dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: This work was funded by a grant from the  European Regional Development Fund (ERDF)  through the  European Territorial Cooperation  for the Indian Ocean 2007-2013,  Sustainable Development  and the  Environment Initiative  (Contract No. 2013-33857 ). Thanks are given to the laboratory technicians at the  Indian Ocean Regional Health Agency (ARS)  for their conscientious care of the animals and for their technical support in field operations. The study involved the use BG Sentinel trap supplied by the  Biogents AG manufacturing company. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contributions:  Gilbert Le Goff ,  David Damiens ,  Jean-SébastienDehecq  and  Louis-Clément Gouagna  conceived and designed the experiments.
Treatment and phylogenetic classification Sequence analysis was performed using the Ribosomal Database Project tools, following RDP Release 11. 4 recommendations  [33]. Sequences generated from the 357F primer were subjected to quality filtering with FastQC for average percentage of A, G, C, and T across the read length, average GC content, location and frequency of N positions, sequence length distribution, duplicate sequences, and overrepresented k-mer sequences. Utilizing quality scores, sequence reads were cleaned by PRINSEQ and RDP. Initial processing used criteria: removal of 5 and 74 nucleotide residues at the 3′ and 5′ ends, respectively; deletion of reads with N residues or with an average quality score ≥32. Chimeric sequences (n = 64108) were also deleted after identification with UCHIIME  [34]  via USEARCH  [35]  and against the RDP Gold v9 database.
After processing, 644019 sequence reads (including the V3 region) were selected for further analyses. Alignment was performed using the HMMER3 model  [36]. The RDP's mcClust algorithm  [37]  and the naïve Bayesian classifier  [38]  were used to define 38730 operational taxonomic units (OTUs) at 98% sequence identity. A multivariate data analysis of OTUs was performed using Phyloseq. Principal component analysis (PCA) and significant features were identified for all treatments using Phyloseq. The R Vegan package  [39]  was used for community dissimilarity calculations (alpha diversity indices combining species richness and abundance into a single value) with for each replicate a OTU number normalized relative to the lowest number of OTUs generated from M2 R1. The Shannon-Weaver index was ranged from 0.
Characterization of genetic diversity by 16S rRNA gene sequencing Bacteria were grown in LB medium over night at 28 °C. Genomic DNA was extracted from cell cultures using the UltraClean®Microbial DNA Isolation kit (MO BIO). Extracted DNA was amplified with 27F and 1492R primers. Amplification was performed under the following conditions: initial denaturation step at 94 °C for 5 min; 40 cycles of denaturation at 94 °C for 40 s, annealing at 55 °C for 40 s, and extension at 72 °C for 1. 5 min; and a final extension at 72 °C for 7 min. PCR products were separated on a 1% agarose gel, were stained with ethidium bromide, and were visualized on a transilluminator. Sequencing was performed in both directions with primers 27F and 1492R, and consensus sequences were obtained using CAP3  [40]. Identification was performed at the genus level by blast against the "procaryota_SSU-rDNA-16S_stringent 277957" leBIBI-QBPP database (https://umr5558-bibiserv.
univ-lyon1. fr/ lebibi/lebibi. cgi). 16S rRNA gene sequences closest to the isolates (98% sequence homology) were recovered for phylogenetic analysis. Two groups were formed and analyzed separately per higher-level taxonomy. Sequence alignment was performed with MUSCLE  [41] , and the phylogenetic tree was constructed using the GTR model in PhyML  [42]  as implemented in the software Seaview4. A bootstrap confidence analysis was performed with 1000 replicates.
Generation of green fluorescent protein-labeled bacterial strains Bacteria were grown in LB medium and washed with icecold 10% glycerol. The pFK78 plasmid  [43]  was transferred into competent cells using the MicroPulser™ electroporation apparatus (Bio-Rad) with recommended protocols for bacteria. Transformed bacteria were selected on LB agar plates supplemented with gentamycin (10 μg ml -1 ) and identified using a LEICA MZFLIII binocular and AxioCamHR camera equipped with AxioVision 4_7 software (Zeiss, Germany). GFP was excited at 440-520 nm light and emission was detected through a 520-600 GFP filter.
Biofilm colonization assay To generate biofilm, roots of tomato seedlings were inoculated for 3 h with 10 ml of P. parasitica zoospore suspension (strain 149, 500 cells μl -1 ) and were washed three times with sterile water  [28]. The biofilm-root complex was inoculated with GFP-expressing bacteria (E. coli and I-1G6) by adding 10 ml of a cell suspension in water (OD = 0. 2) and was incubated for 3 h at 20 °C. Analysis of biofilm-root colonization was performed at 20 °C and was visualized at different time points  (3, 6, 18, 24, 48, 72 , and 96 h) by fluorescence microscopy. For quantitative image acquisition, 8 bit images (512 × 512 pixels) were acquired on a ZEISS LSM 880 laser scanning confocal microscope (λex = 488 nm). In order to measure root and biofilm colonization, a mean fluorescent intensity signal was determined for four 1000 μm 2 areas on each sample (2-biofilm and 2-root material), from 10 serial confocal sections (5 μm) using the ZEN 2012 lite software (Zeiss, Germany).
Partnerships between P. parasitica and bacteria M2 M2 replicates were screened for bacterial species associated with P. parasitica and regulated plant infection. After dissection and dilaceration of mixed-biofilms, a collection of 1200 isolates was recovered from M2 on a P. parasitica crude extract-based medium. A total of 300 isolates were tested for activity towards the in vitro and in planta growth of the oomycete. Eleven percent of the isolates inhibited (9. 8%) or promoted (1. 2%) radial growth of three distinct P. parasitica strains in a confrontation assay on nutrient agar plates (Fig. 4a ). A similar tendency was observed for the growth effect in zoospore germination assay and in the presence of isolate-conditioned V8 medium for each of these isolates (data not shown). Our results suggest in vitro isolates that potentially secrete compounds which interfere either negatively or positively with P.
parasitica growth. Sequencing of 16S rRNA genes and searches of the procaryota_SSU-rDNA-16S_stringent 277957 database of the leBIBI-QBPP  [46]  established that isolates belonged to three phylotypes of Pseudomonas spp. and one phylotype of Enterobacter spp. (Additional file 8: Figure  S4 ). In the M2 treatment, the mean proportion of OTUs affiliated to Pseudomonadaceae or Enterobacteriaceae was 3. 7 and 0. 009%, respectively. Twenty isolates were tested using a co-inoculation assay system for detecting their effect on the ability of a virulent P. parasitica strain to infect tomato roots. Each individual isolate was applied directly to the roots, none was found to cause symptoms on leaves or roots of plantlets for periods of up to 14 days (see Additional file 9: Figure  S5  for Pseudomonas isolates).
No correlation with the in vitro effect of isolates on the oomycete growth were observed. Indeed, for all isolates similar root application at 2 h postzoospore inoculation did not lead to curative protection of plants (data not shown). For four isolates belonging to the Pseudomonas phylotype II and III worsening of symptoms was noted. The rate at which the symptoms appeared and progressed was faster than the appearance of symptoms in control plants inoculated with zoospores or co-inoculated with zoospores and cells of E. coli lab strain. The disease rate was significantly higher for two isolates at 3 and 5 days post-inoculation (Fig. 4b ).
Acknowledgements The authors thank  Renaud Canaguier  ( NIXE, Sophia Antipolis ) for supporting this work,  Mathieu Gourgues  for the helpful discussion,  William Rhamey  for edition of the paper and  Renier van der Hoorm  for the generous gift of pFK78. The authors also thank the  Microscopy Platform-Sophia Agrobiotech Institut-INRA   1355-UNS-CNRS 7254-INRA PACA Sophia Antipolis  for the access to instruments and technical advice. Thanks are extended to expert reviewers for their fruitful suggestions, constructive recommendations, and comments, as well as for their extensive revision of the manuscript.
Availability of data and materials The datasets supporting the conclusions of this article are available in the NCBI repository (BioProject PRJNA354847, https://www. ncbi. nlm. nih. gov/ bioproject/354847). Data for each biological replicate (M1 R1 , M1 R2 , M1 R3 , M2 R1 , M2 R2 and M2 R3 ) can be obtained from the BioSample database (https://www. ncbi. nlm. nih. gov/biosample), accessions SAMN06052955-SAMN06052960.
in 2016  (17) , and that might be involved in its gliding motility phenotype. Further studies about T9SS and gene functional analyses will help clarify the gliding mechanisms of Chryseobacterium sp. JV274. Accession number(s). This draft genome has been deposited at the European Nucleotide Archive under submission ERA783391, BioProject ID PRJEB18783, sample ERS1496757. Scaffold sequences of the de novo assembly have also been deposited at DDBJ/EMBL/GenBank under the accession numbers FTPE01000001 to FTPE01000019.
ACKNOWLEDGMENTS J. V. was supported by  Ph. D. grants from the  Academic Research cluster 3 of the Rhône-Alpes Region. We are grateful to the iBio platform ( UMR CNRS 5557 Ecologie Microbienne ) for helpful discussions. We also acknowledge funding from the  ANR  project  SymbioMaize  ( ANR-12-JSV7-0014-01 ) and the  LABGeM and the National Infrastructure "France Genomique " for support within the MicroScope annotation platform.
Several other active compounds were also discovered, such as miglustat  [25] , glafenine  [26] , latonduine  [27]. The Vertex pharmaceutical company developed two compounds: Lumacaftor (VX-809) and Ivacaftor (VX-770). Lumacaftor is a CFTR corrector which improves F508del-CFTR maturation and F508del-CFTR-mediated currents  [28]. In human bronchial epithelial cells (HBE) from patients (F508del/F508del), it increases CFTR maturation and CFTR-mediated Isc by 4-fold. In a phase IIa trial (ClinicalTrials. gov number NCT00865904), lumacaftor reduced sweat Cl -levels in adult patients (F508del/F508del) but induced no significant changes in lung function or patient-reported outcomes were observed  [29]. This study suggested that a combination of lumacaftor with ivacaftor is necessary to obtain sufficient clinical effects in patients.
Ivacaftor is a CFTR potentiator which was found to potentiate G551D-CFTR-mediated currents by increasing CFTR's opening probability. In primary cultures (G551D/F508del) and in HBE from F508del/F508del patients, it increases CFTR-mediated transepithelial currents. In clinical studies (ClinicalTrials. gov number NCT00909532, NCT00909727 and NCT01521338), ivacaftor was shown to significantly improve the lung function, the body mass index (BMI) and the mucociliary clearance in G551D/G551D patients. In cultured HBE, the application of ivacaftor increased the lumacaftor-corrected F508del-CFTR-mediated transepithelial currents  [28]. This result was the rational of combining correctors and potentiators for CF therapy  [28]. In a phase 2 study (ClinicalTrials. gov number NCT01225211), it was shown that lumacaftor/ivacaftor combination improved lung function and reduced the sweat Cl -levels, in patients having a F508del mutation.
The potential differences and the transepithelial resistances were measured at baseline conditions. After stabilisation of baseline current (10-20 minutes), pharmacologic agents (amiloride, forskolin, genistein, CFTRinh172) were added sequentially to the apical and/or basolateral side. Forskolin (10 μM) was added to the apical and basolateral bath, whereas amiloride (100 μM), genistein (50 μM) and CFTRinh172 (5 μM) to the apical bath. The effect of buserelin (10 -12 M) was measured and data were collected and analysed with DataTrax software (WPI, UK).
The used 5 steps IEF protocol was Step 300 V, 200 V/h; Grad 1000 V, 300 V/h; Grad 5000 V, 4500 V/h; Step 5000V, 9000 V/h; Step 300V. Second-dimension separation was performed immediately after IEF. Immobiline DryStrip gels were equilibrated (15 min at room temperature) in two SDS equilibration buffers (6 M urea, 75 mM Tris-HCl pH = 8. 8, 30% Glycerol, 2% SDS, 0. 0002% bromophenol blue plus DTT or iodoacetamide) before SDS-PAGE. Electrophoresis was performed in 1X Tris-Glycine-SDS buffer (BioRad, USA) at 120 V for 2. 5 hours. The 2-DE gels were stained with a G-250 Coomassie blue solution, scanned and digitalized using a GS-800 Calibrated Densitometer (Bio-Rad, USA). Gel alignment, spots detection and quantification were done using the PDQuest software (PDQuest Basic-8. 0. 1, BioRad, USA). Data are the means of 4 gels from 4 different experiments.
MS analysis of the differentially expressed proteins was performed at the « Plateforme d'Analyse Proteomique de Paris Sud Ouest, France » facility, using LC-MS/MS. Enzymatic digestion was realised by 50ng Trypsin (enzyme/protein ratio: 1/50, 37˚C, overnight). Supernatant was used for extraction. LC-MS/MS analysis was done using a LTQ-Orbitrap Spectrometer. Nano HPLC (Dionex RSLC UltiMate3000) was used, followed by MS (Scan range: 400-1500 m/z, fragmentation: CID, Energy of collision: 35%, Cycle for fragmentation (TopN): Top8). Data were converted to mzXML using MS convert (ProteoWizard v 3. 0. 8934). Databases interrogation was performed by X!Tandem Piledriver (v2015. 04. 01. 1) and X!Tandem Pipeline (v 3. 4. 2 ( Elastine Durcie )).
Although the moss concentration data provide no direct quantitative measurement of total deposition, this information can be derived by statistical approaches relating element concentrations in mosses to measured element concentrations in atmospheric deposition  (Harmens et al. 2010 (Harmens et al. , 2011 (Harmens et al. , 2015)). This way, the spatial resolution of atmospheric HM and N deposition maps can be enhanced  (Schröder et al. 2011 (Schröder et al. a, 2011 (Schröder et al. b, 2012 (Schröder et al. , 2014)). Since 1990, every five years the European Moss Surveys (EMS) have been providing data on concentrations of HM, and since 2005 concentrations of N in moss  (Harmens et al. 2015). Sampling, The EMS 2010 provided data on concentrations of aluminium (Al), arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), lead (Pb), mercury (Hg), nickel (Ni), antimony (Sb), vanadium (V), zinc (Zn) and nitrogen (N) in moss, collected at up to 4499 sample sites across 24 European countries.
(2012)  and  Fawagreh et al. (2014)  compiled and reviewed RF implementations and their features. Internal validation is calculated in terms of the out-of-bag (OOB) error: Each observation is an OOB observation for some of the trees, i. e. , it was not used to construct them. The OOB error is the average error frequency obtained when the observations from the data set are predicted using the trees for which they are OOB. Thus, Random Forests are ensembles of multiple decision trees combined into a single model. Compared with single decision trees, like CART, RF tends to be more robust to outliers and overfitting  (Williams 2011; Ziegler and König 2014). Verikas et al. (2011)  surveyed respective literature and presented comparatively several tests. CART models are prone to overfitting data, which can lead to predictive errors.
Workflow All statistical analysis and modelling were implemented in R (R Core Team 2013, Williams 2011). Analyses with HM and N concentrations in moss as response variable were based on a reasonably large sample size of at least 2154 and at maximum 3664 out of 4499 sampling points. Observations were partitioned into training datasets (90%), which were used to build the RF models, and independent test datasets (10 %) for measuring the quality of the RF models. Some observations from the training datasets had to be excluded from the analyses due to missing information on predictor variables. Based on the null hypothesis principle, the Shapiro-Wilk-test  (Shapiro and Wilk 1965)  was used to assess whether concentrations of HM and N in moss as response variables were normally or lognormally distributed.
In all cases, target variables were log-transformed due to a non-normal distribution. For deciding the number of trees to build, plots of error rates progressively calculated against the number of trees were used. Observations with missing values were removed from the dataset. The number of variables to consider at each split was defined as one-third of the number of predictors  (Williams 2011, p. 263). Models were then optimized using measures for relative variable importance as Increased Node Purity and model accuracy as Pseudo R Squared. Increased Node Purity represents the total increase of decision treenode's purity when splitting the dataset. It is measured for a specific variable as the mean increase of the Gini index over all trees according to Equation 1  (Louppe et al. 2013).
To minimize limitations of the use of Pseudo R 2 (Equation  2 ) for comparing different models measure was preferably compared for the same outcome variables (specifically for elements) and number of observations in the test datasets were set to a minimum of N = 30. Following  Liu et al. (2014) , the goodness of fit of the predictions modelled to the test data were evaluated by use of mean and standard deviation of Pseudo R 2 based on multiple (in this investigation 10) runs. Since a Pseudo R² does not rely on linear relationships between predictors and the response, it could not be tested for significance similar to R², for which the p-value is usually calculated by means of F-statistics testing whether the null hypothesis R²=0 (Wood 1990). Finally, optimized RF models were applied on available spatial information yielding regression maps as results.
All geographic information on the predictor variables such as HM and N deposition, emission, climate, altitude, population and land use features available with blanket coverage of participating countries or regions, respectively, were combined by means of classical GIS functions (overlay, spatial join) as implemented in ESRI's ArcGIS 10. 2. Based on this, RF predictive models were applied to calculate a corresponding number of predictive maps, which result from reasonable combinations of HM and N concentration in moss as dependent variable with relevant predictor variables (Table  2 ) covering Europe at a spatial resolution of 10 km by 10 km for the year 2010. For some regions, element M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 11 concentrations in moss could not be calculated due to missing data on predictor variables (e.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 16 One of the strengths of a single CART is that it is simple to interpret: The relevant predictors are included in the tree and the earlier a variable appears in a tree, the more important it is  (Loh 2011). With RF, this simplicity is lost because many trees (here: 200 -300 trees) have to be considered simultaneously (Ziegler and König 2014). Even if Random Forests are not so easy to understand compared to CART because individual trees cannot be examined separately, it provides several metrics supporting the interpretation of results  (Williams 2011 ). Variable importance is evaluated based on how much worse the prediction would be if the data for that predictor were permuted randomly. The resulting tables can be used to compare relative importance among predictors.
It could be assumed, that land use patterns observed within these ranges should be more relevant compared to other ranges, when taking the land use as a split criterion for generating homogenous subsets in a RF model. In the current study the density of different land use were examined in a range of between 1 and 100 km. The high importance of the 100 km radius (Figure  1, Table 2 ) may indicate that the spatial trends on such distances obscure those on smaller ones. Moss data from the EMS 2005 revealed ranges of 59. 3 km for Cd, 255. 0 km for Pb, and 209. 0 km for N  (Schröder et al. 2012). In Table  2  the slightly raised value of 90. 66 for the relative importance for Cd in a Since RF was used for the first time for mapping geographical patterns of pollutants in terrestrial ecosystems, the results of the regionalizing at hand cannot be compared with results of other RF studies  Meyer et al.
(2015 a)  found, based on data obtained across Norway, comparable predictive relevance for a similar set of regional factors for Cd, Hg, and Pb concentrations in moss by using CART. In addition, Meyer (2015 b) investigated by application of RF the relevance of site and regional factors for HM and N concentrations in moss sampled across Germany in 2005, 2012, and 2013. The results support the findings of the current study. Highest R 2 values of RF models were observed for central and northern European countries. It should further be examined whether this phenomenon is due to low element concentrations, small variability of explanatory variables or due to sampling density  (Harmens et al. 2010 , Ilyin et al. 2011). Country-specific correlations between modelled EMEP deposition and Cd, Hg and Pb concentrations in moss for previous EMS were reported by  Harmens et al.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 19 Regarding the predictor identification and ranking, this study indicates that the radius for examining the the influence of different spatial land use density around the sampling sites could be even more enlarged to find possible maxima (e. g. 150, 200 or 250 km). Precipitation at a higher spatial resolution and a time period corresponding to the 3-year sum of deposition (here: 2008-2010) should be included. Additional, population density could be examined in extended buffers around the sampling sites. The temporal heterogeneity of the deposition data (here: time lag of one year) could affect the significances of the importance metrics for the atmospheric deposition and, respectively, the model accuracy, measured by the Pseudo R². The extent, to which this helds true, should also be investigated in a further study.
Characteristics of optimized RF models calculated for Europe Predictor: e. g. 149. 97 = Relative predictor importance (dimensionless number), the highest relative predictor importance, measured as Increased Node Purity, is depicted in bold; MSE = Mean of squared residuals; Var. explained [%]: Percent variance explained based on training dataset; Pseudo R 2 based on test dataset; For example: 0. 55 = Arithmetic mean of Pseudo R 2 , (0. 0x) = Standard deviation of Pseudo R 2 ; the workflow for model optimization is described in section 2. 3.
(Hornsmann et al. 2008)-specific RF models ELCE = Ecological Landscape Classes of Europe(Hornsmann et al. 2008); n = number of observations in training dataset; ELCE with less than 30 observations in training or test dataset (L2, M5, D_10, U_1, D_8) were excluded; Pseudo R 2 > 0. 4 are depicted in bold; For example: 0. 18 = Arithmetic mean of Pseudo R 2 (0. 09) = Standard deviation of Pseudo R 2 ; Mean = Pseudo R 2 averaged over all rows / columns (median is displayed in box brackets); ---= No Data.
Pseudo R 2 of moss-specific RF models Explanation: n = number of observations in training dataset; moss species with less than 30 observations in training or test dataset were excluded; Pseudo R 2 >= 0. 4 are depicted in bold; For example: 0. 07 = Arithmetic mean of Pseudo R 2 ; (0. 06) = Standard deviation of Pseudo R 2 ; Mean = Pseudo R 2 averaged over all rows / columns (median is displayed in box brackets); ---= No Data.