dataesr/scientific-paragraphs-categorization huggingface dataset:
PFGE, considered as the 'gold standard' among Abbreviations: DI, discriminatory index; MLVA, multiple-locus variable number tandem repeat analysis; PFGE, pulsed-field gel electrophoresis; RU, repeated unit; VNTR, variable number tandem-repeat. molecular typing methods, is routinely used for monitoring and surveillance, as well as investigation of outbreaks. Nevertheless, the discriminatory power for S. Dublin is low and does not enable investigation and tracking of the source of contamination during foodborne outbreaks (FBOs;  Liebana et al. , 2002). A study conducted by the Salmonella Network on a large panel of Dublin strains highlighted the genetic homogeneity of this serovar  (Kerouanton et al. , 1996). Moreover, the ANSES Salmonella Network's PFGE database shows that 84% of the S. Dublin strains collected since 2003 were assigned to the same profile (SDUBXB0003 for 153/183 strains).
, 2004; Hopkins et al. , 2011). Moreover, this method proved very useful in investigating FBOs and facilitated the analysis since it requires no specific technical expertise  (Wattiau et al. , 2011 review). In this study, we developed a new MLVA protocol for high discriminatory typing of S. Dublin in accordance with the guidelines published by  Nadon et al. (2013)  on the development and application of MLVA methods as tools for inter-laboratory surveillance. A reference set of strains and a scheme for harmonization of results are also proposed to allow data exchange between laboratories. In order to assess the discriminatory power of the MLVA protocol developed here and the stability of tandem repeats (TRs), we used a set of 401 strains isolated between 1929 and 2015 from three different collections (Salmonella Network, National Reference Centre for Salmonella and Centre of Expertise for the Food Industry).
Pulsed-Field Gel Electrophoresis Within the panel of 401 strains used for the development of the MLVA scheme, 51 Salmonella Dublin strains, isolated from 2002 to 2011, were PFGE subtyped according to a standardized protocol (PulseNet, 2013, EU) with some modifications in the composition of the cell lysis buffer and the concentration of the enzyme XbaI (Sigma-Aldrich, France). The cell lysis buffer was 1M Tris: 250 mM EDTA, pH 8. 0 + 10% sarkosyl, and the restriction enzyme was five times less concentrated. This analysis of PFGE patterns was performed using BioNumerics R software v. 6. 6 (Applied Maths, Belgium) and comparison of patterns was carried out by building a dendrogram (Dice coefficients, the UPGMA method and position tolerance set at 1%). Each strain profile was assigned to a PFGE pattern corresponding to a unique pattern.
Variable Number Tandem Repeat (VNTR) Selection Twenty-one VNTR markers published from 2003 to 2009  (Kruy et al. , 2011, review)  to discriminate S. enterica subspecies were selected (see Supplementary Table  S2 ) and blasted on the genomic sequence of S. Dublin strains CT_02021853 (accession No. NC_011205. 1/CP001144. 1) obtained from http://www. ncbi. nlm. nih. gov/genome/. The presence of TRs was verified using free access TRs Finder software by  Benson (1999). The in silico analysis performed on the genome of S. Dublin strains CT_02021853 revealed that eight loci, on the 21 searched, presented microsatellite sequences. Among these eight loci, six were tested by PCR on 51 S. Dublin genomes from the Salmonella Network collection to check for the presence of microsatellites and variability of RUs.
Capillary Electrophoresis and Data Analysis The analyses were carried out on an AB3500 capillary electrophoresis system (Applied Biosystems, France) spectrally calibrated to run filter set G5. The instrument was prepared according to the procedures specified by Applied Biosystems. The standard fragment analysis protocol proposed by the manufacturer was used and positive and negative control isolates were included with each run to follow the drift in results due to the use of the instrument over long time periods. Data were automatically saved as. fsa files and imported into GeneMapper software (Applied Biosystems, France), where each fragment was identified according to color and size. The measured lengths attributed to each peak by GeneMapper were transferred to a. txt file to be normalized with the free access MLVA_Normalizer software 1 , following the instructions of the author  (Bachelerie et al.
, 2016). Then, the MLVA profiles were imported into BioNumerics software version 7. 1 (Applied Maths, Belgium) as categorical data. A standard minimum-spanning tree (MST) was generated using the single and double locus variance priority rules, allowed to define the clonality and distance between strains. The discriminatory power of the single VNTR and of the MLVA method compared to that of PFGE was calculated by 1 https://github. com/afelten-Anses/MLVA_normalizer Simpson's index of diversity (DI) according to the formula as described by  Hunter and Gaston (1988).
Reference Strains for MLVA of Salmonella enterica Serovar Dublin Twenty-four reference strains were selected from the 401 examined strains in order to represent most of the allele diversity observed for each locus. The amplicons from each locus were sequenced (Table  2 ). Data on the 24 reference strains are shown in Supplementary Table  S3. This table, once compiled with the raw data, can be used as input file for using the MLVA workflow for normalizing MLVA results cited above  (Bachelerie et al. , 2016). Sequencing confirmed the number of TRs and alignments revealed that the six loci (STTR5, STTR7, STTR3, SENTR3, SENTR1, and SE-2) exhibit no variation in the sequence of the TR unit within a strain and between strains. Single nucleotide polymorphisms (SNPs) were identified in the TRs of the STTR7, STTR3, SENTR3, and SENTR1 loci for 12CEB3654SAL, 09CEB6631SAL and 03EB3784SAL reference strains compared with the genome of S.
This set of reference strains is available from the collection at the ANSES Salmonella Network and National Reference Centre of Institut Pasteur. For some serovars, such as Typhimurium  (Larson et al. , 2009)  and Enteritidis  (Hopkins et al. , 2011) , this reference strain set has already been published with the name of the reference strains, the correct MLVA profile, and the true length of each VNTR locus analyzed. The MLVA_normalizer workflow  (Bachelerie et al. , 2016)  enables correction of the raw data obtained with the Dublin MLVA protocol proposed here. The conversion table described herein to ensure compatibility of S. Dublin MLVA data between laboratories is also available in Supplementary Table  S3. The 6-loci MLVA exhibited high discriminatory power for the 401 strains analyzed.
AVAILABILITY OF DATA AND MATERIAL The reference strains for Dublin MLVA typing are from the Salmonella Network Collection of ANSES and from the National Reference Centre (Institut Pasteur). They are available by writing to: Réseau Salmonella, Laboratoire de Sécurité des Aliments, ANSES; 14 rue Pierre et Marie Curie; 94701 Maisons-Alfort or by emailing: sabrina. cadelsix@anses. fr.
Recent advances in the representation of soil carbon decomposition and carbon-nitrogen interactions implemented previously into separate versions of the land surface scheme JSBACH are here combined in a single version, which is set to be used in the upcoming 6th phase of coupled model intercomparison project (CMIP6). Here we demonstrate that the new version of JSBACH is able to reproduce the spatial variability in the reactive nitrogen-loss pathways as derived from a compilation of δ 15 N data (R = 0. 76, root mean square error (RMSE) = 0. 2, Taylor score = 0. 83). The inclusion of carbon-nitrogen interactions leads to a moderate reduction (-10 %) of the carbonconcentration feedback (β L ) and has a negligible effect on the sensitivity of the land carbon cycle to warming (γ L ) compared to the same version of the model without carbonnitrogen interactions in idealized simulations (1 % increase in atmospheric carbon dioxide per year).
Simulation setup We force the land surface model JSBACH with half hourly climatic data simulated by the MPI-ESM instead of running JSBACH coupled with the atmospheric and ocean components of the MPI-ESM. Therefore, our simulations, in contrast to simulations of the MPI-ESM, do not account for the feedback between land and atmosphere with respect to the water and energy cycle. However, the resulting inconsistencies between climate and land surface should not change the results of the present study and are anyway partly implicit to the underlying CMIP5 simulations because of the prescribed atmospheric CO 2 levels in case of biogeochemical feedbacks  (Taylor et al. , 2012). For the sake of simplicity, we will refer to the JSBACH simulations driven by the climate from respective ESM simulations, with the respective label of the ESM simulations.
Mean annual runoff (q, in mm) was estimated from precipitation and potential evapotranspiration following  (Zhang et al. , 2004) , with ω = 3. Following Xu-Ri et al. (  2008 ), we assumed that leaching losses increase to a maximum dependent on soil water capacity, yielding an annual runoff factor f (q): f (q) = q q + W max , (18) where W max = 150 mm. Mean monthly soil temperatures (T m , in K) were estimated for 0. 25 m depth following Campbell and Norman (1998). We assigned a generic activation energy of E a = 55 kJ mol -1 K -1  (Canion et al. , 2014)  and summed the monthly index values f m (T m ) = exp E a R gas ( 1 T ref - 1 T m ) (19) over the 12 months f (T ) = 12 m=1 = f m (T m ) (20) yielding the annual soil temperature factor f (T ), where T ref = 293 K. The data were then fitted via the gaseous discrimination factor and a constant k by nonlinear least-squares regression to the relationship δ = δ 0 + 1 + k( f (q) f (T ) ) -1 , ( 21 ) where δ is soil δ 15 N, δ 0 is the δ 15 N of the N inputs.
, 2015 (Naipal et al. , , 2016)). Due to the concept of CO 2 -induced nutrient limitation in JSBACH, the nitrogen cycle serves primarily as an additional constraint on the carbon uptake. The advantage of the approach is its low complexity and avoidance of assumptions about the initial state of nutrient limitation thereby taking into account (1) the lack of data regarding the nitrogen cycle  (Zaehle, 2013)  as well as (2) the large uncertainty about the nutrient constraint on plant productivity  (Letters et al. , 2007; Zaehle, 2013). The shortcomings of this approach are that it limits the applicability of the model to carbon cycle projections for scenarios of increasing atmospheric CO 2 and that it cannot capture any stimulation of the plant productivity due to changes in nitrogen availability itself; in addition to direct increase in nitrogen availability by nitrogen deposition and fertilization, a stimulation of plant productivity can occur due to reduced losses of nitrogen by pathways that are not under control of biota, such as fire, leaching, or erosion  (Thomas et al.
mpg. de. Acknowledgements. The authors sincerely thank the editor  Dr. Sato  and the two anonymous referees for their comments which substantially improved the quality of this work. This work was initiated when DSG was funded through the  DFG Cluster of Excellence CLiSAP  ( EXC 177/2 ), now he is funded by the " IMBALANCE-P " project of the  European Research Council  ( ERC-2013-SyG-610028 ). This work contributes to the  H2020  project  CRESCENDO , which receives funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 641816. DN was supported by an  international Macquarie University Research Scholarship  and by the  Ecosystem Modeling and Scaling Infrastructure  (eMAST, http://www. emast. org. au)facility of the  Terrestrial Ecosystem Research Network.
morganii strain LIM90. To identify the SGI1-related island, its chromosomal location, and its resistance gene content, the whole genome of LIM90 was sequenced using an IonProton system (99-fold average read depth). The reads were assembled using MIRA software. The ResFinder and PlasmidFinder tools available at the Center for Genomic Epidemiology were used for identification of acquired resistance genes and plasmid detection, respectively  (27). The complete sequence of SGI1 was assembled using the relevant contigs detected by BLAST searches, PCR gap closure, and PCR product sequencing. The sequence of SGI1 was annotated using the Microbial Genome Annotation and Analysis Platform MicroScope (Genoscope, France) and deposited in the European Nucleotide Archive (ENA) under accession number LT630458  (28).
M. morganii strain LIM90 harbored the SGI1-L variant shown in Fig. 1. SGI1-L-related islands have been previously described in S. enterica serovars and P. mirabilis strains but were never fully sequenced  (10, 14). This SGI1-L variant carried the dfrA15 and bla CARB-2 (previously named bla PSE-1 ) resistance gene cassettes inserted at the two SGI1 integron attI recombination sites, conferring resistance to trimethoprim and ticarcillin, respec-tively (Fig. 1 ). The floR gene, which confers resistance to chloramphenicol and florfenicol, and tetracycline resistance genes tetR(G) and tetA(G) were found to be flanked by these two integron structures (Fig. 1 ). The presence of ISVch4 (also called IS1359) at position 6227 to position 7484 of the SGI1 backbone sequence (ENA accession number LT630458) was found as previously described in a few SGI1 variants (SGI1-H, SGI1-K, and SGI1-L derivatives) in S.
One SNP was identified compared with ISVch4-negative SGI1 of epidemic S. Typhimurium DT104 strains, resulting in an amino acid (AA) change in toxin SgiT (ORF S025) of the sgiAT toxin-antitoxin addiction system (Table  1 )  (28, 29). The putative role of this AA change in the SgiAT toxin-antitoxin system and in the stability of SGI1 remains to be determined. Concomitantly with the first SNP position described above, a second one was found in antitoxin SgiA (ORF S026, no AA change) of ISVch4-negative variants described in other S. enterica serovars and P. mirabilis strains (Table  1 )  (29, 30). Interestingly, the original SGI1 sequence of S. Typhimurium (GenBank accession number AF261825), the recently released SGI1-F sequence of S. Cerro (GenBank accession no. KU847976), and the SGI2 sequence of S.
morganii LIM90 did not carry a conjugative IncA/C plasmid known to specifically mobilize SGI1  (2, 3). This observation is in accordance with the recently described incompatibility between SGI1 and members of the IncA/C plasmid family  (30). The identification of SGI1 in a M. morganii clinical isolate is of great interest, as the spread of this multidrug-resistant genomic island, especially in a naturally ␤-lactamresistant species such as M. morganii, is a nonnegligible threat to public health. The important role of the horizontal transfer of SGI1 is crucial in the dissemination of multidrug resistance and may increase through pathogenic or nonpathogenic bacterial genera as well. Accession number(s). The sequence of SGI1 was deposited in the European Nucleotide Archive (ENA) under accession number LT630458.
Data Analysis Principal component analysis (PCA)  [17, 18]  was performed on all the results. The aim of PCA is to reduce the dimensionality of a dataset by building a new space where each axis (principal component or PC) is a linear combination of all the initial variables. PCA allows us to structure and summarize variables and individuals to better understand the information provided by the variables. PC are calculated to be included in the variance of in the dataset  [17]. Ideally, a limited number of PCs allows a reliable representation of the initial data. All PCA were performed on SAS software: Jump 10.
Copper Copper concentrations in potable water are usually very low. The copper concentrations in water samples were in the range of 0. 07-0. 17 mg/L (Figure  2 ). Copper may occur in natural waters, wastewaters, and industrial waste streams as soluble copper salts or as copper compounds precipitated on suspended solids. Copper is not considered as a health hazard to humans. More than 1 mg/L can impart a bitter taste to water and large oral doses can cause vomiting and may eventually cause liver damage  [18]. component or PC) is a linear combination of all the initial variables. PCA allows us to structure and summarize variables and individuals to better understand the information provided by the variables. PC are calculated to be included in the variance of in the dataset  [17]. Ideally, a limited number of PCs allows a reliable representation of the initial data.
In this part of the study, the data are expressed in the flux of materials that integrates flows. The study carried out shows that the three components can explain nearly 64% of the observed variations. With four components, this figure reached 74%. As a first step, individuals are labelled according to river flows (Figure  9 ). In this case, we observe a partition into three distinct groups that are differentiated by flow. High flow rates are characterized by lower pH, lower conductivity, and higher metal fluxes. An interesting way to differentiate the type of of the river is to take flows into account. The information obtained needs to be supplemented with other samples. In the future, an automatic flow measurement system could be coupled by taking samples analysed. These studies will be carried out with a complete set of data: no points would be deleted to perform the data analysis.
As a first step, individuals are labelled according to river flows (Figure  9 ). In this case, we observe a partition into three distinct groups that are differentiated by flow. High flow rates are characterized by lower pH, lower conductivity, and higher metal fluxes. An interesting way to differentiate the type of metal pollution of the river is to take flows into account. The information obtained needs to be supplemented with other samples. In the future, an automatic flow measurement system could be coupled by taking samples analysed. These studies will be carried out with a complete set of data: no points would be deleted to perform the data analysis. The results suggest that the source of pollution is identical when the variables are highly correlated (Cu and Ni).
Library high throughput insertion tracking by deep sequencing: bio-informatics analysis A dedicated R script has been created to obtain mapping information of the reads on Lp NC8 genome. The script takes in input the alignment sam file of the reads on the reference genome and compares the positions of the reads with an annotation table of the Lp NC8 genome obtained with Geneious from the Lp NC8 PubMed genbank file. It returns a table saying for each read if it maps a gene partially or totally or if it maps an intergenic region.
BLAST Ring Image Generator (BRIG) Visual analysis of the distribution of the transposon insertions was done by using BRIG  (Alikhan et al. , 2011 ; http://sourceforge. net/projects/brig/). Lp NC8 was used as the reference genome. BRIG uses CGView  (Stothard and Wishart, 2005)  to render maps and is operated using a graphical user interface. The BRIG method uses the software BLASTALL v 2. 2. 25+ for the searches. The analysis was done with default settings.
Larval size measurements Axenic adults were put overnight in breeding cages to lay eggs on sterile poor yeast diet. Fresh axenic embryos were collected the next morning and seeded by pools of 40 on 55 mm petri dishes containing fly food. 1x10 8 CFUs (unless otherwise specified) or PBS were then inoculated homogenously on the substrate and the eggs. Petri dishes are incubated at 25°C until larvae collection. Drosophila larvae, 7 days after inoculation, were collected and processed as described by  Erkosar et al. (2015). Individual larval longitudinal length of individual larvae was quantified using ImageJ software  (Schneider et al. , 2012).
Statistical analysis Data representation and analysis was performed using Graphpad PRISM 6 software (www. graphpad. com). Mann Whitney's test was applied to perform pairwise statistical analyses between conditions. Student's T test with Welch correction was performed to determine the significance of differences in gene expression levels between Lp NC8 and ∆dlt op inoculated samples. Kruskal Wallis test was applied to perform statistical analyses between multiple (n>2) conditions. at least partly, peptidase expression in enterocytes. This leads to increased intestinal proteolytic activity, enhanced dietary protein digestion and improved amino-acid uptake in the host, conducting to increased TOR signalling activities in endocrine tissues and sustained production of systemic growth factors despite chronic undernutrition  (Erkosar et al.
Acknowledgments: The authors would like to thank  Maura Strigini ,  Gilles Storelli  and  Dali Ma  for critical reading and editing of the manuscript,  Christophe Grangeasse  for helpful discussions and help with bacterial cells imaging, the  Arthro-Tools  and PLATIM platforms of the  SFR Biosciences  ( UMS3444/US8 ) for providing Drosophila and imaging facilities, the  IGFL  sequencing platform for deep sequencing,  Pascale Serror  for pG+host9 and  Hélène Licandro-Seraut  for P junc -TpaseIS 1223 system. RCM thanks the  "Fondation pour la Recherche Médicale " for financial support through a postdoctoral scholarship SPF20140129318. This work was funded by an  ERC  starting grant ( FP7/2007(FP7/ -2013-N°309704)-N°309704 ). FL lab is supported by the  FINOVI foundation , the " Fondation Schlumberger pour l'Education et la Recherche " and the  EMBO Young Investigator Program.
Acknowledgements This work was supported by the  French National Research Agency (ANR)  through project  ARSENIC  (grant no. 14-CE02-0012 ). AW was additionally supported by the  École Normale Supérieure de Lyon. We thank  Guillaume Chero ,  Alexandre Terrigeol  and  Elise Kerdoncuff , who performed simulations and robustness checks using a preliminary model version during their master internships. We also thank  Youssef Yacine  for helpful discussions and feedback on the manuscript.
Data analysis Data analysis was conducted for each plot (perennial pasture and sugarcane). Data were grouped for each volume of soil described (Figure  1a ), the standard error was calculated, and after verifying data normality, the F-test (analysis of variance) and the Tukey test (comparison of means) were run at 5 % probability on the results for unsaturated hydraulic conductivity, soil bulk density, and porosity of the structures identified by the profil cultural method. The BD and porosity (TP, Ma, and Mi) of each structure described in the soil profiles of the two management systems examined are shown in table 2. Examining each structure separately (CΔ, Fma/gtΔμ, and Cμ in the pasture profile and Lma/gtΔμ, CΔ, and Cμ for the soil profiles under sugarcane), differences in BD and porosity (TP, Ma, and Mi) values were observed at 5 % probability in two profiles.
2015). To gain a better understanding of the lineage and differentiation during sake yeast breeding, we explored single-cell phenomics in sake yeast strains bred by the BSJ and the NRIB. Since the lineage for these strains has been recorded  (Akao 2014 (Akao , 2015)) , we evaluated every step of breeding using lineage relationships. We characterized intraspecific cellular morphology based on microscopic images of cellular, actin, and nuclear DNA morphology  (Ohya et al. 2005 ). After we quantified 501 morphological measurement parameters in 27 sake yeast strains, we obtained. 1,500,000 morphological measurements using the CalMorph image processing software. To normalize the morphological values of each trait, we applied a generalized linear model (GLM). Principal component analysis (PCA) was used to reduce the dimension and to generate orthogonal phenotypic space.