dataesr/scientific-paragraphs-categorization huggingface dataset:
The trees were built on scale in the branch length (scale bars reported). Sequences are identified by the species name and annotation (where applicable), followed by their MMETSP ID deprived of the taxon ID for simplicity or GenBank accession number. Posterior probability (PP) values are represented with symbols at the nodes: star represents PP above or equal to 0. 95; square PP between 0. 75 and 0. 95; triangle below 0. 75. Branch coloring refers to PP, color code is reported in figure. (a) COX and COX-like proteins (2 000 000 generations, split frequency standard deviation (s. d. ) = 0. 002); (b) PTGE synthase proteins (5 000 000 generations, split frequency s. d. = 0. 01 (c) PTGD synthase (10 000 000 generations, split frequency s. d. = 0. 04). For simplicity, large sequence clusters were condensed and identified by numbers and the nine main clades were identified by letters highlighted with vertical bars on the right side of the picture.
Results showed a high variability of expression levels of the three enzymes with no significant differences in silica starvation versus standard growth conditions (Supplementary Figure  S5 ). The effect of stress conditions was extended to all other diatom transcriptomes in the Marine Microbial Eukaryote Sequencing Project database  (Keeling et al. , 2014)  through a bioinformatic analysis. Results (Supplementary Table  S2 ) revealed a huge variability among species and among different culture conditions with only a few species expressing all the genes of the pathway. For the majority of species, COX-1 was not annotated. Moreover, different treatments (nutrient deprivation, temperature, CO 2 levels) did not have the same effect on different species (see COX-1 Fragments Per Kilobase of transcript per Million mapped reads-FPKM value, where present, in Supplementary Table  S2 ).
Acknowledgements This project was financially supported by the  PharmaSea  project funded by the  EU Seventh Framework Program , Contract number  312184 , www. pharma-sea. euand by the SZN internal funding. IO was supported by the  Italian flagship project  ' Ritmare ' (Contract number  2645 ). AA was funded by the  European Union  under  FP7-People  GA n. 600407. We thank  Flora Palumbo  ( SZN ) for figure design and  Mariano Amoroso  ( PharmaSea ) for cell culture cultivation. We are also grateful to  Dr Genoveffa Nuzzo  for chemical extractions and  Dr Salvatore D'Aniello  ( SZN ) for the discussions.
Identification of the degradation products of DTZ were carried out using HPLC-MS method. The HPLC-MS data were recorded using an Agilent Chromatograph equipped with a 996photodiode array detector, an atmospheric-pressure chemical ionization device (APCI), and a reverse Zorbax eclipse XDDC 18 column (5  PS concentration was evaluated by a spectrophotometric method, which is presented in Text S6, Supplementary Material. PS in excess was used in our study and the change of PS concentration within 60 min was negligible. Concentration of sulfate radical at the initial step was also calculated by the method described in Text S8, Supplementary Material, and the values are listed in Table  S4.
Degradation products and reaction pathways Reaction products generated during UV-activated PS oxidation of DTZ were identified by HPLC-MS. The identification was based on the analysis of the total ion chromatograms (TIC) and the corresponding mass spectra obtained by the positive ion electrospray HPLC-MS. Data are provided in Table  S3 , and the proposed degradation pathways is illustrated in Fig. 4. As seen, three pathways take place: pathway a) is the side chain cleavage with formation of an NH 2 group, pathway b) is the deiodination-hydroxylation, and pathway c) is the decarboxylation-hydroxylation. As a strong oxidant, SO ÁÀ 4 tends to attack electron rich sites of the compound. For the reaction with DTZ, the initial step is expected to be an electron-transfer from DTZ to SO ÁÀ  4 , leading to the generation of SO 4 2À and the DTZ radical cation.
Voids in particles introduced by the binarization step were closed (hole fill), particles touching the edge of the images were removed (border kill) and noise at the particle circumference introduced through the binarization step were removed (opening). Overlapping particles may generate artifacts with unreasonably large area. These large particles were removed from the analysis when their equivalent diameter exceeded a manually curated maximum diameter specific to each image. Equally, strongly elongated objects with a circularity below 0. 3 were removed from the analysis as they resulted from artifacts introduced by the petri-dishes. The density distributions were calculated from the particle analysis using the package ggplot2  59  in R version 3. 1. 2  60 and presented as violin plots with the indication of median, the interquartile range and the 95% confidence interval.
This second primer pair targets a 23S rRNA region in cyanobacteria and plastids in algae, specific for phototrophs. In a second PCR reaction of 12 cycles, an index sequence was added and the resulting PCR products were purified and loaded onto the Illumina MiSeq cartridge according to the manufacturer's instructions for sequencing of paired 300 bp reads (v3 chemistry). Library preparation and sequencing was done at the GeT PlaGe Sequencing Center of the Genotoul Lifescience Network in Toulouse, France (get. genotoul. fr). Forward and reverse sequences were assembled using a modified version of the Standard Operation Procedure for MiSeq data  67  in Mothur version 1. 35. 0 68 including preclustering at 4 differences in nucleotides over the length of the amplicon and chimera checking using uchime  69.
All high-throughput sequencing data are deposited in the National Center for Biotechnology Information database with accession numbers KY914576-KY921581 for 23S rRNA sequences and under the SRA bioproject PRJNA393678 for 16S rRNA sequences. We were able to successfully amplify 48 samples for amplicons of partial bacterial 16S rRNA gene sequences and 53 samples for partial cyanobacterial 23S rRNA gene sequences. On average, 16S amplicons had a raw sequence count of 66000 ± 33000 (mean ± standard deviation) per sample, of which on average 48000 ± 24000 were kept after quality checking. The average number of raw sequences for 23S amplicons was 86000 ± 46000, with 44000 ± 29000 good quality sequences after quality checking. It should be noted that the large standard deviations were mostly caused by few samples with more than 120000 good quality reads.
Their occurrence was at random and the number of reads was not artificially reduced for the analysis by subsampling. Archaeal sequences were not considered as they presented on average not more than 0. 02% of reads in granule samples. Using manually curated lists of known genera of nitrifying bacteria  40  , methanotrophic bacteria (www. methanotroph. org), polyphosphate and glycogen accumulating bacteria  72  , or syntrophic bacteria  73  with methanogenic Archaea (Table  S3 ), we selected subsets of sequences from SILVA SSURef NR99 and generated local blast databases using Blast 2. 2. 28+. We then blasted our 16S inventories against these databases. Sequences with close hits to the database (sequence identity >97% and an alignment overlap >98%) were retained and relative abundances in inocula and granules were compared using Wilcoxon Rank Sum Tests in R  60.
Jaccard dissimilarity matrices were calculated for unique sequences using vegdist and later used for Principal Coordinate Analysis (PCoA) with the R package vegan 2. 4. 3  74. The appropriate pair-wise distances in the dissimilarity matrices were selected and compared using Wilcoxon rank sum. Shannon diversity was calculated using renyi in vegan. For the generation of the heatmap, abundances of cyanobacterial 23S rRNA gene fragments were grouped according to their assigned taxonomy in LSURef, release 115 on the order level for Subsections I, II and IV and on the genus level for Subsection III, according to Bergey's taxonomy  40. Unclassified sequences on the order level were blasted against NCBI's nt database and order level taxonomies were manually curated. The average relative abundances of sequence types in the heatmap were calculated per sample.
Acknowledgements This research was supported by the  U. S. National Science Foundation  grants  CBET1335816 ,  CBET1605424 ,  Water Environment Research Foundation 2013 Paul Busch Award , and the  French "Agence Nationale de la Recherche " grant  ANR-16-CE04-0001-01. The  project  ANR-16-CE04-0001-01  is certified by the competitiveness cluster TRIMATEC. We thank the  Egyptian Government Scholarship  for supporting A. Abouhend's visiting research at  University of Massachusetts Amherst. We thank  D. Callaham ,  T. Baskin  and the team of  GeT PlaGe  for SEM and MiSeq sample preparation, respectively. We are grateful to the staff of the wastewater treatments plants in Amherst,  Hadley, Springfield, and Winthrop MA, USA, Neuchâtel (Switzerland) and Narbonne-Ville (France)  for assisting in sampling.
For the DFT calculations, the reactant (H 2 O) and intermediates (OH and H) are first adsorbed on all possible active sites of the catalyst. Afterwards, the VASP software is utilized to optimize the adsorption. For evaluating the energy barrier (E a ¼ E TS À E IS ), the transitional state (TS) was located using the Nudged Elastic Band method. All transition states were verified by vibration analyses with only one imaginary frequency. The p(3 Â 3)-Ni(111), p(3 Â 3)-Mo(110), p(3 Â 3)-MoO 2 (110) and p(1 Â 1)-MoNi 4 (200) surfaces were utilized to simulate the properties of these electrocatalysts.
Acknowledgements This work was financially supported by the  ERC  Grant on  2DMATER  and  EC under Graphene Flagship  (No. CNECT-ICT-604391 ). We also acknowledge the  Cfaed (Center for Advancing Electronics Dresden) , the  Dresden Center for Nanoanalysis (DCN)  at  TU Dresden  and  Dr Horst Borrmann  for the X-ray diffraction characterizations in  Max Planck Institute for Chemical Physics of Solids.
Capillary electrophoresis conditions The capillary electrophoresis system used was a PA800+ automated CE instrument from AB Sciex (Brea, CA, USA) equipped with a photodiode array detection system. The control of CE was performed using 32 Karat software (Beckman Coulter). All through this study, uncoated All rinse cycles were carried out at 30 psi. Hydrodynamic injections were performed at the anodic side of the capillary and normal polarity mode was used with a separation voltage of + 20 kV. Electrophoretic analyses were realized at 25°C in an ammonium acetate buffer (pH 9. 0, ionic strength of 50 mM). The detection of the substrate (HA) and the products was performed at 200 nm (bandwidth 10 nm). The corrected-peak area (CPA) of an analyte represents the peak area, which has been corrected for migration time.
HPTLC analysis HPTLC analyses were done on 10 × 20 cm HPTLC plates (Merck, Germany) by a CAMAG (Muttenz, Switzerland) ATS4 Automatic TLC Sampler controlled by Win-CATS software and were observed with a Reprostar 3 illumination unit. 10 ìL of extract solutions and 4 ìL of standard solutions each at 1 mg. mL -1 were laid down. Plate elution was performed in a horizontal developing chamber. After development, the plate was dried and observed under 254 and 366 nm light. The plate was then sprayed with different specific or non-specific reagents. For each compound family, elution mixture, visualization reagent and standards are mentioned in Table  4.
Acknowledgements We acknowledge support from the  French National Research Agency  ( ANR , Carbiored  ANR-12-BS07-0024-03 ;  Labex program  ARCANE,  ANR-11-LABX-0003-01  and  DYNAMO ,  ANR-11-LABX-0011 ) and from  Fondation de l'Orangerie for individual Philanthropy  and its donors.
This transition is also dependent on the host interactions with its complex bacterial communities  29. Although E. subulatus is a different species, such host-bacterial interactions may also play a role in the adaptation of Ectocarpus sp. to its environment (although this aspect has not been investigated in the study presented here). Analysis of the Ectocarpus sp. genome suggested that this species has evolved effective mechanisms to survive its harsh intertidal environment such as a complex photosynthetic system and a large number of ion channels  1. These characteristics may have contributed to its ability to colonize freshwater environments. However, even with the current improved assembly and annotation of the Ectocarpus genome, about 39% of the predicted genes lack functional annotations based on available databases  32.
95), only one of the markers was retained. Markers with suspect linkages were also filtered out. Finally, a total of 3,588 markers and 80 individuals were used for the construction of the genetic map. Maps obtained with R/qtl and JoinMap using this data were compared and found to be very similar. The 3,588 SNPs were distributed over 28 linkage groups (LGs), with a minimum logarithm of the odd (LOD) score of 4. 0. The total length of the map was 2,585. 7 centimorgans (cM), which accounted for 98. 35% of the estimated genome (Ge) length (Ge = 2629 cM). The average spacing between two adjacent markers was 0. 7 cM and the largest gap was 20. 3 cM (on LG28). The lengths of the 28 LGs ranged from 41. 8 cM with 54 markers to 152. 3 with 217 markers (Table  2 , Supplementary Fig. S2  and Supplementary Dataset).
Gene ontology enrichment test. A genome-wide list of candidate genes was constructed by retaining all loci in the 1. 5-LOD support confidence intervals of the complete set of QTLs. This list of 562 genes included 263 genes that had associated GO terms. In the re-annotated genome of Ectocarpus 32 , 17,418 genes were identified among which 10,688 had predicted functions and 7,383 had associated GO terms. We built a reference list, consisting of genes located in 10 kb windows around the SNPs localized on the genetic map. This gave a list of 2,710 genes among which, 1,156 had associated GO terms (Supplementary Dataset). A GO enrichment test was carried out by comparing the candidate list of 263 genes which had associated GO terms with the reference list of 1,156 genes also having associated GO terms.
A GO enrichment test identified six terms significantly enriched in putative genes lying in the QTL intervals, among which, four were linked to ion and protein transport processes. Of the 562 putative genes in the QTL intervals, 102 were also identified as being significantly differentially expressed between low and high salinity by Dittami et al. 31  (Supplementary Dataset), but more interestingly, the authors also observed an accumulation of intracellular ions at different levels between freshwater and marine strains, suggesting an important role for ion transport in the adaptation to low salinities. The list of putative stress-related genes corresponding to the QTL intervals identified in the current study should be treated with caution because there are uncertainties linked to positions of QTLs and because growth is a complex phenotype integrating numerous metabolic pathways.
Light conditions were 12 h/12 h day/night (~2000 lux light intensity) and the temperature in the growth chamber was set to 13 °C for the salinity experiment. The plates were maintained in the growth chambers for 12 days. Each individual was photographed under a binocular microscope before and after the experiment and thallus area was measured using the program ImageJ. Growth was estimated as the ratio of the thallus area on day 12 to the thallus area on day 0. By treating different experimental conditions as different environments, we determined reaction norms of the mapping progeny and used the slopes of these reaction norms as plasticity traits. The relation of these plasticity traits to specific environmental variations was indicated by giving the two environmental conditions separated by a slash mark (e.
Compared with the original protocol, the barcodes were modified by adding an extra nucleotide, extending to six nucleotides (see Supplementary Dataset). Both an Agilent ® 2100 Bioanalyzer ® (Agilent Technologies) and qPCR were used to quantify the librar- ies generated from each individual. Libraries with distinct multiplexing indices were then combined in equimolar ratios to compose a final pool of libraries for sequencing. An Illumina Hiseq 2500 platform (Rapid Run Mode) was used to generate a total of 470 million (48. 5 Go) high quality (Q30 = 82%) 100 bp paired-end sequence reads at a cluster density of 850 K/mm 2 (HiSeq Rapid SBS Kit v2, 200 cycles). As a test, a subset of the library was also sequenced with Illumina MiSeq, providing 93 million additional reads (MiSeq Reagent Kit v3, 150 cycles) The raw sequence data in fastq format are stored in the Sequence Read Archive (SRA) at National Center for Biotechnology Information (NCBI) and can be accessed at NCBI homepage (https://www.
ncbi. nlm. nih. gov/; bioproject accession PRJNA371840 or study accession SRP099384). Analysis of ddRAD-seq data. The ddRAD-seq sequencing data was analyzed using the Stacks pipeline (version 1. 40)  73. Raw sequence reads were checked for intact barcodes and the restriction enzyme sites. A sliding window of 25% of the length of a read was set to check sequence quality and reads with < 90% base call accuracy were discarded. Because Stacks removes whole reads when adapter contamination is detected, we first used the program Cutadapt (version 1. 8. 3) to check for the presence of adapters and cut only adapter sequences. Paired-end sequencing of shorter fragments generated overlapping reads that were treated with the program PEAR (version 0. 9. 10)  74. PEAR identifies paired-end non-overlapping reads and generates a single consensus sequence from overlapping read pairs.
These single consensus sequences were added to the singleton rem1 and rem2 sequences produced by Stacks for a unique group of singleton sequences. Paired-end reads as well as singletons were then trimmed to 95 bp with the program TRIMMOMATIC (version 0. 32)  75. The paired-end sequences and the singletons were then aligned to the Ectocarpus reference genome 1 using Bowtie 2 (version 0. 12. 9)  76. Output aligned. sam files were subsequently imported in to the Stacks pipeline and the final Genotypes program exported haplotypes encoded as genotypes at the different loci. Construction of the genetic map. After Stacks analysis, genotypes were exported in a generic format that was imported into Excel for filtering. As we used a haploid population, only aa/bb segregation was expected. All non-expected segregation patterns, as well as loci with log likelihood values below -30, were removed.
Loci with over 25% of missing data were also removed. In the finale matrix (see Supplementary Dataset), A genotypes derived from the male parent and B genotypes derived from the female parent. The R package R/qtl (version 1. 39-5)  77  was used for the construction of the genetic map, in conjunction with either R/ASMap (version 0. 4 -7)   or JoinMap ® version 4. 1. At this stage, individuals with more than 40% of missing genotypes were removed. Further filtering was done within the mapping programs. Markers that showed significant segregation distortion (≤ 5%) were removed. Recombination fraction estimates were used to correct erroneous genotypes or switched alleles. Markers were also filtered based on their similarity, on whether they were located on the same contig and based on suspect linkage patterns.
In JoinMap, the maximum likelihood mapping algorithm was applied. Estimated genome (Ge) length was obtained by adding 2 s (s being the average spacing between markers along the LGs) to the length of each LG to account for chromosome ends beyond the terminal markers of each LG  78. The map coverage was then estimated as the ratio between the observed map length and Ge. The R/xoi package (version 0. 66-9) was used to obtain a smoothed estimate of the recombination rate along the LGs, in 1 Mbp sliding windows.
Statistical analyses of phenotypic data. Statistical treatments were done in R. BLUPs were obtained by fitting several linear mixed models (LMM) with the R package lme4 (version 1. 1-12) and selecting the optimal model with AIC: ε = µ + + + Y G R (1) ij i j ij where Y ij represents the value of the trait under investigation for the genotype i at replicate j, μ the general mean of the trait, G i the random effect of the genotype i, R j the random effect of the replicate j and ε ij the random residual effect. Broad-sense heritability of individual traits was calculated as: σ σ = + σ ε H (2) G G J 2 2 2 2 where σ G 2 is the genetic variance, σ ε 2 the environmental variance and J the number of replicates. Some strains did not survive at salinities of 15‰ and 20‰ and hence we introduced a binomial survival trait.
QTL mapping. QTL mapping was performed using the R package R/qtl  77  as well as MapQTL ® version 5. In R/qtl, the scanone function was used to perform interval mapping with the Haley-Knott regression method, including sex as a cofactor. 5% significance thresholds of the LOD scores were obtained by 5,000 permutations. In MapQTL, composite interval mapping was performed with the MQM method. The method uses markers as cofactors in an approximate multiple-QTL model with additive and dominant gene actions. Cofactors were selected in a first round as loci with LOD ≥ 2. 5 after a simple interval mapping. Then this list was extended with markers on a given linkage group selected every 10 or 20 cM and optimized via the automatic cofactor selection method. The method uses a backward elimination procedure to determine which markers show a significant association and which do not, and ultimately produces a list of significant cofactor markers.
For that, we made two lists: a candidate list consisting of all loci in the 1. 5-LOD support confidence intervals of the QTLs, based on the sequenced reference genome and a reference list including all the putative genes in the mapped scaffolds, refining the search only on a 10 Kb window around each SNP on the genetic map. Only loci with clear GO identifications were retained from the two lists. The candidate list was then compared to the reference list using the R/topGO package (version 2. 26. 0), which is designed to facilitate semi-automated enrichment analysis for GO terms. The test was based on gene count and used the "classic" algorithm with the "Fisher" statistic. How to cite this article: Avia, K. et al. High-density genetic map and identification of QTLs for responses to temperature and salinity stresses in the model brown alga Ectocarpus.
Acknowledgements This work was supported by the  Centre National de la Recherche Scientifique , the  University Pierre and Marie Curie , the  Agence Nationale de la Recherche  (project  Idealg   ANR-10-BTBR-04-01  and  ANR-12-JSV7-0008 ), the  Brittany Region  grant  SAD 2015-ECTOQTL 9119  to  Komlan Avia , the  International Research Network "Diversity, Evolution and Biotechnology of Marine Algae"  ( GDRI N° 0803 ) and the  European Research Council  ( ERC , grant agreement  638240 ). We are very grateful to  Philippe Potin  and  Monique Ras  for their management and support. We are grateful to  Akira Peters  for providing different strains for our experiments and for his advice. We thank  Claire Dagin-Thiébaut ,  Charlotte Roby  and  Delphine Scornet  for precious advice during experiments.
We thank  Gildas Le Corguillé  for bioinformatics support. We thank  Ian Probert  and  Jérôme Coudret  for technical support. We thank  Thomas Broquet  for his comments on the paper. We thank the  SeaDAS Development Group at NASA GSFC, Greenbelt, Maryland , the  Ocean Biology Processing Group (OBPG)  at  NASA GSFC, Greenbelt, Maryland and Brockmann Consult GmbH, Hamburg, Germany  for distributing the SeaDAS software and the MODIS data. We thank the  UMR 8199 LIGAN-PM Genomics platform (Lille, France)  which belongs to the ' Federation de Recherche' 3508 Labex EGID (European Genomics Institute for Diabetes ;  ANR-10-LABX-46 ) and was supported by the  ANR   Equipex 2010 session  ( ANR-10-EQPX-07-01 ; ' LIGAN-PM '). The  LIGAN-PM Genomics platform (Lille, France)  is also supported by the  FEDER  and the  Region Nord-Pas-de-Calais-Picardie.