dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS. We thank the  fieldworkers of Possession Island , the fishery observers, and crews of fishing vessels for their help in collecting photo-ID data. We are also grateful to the fishery observers who collected sighting data from fishing vessels and to the  Museum National d'Histoire Naturelle  ( Prof. Guy Duhamel ,  Nicolas Gasco , and  Charlotte Chazeau ) for facilitating access to these data. This work was conducted as part of the  109 program  (Principal Investigator,  Henri Weimerskirch ) with the Institut Polaire Francais. Funding and logistic support were provided by the  Terres Australes et Antarctiques Francaises , the  Reserve Naturelle des Terres Australes , and the  Reunion Island Fisheries Companies  as part of the  ORCADEPRED research program. R. R. R. and C. G. are supported by the  International Whaling Commission's Southern Ocean Research Partnership  ( Agence National de la Recherche, Fondation d'Entreprise des Mers Australes, Direction des Pêches Maritimes et de l'Aquaculture ).
The approach was based on the NU-AGE project (clinicaltrials. gov, NCT01754012) that regrouped 1,250 free-living elderly people (65-79 y. o. , men and women), free of major diseases, recruited within five European centers. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by  Fried et al. (2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centers were selected for untargeted serum metabolomics at T0 (baseline) and T1 (follow-up). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate models.
Serum Metabolomics Analysis Untargeted metabolomics was performed following the procedure described in  Pujos-Guillot et al. (2017). In brief, sera samples (100 µL) were deproteinized using cold methanol. After evaporation under nitrogen, the dry residues were redissolved in 50/50 (v/v) acetonitrile/water containing 0. 1% formic acid. Pooled quality-control samples were prepared by mixing 20 µL from each of the serum samples and prepared similarly. Metabolic profiles were then determined using an ultra-performance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometer (Bruker Impact HD2), equipped with an electrospray source. Separations were carried out using an Acquity HSS T3 column (Waters). Data were acquired in positive and negative ion modes with a scan range from 50 to 1,000 mass-to-charge ratio (m/z).
Data Processing and Metabolite Identification Data were processed under the Galaxy web-based platform Worflow4metabolomics  (Giacomoni et al. , 2015)   1  , using first XCMS, followed by quality checks and signal drift correction according to the algorithm described by van der  Kloet et al. (2009) , to yield a data matrix containing retention times, masses and peak intensities that have been corrected for batch effects. These steps include noise filtering, automatic peak detection and chromatographic alignment allowing the appropriate comparison of multiple samples by further processing methods. After statistical analyses, molecular features of interest were searched against an in-house database containing the reference spectra of more than 1,000 authentic standard compounds analyzed in the same analytical conditions.
Then, the remaining unknown compounds were identified on the basis of their exact masses which were compared to those registered in Metlin 2  , in the Human Metabolome Database (HMDB 3  )  (Wishart et al. , 2007) , or in Kyoto Encyclopedia of Genes and Genomes (KEGG) database 4. Database queries were performed with a mass error of 0. 005 Da and a retention time difference of 0. 1 min (for the in-house database). Database results were confirmed using appropriate standards when available, isotopic patterns, and mass fragmentation analyses, performed on a Thermo Scientific LTQ Orbitrap Velos hybrid mass spectrometer (Thermo Fisher Scientific, San José, CA, United States) using high resolution, at 100,000 resolving power. For unidentified ions, the number of plausible elemental compositions is restricted to a small number (or uniquely identified) with the support of additional chemical information-i.
Statistical Analyses Characteristics of the studied population and sub-groups were analyzed using Wilcoxon test or Fisher's exact tests for numerical or categorical data, respectively. Quantitative variables were expressed as means ± SD. Two-ways ANOVA was applied on metabolomics data at T0 and T1, respectively, with BH correction, to assess the effects of phenotype (pre-frail vs. non-frail) for each gender, with country as cofactor. These analyses were performed using the Galaxy web-service Worflow4metabolomics, first in the whole study population, and then in each sub-groups of interest [(i) stable subjects, (ii) subjects who improved their frailty status, and (iii) subjects who worsened their status]. Calculation of   a p-value for each feature allowed identifying the significant ones (adjusted p-values ≤ 0.
The dataset was iteratively randomly divided into a training set to fit the model and a test set to evaluate it (the two subsets having the same size), and 100 iterations were performed. The prediction model performance was evaluated using a confusion matrix, error rates, and area under ROC curves (AUC)  (Xia et al. , 2013)  [R package "pROC"  (Robin et al. , 2011) ] with a CI estimated with the DeLong's method  (DeLong et al. , 1988).
CONCLUSION In this study, metabolomics allowed discriminating metabolic sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility, with the identification of specific circulating discriminant metabolites. From these results, we were able to build multivariate predictive models for pre-frailty occurrence as well as for 1-year prevalence. Beyond this discovery step, these molecular signatures need to be qualified in large and diverse pre-frail populations in order to study their robustness and precision. Such multivariate strategies based on molecular signatures should then enable the monitoring of the disease progression at an early stage. French infrastructure (ANR-INBS-0010). All the results are available in the NUAGE database that will be open to the scientific community in 2020, as defined in the consortium agreement of the project (http://www.
Data and simulation The dataset used in this analysis was collected during LHC Run 2 in stable beam conditions and with all detector systems operating normally. The event quality was checked to remove events with noise bursts or coherent noise in the calorimeters. Events in the dielectron channel were recorded using a dielectron trigger based on the 'very loose' or 'loose' identification criteria  [17]  with transverse energy (E T ) thresholds between 12 and 24 GeV for both electrons, depending on the data-taking period. Events in the dimuon channel are required to pass at least one of two single-muon triggers: the first requires a transverse momentum (p T ) of at least 50 GeV, while the second has a threshold lowered to 26 GeV but requires the muon candidate to be isolated  [17]. The integrated luminosity of the dataset is determined to be 139.
Signal and background modelling A resonant signal is searched for by fitting the data dilepton mass distribution. The fit function consists of a smooth functional form for the background, and a generic signal shape. The generic signal shapes are constructed from non-relativistic Breit-Wigner functions of various widths convolved with the detector resolution, obtained as described in the previous section. The shape of the dilepton invariant mass distribution for a signal resonance with intrinsic width that is negligible compared with the detector resolution (zero-width signal) is obtained from the mass resolution only. To allow for a generic resonance search, a fiducial region at particle level is defined following the selection criteria applied to the reconstructed lepton candidates: each electron and muon candidate needs to pass |η| < 2.
The spurious-signal yield is calculated independently for the relative signal width assumptions between zero and 10% in steps of 0. 5%. The impact of systematic uncertainties on the signal yield is shown in Table  2. Only systematic uncertainties which change the fitted signal yield by more than 0. 5% at any point in the mass spectrum are considered. The largest systematic uncertainty at low mass in both channels originates from the spurious signals. The largest systematic uncertainty in the dielectron channel at high mass originates from the electron identification efficiency. The uncertainty associated with the 'good muon' requirement is dominant in the dimuon channel at high mass. This uncertainty is estimated with a conservative approach in a dataset collected in 2015-2016, corresponding to 36 fb -1 , by comparing efficiencies obtained in data and in simulation.
In the tested {g q , g } plane the relative width always remains below 10%, and in the {g h , g f } plane (g f ≡ g = g q ) it only exceeds 10% in regions (|g f | > 0. 9 and |g h | > 2. 5) well outside the limit contours. The observed limits can be compared with the limits obtained for the combination of the and ν channels in Ref. [39]  (provided in brackets): for g h = 0 and m Z HVT = 3 TeV, 4 TeV and 5 TeV the |g f | values above 0. 07 (0. 06), 0. 23 (0. 15) and 0. 49 (0. 42) are excluded at 95% CL, respectively. The resulting dilepton-only limits are slightly weaker than those for the and ν channels combined, even with a four times larger dataset, because of the higher W HVT → ν cross-section in this model. A complete set of tables and figures (including additional results for the dielectron and dimuon channels) are available at the Durham HepData repository  [55].
Typing Multilocus sequence typing (MLST) was performed according to the scheme developed by Griffith et al. [20]. Briefly, seven housekeeping genes (adk, atpA, dxr, glyA, recA, sodA and tpi) were amplified by PCR and sequenced as previously described  [20]. The sequence type (ST) was determined as a combination of alleles identified by comparing sequences with sequences available in the C. difficile MLST database (http://pubmlst. org/ cdifficile/). Toxinotyping was performed according to the scheme developed by Rupnik et al. [21].
RA09-070 (A + B -CDT -) infection was associated with caecal histologic damage, but to a lesser extent than TcdB-producing strains (whether or not strains produced TcdA or CDT), with significant neutrophil infiltration and hyperplasia as well as signs of inflammation. Batah et al. [25]  demonstrated in a mouse model that the two toxins of C. difficile strains 630 and R20291 act in synergy with the flagellin, the major component of the filament of the flagella of the bacteria, to produce the strong proinflammatory response observed during the pathogenesis of C. difficile. Because RA09-070 (A + B -CDT -) possesses different regulons coding the proteins of the flagella (GenBank accession no. JPPA00000000), we suggest that TcdA and the flagellin of this strain are responsible of the inflammation observed in our model.
The Direct Radiative Effect (DRE) of aerosols above clouds has been found to be significant over the south-east Atlantic Ocean during the African biomass burning season due to elevated smoke layers absorbing radiation above the cloud deck. So far, global climate models have been unsuccessful in reproducing the high DRE values measured by various satellite instruments. Meanwhile, the radiative effects by aerosols have been identified as the largest source of uncertainty in global climate models. In this paper, three independent satellite datasets of DRE during the biomass burning season in 2006 are compared to constrain the south-east Atlantic radiation budget. The DRE of aerosols above clouds is derived from the spectrometer SCIAMACHY, the polarimeter POLDER, and from collocated measurements by the spectrometer OMI and imager MODIS.
Area-averaged DRE When comparing datasets, and datasets and simulations, sampling is a serious issue. Often, area-and time-averages are compared, to reduce the effects of sampling differences. Here, we show the effect of ignoring the sampling effect, even of areaaverages of, in this case, aerosol DRE over clouds. In Figure  2a , the area-averaged instantaneous aerosol DRE over clouds from all three instruments is given for all available data in the area 10 where often area-averaged DRE values have been reported during this season (e. g. Chand et al. , 2009; de Graaf et al. , 2014; Meyer et al. , 2013; Peers et al. , 2015). Since the instruments have different overpass times, the instantaneous aerosol DRE to SCIAMACHY and POLDER. The events during this period have been investigated often before (e.
g. Chand et al. , 2008; Jethva and Torres, 2011; Yu and Zhang, 2013). The SCIAMACHY DRE values were compared to model calculations from GCMs, particularly HadGEM2 (de  Graaf et al. , 2014). Models were not able to replicate these extremely high aerosol direct radiative effects. The emission of smoke from Africa was possibly strongly peaked in August, but even accounting for such episodic emissions in models did not explain the difference in aerosol effects in models and observations by SCIAMACHY. • N-20 • S,10 • W-20 • E, And Figure  2a  shows that the aerosol DRE values from POLDER are even higher than those from SCIAMACHY. On the other hand, the average OMI/MODIS DRE is never higher than about 60 Wm -2. The largest difference between the datasets was found on 12 August 2006. The differences between the instruments are illustrated using histograms of all noon-normalized aerosol DREs, see Figure 3a.
, 2012)  (not considering overlying smoke). However, to avoid biases from smoke absorption, the MODIS channels at 1. 2 and 2. 1 microns are used to derive COT and CER for OMI/MODIS DRE retrievals. In our retrievals the MODIS reflectances at a resolution of 1 This analysis shows that the aerosol direct effect of aerosols above clouds can be significant on the local scale when smoke is present over clouds. So far, model simulations have been unable to reproduce the high values, and many models underestimate the signal and even simulate a cooling  (Myhre et al. , 2013) , where the datasets in this analysis clearly show that the positive effect is significant and real. However, when observations and model simulations of local effects are compared, sampling issues should be properly accounted for, because area-averaging and time-averaging does not work well for episodic events like smoke plumes, which are short-lived and localized.
The analysis also shows the strengths and weaknesses of the DRE retrieval algorithms for POLDER, SCIAMACHY and OMI/MODIS. Clearly, the latter two still suffer from a bias in the cloud parameter retrieval when smoke is abundant, providing for upcoming missions like METOP-SG 3MI  (Marbach et al. , 2015). This mission combines spectral imaging and polarization measurements. The DAA method would benefit from unbiased COT retrievals, that could be provided with polarization measurements. The assumptions on the spectral dependence of the aerosol absorption in the POLDER-like retrieval can be assessed and improved by the DAA method in a closure study using the instruments on the METOP-SG 3MI platform. This would allow time-dependent retrievals of UV-absorption by aerosols above clouds. The POLDER, SCIAMACHY and OMI/MODIS DRE products provide datasets that can be used to challenge GCMs and test their aerosol intrinsic properties and aerosol-cloud-radiation interaction schemes.
Analysis of the PCR products was performed by capillary electrophoresis on an ABI 3100 automated DNA sequencer (Applied Biosystems, Foster City, California, USA). Data were ❖ www. esajournals. org visualized and allele sizes were scored using GeneMapper software (version 4. 0; Applied Biosystems). In total, all 536 sampled workers were successfully genotyped. The presence of null alleles, errors due to microsatellite stuttering, and large-allele dropout were checked using  Micro-Checker version 2. 2. 3 (Van Osterhout et al. 2006). Tests of deviations from Hardy-Weinberg equilibrium and linkage disequilibrium were conducted in Fstat 2. 9. 3  (Goudet 1995). Genetic diversity parameters (i. e. , the number of alleles, allelic richness, and expected and observed heterozygosities) and fixation indices were estimated using Fstat 2.
N. apis has not been reported in bumble bees. The resulting Nosema sp. group also contains sequences which do not match with ❖ www. esajournals. org N. apis nor N. ceranae, indicating that the initially used multiplex primer set has cross-reactivity with other Nosema species. Species identification of these and whether they cause real infections are beyond the scope of this manuscript. The complete database of parasite prevalence is available from the Dryad Digital Repository: http://da tadryad. org/review?doi=doi:10. 5061/dryad. n8g87.
Statistical analyses To test for the effects of Impatiens glandulifera and parasite species (i. e. , independent variables) on the prevalence of parasites in Bombus pascuorum (i. e. , dependent variable), we performed a generalized linear mixed model (GLMM; lmer function, R package lmerTest;  Kuznetsova et al. 2014)  with the categorical variables Site type (two levels), Parasite species (five levels), and their interaction term as fixed-effect terms, as well as the categorical variables Year (two levels) and Site nested within Site type (five levels per site type) as random-effect terms. Parasite prevalence (which was a proportion variable) was analyzed using binomial model with the number of infected bees (successes) and the number of non-infected bees (failures) as a bivariate response.
After checking for overdispersion, a type III ANOVA was performed to determine the effect of plant invasion and parasite species on the prevalence of parasites (ANOVA function, R package car; Fox and Weisberg 2011). When a significant difference was found, multiple pairwise comparison tests were performed using Tukey contrasts (glht function, R package multcomp;  Hothorn et al. 2008). Besides, we tested whether the prevalence of the different parasites was correlated across sites using Pearson's r correlation coefficients on log-transformed data (rcorr function, R package Hmisc;  Harrell 2015). We used a second Gaussian GLMM to evaluate the effect of the year on the prevalence of parasites in Bombus pascuorum. We included the categorical variables Year (two levels), Parasite species (five levels), and their interaction term as fixedeffect terms, as well as the categorical variable Site (ten levels) as a random-effect term.
The dependent variable (i. e. , parasite prevalence expressed as proportions) was log-transformed to achieve normality of the residuals. Assumptions (i. e. , normality of residuals and overdispersion) were checked, and the effects of fixed and random factors were assessed using the step function. Finally, the impact of I. glandulifera on the parasite community (i. e. , number of parasite species per specimen as dependent variable) was assessed using a third GLMM with a Poisson probability distribution for modeling count data (glmer function, R package lme4;  Bates et al. 2015). We included the categorical variable Site type (two levels) as a fixed-effect term as well as the categorical variables Year (two levels) and Site nested within Site type (five levels per site type) as random-effect terms.
ACKNOWLEDGMENTS We thank  E. Curtis ,  M. Dehon , and  B. Contino  for helping in fieldwork,  BOMB Lab and Agrozoology Lab  for technical help with genetic analyses,  D. Evrard  for technical support, and  T. J. Wood  for proofreading and English improvement. This work was partly supported by the  FRFC  project  2. 4. 613. 12  ( F. R. S. -FNRS-Fonds National de la Recherche Scientifique ) and the  Belspo  project  Belbees  ( BR/132/A1 ). NR and LT are PhD students of the FRIA. MV was F. R. S. - FNRS postdoctoral researcher (grant fellowship  " Charg e de recherches "). DM and MV conceived the ideas and designed methodology; NR, GG, RM, DP, SG, NP, and LT collected the data; MV, NR, GG, MG, RM, SG, GS, IM, and DF analyzed the data; and MV, DM, NR, and RM led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
In light of the spatial heterogeneity of eddy coverage, amplitude, and size, along with the variety of mechanism by which eddies impact biological and biogeochemical processes, we provide a brief analysis of the mesoscale eddy field at each NAAMES stations in the Supplementary Materials of this article. The data presented here are publicly available in NetCDF format as part of the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) maintained by the NASA Ocean Biology  Processing Group and can be accessed at the URL https://seabass. gsfc. nasa. gov/archive/UWASH/gaube/NAAMES/documents.
LAGRANGIAN RE-ANALYSES OF ALTIMETRY Lagrangian diagnostics are generally defined as quantities calculated in the frame of reference of a water parcel that is followed through time  (Kundu et al. , 2008; van Sebille et al. , 2018). In the context of NAAMES, Lagrangian diagnostics can be helpful to identify frontal regions, eddies cores and peripheries and to estimate where the sampled water parcels are coming from. For this specific study we used the LAMTA Lagrangian scheme (available on bitbucket. org/f_nencio/spasso/overview) to advect water parcels using altimetry-derived current velocities (d  'Ovidio et al. , 2015). Since estimating horizontal velocities from altimetry requires making the assumption of geostrophic equilibrium, the velocity field and the derived Lagrangian diagnostics are expected to describe the ocean geostrophic layer and neglect the top surface layer where wind driven circulation is dominant (Ekman layer).
Variability of surface water masses of the Laptev and the East-Siberian seas in August-September 2018 is studied using in situ and satellite data. In situ data was collected during ARKTIKA-2018 expedition and then completed with satellite estimates of sea surface temperature (SST) and salinity (SSS), sea surface height, satellite-derived wind speeds and sea ice concentrations. Derivation of SSS is still challenging in high latitude regions, and the quality of Soil Moisture and Ocean Salinity (SMOS) SSS retrieval was improved by applying a threshold on SSS weekly error. The validity of SST and SSS products is demonstrated using ARKTIKA-2018 continuous thermosalinograph measurements and CTD casts. The surface gradients and mixing of river and sea waters in the free of ice and ice covered areas is described with a special attention to the marginal ice zone.
The sea level anomalies over the Eastern Arctic shallow seas are positive and largest in summer (up to 10 cm at 75 • N, down to 3 cm at 80 • N, as reported by  Andersen and Johannessen (2017) ). The Laptev sea considered in this article is not at all sampled by Argo products, so the ARKTIKA-2018 measurements offer a good opportunity to validate SST and SSS. Accurate measurements of surface salinity can enrich our understanding of freshwater content in the Arctic  (Rabe et al. (2011) ) and SSS estimates from L-band satellites can bring a valuable input. In this paper, the surface water evolution of the Laptev and the East-Siberian seas will be described and analysed considering wind speed and direction during the period of the ARKTIKA-2018 expedition. Firstly, data and methods used for the following study will be presented.