dataesr/scientific-paragraphs-categorization huggingface dataset:
The LGC and LW datasets (https://github. com/MicroB3-IS/osd-analysis/wiki/Guide-to-OSD-2014-data) were analyzed with the same pipeline using mothur software v. 1. 35. 1  27. Reads were filtered to keep only sequences without ambiguities (N) and longer than 300 bp. Reads were aligned with SILVA seed release 123 reference alignment  28  and corrected by hand to remove gaps at the beginning and at the end of alignments. The aligned datasets were filtered by removing columns containing only insertions. Chimeras were detected using Uchime v. 4. 2. 40  29  as implemented in mothur. Representatives of sets of identical sequences, referred to here as Amplicon Single Variants (ASVs, following Callahan et al. 30  ) were assigned using the Wang classifier as implemented in mothur and the PR 2 reference database 31 version 4.
2 (https://figshare. com/articles/PR2_rRNA_gene_database/3803709/2) for which the Chlorophyta sequences had been recently curated  6. To confirm the assignation and explore the genetic diversity of Mamiellophyceae, ASVs from Bathycoccus, Micromonas, Ostreococcus and Mantoniella represented by more than 200 reads in either the LW or LGC dataset were selected. These selected ASVs were aligned to GenBank reference sequences using MAFFT v. 7. 017  32. Maximum likelihood (ML) phylogenies were built using FastTree v. 1. 0  33, 34  as implemented in Geneious v. 7. 1. 9  35. Bayesian phylogenies were built with MrBayes v. 3. 2. 6  36. implemented in Geneious. Clades were defined by both the presence of clear signatures in the alignments and phylogenetic features  6, 37, 38  : a clade had to be monophyletic, found with the two different construction methods (i.
e. , ML and Bayesian) and supported by bootstrap values greater than 70 %. We selected a subset of 92 samples with more than 100 Mamiellophyceae reads (Table  S1 ) to compute the relative abundance of selected ASVs using R software version 3. 3. 1 (http://www. R-project. org/). Graphics were created using the R packages ggplot2, ComplexHeatmap  39  and treemapify.
Results and Discussion We analyzed unique sequences (ASVs 30 ) of two separate OSD datasets: LGC and LW. We focused on the LGC dataset which encompasses a much larger number of samples than the LW dataset which corresponds to a subset of the OSD samples that were processed in a completely independent manner compared to the LGC dataset (differences in filtration, DNA extraction, PCR and Illumina sequencing). The LW dataset was mainly used to confirm that the LGC ASVs were not artifacts by verifying that any major LGC ASV corresponded to an LW ASV with a strictly identical sequence. Mamiellophyceae represented 55% of the Chlorophyta reads found in the OSD 2014 surface samples  25. Overall 10,447 Mamiellophyceae ASVs (97. 5% of the ASVs) were assigned to seven genera, while the other ASVs were classified only at the family or order level.
Ostreococcus. In total, 4,223 ASVs from the LGC dataset were assigned to the genus Ostreococcus, among which 10 had more than 200 reads (Table  1 ). For each of these abundant LGC ASVs, we found a corresponding ASV in the LW dataset. These sequences constituted five clades (Fig. 2A ), four of which were previously described and a new one that we named clade E, following the previous conventions for naming Ostreococcus clades  16. The same tree topology was recovered with ML and Bayesian methods. Alignments (Fig. 2B ) confirmed that the V4 region exhibited clear signatures delineating the five Ostreococcus clades. In this region, the genetic variation between clades (Table  S2 ) was too low to discriminate the different clades when V4 operational taxonomic units (OTUs) are built with a 99% identity threshold, except for O.
mediterraneus (98. 3%). In terms of overall distribution, Ostreococcus was completely absent from high latitudes beyond 65° N (Iceland, Greenland and Nunavut, Fig. 3 ). O. "lucimarinus" was the most widely distributed (Fig. S1 ). In general none of the different Ostreococcus species/clades seemed to co-occur, with the exception of O. tauri and O. "lucimarinus" and to a lesser extent, clades B and E (Fig. S2 ). Three ASVs were assigned to the first described species of Ostreococcus, O. tauri, with a number of reads ranging from 1,000 to 3,500 (Table  1 ). These ASVs did not form a monophyletic clade in either ML and Bayesian tree (Fig. 2A ). The two major ASVs (ASV_LGC_00012 and ASV_LGC_00013) were closely related to the 18S rRNA sequence recovered from the genome of O. tauri (GQ426344). The third ASV (ASV_LGC_00018) was identical to the sequence with accession number Y15814 which was obtained more than 20 years ago for the type strain of O.
, see below). A single ASV was assigned to Ostreococcus clade B, initially described as a low-light-adapted clade  19  , representing 6,207 reads, and accounting for 62% of the Mamiellophyceae reads off Panama (OSD51, Fig. 3 ). Clade B constituted more than 10% of the Mamiellophyceae reads at seven tropical and subtropical stations from various oceans (OSD60 in South Carolina; OSD25, OSD37 and OSD51 in Florida; OSD95 in Singapore; OSD122 in the Red Sea; OSD147 in Sri Lanka, Fig. 3 ), which is consistent with previous results obtained by qPCR which detected clade B at warm oligotrophic sites  40, 41. The novel Ostreococcus clade E was represented by a single ASV in the LGC dataset (22,720 reads) and an identical sequence was also found in the LW dataset, suggesting that the detection of this clade was not an artefact.
The clade E sequence is very similar to that of clade B, with two clear bp differences (Fig. 2B ), and is 100% similar to a single sequence available from GenBank (accession number MH008654) also obtained by Illumina sequencing from South China Sea waters  42. The V4 sequence of clade E is 99. 4% similar to that of clade B, such that these two clades may have been lumped together in many metabarcoding studies that considered OTUs rather than ASVs. Clade E could be locally dominant in OSD samples representing up to 70% of the Mamiellophyceae reads (OSD111, off Portugal). The clade dominated coastal warm temperate stations (Fig. 3 ) on both sides of the Atlantic Ocean (Southern USA: OSD39, OSD58 and OSD143; Portugal: OSD81, OSD111, OSD117 and OSD153; and France: OSD154) and the Mediterranean Sea (Adriatic Sea off Venice: OSD69).
5 ): at the three Hawaii stations (OSD56, OSD57, and OSD144), off Florida (OSD37), off Portugal (OSD101), off Singapore (OSD95) and in Venice Lagoon (OSD69 and OSD70). This distribution was consistent with data from available GenBank sequences 11 : one representative strain was isolated in the Mediterranean Sea in summer (RCC1109) and environmental clones weren recovered in the Red Sea  45  , in the South China Sea ("unknown clade"  46  ) and off Taiwan (Micromonas clade VI  44  ). M. commoda A2 ASV (10,787 reads) represented more than 1% of the Mamiellophyceae reads at 57 stations (Fig. S1 ) and up to 35% off the Atlantic coast of Canada (OSD152). This ASV was found in the North Atlantic up to Iceland and off New Zealand (Figs  5  and S5 ). In contrast, this ASV was almost completely absent from the Mediterranean Sea as well as from tropical stations.
The frequency of M. commoda A1 (5,465 reads) was above 1% at a much lower number of stations (16, Fig. 1 ) and accounted for up to 40% of the Mamiellophyceae reads off Sri Lanka (OSD147). M. commoda A1 was distributed in tropical and subtropical waters (Figs  5  and S5 ), in particular, off Florida, Singapore and Hawaii as well as in the Eastern Mediterranean Sea off Israel. M. commoda was described by Van Baren et al. 47  who mentioned only that this species had not been recorded yet at high latitudes (beyond 60 North or 60 South). The species was then revised by Simon et al. 11  , who described the distribution of this species as ubiquitous based on available GenBank sequences. The genetic variability within this species had already been highlighted previously  48, 49  and was confirmed by the OSD data since the two subclades A1 and A2 had clearly distinct distributions.
Simon et al. 11  proposed the hypothesis that speciation events may be ongoing within M. commoda. The ASV (1,616 reads) corresponding to Micromonas sp. clade B4 ("candidate species 1" according to Simon et al. 11  ) reached a frequency of 15% off Florida (OSD29) and was found in rather warm waters (off Hawaii, Israel, and Morocco, Fig. 5 ), which matches the distribution of sequences available in GenBank that had been previously recovered from the Mediterranean and Red Seas as well from the Pacific Ocean  11. M. polaris ASV (2,200 reads) was the major Mamiellophyceae contributor (Fig. 5 ) at two stations in Arctic waters (73% in Nunavut OSD105, 66% in Fram Strait OSD146) and was also present in the Gulf of Finland (OSD30). This pattern is consistent with current knowledge of this species. M.
polaris was first isolated from the Arctic Ocean 50 and shown to be the dominant picoeukaryote in the Beaufort Sea in the summer  51. This ASV was also recently recorded in the Southern Ocean  52  , although its presence appears to be less prevalent since it is absent from environmental Antarctic clone libraries  6. A new Micromonas clade closely related to M. polaris (B3, 1,096 reads) had a maximum contribution off Canada (32% in Bedford Basin OSD152) and represented more than 10% of Mamiellophyceae reads at four subarctic stations off Maine and Iceland as well as at a temperate location off the UK coast in the North Sea (Fig. 5 ). This ASV is 100% similar to GenBank sequences recently obtained in the White Sea  53. Finally, the ASV corresponding to the first described Micromonas species, M.
Bathycoccus. The ASV corresponding to Bathycoccus was both the most abundant (24,391 reads) and the most prevalent, accounting for more than 1% of the Mamiellophyceae reads at 72 stations (Fig. S1 ) distributed all over the coastal ocean from tropical to polar waters (Figs  6  and S6 ). This worldwide distribution of the genus matches what has been observed based on the Tara Oceans dataset  21  where the metagenome of Bathycoccus was recovered at a wide range of stations. Bathycoccus is now known to be composed of two cryptic species with identical 18S rRNA sequences (that therefore cannot be distinguished in the OSD dataset) but differences in the ITS sequences as well as at the genomic level  21, 54. The distribution of these two cryptic species (BI-genome RCC1105 and BII-genome TOSAG39-1) determined by metagenomic analysis and qPCR suggest that BI could be coastal and extending to polar waters, while BII is adapted to warmer oceanic waters  21, 22, 55, 56.
However, this ASV was found at stations with very different environmental conditions, including off Hawaii and in a lagoon on the coast of Uruguay (Fig. 8 ). This finding is consistent with previous descriptions of M. squamata as a cosmopolitan species  3  and several studies reporting in particular its presence in northern high latitudes  51, 57. The ASV corresponding to Mantoniella clade B (4,070 reads) was more widespread, representing more than 1% of the Mamiellophyceae reads at 16 stations (Fig. S1 ), especially in Venice Lagoon (53% at OSD47, 36% at OSD70 and 12% at OSD69) but also in other moderately warm waters (eastern Mediterranean Sea, Gibraltar, California, Hawaii, Figs 8 and S7), matching GenBank sequences previously found in the Mediterranean Sea  8. No ASV corresponding to Mantoniella clade A was found in the LGC dataset but one ASV from the LW dataset (841 reads) matched this clade.
This clade was present only off Greenland (OSD80) which matches the environment where other GenBank sequences from this clade have been obtained (sea ice from the Arctic Ocean  58  , the White Sea  53  and the Baltic Sea  59  ), suggesting that Mantoniella clade A is an ice alga. Finally, no major ASV corresponding to the species M. antarctica was found in either the LGC or LW dataset. In particular, this species was absent from the single station sampled in Antarctica (OSD187, Palmer Station) in the middle of the austral winter, where only 85 Mamiellophyceae reads were recorded. Similarly, no major ASVs corresponding to the newly described species M. baffinensis and M. beaufortii were found in either the LGC or LW datasets, although a minor ASV corresponding to the latter species was found off Greenland (OSD80)  15.
Limitations of the OSD Dataset The OSD metabarcoding dataset is invaluable to determining the distribution of many phytoplankton groups in coastal waters  25, 26, 60. However, it must be always emphasized that metabarcoding is not a quantitative method because of biases in PCR and because of the variation in the number of 18S rRNA gene copies per organism  61. In the case of Mamiellophyceae, because they are small, the number of copies is low  61  and does not vary greatly between the different genera (e. g. two and four copies in Bathycoccus and Ostreococcus, respectively  62, 63  ). This low variation supports the use of relative number of reads as a semiquantitative proxy of Mamiellophyceae contributions. Another potential problem of the OSD dataset is the fact that sampling was conducted everywhere on almost the same date (in most cases on June 21).
Such simultaneous sampling has the advantage of providing a snapshot of the global coastal ocean, which allows analysis of spatial distributions without the impact of seasonality, in contrast to oceanographic expeditions such as Tara Oceans 5 , which sampled different ecosystems at different times of the year. One limitation of OSD is that the Northern and Southern Hemispheres were sampled at opposite points of the yearly cycle: at the summer and winter solstices, respectively. However, since sampling was mostly conducted in the Northern Hemisphere, this limitation has a low impact on data interpretation. The existence of phytoplankton cycles driven by temporal changes and nutrient cycles in coastal environments  [64] [65] [66]  may explain why some species were not found in the OSD dataset in regions where strains or clones corresponding to the same species have been isolated before.
As an example, Bathycoccus initially isolated from the Gulf of Naples in the Mediterranean Sea  20  , was not recovered at the Naples OSD station (OSD4), where only 2 Mamiellophyceae reads were obtained. Metabarcoding analyses at the Long Term Ecological Research station in the Gulf of Naples showed that Mamiellophyceae were not found in June 67 , which may explain why they were absent from the OSD dataset. In contrast, analysis of several time series  68  led to the conclusion that M. bravo (previously non Arctic B. E. 3 clade) dominated the Micromonas community in summer and should be adapted to warm well-lit coastal waters which is consistent with what we observed in the OSD dataset for which sampling was performed in June.
Since some oceanic regions are covered by the OSD dataset, it is possible that yet undiscovered Mamiellophyceae clades/species exist, especially in the Southern Hemisphere. To obtain more information on the new clades reported here and to determine whether they correspond to potentially novel species, several strategies are possible. First, now that the geographical distribution of these taxa is being uncovered, we may target specific environments and obtain isolates. Such targeting will allow analysis of finer resolution markers such as the ITS  11  and determination of physiological preferences, for example, in terms of temperature  70. Another strategy for examining clades that are hard to isolate in culture would be to determine longer sequences of the ribosomal operon, including in particular the ITS region, directly from the environment either by long-amplicon PCR using novel technologies such as nanopore sequencing  71  or by extracting them from existing metagenomics datasets such as those obtained during the Tara Oceans project  21.
Acknowledgements Financial support for this work was provided by the  European Union  project  MicroB3  ( UE-contract-287589 ), the  ANR PhytoPol  ( ANR-15-CE02-0007 ) and  TaxMArc (Research Council of Norway ,  268286/E40 ). M. T. was supported by a PhD fellowship from the  Université Pierre et Marie Curie  and the  Région Bretagne (ARED GreenPhy ). We would like to thank the  Ocean Sampling Day consortium  for providing sequence data and the ABIMS platform in Roscoff for access to bioinformatics resources.
Data Availability Scripts for mothur and R, and Mamiellophyceae OTU sequences, alignments, assignation and abundance for the LGC and LW datasets are provided as Supplementary Files at https://github. com/vaulot/Paper-2018_Tragin_ Mamiellophyceae-R-scripts (https://doi. org/10. 6084/m9. figshare. 7233110). The R processing script is detailed at https://vaulot. github. io/papers/OSD_Mamiello. html.
The SR'18 shared task includes two tracks: a shallow track where the input is an unordered, lemmatised dependency tree and a deep track where function words are removed and syntactic relations are replaced with semantic ones. In this paper, we focus on the shallow track of the SR'18 Shared Task and we propose a neural approach which decomposes surface realisation into three subtasks: word ordering, morphological inflection and contraction generation (e. g. , clitic attachment in Portuguese or elision in French). We provide a detailed analysis of how each of these phenomena (word order, morphological realisation and contraction) is handled by the model, and we discuss the differences between languages. For reproducibility, all our experiments including data and scripts are available at https:// gitlab.
Data The SR'18 data (shallow track) is derived from the ten Universal Dependencies (UD) v2. 0 treebanks  (Nivre et al. , 2017)  and consists of (T, S) pairs where S is a sentence, and T is the UD dependency tree of S after word information has been removed and tokens have been lemmatised. The languages are those shown in Table  1  and the size of the datasets (training, dev and test) varies between 7,586 (Arabic) and 85,377 (Czech) instances with most languages having around 12K instances (for more details about the data see  Mille et al. (2018) ).
Morphological Realisation The morphological realisation (MR) module consists in producing inflected word forms based on lemmas coupled with morphological features. For that module, we used a model recently proposed by  Aharoni and Goldberg (2017) , which achieves state-of-the-art results on several morphological inflection datasets: the CELEX dataset  (Baayen et al. , 1993; Dreyer et al. , 2008) , the Wiktionary dataset  (Durrett and DeNero, 2013)  and the SIG-MORPHON2016 dataset  (Cotterell et al. , 2016). Their model is based on a neural encoder-decoder architecture with hard monotonic attention and performs out-of-context morphologic realisation: given a lemma and a set of morpho-syntactic features, it produces a corresponding word form. We trained the model of Aharoni and Goldberg (2017) on (lemma+morpho-syntactic features 4  , form) pairs extracted from the SR'18 training data.
Contraction Generation Contraction handling is the last step of our surface realisation pipeline. Example 2 shows some types of contractions. The sequence-to-sequence model is trained on pairs of sentences without and with contractions. The sentence with contraction (S +c ) is the final sentence, i. e. , the reference sentence in the SR'18 data. The sentence without contraction (S -c ) is the corresponding sequence of word forms extracted from the UD CoNLL data. The regular expression module is inspired by the decomposition of multi-word expressions, such as contractions, which is applied during the tokenisation step in parsing  (Martins et al. , 2009). We reversed the regular expressions given in the TurboParser 6  for the surface realisation task, and also added our own to tackle, for example, elision in French.
Word Ordering Constraints We also investigate the degree to which our results conform with the word ordering constraints of the various languages focusing on the following dependency relations: DET (determiner), NSUBJ (nominal subject), OBJ (object), AMOD (adjectival modifier) and ACL (nominal clausal modifier). For each of these dependency relations, we compare the relative ordering of the corresponding (head, dependent) pairs in the reference data and in our system predictions. To determine whether the dependent should precede or follow its head, we use the gold standard dependency tree of the UD treebanks. Since for the system predictions we do not have a parse tree, we additionally record the distance between head and dependent (in the reference data) and we compare it with the distance between the same two items in the system output.
Morphological Realisation Table  3  shows the results for the WO+MR model. The top line (MR Accuracy) indicates the accuracy of the MR model on the SR'18 test data which is computed by comparing its output with gold word forms. As the table shows, the accuracy is very high overall ranging from 87. 6 to 99. 07, with 9 of the 10 languages having an accuracy above 90. This confirms the high accuracy of the model when performing morphological inflection out of context. The third line (WO+MR (S -c )) shows the BLEU scores for our WO+MR model, i. e. , when the MR model is applied to the output of the WO model. Here we use an oracle setting which ignores contractions. That is, we compare the WO+MR output not with the final sentence but with the sentence before contraction applies (the ability to handle contractions is investigated in the next section).
Linguistically, this confirms the intuition that the mapping between shallow dependency structure and word order can be learned independently of the specific words involved. We further carried out a detailed evaluation of how our word ordering model performs on the ten languages of the SR'18 shallow track. While differences in annotation consistency, number of dependency relations 10  and frequency counts for each dependency relations in each dataset make it difficult to conclude anything from the differences in overall scores between languages, the evaluation of head/dependent word ordering constraints highlighted the fact that long-distance relations, such as ACL, and irregular word ordering constraints (e. g. , the position of the verb in Dutch main and subordinate clauses) negatively impact results.
| Data collection A standardized case report form (CRF) designed for this study was used to collect data on patient demographics, vital status, laboratory data, TB treatment regimens and TB treatment outcomes. The CRF was developed in Research Electronic Data Capture (REDCap) and was available in English, French and Spanish  [15]. Local IeDEA site investigators completed a CRF for each TB case after medical record review from January 2012 to January 2016. Routine data audits for quality assurance were completed by the principal investigator (PI). Information on ART initiation dates were obtained from the electronic regional IeDEA data repositories.
Unevaluable TB treatment outcomes included those lost to follow-up or without an assigned TB treatment outcome. Our primary exposure of interest was receipt of TB treatment by DOT during the entire continuation phase of therapy. DOT data were obtained at the site-level and applied to each individual patient receiving care at the site. Receipt of DOT was defined as ≥5 days of DOT per week; <5 days of DOT was defined as SAT. DOT was defined according to local guidelines with respect to the person administering therapy (family member/friend vs. healthcare worker) and the location of treatment (home/community-based vs. healthcare facilitybased). Covariates identified for inclusion in multivariable models a priori included age at TB diagnosis, sex, baseline body mass index (BMI), change in BMI from the date of anti-TB treatment start to the date of anti-TB treatment stop, smear status, culture status, bacteriologic TB confirmation, site of disease (extrapulmonary, pulmonary/both/unknown), CD4 + count at TB diagnosis, IeDEA region, year of TB diagnosis and ART initiation timing in relation to TB diagnosis.