dataesr/scientific-paragraphs-categorization huggingface dataset:
07 to 2250 μM in 100 μl of the same buffer that the one used in quenching experiments, according to the protocol in ref. 53. After 15 min of incubation at room temperature, samples were centrifuged at 220,000 g for 1 h at 4 °C using a Beckman Coulter's TLA 100 rotor. The top 80 μl was considered as supeRNAtant (S) and the remaining 20 μl diluted with 60 μl of working buffer as pellet (P). Pellet and supeRNAtant were analyzed on a 10% SDS-PAGE and stained using coomassie blue. Quantifications were made using Image J software (National Institutes of Health, MD, USA). SLBs were prepared with 0. 1 mg. mL -1 liposomes flowing at 10 μL. min -1 for 10-20 min on a UV-treated SiO 2 surface of Q-sensor fixed in a Q-Sense Flow module, QFM 401 Biolin Scientific, Sweden). Stable SLBs were rinsed with citrate buffer (NaCitrate 10 mM, 100 mM NaCl, and 0.
Acknowledgements We are thankful to  Dr. Olivier Coux  for his help with protein purification and  Dr. Michel Franco  for the gift of PH-EFA6 plasmid. We thank the  Montpellier RIO Imaging (MRI)  microscopy facilities. This work has been funded by  ANR  ( ANR-13-BSV5-0006-01 ). C. P. acknowledges the  European Commission  ( FP7)  for a grant from the  European Research Council  ( GA259370 ). N. Y. is a recipient of the  European Community Erasmus Mundus PhD  fellowship. C. F. and D. M. are part of the  CNRS GDR MIV consortium.
The main difficulty in measuring the relative angular positions of the successive blades is linked to the lack of rigidity of algae (as compared to plants). For this reason we used a small apparatus described in Peaucelle  [30]  in which the branch is attached at both ends and held vertically. The axis of the branch coincides with the center of a 360° protractor. By sliding the protractor vertically we can measure the successive positions of the laterals. The extremity of a branch is a tightly packed structure similar to the terminal bud of plants. The outer blades that cover it were cut so that the central region was observed with a low magnification microscope. After identification of the order of the successive primordia we measured their relative positions, using an ImageJ software  [30, 31].
Acknowledgments A. P. is grateful to  Elliot Meyerowitz  for support and discussions and the  SLCU microscopy facility  supported by the Gatsby foundation. Y. C. is grateful to  Agnes Schermann  from  Université de Rennes  for introducing him to the complex world of algae during a CNRS summer school held in Berder and to  Stéphane Douady  for a long companionship in phyllotaxis investigations.
First cultivated in Greece  [5] , sweet cherry was later spread all over Europe. Its cultivation seems to be very old, its grafting technique was already described by the Roman writer Varo BC, and Pliny (23-79 AD) gave information of eight distinct cultivars  [6, 7]. As a result of centuries of natural and human selection a multitude of cherry landraces were raised in Europe. The economic and social status of cherries has changed in European societies between classical and medieval times  [8]. These fruits played an important social role in the medieval elite diet regime  [9]  before becoming a more common fruit during the later centuries  [8, 10]. Although many landraces have been lost, a large diversity still exists in Europe (i. e. : 900 cherry landraces are reported in the European Prunus database).
SNPs genotyping All accessions were genotyped using the RosBREED cherry 6 K Illumina Infinium II® SNP array v1  [24]. Genotype differences were recorded in the iSCAN platform and SNP genotypes were determined using Genome Studio Genotyping Module (Version 1. 8. 4, Illumina™) as described in  [24]. The RosBREED cherry 6 K SNP array v1 markers used in this work were deposited in NCBI's dbSNP repository available at www. ncbi. nlm. nih. gov/projects/SNP [25]  and each SNP was given a unique accession number that starts with the prefix 'ss' (SNPs NCBI ss# database names). More information associated with these SNPs is available at the Genome Database for Rosaceae (GDR; www. rosaceae. org [26] ). Physical positions of the SNPs  [24] were inferred from the peach genome  [27]  and the macrosynteny of peach-sweet cherry genomes  [28].
SNP positions of the ROSBREED cherry 6 K array v1. 0 on the peach genome v2. 0 were redefined using batch BLAST function available at the GDR's website (GDR; www. rosaceae. org [26] ) (Additional file 1: Table  S2 ). Illumina's GenCall software algorithms for clustering, calling and scoring genotypes were first used to assure SNP quality. SNPs below 0. 2 10 %-Gen-Call were removed. Initial clustering was done using Gentrain2, a GenomeStudio build-in clustering algorithm  [29]. Following the clustering by Gentrain2, all SNPs were visually examined for appropriateness of clustering, cluster separation, number of clusters, presence of null alleles and paralogs. A SNP was considered 'failed' if it showed (1) overlapping clusters or ambiguous clusters which could not be improved by even manual clustering (2) more than 3 clusters suggesting presence of paralogs or (3) very low call frequency  [29].
Linkage disequilibrium Because LD can affect both Principal Coordinate Analysis (PCoA) and STRUCTURE analysis, the marker set was pruned by excluding SNPs in strong LD using PLINK software  [30]. SNPs were pruned with a window of 50 SNPs and a step size of 5 makers. The r 2 threshold was 0. 5. Pairwise LD measures for multiple SNPs were calculated using PLINK  [30]. Correlations based on genotype allele counts, i. e. not phased genotypic data, were used to estimate the LD using PLINK  [30]. The squared correlation based on genotypic allele counts is therefore not identical to the r 2 as estimated from haplotype frequencies, although it will typically be very similar. Because it is faster to calculate, it provides a good way to screen for strong LD  [30]. Total length of each chromosome was chosen as window size and all SNP pairs were reported within each chromosome.
Population structure PCoA (also referred to as Classical Multidimensional Scaling), Bayesian-based (STRUCTURE software  [38] ) and Discriminant Analysis of Principal Components (DAPC) analysis were used to investigate the pattern of population structure. PCoA is a distance-based model which uses jointly a dissimilarity matrix calculated with a simple-matching index, and a factorial analysis. PCoA was performed using DARwin 6. 0. 010 software (Dissimilarity Analysis and Representation for Windows)  [39, 40]. This software produces graphical representations on Euclidean plans which preserve at best the distances between units  [39, 40]. The model-based approach implemented in the software package STRUCTURE  [38]  was also applied to infer population structure. Structure software options offers to split the Graphic User Interface from the main algorithm helping to set large numbers of runs on a computing cluster (Additional file 2: Figure  S1 ).
[38]  and the ΔK method, described by Evanno et al. [37]  and implemented in Structure Harvester  [41] , were used. In order to assess assignment success, STRUCTURE was run by enforcing K to its true value. For a given K, we used the run that had the highest likelihood estimate to assign cluster proportions to individuals. Accessions with estimated memberships above 0. 8 were assigned to corresponding groups whereas accessions with estimated memberships below 0. 8 were assigned to a mixed group. We ran STRUCTURE on partitioned datasets in order to investigate lower levels of structure, in relation to the results obtained. For the partitioned datasets, K was allowed to vary from one to four for the 'Bred cultivars' subgroup and from one to 11 for the 'Landraces' subgroup, in agreement with the number of countries of origin of the accessions in each subgroup.
Pairwise F st  [42]  among the subpopulations identified by STRUC-TURE were calculated using adegenet 2. 0. The assumptions underlying the population genetics model in STRUCTURE may limit its use in crops. Unlike natural populations, crops are subjected to displacements, breeding, clonal propagation, absence of panmictic conditions. Thus, we complemented the STRUCTURE analysis with the DAPC. The absence of any assumption about the underlying population genetics model, in particular concerning Hardy-Weinberg equilibrium or linkage equilibrium, is one of the main assets of DAPC analysis  [43]. DAPC was used to identify and describe clusters of genetically related individuals, as implemented in the R's package adegenet 2. 0  [31, 32]. DAPC transforms the data using PCA, and then performs a Discriminant Analysis on the principal components (PC) retained using a cross-validation method.
This multivariate method is suitable for analyzing large numbers of genome-wide SNPs, and it provides individuals' assignment to groups as well as a visual assessment of between-population differentiation. The number of PCs retained can have a substantial impact on the results of the analysis. Indeed, retaining too many components with respect to the number of individuals can lead to over-fitting and instability  [31]. We used the optimization procedure proposed by the R's package adegenet to assess the optimal number of PCs to be retained  [32]. The cross-validation procedure implemented with the function xvalDapc performs stratified cross-validation of DAPC using varying numbers of PCs (and keeping the number of discriminant functions fixed)  [31]. Pairwise F st  [42]  among the DAPC clusters were calculated using adegenet 2.
Population structure The genetic structure of the INRA's Sweet Cherry genetic resources collection was analyzed using STRUCTURE, PCoA and DAPC. All analyses were performed with the LD-pruned 326 SNP set. Thanks to the scalability of STRUCTURE software and MCIA multi-core infrastructure, we reduced the computing time from one year to few days. In STRUCTURE the most likely number of clusters was evaluated considering the ΔK method  [48]  and the plateau criterion  [38]. The ΔK criterion gave the highest value for K = 2 (Additional file 2: Figure  S3 ; Additional file 1: Table  S3 ). This method is known to give rise to the first structural level in the data, here two ancestral populations were identified (Fig. 2 ). The   (Fig. 3 ). Cherry accessions formed two main clusters corresponding to the two ancestral populations identified with STRUCTURE.
The landraces cluster was more scattered than the breeding cultivar one. The admixed accessions were dispersed between these two clusters along the axis 2 (Fig. 3 ). Pairwise F st values among STRUCTURE clusters ranged from 0. 022 (Admixed-Bred cultivars) to 0. 058 (Landraces-Bred cultivars) (Additional file 1: Table  S5 ). As the Evanno ΔK preferentially detects the uppermost level of structure of the data  [47] , we analyzed each cluster independently to explore whether a substructure could be detected within each group. The two partitioned datasets comprised 72 accessions of the 'Bred cultivars' ancestral population and 50 accessions of the 'Landraces' ancestral population. The 88 accessions considered as admixed were discarded from further analyses. Within the two groups, 'bred cultivars' and 'landraces', STRUCTURE allowed the identification of two subgroups in each group (Additional file 1: Table  S4 ).
The functions 'find. clusters' and 'k-means' algorithm were used to determine the number of clusters maximizing the variation between clusters  [31]. To avoid the loss of Fig. 2  Inferred population structure of the collection using STRUCTURE software. Bar plot of individual ancestry proportions for the genetic clusters inferred using STRUCTURE (K = 2) and the reduced dataset (326 SNP data). Individual ancestry proportions (q values) are sorted within each cluster. Admixture model, independent frequencies, 10,000 burn-in iterations, 100,000 Markov Chain Monte Carlo iterations were used for this analysis. Bred cultivars and landraces ancestral populations are shown in green and red, respectively information these two functions performed with 170 Principal Components, accounting for more than 98 % of the variance (Additional file 2: Figure  S5 ).
The Bayesian Information Criterion (BIC) was used to identify the optimal number of clusters, 9, indicated by an elbow curve of BIC values as a function of k (Additional file 2: Figure  S6 ). The number of retained PC for DAPC analyses was calculated using a cross validation method implemented in 'xvalDapc' function from R adegenet 2. 0 package  [31, 32]. 'xvalDapc' function minimized the mean square error using 20 PC (Additional file 2: Figure  S7 ). Also, a bar plot of eigenvalues for the discriminant analysis was used to select eight discriminant functions to be retained (Additional file 2: Figure  S8 ). Thus, a scatter plot was drawn using nine clusters obtained by BIC, 20 PCA obtained by xvalDapc, and the two main axes of the discriminant analysis (DA) (Fig. 4 ). Pairwise F st values among DAPC clusters ranged from 0.
), 47 % of the American ones in particular 'Hardy Giant' and 'Garnet', and 61 % of the French ones, with 'Fercer' and all its derived hybrids ('Ferprime', 'Ferdiva', 'Ferdouce', 'Feria'), except 'Folfer' and 'Ferlizac' which are included in DAPC clusters 3 and 5, respectively. Most of the accessions comprised in cluster 9 are landraces with a short flowering-maturity period. Clustering performed by DAPC is consistent with the available information on pedigree data (Additional file 1: Table  S4 ). For example, 'Burlat' and its descendants clustered together in group 3. Also, DAPC clustering was represented according to the countries of origin (Additional file 2: Figure  S10 ). The plant material analyzed in this study from countries such as Canada, Italy, Spain or USA, showed a narrow genetic diversity, with most of each country's cultivars included in only one or two clusters.
Our results are in agreement with a previous structuration of sweet cherry cultivars into landraces and modern varieties  [11]. We completed the analysis using the maximum likelihood parameter as recommended by Pritchard et al. [30] , in that case K was set to nine. This value of K appeared to fit with the origin and the pedigree of the accessions. The DAPC method provides an interesting alternative to STRUCTURE software as it does not require that populations are in HW equilibrium and can handle large sets of data without using parallel processing software. However, as for other multivariate analyses, the reduction of genetic information to interindividual or interpopulation distances may represent a substantial loss of information  [63]. Nevertheless, our results showed a good consistency between STRUCTURE and DAPC analyses when no admixed individuals were considered.
Also DAPC analysis provided a more detailed clustering within landraces and bred cultivars compared STRUCTURE analysis either in our study or in previous analysis using SSR  [11]. Regarding membership to clusters, DAPC provides membership values that are different from admixture coefficients from STRUCTURE, but they can still be interpreted as proximities of individuals to the different clusters  [32]. However, group membership provided by R's adegenet package is more useful for groups defined by external criteria (i. e. biologically) rather than by k-means, as kmeans provides optimal groups for DAPC and therefore both classifications will be mostly consistent  [31]. Clustering of individuals presented in this study may give interesting cues for increasing diversity in breeding programs and germplasm collections.
Acknowledgements We thank French ministry for agriculture and food (MAAP n°C-2011- 05  project) for financing part of this work. We thank  INRA, Aquitaine Region and CEP Innovation  ( AQUIPRU  project  2014-1R20102-2971 ) for financing doctoral and postdoctoral fellowships to RB and JAC, respectively. We thank  Dr Angel Fernandez  from  CITA (Zaragoza, Spain ) and  M. Jose Luis Segui  from  Almudaina (Alicante, Spain ) for providing seven cherry accessions, including six Spanish landraces from  Sierra de Aitana (Alicante). We thank  Sabine Rauzier  from  "Centre National de Pomologie" at Alès  for facilitating the access to the French national landraces historical information. We acknowledge  Jean Claude Barbot  for his help in sampling leaves of sweet cherry collection,  Sandra Robert  for preliminary analysis of data, the technical staff of the  Genome-Transcriptome facility (PGTB) at INRA Pierroton  for DNA quality assessment and  RosBREED  project and  Michigan State University  for the genotyping facilities.
Whole mount in situ hybridization In situ hybridization was carried out as previously described  [36]  with several modifications as previously specified  [32]. Templates for the PRIle-piwi1, PRIle-vasa, and PRIle-nanos probes were, respectively, ~1900 bp (positions 990-2968 in GenBank KM078049), ~2250 bp (positions 229-2481 in GenBank KM078051), and ~1450 bp (GenBank GQ369728. 1) [32]. Following development of in situ hybridization signal, samples were mounted in 25 % PBS/75 % glycerol and imaged using a Zeiss Axioplan2 microscope, a Zeiss AxioCam HRc camera, and AxioVision (v4. 8. 2) software.
Gonads regress upon starvation and regrow upon refeeding We investigated whether the gonads of P. leidyi are sensitive to feeding regime by starving and refeeding worms over several weeks and assessing gonad expression of three germline/multipotency genes, homologs of piwi, nanos, and vasa ("Methods, " Experiment 1). We also scored specimens for the presence of fission zones and expression in developing regions, namely in fission zones and the posterior growth zone. Consistent with our previous study  [32] , we found that all three genes were expressed in gonads and actively growing tissues but we determined that our PRIle-piwi1 probe typically yielded the strongest and most robust expression in the gonads; scorings and data presented below therefore emphasize results from this gene. We found that starvation causes a marked body-wide down-regulation of germline/multipotency gene expression and appears to cause gonad size to decrease.
Changes in conductivity can be used to evaluate the cmc of surfactants. In Fig. 2b , the conductivity of 2NapFF solutions is shown as a function of concentration. A detailed analysis of this measurement shows a significant reduction in the rate of increase in conductivity in the concentration range from 0. 008 to 0. 04 wt% (Fig. 3a ), suggesting that there is a cmc transition in this concentration range. These data are typical for the conductivity measurements of a surfactant's cmc. As the micellar structures become the dominant species in solution, they decrease the exchange rate of OH À with the electrode surfaces and slow down the diffusion of OH À , thus decreasing the rate of increase in conductivity. These results agree with the surface tensiometry data, which suggest that the micellar structures start to form above 0.
33 Our conductivity data suggest that for 2NapFF, there is a first cmc (cmc 1 ) in the region from 0. 008 to 0. 02 wt% (region I shown in Fig. 2 ). From 0. 05 to 0. 1 wt%, there is a significant increase in the conductivity with a peak in the data at 0. 07 wt% (Fig. 3b ), suggesting a second transition within this concentration range (region II, shown in Fig. 2 ). We assign the second transition to a second cmc (cmc 2 ). Linear fits to the conductivity data (Fig. 3 ) result in an accurate quantification of the critical micellar concentrations. These transitions in conductivity are consistent with a first aggregation into spherical micelles, followed by the formation of elongated worm-like micelles. The second transition was further investigated using rheological measurements. These measurements show that there is a gradual change in viscosity of the solution (Fig.
3 wt% (Fig. S19  and S20, ESI †). However for concentrations below 0. 3 wt%, a distinct peak is visible at 1643 cm À1 in the hydrogel, which is not present in the solution. These IR data imply that there is possibly a molecular packing and structural transformation, at least between 0. 1 wt% and 0. 3 wt% when the solution phase is triggered to form a hydrogel. 4] [45]  In addition, peaks at 1548 cm À1 and 1585 cm À1 in the solution state, and at 1585 cm À1 and 1602 cm À1 in the hydrogel state can be associated with different naphthalene and phenylalanine packing. 42, 46, 47 To further compare the solution and gel phases, we investigated the hydrogels by microscopy. Optical microscopy did not result in any visible structures, either under bright field or cross-polarised light (data not shown).
Attempts were made with AFM, TEM, and SEM. As an Fig. 5  FTIR spectra of (a) 2NapFF solutions at high pD 11. 5 AE 0. 5 (0. 05 to 0. 5 wt%) and (b) the corresponding hydrogels (0. 05 to 0. 5 wt%) at the lower limits of detection. For the complete data set for the solution and gel phase, see Fig. S19  and S20 (ESI †). example, we imaged the samples using SEM after addition of calcium nitrate. We obtained results that initially appear to agree with our suggested sphere-to-worm transition for the samples of 2NapFF Ca-hydrogels when no coating was used and at low keV. At 0. 06 wt% and above, only fibrous, worm-like structures were imaged (see Fig. S22  and S23, ESI †). Between 0. 01 wt% and 0. 05 wt%, spherical structures apparently co-exist with fibrous structures (example data for 0. 03 wt% is shown in Fig.
S24b, ESI † ). However, a blank of a salt solution prepared under the same conditions (Fig. S24f , ESI †) shows similar spherical structures and hence we assume that the spheres imaged in the 0. 03 wt% gel, and most likely at other low concentrations (Fig. S24 , ESI †), are also from drying artifacts from the salt. As a result, we highlight the difficulties in imaging samples at such low concentration. The primary self-assembled structures within both the wormlike micellar solution and the gel phase were probed further in situ using small angle neutron scattering (SANS). 9] [50] [51]  The wide Q range accessed here provides information on the primary fibril dimensions and aspects of the network structure. As discussed above, as with many analytical techniques, the results from the lowest concentrations of this study are limited by the sensitivity of the technique and the measurement time required to collect data of sufficient quality, which is particularly important as access to SANS beam time is a limited resource.
The samples were left to stand for a 24 hours incubation period at 22 1C. Surface tension. The surface tension measurements were performed on a high throughput Kibron Delta-8 Surface Tensiometer which uses a Du Nouy-Padday method (maximum pull on a rod). The results were analysed by the Delta-8 Manager software. The pre-gelation samples were prepared as described above, however the dilutions were performed using an Eppendorf epMotion 5072 for a preliminary scan and subsequently the data shown here were obtained with solutions prepared directly at the required concentrations as highlighted above. For the robotic system, 200 mL of the concentrated 2NapFF solution was transferred onto the first column of a standard Nunclon 96-well plate. A series of concentrations were prepared by sequential dilution of 2NapFF using pH 10.
001 M Nile blue in the initial solution (pipetting a selected amount from a 0. 01 M stock solution). Typical pictures selected from over the 50 captured were chosen and measured for this study. The images were analysed using the Zeiss AIM software (Zeiss, Jena, Germany) and ImageJ software. 3 ourier transform infrared spectroscopy (FTIR). A Bruker Tensor 27 spectrometer at a resolution of 2 cm À1 , averaging over 64 scans from 4000 cm À1 to 800 cm À1 was used to make these experiments. The hydrogels were prepared with D 2 O, NaOD and DCl for these experiments. Scanning electron microscopy (SEM). SEM images were obtained using a Hitachi S-4800 FE-SEM at 0. 5 to 1 keV. 5 Â 5 mm diced silicon wafers were used to probe the gels. The gels were prepared as described above in volumes of 2 mL. A Si-wafer was dipped for 30 minutes in the solution/gel sample and subsequently allowed to dry for a further 30 minutes under a nitrogen flow (approximately 0.