dataesr/scientific-paragraphs-categorization huggingface dataset:
2016) , OBITOOLS  (Boyer et al. 2016) , DADA2  (Callahan et al. 2016) , FROGS  (Escudié et al. 2018) , or the webapplication SLIM  (Dufresne et al. 2019). First, bioinformatic processing usually includes various quality-filtering steps, where primers, sample tags, and sequencing adaptors are removed from raw sequences. These are then trimmed to remove low-quality ends and quality-filtered (based on nucleotide quality-scores or error rates). Next, a key choice one has to make is to define the molecular entity that will serve as a proxy for species in the dataset; this can result from either clustering or denoising sequences, resulting in OTUs or amplicon sequence variants (ASVs), respectively  (Macheriotou et al. 2020 (Macheriotou et al. , 2018)). These two options represent distinct strategies for countering errors generated during sequencing; the former does so by grouping (clustering) processed sequences within a user-defined similarity threshold, while the latter applies a data-based and quality-aware correcting algorithm to the sequences.
g. , an undetected chimera, a pseudogene), or because of tag switching. It is thus current practice to remove singletons and filter molecular clusters based on their relative abundance per sample or in the total dataset  (Wangensteen and Turon 2016). These minimal abundance filters have to be chosen with caution as they significantly affect qualitative detection measures. To avoid arbitrary filtering based on relative abundance,  Froslev et al. (2017)  have developed an alternative curation algorithm, LULU, which filters OTUs/ASVs based on their identity and cooccurrence rates to more abundant OTUs/ASVs. Although ecological patterns can be investigated without taxonomic identities, species names are useful for inferring biological traits or ecosystem function, as behind each name, there is a phenotype, an ecological role, and a geographic distribution.
Therefore, once ASVs/OTUs have been generated, one can identify their Linnaean identity through taxonomic assignment by which each ASV/OTU sequence is compared to a reference sequence of known taxa. The comparison can be tree-, alignment-, phylogeny-, or probability-based, the choice of which can affect the resulting taxonomy  (Holovachov et al. 2017). In this respect, the Ribosomal Database Project's (RDP) naïve Bayesian classifier has been the suggested methodology for 16S ribosomal OTUs  (Navas-Molina et al. 2013)  providing up to 89% genus level accuracy  (Wang et al. 2007 ). However, a recent study comparing taxonomy prediction algorithms on 16S rRNA and ITS sequences found that alignmentbased methods (e. g. , BLAST) allowed similar accuracy than probabilistic methods, although the latter have the advantage of providing a confidence level for each taxonomic rank  (Edgar 2018).
The limitations in taxonomic assignment quality are therefore mostly due to the limited amount of data available rather than algorithms, and this is especially true for understudied ecosystems such as the deep-sea. Arbitrarily large and uneven databases containing sequences that have not been truncated to the target sequence length, can decrease the number of accurate taxonomic assignments  (Werner et al. 2012; Ritari et al. 2015; Edgar 2018; Macheriotou et al. 2018) , and recent work shows that taxonomic assignments based on public databases remain poor for deep-sea metabarcoding data generated with "universal" primers  (Brandt et al. 2021b). For this reason, the development of smaller, taxon-specific and ecosystem-specific reference databases will be necessary to obtain reliable taxonomic information from metabarcoding studies performed in poorly characterized areas such as the CCFZ.
5% LC-MS grade water, and 2. 5% trifluoroacetic acid is used. Depending on size, 2-5 μL of HCCA are applied to a specimen in a microcentrifuge tube after complete evaporation of ethanol  (Volta et al. 2012; Bode et al. 2017; Rossel and Martínez Arbizu 2018). After 5 min of incubation, the protein solution is transferred for measurement to a target plate. After co-crystallization of peptides, proteins and matrix solution the target plate can be measured. Usually, protein sizes for identification of metazoans are measured within a range from 2000 to 20,000 Da  (Laakmann et al. 2013; Mazzeo and Siciliano 2016; Rossel and Arbizu 2018). To avoid misidentifications based on poor signal quality, a data processing pipeline to recognize mass spectra of minor quality should be applied  (Palarea-Albaladejo et al.
A combination of both approaches can be used for areas with an incomplete reference library. During the first step, specimens can be identified using RF, followed by an analysis of the remaining specimens using the clustering approach to generate species data across stations. Until present, only few studies provided accessible data, since so far no data depository for mass spectra data (comparable to GenBank or BOLD for DNA sequences) is widely used. However, data can be stored either processed or unprocessed at the Dryad data depository (https://datadryad. org/). Because of the variability in measured protein sizes, data has to be processed conjoined to ensure protein sizes to be put into homologous bins for analysis. Hence, it is advisable to store unprocessed data alongside processed data matrices.
Traits (e. g. , size, mobility, feeding strategy, etc. ) may be obtained from direct measurements of individuals and/or from bibliography. Some trait databases exist where scientists may retrieve information, e. g. , polytraits (database on biological traits of polychaetes, http://polytraits. lifewatchgreece. eu/)  (Faulwetter et al. 2014) , sFDVent (a global trait database for deep-sea hydrothermal vent fauna, https://datadryad. org/stash/ dataset/doi:10. 5061/dryad. cn2rv96) (Chapman et al. 2019) , and WoRMS (WoRMS Editorial Board 2020) (http://www. marinespecies. org/). Multiple analysis frameworks and indices are readily available, allowing the analyses of categorical and continuous traits  (Villéger et al. 2008; Laliberte and Legendre 2010; Mouchet et al. 2010). Contrary to trophic markers (see below), trait-based methods are non-destructive, and can be applied regardless of the available number of specimens or biomass.
In the CCFZ, simple spatial interpolation has been used in the proposal of marine protected areas (MPAs)  (Wedding et al. 2013 ) that have been adopted in a modified version by the ISA in 2012 to designate Areas of Particular Environmental Interest (APEIs)  (Wedding et al. 2015). This approach already included a continuous spatial dataset on invertebrate macrofauna abundance, but the framework was mainly based on abiotic variables such as nitrogen flux, nodule abundance, and water depth  (Wedding et al. 2013). Kuhn et al. (2020)  applied RF regression to predict the continuous spatial distribution of meiofauna and macrofaunasized epifauna abundances in the German exploration area (BGR). On high taxonomic level, the abundance of polymetallic nodules is an important predictor, influencing the different size classes of organisms in different ways and should be considered in models.
As taxonomic databases are still very incomplete regarding deep-sea taxa, metabarcoding studies should integrate taxonomy and barcoding in order to fill database gaps. While taxonomic identification is useful for inferring ecological state, biotic indices can also be derived from taxonomy-free machine learning approaches. • Proteomic fingerprinting may also be a promising avenue and can be a faster and cheaper alternative to metabarcoding. • The measurement of aspects of diversity other than species and their abundances including biomass, functional traits, and trophic markers may provide critical insights that can help to answer current and future key questions and can be combined with more classical biodiversity assessments. • Statistical modeling facilitates predicting patterns spatially across point-source data and it is necessary for designing future sampling efforts, and predict species distributions ranges and thus, conservation management.
Acknowledgments The authors thank the western  France laboratory cluster (Laboratoire d'Excellence) LabexMER  ( ANR-10-LABX-19 ), the program " Ressources Minérales Marines " (REMIMA) at the  French Research Institute  for the exploitation of the  Sea (Ifremer) , the  Senckenberg Institute , the  Ghent University  for funding the "NemAtodes from deeP-sea pOLymEtallic nOdules: implications to improve miNing pratices" (NAPOLEON) Workshop, held the 18th-22nd June 2018 in Brest, France. From their work, the invited speakers and other participants in the workshop participated in generating the manuscript. S. Kaiser a grant from the  Narodowa Agencja.
Comparison of global mean temperature metrics Annual mean global mean temperature anomalies in the HadCRUT4  11  dataset, relative to 1850-1900, based on an area-weighted global mean of monthly-mean anomalies are shown in Figure  1a. These are compared with global mean blended sea surface temperature over ocean and near surface air temperature over land and ice masked with HadCRUT4 coverage  5  (GMST, see Methods) in individual CMIP6  12  historical simulations merged with SSP2-4. 5  13  simulations (historical-ssp245 simulations hereafter). The simulated warming in 2010-2019 is 17% (5-95% range of 10%-24%) stronger in globally-complete GSAT than in HadCRUT4-masked GMST (Figure  1a ), similar to previous results based on CMIP5  14, 15  , demonstrating the importance of the choice of metric for assessing attributable warming.
Comparing globally-complete versions of GSAT and GMST, the simulated warming in GSAT is only 6% stronger (5-95% range of 2%-8%). Hence the largest contribution to the enhanced warming in globally-complete GSAT versus HadCRUT4-masked GMST warming comes from the observational masking. Multiplying the observed 2010-2019 warming in HadCRUT4 GMST of 0. 94 °C (5-95% range of 0. 90-0. 99 °C, see Supplementary Table  1 ), by the ratio of simulated warming in globallycomplete GSAT to HadCRUT4-masked GMST (1. 17), we infer a best estimate of observed 2010-2019 warming in GSAT of 1. 10 °C (5-95% range of 1. 01-1. 20 °C). Similar calculations using GISTEMP  16  and NOAAGlobalTemp 17 yield estimates of observed GSAT warming in 2010-2019 of 1. 18 °C and 1. 12 °C respectively (Supplementary Table  1 ). For the remainder of the study we primarily report results based on the non-infilled HadCRUT4 dataset, and to ensure a like-for-like comparison, we use masked and blended model output when comparing with HadCRUT4 observations, including in all regressions.
Attribution of global mean temperature changes In order to quantify the contributions of individual forcings to observed trends we used the CMIP6  12  DAMIP 18 simulations from the thirteen CMIP6 models for which the necessary simulations were available (Figure  1b , Extended Data Figure  1 , Supplementary Table  2 ): ACCESS-ESM1-5  19  , BCC-CSM2-MR 20 , CanESM5 21 , CESM2  22  , CNRM-CM6-1  23  , FGOALS-g3  24  , GFDL-ESM4  25  , GISS-E2-1-G 26 , HadGEM3-GC31-LL  27  , IPSL-CM6A-LR  28  , MIROC6  29  , MRI-ESM2-0  30  and NorESM2-LM  31. We primarily used output from four experiments: historical-ssp245 (driven with changes in all anthropogenic and natural forcings), hist-aer (driven with changes in anthropogenic aerosol emissions and burdens only), hist-nat (driven with changes in natural forcings only), and hist-GHG (driven with changes in well-mixed greenhouse gas concentrations only).
Methods We downloaded monthly mean near-surface air temperature (tas), sea surface temperature (tos), and sea ice concentration (siconc) from all the CMIP6 models for which the necessary CMIP6 historical  12  , ScenarioMIP 13 SSP2-4. 5 and DAMIP  18  hist-nat and hist-aer simulations were available (Supplementary Table  2 ). SSP2-4. 5 forcings were used in the DAMIP simulations for the 2015-2020 period  18  , so we merged CMIP6 historical simulations with SSP2-4. 5 simulations for the period 2015-2019 for consistency. We used ESMValTool  42  to preprocess the model output, and used Cowtan 5 code to calculate masked and blended temperature from the model output using HadCRUT4 11 observational masking, and using anomalies and variable sea ice concentration  5. We calculated 5-year mean global means of these data using area-weighting, for the period January 1850 to December 2019 to give a vector with 34 elements, and then subtracted the long-term mean to give anomalies.
Due to limited availability of the land-sea mask from some models, the land-sea mask from CNRM-CM6-1, regridded onto a 5°×5° grid, was used for all models. Observed GMST was calculated from HadCRUT4 11 monthly anomalies by area weighting, taking 5-year means, and subtracting the long-term mean to give anomalies. The median dataset was used for the main analysis results, and each of the 100 members of the ensemble dataset were treated in the same way and used to derive uncertainties in the multi-model attributable warming estimates (see also Extended Data Figure  6 ). The uncertainty range in inferred observed GSAT warming was obtained by randomly sampling a HadCRUT4 ensemble member, and the ratio of GSAT to GMST warming from an individual historical-ssp245 simulation, taking the product, and repeating 10000 times, with equal weight given to each CMIP6 model.
The NOAAGlobalTemp 17 (v5) dataset starts in 1880, but our analysis required data from 1850, so we concatenated HadCRUT4 anomalies relative to the NOAAGlobalTemp 1971-2000 base period over the 1850-1879 period with NOAAGlobalTemp, and then calculated global mean 5-yr mean anomalies as for HadCRUT4. The GISTEMP  16  (v4) data are available on a 2°×2° grid, so we first interpolated onto the HadCRUT4 5°×5° grid. We then concatenated with HadCRUT4 anomalies relative to the GISTEMP base period of 1951-1980 over the period 1850-1879, since GISTEMP starts in 1880. We then calculated global-mean 5-yr anomalies as for the other datasets. Five-year mean hemispheric means (Extended Data Figure  5 ) were calculated in the same way from gridded anomalies in masked and blended model output and observations.
Data availability All figures in this manuscript use CMIP6 data available here (https://esgfnode. llnl. gov/projects/cmip6/). The DOIs of the CMIP6 datasets used from each model were: ACCESS-ESM1-5: 10. 22033/ESGF/CMIP6. 2288, 10. 22033/ESGF/CMIP6. 14362, 10. 22033/ESGF/CMIP6. 2291; BCC-CSM2-MR: 10. 22033/ESGF/CMIP6. 1725, 10. 22033/ESGF/CMIP6. 1726, 10. 22033/ESGF/CMIP6. 1732; CanESM5: 10. 22033/ESGF/CMIP6. 1303, 10. 22033/ESGF/CMIP6. 1305, 10. 22033/ESGF/CMIP6. 1317; CESM2: 10. 22033/ESGF/CMIP6. 2185, 10. 22033/ESGF/CMIP6. 2187, 10. 22033/ESGF/CMIP6. 2201; CNRM-CM6-1: 10. 22033/ESGF/CMIP6. 1375, 10. 22033/ESGF/CMIP6. 1376, 10. 22033/ESGF/CMIP6. 1384; FGOALS-g3: 10. 22033/ESGF/CMIP6. 1783, 10. 22033/ESGF/CMIP6. 2048, 10. 22033/ESGF/CMIP6. 2056; GFDL-ESM4: 10. 22033/ESGF/CMIP6. 1407, 10. 22033/ESGF/CMIP6.
89 (ns), p Glu-4h = 0. 6 (ns), p Glu-6h = 0. 54 (ns), p Glu-24h = 0. 78 (ns), p Glu+BINA-2h = 0. 39 (ns), p Glu+BINA-4h = 0. 2 (ns), p Glu+BINA-6h = 0. 43 (ns), p Glu+BINA-24h = 0. 83 (ns), n = 4 independent biological samples examined over 3 independent experiments. h Scatter plot of ΔEC 50 , i. e. the difference in EC 50 obtained in presence and absence of BINA, at time 24 h at RT (y-axis) vs. at time 0 h (x-axis), for membrane fractions and detergent mixtures. The conditions along the diagonal represent those experiencing the lowest changes over time. Source data of panels b-h are provided as a source data file. of the major population to lower FRET, a portion of the HF population remained for the full-length receptor. Next, we explored the effect of a PAM at saturating concentrations. Alone, BINA had no effect (Fig.
Methods Chemicals. All chemicals were purchased from Sigma-Aldrich, Merck and Roth unless otherwise noted. n-dodecyl-β-D-maltopyranoside (DDM), lauryl maltose Fig. 5  Different efficacies of orthosteric ligands on mGlu2 ECD rearrangement. a-h FRET distributions were obtained in the presence of partial agonists LCCG-I (a) and DCG-IV (b) or full synthetic agonist LY379268 (g) alone or in the presence of BINA (c, d, h respectively) or G i (e and f, respectively). i Comparison of the fraction of the active state (LF/(LF+HF)) in response to different orthosteric and allosteric ligands. n = 3 independent biological replicates examined over 3 independent experiments for each condition. The scatter plot shows data together with the mean± SD. Statistical differences were determined using a one-way ANOVA with Sidak multiple comparisons test and are given as: p LY35/Glu = 0.
0068 (**), p Glu/LY37 = 0. 0006 (***), p DCG-IV+BINA/Glu+BINA = 0. 0028 (**), p Glu+BINA/LY37+BINA = 0. 61 (ns). j Comparison of the fraction of all states in response to different orthosteric and allosteric ligands. Data represent the stacked means ± SD from 3 independent biological replicates for each state with error bars centered around the mean for the VLF, LF, HF, and VHF from top to bottom, respectively. Source data of panels a-j are provided as a source data file. neopentyl glycol (LMNG), and cholesteryl hemisuccinate (CHS) tris salt were purchased from Anatrace (through CliniSciences, France). Glyco-diosgenin (GDN) was purchased from Avanti Polar Lipids through Merck. SNAP-Lumi4-Tb, SNAPgreen, SNAP-Cy3B, and SNAP-d2 were obtained from Cisbio Bioassays (Codolet, France). DCG-IV, LY341495, LY379268, LY354740, LCCG-I, BINA hydrochloride, and Ro64-5229 were purchased from Tocris Bioscience (Bristol, UK).
05 U/µl of apyrase (Sigma-Aldrich, France) and incubation for another 30 min before acquisition. The effect of 100 µM Glu on VFT reorientation was reversed by the addition of 1 mM of the competitive antagonist LY34. smFRET data analysis. Data analysis was performed using the Software Package for Multiparameter Fluorescence Spectroscopy, Full Correlation and Multiparameter Fluorescence Imaging developed in C. A. M. Seidel's lab (http:// www. mpc. uni-duesseldorf. de/seidel/). A single-molecule event was defined as a burst containing at least 40 photons with a maximum allowed interphoton time of 0. 3 ms and a Lee-filter of 20. Photobleaching events were identified base on | TGX-TRR | < 1 ms as described  57. τ DA vs E analysis and time windows analysis were performed using the PAM 1. 3 software  58.
To display the 1D FRET efficiency histograms and for further analysis, doubly labeled (Donor-Acceptor) molecules were selected using the stoichiometry S factor using (0. 3-0. 35 < S < 0. 6-0. 65). Additional software. The structures shown in Fig. 1  were generated using PYMOL 2. 3. 3. LRET data was analyzed using MARS (BMG Labtech) and displayed in GraphPad PRISM 7. 05. FRET histograms were fitted and displayed using Origin 6 (Microcal Software, Inc. ) and PRISM 7. 05 (GraphPad).
Acknowledgements We thank  Sebastien Granier  and  Rémy Sounier  ( IGF, Montpellier, France ) for providing the heterotrimeric G protein;  Gilles Labesse  ( CBS, Montpellier ) for helping in mGlu2 modeling;  Suren Felekyan  ( U. Dusseldorf, Germany ) for discussion on data analysis;  Guillaume Lebon ,  Alexandre Bouyssou  ( IGF, Montpellier, France ) and the members of the  IBM team (CBS, Montpellier)  for fruitful discussions; the  Arpège platform (IGF, Montpellier ) for providing facilities and technical support,  Perkin Elmer Cisbio  for providing reagents. Our research is supported by grants from the  Agence Nationale pour la Recherche  ( ANR 17-CE09-0026-02  to EM and  ANR 18-CE11-0004-02  to EM and JPP), and the  Fondation Recherche Médicale  ( DEQ20170336747  to JPP). AMC was supported by the  Labex EPIGENMED.
As these damages are due to traffic, a full-scale experiment, done with the pavement fatigue carrousel of the university Gustave Eiffel, has focused on the detectability of different kinds of artificial debonded areas in a pavement structure test by few GPR under a controlled traffic. The interest of such study is to monitor the test section along its service life, to create a large GPR database while studying the evolution of the test structure in terms of damage level and lateral extension, of the defects at different loading cycles. The data collection was organized in a two-stage experiments and covers the full lifecycle of the pavement structure. During the first stage, which took place in 2012  [12] , 300 K loading cycles were performed, and degradation, either in extension or evolution, was expected.
4 GHz) and the center of the GPR bandwidth (~5. 8 GHz). The univ. Eiffel's stepped-frequency radar (SFR) relies on ultra-wide-band (UWB) radar technology. Data were collected in the frequency domain within the bandwidth 0. 8 GHz-10. 8 GHz using a vector network analyzer (VNA) and two air-coupled UWB Vivaldi antennas  [16]. Inverse fourier transform is conventionally used to provide time domain radar data (B-scans), comparable to a 5 GHz impulse system, in the time domain. The 3D-radar manufacturer-provided stepped-frequency 3D radar array system is composed of 21 ground-coupled antennas separated by 7. 5 cm providing a sweep width of ~1. 40 m. The frequency band is ranging from 40 to 3000 MHz, working with the highest range of frequency, leading, after an inverse Fourier transform, to radar data equivalent to multiple 1.
Acquisition and Processing Methodologies During the experiment, radar surveys were performed at periodical loading stages. In 2012, they were focused on the largest defects, and done after the construction of the test zone, at 10 K loadings (the structure being considered as consolidated), and at around 50 K, 100 K, 200 K, 250 K, and 300 K loadings. In 2019-2020, the experiment was completed by measurements done on every defect (except the univ. Eiffel's SFR focused on I-11, I-12, and I-13) at about 310 K, 396 K, 420 K, 500 K, 600 K, 720 K, and 800 K loadings (end of the APT). All the data were realized in the summer period, except for 720 K loading stage done end of January. For the data collection, raw B-scans from impulse radar systems and univ. Eiffel's SFR were taken at each loading stage, in two major directions: Transverse and longitudinal at the center of the defects.
Transverse profiles were performed from the inner to the outer radius of the traffic path, and the longitudinal ones in the direction I-13 to I1. For the 3D-radar system, data are presented as horizontal maps (C-scans) of the surface echoes and the ones from the interface between the two bituminous layers. From these data, longitudinal B-scans have been extracted from the center of the largest defects. The data pre-processing differs from the radar technology. The data pre-processing steps for impulse radar is specified in Table  2. Because of the technology of acquisition, the stepped-frequency radar data are basically required to perform inverse Fourier transform to provide the temporal B-scan images (with some time gating to limit the time horizon) and some calibration steps beforehand to take into account the antenna response in free space.
Introduction This global experiment, done in two series over a period of eight years, led to the acquisition of more than 360 B-scans from impulse systems and more than 140 from stepped-frequency ones, with open-access data available in  [16, 17]. Processing methodology, presented in Section 2. 3, has been performed on most of these data. Nevertheless, for reasons of clarity, only the major ones are presented and commented on, with the others being referenced in appendices A to D. Moreover, several cross-section profiles, done with a laser rugosimeter system, were performed showing the rutting resulting from the heavy traffic. Figure  7  presents, as an example, the cross-section profiles done in the center of I-13 and in two healthy zones (between I-11 and I-13 and between I-3 and I-4), from the inner to the outer radius of the traffic path.