dataesr/scientific-paragraphs-categorization huggingface dataset:
The aligned reads were then assembled into transcripts using Stringtie with default parameters  33. Transdecoder 34 was used to predict the coding regions of those transcripts using Pfam hits to select the best frame of each transcript. In parallel, we used the RNA-seq libraries to build a de novo transcriptome assembly using Trinity  34  , which was also processed using Transdecoder. We used the resulting assemblies to characterise the actively transcribed genes in S. kawagutii. In parallel, we used manually curated Symbiodinium retrotransposon sequences as query in a tblastn search against the genome assembly. Those hits were transformed into a hints file for Augustus ab initio gene prediction software  35  , which was run in a mode to detect 'intronless' gene models guided by the hints.
Each RNA-seq library was then quantified using Kallisto 0. 42. 3  39  and Hisat2 uniquely mapped reads. DNMT and retrotransposon searches. In all our searches, we used a data set of eukaryotic proteomes (Supplementary Data 1) selected to maximise the span of phylogenetic diversity. To scan the proteomes, we used Hmmer3 40 using candidate Pfam domains as query (e. g. , DNA_methylase PF00145). For the resulting protein hits, we obtained the complete domain architecture using Hmmscan with PfamA database as obtained in March 2015  41. For the dinoflagellate species without reference genome, we downloaded transcriptome assemblies from the MMETSP project  42  and we used Transdecoder to obtain coding sequences. Reference retrotransposons sequences were obtained from RepBase (download February 2014)  43.
Viral sequences were obtained from NCBI non-redundant database using retrotransposon DNMT sequences as query. Symbiodinium DIRS elements have a divergent reverse transcriptase that does not align well with current Pfam domain seed alignments. To increase our recovery rate of DIRS retrotransposons in Symbiodinium, we generated an alignment using representative sequences from divergent clades within DIRS retrotransposons that we trimmed manually and converted into an HMM profile using hmmbuild. Phylogenetic reconstruction. Multiple sequence alignments were built using MAFFT  44  , choosing L-INS-I mode for smaller data sets and automated mode for more than 1000 sequences. Resulting alignments were trimmed using TrimAL with '-automated1' mode  45. For phylogenetic reconstruction, we used IQ-TREE with the in-built automated test to choose the best substitution model for each tree  46.
To reveal how many active lineages there were among the young transposons, we built a network using the identity values as edges and the sequences as nodes, obtaining the total number of orthologous clusters using the 'igraph' package for R in CRAN. To characterise the evolution of specific transposon subfamilies, we first clustered transposable element sequences belonging to all Symbiodinium species using blastp pairwise 0. 6 identity threshold to cluster modules of conserved subfamilies across species. We then counted the numbers of each subfamily in each genome, and inferred the evolutionary dynamics using Count 47 under Dollo parsimony. DNMT and non-DNMT encoding transposons were analysed separately. The observed versus expected ratio of CG dinucleotides on transposons and genomes data was computed as: (total number of CG dinucleotides)/((total number of Cs + total number of Gs)/2) 2 as in ref.
48. To obtain the Ka/Ks ratios for the DNMT domain in retrotransposons, we gathered all retrotransposon sequences encoding DNMTs and extracted the DNMT and reverse transcriptase domain using Hmmer3 40 with custom hmm profiles. We removed identical sequences at the amino acid level and we used MAFFT  44  to align the proteins and PAL2NAL  49  to obtain the codon alignments. We filtered out sequences with too many gaps as they dramatically decreased the final size of the codon alignment. Furthermore, we only used transposable elements were both DNMT and reverse transcriptase domains passed the quality filters. Finally, we obtained the Ka, Ks and Ka/Ks values using the 'seqinr' package for R in CRAN. For interspecies comparisons, we preselected candidate sequences from all lineages depicted in Fig.
For the mapping to the reference genomes, we used BS-Seeker2 50 , relying on Bowtie2 end-to-end alignments, and using Sambamba rmdup to eliminate PCR duplicates. Methylation calls were genotype corrected using read data from BS-Seeker2 ATCGmap output file. The scaffolds corresponding to plastid or mitochondrial sequences were excluded for global methylation analysis. In each library, we spiked in an unmethylated lambda phage genome as control, which then we used to calculate the bisulfite reaction non-conversion rate (% of mC for all covered Cs) for each library. Methylation data were visualised using IGV genome browser and DeepTools2 51. To obtain differentially methylated regions, we used the DSS package from Bioconductor, allowing a minimum delta of 0. 2 spanning at least 5 CGs, with a p value threshold of 0.
Acknowledgements We thank  Geoff Faulkner ,  Marina Oliva  and  Ozren Bogdanovic  for critical reading of this manuscript,  Sophie Dove  (the  University of Queensland ) for Symbiodinium sp. cultures and  Oliver Rackham  for pYES2 plasmids. This work has been supported by the  Australian Research Council (ARC) Centre of Excellence program in Plant Energy Biology  ( CE140100008 ) and the  Natural Science Foundation of China  (grant # 31661143029 ) and the  National Key Research and Development Program of China  grant  2016YFA0601202. R. L. was supported by an  ARC   Future Fellowship  ( FT120100862 ), a  Sylvia and Charles Viertel Senior Medical Research Fellowship  and a  Howard Hughes Medical Institute International Research Scholarship. A. d. M. has been supported by a  EMBO long-term fellowship  ( ALTF 144-2014 ).
S. L. has in part been supported by  Gordon and Betty Moore Foundation  (GBMF grant # 4980 ). P. L. was supported by intramural funding from  CNRS , the  Université Paris Diderot  and the  Institut National de la Santé et de la Recherche Médicale , A. B. has been supported by a post-doctoral fellowship from the  ANR  through  the initiatives d'excellence (Idex   ANR-11-IDEX-0005-02 ) and the  Labex 'Who am I?'  ( ANR11-LABX-0071 ). All data sets have been deposited in GEO under the accession number  GSE104474.
2 Fig. 5 25 Fig. 5 De novo methylation of CGs by retrotransposon-encoded DNMTs. a Schematic representation of the pYES2 construct and the list of DNMT domain inserts tested. M starting methionine, NLS nuclear localisation signal, HA human influenza hemagglutinin tag, Hsap Homo sapiens, Skaw S. kawagutii, Knit K. nitens. b Methylation induction in pYES2-DNMT-expressing yeast lines measured in duplicates (r1 and r2) as a percentage of mC calls against all Cs in each of the four dinucleotide sequence contexts. Independent measures of methylation levels reported for the episomal pYES2 construct and the native yeast genome. Data for the non-conversion for each experiment (spiked-in lambda genome) is in Supplementary Fig. 15b.
In addition, in up to 39 % of samples in which UCYN-A1 and A2 were detected, their respective hosts were bd. Pairwise comparisons of the nifH abundances and various environmental parameters supported two groups: a deep-dwelling group (45 m) comprised of UCYN-A1 and A2 and a surface group (0-15 m) comprised of Trichodesmium, het-1 and het-2. Temperature and photosynthetically active radiation were positively correlated with the surface group, while UCYN-A1 and A2 were positively correlated with depth, salinity, and oxygen. Similarly, in a meta-analysis of 11 external datasets, all diazotrophs, except UCYN-A were correlated with temperature. Combined, our results indicate that conditions favoring the UCYN-A symbiosis differ from those of diatom diazotroph associations and free-living cyanobacterial diazotrophs.
An earlier investigation along a transect in the western equatorial Pacific estimated that 74 % of the total N 2 fixation could be attributed to the < 10 µm size fraction, although abundances of unicellular cyanobacteria were moderate (17 cells mL -1 )  (Bonnet et al. , 2009). However, diazotroph quantification is lacking further south in tropical waters, despite being recently recognized as a hot spot of N 2 fixation, with average rates of ∼ 570 µmol N m -2 d -1  (Bonnet et al. , this issue) , i. e. , in the upper range (100-1000 µmol N m -2 d -1 ) of rates gathered in the global N 2 fixation MAREDAT database  (Luo et al. , 2012). The primary aim of this study was to quantify diazotroph abundance and distribution in the WTSP, with an emphasis on symbiotic N 2 -fixing populations, both by at-sea and labbased quantitative approaches.
Since earlier expeditions to the SW Pacific identified it as a potential hotspot for diazotrophy  (Bonnet et al. , 2015 (Bonnet et al. , , 2016 (Bonnet et al. , , 2017;; Moisander et al. , 2010) , the at-sea quantitative polymerase chain reaction (qPCR) was performed in order to make informed decisions about the presence, absence, and relative abundance of diazotrophs so that stations could be selected for other assays and characterization of the hydrographic conditions (e. g. , incubation experiments, sediment and drifter deployments; see  Bonnet et al. , 2018). For a more comprehensive investigation of the symbiotic diazotrophs, we developed a new primer and probe set for quantification of the UCYN-A1 host. We also identified key environmental parameters, both biotic and abiotic, which influenced the distribution of diazotrophs in the WTSP and tested the congruency of these parameters in an additional 11 publicly available datasets.
Oligonucleotide design A new primer and probe set was designed to amplify the UCYN-A1 host and was based on a published 18S rRNA sequence (accession number JX291893) reported from the North Pacific gyre (station ALOHA)  (Thompson et al. , 2012). The design utilized the same 96 bp target region of the 18S rRNA used to amplify UCYN-A2 hosts described in  Thompson et al. (2014)  (Table  S1  in the Supplement). The primers and probe for the UCYN-A1 host 18S rRNA gene assay are as follows: forward, 5 AGGTTTGCCGGTCTGCCGAT-3 ; reverse, 5 GAGCGGGTGTCGGAGACGGAT-3 ; probe, 5 -FAM-CTGGTAGAACTGTCCT-TAMRA-3. The forward, reverse, and probe contain two to four, one, and five mismatches, respectively, to UCYN-A2 host sequences (accession number KF771248-KF771254) and the following closely related sequences (98-100 %): uncultured eukaryote clones (station ALOHA: EU50069; Cariaco Basin: GU824119) Chrysochromulina parkeae: AM490994; Braarudosphaera bigelowii TP056a: AB250784; B.
bigelowii Furue-15: AB478413; B. bigelowii Funahama T3: AB478413; and B. bigelowii Yastushiro-1 AB478414. The UCYN-A1 host oligonucleotides specificity was tested de nova against the following closely related sequences derived from uncultured eukaryotic clonal sequences (accession numbers: EU500067-68, FJ537341, EU500138-39, EF695227, EU500141, EU499958, EF695229, EF695220). Only one mismatch was found in the forward probe for one sequence (EU500138) (Table  S1 ). The nucleotide sequence identity for UCYN-A1 and UCYN-A2 host sequences is 97. 95 %. Finally, a cross-reactivity test between the newly designed UCYN-A1 host oligonucleotides and a dilution series of the UCYN-A2 host template was run (see below).
Statistics and data analysis Skewness and normal distribution tests by descriptive statistics were performed in IBM SPSS (version 23) on the following parameters recorded during sample collection in the WTSP from the CTD package: depth (m), oxygen (mL L -1 ), temperature ( • C), chlorophyll fluorescence (µg L -1 ), photosynthetically active radiation (PAR; µmol photons m -2 s -1 ), salinity (PSU), and gene copy abundances determined by qPCR. Since fewer samples and targets were processed in the at-sea qPCR, only the lab-based qPCR results from the archived samples was used for statistical analysis. Significant skew was noted when skewness, divided by its standard deviation, exceeded 1. 95. All but three targets (het-1, UCYN-B, and Trichodesmium spp. ) and three environmental parameters (temperature, salinity, and oxygen) were significantly skewed (not normally distributed) even after LOG10 transformation.
Therefore, a nonparametric Spearman's rank correlation was conducted to test possible correlations between the targets and environmental parameters, where we assume that the het groups and UCYN-A clade are symbiotic, while UCYN-B is free living. The resulting correlation matrices were visualized in the form of a heat map of hierarchical clustering in R (version 3. 2. 2) using packages "hmisc" and "gplots". Multivariate statistics by redundancy analysis (RDA) was conducted using the R package "vegan". T tests, in IBM SPSS (version 23), were performed to characterize the MA and SG regions and used the mean concentrations of nutrients at each station as input. For meta-analysis on the external data from 11 publically available datasets, sampled in the Atlantic and Pacific oceans and the South China Sea, data were acquired from the PANGAEA database and previous publications  (Benavides et al.
, 2016; Bombar et al. , 2011; Church et al. , 2005 Church et al. , , 2008;; Foster et al. , 2007 Foster et al. , , 2009;; Goebel et al. , 2010; Kong et al. , 2011; Langlois et al. , 2008; Moisander et al. , 2008 Moisander et al. , , 2010)). We included only datasets with a minimum of 10 data points on the previously mentioned diazotrophic targets. Note that in all datasets the two UCYN-A phylotypes (A1 and A2) were not distinguished, and het-3 was excluded since it was rarely quantified. The meta-analysis was conducted using the software OpenMEE  (Wallace et al. , 2017)  (based on R package "metafor"), where correlation coefficients from Spearman's rank were z-transformed (Fisher's) and tested using weighted random effect models. Graphical visualization of the mean abundances of the most numerous diazotrophs across the cruise transect was also performed in IBM SPSS (version 23).
Global drivers of diazotrophic abundance We found consistency between our results in the WTSP and the correlations identified in the 11 external datasets using the nonparametric correlation analyses and meta-analyses (Fig. 5a-d ; Table  S6 ). For example, in three of the exter- nal datasets, nifH gene abundances of Trichodesmium spp. , UCYN-B, and het-1 were significantly positively correlated with temperature and negatively correlated with the same three parameters as in our study in the WTSP: salinity, DIP, and DIN. The latter correlations were identified in two regions of the WTSP (tropical and subtropical) and in the northern South China Sea (NSCS). In contrast to a significant positive correlation between UCYN-A nifH gene abundance and depth reported here in the WTSP, UCYN-A nifH gene abundance was negatively correlated with depth in 4 of the 11 external datasets (two regions of the WTSP, tropical Atlantic (TA), and NSCS).
Moreover, and consistent with several of the other diazotrophs (Trichodesmium, UCYN-B, het-1), UCYN-A nifH gene abundance was negatively correlated with DIP and DIN concentrations (five and three additional external datasets, respectively) (Table  S6 ). Meta-analysis revealed similar groupings (e. g. , shallow and deep) as observed in the WTSP; however, the significance was less robust (Fig. 5a-d ; Table  S6 ). For example nifH gene abundances of Trichodesmium and het-1 and het-2 were significantly positively correlated with temperature and negatively correlated with salinity (p < 0. 05). No significance was found for UCYN-A nifH gene abundance for the latter parameters, and UCYN-B nifH gene abundance was not significantly correlated with salinity, but was sig- nificantly positively correlated with temperature (p < 0.
The maximum abundances at depth for UCYN-A1 and A2 were slightly above or at the nitracline and coincided with higher measures of fluorescence from the CTD. The latter is consistent with observations of high UCYN-A abundances in coastal habitats  (Bombar et al. , 2014) , estuaries  (Messer et al. , 2015) , or in waters that have been recently entrained with new nutrients  (Moisander et al. , 2010). Increased nifH copies and/or nifH gene expression for UCYN-A have also been reported from bioassay experiments amended with nutrients, including DIN, phosphate, and iron  (Krupke et al. , 2015; Langlois et al. , 2012; Moisander et al. , 2012). The latter is in contrast with the data reported here in the WTSP (including the meta-analysis) and several of the external datasets (e. g. , WTSP, TA, NA, NSCS), which find a negative correlation between DIN and DIP concentrations and nifH gene abundance of most of the diazotrophs, including UCYN-A.
Interestingly, we found no significant correlation between PAR and the UCYN-A host lineages, which is unexpected since the host partners require light for photosynthesis. In an attempt to identify the consistency in the correlation patterns identified in the WTSP with other regions of the world's ocean, the same statistical analyses were performed on 11 publically available datasets and subsequently run through a meta-analysis. Our statistical analyses provided coefficients and p values for easy evaluation and comparisons between data sets for the influence of environmental parameter(s) and diazotroph abundance. The analyses confirmed that UCYN-A indeed stands out from the other diazotrophs in terms of environmental parameter influence, mainly by not being significantly correlated with temperature, which for all other diazotrophs was a significant positive correlation.
In terms of temperature, UCYN-A has the lowest temperature optimum among the target cyanobacterial diazotrophs, which will influence their distributions and subsequent correlation with temperature both regionally and vertically. The studies in the meta-analysis include a wide geographical range and, independent of ocean basin, the majority of the datasets found no significant correlation between UCYN-A and temperature. For most other environmental parameters the pattern for UCYN-A in the WTSP does not hold true in the meta-analysis. For example a significant negative correlation with depth was found for UCYN-A and perhaps related to high surface temperatures, given that  Moisander et al. (2010)  observed UCYN-A closer to the surface in the southern SP where surface temperatures were typically cooler.
In summary, the correlations observed in the WTSP were not always consistent with the meta-analysis of the external datasets. We attribute the inconsistencies in part to seasonal differences in sample collections and the impact of an individual environmental parameter or sets of parameters on a local and regional scale, particularly for coastal studies that make it difficult to unambiguously explain the abundance and distribution patterns. Determining the condition or sets of conditions favoring one diazotroph or life history strategy (free-living vs. symbiotic) is complex and likely not all diazotrophs are influenced by the same condition in time and space.
Our initial hypothesis was that the condition or sets of conditions, which promote the distribution of one diazotroph, would differ. Moreover, the parameters for symbiotic diazotrophs should also differ from those of free-living phylotypes, and given the vast difference in hosts (diatoms and prymnesiophyte) and genome content for the het and UCYN-A symbionts, we further hypothesized divergent conditions favoring one symbiosis over another. In the WTSP, the same conditions favored abundances of both the free-living phylotypes and the diatom (het groups) symbioses. However, the same conditions impacted the abundance of UCYN-A-based symbiosis negatively, hence somewhat supporting our initial hypothesis that conditions for one symbiosis type would differ. In the external datasets, however, we observed differences in environmental conditions favoring abundances of the investigated diazotrophs compared to the WTSP, which underscores that diazotrophs are not similarly influenced by the same condition in time and space.
Data availability. Underlying data are reported in the Supplement, and all cruise and meta-data can also be accessed at http://www. obs-vlfr. fr/proof/php/outpace/outpace. php. The qPCR data will also be made available at https://pangaea. de. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-15-1559-2018-supplement. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Interactions between planktonic organisms and biogeochemical cycles across trophic and N 2 fixation gradients in the western tropical South Pacific Ocean: a multidisciplinary approach (OUTPACE experiment)". It is not associated with a conference.
Acknowledgements. The participation ( Marcus Stenegren  and  Andrea Caputo ), sample processing, and work presented here is supported by the  Knut and Alice Wallenberg foundation  (to  Rachel A. Foster ). This is a contribution to the  OUTPACE (Oligotrophy from Ultra-oligoTrophy PACific Experiment ) project funded by the  French research national agency  ( ANR-14-CE01-0007-01 ), the  LEFE-CYBER program  ( CNRS-INSU) , the  GOPS program (IRD) , and the  CNES  ( BC T23 ,  ZBC 4500048836 ). The OUTPACE cruise (https://doi. org/10. 17600/15000900) was managed by  Thierry Moutin  and  Sophie Bonnet  from the  MIO (Mediterranean Institute of Oceanography). Thierry Moutin  is specifically thanked for invaluable input and feedback on this paper. The authors thank the crew of the  R/V L'Atalante  for outstanding shipboard operation.
Gilles Rougier  and  Marc Picheral  are thanked for their efficient help in CTD rosette management and data processing, as is  Catherine Schmechtig  for the LEFE-CYBER database management. We especially acknowledge  Andreas Krupke  (formerly of WHOI),  Kyle Frischkorn  (WHOI),  Mar Benavides  ( MIO/IRD ),  Hugo Berthelot (MIO) , and  Mathieu Caffin  (MIO). Olivier Grosso (MIO) and  Sandra Hélias  (MIO) are acknowledged for nutrient analyses. Additionally we thank  Lotta Berntzon  for assisting in sample processing and  Konrad Karlsson  for assistance with the multivariate statistics. Lastly we thank  Bethany Jenkins ,  Anders Bignert  and two anonymous referees for invaluable feedback and suggestions on our paper. Edited by: Gerhard Herndl Reviewed by: two anonymous referees.
A recent meta-analysis revealed that being obese before CRC diagnosis whatever the stage was associated with increased colorectal cancerespecific mortality and allcause mortality  [4]. Moreover, the analysis of the Adjuvant Colon Cancer End Points database revealed that patients with stage II or III disease who were either underweight or obese had worse overall survival (OS) and disease-free survival (DFS) compared to patients with a normal BMI  [5]. Another large recent database analysis of patients treated for a stage I to III CRC stated that patients with normal weight, overweight, and moderate obesity have a better prognosis compared to patients underweight or with severe or morbid obesity  [6]. In a metastatic setting, conflicting data were reported for the prognostic effect of BMI. A post hoc analysis of two prospective randomised trials, CAIRO and CAIRO 2, revealed that a high BMI was associated with longer OS in the CAIRO trial, which evaluated chemotherapy alone, but not in the CAIRO 2 study, which evaluated chemotherapy in combination with antiangiogenic targeted therapy  [7].