dataesr/scientific-paragraphs-categorization huggingface dataset:
Association phases were monitored during injections over the functionalized surfaces of analyte solutions of different concentrations for 300 s, and the dissociation phases of analysis buffer for 300 s. To assess and monitor analyte unspecific binding, blank surfaces were treated with the same procedures but replacing the ligand solutions by analysis buffer. All surfaces were fully regenerated between experiments with different BMPR-Fc by injecting for 30 s regeneration buffer. Two independent experiments were performed. Kinetic data were processed with the manufacturer software (Biacore Evaluation software v3. 1). Signals from the reference surface were subtracted from the signals obtained from each functionalized ship. The resulting specific kinetic signals were then fitted using the 1:1 Langmuir model.
" While the type-I interface is a large continuous area formed by residues from both BMP monomers, the interface with type II is composed only of amino acids from one BMP monomer, 5 as seen in the example of BMP-2/ALK3/ACTR-IIA (pdb: 2H64)  50  [Fig. 3(a) ]. Thus, a one to one binding is expected. , and a more complicated model should be applied. Nevertheless, to be consistent with the literature, we applied the commonly used 1:1 Langmuir model to fit the experimental data as it has been regularly employed in previous studies, notably in the ones with an Fc adsorption strategy, and in the one using BLI technique to determine kinetics parameters. 25, 29, 31, 36 urthermore, the R 2 values of the fits, presented in Tables  S3  and S4  in the supplementary material, 70 are in majority around 0.
ACKNOWLEDGMENTS The authors acknowledge  Anne Chouquet  from the ISBG platform for her help and  Marianne Weidenhaupt  ( INPG ) for discussions regarding the immobilization protocol. They are grateful to  Sabine Bailly  and  Corinne Albiges-Rizo  fruitful discussions and advice. The study was supported by the  Agence Nationale de la Recherche  ( ANR CODECIDE ,  ANR-17-CE13-022 ) to C. P. , the  European Research Council  under  FP7 programm (ERC Biomim   GA259370 ) and by the  Fondation Recherche Medicale (FRM , grant  DEQ20170336746 ) to C. P. V. K. was supported by a Ph. D. fellowship from  Grenoble Institute of Technology. This work used the platforms from  Grenoble Instruct-ERIC Center  (ISBG:  UMS 3518 CNRS-CEA-UGA-EMBL ), within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  (No.
Here, we make the hypothesis that protected areas host higher α-diversity than their non-protected counterparts with a nested pattern in ß-diversity where non-protected areas host only a subset of those species present in nearby protected areas (Fig  1 ). As an alternative, protected and non-protected may host different species inducing a mechanical increase in γ-diversity (Fig  1). More specifically, we use 3 extensive and species-rich datasets across many regions (1,447 reef fish species in 44 protected areas, 3,532 plant species of the French Alps in 192 protected areas, and 639 North American bird species in 415 protected areas; see S1 Table ) to compare Illustration of species dissimilarity and ß-diversity partitioning between 2 components-species turnover and species nestedness with a theoretical comparison between a protected and a non-protected area sharing some species.
e. , whether or not each environment and protection level had a significant association with observed species turnover. The raw data can be found in https://github. com/LoiseauN/Betadiversity-protected-areas. https://doi. org/10. 1371/journal. pbio. 3001195. g002 region as a Strictly Protected or Restricted area and its surrounding Non-Protected areas within a 50-km buffer, the 3 protection levels being present together in 45 regions. We also extracted 6, 20, and 21 environmental variables recognized as major drivers of species composition for reef fishes, birds, and plants, respectively, on the same surveys (see Materials and methods). At the scale of individual surveys, we observe a high species turnover within regions with on average 62% (+/-14) for fish, 64% (+/-17) for birds, and 97% (+/-1) for plants, suggesting a high heterogeneity between surveys.
High species turnover between protection levels Using the same protocol, we show that the level of species dissimilarity between protection levels within the same region is consistently high in the 3 datasets, with a median ß-diversity value ranging between 40% (for birds between Strict Protected and Non-Protected areas) and 80% (for plants between Restricted and Non-Protected areas) (Fig  5 ) suggesting that adjacent areas (<50 km) with different protection levels host very dissimilar species. Since this result can be due to species richness and occurrence patterns without any effect of protection, we tested whether our observed ß-diversity values among protection levels were larger or smaller than expected under a random assembly model (see Materials and methods). Overall, observed ß-diversity values between protection levels are higher than expected by chance (combined Fisher p-value < 0.
001, This exception highlights that only Strictly Protected areas play a key and unique role in sustaining regional fish biodiversity. All datasets combined, the high species dissimilarity between protection levels within a region is primarily due to species turnover (74% ± 20% of the total dissimilarity) and not nestedness (26% ± 20% of the total dissimilarity or ß-diversity). On average, 29% (±14%) of bird, 32% (±8%) of plant, and 43% (±16%) fish species are replaced between adjacent areas under different protection levels, while nestedness represents, on average, between 9% (fish) and 30% (plant) of total species dissimilarity (Fig  5 ). This proportion of species turnover is also significantly higher than expected at random (combined Fisher p-value < 0. 001, We then tested the correlation between the difference in environmental conditions and species turnover for each pair of protection levels within each region for each taxon (Fig  6 ).
CR, EN, VU, and NT species are considered as "Imperiled" (D-F) while LC as "Low risk" (G-I). The raw data can be found in https://github. com/LoiseauN/Betadiversity-protectedareas. CR, Critically Endangered; EN, Endangered; LC, Least Concern; NPA, Non-Protected Areas; NT, Near Threatened; RA, Restricted Areas; SPA, Strictly Protected Areas; VU, Vulnerable. https://doi. org/10. 1371/journal. pbio. 3001195. g007 Protected areas host unique trophic structures and species compositions, different from those observed in Non-Protected or even Restricted areas  [29]. Thus, species appear to be "sorting" among different protection levels, and management diversity is needed to maintain larger scale biodiversity. A non-mutually exclusive but alternative explanation is that protected areas are placed in a way that captures the geographic range of some species but not others  [30] [31] [32].
Species datasets Reef fishes. We used the global Reef Life Survey (RLS) database  [45]  and data from coral reef surveys  [46]. These reef fish surveys involve underwater visual census (UVC) by SCUBA divers along a 50-m transect line, laid along a depth contour on hard substrata (coral or temperate rocky reef). Observers included highly trained volunteers who produce survey counts statistically indistinguishable from professional researchers  [47]. All fish species observed within 5 m of the transect line were recorded as the diver swam slowly along the line. Full details of methods can be found in an online method manual available at www. reeflifesurvey. com  [45]. Initially, 39 no-take Strictly Protected areas and 40 Restricted areas were used. However, because habitat was unavailable for most of the reefs, we standardized the habitat of surveys a priori to those performed on reef within the depth range 4 to 20 m.
This choice greatly reduced the number of analyzed marine protected areas but minimized any bias due to dissimilarity in habitat. Thus, analyses were reduced to 25 Strictly Protected areas and 19 Restricted areas, plus respective Non-Protected areas. A total of 1,447 species were surveyed along 2,156 transects. We did not compare surveys across the 2 datasets to avoid any methodological bias. French alpine plants. We used a database of vegetation surveys provided by the National Alpine Botanical Conservatory (CBNA) over the French Alps region which covers over 26,000 square kilometers  [26]. Database included 42,290 community plots sampled in natural or seminatural areas from 1980 to 2009, encompassing a total of 3,532 plant species. Each plot was approximately 10 m × 10 m and within each sample, species occurrences were recorded.
Five Strictly Protected areas and 191 Restricted areas were analyzed. Birds. We use the North American Breeding Bird Survey (BBS), which estimates species bird abundances in 48 US states along 40 km routes. In BBS, yearly, during the breeding season, observers census birds along c. 50 km survey routes with stops separated from 1 km apart and counting all bird species present. We selected the 2,411 routes with surveys from 2010 to 2015. A total of 639 bird species were surveyed. OneAU : PerPLOSstyle; numeralsarenotallowedatthebe hundred thirty-two Strictly Protected areas and 283 Restricted areas were analyzed. IUCN status. For each taxon, we grouped species in 3 categories depending on their threat level according to IUCN red list: Critically Endangered (CR), Endangered (EN), Vulnerable (VU), and Near Threatened (NT) as "Imperiled"; Least Concern (LC) species as "Not Threatened"; Data Deficient (DD) species and species without known status as "Not Assessed" species.
Protection levels We distinguished 2 different statuses of protected areas: Strictly Protected areas where human activities are prohibited (IUCN I-II) and Restricted areas (IUCN III-VI) where limited extractive activities are authorized  [48]. For birds and plants, we intersected all sites using official coverage of all existing protected areas in US and France (Protected Areas Database of the United States and The World Database on Protected Areas, WDPA). For reef fishes, we used management available data  [45, 46]. For each survey, we assessed if it was unfished, that is within the borders of a no-take marine reserve; restricted, with active restrictions on particular gear types (for example, bans on the use of nets, spear guns, or traps) or fishing effort (which could have included areas inside marine parks that were not necessarily no-take); or fished, that is, regularly fished without effective restrictions.
Environmental conditions with habitat and climatic data Reefs. For global RLS, we used the data from benthic photoquadrats taken along the same transect lines surveyed for fishes to quantify local habitat differences between sites. Photoquadrats were taken vertically downward of the substrate every 2. 5 m along each of the same transect lines, and later scored using a grid overlay of 5 points per image, 100 points per transect. Categories of benthic cover scored were from a set of 50 morphological and functional groups of algae and corals. Substratum categories used for analyses here were the sum of all live hard coral categories, macroalgaes, and others. Full details of the photoquadrat method and scoring procedure are already provided  [49]. We also included depth of the transect as a local environmental variable.
For other data  [46] , we used habitat (whether the reef is a slope, crest, flat, or back reef/lagoon) and depth recorded along transects. For both databases, we also extracted sea surface temperature and chlorophyll-a concentration using Bio-Oracle at 10 km of resolution average on the period 2000 to 2014  [50]. These variables were all recognized as major drivers of fish diversity on coral reefs  [51, 52]. As we did not compare surveys across the 2 datasets, differences in environmental datasets should not affect outcomes. Some environmental data were not available for all sites (34% for RLS surveys and 38% for other data  [46] ). We removed these surveys when testing influence of environmental conditions on diversity. French Alps. We used the official coverage of land cover in France (Theia Land, 2016, 20 meters of resolutions).
This dataset included 17 variables impacting plants: winter agriculture, summer agriculture, hardwood forest, coniferous forest, lawns, woody moors, dense urban, sprawling urban, industrial and commercial areas, roads, mineral surfaces, beaches and dunes, water, glaciers or snow, grasslands, orchards, vineyards. We chose a 1-km buffer as a reasonable range at which principal habitats of recorded species lived and to capture the relevant habitat. We also included average annual precipitation and annual temperature from 1979 to 2013 using Chelsa at 1 km of resolution (http://chelsa-climate. org/). US land cover and climate. We used the official coverage of land cover in the US (The National Land Cover Database (NLCD, 2016, 30 meters of resolution). NLCD land cover classes  (20)  were water, perennial ice snow, low intensity residential, high intensity residential, developed/open space intensity, developed/medium intensity, developed high intensity, bare rock/sand/clay, deciduous forest, evergreen forest, mixed forest, shrub/scrub, grasslands/herbaceous, pasture/hay, row crops, woody wetlands, emergent herbaceous wetlands.
We created a 19. 7-km radius circle (one half the length of a BBS route) around the centroid of each BBS route  [53] [54] [55] [56]  and used this to extract land cover. We chose this radius because it encompassed the entire BBS route, regardless of route path, and a circle because it provided a uniform area and shape around each BBS route. We also included average precipitation and temperature from 1979 to 2013 using Chelsa at 1 km of resolution (http://chelsa-climate. org/). We averaged the climatic data within a 19. 7-km radius circle. All data sources are provided in S2 Table.
dbRDA were centered on Strictly Protected areas. First, we used the extracted environmental conditions data and computed Euclidean environmental distance between each pair of surveys. Then, we performed a PCoA and extracted the first 2 axes. Second, for each Strictly Protected area, we selected all surveys inside and outside the protected area in the buffer of 50 km. Each survey was associated with its protection level and when environmental conditions were available, 2 PCoA axes as explanatory variables. Turnover was calculated for presence-absence matrices. Significance of the models as well as the significance of each axis and of the marginal effect of each variable were tested using ANOVA-like permutation tests with 9,999 permutations. Finally, we computed 2 partial dbRDA using the Jaccard turnover distance to (i) isolate the exclusive effect of protection after accounting for environmental conditions and (ii) isolate the exclusive effect of environmental conditions after accounting for protection.
Funding and support come from the European project  RESERVEBENEFIT  (European call BIODIVERSA3 2015-2016 call, SM). Additional support was provided by  Australian Research Council  [ CE140100020 ,  FT160100047 ] (JEC) &  Australian Research Council  [ LP150100761 ] (GJE), the  Ian Potter Foundation (GJE) , the  Royal Society University Research Fellowship  ( UF140691 ,  NAJG) , the  ANR  project  Origin-Alps  ( ANR-16-CE93-0004 ,WT & JR) and from ' Investissement d'Avenir ' grants managed by the  ANR  ( Trajectories :  ANR-15-IDEX-02 ; Montane: OSUG@2020:  ANR-10-LAB-56 , WT & JR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Only the beginning of scaffold112 of the D. alata genome v1 was actually mapped on the draft D. alata chromosome 1 (see in  [16] ) and the sequence homology between D. alata and D. rotundata was good (Additional file 3: Fig. S3A ). D. rotundata was thus used as a reference upon which the D. alata transcriptome was aligned in order to avoid issues related to D. alata scaffolding and to extend our research of candidate genes prior to the beginning of scaffold112. Gene ontology analysis revealed that the genomic region encompassing the QTN related to flowering ability was significantly enriched in genes involved in the reproduction process and gamete generation (Additional file 3: Fig. S3B ). This was due to the presence of two genes expressed in D. alata: the first one at around 125 kb (D. alata transcriptome contig7439) annotated as a cyclin-dependent kinase F-4 CDKF4 and the second around 167 kb (D.
Detection of sex-determination loci GWAS was also conducted using flowering accessions to identify sex-related loci. Using an FDR threshold of 1% and a (P + K) model, significant sex-linked QTNs were only located on chromosome 6 (Fig. 2 ). Those five QTNs were positioned from 9,886,520 to 19,660,282 bp on the D. rotundata pseudo-chromosome 6 v1 and their -log10(p-value) ranged from 5. 7 to 13. 37. At the most predictive QTN, the allelic composition assessed by GBS was in agreement with the observed sex for 89% of the accessions (76/85; Table  1 ). This locus was positioned at 19,660,282 bp, with females being mostly homozygous (A/A) and males mostly heterozygous (A/G), or homozygous (G/G) for one accession. Flanking sequences of the corresponding Single Nucleotide Polymorphism, SNP (06. 1_19660282) were extracted to design allele-specific KASPar primers (X = A; Y = G).
Then this KASPar assay was validated on 42 different accessions (Table  2 ; Additional file 4: Fig. S4 ). All tetraploid males were genotyped as XXXY, indicating that their allelic composition was AAAG. Diploid males were genotyped as XY (AG) and diploid females as XX (AA). The only two exceptions were: a XXXY (AAAG) tetraploid female ('Noulelecae') and a XX (AA) diploid male ('Peter'). To conclude, the efficiency of this KASPar assay as a diagnostic tool for sex determination was thus estimated at 95%  (40/42). Moreover, the hypothesis of the presence   Accessions that were only used in the GWAS for sex determination and not for flowering ability are shown in brackets. NA; missing data of a dominant Y allele and a recessive X allele, in agreement with the XX/XY sex-determination system, appeared to be validated.
alata. Evidence of the involvement of the genomic region in the reproduction process (i. e. male gamete generation) was strengthened via the presence of two candidate genes: one homologous to CDKF4 of Oryza sativa L. and another to SINAT2 of Arabidopsis thaliana. CDKF4 belongs to a large family of serine/threonine protein kinases conserved among eukaryotes and involved in cell cycle regulation  [23]. It was found to be highly expressed in O. sativa L. flower buds (NCBI BioProject: PRJNA243371), especially in mature pollen (  [24] , as well as in cotton petals and stamens  [25]. Moreover, SINAT2 belongs to E3 ubiquitin-ligases, which are known to be key phytohormone signalling regulators  [26]. In A. thaliana, it is highly and mostly expressed during pollen development (The Arabidopsis Information Resource  [27] ;).
Plant materials A total of 124 yam (D. alata) accessions maintained in the West French Indies (Guadeloupe) at the Tropical Plants Biological Resources Centre (CRB-PT) and CIRA D were used for GWAs analysis in order to identify regions related to sex determination and flowering capacity (Additional file 9: Table  S1 ). Sex was determined by mining CRB-PT agro-morphological description data (available at http://intertrop. antilles. inra. fr) and by assessing CIRAD accessions during the flowering period (December to January). Female and male phenotypes were coded as 0 and 1, respectively, for a total of 90 accessions. Regarding non-flowering phenotypes, accessions were assessed as non-flowering forms if they had been morphologically characterized by CRB-PT, however no information had ever been recorded regarding their sex.
Genotyping-by-sequencing and SNP filtering Exactly the same DNA extraction, genotyping-bysequencing (GBS) and SNP calling and prefiltering protocols described in  [16]  were applied. They were based on the DNA extraction procedure described in  [38] , the genotyping by sequencing described in  [39]  and the SNP calling and prefiltering using process reseq. 1. 0. py software and VcfPreFilter. 1. 0. py implemented in the VcfHunter package  [40]. Raw sequencing reads were obtained from  [9]  and aligned on the D. rotundata reference genome v1 (pseudo chromosome BDMI0100001-21  [18] ; to detect SNPs. This procedure was applied to the two panels used in GWAS and to the biparental population (population A, 74F × Kabusa) used to reconstruct sex chromosomes. Concerning the datasets from the pre-filtered vcf files used in GWAS, SNPs and accessions were filtered using the following filters: minimum depth 8, minor allele frequencies per site > 5%, maximum missing data per loci < 10% and maximum missing data per accession < 20%.
Then SNPs were assigned to male and female datasets regarding their segregation pattern within the parents. This resulted in a genotyping male matrix of 110 progenies × 5473 SNPs consisting of SNPs heterozygous in the male parent and homozygous in the female parent, or heterozygous in both parents; and a female matrix of 110 progenies × 5866 SNPs consisting of SNPs heterozygous in the female parent and homozygous in the male parent, or heterozygous in both parents. SNP and accession filtering was conducted with R scripts (R 3. 4. 4, R Core Team, 2017) using the vcfR 1. 5. 0 package  [41].
Genome-wide association studies SNP-trait associations were first tested using a generalized linear model coded in R using the glm function. According to the  [40]  method, based on principal component analysis (PCA) of a modified genotyping incidence matrix, the panel structure was investigated and tested using a Tracy-Widow test. As only the largest eigenvalues were assessed as being significant (p < 0. 001), the panel structure was estimated using accession coordinates on the first PCA axis. Then the mixed model procedures Q (i), K (ii), and Q + K (iii) developed by  [42]  were applied using the ASReml-R package  [43]  and expressed as: y ¼ 1μ þ Q þ Sα þ ε i ð Þ y ¼ 1μ þ Sα þ Zu þ ε ii ð Þ y ¼ 1μ þ Q þ Sα þ Zu þ ε iii ð Þ where y is a vector of phenotypes coded as 0 or 1; 1 a vector of 1; μ the intercept; Q is the vector of accession coordinates on the significant PCA axis resulting from the panel structure analysis; α is the additive effect of the tested SNP; u is a vector of random polygenic effects assumed to be normally distributed N(0,σ 2 yK); where K is a matrix of relative kinship computed as the percentage of shared alleles, S and Z are incidence matrices, and ε is a vector of residual effects.
Validation Regarding the results, two types of validation were performed: genotyping using KASPar technology or a gene ontology study. SNP conversion for the KASPar assay was conducted as described in  [44] , except that the wet chemistry was conducted at the CIRAD Roujol research station (Guadeloupe, France). Polymorphic SNP flanking sequences (60 bp upstream and 60 bp downstream around the variant position) were selected using SNiPlay3  [45]. The LGC KASP master mix (standard protocol with 31 PCR cycles) was used once DNA had been extracted from leaf tissue using the DNeasy® Plant Mini Kit (standard extraction protocol, Qiagen). Overall, 42 accessions with known ploidy levels  [9]  were genotyped and 8 negative controls (water or water and mix) were included in the experiment. Gene ontology (GO) enrichment was tested using the TopGO R cran package ("classic" Fisher test options).