dataesr/scientific-paragraphs-categorization huggingface dataset:
The Supplement related to this article is available online at doi:10. 5194/acp-17-1901-2017-supplement. Acknowledgements. This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  through the  EUROCHAMP-2020 Infrastructure Activity  under grant agreement no. 730997. It was supported by the  French national programme LEFE/INSU , by the  OSU-EFLUVE (Observatoire des Sciences de l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie)  through dedicated research funding, by the  CNRS-INSU  by supporting  CESAM  as national facility, and by the project of the  TOSCA program  of the  CNES (Centre National des Etudes Spatiales). Claudia Di Biagio  was supported by the  CNRS  via the Labex L-IPSL, which is funded by the  ANR  (grant no. ANR-10-LABX-0018 ).
| Sampling In the aim of exploring the Pocillopora genus diversity and in fine studying population connectivity, colonies of Pocillopora genus were sampled [tip of branches + photographs except for Tromelin Island (Scattered Islands) and Polynesia] independently of their corallum macromorphology, as it is not a diagnostic character in Pocillopora genus. So species identification was realized molecularly a posteriori of sampling and a priori of data analyses (see below), leading to a subset of 2,507 colonies corresponding to PSH09 sensu  Gélin, Postaire, et al. (2017). The sampling was achieved from April 2011 to October 2016, in three marine provinces extended over six ecoregions  (Spalding et al. , 2007) : the Western Indian Ocean (WIO), the Tropical Southwestern Pacific (TSP), and the Southeast Polynesia (SEP).
Because colonies were sampled based on their corallum macromorphology, P. eydouxi lineage identity was verified a priori using assignment tests performed with Structure  (Pritchard, Stephens, & Donnelly, 2000). For this purpose, we combined all sampled colonies for this study and the 943 colonies from  Gélin, Postaire, et al. (2017)  that were assigned to the different PSHs as in  Gélin, Postaire, et al. (2017). From that, a total of 2,507 colonies grouped together in PSH09 and constituted the final dataset.
| MLG identification and assignment to the SSHs To check for clonal propagation among the sampled colonies, identical multilocus genotypes (MLG) were identified using the R (R Development Core Team 2016) package RClone  (Bailleul, Stoeckel, & Arnaud-Haond, 2016). Then, keeping one representative per MLG, we assigned the colonies to the three SSHs (SSH09a, SSH09b and SSH09c) previously identified in  Gélin, Postaire, et al. (2017). To do so, we performed assignment tests on the 2,507 genotyped colonies, including the 257 colonies from PSH09 used in  Gélin, Postaire, et al. (2017)  and using Structure  (Pritchard et al. , 2000)  for K = 3. Five chains with 2 × 10 6 generation steps after a burn-in of 2 × 10 5 were run assuming admixture and correlated frequencies. The Structure outputs were summarized with clumpp v.
NewHybriDS calculates the posterior probability that sampled colonies fall into each of a set of hybrid categories (Parent 1, Parent 2, F1, F2, backcross to Parent 1, backcross to Parent 2). Once identified, the admixed colonies were not considered in the subsequent analyses. The genotypic linkage disequilibrium was assessed with FStat v. 2. 9. 3 both on the whole dataset and for each SSH separately  (Goudet, 2001) , and pairwise differentiation among SSHs was estimated using the F ST  (Weir & Cockerham, 1984)  with arlequiN v. 3. 5  (Schneider, Roessli, & Excoffier, 2000)  and D est using the R package DEMEtics  (Gerlach, Jueterbock, Kraemer, Deppermann, & Harmand, 2010). For each SSH, the allelic frequency distributions were plotted, and the number of alleles (Na) and the number of private alleles (Np).
| Structuring analyses within each SSH Further analyses were performed on each SSH separately. First, to determine the most likely number of genetically homogenous groups (K) within each SSH, a Bayesian analysis was performed using Structure v. 2. 3. 4  (Pritchard et al. , 2000)  with the same conditions as described above. This analysis assumes that, within a set of sam- (it transforms genotypes using PCA as a prior step to a discriminant analysis). DAPC was applied using the adegenet package  (Jombart, 2008)  for R (R Development Core Team 2016). Then to evaluate the congruency between Structure and DAPC for a given K, the number of colonies that were assigned differently between both methods was estimated. In a hierarchical approach, these analyses were repeated on each cluster found in each SSH separately.
Commonly, using Structure and DAPC, when the finest level of structuring is reached, adding a supplementary cluster leads to inconclusive assignments with colonies assigned to several clusters in the same proportions. Here, some colonies kept ongoing assigned to clusters when K increased above the most likely K, either using Structure or DAPC. Moreover, because the methods traditionally used to detect the most likely number of genetic groups within a dataset [Pr(X|K)  (Pritchard et al. , 2000) , ΔK method  (Evanno, Regnaut, & Goudet, 2005) , the deviance information criterion (DIC)  (Gao, Bryc, & Bustamante, 2011) , the Bayesian information criterion (BIC)  (Jombart et al. , 2010) , and the thermodynamics integration (TI)  (Verity & Nichols, 2016) ] might provide different outputs, and because they are purely mathematical, they might not reflect the biolog- Considering the clusters finally kept within each SSH, the pairwise differentiation among clusters within each SSH was estimated using the F ST  (Weir & Cockerham, 1984 ) with arlequiN v.
3. 5  (Schneider et al. , 2000)  and D est using the R package DEMEtics  (Gerlach et al. , 2010). Then, for each cluster, the allelic frequencies spectrum for each locus were plotted, and considering each SSH, the global F ST  (Weir & Cockerham, 1984)  for each locus was calculated using FStat v. 2. 9. 3  (Goudet, 2001). Additionally, minimum spanning trees based on the shared alleles distance (DAS) were performed with eDeNetwork  (Kivelä, Arnaud-Haond, & Saramäki, 2015)  and drawn with colonies colored according to the clusters found in the assignment analyses. Moreover, we used NewHybriDS v. 1. 1  (Anderson & Thompson, 2002)  between pairs of clusters within each SSH to detect whether the admixed colonies found in Structure could be considered as hybrids (same conditions as above). Finally, a hierarchical analysis of molecular variance (AMOVA) was performed using arlequiN v.
| Is this the real life? Is this just fantasy? Here, we used microsatellite genetic data coupled with different methods to refine lineage boundaries within the P. eydouxi complex (PSH09;  Gélin, Postaire, et al. (2017) ). Thus, despite the fact that microsatellites are not commonly used in species delimitation but rather in population genetics, their high polymorphism can help in refining boundaries between closely related species (e. g. ,  Dawson et al. , 2010). Although Structure and DAPC differ in their a priori hypotheses, we observed very few differences (3. 6% over the whole dataset) between the outputs of these two methods, highlighting their congruency and comforting us that the observed clusters were independent of the method. The main problem when using clustering methods is to determine the true number of genetically differentiated clusters, K, present in the dataset, subject of debate over the last decade.
Nevertheless, all the estimators of K traditionally used can all give different values for a given dataset, and not a single estimator can provide a true value of K, as suggested by  Jombart et al. (2010). Recently,  Verity and Nichols (2016)  suggested that K should be viewed as a flexible parameter that describes just one point on a continuously varying scale of population structure. Therefore, comparing the outputs of the different analyses and keeping the highest value of K that gave congruent results among analyses seemed to be a good compromise to estimate the best number of genetic groups. Overall in the whole sampling (2,507 colonies), we did not find at least two colonies sharing the same MLG, suggesting that the analyzed colonies do not exhibit clonal propagation, in accordance with their massive morphology and their stout branches, limiting fragmentation.
So the genotypic linkage observed in the whole dataset was not due to the presence of repeated MLGs (clones), but to some particular alleles that were preferentially associated within SSH, as well as in SSH09b considering the non-random association of alleles within each cluster. Thus, (1) the number of private alleles found in each SSH and in each cluster within SSH along with (2) the variation of allele frequencies among SSHs and also among clusters within SSH (high values of global F ST per locus) both played a role in the genetic differentiation observed among SSHs and clusters. Finally, rather than structured populations within a single species, these three SSHs, and even the eight clusters, likely represent distinct genetic lineages, though incompletely sorted for the ORF gene, engaged in a speciation process (half-way in the "gray zone" between two populations highly differentiated and two sister species) or real species following the unified concept of De  Queiroz (2007).
| CONCLUSION AND PERSPECTIVES Examining the population structure of the Pocillopora eydouxi species hypothesis (PSH09;  Gélin, Postaire, et al. 2017 ) revealed a nested partitioning of the different SSHs, obliging to think about the unit on which connectivity should be assessed. Whatever the causes, facing to this over-partitioning of our dataset, the matter is not how to estimate connectivity but on what. As each SSH is a mix of several genetically differentiated clusters found in sympatry, we prefer considering the eight different clusters as our reference unit to assess genetic differentiation among populations. Several hypotheses have been proposed to explain the observed pattern, but more investigations are needed to understand the structuring pattern of this species complex. Knowing more about Pocillopora genome seems now to be a fundamental key to improve the understanding of its history of divergence and would offer clues to favor one or another exposed hypothesis.
each SSH, only F2 hybrids were detected using NewHybriDS between pairs of clusters. Nevertheless, colonies assigned to a unique cluster with Structure (assignment probability > 0. 75) were mainly assigned in pure lineages with NewHybriDS (i. e. , 97, 89. 4 and 98. 7% in average among the clusters of SSH09a, SSH09b, and SSH09c, respectively). Finally, considering this hierarchical clustering of the whole dataset (three SSHs splitting in several clusters), the AMOVA revealed that nearly 15% of the overall genetic variation was explained by the partition into three SSHs and 12% by the partition into clusters within SSH (Appendix S6).
ACKNOWLEDGMENTS Sampling of corals in New Caledonia (HM) was carried out during COBELO (https://doi. org/10. 17600/14003700 ),BIBELOT (https://doi. org/10. 17600/13100100 ),and  CHEST  (https://doi. org/10. 17600/15004500 )oceanographic campaigns on board of RV Alis (IRD), and in the northeast and northwest of Madagascar during MAD (https://doi. org/10. 17600/16004700 )oceanographic campaign on board of  RV Antea (IRD). Sampling in Reunion Island (HM, PG)  was supported by program  CONPOCINPA  ( LabEx CORAIL  fund); in the South of Madagascar (HM) in collaboration with the  Institut Halieutique des Sciences Marines (Tulear) and in Rodrigues Island (HM)  with the collaboration of the  Rodrigues Regional Assembly  and the  South-East Marine Protected Area  supported by project  Biodiversity  ( POCT FEDER  fund); in Europa,  Juan de Nova , and  Glorioso Islands  (HM) by program  BIORECIE  (financial supports from  INEE ,  INSU ,  IRD ,  AAMP ,  FRB ,  TAAF , and the foundation Veolia Environnement);  in Tromelin Island (HM) by program  ORCIE (INEE); and in Mayotte (HM) by program  SIREME (FED).
HM thanks all the buddies who helped in photographs during diving ( J. Butscher ,  S. Andréfouët ,  M. Pinault ,  A. Renaud ). We acknowledge the  Plateforme Gentyane of the Institut National de la Recherche Agronomique  ( INRA, Clermont-Ferrand, France ) for guidance and technical support. PG was financially supported by a PhD contract from the  LabEx CORAIL.
Conclusions: The presented transcriptomic and biochemical datasets demonstrate that pretreatments caused modifications of the lignocellulose, to both specific structural features as well as the organisation of the overall lignocellulosic structure, that determined A. niger transcript levels. The experimental setup allowed reliable detection of substrate-specific gene expression patterns as well as hitherto non-expressed genes. Our data suggest beneficial effects of using untreated and IL-pretreated straw, but not HT-pretreated straw, as feedstock for CAZyme production.
Previously, various untreated lignocelluloses as well as individual polysaccharides have been used to study the transcriptional response of the ascomycetes Aspergillus niger  [9] [10] [11] [12] [13] , Aspergillus nidulans  [14] , Trichoderma reesei  [15] [16] [17] [18] , Neurospora crassa  [14, 19, 20]  and Myceliophthora thermophila  [21]. Transcriptional responses of mixed cultures of ascomycete fungi with untreated lignocellulose have also been studied  [22]. A limited number of studies have used pretreated substrates, namely where A. niger was exposed to steam-exploded bagasse  [10]  or where T. reesei was exposed to steam-exploded bagasse, wheat straw or spruce  [15]. The most prominent feature of these studies was the induction of many genes encoding Carbohydrate-Active enZymes (CAZy, as found in the CAZy database, http://www.
Bi-modal CAZy gene transcript patterns exclusive to IL pretreatment The increased digestibility of IL-pretreated substrates in the cultures and the corresponding accumulation of xylose and arabinose (Fig. 2 ) had a pronounced effect on the expression of CAZymes. The abundance of total CAZy transcripts in A. niger had a bi-modal pattern in the ILS cultures, whereas there was a mono-modal pattern in the untreated and HTS and KMS cultures (Fig. 4 ). Correlation between high xylose and arabinose concentrations in the middle (9-24 h) IL time points and gene expression patterns was investigated using the Mfuzz clustering analysis of genes annotated as having a signal peptide. These genes encode the potential secretome, as well as some proteins that are deposited in membranes such as transporters  [42].
6b ). Genes with this pattern included four uncharacterised genes annotated with the Pfam domain for sugar transporter (PF00083). Of the CAZy genes with the bi-modal pattern, 19 of these are regulated by the CAZyme-activating transcription factors XlnR or AraR based on the study of de Souza et al. [30]  (Additional file 6: Table  S5 ). Other genes did not display this bi-modality in transcript level pattern on ILS around the time points where the pentose sugar concentration was higher, such as An01g01870. Aspni5_20633 and cbhB from clusters 5 and 9 from the ILS time course, respectively (Fig. 6c ; Additional file 5: Table  S4 ). A subset of the proteins encoded by CAZy genes was analysed with targeted proteomics. Generally, there was no bi-modal pattern on the ILS compared to the KMS for  the proteins whose genes had a bi-modal transcript level pattern.
In total, 5 genes were identified on all lignocellulosic substrates. The encoded proteins formed 4 main groups: 26 proteins with transmembrane domains, at least 9 proteins related to secretion stress or potential fungal cell wall proteins, a set of proteins for which no function or similarity could be derived, and 5 proteins, discussed in detail, similar to CAZymes. Identified in KMS cluster 16, which contains >50% of genes encoding CAZymes, An16g00670. Aspni5_127791 showed increased expression on KMS from 9 to 24 h. A Phyre2 HMM-based search for structurally similar proteins  [48]  identified similarity to B. subtilis YuiC (PDB 4wjt), which cleaves chitin oligosaccharides, with conservation of the catalytic residue  [49]. The mycoCLAP database for characterised lignocellulolytic enzymes  [50]  indicated similarity to the linker and chitosan-binding domain  [51]  of GH75 chitosanases.
The encoded protein has an IPR008928 domain, a six-hairpin glycosidase-like structure shared by a broad range of enzymes. An13g02450. Aspni5_54830 shares overall structural similarity with many of these enzymes with conservation of the catalytic acid residue, but it lacks key catalytic or characteristic residues of the most structurally similar rhamnosidases and disaccharide phosphorylases. The automated CAZyme annotator dbCAN  [53]  indicates that the protein belongs to GH121, a bacterial sequence family of which the only characterised enzyme liberates Araf-β1, 2-Araf from the glycan moiety of plant protein extension  [54]  and also has a IPR008928 domain. Together with the absence of gene expression on HTS, which lacks arabinose, this suggests a possible role of An13g02450. Aspni5_54830 in degrading arabinose-containing carbohydrates.
Novel potential candidates for transcription factor-dependent engineering strategies for boosting CAZyme expression The Zn 2 Cys 6 transcription factors that play a major role in regulating biomass degradation were identified in the MFuzz KMS transcription profile clusters that had a peak in transcript abundance earlier than the clusters containing the biomass-degrading CAZy genes: xlnR (cluster 11) and araR (cluster 1) (Additional file 3: Table  S2 ). Uncharacterised Zn 2 Cys 6 transcription factors were also identified in clusters with characterised transcription factors or with CAZy genes, prompting more detailed analysis of transcription factor expression on all three of the lignocellulosic substrates using the MFuzz clustering of all genes datasets. Uncharacterised transcription factors that were annotated with at least one of four Pfam transcription factor domains were analysed to identify those that were putative activators of plant biomass-degrading CAZymes (Additional file 10: Table  S8 ).
Three of the transcription factors (An16g01390. Aspni5_130681, An16g02920. Aspni5_53646 and An11g04160. Aspni5_178503) are annotated as being similar to AmyR which in A. niger was shown to have a broader regulatory role beyond maltose regulation  [59] , and this dataset supports a broad role for these three transcription factors. The putative activating roles of the 24 transcription factors suggested engineering strategies to increase the production of CAZymes. Overexpression of the transcription factors that are repressed on ILS 9-24 h could drive the transcription of the CAZymes repressed in the presence of higher pentose sugar concentrations. Overexpression of An14g05670. Aspni5_41900, which is a putative activator of the pectinases induced on KMS, could be used to drive the transcription of these pectinases on the pretreated substrates.
Clustering analyses of gene expression patterns Hierarchical clustering of the 27 conditions was conducted with the function heatmap2 within the R package gplots using the gene expression values (FPKM), which were first log-transformed and then quartile-normalised. Only genes with an FPKM value >1 in at least one of the conditions were included. C-means clustering of the normalised gene expression values (FPKM) over time was calculated using the R package Mfuzz  [33]. The c-means clustering was performed on the subset of genes annotated with a signal peptide as well as all the genes. The optimal range of clusters was first explored for each of the time courses using the corresponding Mfuzz function "cselection". Clustering of genes based on these range values was then conducted, and the genes within each cluster were determined.
Gene ontology (GO) enrichment analysis Gene Ontology (GO) enrichment analysis of each set of selected genes was performed using the Bioconductor package GOseq (v1. 18. 0)  [69]. GO annotations for the genes were from the FetGOat resource (http://www. broadinstitute. org/fetgoat/) [70]. The GO annotations for the ATCC 1015 gene model and the CBS 513. 88 gene model were used and combined to make a non-redundant list of GO annotations for a particular gene. The input for the enrichment analysis was the lists of significantly different genes from the DESeq2 analysis that had a log2 fold change as calculated by DESeq2 of ≤ -1 or ≥1 and a p adj ≤ 0. 05. Genes with an FPKM < 1 in both conditions being compared were excluded. The DESeq2 lists from the comparison of the Glu 48 h control with each of the other 26 conditions were used.
The data were acquired with Agilent MassHunter Workstation Software, LC/MS Data Acquisition B. 06. 00 (Build 6. 0. 6025. 3 SP3). Skyline version 3. 1. 0. 7382 software (MacCoss Lab, University of Washington) was used to determine 2-4 peptides per protein and optimal SRM transitions (3-5 transitions per peptide). Quantification of the SRM peak areas was processed using the mProphet algorithm  [71]  that is part of Skyline. Only peaks that passed a 0. 05 q value cut-off were quantified and analysed further.
Background Myelodysplastic syndromes (MDS) occur predominantly in patients aged ≥60 years, many of whom have limited treatment options due to comorbidities, functional impairment, or poor medication tolerance  [1, 2]. Studies reporting the effect of age on baseline characteristics and treatment outcomes in MDS are limited and usually involve small patient cohorts  [3] [4] [5]. This study aims to assess age-related differences in baseline characteristics and outcomes with lenalidomide in red blood cell (RBC) transfusion-dependent patients with del(5q) included in two multicenter studies (MDS-003 and MDS-004; ClinicalTrials. gov NCT00065156 and NCT00179621)  [6, 7].
Although thrombocytopenia was one of the most common AEs with lenalidomide, the incidence of grade 3-4 bleeding events remained low across all age groups (data not shown). The cumulative incidence of AML was significantly lower in the ≥75 years group than in the ≥65 to <75 years (p = 0. 010) and <65 years age groups (p = 0. 006; Fig. 1a ). Similarly, the incidence of AML after treatment failure was significantly lower in the ≥75 years group than in the ≥65 to <75 years (p = 0. 006) and <65 years groups (p = 0. 022; Fig. 1b ). To assess whether this difference could be attributed to differences in duration of MDS between age groups, rates of AML progression were analyzed according The ECOG performance status score could only be determined for patients with this information entered into the study database c The number of cytopenias at baseline could not be determined for one patient due to missing ANC data    S1 ).
These results should be interpreted with caution given certain limitations of our analysis. For example, some mutations such as the TP53 gene mutation and possibly mutations of other genes (i. e. , CSNK1A1, ASXL1, TET2) are associated with a poor prognosis in patients with lower-risk MDS with del(5q)  [2, 17]. Mutation data were not collected in the MDS-003 and MDS-004 studies, and therefore, it could not be determined if certain mutations were more frequent in the <65 years age group. Of note, however, p53 protein overexpression, a validated surrogate marker of TP53 gene mutation, was not influenced by age in the present study.