s for a single sample is given as: s = (b -a) / max(a,b) Where a is the mean distance between a sample and all other points in the same class and b is the mean distance between a sample and all other points in the next nearest cluster. We used the value of s, in addition to bootstrap values, to partition each tree into genomic provinces (see Supplementary Information 2 for further details on statistical validation of genomic provinces).
Additionally, we used the Radial Reingold-Tilford Tree representation from the JavaScript library D3. js (https://d3js. org/)  43 o visualize sample partitions from the dendrogram. Single samples were not considered as genomic provinces. In a complementary approach, we performed a principal coordinates analysis (PCoA) with the R command cmdscale (eig = TRUE, add = TRUE) from the package stats 3. 2. 1  34  on the matrices of pairwise metagenomic dissimilarities calculated by Simka (or OTU dissimilarity measured with the Jaccard index) within each size fraction and kept only the first 3 principal coordinates. We then converted those coordinates to a color using the RGB encoding described above, with one modification: scaling factors λr, λg and λb were calculated as the ratios of the second and third eigenvalues to the first (dominant) eigenvalue to ensure that the dispersion of stations along each color channel reproduced the dispersion of the stations along the corresponding principal component (the ratio for the color corresponding to the dominant eigenvalue is 1).
The components (x, y, z) were then mapped to color channel values (r, g, b) between 0 and 255 as r = 128 * (1 + λcx / max(abs(x)), where λc is the ratio of the eigenvalue of color c to the dominant eigenvalue. We represented number and PCoA-RGB color of genomic provinces for each sample on a world map (Fig. 1 , Supplementary Fig. 4a-f ) generated with the R packages maps_3. 0. 0. 2  44  , mapproj 1. 2-4  45  , gplots_2. 17. 0  35  and mapplots_1. 5  46. We also plotted phosphate and temperature (Supplementary Fig. 4a-f ) obtained from the Csiro Atlas of Regional Seas (CARS2009, http://www. cmar. csiro. au/cars) using the phosphate_cars2009. nc and temprerature_cars2009a. nc files and the R package RNetCDF  47.
Comparison of genomic provinces to previous ocean divisions To evaluate the spatial similarity between the clusters obtained in our study for each size fraction and previous biogeographic divisions, we performed an analysis of similarity (ANOSIM, Fathom toolbox, matlab®). First, we collected coordinates for three spatial divisions at a resolution of 0. 5° x 0. 5°: biomes, biogeochemical provinces (BGCPs)  3, 48  and objective global ocean biogeographic provinces (OGOBPs)  49. Second, we assigned Tara Oceans stations to biomes, BGCPs, and OGOBPs based on their GPS coordinates. Third, for each size fraction we performed an ANOSIM with the metagenomic dissimilarity matrix calculated by Simka, using biogeographic clusters (biome, BGCP, OGOBP) as group membership for each station. Each ANOSIM was bootstrapped 1,000 times to evaluate the interval of confidence around the strength of the relationships we detected (Supplementary Fig.
Environmental differences among genomic provinces For each size fraction, we tested which environmental parameters significantly discriminated among genomic provinces (Supplementary Fig. 7 ). A total of 12 parameters characterizing each sample, grouped by genomic provinces, were evaluated with a Kruskal-Wallis test within each size fraction with a significance threshold of p < 10 -5. Selected parameters for each size fraction were then used to perform a principal components analysis of the samples using the R package vegan_1. 17-11  37. Samples were plotted with the same PCoA-RGB colors used in the genomic province maps above and each genomic province surrounded by a grey polygon. In analyses where Southern Ocean (including Antarctic) stations were considered independently from other stations, the following were considered Southern Ocean stations: 82, 83, 84, 85, 86, 87, 88, 89.
Ocean circulation simulations We derived travel times from the MITgcm Darwin simulation  50  based on an optimized global ocean circulation model from the ECCO2 group  51. The horizontal resolution of the model was approximately 18 km, with 1,103,735 total ocean cells. We ran the model for six continuous years in order to smooth anomalies that might occur during any single year. We used surface velocity simulation data to compute trajectories of floats originating in ocean cells containing all Tara Oceans stations, and applied the following stitching procedure to generate a large number of trajectories for each initial position. (The use of surface velocity data implies that Ekman transport also influences trajectories within the simulation. ) First, we precomputed a set of monthly trajectories: for each of the 72 months in the dataset, we released floats in every ocean cell of the model grid and simulated transport for one month.
Exponential fits for size fractions 0-0. 2 µm and 5-20 µm were not calculated due to an insufficient number of sampled stations in the North Atlantic (Supplementary Information 6). The synthetic map (Supplementary Fig. 10a ) was generated with the R packages maps_3. 0. 0. 2, mapproj 1. 2. 4, gplots_2. 17. 0 and mapplots_1. 5. We derived dynamic sea surface height from the Csiro Atlas of Regional Seas (CARS2009, http://www. cmar. csiro. au/cars) using the hgt2000_cars2009a. nc file and plotted with the R package RNetCDF. Foundation EDF Diversiterre, FRB, the Prince Albert II de Monaco Foundation, Etienne Bourgois, the Tara schooner and its captain and crew. We thank MERCATOR-CORIOLIS and ACRI-ST for providing daily satellite data during the expedition. The bulk of genomic computations were performed using the Airain HPC machine provided through GENCI-[TGCC/CINES/IDRIS] (grants t2011076389, t2012076389, t2013036389, t2014036389, t2015036389 and t2016036389).
g. , community A is closer to B than to C). We compared the genomic provinces to clusters found by K-means and spectral clustering for all values of the tuning parameter using the Rand Index (RI; from the GARI function of the loe R package version 1. 1  64  ), a score of agreement between partitions. Results are reported as mean +/-s. d. of the RI: 1 means perfect agreement and 0 complete disagreement. Fourth, in order to assess the significance of the genomic provinces, we performed a multivariate ANOVA to partition metagenomic dissimilarity across regions, using the adonis function of the vegan R package version 2. 5-4  37. Note, however, that since the same data were used both to construct the genomic provinces and to assess their significance, the p-values estimated by ADONIS might be anti-conservative.
A bimodal zooplankton species distribution (split into subtropical and subpolar communities, with ubiquitous warm water species) was also detected by a recent study on copepod population dynamics that used alternative approaches to analyze the same metagenomic dataset 66 (see their Fig. 2 ). More locally, within the North Atlantic (see also Supplementary Information 6), along the northern boundary of the subtropical gyre, cold and warm copepod species overlapped because of cross-current dispersal. Nonetheless, although both cold and warm species appeared to be able to travel long distances, mixing among them was not sufficient to create a local genomic province in our data. We interpret the difference in genomic province sizes between smaller and larger size fractions as the result of various factors.
We measured the time window of transport by currents separating two samples during which the changes in their community composition were maximally correlated with travel time, resulting in a global average of Tmin < roughly 1. 5 years. This represents the travel time during which predictable continuous turnover occurs in our dataset. Notably, Tmin does not necessarily define the turnover rate itself which depends on how strongly different seascape processes affect communities with differing biological characteristics (see Supplementary Information 6). The global ocean current system is composed of a series of large-scale main currents and associated recirculations (which are also referred to as gyres). Therefore, we present the following hypothesis as a potential explanation of our results: the average global timescale of 1.
8  (Supplementary Fig. 10f-i ). Second, their turnover rates were calculated separately for individual plankton groups (the 9 main groups were prokaryotes, coccolithophores, dinoflagellates, diatoms, all microbial eukaryotes, gelatinous zooplankton, mesozooplankton, macrozooplankton and myctophids), whereas our metagenomic data represent samples of the full plankton community within each size fraction. Among these, several groups (e. g. , dinoflagellates or mesozooplankton) would be expected to be found across multiple Tara Oceans size fractions, blurring potential comparisons. Thus, our study and Villarino et al. calculated rates of change using broadly similar approaches, but based on very different underlying biological substrates.
Acknowledgements We acknowledge  Oliver Jahn  and  M. J. Follows  for providing numerical simulations of particle trajectories from Tara Oceans stations. We thank the commitment of the following people and sponsors who made this expedition possible: CNRS (in particular Groupement de Recherche  GDR3280 ),  European Molecular Biology Laboratory  (EMBL),  Genoscope/CEA , the  French Government 'Investissement d'Avenir'  programs  OCEANOMICS  ( ANR-11-BTBR-0008 ) and  FRANCE GENOMIQUE  ( ANR-10-INBS-09 ),  Fund for Scientific Research -Flanders ,  VIB ,  Stazione Zoologica Anton Dohrn ,  UNIMIB ,  MEMO LIFE  ( ANR-10-LABX-54 ),  Paris Sciences et Lettres (PSL) Research University  ( ANR-11-IDEX-0001-02 ),  ANR  ( projects POSEIDON/ANR-09-BLAN-0348 ,  PROMETHEUS/ANR-09-PCS-GENM-217 ,  MAPPI/ANR-2010-COSI-004 ,  TARA-GIRUS/ANR-09-PCS-GENM-218 ,  HYDROGEN/ANR-14-CE23-0001 ),  EU   FP7 MicroB3 /No.
XRD measurements and hydrogen concentration in quenched liquid. The present melting experiments (runs #1-12) were carried in combination with XRD measurements at the beamline BL10XU, SPring-8 synchrotron facility (Supplementary Fig. 3 ). Angle-dispersive XRD patterns were collected in-situ at high P-T by a flat panel detector (Perkin Elmer) using a 6 µm X-ray beam (full width at half maximum) with an energy of ~30 keV. Exposure time was 0. 2 s during laser heating and 1 s after quenching temperature. Obtained 2-D XRD images were integrated to conventional 1-D profiles using the IPAnalyzer software  62. The XRD profiles demonstrate that fcc-, hcp-, or dhcp-FeH x was formed from H-rich liquid alloys upon quenching temperature. Since hydrogen escapes from iron lattice when its structure changes into bcc during decompression  23  , the hydrogen content x in FeH x was determined from its volume considering volume expansion due to the incorporation of hydrogen (Supplementary Table  1 ): x ¼ V sample -V Fe 4V H ð1Þ in which V Fe is the volume of Fe (ref.
Acknowledgements We thank  N. Hirao  and  S. Kawaguchi  for their assistance in experiments at  BL10XU , SPring-8 (proposal no. 2019A0072  and  2019B0072 ). We also thank  K. Yonemitsu  for help in sample analyses with FIB and EPMA. This work was supported by  JSPS Kakenhi  to K. H.
Data were plotted and analyzed with the Origin software package (OriginLab Corporation, Northampton, MA). Small-and wide-angle X-ray scattering. The dispersed lipid samples for X-ray measurements were prepared essentially as described for DSC measurements, with the exception that the concentration of lipid was 150 mM. To ensure homogeneous hydration of the samples, the hydrated samples were incubated at 75 °C for 5 min and cooled on ice for 5 min. This heating-cooling cycle was repeated at least three times before the X-ray measurements. Small-angle X-ray scattering (SAXS) and wide-angle X-ray scattering (WAXS) measurements were carried out at RIKEN Structural Biology Beamline I (BL45XU)  [70] [71] [72] [73]  at SPring-8, 8 GeV synchrotron radiation source, Hyogo, Japan. The X-ray wavelength used was 0.
Acknowledgements We are grateful to  M. Abe ,  K. Ishii ,  Y. Ueda ,  K. Murase-Tamada ,  R. Ishitsuka ,  M. Murate  and  H. Shogomori  for their help in X-ray measurements at SPring-8. We thank  F. Hullin-Matsuda ,  N. Tomishige  and  K. Takahashi  for critically reading the manuscript. This work was supported by grants from the  Ministry of Education, Science, Sports, and Culture of Japan  ( 19390027  to T. K. and  21790105  to A. M. ) and Lipid Dynamics Research Project, Integrated Lipidology Project and  Glycolipidologue Initiative Project of RIKEN  (to T. K. ). T. K. also acknowledges funds from  Institut National de la Santé et de la Recherche Médicale ,  Centre National de la Recherche Scientifique , and  Université de Strasbourg , and grants from  Agence Nationale pour la Recherche  ( ANR-19-CE16-0012-02  and  ANR-22-CE44-0019-01 ).
Funding This research was supported by  National Institutes of Health (NIH)  grant  HD030284  and the  Ben F. Love Endowment  to RB,  Agence Nationale de la Recherche (ANR)  grant  ANR-08-JCJC-0059  to SJ, and  NIH  grant  AR60899  to PM. MM was supported by  NIH   F31HD106600. DI was supported by  NIH   R25CA240137. Confocal microscopy was supported by  NIH  shared instrument.
The passport data for Collect-1 samples included the village name, the variety name, the species (O. sativa or O. glaberrima) and, occasionally, the rice growing ecosystem  (upland, hydromorphic, lowland)  and the type of collect (field/granary, bulk/panicle). Approximately 40% of the accessions were collected in the field. To prepare the Collect-2 campaign, the geographic coordinates of a large share of the villages visited during Collect-1 was retrieved using Google-Earth software. The collect team borrowed the track of Collect-1 campaign but did not limit its collect to the Collect-1 villages, especially as some villages could not be found. The passport data for samples from Collect-2, included the village name and its geographic coordinates, the variety name and tentative species membership, the rice growing ecosystem and the type of collect.
Genotyping All Collect-1 and Collect-2 accessions were first genotyped with 16 SSR markers using DNA from a plant obtained with a seed visually most representative of the seed stock of each accession. These data served to (i) ascertain the membership of accessions to one of the three genetic groups (O. sativa indica, O. sativa japonica, and O. glaberrima), (ii) detect redundant accessions, (iii) assess the pattern of genetic diversity within each genetic group, for each collect. Based on such information and logistic issues, 600 accessions were selected for genotyping by sequencing (GBS). DNA libraries were prepared at the Regional Genotyping Technology Platform (http://www. gptr-lr-genotypage. com), using the MATAB method for DNA extraction and the ApekI enzyme for DNA digestion. The libraries were single-end sequenced using Illumina HiSeq™2000 (Illumina, Inc.
) at the Regional Genotyping Platform (http://get. genotoul. fr/). The Fastq sequences were aligned to the rice reference genome, Os-Nipponbare-Reference-IRGSP-1. 0 with Bowtie2 (default parameters). Non-aligning sequences and sequences with multiple positions were discarded. Single nucleotide polymorphisms (SNP) were called using Tassel GBS pipeline v5. 2. 29  (Bradbury et al. , 2007). First, the genotypic data for all accessions taken together were filtered for quality score (> 20) and bi-allelic status of SNPs. Second, the genotypic data of accessions from each genetic group were filtered separately for minor allele frequency (MAF > 1%), rate of missing data (< 20%), and heterozygosity (< 5%). The missing data were imputed, separately for each group, using Beagle v4. 0. Table  1  summarizes the results of the process.
glaberrima. The relative importance of genetic diversity among subpopulations, among individuals within a subpopulation and within individuals, was assessed by analysis of molecular variance AMOVA with Arlequin software version 3. 5. 2. (Excoffier et al. , 2010). The same software served to compute nucleotide diversity, expected heterozygosity per locus, the  Wright (1965)  F-statistics (F IS and F ST ) and the population parameter ߠ ൌ 4ܰ μ, where ܰ is the effective population size and µ is the overall mutation rate at the haplotype level. The speed of decay of linkage disequilibrium (LD) in each subpopulation was estimated by computing r² between pairs of markers on a chromosome basis, using the "full matrix" option of Tassel 5. 2. 6  (Bradbury et al. , 2007) , and then by averaging the results by classes of distance.
, 2019 ; http://GeneOntology. org), that use RAP and MSU gene identity, respectively, as input. The chromosomic segments bearing loci under selection were also investigated for genes known to be involved in reproductive development processes, using available literature  (Wei et al. , 2020;  http://www. modelcrop. org/). Likewise, chromosomic segment bearing loci under selection were investigated for enrichment in quantitative trait loci (QTL) involved in DTHD, using the Gramene QTL database (http://www. gramene. org/).
When all the 31 ILUS were considered, three additional known genes involved in biological processes related to heading were identified (Table  3 , S Table  7 ). Investigation of the chromosomic segments bearing the 24 M2 type ILUS for the presence of QTLs involved in DTHD, counted 112 such QTL out of the 619 listed in the Gramene QTL database (http://www. gramene. org/) (Table  3 ; S Table  7 ). This represented a density of one DTHD QTL every 0. 21 Mb, approximately 3. 0-fold higher than the expected average density of DTHD QTL, one every 0. 62 Mb. The 112 DTHD-QTL counted included a number of major QTL such as Hd1, Hd2, Hd5, Hd6, Hd8 and Hd11. When all the 31 ILUS were considered, the count of DTHD QTL reached 162, representing a 3. 3-fold enrichment.
(i) Penetration of improved O. sativa rice varieties, with shorter duration, in the slashand-burn based upland rice cropping system of our study area was reported to be extremely limited  (Barry et al. , 2009)  and no breeding program exists for the improvement and release of new O. glaberrima varieties. (ii) Distance based neighbour-joining tree did not show any structuring in relation with collect time. (iii) Genetic differentiation between Og-1 and Og-2 and between Osj-1 and Osj-2 was very low (F ST < 0. 001). (iv) The genetic diversity (nucleotide diversity and θ parameter) of the Collect-1 and Collect-2 subpopulations had the same order of magnitude. The two latter observations also argue for the fact that the combined human and environmental selection pressure has not given rise to the emergence of a few well-adapted individuals (i.