dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This work was supported by the  Academy of Finland ; the  Emil Aaltonen Foundation ; the  Sigrid Jusélius Foundation ; the  Novo Nordisk Foundation  (to N. K. ); the  Turku Doctoral Programme of Biomedical Sciences  (to M. D. R. ); the  National Institutes of Health  ( 5R01GM062534  to G. J. H. ); a kind gift from  Kathryn W. Davis  (to G. J. H. ). G. J. H. is an investigator at the  Howard Hughes Medical Institute. K. A. W. is a George A. and  Marjorie H. Anderson Fellow  at the  Watson School of Biological Sciences. We thank the  Tartu University Proteomics Unit  for the MS analysis, the  Turku Centre for Biotechnology ,  Sanja Vanhatalo , and the whole staff of the  Department of Physiology  for their contributions and technical support. We thank  Ralph Burgess  for sorting the round spermatids and  Michelle Rooks ,  Dick McCombie ,  Elena Ghiban ,  Danea Rebbolini , and  Laura Cardone  for help with the Illumina sequencing.
Free Cytosolic Calcium Concentration Variation Analysis Measurements of the [Ca 2+ ] cyt were based on aequorin bioluminescence using a luminometer (Lumat LB 9507, Berthold, Evry, France). In vivo reconstitution of aequorin was performed by the addition of 6 ml of coelenterazine (5 mM stock solution in DMSO) to 10 ml of aequorin-transformed cell suspension for at least 3 h in the dark. Luminescence was recorded continuously and data converted to free cytosolic calcium concentration using the calibration equation described in Vandelle et al. [31].
Data extracted with Microarray Imager (Combimatrix, Irvine, CA, USA) were normalized by median scaling before deposition in NCBI GEO database (ID: GSE42972). Differentially-expressed genes were identified by pairwise comparison of plants treated (PS3, Lam or SA, JA) versus control plants (Adj or DMSO ) as described by Tsai et al. [35]. GO enrichment analysis was performed on PS3-up-regulated genes with the AgriGO software  [36].
Quantification of JA and SA Metabolites by LC-MS Fifty mg of freeze dried leaves were harvested in parallel of microarray samples and homogenized after addition of a mixture of internal standards containing 100 ng dihydro-JA and D 6 -SA  [39]. After centrifugation the supernatant was purified by organic partitioning, as previously described  [40] ,  [41]. A dried aliquot was re-suspendended in 1 ml of MeOH: H 2 O (10:90) and injected into the HPLC system. Analyses were carried out using a Waters Alliance 2690 HPLC system (Milford, MA, USA) with nucleosil ODS reversed-phase column (10062 mm i. d. ; 5 lm; Scharlab, Barcelona, Spain) coupled to a Quatro LC (quadrupole-hexapolequadrupole) mass spectrometer (Micromass). Data was quantified after comparative analysis with standards using Mass Lynx (v 1.
Statistical Analysis In order to compare P. viticola infection levels, analysis of variance (ANOVA) was performed with the statistical program Stat-Graphics 5. 1 (Manugistic, Inc. , Rockville, MD, USA) using the least significant difference (LSD) test to detect significant differences (P,0. 05) between treatments. For microarray experiments, differentially expressed transcripts were identified by pairwise comparison  [35]  of three independent experiments (n = 3 per treatment) with a fold change cut-off $2 or #0. 5 at least in one condition, and a FDR of 5% (P,0. 05; t test). P values are indicated in each sheet of the Table  S1. For qPCR experiments, transcripts differentially expressed between PS3-or Adj-treated and subsequently infected plants were identified by comparing means of the data obtained in each modality (technical triplicates of 4 biological replicates; n = 12), using unpaired heteroscedastic Student's t test (P,0.
PS3 and Lam Share a Common Stress-responsive Transcriptome that Partly Overlaps the SA-and JAdependent Ones Combining data from the 18 hybridizations realized for the microarray experiment, 200 genes were significantly modulated by at least one of the treatments (P,0. 05). The relative fold changes were determined compared to their respective control: 0. 05% adjuvant for PS3 and Lam, and 0. 1% DMSO for SA and JA. Based on these significantly modulated genes, a hierarchical clustering revealed three different transcriptomic clusters (Figure  2A ). The first cluster shows genes co-induced by the 2 bglucans Lam and PS3 (group 1); the second corresponds to genes modulated after SA treatment (group 2) and the third one is specific to JA treatment (group 3). Among the up-regulated genes (FC. 2), 132, 94, 67 and 27 genes were identified after PS3, Lam, SA or JA treatment, respectively (Figure  2B , Table  S1 ).
At this time point, pathogen spreading and sporulation were undetectable (Figure  S1A ). In parallel, a non-significant variation in JA content   S2  for gene identifiers). C. Gene Ontology enrichment analysis of the PS3-up-regulated genes at 12 hpt realized with the AgriGO software  [36]. An asterisk (*) implies statistical significance between PS3-induced transcripts vs reference transcripts (Fisher's exact test with Benjamini-Yekutieli false detection rate correction, Q,0. 05; Foldchange cut-off 2 used). A hash mark (#) indicates a significant difference between PS3-induced transcripts vs reference transcripts when a more stringent fold-change cut-off of 4 was used. doi:10. 1371/journal. pone. 0088145. g002 was detected in inoculated PS3-treated compared to adjuvanttreated plants (data not shown).
Four independent biological experiments were performed and, in each experiment, the 2nd and 3rd youngest leaves of four plants were sampled and pooled. Means of relative expression after PS3 or Adj treatment were compared within a time point. Asterisks indicate significant differences using unpaired heteroscedastic Student's t-test (P,0. 05). The relative expression in Adj at 0 dpi was set as 1 and the others were adjusted accordingly. The error bars represent the standard error of the mean. D. The SA analogue benzothiadiazole (BTH) induces grapevine resistance against P. viticola. Leaf sporulating area evaluated after 8 dpi. Different letters indicate statistically significant differences (P,0. 05; ANOVA followed by LSD test). Data are representative of three independent biological experiments (n = 3).
The relative expression in Adj at 0 dpi was set as 1 and the others were adjusted accordingly. The error bars represent the standard error of the mean. C. Diphenylene iodonium (DPI) partly abolishes the PS3-IR to P. viticola in grapevine. Leaf disks were treated during 1 day with 10 mM DPI, washed and then treated with 2. 5 g l 21 PS3 during 1 day, washed and, finally inoculated with P. viticola. Leaf sporulating area evaluated at 8 dpi. Different letters indicate statistically significant differences (P,0. 05; ANOVA followed by LSD test). Data are representative of three independent experiments (n = 3). D. Microscopic analyses on the same grapevine leaf discs show that DPI inhibits the primed H 2 O 2 production (black arrows and inset) and callose deposition (white arrows and inset) during PS3-IR, leading to P.
Means of relative expression after PS3 or Adj treatment were compared within a time point. Asterisks indicate significant differences using unpaired heteroscedastic Student's t-test (P,0. 05). The relative expression in Adj at 0 dpi was set as 1 and the others were adjusted accordingly. The error bars represent the standard error of the mean. D. The SA analogue benzothiadiazole (BTH) induces grapevine resistance against P. viticola. Leaf sporulating area evaluated after 8 dpi. Different letters indicate statistically significant differences (P,0. 05; ANOVA followed by LSD test). Data are representative of three independent biological experiments (n = 3). doi:10. 1371/journal. pone. 0088145. g003.
C. Diphenylene iodonium (DPI) partly abolishes the PS3-IR to P. viticola in grapevine. Leaf disks were treated during 1 day with 10 mM DPI, washed and then treated with 2. 5 g l 21 PS3 during 1 day, washed and, finally inoculated with P. viticola. Leaf sporulating area evaluated at 8 dpi. Different letters indicate statistically significant differences (P,0. 05; ANOVA followed by LSD test). Data are representative of three independent experiments (n = 3). D. Microscopic analyses on the same grapevine leaf discs show that DPI inhibits the primed H 2 O 2 production (black arrows and inset) and callose deposition (white arrows and inset) during PS3-IR, leading to P. viticola spreading. Aniline blue and DAB staining were realized to detect callose and H 2 O 2, respectively. Pictures are representative of three independent experiments.
Predominance of differentially expressed genes at 1 h in the time-course analysis of L. lactis MG1363 transcriptome Transcriptomic analysis was performed on the whole genome of strain MG1363. Differentially expressed (DE) genes in condition O2 in comparison to condition N2 were determined at different time points corresponding to stage A (1 h), stage B (5 h), stage C (8 h) and stage D (24 h). All DE genes are described in the supplementary data (Additional file 1: Table  S1 ) and DE genes discussed in the text are gathered in Table  1. Figure  2  represents the number of DE genes per known COG category, for each time point except 24 h (only one DE gene). The adaptation to oxygen strikingly occurred at 1 h (Figure  2. 1) of culture (53 genes over-expressed and 37 genes underexpressed). All the COG categories were represented, indicating a global adaptation to oxidative stress.
Each spectrum consisted of 64 scans of 32768 complex data points with a spectral width of 8389 Hz (14 ppm). The nuclear Overhauser effect spectroscopy (NOESY) pre-saturation experiment was used for each sample using a low power irradiation at the water frequency during the recycle delay to suppress the massive water signal. Spectra were Fourier transformed with 0. 3 Hz line broadening, automatically phased and manually baseline corrected using the TOPSPIN software. The concentrations of metabolites were measured by a previously validated NMR targeted profiling method  [51]. Spectra referenced to TSP (0 ppm) were uploaded in the Chenomx NMR suite version 6. 0 software (Chenomx Inc. ) and compounds identified and quantified by fitting library reference spectra of individual metabolites to the experimental spectrum.
Genomic expression analysis Home-made microarrays containing 2459 PCR amplicon probes representing the coding sequences of the genome of L. lactis MG1363 were designed. Probes coding sequences from the lactococcal plasmid pLP712 were added  [50]. The design is available on the Arrayexpress database under the accession number A-MEXP-2216. Ten μg of total RNA were labeled overnight at 42°C using 2 μl of Cy5-dCTP (Amersham Pharmacia), 4 μl of reverse transcriptase (AffinityScript, StrataGene) and 5 μg of random hexamers (Bioprime DNA labeling System, Invitrogen). Labeled cDNA was purified using QIA-Quick PCR purification kit (Quiagen) and eluted in 50 μl of RNAse-free water. For each microarray sample, 0. 3 μg of gDNA were labeled with Cy3-dCTP using the same protocol. The expression profile of L.
Metabolomic data with growth/pH kinetics have been analyzed using a repeated series ANOVA (library nlme) in software R  [52]. Models taking time and conditions (O2 and N2) as factors were tested. Time was introduced as a quantitative factor at degree-1 polynomial equation for the model. The results give the significance for each factor and their interaction (condition, time, time:condition). In this study, we focused on the significance of condition effect over the 24 h-time-course.
Quantitative RT-PCR for all experimental time points was performed in triplicate (independent biological triplicates). Standard curves were generated to calculate the copy number of each gene in each sample. The most stable control genes were determined by geNorm among five potential genes. Real-time quantitative PCR data were normalized by geometric averaging of three internal control genes (parA, tuf and hllA). Gene expression was thus expressed as relative expression with regard to the normalization factor calculated by geNorm. Statistical analysis was performed using ANOVA test considering a p-value lower than 0. 05 to identify genes showing significant change in expression with time.
Acknowledgements This work has been supported by the  European Union  with a  Marie Curie Intra-European Fellowship  (Project number:  41161 , acronym: OXYRED). The authors are grateful to  Pascal Pachot, Stat-Plan , (http://pachot. wix. com/stat-plan) for statistical analysis of the NMR spectrometry and kinetics data. The authors would like to thank  Hélène Falentin  for improving the manuscript and to  Alyson Yee  for correcting the English.
Acknowledgments The data used for this paper were collected within the European  BeFoFu  project (www. befofu. org). BeFoFu is funded by national funding agencies ( Austrian Wissenschaftsfond , the  French Agence Nationale de la Recherche , the  German Federal Ministry of Education and Research, The Netherlands Organisation for Scientific Research , the  UK Natural Environment Research Council  and the  UK Department for Environment, Food , and Rural Affairs). We are grateful to our funders and the experts that devoted their time for the interviews.
Acknowledgements The present study was supported by the  Natural Science Foundation of China  ( 31101622 ,  NSFC-J1210002 ) and the  National Special Transgenic Project  ( 2008ZX08012-005 ;  2011ZX08012-005 ). The  Muséum national d'Histoire naturelle of Paris  funded the collecting trips to New Caledonia of the second and third authors, and in part funded the study of the material for the first author in Paris.
Comparative phylogeography is an effective approach to assess the evolutionary history of biological communities. We used comparative phylogeography in fourteen tree taxa from Lower Guinea (Atlantic Equatorial Africa) to test for congruence with two simple evolutionary scenarios based on physio-climatic features 1) the W-E environmental gradient and 2) the N-S seasonal inversion, which determine climatic and seasonality differences in the region. We sequenced the trnC-ycf6 plastid DNA region using a dual sampling strategy: fourteen taxa with small sample sizes (dataset 1, mean n = 16/ taxon), to assess whether a strong general pattern of allele endemism and genetic differentiation emerged; and four taxonomically well-studied species with larger sample sizes (dataset 2, mean n = 109/species) to detect the presence of particular shared phylogeographic patterns.
When grouping the samples into two alternative sets of two populations, W and E, vs. N and S, neither dataset exhibited a strong pattern of allelic endemism, suggesting that none of the considered regions consistently harboured older populations. Differentiation in dataset 1 was similarly strong between W and E as between N and S, with 3-5 significant F ST tests out of 14 tests in each scenario. Coalescent simulations indicated that, given the power of the data, this result probably reflects idiosyncratic histories of the taxa, or a weak common differentiation pattern (possibly with population substructure) undetectable across taxa in dataset 1. Dataset 2 identified a common genetic break separating the northern and southern populations of Greenwayodendron suaveolens subsp. suaveolens var.
Given the paucity of well-established knowledge on the evolution and species delimitation of African rainforest trees, we opted for two complementary sampling strategies. (i) First, we considered fourteen tree taxa with limited sample sizes (12 to 20 individuals) and possibly encompassing a wide range of phylogenetic depths (dataset 1); i. e. some of these taxa may not match the biological species concept, containing more than one genetic species according to current taxon delimitation. This sampling allowed us to assess whether a very general phylogeographic pattern emerges given a wide range of phylogenetic depths among different lineages. (ii) Second, we analyzed larger sample sizes (66 to 169 individuals) in four tree species that are taxonomically very well studied (dataset 2). This sampling provided increased power to detect particular shared phylogeographic patterns among the four species, though at the cost of generality.
Ethics Statement We sampled fourteen common rainforest tree taxa typically occurring in evergreen and semi-evergreen rainforests of the Lower Guinea. Sampling sites included national parks and public land. Sampling permits were obtained from the CENAREST (Gabon) and the Ministry of Scientific Research and Innovation (Cameroon) and sample collection was facilitated by the Wildlife Conservation Society (La Lope, Gabon), the CIRMF (Franceville, Gabon), the logging company Pallisco (Cameroon), the Missouri Botanical Garden and the NGO Nature Plus. According to IUCN, Baillonella toxisperma Pierre (Sapotaceae) is listed as ''vulnerable A1cd ver 2. 30, Milicia excelsa (Welw. ) C. C. Berg (Moraceae) as ''lower risk/near threatened ver 2. 30, and Greenwayodendron suaveolens (Engl. & Diels) Verdc. as ''least concern'', while there is no data on the remaining taxa.
Sampling Strategy and Molecular Methods In a first stage (dataset 1), we sampled fourteen common tree taxa typically occurring in evergreen and semi-evergreen rainforests of the LG (Table  1 , Figure  1 , Table  S1 ). Thirteen of the sampled taxa are considered as species according to most taxonomic reference books (e. g. , Flore d'Afrique Centrale, Flore du Gabon, Flora Zambesiaca) whereas Erythrophleum harbours two vegetatively similar and closely related sister species traded under the same timber name (tali)  [34]. However, recent or on-going taxonomic revisions or genetic work suggest that the LG study region harbours several species in the genera Carapa  [46, 47] , Strombosiopsis (  [48] , F. J. Breteler, pers. com. ) and Greenwayodendron  [38]. Current knowledge is insufficient to establish whether any of the used taxa matches the biological species concept, and there is evidence that several taxa correspond to closely related species or species complexes (e.
Taxa represent a wide range of life history traits with respect to successional behaviour, seed dispersal syndromes, size of geographic range and tree strata in the rainforest communities (  [49] [50] [51] [52] [53] [54] , Table  S2 ). In a second stage, we included additional samples for four species (dataset 2, Figure  3 ): Greenwayodendron suaveolens subsp. suaveolens var. suaveolens (Engl. & Diels) Verdc. , totalling n = 169 individuals from 41 locations published by Dauby et al. [38] ; Milicia excelsa (Welw. ) C. C. Berg, n = 127, 19 locations, published by Daı ¨nou et al. [40] ; and new data in Symphonia globulifera Lf. , n = 67, 20 locations and Trichoscypha acuminata Engl. , n = 73, 28 locations. Complementary information indicated that these taxa most likely matched the biological species concept; specifically, in G.
PCR products were purified on filter columns (MSB HTS PCRapace/C(96) kit, Invitek, Berlin, Germany) and sequenced using Big Dye v. 3. 1 chemistry (Applied Biosystems, Lennik, Belgium) and an ABI3100 sequencer (Applied Biosystems). (CodonCode Corporation Dedham, MA, USA). Within-taxon alignments of individual sequences were done with MUSCLE 3. 6  [61]. To characterize genetic variation in each taxon, we recorded the number of segregating sites S, the number of singletons, the number of insertion/deletion (indel) polymorphisms, inversions and microsatellite (SSR) length polymorphisms. Considering point mutations only, we used DnaSp v. 5. 10. 1 software  [62]  to compute haplotype diversity Hd and two estimators of the population mutation parameter, h = 4N e m, namely, nucleotide diversity (p), which represents the average number of nucleotide differences per site between two sequences  [63] , and Watterson's estimator h W  [64]  based on S.
We then considered all polymorphism types (except in the large S. globulifera data set, where only SNPs were considered because complex indel polymorphisms prevented unambiguous coding of all polymorphisms) to define haplotypes and computed a haplotype distance matrix for each taxon, defining the distance between two haplotypes as the proportion of polymorphisms differing between them. Statistical parsimony networks among haplotypes were constructed based on these distance matrices using the program TCS 1. 21  [65]. The totality of polymorphisms was used for all further analyses except in the large S. globulifera data set. We computed genetic diversity h and v  [66] , based respectively on ordered or on un-ordered haplotypes, in SPAGeDi v. 1. 3. [67]. The frequencies of haplotypes per sampling location were represented in haplotype maps using ArcGIS 10 software (ESRI, Redlands, CA, USA).
g. , past population differentiation, or recent common origin in a single region), samples were classified as belonging to generally evergreen Atlantic forests or to semi-evergreen inland forests  [19]. For the N-S scenario (2), which emphasizes the role of the climate inversion and seasonality differences across the climate hinge (possibly in conjunction with Plio-Pleistocene forest history, see Introduction), samples were classified as located N or S of the seasonal inversion line (Figure  2 ). Within each of the regions defined according to the two scenarios, we recorded the number of haplotypes observed (Nhaps), the rarefied haplotype richness (Ar x , the number of haplotypes expected in a sample of size x), the number of private haplotypes (Npriv) and the rarefied private haplotype richness (Arpriv x , the number of private haplotypes expected in a sample of size x) using the program HP-Rare  [69] , and we computed genetic diversity and its standard error using the program CONTRIB  [70].
In dataset 1, haplotype richness statistics were collected separately for each taxon and for haplotypes pooled across the 14 taxa. In dataset 1, we carried out one-way analysis of variance in the R software  [71]  to test for the effects of taxa and of geographical regions on genetic diversity statistics in each of the two scenarios. We then computed the differentiation statistics F ST for unordered and N ST for ordered alleles  [66]  for each scenario (both datasets) and tested against the null hypothesis of no population differentiation using permutation tests in SPAGeDi. The number of significant differentiation tests between regions was recorded for each scenario and the contribution of mutations to differentiation was assessed with permutation tests (N ST. N ST(permuted) ,  [72] ). We also evaluated the relative support of both scenarios counting the taxa with stronger differentiation (F ST ) in the candidate vs.
alternative scenario (in dataset 1). To assess the power of our approach to identify population subdivision, discriminate between the tested scenarios, and to assess the rate of false detection of population subdivision when it is absent (type I error), we applied a coalescent simulation approach using simulated sample sizes mimicking our initial (n = 8/population) and enhanced data sets (n = 30/population). We used the software fastsimcoal  [73]  to simulate two simple evolutionary scenarios (Figure  4 ): in each scenario, two samples of n = 8 or n = 30 genes were taken at generation 0 (present time) in each of two populations, their genealogy was simulated backward in time, and then mutations were added onto the genealogy  forward in time. In the ''divergence'' scenario (Figure  4 , A), we modelled one ancient divergence event at t3 = 1600 generations ago, and a more recent divergence at t2 = 200 generations leading to two demes in each population.