dataesr/scientific-paragraphs-categorization huggingface dataset:
Specifically, based on available studies of the effects of humidity on BB aerosol (e. g. ,  [55, 56] ), we disregarded the observed cases where the average relative humidity (RH) in the aerosol column was greater than 60%. Values of RH were evaluated as described below in Section 2. 1. 2. Finally, the third selection criterion was introduced to minimize the possibility that observed aerosol optical properties are strongly affected by changes in particle morphology and composition that may occur  [57, 58]  as a result of atmospheric aging of BB aerosol. The selection was based on our estimates of the photochemical age of BB aerosol (see Section 2. 1. 2): observations of aerosol aged more than 30 h were excluded from the analysis. While choosing the threshold age value (which, ideally, should be as small as possible), we tried to ensure that the amount of the remaining data would be sufficient for statistical inferences.
The HTAP data for the year 2010 were used because the corresponding 2012 data were not available; we presume that changes in annual anthropogenic emissions in the Siberian region could hardly be considerable during the period of three years and can be disregarded. The BB emissions were estimated using the FRP observations mentioned above by applying to them, sequentially, the FRP to biomass burning rate conversion factor and the emission factors (species and land cover dependent). The methodology used to process and re-grid the FRP data, as well as the procedure aimed at the estimation of the conversion factor relating FRP and biomass burning rate values were developed earlier and are described elsewhere  [4, 30, 67]. In this study, the conversion factor was calibrated by minimizing the root mean square difference between the AOD values obtained from CHIMERE for the 500-nm wavelength and corresponding AOD values available from the AERONET sites considered (see Section 2.
1. 1) for summer months of 2012. The optimal value of the conversion factor was found to be 6. 81 × 10 -4 g s -1 W -1. For comparison, a similar calibration procedure applied earlier to the satellite AOD data in the same region yielded a 40% larger value of the conversion factor  [20]. The difference may, in particular, be due to spatial and temporal inhomogeneity in burning conditions, as well as due to the fact that the previous study  [20]  utilized the Collection 5 fire data product, while the more recent Collection 6 data were used in this study. The emission factors for aerosol and gaseous species used in this study were specified using the recommended emission factor values for boreal forest, savanna and agricultural fires from the GFED4 (Global Fire Emissions Database) emission inventory  [68].
The procedure comprised 5000 iterations involving random sampling of N (20, in our case) data points from the original dataset (including the same number, N, of data points). The sampled data were then used in each of the iterations to obtain perturbed estimates of a given parameter (e. g. , the slope of the linear regression in Equation  (5)  or the mean of the estimates for individual observations) in the same way as the original data were used to obtain a corresponding optimal estimate. The underlying assumption in this procedure is that the errors of individual SSA observations are statistically independent. Wherever an estimation involved uncertainties in input parameters (as, e. g. , in the case of the estimation given by Equation (  8 )), such uncertainties were taken into account by randomly perturbing the corresponding parameter values (within the range of their uncertainties; see Table  2 ) in each iteration of the same Monte Carlo procedure.
Acknowledgments: This study was supported by the  Russian Science Foundation  (Grant Agreement No. 15-17-10024 ). The authors acknowledge the free use of the AERONET data available from https://aeronet. gsfc. nasa. gov. Author Contributions:  I. B. Konovalov  conceived of the idea of the study, performed the analysis of the AERONET data and wrote the paper;  D. A. Lvova  performed processing of the AERONET data and simulations with the CHIMERE chemistry transport model;  M. Beekmann  provided recommendations with respect to application of the CHIMERE model to this study and contributed to the interpretation of the analysis of the AERONET data.
9 μL of this mix was added to 2. 1 μL of diluted DNA (0. 05 ng μL -1 ). Real-time PCR was performed on a LightCycler 480 (Roche Life Science, Germany) in white 384-well plates, sealed with adhesive foil. A 5-min denaturation step at 95 °C was followed by 55 cycles of 10 s at 95 °C and 15 s at 60 °C and 15 s at 72 °C. After each run, a dissociation curve was obtained by heating the samples from 65 to 97 °C. The dissociation curves indicated a single product for both primer pairs (data not shown). All samples were run in triplicates, as recommended by  Pfaffl (2004) , and autoclaved milliQ H 2 O was used as negative control. For relative quantification, the differences between the quantification cycles (ΔC q ) obtained by two qPCRs with the different primer pairs run in parallel on the same DNA sample were measured, as by  Gachon et al.
Data analysis Cycle thresholds were calculated with the LightCycler 480 Software (Roche, Germany) and exported to Excel 2013 (Microsoft, USA) where ΔC q values of each DNA sample were determined. Values are reported as average ± standard deviation. Graphs of the standard curves were drawn with GraphPad Prism (GraphPad Prism Software, Inc. , USA), and the heat map was constructed in R Studio (RStudio, Inc. , USA). SPSS (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22. 0. Armonk, NY: IBM Corp. ) was used to perform statistical analyses. Normality of the data was tested with the Shapiro-Wilk test and homogeneity of variances with the Levene test. Data with normal distribution and homogeneous variances was analysed with oneway ANOVA. In the case of heterogeneous variances, the non-parametric Kruskal-Wallis test was used.
Natural infection of laboratory-grown samples in a seaweed farm The course of natural infection of S. latissima with L. elsbetiae was explored by an experimental set-up where laboratory-grown samples were transferred to a seaweed farm for 6 months and infection rates were compared to samples from a wild population. The number of thalli infected with L. elsbetiae was more than four times higher in wild samples (87% of infected thalli) than in the samples grown in the seaweed farm in close vicinity to the wild population (19%, Fig. 4a ). No endophytes were detected in the laboratory controls (Fig. 4a ). While the laboratory-grown samples in the seaweed farm were heavily covered with epiphytes (data not shown), the qPCR revealed significantly lower infection rates by the endophyte L. elsbetiae (ΔC q = 11.
Funding information The presentation of this work at the ISAP2017 was supported by a fellowship provided by the  Global Seaweed Network (UK NERC IOF Pump-priming + scheme   NE/L013223/1 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skladowska-Curie grant  agreement  N°642575  and by the project  IDEALG (France :  ANR-10-BTBR-04 ).
Computational Details Eleven thermodynamic state points of SCW were simulated in the framework of the present study. Ten molecular dynamics (MD) atomistic simulations of SCW were performed at a constant near-critical temperature of T = 666 K (T c = 647. 1 K) and for densities in the range 0. 0644 -0. 644 ) in order to reveal the effect of hydrogen bonding interactions on the local structural order and density inhomogeneities in SCW. An additional MD simulation was later performed at the lower density of 0. 0322 3 / cm g and at the same temperature, in order to investigate the possible existence of structural transitions in SCW. The simulated state points of SCW are presented in Table  I. The simulations were carried out with 500 molecules, using the DL_POLY software  [47]. Each simulation was extended to 1 ns to achieve equilibrium, starting from an initial configuration created with the packmol software  [48] , and the properties of SCW were evaluated in subsequent simulations with duration of 2 ns.
Acknowledgements I. S. acknowledges the financial support from the  group of Professor Elvira Guardia (Computer Simulation in Condensed Matter Research Group -SIMCON ), which allowed him to visit the  Department of Physics of the Technical University of Catalonia (UPC)  for one month, in order to work on the structure and dynamics of water at several different thermodynamic conditions. The use of the computational facilities of the  SIMCON group  and of the  Physical Chemistry Laboratory at the Department of Chemistry -University of Athens  is also gratefully acknowledged. E. G. acknowledges financial support from the  Ministerio de Economía y Competitividad of Spain , Grant  FIS2015-66879-C2-1-P (MINECO/FEDER ).
sp. "atroviride B" LU660, T. asperellum LU1370) DR5::GUS seedlings were removed and incubated at 37 • C overnight in GUS staining solution (50 mM sodium phosphate buffer, pH 7. 0, 0. 1% Triton X-100, 1. 5 mM potassium ferricyanide, 1. 5 mM potassium ferrocyanide, and 1. 5 mg/mL X-Gluc). After staining, the samples were cleared by incubation with 7% NaOH for 20 min and 60 min with 100% ethanol followed by 60 min hydration with serial ethanol dilutions 70, 60, 40, 20, and 10%. Then the seedlings were incubated for 30 min with 25% glycerol and overnight with 50% glycerol at room temperature. Seedlings were visualized using an Olympus BX51 compound microscope. Images were captured using an Olympus DP70 digital camera system and processed with the software Cell F (Olympus). The experiment was repeated once with similar results using 10 replicate plants for each repeat.
For the localization of the different auxin transporters, confrontation assays were performed as described above using T. virens Gv29. 8 for inoculum and multiple Arabidopsis transgenic lines. These lines correspond to the marker for auxins DR5::GFP, and different auxin transporters fused to the green fluorescent protein: PIN1::PIN1-GFP, PIN2::PIN2-GFP, PIN4::PIN4-GFP, PIN7::PIN7-GFP and PIN3::PIN3-GFP (Supplementary Table  2S ). After 5 d interaction, seedlings were removed from the interaction plate and directly mounted on a slide for observation on an Olympus BX51 compound microscope. Images were captured using an Olympus DP70 digital camera system and processed with the software Cell F (Olympus). The experiment was repeated once using 10 replicate plants each time.
The vials were incubated at 25 • C for 24 h before sampling of the headspace VOCs was conducted according to  Stoppacher et al. (2010). An SPME fiber coated with 65 µm polydimethylsiloxane/divinylbenzene was used to extract the fungal VOCs from the headspace vial for 30 min without agitation. After injection, the compounds were desorbed for 2 min in a split/splitless injector at 250 • C. The oven temperature was held at 40 • C for 2 min, then raised to 200 • C at 10 • C min -1 and 260 • C at 25 min -1 and then held at this temperature for 5 min. Helium was used as carrier gas at a constant flux of 1 mL min -1. Compounds were identified by matching their mass spectra and linear retention indices using GCMS solution v. 2. 72 (Shimadzu Corporation, Japan) software with NIST 11 and Wiley 10 mass spectrum libraries and by using the software MassFinder4 with a specialized terpenoids library (Hochmuth Scientific Software, Hamburg, Germany).
Statistical Analyses Data analyses from fresh weight of Arabidopsis plants grown in sterile soil (Figure  1 ), effect of Trichoderma on A. thaliana root architecture (Figure  2 ), and the effect of Trichoderma volatile organic compounds on Arabidopsis plant biomass using a split-plate assay (Figure  7 ) were analyzed by Analysis of Variance (ANOVA) using the statistical software GenStat Version 18. Differences between treatment means were compared by the unrestricted least significant difference (LSD) analysis at P < 0. 05.
virens which produced a compound that migrated to the same Rf as IAA but with a different color; this was present but less evident when Trp was added (Figure  5A ). Additionally T. virens synthesized additional compounds in standard MS independently of the presence of Trp. Four strains from the first experiment were used to assess the IAA production in PDB: T. atroviride IMI206040, T. virens Gv29. 8, T. sp. "atroviride B" LU132, and T. reesei QM6a. All strains were able to synthesize a molecule that migrated to the same Rf as commercial IAA (Figures  5A, B ).
ACKNOWLEDGMENTS We acknowledges  Jiri Friml  ( IST, Austria ) for kindly providing us with seeds of fluorescent transgenic lines of A. thaliana;  Alfredo Herrera-Estrella  ( Langebio, Mexico ) and  Charles Kenerley  ( Texas A&M, USA ) for having provided us with Trichoderma atroviride IMI206040 and T. virens Gv28. 9, respectively.
FUNDING This work was supported by the  Pre-Seed Accelerator Fund (PSAF) , the  Tertiary Education Commission  (contracts 38631 and  38651 ) and the  Ministry for Science and Innovation. Fatima Salazar  was supported by  Tertiary Education Commission  and  Conacyt-Mexico  for her stay at  Lincoln University. JT acknowledges the financial support of  CNPq  and  CAPES.
To align with current treatment guidelines, we present data from those patients with HCV genotype 1b infection who received RBVfree treatment. For patients with HCV genotype 1a or 4 infection, RBV dosing was weight-based at 1000 or 1200 mg divided twice daily, with the exception of patients with creatinine clearance <50 mL/min who received renally adjusted doses of RBV as per label guidelines  [18]. The primary endpoint for Part 2 of this study was the proportion of patients achieving SVR12; plasma HCV RNA levels were quantified using the Roche COBAS TaqMan real-time reverse-transcriptase polymerase chain reaction assay, version 2. 0. The LLOQ for this assay is 25 IU/mL. Unless otherwise indicated, analyses were conducted in the intention-to-treat population (ITT), which includes all patients who received at least 1 dose of study drug.
Acknowledgments We thank the study participants and their families. AbbVie participated in the collection, analysis, and interpretation of the data and in the writing, review, and approval of the manuscript. Medical writing support was provided by  Dr. Zoë Hunter  of  AbbVie. Financial support. This work was supported by  AbbVie, Inc. Potential conflicts of interest. J. K. R. reports the following: Grant/ Research support -Gilead; Consultant/Advisor -Abbott, Abbvie,  BMS, Bionor ,  Cipla ,  Gilead ,  Janssen ,  Merck , and ViiV; Speaker at educational events -AbbVie,  BMS ,  Gilead ,  Janssen , and  Merck. D. W. reports the following: Grant/Research support - AbbVie ,  BMS ,  Gilead ,  Merck , and  Tacere Therapeutics ; Consultant/Advisor -AbbVie,  BMS ,  Gilead ,  Janssen , and  Merck. A F.
" Each track was divided into homogeneous segments (in terms of mean and variance in first passage time) using the Lavielle segmentation  (Barraquand & Benhamou, 2008)  implemented in the R package ade-habitatLT  (Calenge, 2006). These segments were then considered to be periods of area-restricted search if the mean first passage time during this period was higher than the mean across the foraging trip. Periods where the first passage time was lower than the mean were considered to be non-area-restricted search zones  (Pinaud & Weimerskirch, 2005 , 2007; Weimerskirch et al. , 2007). In zones of area-restricted search we calculated the number of landings (Speed <10 km/h) per zone-a proxy of energetic costs  (Shaffer, Costa, & Weimerskirch, 2003) , and this was used as "foraging effort. " The time spent in area-restricted search zones was measured as the time the bird exited the area-restricted search zone-the time the bird entered the zone and this was used as "time in patch.
" The number of area-restricted search zones per trip was used as an estimate of the "number of patches" (Figure  1 ). In total 816 foraging patches were identified from 276 individual trips, with a mean of 3. 15 ± 2. 10 patches per individual per trip. We subset the data to include measures of EE trade-off components per foraging trip, with a measure of number of patches and size of patch calculated across the whole trip, and randomly selected measures of time in patch and foraging effort. We also conducted the analyses on the full dataset and a dataset subsampled to include only one randomly selected trip per bird (N patches = 292; N trips = 274; N birds = 228). These results supported those in the main paper (Appendix 1 Table  S1 ; Appendix 2 Tables  S1-S6 ) and full details of sample sizes for each analysis and given in all table legends.
| EE trade-off and individual strategy We estimated the correlation between four foraging traits (size of patch, time in patch, number of patches and foraging effort) using Pearson's correlation coefficients and associated p values to examine whether there was evidence of correlated traits within the trade-off. A positive correlation is representative of groups of traits being consistently displayed by the same individuals. We also used the R package prcomp to conduct a principal component analysis to extract a single metric representative of an EE strategy. Models were also run as multivariate analyses in MCMCglmm  (Hadfield, 2010) , and the covariation between foraging traits and personality estimated. Using multivariate models we failed to reach full convergence, however, results supported those presented here (Appendix 3; Table  S1 ).
The relationship between boldness, age and sex with component traits of the EE trade-off and principal component one (EE strategy). Estimates from general linear mixed models of slopes and intercepts are presented for all effects and significant results are shown in bold. Non-significant interactions were dropped from all models and are shown in italics. Dataset includes one measure of EE traits per trip (N patches = 197; N trips = 186; N birds = 159). M = male; F = female p <. 001) and the maximum range (χ 2 1 = 14. 82; p =. 001). This showed that individuals favouring exploitation had longer trip duration (Estimate [logged scale] ± SE: 0. 23 ± 0. 04; Figure4a), travelled further in total (Estimate [logged scale] ± SE: 0. 19 ± 0. 04; Figure4b) and had a larger maximum range (Estimate [logged scale] ± SE: 0.
Foraging metrics are grouped for plotting purposes. Raw data are shown in Appendix 2 Figure S2 [Colour figure can be viewed at wileyonlinelibrary. com] T A B L E 5 The relationship between the EE strategy and individual components with reproductive success. Estimates from general linear mixed models of slopes and intercepts are presented for all effects and significant results are shown in bold. Dataset includes one measure of EE traits per trip (N patches = 282; N trips = 267; N birds = 223).
ACKNOWLEDGEMENTS Thanks to  John Quinn ,  Claudia Mettke-Hofmann  and  Jon Green  for comments on the manuscript,  Julien Martin ,  Orr Spiegel ,  Pierre-Olivier Montiglio  and  Jon Wright  for helpful discussions and fieldworkers who collected data at Possession Island over the past four decades, particularly  Julien Collet ,  Susan Waugh ,  Timothée Poupart  and  Franck Théron. We thank  Karine Delord  and  Dominique Besson  for database. The  Institut Polaire Français Paul Emile Victor  (IPEV, programme  109 ) and the  Terres Australes and Antarctique Françaises (TAAF)  provided logistical and financial support, and  SCP  was partly funded by a  Marie Curie Intra-European fellowship (ALBASPECIALISATION).
T A B L E 4 4 The relationship between boldness, age and sex with component traits of the EE trade-off and principal component one (EE strategy). Estimates from general linear mixed models of slopes and intercepts are presented for all effects and significant results are shown in bold. Non-significant interactions were dropped from all models and are shown in italics. Dataset includes one measure of EE traits per trip (N patches = 197; N trips = 186; N birds = 159). M = male; F = female p <. 001) and the maximum range (χ 2 1 = 14. 82; p =. 001). This showed that individuals favouring exploitation had longer trip duration (Estimate [logged scale] ± SE: 0. 23 ± 0. 04; Figure4a), travelled further in total (Estimate [logged scale] ± SE: 0. 19 ± 0. 04; Figure4b) and had a larger maximum range (Estimate [logged scale] ± SE: 0.
Flow cytometry. Determination of antigen level on cell surface. The number of TSPAN8 and CEA antigens respectively present on HT29 and A431-CEA-Luc cells' surface was quantified using an in vitro kit assays (CellQuant Calibrator ® , Biocytex, France) and a flow analyser (BD-LSRII flow cytometer with FACSDiva Software (BD Biosciences)). All experiments were made according to the manufacturer instructions and in triplicates. The statistical significance was tested with a PLSD Fisher test and considered to be significant when p < 0. 05. Assessment of the interaction between TCO and TZ. After trypsinization 1. 10 6 HT29 or A431-CEA-Luc cells were incubated 30 min at 4 °C with 0. 03 nmol of Ts29. 2-1-3 or 35A7-1-3 then washed in Dulbecco's phosphate buffered saline (DPBS), centrifuged 5 min at 461 g and supernatants were removed to avoid unspecific ligation.
5% or 10-14 equivalent of TZ-Cy3 with respect to the number of TCO diluted in DPBS-BSA 2. 5% -DMSO 0. 5%. Cells were then fixed with 10% formalin (Sigma Aldrich, France). Chambers were finally removed from the labteks and blades were mounted in vectashield-DAPI mounting medium then observed using a confocal imager (Leica SPE, ICCF platform, Clermont-Ferrand, France). Region of interest (ROI) was quantified on the entire image using ImageJ software. Three fields per well, all Z-sections every 2 µm, were randomly imaged and quantified with an automated 3D-strategy. Imaging settings and quantification procedure were detailed in supplementary methods. Animals. This investigation is conforms to the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH Publication n°85-23, revised 1996).
Acknowledgements A. R. received a PhD fellowship from  FEDER Région Auvergne-Rhône-Alpes. This project is also sustained by Cyclopharma. We thank  Caroline Vachias  and  Pierre Pouchin  from their expertise for confocal imaging studies performed on the ICCF platform (Clermont-Ferrand, France). Flow cytometry experiments were performed on the  CICS platform (Clermont-Ferrand, France)  with the technical help of  Christelle Blavignac. In vivo imaging was made on the multimodal imaging IVIA platform (Clermont-Ferrand, France).
de novo genome assembly Various sets of the longest MinION 2D reads, which refer to various theoretical genome coverage (10x, 15x, 20x and all the 2D reads) taking 15 Mb as genome size estimate (Table  S1 ) were subjected to four assemblers: ABruijn (v0. 3b)  (Lin et al. 2016 ) Canu (v1. 1)  (Berlin et al. 2015) , miniasm (v0. 2-r137-dirty)  (Li et al. 2016)  and SMARTdenovo (https://github. com/ruanjue/smartdenovo). ABruijn and miniasm were run with default parameters, while "genomeSize=13m, minReadLength=2500, mhapSensitivity=high, corMhapSensitivity=high and corOutCoverage=500" was set for Canu and "-c 1 -k 14 -J 2500 -e zmo" for SMARTdenovo. After the assembly step, we polished each set of contigs with Pilon (v1. 18)  (Walker BJ et al. 2014) , using ~100X of Illumina 2 x 100 bp paired-end reads.
Results and Discussion Three D. bruxellensis isolates (UMY321, UMY315 and 133) were sequenced in this study (Table  1 ). These strains were determined to be diploid based on flow cytometry analysis and were all isolated from wine or grape must in Italy or South Africa. The genome of the UMY321 isolate was sequenced using a combination of Nanopore long-read and Illumina short-read sequencing data to obtain a highquality assembly. By contrast, the UM315 and 133 isolates were only sequenced using a short read strategy. In addition, these genomes were compared to previously genome sequences of 6 other D. bruxellensis isolates (Table  1 )  (Piskur et al. 2012 , Curtin et al. 2012 , Borneman et al. 2014 , Crauwels et al. 2014 , Olsen et al. , 2015).