dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  The Sainsbury Laboratory  for hosting the  Open Ash Dieback Git repository  (oadb. tsl. ac. uk), and  M. Caccamo  and  S. Janacek  for assistance with project management. The submission of sequencing data was brokered by the COPO platform (https://copoproject. org), funded by the  Biotechnology and Biological Sciences Research Council (BBSRC)  ( BB/L024055/1 ) and supported by  CyVerse UK , part of the  Earlham Institute National Capability in e-Infrastructure. The Ash Dieback research project was funded jointly by the  BBSRC  and the  Department of Environment Food and Rural Affairs (Defra)  via the  Nornex  project ( BBS/E/J/000CA523 ), the  Economic and Social Research Council , the  Forestry Commission , the  Natural Environment Research Council (NERC)  and the  Scottish Government , under the  Tree Health and Plant Biosecurity Initiative  ( BB/L01291X/1 ).
The UMR IAM is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  Investissements d' Avenir programme  ( ANR-11-LABX-0002-01 ,  Laboratory of Excellence ARBRE). Genome sequencing was done by  Edinburgh Genomics , and the  Earlham Institute (formerly The Genome Analysis Centre, Norwich ), by members of the Platforms and Pipelines Group funded by  BBSRC National Capability in Genomics  grant ( BB/J010375/1 ). Additional research funding was via a  BBSRC Institute Strategic Programme Grant for Bioinformatics  ( BB/ J004669/1 ) to the  Earlham Institute.
Data availability. All MP and LMP sequencing data generated for the Hf-v2. 0 genome (PRJEB21027), population genetics and parental cross reads have been submitted to the European Nucleotide Archive (under projects PRJEB21059, PRJEB21060, PRJEB21061, PRJEB21062, PRJEB21063, PRJEB21064 and accessions ERS480843-ERS480865; see Supplementary Table  1 ). The genome annotation is available at the Earlham Institute Open Data site (http://opendata. earlham. ac. uk/ Hymenoscyphus_fraxineus/EI/v2/).
For instance, SRC and MEK inhibitors, small molecules and antibody inhibitors of growth factor receptors have shown promising anti-EMT effects  [7, 8]. However, from a clinical perspective, their efficacy is hindered by the development of resistance making it an urgent need to combine the existing disparate and yet conflicting knowledge of EMT systematically to expedite the translational steps of selecting efficient drug combinations. To tackle this complexity, high-throughput experiments have begun to shed light on details of various stimuli and mechanisms leading to EMT  [9] [10] [11] [12] [13] [14] [15] [16]. We are thus witnessing an increasing number of published gene lists demonstrating EMT portrait in aggressive cancers. Efforts have been also made in performing meta-analyses to integrate a number of expression datasets with several EMT inducers to provide a more unified description of EMT irrespective of nuance differences in the experimental conditions  [17, 18].
Enrichment-based and analytical tools Enrichment tests were performed in "BiNGO" Cytoscape plugin  [22] , Gene set enrichment analysis (GSEA) in "Molecular Signatures Database (MSigDB) version 6. 0" (software. broadinstitute. org/gsea/msigdb) [23]  and also Enrichr (amp. pharm. mssm. edu/Enrichr) web platform as of Feb 2017  [24]. "CancerMA" database (www. cancerma. org. uk/information. html) accessed on Feb 2017 was used to perform meta-analysis across 80 cancer datasets for 13 different cancer types  [25]. "L1000CDS 2 " search engine (amp. pharm. mssm. edu/L1000CDS2) accessed date on Feb 2017 was used to query L1000 datasets and identify EMT reversing drugs  [26, 27]. "Expression2Kinases (X2K)" downloaded in Nov 2016 (www. maayanlab. net/X2K) was used to identify EMT-associated kinome which utilizes enrichment analysis of kinases and their substrates  [28].
Initially, a bottom-up approach was taken starting from identification of transcription factors as described previously in  [29]  and  [30]  which mainly identified intracellular non-receptor kinases linking signal transduction pathways to gene expression changes in the nucleus. Individual EMT gene signatures were also directly submitted to KEA module of X2K to identify kinases regulating (LINCSCP_34467 and LINCSCP_5717) in MCF-7 and A549 cell lines. Expression of drug signatures in murine hematopoietic cells was analyzed with "my gene set" tool in "ImmGen" project (immgen. org) [38].
Statistical analysis For enrichment tests FDR-adjusted P-value <5% , for meta-analysis and -1. 5 <fold change <+ 1. 5 and in co-expression analyses, Pearson's correlation co-efficient >0. 8 were considered significant. In cell-based experiments two tailed t-test with statistical significance of was level < 0. 05 in GraphPad Prism 6 (GraphPad Software; La Jolla, CA). Quantification of cell-based experiments was performed with MATLAB 2014b (MathWorks; Natick, MA). 3.
Unified EMT gene set library expression in cancers EMT-related signatures were compiled from four published studies (for details see methods) and a library of 962 genes was generated. Taking into account the importance of cell adhesion-related genes in initiation of EMT, the biological process gene cluster under "Cell-adhesion, GO:0007155" term was dissected from the main library resulting in a subset of 93 genes. The status of their differential expression was assessed by performing meta-analysis across eighty datasets and thirteen cancer types using "CancerMA" database. Complete list of differentially regulated celladhesion genes across multiple cancers is provided in (Supplementary File 1). This approach also allowed converting the gene set into an upregulated (regarded as mesenchymal-related genes) and downregulated (regarded as epithelial-related genes) signature in a cancer-dependent manner.
Except for adrenal cancer, HDACIs significantly reversed EMT-associated gene sets in ten solid cancers (Figure  3B ). Intermediate states of EMT are associated with deregulation of varying degrees of epithelial and mesenchymal genes in the cell surface. To further extend these deregulated genes with patient data across multiple cancers, the selected upregulated genes for each cancer were used as seed and were expanded with Cancer Genome Atlas (TCGA) datasets (Supplementary File 2) using co-expression (Pearson's r>0. 8) toolbox in "cBioPortal" web tool  [34]. In resubmitting the expanded gene lists to L1000CDS2, HDACIs were observed among the top ranked drugs to reverse expression pattern of the extended signatures in eight cancers which are marked with asterisk in Table  1. To elucidate the underlying biology of the pan-cancer effects HDACIs on EMT, we hypothesized that these drugs may modify the epigenetic mechanisms governing mesenchymal plasticity related to stemness features and pluripotency which are conserved across cancers  [42].
Accordingly, downregulated genes by Vorinostat in multiple cell lines were significantly enriched (FDR< 0. 05) in stemness-related gene sets in MSigDB (Figure  3D ). Most significantly downregulated genes by Vorinostat were found to be significantly expressed in stem cells and stromal fibroblast cells compared to their expression profile in mouse hematopoietic cell strains deposited ImmGen database (Figure  3E ). These results pinpoint the role of epigenetic plasticity in aggressive behaviors of carcinoma cells which would be inhibited by Vorinostat. Since a strong correlation between expression of embryonic plasticity gene signature and poor survival due to metastasis was observed in this study  [43] , we were also interested to see if the 13 plasticity-related gene set reversed by Vorinostat were associated with survival with DRUGSURV database which connects targets of drugs with survival.
We thus sought to determine EMT-associated kinome by performing kinase-substrates enrichment analysis on the EMT library using "Expression2Kinases" software. The identified kinases were connected using "STRING 10. 0" database to create a kinase interaction network (Supplementary File 3). The network was consisted of four highly connected sub-communities (modules) regulating different pathways in EMT (Supplementary File 4). Calculation of main network centralities proper for our network  [51]  including degree, betweenness and closeness showed that the proto-oncogene SRC with 45 interactions was the hub node (a node with high degree of interactions) in the whole network. SRC also owned the highest betweenness (0. 27) as a measure of shortest paths which pass through this node and also highest closeness value (0.
SRC and IKBK overexpression leads to induction of EMT in epithelial cells To confirm the relevance of SRC and IKBK kinases in EMT, "CREEDS" database of gene perturbation signatures  [37]  was queried and differentially expressed genes (DEGs) by SRC and IKBK overexpression or constitutive activation were identified. Biological functions of DEGs in the SRC overexpression signatures were associated with cell adhesion and actin cytoskeleton organization. In IKBKB over-activation signatures, epithelial cell differentiation, extracellular matrix organization, cell junction organization, regulation of inflammatory response were observed (adjusted P-value<0. 05), all of which are evidently associated processes in EMT. Consistently, the overlap between DEGs in the SRC and IKBK perturbation signatures and the 962-EMT gene set was significant (P-value< 0.
The zscore expressions of drug targets with relevance to EMT were then extracted from CellMiner and were compared across mesenchymal ovarian cell lines. A positive correlation (r >0. 5) between increase in the EMT score and expression of FYN (a member of SRC family of kinases), PDGFRB, HDAC2, CHUK and IKBK which are all inhibited by our proposed drug combination was observed implying that by increasing mesenchymal properties in these cell lines, the expression of these drug targets increased. To see if the pattern was maintained in datasets of clinical samples, the association of these drug targets with various histopathological characteristics of ovarian cancers in CSIOVDB database was assessed (Supplementary file 7). Among the drug targets, FYN and PDGFRB, CHUK and HDAC2 were correlated with mesenchymal subtype.
(D) Summary of drug classes in combination and their prospecting effects which is hypothesized to inhibit multiple aspects in EMT. Fig. 2. Meta-analysis shows inconsistent expression of cell adhesion-related genes across various solid cancers. (A) Upregulated cell adhesion-related genes dissected from collated EMT signatures. Expression status was determined by performing meta-analysis in CancerMA database for different cancer types. These upregulated genes were considered as mesenchymal genes (B) Downregulated cell-adhesion related genes from collated EMT signatures in various cancers obtained by meta-analysis. These downregulated genes were assumed to be associated with epithelial characteristics. The outer layer shows de-regulated genes and the inner layer represents cancer types. Edge thickness in the networks signifies folds change (FC) range.
±1. 5 FC was considered significant. Arrows indicate most similar genes in different cancers. Cellular location of vorinostat-induced gene products using Enrichr. FDR adjusted P-value (q-value) <0. 05 was considered significant for cellular component enrichment. (E) Significantly enriched (q-value < 0. 05) biological processes for shared genes between collated EMT signatures and Vorinostatinduced differentially expressed genes (DEGs) from iLINCS database. *Drugs that reverse co-expressed cell adhesion related genes in EMT are shown with asterisk.
Study design The questionnaire was prepared by the steering committee of the Trainee Association of the European Society of Clinical Microbiology and Infectious Diseases (TAE) to investigate the current status of supervision among European trainees during their training. A pilot phase involving 36 participants from different countries (Germany, Italy, The Netherlands, Spain, Sweden, and Turkey) was performed in order to assess the feasibility of the questionnaire and to allow for amendments. The final questionnaire was made available online between 1 June and 30 September 2017 using the SoSci Survey platform, which is a free open source professional software made in Germany (www. socscisurvey. de). Whereas no specific inclusion criteria were applied, participants who did not answer all questions were excluded for this analysis.
Statistical analysis The results were collected using SoSci Survey version 2. 5. 00-Categorical variables were summarized by frequencies and percentages. Continuous variables were summarized as mean and standard deviation (SD) or median/IQR depending on the distribution. Differences between the regions were investigated using chi-square or Fisher's tests as appropriate for categorical variables and Kruskal-Wallis tests for continuous variables. Statistical significance was set at p < 0. 05. All analyses were performed using IBM SPSS Statistics for Macintosh version 25. 0 (IBM Corp. , Armonk, NY).
Demographic characteristics After exclusion of 172 fully incomplete questionnaires, data from 393 participants were analyzed. Table  1  presents the main characteristics of these respondents, coming from 37 countries. The mean age was 34 years (SD ± 7 years). Most respondents worked in Spain (n = 60, 15. 3%), Italy (n = 40, 10. 2%), The Netherlands (n = 40, 10. 2%), Turkey (n = 35, 8. 9%), and the UK (n = 30, 7. 6%) (Fig. 1 ). Thirty-one percent of participants worked in countries which belong to Western Europe (n = 123), 14% Northern (n = 55), 7. 6% Eastern (n = 30), 27. 7% Southwestern (n = 109), 16. 5% Southeastern (n = 65), and 2. 8% other countries (n = 11). There were 265 trainees (67. 4%) and 128 recently finished medical specialists (32. 6%), with a CM:ID ratio of 1 to 1. 14 for trainees and 1 to 1.
We did so by applying meta-analysis to a database (MycoDB) of plant responses to mycorrhizal fungi with unprecedented taxonomic breadth and sampling depth  22. We tested the influence of early phylogenetic and recent diversification among plant species and fungal genera, non-independence of plant and fungal diversification (i. e. , specificity of plant response to particular fungi due to non-independent evolution of plants and fungi); independent evolutionary origins of ectomycorrhizal symbiosis in plants and fungi; artificial selection through human domestication of plants; plant traits including functional groups and life history; and ecological factors, including nitrogen (N) and phosphorus (P) fertilization and the presence of additional non-mycorrhizal microbes. We find that evolutionary history explains a substantial proportion of variation in plant responses to mycorrhizal fungi, and has different influences on outcomes of ectomycorrhizal versus arbuscular mycorrhizal symbioses.
Methods Overview and data. We conducted separate phylogenetic mixed-model metaanalyses for arbuscular mycorrhizal and ectomycorrhizal symbioses using the most recent version of the MycoDB database and associated fungal and plant phylogenetic trees (MycoDB_version4, FungalTree_version2, and PlantTree_version2), which contain data on plant biomass responses to inoculation with arbuscular mycorrhizal and ectomycorrhizal fungi, biotic and abiotic contextual factors varying among trials, species traits, and evolutionary origins and phylogenetic relationships of plant host species and fungal symbiont genera  22. Fungal identities in MycoDB are coded to genus and not species because in many cases, recent revisions in fungal systematics make assigning taxa in older papers to new groups problematic, and because fungal species names are inconsistent among publications and thus difficult to definitively link to particular taxa.
Compared to previous versions of the database, MycoDB_version4 and the associated phylogenetic trees (FungalTree_version2 and PlantTree_version2) exclude observations on a small number of possibly non-mycorrhizal or misidentified fungal taxa, update or correct nomenclature and/or phylogenetic placement of some plant and fungal taxa, and add new variables on independent evolutionary origins of mycorrhizal lifestyle in ectomycorrhizal plants and fungi. Our analyses were conducted on three subsets of MycoDB_version4: one (AM-full) in which plants were inoculated with arbuscular mycorrhizal fungi belonging to one or more fungal genera (2984 studies across 293 plant species and 14 fungal genera from 359 publications), a second (AM-sub) that was a subset of AM-full in which plants were only inoculated with a single arbuscular mycorrhizal fungal genus (2398 studies across 234 plant species, 14 fungal genera, and 456 unique plant-fungus combinations, from 297 publications), and a third (EM-sub, hereafter EM) in which plants were inoculated with ectomycorrhizal fungi belonging to only a single fungal genus (1001 studies across 62 plant species, 40 fungal genera, and 190 unique plant-fungus combinations, from 83 publications).
Very few studies were on plants inoculated with species from more than one genus of ectomycorrhizal fungi, or with both arbuscular mycorrhizal and ectomycorrhizal fungi, so those studies were not included in our analyses. Because the studies in the AM-sub and EM data sets used inoculation with a single fungal genus, analyses of those data could include fungal genus, fungal phylogeny, and plant × fungal interactions in meta-analytic models. For a brief discussion of how the scope of inference from meta-analysis of MycoDB may be affected by the nature of the studies included, see Supplementary Methods. Calculation of effect size and estimated sampling variance. For all analyses, the response variable was the effect size of plant biomass response to mycorrhizal inoculation, expressed as a log response ratio  34  : LRR ¼ ln x inoc x ctrl ; where x inoc and x ctrl are mean plant biomass (total biomass if available, otherwise shoot biomass) in an inoculated treatment and a non-inoculated control, respectively.
6% of studies in AM-full, 3. 5% of studies in AM-sub, and 21. 2% of studies in EM). This imputation was performed separately for each data set (AM-full, AM-sub, and EM). We also explored the robustness of results to alternative imputation methods (see Supplementary Methods). Random factors included in meta-analysis models. All models of both arbuscular mycorrhizal and ectomycorrhizal symbiosis potentially included these 11 random effects: plant phylogeny, plant species, fungal phylogeny, fungal genus, plant phylogeny × fungal phylogeny interaction, plant phylogeny × fungal genus interaction, plant species × fungal phylogeny interaction, plant species × fungal genus interaction, study ID, control set, and paper. The first four of those random effects correspond to the phylogenetically heritable and non-heritable variance components (for plants and fungi, respectively) of the phylogenetic mixed model described by Housworth et al.
e. , greenhouse or growth chamber, or in the field), and Plant Functional Group (AM: C4 grass, C3 grass, nitrogen-fixing forb, non-nitrogen-fixing forb, nitrogen-fixing woody, or non-nitrogen-fixing woody; EM: nitrogen-fixing woody or nonnitrogen-fixing woody). The data for arbuscular mycorrhizal symbiosis allowed us to test two additional fixed-effect predictors on arbuscular mycorrhizal plant traits: plant life history (annual/biennial or perennial) and domestication (whether the host plant was a wild variety, a forage crop, or a domesticated variety). Finally, the AM-full data subset allowed us to test an additional fixed-effect predictor, Inoculum Complexity (single fungal species, multiple fungal species, or whole soil inoculum). Additional details on construction of these nine fixed-effect predictors can be found in Chaudhary et al.
22. Replication in our updated version of MycoDB was sufficient to test some twoway interactions between fixed-effect predictors, unlike a previous analysis of an earlier version of MycoDB 13. To simplify the candidate set of models, two-way interactions between pairs of fixed-effect predictors were selected for analysis only when we could conceive hypotheses on how they would influence the response variable  39  , and when the structure of the data allowed testing of those interactions. The latter criterion was never satisfied for two-way interactions of interest for the ectomycorrhizal symbiosis. In the saturated model for arbuscular mycorrhizal symbiosis, the following two-way interactions were included: N-fertilization × Pfertilization, N-fertilization × plant functional group, P-fertilization × plant functional group, and sterilization × microbial control.
In addition, we checked the sensitivity of these results to the model fitting approach by using Bayesian model fitting with saturated models containing all random and fixed effects, and examining the 95% credible interval and Bayesian P-values for fixed effects to determine their significance relative to an alpha of 0. 05. For further details of how likelihood and Bayesian methods were used to determine the importance of fixed-effect predictors, see Supplementary Methods. Estimating magnitudes of random effect variance components. To characterize random effects, we used restricted maximum-likelihood estimation (REML) to fit models that were determined to be the best (with respect to which fixed effects were included) according to AICc-based model selection, as described above. From these models, the influences of random effects were ascertained by examining estimated magnitudes of associated variance components, along with their associated profile likelihood confidence intervals, which were estimated using the confint() function of the R package metafor.
1 ). One of the plant evolutionary origins, Phyllanthaceae, was represented in the data by only a single plant species (Uapaca somon), so it was not labeled on Fig. 1. To visualize the random effects of ectomycorrhizal plant phylogeny × fungal phylogeny (plant/fungus bubbles in Fig. 1 ) and arbuscular mycorrhizal plant phylogeny × fungal genus (plant/fungus bubbles in Fig. 2 ), we generated 2-phylogeny heat maps of the corresponding BLUPs using the input_trees() and plot_trees() functions in the dualingTrees package of R (available at github. com/jfmeadow/ dualingTrees-pkg). Fungal genus and plant species means, calculated as marginal means across plant-fungus combinations, are shown at the bottom and right sides of the heat maps, respectively. For arbuscular mycorrhizal and ectomycorrhizal symbiosis, these BLUPs were taken from analysis of the best models of the AM-sub and EM data, respectively.
Acknowledgements This research was supported by the  National Evolutionary Synthesis Center , which received support from a  National Science Foundation (NSF)  grant ( EF-04-23641 ),  Duke University , the  University of North Carolina at Chapel Hill , and  North Carolina State University. This work was also supported by a  Distributed Graduate Seminar (DGS)  project conducted through the  National Center for Ecological Analysis and Synthesis , a Center funded by  NSF  (award  EF-05-53768 ), the  University of California, Santa Barbara , and the  State of California. We are grateful to the more than 50 graduate students who contributed to the MycoDB database through the  DGS  project, and to  P. Antunes ,  M. Ha ,  J. Hopkins ,  L. Van , and  M. Woods  for help in curating MycoDB. J. D. B.
and J. U. were supported by  NSF  award  DEB-1556664. M. G. was supported by the  French Laboratory of Excellence "TULIP "  project ( ANR-10-LABX-41 ,  ANR-11-IDEX-0002-02 ). C. G. was supported by the  NSF  award  EF-1340852. J. H. was supported by the  NSF  award  DEB-1119865. E. A. H. was supported by the  NSF  award  DMS-1206405. W. K. was supported by a  Research Grant for   Development and Promotion of Science and Technology Talents Project (DPST)  (Grant No. 21/2557 ) graduate with First Placement, the  Royal Thai Government. M. J. L. was supported by the  NSF  award  DBI-1262545. M. A. R. was supported by the start-up funds from  Wright State University. G. W. T. W. was supported by the  NSF-LTER  award  DEB-1354098.