dataesr/scientific-paragraphs-categorization huggingface dataset:
Using the Mulligan-cohort, including patients treated with bortezomib after relapse, only the Mu-HA score kept prognostic value (Figure  2C ). Next, we evaluated whether the scores could be used to predict response to therapy. Using the HM-cohort, we could not find a significant difference in the distributions of low scored and high scored patients between different response groups following high-dose therapy and autologous stem cell transplantation (data not shown). For the Mulligan-cohort we only tested the Mu-HA score. The distributions of low scored and high scored patients were significantly different between non-responders (no change + progressive disease) compared to responders (complete response + partial response + minimal response) (Supplementary Table  S3 ). The prediction had a high specificity (97.
In vivo quisinostat or decitabine treatment altered the expression of genes involved in immune pathways Using DAVID gene ontology (GO) software, the enrichment of the in vivo deregulated genes with a biological process was investigated  [63, 64]. GO analysis showed a significant enrichment of genes involved in immune regulation, metabolism/homeostasis, development and differentiation, cytoskeleton/migration, regulation of gene expression and cell death (Supplementary Table  S5 ). The hierarchical clustering of the co-enriched gene signatures is shown in Figure  4  and 5. Looking to the genes included in the scores in more detail showed that these genes were dispersed across all the different enriched GO sets. STRING protein network analysis confirmed the GO analysis and showed network interactions linked with immune cell regulation, cell death pathways and metabolism (Supplementary Figure  S2, S3 )  [65].
Gene expression profiling Samples with more than 95% plasma cells (N=4 in each group) were used for RNA isolation using the RNeasy Kit (Qiagen, Venlo, The Netherlands). RNA was further processed and hybridized to the Mouse Genome 430 2. 0 Array (Affymetrix, Santa Clara, CA, USA) as described in Moreaux et. al  [35]. Microarray data are available at ArrayExpress database (Accession number: E-MTAB-3178).
Microarray data of primary multiple myeloma cells and human myeloma cell lines Affymetrix data of two independent cohorts of previously untreated MM patients was used. The training cohort consists of 206 MM patients and is termed the Heidelberg-Montpellier (HM)-cohort. This cohort also includes 7 bone marrow plasma cell (BMPC) samples from healthy donors. The samples were obtained after written informed consent in accordance with the Declaration of Helsinki and after approval of the ethics committee of Montpellier and Heidelberg. These data are publically available through ArrayExpress database (E-MTAB-372). The validation cohort contains 345 MM patients from the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA) and is termed the TT2-cohort. These data can be accessed at the online Gene Expression Omnibus (GSE2658).
We also used Affymetrix data of 152 relapsed MM patients subsequently treated with bortezomib (GSE9782) from the study by Mulligan et al. [57]. The clinical characteristics of the cohorts have been described previously and are summarized in Supplementary Table S8  [54] [55] [56] [57]. In addition, we used the Affymetrix data of 40 human myeloma cell lines (HMCLs) stored in the ArrayExpress database (E-TABM-937 and E-TABM-1088)  [58]. The derivation of the HMCLs as well as their phenotypic and molecular characteristics were previously published  [58].
Results were plotted using the Kaplan-Meier method. Multivariate analysis of the scores was done using Cox proportional hazard model. The above analyses were performed using R 2. 15. 1 and Bioconductor 2. 0 software. Gene ontology (GO) analysis was done by the online Database for Annotation, Visualization and Integrated Discovery (DAVID)  [63, 64]. Pathway analysis was conducted by Reactome software, Pathway-Guide and STRING analysis.
ACKNOWLEDGMENTS We thank  Carine Seynaeve  for her expert technical assistance. Decitabine and quisinostat were kindly provided by  Johnson & Johnson (Beerse, Belgium ). This work is funded by a  Brian D. Novis  research grant of the  International Myeloma Foundation ,  Vlaamse Liga tegen Kanker , Stichting tegen kanker, Fonds voor  Wetenschappelijk Onderzoek ,  ARC  ( SF120121205949 ,  Paris, France ),  ANR emergence (ETTMM) ,  CRLR grant  ( R14026FF ), 7 th  EU-framework program  " OverMyR ", the  German Research Foundation  (DFG;  SFB/TRR79 ) and the  Federal Ministry of Education and Research  (BMBF; CAMPSIMM and CLIOMMICS),  Bonn ,  Germany. Elke De Bruyne  and  Els Van Valckenborgh  are fellows of  FWO-Vlaanderen. This work was published with the support of the  University Foundation Belgium.
EBT informs a validation process that can be used to assess the PoT approach and any associated ITS  (Stephens et al. , 2013). A proposal for a "mechanistic validation" using EBT principles has recently been put forward  (Hartung et al. , 2013b). EBT principles are transparency, consistency and objectivity that help to limit the bias in the review of relevant studies while still leaving room for professional judgment on how to inform policy/decision making. Using these approaches (PoT, ITS and EBT), CAAT has formed a multi-stakeholder team to map the human toxome. The input into this process includes in vitro characterization of model systems combined with high content omics data and statistical modeling and visualization programs. In October 2012, CAAT hosted a workshop on tools for PoT development -including a PoT database (because on one hand existing toxicity databases are not pathway oriented and on the other hand existing pathway databases are not toxicity oriented).
Virtual Liver: Predicting Liver Injury -Imran Shah From the information on 544 environmental chemicals in EPA's Integrated Risk Information System (IRIS) database, human health risk assessments for 20% are based on liver toxicity. The liver is a complex, dynamic system and not simply a bag of enzymes. How do you assemble the pieces in a meaningful way so that we can predict toxicity? One objective is to come up with screening tools that could begin to point out and connect MIEs that lead to adverse downstream effects; however, there are many pathways to any given adverse effect. So, perhaps a more fruitful approach is to start with the histopathology and work backwards to the MIE. A main objective of the high throughput toxicology approach being taken by the Tox21 projects is the identification of reproducible biomarkers; however, this is a very difficult problem in the absence of biological context.
The utility of the model is to aid drug design; to maintain potency while minimizing toxicity. The model is not a database of interactions, and it is not a QSAR model. The virtual liver consists of processes, pathways, etc. , that are quantitatively linked and allows one to run simulations, make predictions and develop insights into the toxicity process. It is a model of biology; it is not "trained" using existing drugs or chemicals, and therefore is not limited by a chemical space (but rather is limited by our knowledge of biological space). The virtual liver can model hepatotoxicity, metabolic disease and cardiovascular disease. The Virtual Liver project takes a top-down, integrated approach, similar to completing a jigsaw puzzle by piecing together the outline first: the boundary is the phenotype.
The elaborated pathway was evaluated according to OECD guidance: 1) concordance of dose-response relationships: no available dose-response curves but sporadic numerical data; 2) temporal concordance among the key events and adverse outcome: the sequence of the events is in strong agreement; 3) strength, consistency and specificity of association of adverse outcome and initiating event: strong and consistent association but not specific; 4) relevance: LXR expression and all the other elements are considered adequately conserved from rodents to humans; 5) uncertainties, inconsistencies and data gaps: there are differences in LXR expression between cell lines; the MoA could be affected by ethnicity and the life stage of the individual; there are possible effects by factors related to the formation of steatosis including trends in adipose tissue deposition, total body fat, and visceral and subcutaneous adipose tissue (which vary among different life stages, sexes and ethnicity).
Bioinformatics and modeling approaches can also be used to incorporate the influences of genetic variability on normal and perturbed pathways. To fully inform pathway development, it is also essential to incorporate the knowledge from histologists and clinicians familiar with the biological outcomes at the tissue and individual levels. In the case of DILI, there are also large human databases that contain information that can be used to inform idiosyncratic factors. Some important considerations for the future of AOP development in general and liver AOPs in particular that were discussed included: accounting for context -AOPs are species, tissue, timing and dose-dependent; consideration of metabolism, both before entering the liver and within the liver; the fact that more information is not always better -a consideration of signal-to-noise and knowing what information is relevant is critical; identification of appropriate tools to test integrated strategies including appropriate cell types and tissue constructs; incorporation of severity -prediction when the "tipping-point" of adversity has been reached (quantification of the relationships between intermediate effects is necessary); appropriate design of assays to inform the quantitative relationships between intermediate events; the importance of a centralized and accessible knowledge base.
2012 ). E. sections Goniostema, Denisophorbia and Deuterocalli were represented, respectively, by 203, 34, and 12 samples, corresponding to approximately 63, 8, and 2 species, representing 90% of the estimated diversity in the Euphorbia GDD clade (Supplementary Fig. S1  and Table  S1 , available from http://doi. org/10. 5061/dryad. vq6mp). DNA was sequenced for six chloroplast markers  (atpI-atpH, psbA-trnH, ndhA, matK, trnQ-5'rps16, and rbcL)  and two nuclear regions (ITS and ETS), resulting in a data matrix of 8507 bp (<1% missing data). Our approach was to use all of the available data (sequences) in order to verify operational taxonomic units (OTUs) and estimate phylogeny, then trim the trees to retain a single sample per ingroup OTU (i. e. , removing duplicates), retaining all samples that were associated with both an unambiguous taxonomic circumscription and reliable locality data (Supplementary Fig.
S1 ; http://doi. org/10. 5061/dryad. vq6mp). Rerunning the phylogenetic analysis with one sample per final retained OTU (67 taxa) yields essentially the same topology. Sequence matrices were aligned using MUSCLE  (version 3. 8; Edgar 2004)  and  MAFFT (version 6; Katoh et al. 2002) ; the aligned data are available at doi:10. 5061/dryad. vq6mp. The best-fit model of nucleotide substitution evolution for each region was determined using Akaike's information criterion implemented in MrModelTest 2. 2  (Nylander 2004 ): GTR+ +I for ITS, ETS, atpI-atpH, GTR+ for psbA-trnH, ndhA, matK, trnQ-5'rps16, and GTR+I for rbcL. Phylogenetic analysis of the nuclear and chloroplast markers separately revealed topological incongruence between them: chloroplast markers, which are lacking in sequence variation, do not recover as monophyletic E.
Locality and Climate Data We assembled an initial database of 653 occurrences of members of the GDD clade, approximately 75% from museum collections, mainly the MNHN and MBG herbaria (available on SONNERAT [http:// science. mnhn. fr/institution/mnhn/search, last accessed May 28, 2014] and TROPICOS [http:// www. tropicos. org/, last accessed May 28, 2014]). The remaining localities were derived from monographs, unpublished records, and expert knowledge  (Aubriot X. , Haevermans T. , unpublished data). All locality data were carefully evaluated; some records were discarded because of imprecision, species misidentification, or duplication. The final data set contained 529 occurrences (Fig. 3a ), which were used for climatic data extraction. Climate data were the 19 "Bioclim" variables available from worldclim (http://www.
Our tests accounted for the relatedness of species by including a correlation structure derived from phylogenies (implemented using the MCMCglmm package in R;  Hadfield 2010; Hadfield and Nakagawa 2010). Prior distributions for the climate effects were normally distributed centered on zero, with variance proportional to the estimated residual variance  (Hadfield 2012). Based on convergence and autocorrelation statistics from initial simulations on the trimmed maximum clade credibility tree, we ran simulations of 1. 5 × 10 6 iterations, eliminating the first 5 × 10 5 as burn-in, and thinning the chains to 1/1000 samples, yielding 1000 samples per chain. To account for phylogenetic uncertainty, including uncertainty about the monophyly of Euphorbia sections Goniostema and Denisophorbia, we repeated MCMCglmm tests (one simulation per tree) using a random sample of 10 000 trees taken from the post burn-in phase of the three BEAST runs described above.
Ordination of Climate PCA and OMI eigenvectors were oriented similarly, but differed in magnitude (Fig. 3c  vs. Supplementary Fig. S2b ; http://doi. org/10. 5061/dryad. vq6mp). We preferred the output from the OMI analysis over PCA because it nicely captured the four major bioclimatic regions defined by  Cornet (1974) -the humid, warm eastern forest; the cool central highlands; the subarid south and southwest; and the climatically heterogeneous west-each one falling into one-quadrant of the OMI axis 1 versus 2 climate space (Fig. 3b ). Further, species were better separated from one another in OMI axis 1 versus 2 space, compared with PCA axis 1 versus 2 space (Fig. 3b  vs. Supplementary Fig. S2a ; http://doi. org/10. 5061/dryad. vq6mp). MAT is highest in the upper left and lowest in the lower right of the OMI ordination diagram, whereas MAP is highest in the lower left and lowest in the upper right (Fig.
Life Form The 14 "cactiform" species tend to occur in the warmest region of the climate space (Fig. 4c  and Supplementary Fig. S3a , http://doi. org/10. 5061/ dryad. vq6mp), with the notable exceptions of Euphorbia horombensis, E. lophogona, and E. boissieri (MAT = 20. 5 • C, 22. 9 • C, and 23. 7 • C, respectively). The effect of MAT on the cactiform life form is not statistically significant on any of 10 000 trees considered, nor is the effect of MAP. But the evolution of two growth forms with a majority of their biomass below groundgeophytes and dwarf chamaephytes-is significantly associated with low average annual precipitation and/or low average annual temperature. Evolution of geophytism is significantly associated with low MAT (on 100% of 10 000 trees), whereas evolution of the dwarf chamaephyte form is significantly associated with low MAP (on 88% of 10 000 trees).
Climate data for the extant taxa are shown in Table  1. Climate at interior nodes was reconstructed using the function phenogram (package phytools, R; Revell 2013); stochastic character mapping was performed using make. simmap (also in the package phytools). A single stochastic realization of character mapping is shown for each trait. Upper panels: predicted probability of having the succulent character state as a function of climate, from a logistic regression not taking into account phylogeny. a) Leaf succulence converted to a binary trait; phylogeny plotted with respect to MAP. b) As in panel a, but the tuber character. c) Growth form converted to a binary trait, contrasting geophytes and dwarf chamaephytes against all other growth forms; phylogeny plotted with respect to MAT. with a linear term for MAT, and the preceding models without two outliers with respect to MAP (Euphorbia lophogona and E.
boissieri). This is in spite of trends in the data (Supplementary Fig. S3a, b ; http://doi. org/10. 5061/dryad. vq6mp). We suspect the lack of statistical significance is a consequence of low statistical power, due to a small number of transitions in and out of the cactiform state (Supplementary Fig. S3c ; http://doi. org/10. 5061/dryad. vq6mp).
Contrary to a layperson's view of cactiforms, they are not prevalent in the driest places occupied by the Euphorbia GDD clade (upper right quadrant, Fig. 4c ); instead, this growth form is most prevalent at intermediate levels of MAP (Supplementary Fig. S4b , http://doi. org/10. 5061/dryad. vq6mp). Cactiform stem succulence is also associated with warm temperatures: 11 of the 14 cactiform species are found in places with MAT between 24 • C and 26 • C (Table  1  and Supplementary Fig. S3a , http://doi. org/10. 5061/dryad. vq6mp). These patterns are consistent with the literature  (Gibson and Nobel 1986; Burgess and Shmida 1988; Burgess 1995; Eggli and Nyffeler 2009 ), but they were not statistically significant, probably because of a lack of power (Supplementary Fig. S3c , http://doi. org/ 10.
5061/dryad. vq6mp) and the presence of "climate outliers," that is, Euphorbia GDD species that are found in surprisingly mesic places, given their morphological characteristics. For example, E. lophogona is cactiform, yet it occurs in the eastern mesic forest biome, where MAP is 2172 mm per year. However, many of these "climate VOL. 63 outliers" are found on edaphically arid substrates (Table  1 ): E. lophogona grows on sand, and thus, at a microenvironmental scale, is subjected to aridity, a phenomenon arising from the interaction between climate and substrate  (Nobel 1983; McAuliffe 1994; Schwinning and Ehleringer 2001; Schwinning 2010). We return to this point below regarding niche conservatism versus lability. There is still much to be learned about the resource strategies of succulent plants.
MAT data for the extant taxa are shown in Table  1 ; ancestral reconstruction of MAT used the "pic" method in traitgram (package picante, R). Colors indicate the seven strongly supported subclades illustrated in Figure  3. Horizontal bars indicate the range of values for each subclade. b) As in a, but MAP. more than 290 cm per year, nearly a 10-fold difference, and MAT from 17 • C to 27 • C (Table  1 ). Multiple, independent colonizations of mesic environments are suggested by the fact that the species occupying these areas are derived from five separate, well-supported subclades (Figs. 2  and 6b ): section Deuterocalli (red); section Denisophorbia (pink); a pair of sizeable clades with species in section Goniostema that are found in northern Madagascar, which we refer to as the "thuarsiana" clade and the "boissieri" clade (after the first-named species in each; yellow and orange, respectively); E.
This is particularly significant in Madagascar, one of the world's most important biodiversity hotspots, where succulent and spiny Euphorbia are a dominant component of many vegetation types. In fact, climate effects on succulent Euphorbia seem to have already taken hold in South Africa, where there is widespread mortality of Euphorbia ingens trees (Van der Linde et al. 2012). SUPPLEMENTARY MATERIAL Data available from the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. vq6mp.
ACKNOWLEDGEMENTS The authors thank  A. Soulebeau  for assistance with the Euphorbia section Denisophorbia species, and the  BoEM molecular laboratory (Muséum National d'Histoire Naturelle, Paris [MNHN])  for assistance with molecular work. Some of the sequencing was performed under agreement No. 2005/67  between Genoscope and the  MNHN  under the project " Macrophylogeny of life " directed by  Guillaume Lecointre. The authors are grateful to  J. Hadfield  and  K. Holsinger  for their assistance with MCMCglmm. Thanks go to the  JCVI, Inc. , for support, access, and use of the computational grid employed for comparative analyses. Comments from  M. Ogburn ,  K. L. Prudic ,  B. Boyle , and members of the  Sanderson laboratory  significantly improved the manuscript.
2 to 6 folds in the polyassociated flies compared to their GF siblings (Fig. 1B  and Fig. 2 ). We classified the 105 selected genes according to their annotated tissue expression profile using the FlyMine tool  [19]  based on the FlyAtlas dataset  [20]. Interestingly, despite the fact that the transcriptomic analysis was conducted on whole adult animals, we found that most (100/105) of the selected transcripts are expressed in the midgut of conventionally (CONV) raised adults (i. e with a microbiota) (Fig. 1C ). Recently, Buchon et al. and Marianes et al. reported that 60-65% of the Drosophila genes are detected in the midgut of CONV adults with specific patterns of expression in this tissue  [21, 22]. We therefore examined whether our list contains enrichment for genes sharing a given spatial pattern of expression in the midgut but we failed to detect any (data not shown).
Nonetheless, the marked over-representation of midgut genes in our dataset indicates that the host transcriptional response to the microbiota is localized and occurs mainly in the Drosophila midgut. We then verified that microbiota-regulated transcripts were indeed enriched in the midgut upon microbiotaassociation. To do so, using RT-qPCR, we compared the expression levels of a set of the most strongly microbiota-regulated genes in dissected midguts from GF and microbiota-associated animals and found that the tested genes were all up-regulated in adult midguts associated with four commensal bacterial strains compared to GF controls (Fig. 1D ). These results therefore demonstrate that microbiota association impacts on genes expression in the midgut.
Drosophila microbiota sustains metabolic genes expression We next used Gene Ontology (GO) clustering and enrichment analysis tools (Database for Annotation, Visualization, and Integrated Discovery or DAVID;  [23] ) to identify the functional categories within our gene list. We found that 74 out of 105 genes on the list are associated with metabolic processes (Fig. 1E , Fig. 2 ). Among the 74 genes, 33 are involved in proteolysis, 11 in carbohydrate metabolic process, six in lipid metabolic process and four in oxydo-reduction. From the same analysis, we found that the terms ''metabolic process'', ''proteolysis'', ''primary metabolism'' and ''carbohydrate metabolism'' are the most significantly enriched terms (Fig. 1E -F, Fig. 2 ). Among the remaining 31 genes, several are involved in processes that could either be connected to metabolic activities such as transport across membranes (4/31), chitin metabolism (4/31) and cellular ion homeostasis (2/31) or linked to host response to bacterial colonization (i.
Trade-off between microbiota-mediated and infectionmediated midgut genes expression Buchon et al. characterized the transcriptional signatures in dissected CONV adult midguts after an acute oral infection with the bacterial strain Erwinia carotovora carotovora 15 (Ecc15). Our current study shares many common features with that of Buchon et al. , in that we adopted similar experimental protocols to study changes in transcriptomes after exposing the host to a specific set of bacteria and we both found a marked transcriptional response localized to the midgut. We therefore compared our dataset to that of Buchon et al. and found an evident overlap between the two lists: half of our microbiota-regulated genes are also modulated upon intestinal infection (52/105 genes, Fig. 2 ). As expected, like in Buchon et al.