dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The naturalist surveys carried under the umbrella of the Tropical Deep Sea Benthos program (TDSB) provide the only cohesive data set to describe the structure of deep-sea biodiversity in French Polynesian EEZ. Based on the results of the three TDSB expeditions, we inventoried 471 invertebrate species. Among these 471 species, 169 are newly described species based on TDSB sampling, and 30 are either under description, or not yet attributed to a species name. This high proportion of species newly described, together with the amount of material still left to be examined by taxonomists, stresses how much diversity remains to be explored for these environments, especially in such remote areas. Nonetheless, the analyses of these data allowed us to provide a first insight into   the biodiversity patterns of the deep-sea benthic communities of French Polynesia, in a context of potential deep sea mining activities.
2010), this value is revised to 70%, which is still a high proportion of endemics. Furthermore, these endemic galatheid species are equally distributed over the three prospected areas, and over the different explored habitats. Cone snails represent another example. In our dataset, among the 22 species of Conidea sampled below 200 m in French Polynesia, 30% can be considered as endemic to this area. However, this number should be taken with caution since recent studies showed that, among the listed species, some belong to species complex pending revision. For example, based on available literature data, we considered Conus orbignyi as cosmopolite. However ongoing molecular studies revealed that under the name C. orbignyi, a complex of at least nine species is concealed, some of them being geographically limited to one archipelago  42.
Since, only dead shells of cone species were collected in the deep waters of French Polynesia, it is likely that our study underestimates their endemicity. Results tend to show that the deep-sea communities of French Polynesia have an especially high score of endemism and rarity. Rarity is a relative concept. Usually, the species falling in the first quartile of the frequency distribution of species occurrences are considered as "rare"  43. Here, 40% of the species in the dataset #1 were only sampled once (Fig. 3d ). Hence, it would be meaningless to apply the 25% cut-off to this dataset. Moreover, the species that were sampled only once were often the ones sampled with only a few specimens. The interpretation of the data, in terms of endemism, may thus be obstructed by the apparent rarity of most sampled species, resulting from the shortfall of sampling over such a large area.
Indeed, our sampling is not saturated for any of the three regions (Fig. 3a ), and the heterogeneity of the upper bathyal region of the whole French Polynesia is far from covered with 391 stations. In addition to the limited number of sampling stations in relation to the size of the French Polynesian EEZ, it is also worth considering the taxonomic coverage of the available dataset. Indeed, although all the phyla have been collected during these cruises, only the Molluscs, Crustaceans, Crinoids and Brachiopods have been extensively identified. It should be noted that Bertrand Richer de Forges, the PI of MUSORSTOM 9 and BENTHAUS cruises is a carcinologist and that Philippe Bouchet, the PI of TARASOC cruise is a malacologist. On that account, it is likely that their networks of collaborators include more malacologists and carcinologists than specialists of other taxa.
The time spent between sampling and species descriptions -the shelf life of specimens  44  -is thus highly contingent, and results in a bias in taxonomic coverage. For example, survey logbooks from MUSORSTOM 9 stated that large amounts of Echinoids and Asteroids were sampled  26. Moreover, the field photos and the logbooks both confirm the presence of Cnidaria and Porifera in the sampled stations, but in very low abundances. These taxa are lacking in our dataset because none have been identified by a taxonomist yet. They include habitat-forming species which often dominate the biomass and shape the habitats  45. As such, they are used as indicator taxa for Vulnerable Marine Habitats (VME) in the deep sea  46. However, no coral gardens, sponge grounds, or crinoid fields, which have been described on some cobalt-rich seamounts in the Central Pacific  9, 47  , were observed in the three surveys in French Polynesia.
The analysis of the spatial distribution of such abundant and large organisms is possible using imagery data. But such techniques are not well-suited for surveying environments inhabited by small organisms living in low densities. In the largest known oceanic desert, the alternative sampling method, employing physical sampling of biological specimens and precise taxonomic identifications, reveals that polymetallic crusts, are inhabited by distinct macrobenthic communities. Submarine imaging techniques would certainly not have allowed us to obtain this result. The analyses of the molluscan dataset allowed us to reveal the differences in size spectrum, between the communities living on seamounts or polymetallic crusts, and between the communities living on island slopes. We should stress that, had we considered only the available taxonomic literature, our analysis would not have revealed the size spectra.
Indeed, for the Molluscs only 11 out of 48 articles considered for dataset #2, included description of micromolluscs. This bias in the published literature is due to the time needed for the identification of smaller molluscs  22, 55, 56. Among the molluscs sampled during these three cruises, the smaller specimens are still mostly identified only at the family level. In our dataset, the smaller size of molluscs on seamounts, in comparison to island slopes, could reflect the impoverishment in food input on isolated seamounts. Contrary to isolated seamounts, island slopes are supplied with terrestrial inputs. Abundance and biomasses of animals decrease with depth, but this decrease is more important for large animals than for small ones  57. Deep-sea environments with unusually high food inputs (like hydrothermal vents or, animal or plant falls), have high abundances of large animals  58.
Acknowledgements We would like to thank the crew and scientific teams who, onboard of the RV Alis (belonging to the  Institut de Recherche pour le Développement, IRD) , collected and sorted the material. We would like to thank the team of MNHN curators and volunteers, who manage the deposited collections, and the network of taxonomists that studies these collections. We thank  Sophie Bary  for helping us with literature compilation and  Nicolas Puillandre  for the Conoidea data derived from the  CONOTAX  project (Grant Number  ANR-13-616 JSV7-0013-01 ). We specially thank  Philippe Bouchet  and  Bertrand Richer de Forges  as PIs of the three TDSB cruises in French Polynesian EEZ and  Eric Pante  for discussions and suggestions during the preparation of the manuscript. This work was supported by a grant from  Agence Nationale de la Recherche  under the LabEx  ANR-10-LABX-0003-BCDiv , in the program " Investissements d'avenir" n°   ANR-11-IDEX-0004-02.
Field measurements The Table  6  presents an overview of the whole dataset acquired specifically during the LANDEX field campaign. The data validation rate was greater than 72 % over the 27 d of the campaign. The two major monoterpenes observed were β-pinene and α-pinene, representing 60 % of the terpenoids measured on average, where isoprene represented about 17 %. In order to give insight into the performance of the method, the time series of the uncertainty apportionment is shown for β-pinene for the whole campaign (see Fig. 8 ). As reported in Sect. 3. 3, β-pinene uncertainty was driven by the precision term, but, depending on the period, the integration term became significant due to asymmetric peaks. The linearity term also had an important influence for mixing ratios higher than the calibration values.
To determine the metabolic response of parsnip leaves to O 3 and wounding stresses, we designed a liquid chromatography-mass spectrometry (UHPLC-MS) untargeted metabolomic approach. Parsnip plants were subjected to acute O 3 stress or mechanical wounding (MW). The metabolite composition of the leaves was determined 24 h, 48 h, and 72 h after the initiation of the stresses. The datasets generated were analyzed by multivariate and univariate statistical approaches. Metabolite signals differentially accumulated between treated and control plants were identified and replaced in the corresponding metabolic pathways. MW induced a stronger plant response than did the O 3 stress. In addition to furanocoumarin accumulation, our study depicted a large modification of the parsnip metabolome in response to MW that included monoterpene accumulation and flavonoid depletion, together with a modulation of the lipid pathway.
4-Methylumbelliferone at a concentration of 5 µM was used as an internal standard for mass quantification. The results of this preliminary experiment are available in Supplemental Data 1. Briefly, the cumulative concentrations of all the metabolites measured were not significantly different between the five leaflets from the same parsnip stem. Therefore, the leaflets sampled at T0, T1, T2, and T3 correspond to leaflets from the same stem.
Metabolic Data Analysis The UHPLC-MS raw data were converted to. cdf files using the LabSolution software (Shimadzu, Japan) and were pre-processed using the XCMS Online platform (https://xcmsonline. scripps. edu). This pre-processing consisted of peak detection, retention time correction, and alignment of the molecules. Peak detection was performed with the CentWave method (∆m/z = 2. 5 ppm, peak width (5-50), Signal-Noise threshold = 20). Retention time correction was performed with the obiwarp method, and alignment was performed with the following parameters (minfrac = 0. 5, bw = 5, mzwid = 0. 015). An analysis was performed by comparing the eight replicates of each treatment at a given harvest time (T0, T1, T2, and T3). The pre-processed datasets, corresponding to 1592, 1077, 1091, and 1260 features in positive mode at T0, T1, T2, and T3, respectively, were imported into Microsoft Excel and converted to.
csv files. Multivariate and univariate statistical analyses were performed with the MetaboAnalyst 4. 0 online platform (https://www. metaboanalyst. ca) [28]. Prior to the statistical analyses, datasets were filtered based on the relative standard deviation (RSD/mean) to remove the features with low repeatability (40% of the filtered features). Then, data were normalized using the Pareto method (each feature was mean centered and divided by the square of the standard deviation). A principal component analysis (PCA) was conducted on the filtered and normalized features, corresponding to 956, 641, 655, and 755 features for T0, T1, T2, and T3, respectively. A two-way analysis of variance (ANOVA, p < 0. 001) was performed to identify metabolic features affected by treatment and sampling date. A one-way analysis of variance (ANOVA, p < 0.
05, Fisher's least significant difference (LSD) post hoc test) was performed to identify the metabolic features significantly affected by O 3 and MW stresses at a specific sampling date. In each case, a false discovery rate approach was performed as multiple testing correction. Metabolite identification was performed based on significantly affected metabolic features. Raw HPLC-MS LTQ-Orbitrap data files containing exact mass and mass fragmentation data were converted to. abf and analyzed using MS-Dial (v. 3. 20) and MS-Finder (v. 3. 12) software. HRMS data allowed the determination of the molecular formula, while MS/MS data were used to propose molecular structures by comparison with 21 metabolomic databases, including UNPD, LipidMAPS, HMDB, MassBank, METLIN, and ReSpect. When possible, the identification of metabolites was confirmed by comparison with authentic standards.
Impact of Treatment on the Oxidative Status of Parsnip Leaves To characterize the intensity of oxidative stresses, the hydrogen peroxide (H 2 O 2 ) concentration was assessed in parsnip leaves that were exposed to O 3 or mechanically wounded (MW). After three days of O 3 exposure (250 ppb), parsnip leaves did not show any chlorosis or early senescence symptoms. H 2 O 2 content was measured for the O 3 , MW, and control plants. Although H 2 O 2 content tended to be higher for the O 3 treatment, this observation was not statistically significant (Supplemental Data 2).
Global Overview of the Metabolomic Profile. To determine the global metabolic response in MW-and O 3 -treated parsnip leaves, we developed an untargeted metabolomics strategy. For this purpose, LC-MS analyses were performed on the hydro-methanolic extracts prepared from the leaves collected 24, 48, and 72 h after the beginning of the stresses. Metabolic features, characterized by a mass on charge ratio in positive mode (m/z) and the retention time, were detected and quantified in each sample (Supplemental Data 3). In order to assess the impact of treatment on the parsnip leaf metabolome, we performed a PCA on the metabolic features at each sampling date (Figure  1 ). HMDB, MassBank, METLIN, and ReSpect. When possible, the identification of metabolites was confirmed by comparison with authentic standards.
Impact of Treatment on the Oxidative Status of Parsnip Leaves To characterize the intensity of oxidative stresses, the hydrogen peroxide (H2O2) concentration was assessed in parsnip leaves that were exposed to O3 or mechanically wounded (MW). After three days of O3 exposure (250 ppb), parsnip leaves did not show any chlorosis or early senescence symptoms. H2O2 content was measured for the O3, MW, and control plants. Although H2O2 content tended to be higher for the O3 treatment, this observation was not statistically significant (Supplemental Data 2).
Global Overview of the Metabolomic Profile. To determine the global metabolic response in MW-and O3-treated parsnip leaves, we developed an untargeted metabolomics strategy. For this purpose, LC-MS analyses were performed on the hydro-methanolic extracts prepared from the leaves collected 24, 48, and 72 h after the beginning of the stresses. Metabolic features, characterized by a mass on charge ratio in positive mode (m/z) and the retention time, were detected and quantified in each sample (Supplemental Data 3). In order to assess the impact of treatment on the parsnip leaf metabolome, we performed a PCA on the metabolic features at each sampling date (Figure  1 ). The red, green, and blue areas and dots are related to control, mechanical wounding (MW), and ozone stress (250 ppb), respectively.
001). The heatmap constructed with the significant metabolic features (Figure  2 ) led to identifying clusters of metabolic features that were positively (Cluster I, Figure  2 ) or negatively (Cluster III, Figure  2 ) affected, depending on the sampling time. In addition, three main trends can be visualized regarding the impact of treatments: Cluster II gathered metabolic features that tended to decrease both under O 3 and MW; Cluster IV contained few metabolic features accumulating under O 3 in comparison with the control; and Cluster V gathered metabolic features gradually accumulating under MW in comparison to both O 3 and control samples (Figure  2 ). Figure  1. Principal component analysis (PCA) of the parsnip metabolomic datasets. PCA analyses were performed at T0, T1 (24 h), T2 (48 h), and T3 (72 h) after the application of the stresses.
Metabolite Identification Metabolite identification was performed on the DAMFs at T1, T2, and T3, which represented a dataset of 544 non-redundant entities. This dataset was filtered by subtracting fragment ions and metabolic features with a small peak area or with non-exploitable MS/MS data, leading to a final dataset of 245 DAMFs. High-resolution mass spectrometry and MS/MS fragmentation data led to the identification of 39 metabolites (Table  2 ). The identified DAMFs were mostly related to MW rather than to O 3 exposure (250 ppb). According to the Classyfire metabolic annotation  [29] , the putatively-identified metabolites were distributed among eight major classes: flavonoids, fatty acyls, coumarins and derivatives, indoles and derivatives, tetrapyrroles and derivatives, prenol lipids, glycerophospholipids and glycerolipids (Table  2 ).
Description of the datasets generated by the XCMS process and used for MetaboAnalyst statistical analyses. The list of DAMFs determined by ANOVA is provided. Supplemental Data 4: MS2 fragmentation of M677T8 putative NCCs. A: The structure and fragmentation pattern of M677T8 identified as (2S,2(1)R)-2(1),2(2)-dicarboxy-8-ethenyl-2,7,12,18-tetramethyl-2,2(1)-dihydrobenzo[b]porphyrin-13,17-dipropanoic acid. B, C, D, and E describe the fragmentation pattern of M693T9, M691T10, M707T9, and M661T9. On each fragmentation pattern, the mass-loss specific for NCCs is described (in blue) according to Roca et al. , 2018. Author Contributions: A. H. , R. L. , and G. G. of conceived the experiment and the study. G. G. , R. L. , and A. G. performed the experiments and analyzed the data. G. G. , A. G. , Y.
Data 3 : 3 Description of the datasets generated by the XCMS process and used for MetaboAnalyst statistical analyses. The list of DAMFs determined by ANOVA is provided. Supplemental Data 4: MS2 fragmentation of M677T8 putative NCCs. A: The structure and fragmentation pattern of M677T8 identified as (2S,2(1)R)-2(1),2(2)-dicarboxy-8-ethenyl-2,7,12,18-tetramethyl-2,2(1)-dihydrobenzo[b]porphyrin-13,17-dipropanoic acid. B, C, D, and E describe the fragmentation pattern of M693T9, M691T10, M707T9, and M661T9. On each fragmentation pattern, the mass-loss specific for NCCs is described (in blue) according to Roca et al. , 2018. Author Contributions: A. H. , R. L. , and G. G. of conceived the experiment and the study. G. G. , R. L. , and A. G. performed the experiments and analyzed the data. G. G. , A.
The Atlantic Meridional Overturning Circulation (AMOC) impacts ocean and atmosphere temperatures on a wide range of temporal and spatial scales. Here we use observational datasets to validate model-based inferences on the usefulness of thermodynamics theory in reconstructing AMOC variability at low frequency, and further build on this reconstruction to provide prediction of the nearfuture (2019-2022) North Atlantic state. An easily observed surface quantity -the rate of warm to cold transformation of water masses at high latitudes -is found to lead the observed AMOC at 45 • N by 5-6 years and to drive its 1993-2010 decline and its ongoing recovery, with suggestive prediction of extreme intensities for the early 2020s. We further demonstrate that AMOC variability drove a bi-decadal warming-tocooling reversal in the subpolar North Atlantic before triggering a recent return to warming conditions that should prevail at least until 2021.
The primary purposes of the present study are (1) to validate with observational data the predictive skill of surfaceforced water mass transformation for AMOC variability and (2) to assess the causal link between AMOC variability and decadal OHC changes in the SPG and perform near-future prediction of those quantities. Regional variability will also be documented, with details on the capability of the in situ OSNAP array in monitoring the basin-wide AMOC σ. The paper is structured as follows. Section 2 presents the observational datasets and the methodology used to compute AMOC σ , SFOC σ , and OHC. Section 3 gathers the main results of the study and Sect. 4 summarizes and discusses them.
Data Monthly gridded potential temperature (θ ) and practical salinity (S) profiles from four in situ hydrographic datasets were used. Details on those datasets (EN4, CORA, ISHII, and ARMOR3D) are provides in Table  S1  in the Supplement. For each product and at each grid point, the θ and S profiles were interpolated to a regular 20 db vertical spacing. Using the TEOS-10 Gibbs-SeaWater (GSW) toolbox, practical salinity was converted to absolute salinity and potential temperature to conservative temperature, and σ 0 and σ 1 (potential density relative to sea-surface and 1000 m, respectively) were computed. Air-sea heat fluxes (radiative and turbulent) and freshwater fluxes (evaporation and precipitation) were obtained from three atmospheric reanalyses (NCEP2, ERA-I, and CERES, see Table  S1 ).
Absolute dynamic topography and associated surface meridional geostrophic velocities were obtained from the AVISO platform and combine sea-level anomalies from multi-mission satellite altimeters and mean dynamic topography from GOCE (Gravity field and steady-state Ocean Circulation Explorer), GRACE (Gravity Recovery and Climate Experiment), altimetry, and in situ data (https://www. aviso. altimetry. fr/en/data/products/ auxiliary-products/mdt. html, last access: 20 June 2019). Data were downloaded on 12 September 2017 for the years 1993-2015 and in August 2018 for the years 2016-2017. The various integrated quantities derived from those data products (such as ocean heat content or overturning stream functions -see description below in Sect. 2. 2 and 2. 3) were then combined into ensemble mean over the period  (1993-2017 for altimetry-related quantities, 1985-2017 otherwise) , with associated ensemble standard errors computed as σ √ N -1 , where σ is the standard deviation and N = 4 the number of data products used in the mean.
Acknowledgements. Damien G. Desbruyères  and  Guillaume Maze  were supported by  Ifremer. Herlé Mercier  was supported by  CNRS. Nathalie Daniault  was supported by the  University of Brest. This work was supported by the  French national programme LEFE/INSU: OBLADY  and SOMOVAR, led by  Damien G. Desbruyères  and  Guillaume Maze , respectively. All datasets used herein are available online (see the Supplement for references). Review statement. This paper was edited by  David Stevens  and reviewed by two anonymous referees.
Data availability. The EN4. 2. 0 dataset is available online at https://www. metoffice. gov. uk/hadobs/en4/download-en4-2-0. html and described in Good et al. (2013). The CORA dataset is available at https://doi. org/10. 17882/46219 and described in  Szekely et al. (2016). The ISHII dataset is available at https://climate. mri-jma. go. jp/pub/ocean/ts/ and described in  Ishii et al. (2017). The ARMOR3D dataset is available at http://resources. marine. copernicus. eu/?option=com_csw&view= details&product_id=MULTIOBS_GLO_PHY_REP_015_002 and described in Guinehut et al. (2012). NCEP_Reanalysis 2 data are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at http://www. esrl. noaa. gov/psd/ and are described in  Kanamitsu et al. (2002). ERA-I data are available online at https://apps.
ecmwf. int/datasets/data/ interim-full-daily/levtype=sfc/. The CERES/FMASS product is D. G. Desbruyères et al. :  Surface predictor of overturning circulation in the subpolar North Atlantic available at https://doi. org/10. 17864/1947. 111 and described in  Liu et al. (2017). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/os-15-809-2019-supplement.
Electronic cognitive tests used in oncology Among electronic batteries of cognitive tests used in oncology studies with adult patients excluding CNS cancer (Table  2 ), the most frequently used are CogTrack, the Cambridge Neuropsychological Test Automated Battery (CANTAB), CNS Vital Signs, CogState and BrainBaseline. They assess various cognitive domains pertinent in oncology such as attention, processing speed and memory. For each of them, patient can perform the assessment without professional assistance. The Cognitive Drug Research System (CDR), or CogTrack, was developed to assess cognition in clinical trials. The normative data are based on a large sample size  (Wesnes, McNamara, & Annas, 2016). Psychometric properties have recently been described  (Wesnes et al. , 2017)  and this battery showed a beneficial effect of modafinil on memory speed, episodic memory and attention in breast cancer survivors  (Kohli et al.