dataesr/scientific-paragraphs-categorization huggingface dataset:
The emissions of 1700 are derived from Table  1  of ref. 43  except pyrogenic sources (see below). Reference  65  provides gridded (0. 5°× 0. 5°) historical anthropogenic methane emissions from 1850 to 2000. This dataset includes fossil fuel, waste treatment and landfill, and different pyrogenic sources (agricultural waste burning, forest burning, and grass burning), but gathers rice cultivation, livestock enteric fermentation, manure management as a single agricultural sector source. To separate grass burning into C3 grass burning and savanna (C4 grass) burning, we applied the gridded C3-C4 grass distribution derived from the approach described in ref. 38  based on growing season temperature. The EDGAR v4. 3. 2  10  provides emissions from fossil fuel, waste treatment and landfill, rice cultivation, livestock enteric fermentation, manure management, and agricultural waste burning for the period of 1970-2012.
Acknowledgements J. C. and P. C. acknowledge support from the  ERC Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. S. P. acknowledges support from the  National Natural Science Foundation of China  (grant number  41722101 ). H. T. and S. D. acknowledge support from  NSF  (grant numbers:  1903722 ,  1243232 ) and  NKRDPC  (grant number  2017YFA0604702 ).
Data availability The data that support the findings of this study are available at the public Data Repository of the International Institute of Applied Systems Analysis (IIASA DARE; https://doi. org/10. 22022/ESM/06-2019. 45). The source data underlying Figs. 1, 3, and 6 are provided as a Source Data file. The source data underlying Fig. 2 are provided by Supplementary Table 2. The source data underlying Figs. 4 and 5 can be found in the IIASA DARE (https://doi. org/10. 22022/ESM/06-2019. 45).
Confirming the previous work on the effects of retromer dysfunction on seed storage proteins, the seed proteome changes in vps29 mutant indicate a deeply modified processing of storage proteins (Figure  2 ). Comparison of the 2D electrophoretic patterns of seed proteomes with the reference database shows a decrease in abundance of 12S αand β-cruciferin spots in vps29 seeds (Figure  2 ). Concomitantly, several spots corresponding to precursors of these storage proteins, such as 12S seed storage protein precursors, were more abundant in vps29 seeds, indicating that defects in retromer function do not alter seed storage protein production, but instead inhibit their maturation. Indeed, the shotgun proteome analysis showed a similar quantity of peptides belonging to 12S cruciferins in vps29 and Col-0 seeds (Table  S1 ), which is consistent with an increased abundance of precursors, but an inhibition of their maturation, as already reported for vps29  [9].
Protein Identification and Quantification A database search was performed with XTandem (http://www. thegpm. org/TANDEM/). Enzymatic cleavage was declared as a trypsin digestion with two possible missed cleavages. Carbamidomethylation of cysteine residues was set to static modifications. Methionine oxidation was set as a possible modification. Precursor mass and fragment mass tolerance were 10 ppm and 0. 02 Da, respectively. A refinement search was added with similar parameters, except that semitrypsic peptides and possible N-terminal protein acetylation or deamidation were searched for. The Arabidopsis genome database (http://www. arabidopsis. org) and a contaminant database (trypsin, keratins) were used. Only peptides with an E value smaller than 0. 05 were reported. Identified proteins were analyzed using XTandem (http://www.
Funding:  Institut Jean-Pierre Bourgin  (IJPB) benefits from the support of the  LABEX Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ), and work in  Laboratoire Reproduction et Développement des Plantes was supported by  grant  ANR-08-BLAN-0142-01. Thanks are given to  Olivier Langella  from  PAPPSO (Plateforme d'Analyse Protéomique de Paris Sud-Ouest ) for making the proteomic data available on PROTICdb.
Isolation and growth conditions In 2016, we isolated from the stool specimen of a 38-month-old healthy girl from Senegal, the bacterial strain mt20 T. Screening was performed by MALDI-TOF MS on a Microflex LT spectrometer (Bruker Daltonics, Bremen, Germany) as previously described  [7]. The obtained spectra (Fig. 1 ) were imported into MALDI BIOTYPER 3. 0 software (Bruker Daltonics) and analysed against the main spectra of the bacteria included in two databases (Bruker and the constantly updated MEPHI databases (http://backup. mediterranee-infection. com/article. php? larub=280&titre=urms-database)). The study was validated by the ethics committee of the Institut Fédératif de Recherche IFR48 under number 09-022. Initial growth was obtained after 72 h of culture in a liquid medium containing 37 g of Difco Marine Broth (Becton Dickinson, Le Pont de Claix, France) per litre of sterile water at 37°C and on Columbia agar enriched with 5% sheep blood in strict aerobic conditions at 37°C.
Strain identification The 16S rRNA gene was sequenced to classify this bacterium. Amplification used the primer pair fD1 and rP2 (Eurogentec, Angers, France) and sequencing used the Big Dye® Terminator v1. 1 Cycle Sequencing Kit and ABI Prism 3130xl Genetic Analyzer capillary3500xLGenetic Analyzer capillary sequencer (Thermofisher, Saint-Aubin, France), as previously described  [8]. The 16S rRNA nucleotide sequences were assembled and corrected using CODONCODE ALIGNER software (http://www. codoncode. com). Strain mt20 T exhibited a 98. 74% sequence identity with Enterobacter cloacae strain ATCC 23373 T (GenBank Accession number NR_118011. 1), the phylogenetically closest species with standing in nomenclature (Fig. 4 ). Based on the phylogenetic tree, we found that strain mt20 T do not reach the threshold of 16S required to identify a new species.
Genome sequencing Genomic DNA was extracted using the EZ1 biorobot (Qiagen, Courtaboeuf, France) with the EZ1 DNA tissue kit and then sequenced on the MiSeq technology (Illumina, San Diego, CA, USA) with the Nextera Mate Pair sample prep kit (Illumina), as previously described  [9]. The assembly was performed with a pipeline incorporating different softwares (VELVET  [10] , SPADES  [11]  and SOAP DENOVO  [12] ), on trimmed data (MISEQ and TRIMMOMATIC  [13]  softwares) or untrimmed data (only MISEQ software). GAPCLOSER was used to reduce assembly gaps. Scaffolds <800 bp and scaffolds with a depth value < 25% of the mean depth were removed. The best assembly was selected by using different criteria (number of scaffolds, N50, number of N). The genome of strain mt20 T is 4 199 690 bp long with a 56.
8 mol% G+C content and 13 contigs. The degree of genomic similarity of mt20 T with closely related species was estimated using the ORTHOANI software  [14]. Values among closely related species (Fig. 5 ) ranged from 79. 29%, between Enterobacter soli and Enterobacter kobei, to 91. 59%, between Enterobacter asburiae and Enterobacter bugandensis. When the isolate was compared with these closely related species, values ranged from 82. 03% with Enterobacter soli to 83. 18% with Enterobacter asburiae. Based on the whole genome sequence, we consequently classify this strain as a member of a new species within the genus Enterobacter, family Enterobacteriaceae, phylum Proteobacteria.
Acknowledgements We sincerely thank  Sakazume Taku ,  Takashi Irie ,  Kyoko Imai ,  Shigeki Matsubara ,  Akiko Hisada ,  Yusuke Ominami , and all the  Hitachi Team in Japan  for the collaborative study we are conducting together between  Hitachi High-Technologies Corporation  and the  Institut Hospitalo-Universitaire Méditerranée-Infection , and for the installation and servicing of the TM4000Plus microscope in our facility.
Geomorphic strain markers accumulating the effects of many earthquake cycles help to constrain the mechanical behaviour of continental rift systems as well as the related seismic hazards. In the Corinth Rift (Greece), the unique record of onshore and offshore markers of Pleistocene ~100-ka climate cycles provides an outstanding possibility to constrain rift mechanics over a range of timescales. Here we use high-resolution topography to analyse the 3D geometry of a sequence of Pleistocene emerged marine terraces associated with flexural rift-flank uplift. We integrate this onshore dataset with offshore seismic data to provide a synoptic view of the flexural deformation across the rift. This allows us to derive an average slip rate of 4. 5-9. 0 mm•yr -1 on the master fault over the past ~610 ka and an uplift/ subsidence ratio of 1:1.
north-dipping fault system  10, 17, 18. Here we improve this onshore record of the deformation and link it directly to the now well-resolved tectono-stratigraphic framework deduced from offshore studies in the gulf (Nixon et al. 19  and references therein). We use a high-resolution Digital Surface Model (DSM) to resolve more accurately the terrace uplift and onshore flexural pattern, and complement this analysis with depth-converted offshore seismic data  20. This exceptional dataset provides a unique integrated view of the flexure resulting from continental rifting. Our onshore-offshore description of the flexural uplift and subsidence is best analysed using a new modelling approach at crustal-scale that refines previous attempts that were able to reproduce the uplift pattern of the terraces using an extremely weak (E = 0.
The slope of the Holocene seacliff was measured at 48 locations and its value ±1σ was used to estimate the horizontal and vertical position of the shoreline angles for ~700 palaeocliffs with TerraceM  56, 57  (Supplementary Figs  2  and 9 ). To reduce the influence of fluvial and gravitational erosion, we used the maximum topography of 100 m-wide swath profiles perpendicular to the cliffs, the size preferred by Jara-Muñoz et al. 57. All swath profile and shoreline angle locations are included as supplementary Google Earth and ESRI Shapefile data files. The terraces were correlated laterally using satellite imagery, mapview and profile view of shoreline angles in combination with a N130°E coast-parallel swath stack  58  of 500 average elevations of swath profiles (Fig. 2d ). The uplift rate U for individual shoreline angles was calculated using U = (H T -H SL )/T, where H T is the present elevation above the modern mean sea-level, H SL is the eustatic sea-level elevation for the time interval of terrace formation and T is the age of terrace formation.
Although the error bars of the older terraces are smaller due to the smaller influence of age uncertainty (see equation above), we note that the actual uncertainty of the rates derived from those terraces is much higher since those levels have not been directly dated. The uplift rate at the onshore Xylokastro Fault was estimated by combining the New Corinth (II), Old Corinth (II), Temple (II) and Laliotis uplift rates and extrapolating a best fitting quadratic curve with MATLAB (Supplementary Fig. 4 ). A critical χ 2 test was done to confirm that the residuals follow a Gaussian distribution and the uplift rate dataset is well described by the curve  61  , which was the case when excluding the New Corinth (II) terrace, but not when including that terrace. Within this test we excluded the New Corinth (II) datapoints between 13 and 18 km distance from the fault, since their elevation appears to be disturbed by sedimentary processes on the Vokha plain (Fig.
Best-fitting quadratic curves for the New Corinth (II), Old Corinth (II), Temple (II) and Laliotis terraces were extracted with MATLAB. The Laliotis curve, assuming an age of 409 ka and a eustatic sea-level correction of + 5 m, was extrapolated linearly to estimate total uplift at 605 ka and 1050 ka, approximately corresponding to the sea-level highstand following the oldest marine incursion interpreted offshore (Fig. 3b ) and the age to match the position of the Klimenti Delta overlying the terrace sequence (Fig. 3a ). The sill depth is chosen at 62 m 62 , and for simplicity chosen as constant through time. Given the fast rate of sea-level rise before major interglacial highstands, uncertainty in sill depth does not change the age of the interpreted offshore horizons much, nor does the depth-uncertainty in the sea-level curve.
Fault modelling. For the fault modelling we used PyLith  66  , an open-source finite element code for dynamic and quasi-static simulations of crustal deformation. We used a starting model with a 10 km upper crustal thickness, adopting the peak in microseismicity depth (Supplementary Fig. 6 ) around the cross-section of Fig. 3 , and a 35 km crustal thickness following Moho depth estimates from Ps receiver functions  67  and tomographic inversion of PmP reflection times  68. Listric and biplanar fault geometries were excluded from our models, since they are not expected to give the significant footwall uplift that our data suggests  46. For model simplicity we exclude erosion and sedimentation processes, to which previous numerical models with much lower upper crustal Young Modulus were relatively insensitive  10.
Acknowledgements We thank  Aurélia Hubert-Ferrari ,  Lisa McNeill  and two anonymous reviewers for commenting on the manuscript. The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  under the  ITN  project  ALErT  (Grant FP7-PEOPLE-2013-ITN number  607996 ) and by the  ISIS program  of  CNES. The authors thank  Arthur Delorme  for his technical assistance in producing the DSMs, and  Raphaël Grandin  for discussing an earlier version of the manuscript. Numerical computations for the DSM were performed on the S-CAPAD platform,  Institut de Physique du Globe de Paris (IPGP) ,  France. Seismic reflection data from EW0108 is available from the Marine Geoscience Data System.
Data Availability The Pleiades satellite imagery was obtained through the ISIS and Tosca programs of the Centre National d'Etudes Spatiales (CNES, France) under an academic license and is not for open distribution. On request, we'll provide the DSM calculated from this imagery to any academic researcher who gets approval from CNES (contact isis-pleiades@cnes. fr for quoting this paper, and with lacassin@ipgp. fr in copy). We do share a georeferenced hillshade image and slope map of the 2 m-resolution Digital Surface Model that was developed from Pleiades satellite imagery. This image can be retrieved with these links: https://figshare. com/s/05d6610458391e9da3d7(hillshade image) https://figshare. com/s/a50519854408656e2532(slope map). The other data that support the findings of this study are available within the publication, referenced studies and/or from the corresponding author on request.
, 5. Using databases of elliptic curve data such as  [11]  or  [1] , we checked that for every i = 2, E i has no torsion points defined over Q and that E i (Q) has rank one. We also found in the tables an explicit generator g i for E i (Q) and we computed ĥ(g i ) using the function ellheight of PARI/GP  [43]  (notice that the canonical height of PARI/GP is two thirds of ours). A generator for the curve E 2 , which has a conductor too big to appear in Cremona's tables, was given in  [41, Example 3]. Collecting these informations we have that the generators of E i (Q) are: where ĥ(g 1 ) 0. 377, ĥ(g 2 ) 47. 888, ĥ(g 3 ) 17. 649, (9. 2) ĥ(g 4 ) 60. 674, ĥ(g 5 ) 136. 823. g 1 = (1, We Proof. The proof is an application of Theorem 7. 3. First, we need to compute all the invariants intervening in the bounds.
2) and also better bounds on c 1 (E) and c 2 (E). We compare this with the bound obtained in  [25]. There curves with a = 1 are studied, where b (denoted t in  [25] ) can be rational. Then (if E(Q) has rank 1) they show that for all P ∈ C(Q) h W (x(P)) 7  2 h(b) + 1 2 log 81468 7  2 h(b) + 5. 654. Since the x-coordinates of the images of P on E are given by 9x(P) ±2 + 3b, this translates into C : y 2 = x 6 + x 4 + x 2 + 1. Then E is the curve 128a1 in the Cremona database  [11]  (and [1, 128. a2]), and E(Q) ∼ = Z/2Z × Z. We have ĥ(P 0 ) > 0. 6485. The bound in the theorem above (using the bound for h 2 (C ) in (A. 2) and the bounds for c 1 (E) and c 2 (E) from five elliptic curves E = E 1 ,. , E 5 as defined in the Introduction. The second family consists of the curves D n (E i ) given by Φ n (x 1 ) = y 2 , where Φ n is the nth cyclotomic polynomial, for the same set of elliptic curves E i.
Acknowledgements We are indebted to  K. Belabas  for writing the algorithm to conclude the proof of Theorem 1. 5 and for his kind answers on some technical aspects of PARI/GP. We are thankful to  M. Stoll  for his useful remarks which helped us to improve the paper and for his nice appendix. We warmly thank  J. H. Silverman  for his useful suggestions and for his interest in our work. We are grateful to  P. Philippon  for answering some questions on the comparison of several height functions. We also thank  Ö. Imamoglu  for her comments on an earlier version of this paper. We finally thank the referee for several useful comments and for his/her work. S. Checcoli 's work has been funded by the  ANR  project  Gardio   14-CE25-0015. E. Viada  thanks the  FNS (Fonds National Suisse)  Project  PP00P2-123262/1  for the financial support.
g. , through the distortion or disruption of animalborne tag transmissions to an orbiting satellite). State-space models have been used to advance knowledge based on large telemetry datasets  (Block et al. , 2011; Strøm et al. , 2018) , to infer unobservable behaviors  (Leos-Barajas et al. , 2017; Michelot et al. , 2017) , and to understand how movement behaviors are influenced by environmental drivers  (Patterson et al. , 2009; McClintock et al. , 2012; Bestley et al. , 2013; Jonsen et al. , 2018). FIGURE 1 | Schematic illustrating the many parameters that can be measured by animal-borne packages, using archival, acoustic, or satellite telemetry. The environmental, physiological, and ecological data collected by the illustrative marine animals (penguin, seabird, fish, seal) may be measured in multiple ways and stored or transmitted or both.
Oceanographic Findings Enabled by Animal Telemetry Over the Last Decade The functionality of Argo floats is limited in polar regions due to the seasonal presence of sea ice that prevents floats from returning to the surface. Instrumenting free-ranging, airbreathing animals that move through sea-ice covered areas, such as seals, with temperature and salinity sensors can help fill data gaps in the Argo dataset in sea ice regions  (Fedak, 2013; Treasure et al. , 2017). To date, over 540 000 profiles have been collected by marine mammals and have been made available to the broader operational and research oceanography communities  (Roquet et al. , 2014)  (Figure  3 , http://www. meop. net/). The great potential provided by animal-borne sensors for monitoring Southern Ocean hydrographic conditions and how animals respond to them was demonstrated following the deployments of CTD-SRDLs on southern elephant seals in 2004-2005  (Biuw et al.
Costa et al. (2008)  analyzed the upper ocean heat content variability in the west Antarctic Peninsula using instrumented seals, providing a valuable reference to evaluate numerical circulation models. Using the maximum depth of benthic dives,  Padman et al. (2010)  identified troughs in the continental shelf that allow intrusions of Circumpolar Deep Water under the Wilkins Ice Shelf, accelerating its collapse  (Padman et al. , 2012). Animalcollected data also helped to characterize the exchange of properties across the shelf break in the Weddell Sea, linked to eddy overturning  (Nost et al. , 2011)  and wind forcing variability  (Arthun et al. , 2012). Zhang et al. (2016)   (2010) used hooded seal (Cystophora cristata) data from the East Greenland Shelf to estimate seasonal temperature variations of subtropical waters sitting near the entrance of a major glacial fjord.
Variability of these continental shelf waters was further investigated by  Sutherland et al. (2013). Instrumented ringed seals (Pusa hispida) have also proved useful to investigate freshwater runoff from the Greenland Ice Sheet  (Mernild et al. , 2015)  and freshwater discharge plumes from glaciers in Greenland  (Everett et al. , 2018)  by providing observations from directly adjacent to the glacier tongue. Grist et al. (2011)  used Argo and marine mammal profiles to produce a gridded data set that revealed distinctive boundary current-related temperature minima in the Labrador Sea and at the East Greenland coast  (Isachsen et al. , 2014). Isachsen et al. (2014)  used data collected by instrumented hooded seals as well as Argo floats to reveal warmer and saltier conditions over much of the Nordic Seas in 2007-2008 compared to the 1956-2006 climatology.
Benefits for Local Conservation Animal telemetry has immense value for aiding understanding and conservation of ocean habitats  (Hays et al. , 2019). At the same time as measuring animal movement, tracking devices can collect important observations on poorly monitored physical environments through which these animals' transit. The complex and diverse marine systems in tropical regions support a rich diversity of life including tens of marine mammal and sea turtle species, many of which are endangered. Compared to the relatively abundant data available in many other regions (e. g. ,  Hussey et al. , 2015; Sequeira et al. , 2018) , information about ocean use by marine fauna in tropical Asia is limited. To address these data and knowledge gaps, researchers have started data collection using available technologies.
Collectively, such repositories are globally accessible, strive to use open source software and common (or at least easily mappable) formats, and amass and curate large quantities of movement data and metadata across thousands of species extending across most regions of the globe  (Hussey et al. , 2015; Kays et al. , 2015). Analysis of the large spatial and temporal datasets now available will allow establishment of baselines from which large-scale (1000s km) and long-term (decadal) changes can be identified, which in turn will provide conservation benefits through informed planning and management  (Allen and Singh, 2016; Fraser et al. , 2018) , and will "increase global communication, scope for collaboration, intellectual advancement, and funding opportunities"  (Hussey et al. , 2015). Pooling datasets collected in disparate ways with different instruments, or secondarily derived from other primary data (e.
The need for international coordination of deployment of animal-borne sensors to address data coverage gaps was recognized more than 15 years ago, in particular the challenge of coordinating resources to enable simultaneous deployments across several sub-Antarctic sites. To address this, the project SEaOS (Southern Elephant seals as Oceanographic Samplers) was launched in 2003 with participants from five national groups. This led to the MEOP program that began during the International Polar Year period in  2008-2009. MEOP has now transformed into a large consortium that acts to bridge the scientific teams deploying the tags and those using the data  (Treasure et al. , 2017). The MEOP data (Figure  3 ) are made available to the global scientific community through the data portal (http://www.
Andrews), where the data are decoded and the profiles rebuilt before distribution through the British Oceanographic Data Center (BODC). At the BODC, the data are then automatically converted into either TESAC or BUFR formats and sent to the GTS for operational use. However, in contrast to what is done for Argo float data, BODC has to date no dedicated resources to quality control and flag the animal data before transmission to the GTS, limiting importantly the usability of the data for data assimilation. The complexity of encoding, decoding, and transmitting these data and posting them on the GTS is in part the result of unavoidable constraints imposed by both the animals' behavior, the small size of the tags and the CLS Argos data transmission system. Tags on animals must be as small as possible so as to not adversely affect the animal; therefore, the energy available to the tags is extremely limited.