dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analyses Statistical analyses were performed with SAS  [20]. Egg hatching and nymph survival rates were compared with a chi-square goodness-of-fit test among the various RH levels. The effects of the RH on the development of the immatures, adult longevity and fecundity of each Adelphocoris species were analyzed with oneway analysis of variance (ANOVA), and the means were separated by Tukey's honest significant difference (HSD) test (P50. 05). Before ANOVA, the adult fecundity data were log 10 (n+1) transformed (all data sets were tested for normality prior to ANOVA). Nymphs that died prior to becoming adults and adults that died without producing any eggs were excluded from analysis of nymph development and adult fecundity, respectively. The following life table parameters were calculated for each species for all RH levels: the net reproductive rate, (R 0 )5gl x m x ; the intrinsic rate of increase (r m ), which was calculated by iteratively solving the equation gl x m x e -rmx 51; the mean generation time, (T)5lnR o /r m ; and the finite rate of increase, (l)5e rm.
, 1992; Henson et al. , 2003). Climatic data show two seasons: a dry season from November to April (the driest months being December and January) and a wet season which occurs from May to October providing an average annual rainfall of 2062 mm (Fig. 1 ). Rainfall is well distributed and a moderate water deficit occurs. Under these conditions and unless an exceptionally dry period occurs (which did not happen to date) it is not very likely to monitor a response of the oil palm to water stress which depends on the planting density  (Coomans, 1974 , Bonneau 1991; Palat et al. , 2012). The planting density experiment was carried out in a second generation plantation established in July 2005, on a previous oil palm cover which occupied the plot for 25 years. The stems of the felled old palms were pushed to the edge of the experimental plot.
Fertilization regime Palms under study were given a uniform fertilization regime per palm for the first four years then uniform fertilization per unit area from the fifth year onwards (Tab. 2). The aim was to adjust the fertilizer's input so that mineral nutrition never acted as a limiting factor. Annual leaf analyses were used in order to continuously monitor the mineral nutrition status of palms and to adjust the fertilization regime accordingly. Table  3  describes changes in nutrition data for the whole experiment through the analysis of leaf mineral contents for the main nutrients.
A charcoal hearth that is currently in use  Data were determined using LiDAR measurements  [37]. doi:10. 1371/journal. pone. 0091114. t004 was identified at short distance from the Val di Pejo, where expert charcoal makers repeat traditional charcoal production mainly for didactical purposes. They also recorded, year by year, the exact amount of wood used and of charcoal produced. The amount of pyrogenic C left on hearth soil was quantified using the loss-on-ignition (LOI) method  [39]  using four replicates randomly selected within the hearth area. w was determined as the ratio between the sum of C contained in the charcoal that was made over the last ten years and the corresponding amount of C that was found in the soil. The uncertainty was estimated as the standard error of the mean; -C content of freshly produced larch wood charcoal (r 0 ) was also assessed experimentally.
Known amounts of larch wood taken from wood disc collected in the proximity of Val di Peio charcoal hearths were pyrolized at different temperatures in a muffle furnace. The C concentration was measured on each charcoal sample using a CHN elemental analyzer. Measured C content data were fitted to production temperature using a second order polynomial relationship and C content at a reference temperature of 450uC was assumed to be an analog of the charcoal originally produced  [40].
Acknowledgments The authors would like to thank  Federica Camin  and  Luca Ziller ,  Matteo Girardi ,  Michele Dalponte  and  Damiano Gianelle  ( E. Mach Foundation ,  San Michele a/Adige )  Luisa Andrenelli ,  Adriano Baglio ,  Fabio Zuliani  ( Stelvio National Park ) for their support in field and laboratory work, as well as  Christa Backmeroff ,  Fabio Angeli ,  Dino Buffoni ,  Jean Christophe Lata ,  Marie Alexis  and  Katell Quenea  for useful discussions.
This study was financially supported by the  European Commission  through the  EuroChar  project ( FP7-ENV-478 2010ID-265179 ) and AgroPyroGas funded by  Regione Toscana, Italy. I. C. was supported by a Ph. D. grant from  Fondazione Edmund Mach, Trento. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS². In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.
UPLC-Q-ToF-MS DATA PRE-PROCESSING Calibration, alignment and peak picking of individual LC-MS runs were performed using the Genedata Expressionist for MS 8. 0 software (Genedata AG, Basel, Switzerland). Internal recalibration was based on 1:4 diluted low concentration tune mix (Agilent, Waldbronn, Germany), which was injected prior to each run using a 6-port valve mounted to the MS. Individual steps of data pre-processing are described in the Results and Discussion Section. Briefly, the complete processing consisted of three stages: Stage 1 performed chemical noise subtraction; Stage 2 performed recalibration and alignment and Stage 3 achieved peak picking and export.
STATISTICAL ANALYSIS Filtering of masses was performed in MS Excel 2010 (Microsoft, Redmond, USA). All further statistical analyses were performed with Genedata Expressionist for MS 8. 0 (Genedata, Basel, Switzerland). Principal Component Analysis (PCA) is an unsupervised method with the capacity to reduce the complexity of a huge dataset. Its goal is to extrapolate important information and display it as a set of new independent variables called principal components. This method discloses the similarity pattern of the observations or variables.
MS/MS SPECTRA PROCESSING Marker peaks from statistical analyses were subjected to tandem MS. Target lists for fragmentation experiments were converted to MS/MS acquisition methods using MetShot  (Neumann et al. , 2013). After acquisition, MS/MS spectra were manually extracted using Bruker Data Analysis 4. 1 (Bruker Daltonic, Bremen, Germany) and files were exported to. mgf format and converted to MetFusion batch files using a custom Perl script.
PRE-PROCESSING OF FTICR-MS DATA Non-targeted FTICR-MS analysis generates a tremendous amount of data and requires pre-treatment prior to the application of statistical tools. Raw data were first aligned in order to discover occurring patterns, to identify outliers, to reduce the dimensionality of the data, and also to compress large datasets into smaller and more discernable ones  (Lucio, 2009). A common approach for the identification of unknown molecules is the calculation of possible elemental formulas. Molecular formulae were calculated using an in-house software tool with the following chemical constraints: N rule; O/C ratio ≤ 1; H/C ratio ≤ 2n + 2; element counts: C ≤ 100, H ≤ 200, O ≤ 80, N ≤ 3, S ≤ 3, and P ≤ 1  (Gougeon et al. , 2009; Schmitt-Kopplin et al. , 2010; Roullier-Gall et al.
AUTOMATED DATA PRE-PROCESSING OF UPLC-Q-ToF-MS DATA A total of 286 chromatograms were obtained, including blank injections and QC samples. To analyze this amount of data, automated pre-processing is needed. Several open-source software packages exist for this task. We employed Genedata Expressionist for MS 8. 0, which allows workflow-based data processing in a user-friendly graphical user interface. Our processing strategy consisted in three stages. Stage 1 mainly aims at data reduction by use of chemical noise subtraction. This is performed in a so-called repetition activity, which handles only one chromatogram at a time, enabling faster processing, because memory of the employed computer system is optimally used and no files have to be paged to cache plates. Additionally, this prevents an overhead of the memory of very big studies with >250 chromatograms.
METABOLITE ANNOTATION USING DIFFERENT METABOLOMICS DATABASES A first overview of potential structures corresponding to detected metabolites was obtained by metabolite annotation using both the MassTRIX interface and a home-build (including grape and wine) metabolite database  (Suhre and Schmitt-Kopplin, 2008; Wägele et al. , 2012; Roullier-Gall et al. , 2014a,b)  (Figure  2 ). A search against KEGG, HMDB and LipidMaps with maximum error of 3 ppm was performed. MassTRIX and KEGG enable the visualization of compounds annotation on pathways of a chosen organism (Vitis vinifera in this example) (Figures  2A, B ). In total, 3351 detected masses from FTICR-MS could be annotated using MassTRIX, whereas 2613 detected masses could be annotated using our home-build metabolite database (Figure  2C ).
UPLC-Q-ToF-MS AND FTICR-MS ALIGNMENT Classical approaches where FT-MS would have been coupled to LC, would have required a reduction of the resolution in order to increase the scan rate. Here, by doing the UPLC-UHR-ToF-MS and FTICR-MS combination in silico, we get the best of both worlds, i. e. , a high resolution in LC and ultra-high resolution provided by FTICR-MS. In order to compare both datasets and confirm exact masses for the UPLC-Q-ToF-MS data, we performed the alignment of both datasets using a custom Perl script. In order to find optimal alignment conditions we used maximum error thresholds from 1 to 15 ppm in 1 ppm steps and compared the number of unique, double, triple, or more hits. The procedure of alignment gives us the remarkable advantage of combining exact mass information from FTICR-MS with retention time data from UPLC-UHR-ToF-MS.
Although this was not the purpose of that paper, this result illustrates the potentiality of non-targeted MS-based analyses for the discrimination of wines according to their appellation of origin (geographical origin of grapes), regardless of the vintage, and thus nicely confirms that wine's metabolic diversity holds various chemical fingerprints that advanced non-targeted metabolomics can read. Possible structural assignments for specific masses for geographical origins (Figure  5B ) could be obtained from databases or interfaces such as SciFinder Scholar or MassTrix  (Wägele et al. , 2012). However, on average more than 80% of the discriminant masses could not be annotated. Through the MassTRIX interface, 56 elemental formulas specific to Chablis wines, 59, 50, and 3 elementals formulas specific to Meursault 1, Meursault 2 and Corton Charlemagne wines, respectively (out of 304, 327, 204, and 267 masses, specific to Chablis, Meursault 1, Meursault 2, and Corton Charlemagne wines, respectively) could be correlated to hypothetical metabolite structures from the various metabolic pathways of the Vitis vinifera organism (Figure  5B )  (Suhre and Schmitt-Kopplin, 2008; Wägele et al.
ANALYSIS OF MARKER PEAKS Putative annotations of significantly different metabolites only give small evidence on their real structure, because several possible isomers and isobars might exist, especially for secondary metabolites. Therefore, we performed MS/MS experiments on statistically identified markers from the different data sets. We used the R package MetShot to sort given RT and m/z into different subgroups for MS/MS to have a minimum number of acquisition methods that cover all features  (Neumann et al. , 2013). MS/MS spectra were manually extracted and exported to. mgf files from which MetFusion batch files were generated using a custom Perl script. Identification of metabolites is the current bottleneck of metabolomics  (Evans et al. , 2009). Although tandem MS libraries are growing fast, numerous metabolites are still missing.
The most promising solution to overcome this problem is in silico fragmentation. Several tools exist and we used MetFusion for analysis  (Tohge and Fernie, 2009; Heinonen et al. , 2012; Gerlich and Neumann, 2013; Allen et al. , 2014). This web-based tool uses the in silico fragmentation tool MetFrag in combination with different MS/MS databases, like MassBank or Metlin. Results were ranked based on spectral similarities to the in silico spectrum of compound matching the precursor mass and spectral similarities to spectra stored in different databases. All spectra were uploaded to a MetFusion batch client. To prove the applicability of this approach for metabolite identification we have chosen an example, which will be discussed in more detail. As example we show results for m/z 311. 0406 at retention time 3.
46 min. This mass was putatively annotated as caftaric acid (Figure  6A ) with an error of -0. 79 ppm. Cafltaric acid is known to take part of the color of white wine and is considered to be one of the strongest natural antioxydant of Chardonnay  (Cheynier et al. , 2006) , and it's concentration is known to decease upon oxidation  (Fernández de Simón et al. , 2014; Gawel et al. , 2014). From FTICR-MS, the exact mass of 311. 0408523 was aligned to this feature. Using our MetShot based approach we obtained a MS/MS spectrum suitable for further analysis. We uploaded the most intense fragment peaks to MetFusion, then searched the ChemSpider database with an error of 1 ppm and used the European mirror of MassBank for MS/MS search. Six potential candidates were found and caftaric acid yielded the highest score with 5 explained peaks (Figures  6C, D ).
ACKNOWLEDGMENTS The authors would like to gratefully thank  Steffen Neumann  and  Michael Gerlich  for their help with MetFusion. We thank the  Domaine de la Romanée Conti (Vosne Romanée, France), Domaine Vincent Dauvissat (Chablis, France) ,  Domaine des Comtes Lafon (Meursault, France) ,  Maison Bouchard Père & Fils  ( Beaune, France ) and  Domaine de la Velle (Meursault, France)  for providing wine samples. This work was financially supported by the  Région Bourgogne , the  Bureau Interprofessionnel des Vins de Bourgogne (BIVB)  and the  Comité Interprofessionnel des Vins de Champagne (CIVC).
The percentage of motile spermatozoa and their VAP (Velocity of the Average Path) were assessed using a CASA plug-in developed for the Image J software  [13]. Calibration settings of the software were as follows, frame rate: 25 frame sec 21 ; sperm size range: 0. 9 to 7. 5 mm; minimum VAP for motile sperm: 10 mm sec 21 ; minimum track length: 6 frames; minimum number of sperm observed: 30. The fertilization capacity of the four sperm pools (13. 1, 13. 2, 24. 1, 24. 2) was then estimated in triplicate (50 000 oocytes for each pool; fertilization volume 100 ml)  [10]. Firstly, each sperm pool was used to fertilize a pool of oocytes collected from the control group (four crosses). Secondly, the sperm was used to fertilize the oocytes of its respective female groups (four crosses). Because sperm quality was being studied, a limiting sperm to oocyte ratio of 300, corresponding to 150 000 sperm ml 21 , was used.
However, how LntA impairs the function of BAHD1 remains to be deciphered; in particular, whether a direct interaction between LntA and BAHD1 is required for such inhibition is unknown. LntA is a small basic protein with no sequence homology to any known protein. We have previously solved the structure of LntA and shown that it folds into a compact, elongated five-helix bundle (Protein Data Bank [PDB] accession number 2XL4; see also  reference 10). In this study, we unravel the determinants involved in the LntA-BAHD1 interaction using crystallography, in vitro interaction assays, immunofluorescence, and functional assays after Listeria infections of human cells. Our results provide evidence that a direct interaction between the elbow domain of LntA and a proline-rich region in BAHD1 is required for stimulating innate immune gene expression, thus adding a molecular basis for the LntA-mediated inhibition of BAHD1.
The coexpression and GST pulldown assays subsequently allowed us to devise a rapid method to test protein complex formation and was useful for screening mutants, as shown below. LntA can therefore interact in vitro with the central region (amino acids 187-428 of BAHD1). This and our previous results  (10)  thus support a direct interaction between LntA and BAHD1 without any additional factor, as it was observed in three independent systems: in E. coli using recombinant proteins, in Saccharomyces cerevisiae by two-hybrid screening, and in human cells by pulldown and microscopy assays. A basic patch on the surface of LntA is involved in the interaction with BAHD1 187-428. LntA folds into a compact structure composed of five helices (PDB accession number 2XL4) (Fig. 2A  and S1C): three long antiparallel helices (H1 to H3), which we refer to as the trunk of the protein, and two smaller ones (H4 and H5), which form a protruding elbow.
We crystallized and solved the threedimensional structure of LntA K180D/K181D to a 2. 22-Å resolution (Fig. 4 ). The structure was solved by performing a MR-SAD (molecular replacement combined with single-wavelength anomalous dispersion) experiment with data for the selenomethionine edge collected at the BM14 beamline of the European Synchrotron Radiation Facility (ESRF) and by using the model of wild-type LntA (LntA WT ) (PDB accession number 2XL4) for the MR phasing protocol. Data collection and refinement statistics can be found in Table  S1. The overall fold of LntA K180D/K181D is very similar to that of the wild-type protein (Fig. 4A , ribbon overlay), in agreement with a measured root mean square deviation of only 0. 47 Å for 139 ␣ carbons when the two structures were compared. The main difference involves the local modification of charges in the region of the double mutation, with disappearance of basic patch 3.
The complexes were eluted with the same buffer containing 20 mM reduced glutathione (GST-Trap) or 500 mM imidazole (His-Trap). All fractions were analyzed by SDS-PAGE and immunoblotting. Pulldown experiments were repeated four times on the GST-Trap and twice on the His-Trap columns. Crystallization, data collection, structure solution. For the crystallization of Se-methionine LntA K180D/K181D , the protein was expressed as a GST fusion in M9 minimal medium containing 200 mg/liter thiamine. When cultures had attained an OD 600 value of 0. 5, 60 mg of Se-Met, 100 mg of L-Lys, L-Phe, and L-Thr, and 50 mg of L-Iso, L-Leu, and L-Val were added per liter of culture. After 30 min of growth, expression was induced by the addition of 1 mM IPTG. The protein was then purified as described previously for wild-type LntA  (10)  except that the last step of purification was on a Superdex 200 HR 10/30 column in 25 mM KPi (pH 7.
0), 0. 2 M NaCl, 1 mM dithiothreitol (DTT). Full labeling by selenium atoms was confirmed by mass spectrometry. Crystallization tests were performed at 4°C on the HTX-PSB platform in Grenoble, France. Crystals were grown with a protein concentration of 5 mg/ml at 20°C using the hanging-drop technique in 0. 1 M citrate (pH 5. 0), 1 M LiCl 2 , and 21% polyethylene glycol 3350. Crystals were cryoprotected in 25% glycerol prior to data collection. A single data set was collected on beamline BM14 (ESRF, Grenoble, France), and MR phasing was performed by Phaser (25) using the structure of wild-type LntA (PDB accession number 2XL4) as a search model. Further model improvement was done by manual model building using Coot  (27)  and refmac (  28 ) by an amplitude-based twin-refinement protocol. Final data collection, structure solution, and refinement statistics are shown in Table  S1.
Images were acquired with Apochromat 100ϫ (numerical aperture, 1. 4) objective lenses in 2-mm-high, 200-m-step Z-stacks with the MetaMorph software, version 7. 7. 9. 0 (Molecular Devices). Acquisition parameters were similar for all samples. For snapshot display, maximum intensity signals from 4 successive stacks (i. e. , 0. 6-m slices) were integrated with ImageJ. Each picture is representative of the transfected cell population observed in two independent experiments. Cellular infections. Cells were seeded in 24-well plates to achieve a density at the time of infection of 2. 5 ϫ 10 5 cells/well for LoVo cells and 1 ϫ 10 5 cells/well for JEG-3 cells. Listeria strains were grown in brain heart infusion (BHI) medium to an OD 600 of 3, washed in PBS, and diluted in culture medium without serum to achieve a multiplicity of infection (MOI) of 10.
Genomic DNA was removed by treatment with a Turbo DNA-free kit (Ambion). cDNAs were generated from 1 to 2 g total RNA using the RT 2 HT first-strand kit (Qiagen/SABiosci- ences). qPCR was performed on a CFX384 instrument (Bio-Rad) using Kapa SYBR Fast universal master mix (Kapa Biosystems) as specified by the supplier. Each reaction was performed in triplicate. RT-qPCR primers for YWHAZ, IFI44L, IFITM1, and IL6 were predesigned, validated RT 2 qPCR primer pairs from Qiagen/SABiosciences. Other primer pairs were selected from the literature, from primer databases (RTPrimerDB, http: //medgen. ugent. be/rtprimerdb/, or qPrimerDepot, http://primerdepot. nci. nih. gov/) and validated for efficiency. Data were analyzed by the ⌬⌬C T method. Target gene expression data were normalized to the relative expression of the YWHAZ reference gene.
ACKNOWLEDGMENTS We are grateful to  Morwena Leguillou  and  Jean Feunteun  for providing the BAHD1 ⌬cPRR construct,  Alexandre Leclercq  for help with the L. monocytogenes strain database,  Goran Lakisic  for help with protein production,  Marjolaine Noirclerc-Savoye  for help with testing the solubility of LntA variants,  Adrien Favier  for performing 1D-NMR experiments on LntA mutants,  Luca Signor  for performing spectrometry measurements on LntA,  J. Marquez  and the  HTX Lab team  for access to and help with high-throughput crystallization, the  ESRF  for access to beamlines,  Babu Manjasetty ,  Hassan Berlhali  ( BM14 ,  ESRF ), and  Carlos Contreras-Martel  ( IBS ) for help with data processing and structure solution, and  Andrey Grishin  for critical reading of the manuscript. This work used the platforms of the  Grenoble Instruct Centre (ISBG ;  UMS 3518 CNRS-CEA-UJF-EMBL ) within the  Grenoble Partnership for Structural Biology (PSB).
This work received funding from  INRA ,  CNRS , and the  Pasteur Institute , as well as  French National Research Agency  grants  EPILIS  ( ANR-11-BSV3 003-01 ),  FRISBI  ( ANR-10-INSB-05-02 ), and  GRAL  ( ANR-10-LABX-49-01 ), and from  European Research Council (ERC) Advanced  grant  233348. P. Cossart is a  Senior International Research Scholar  of the  Howard Hughes Medical Institute.
Data Analysis The comparison between each concentration of bark, seed and leaf extract from the initial examination (step 1) was analyzed by one-way ANOVA using SPSS version 15. 0 (SPSS Inc. , Chicago, IL, USA). Multiple comparisons between factor levels were done by Duncan's posthoc test. In the second step the relationship between dose and mortality in the insecticide susceptible mosquito strain was analyzed by probit analysis using SPSS software (IBM SPSS Statistics for Windows, Version 21. 0. IBM Corp. , Armonk, NY, USA), which provides an estimation of the median lethal doses for each of the two compounds compared (Soxhlet and ASE bark Z. heitzii extracts). Median lethal doses (LD50 and LD95 with 95% confidence intervals) are expressed in nanograms of active ingredient per mg of average mosquito body weight (calculated from 50 live mosquitoes).