dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009). The resulting micrographs were dose-filtered according to  (Grant and Grigorieff, 2015)  by means of Matlab scripts (https://github. com/C-CINA/TomographyTools). During optimization of cryo-ET samples, we observed that protein A gold beads bound with high affinity to VAP-A. To reduce binding, PEG-coated gold beads were added to samples employed for cryo-ET  (Duchesne et al. , 2008). We observed that much more gold beads were at the proximity of MCS formed by OSBP than with N-PH-FFAT. Cryo-ET (see Figure  3D ) revealed that less molecules of VAP-A were engaged in contacts with OSBP and thus more accessible for binding gold particles. However, this prevents further analysis by sub-tomogram averaging of OSBP-VAP-A MCS. Data collection are presented Table  S1.
Molecular Dynamics (MD) simulation of dimer model The 3D model of 195-335 OSBP (Model 1, Figure  S5B ) generated from Robetta was duplicated to create two chains and positioned with on average 9Å distance between them with Pymol software  (Schrödinger, 2010). MD simulations were performed with GROMACS 2019. 4  (Abraham, M. J. et al. , 2015)  with the CHARMM36 force field  (Huang, J. , and MacKerell, 2013). The dimer was centered in the cubic box, at a minimum distance of 2. 5 nm from box edges. Solvent molecules were added in the coordinate file. The TIP3P water model configuration was used  (Jorgensen et al. , 1983). Na and Cl ions were added to neutralize the simulation box and at a minimum concentration of 120 mM. The total number of atoms (protein + solvent + counterions) was 958,802.
A restricted alignment was run to confirm that subvolumes were properly subboxed. Symmetry was set to c2 based on the evident twofold symmetry observed, the symmetry test performed under Dynamo and on biochemical data that have shown that N-PH-FFAT is a dimer  (Jamecna et al. 2019). Step 10a The last step consisted of an adaptive band-pass filter alignment (FSC set to 0. 143). Initial parameters of the angular search were set to a cone range of 16° (increments of 4°) and in-plane range of 180° (increments of 10°) while shifts were set 8, 6, 6 voxels along the x, y and z axes. Symmetry was kept as c2. Both, the angular search and shifts were gradually decreased, and the band-pass filter moved towards higher resolution. Step 11a, 8b The final converged average consisted on 15,597 and 6,948 subvolumes for N-PH-FFAT and VAP-A respectively, and their respective FSC were calculated over the two half-averages obtained upon randomly splitting the subvolumes in two datasets.
N-PH-FFAT path Step 6a Step 7a Step 8a Step 9a Step 10a Step 6b Step 7b Step 8b VAP-A FSC curve SUPP All steps of the following pipeline were performed under Dynamo  (Castaño-díez et al. , 2012; Castaño-Díez et al. , 2017). FIGURE 4B Step 1 During the first step the whole dataset composed of 63,348 oversampled subvolumes of 88 3 voxels (59. 8 3 Å) belonging to class III (Figure  4B ) were extracted from binned 4 (voxel size = 6. 8 Å) tomograms, from which an initial model-free template was obtained by averaging a set of 2,000 randomly chosen subvolumes. Step 2 Membranes from tubes and vesicles were used during the first rounds as references to align apposed membranes. A cylindrical mask covering half of the tube and the upper region corresponding to vesicles membrane was selected. Particles were binned 2X and the angular search was restricted to a cone range of 24° (steps of 6°) and to an in-plane range of 32° (steps of 8°).
ACKNOWLEGMENTS We are grateful to  A. Bertin  for 2D image analysis,  P. Cuniasse  and  P. Sens  for fruitful discussions, D. Woolfson for feedback on coiled-coil analysis,  N. Charmel  for the picture of Figures  2A  and  3C  and  L. Duchesne  for providing PEG-coated gold beads. We thank  G. Schoehn  and the EM facilities at the  Grenoble Instruct-ERIC Center (ISBG ;  UMS 3518 CNRS CEA-UGA-EMBL ) with support from the  French Infrastructure for Integrated Structural Biology (FRISBI  ;  ANR-10-INSB-05-02 ). We are deeply grateful to  W. Hagen  for the quality of the cryo-electron tomography data acquired at the cryo-electron microscopy platform of the  European Molecular Biology Laboratory  (EMBL) in Heidelberg. We thank the  Cell and Tissue Imaging core facility (PICT IBiSA) ,  Institut Curie , member of the  French National Research Infrastructure France-BioImaging  ( ANR10-INBS-04 ).
This work was supported by  CNRS ,  Institut Curie , and the  Agence Nationale de la Recherche  ( ANR-15-CE11-0027-02 ). This work was also supported by iNEXT, project number  iNEXT PID7315 , funded by the  Horizon 2020 program  of the  European Union. E. de la Mora  was funded by a grant from  ANR  ( ANR-15-CE11-0027-02 ) and by a grant from the  Labex CellTisPhysBio  ( ANR-11-LABX-0038 ,  ANR-10-IDEX-0001-02 ). The Antonny lab is supported by a grant from the  Fondation pour la Recherche Médicale  (Convention  DEQ20180339156 Equipes FRM 2018 ), by the  LABEX signalife  ( ANR-11-LABX-0028-01 ) and by the  university Côte d'Azur  (IDEX académie 4masters environnés).
ImageJ  (Schneider et al. , 2012)  https://imagej. nih. gov/i j/ SerialEM (Mastronarde, 2005) http://bio3d. colorado. e du/SerialEM/ Matlab R2019a Mathworks https://fr. mathworks. co m CTFPlotter and CTFphaseflip (Xiong et al. , 2009) https://bio3d. colorado. edu/imod/doc/man/ctfp lotter. html Dynamo (Castaño-díez et al. , 2012; Castaño-Díez et al. , 2017) https://wiki. dynamo. bi ozentrum. unibas. ch/w/ index. php/Main_Page IMOD (Mastronarde and Held, 2017) http://bio3d. colorado. e du/imod/ UCSF Chimera (Goddard et al. , 2007) https://www. cgl. ucsf. e du/chimera/ Robetta (Raman et al. , 2009; Song et al. , 2013; Yang et al. , 2020) http://robetta. bakerlab. org Phyres2 (Kelley Lawrence A, et al. , 2016) http://www. sbg. bio. ic. ac. uk/phyre2/html/pag e. cgi?id=index PSIpred (Buchan and Jones, 2019) http://bioinf.
JMDC catalogue The most complete and up-to-date catalogue that lists all the interferometric measurements that have been done so far is the JMMC Measured stellar Diameters Catalog 1   (Duvert 2016). As of February 2020, this catalogue contains 1672 rows. Among all these measurements, the current number of individual stars with observed diameters is 885. The catalogue lists the uniform disc angular diameter θ UD , the limb-darkened angular diameter θ LD, and the θ UD to θ LD conversion factor µ λ if available. A "notes" column is included and contains some information about the star. The observing technique is indicated: optical interferometry, lunar occultation or intensity interferometry. We cross-matched the Simbad database with the JMDC catalogue to obtain photometric information (see Sect.
Surface brightness-colour relations for Gaia In this section, we convert our SBCRs in the Gaia photometric band G. The G photometry of the stars in our sample is found in the Gaia DR2 database  (Gaia Collaboration et al. 2018). In order to determine the corresponding extinction (A G ), we used an analytic model established by  Danielski et al. (2018) : A G = a 1 + a 2 (G -K) 0 + a 3 (G -K) 2 0 + a 4 (G -K) 3 0 + a 5 A V + a 6 A 2 V + a 7 (G -K) 0 A V , (11) with a 1 = 0. 935556283, a 2 = -0. 090722012, a 3 = 0. 014422056, a 4 = -0. 002659072, a 5 = -0. 030029634, a 6 = 0. 000607315, and a 7 = 0. 002713748. The SBCRs based on the Gaia photometry are shown in Fig. 8 , while their coefficients are listed in Table  5. We find a good consistency with the SBCR based on the V band. Precision ranges from 1.
2018) , which should definitively improve our knowledge of SBCRs. In this context, using the Gaia and 2MASS photometric systems, which both have the largest databases, seems to be the best approach. Since the Gaia photometry is among the most homogeneous over the full sky, we calibrate SBCRs using this precise photometry for the first time. We reach a precision on the angular diameter between 1. 1% and 2. 4%. A&A proofs: manuscript no. AS_2019 0. 0e+00 5. 0e+07 1. 0e+08 1. 5e+08 2. 0e+08 2. 5e+08 3. 0e+08 3. 5e+08.
Acknowledgements. A. S. acknowledges  F. Arenou  and  N. Leclerc  for their contribution to the computation of the interstellar extinction with Stilism. This work is based upon observations obtained with the  Georgia State University Center for High Angular Resolution Astronomy Array at Mount Wilson Observatory. The  CHARA Array  is funded by the  National Science Foundation  through  NSF  grants  AST-0606958  and  AST-0908253  and by  Georgia State University  through the  College of Arts and Sciences , as well as the  W. M. Keck Foundation. This work made use of the JMMC Measured stellar  Diameters Catalog  ( Duvert 2016 ). This research made use of the SIMBAD and VIZIER 4 databases at  CDS, Strasbourg (France)  and the electronic bibliography maintained by the  NASA /ADS system. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www.
Reliable cross-section data on these two reactions may also help refine theoretical models of charged particle emission from light nuclei, better characterizing pre-equilibrium effects. The cross-section of the 12 C(n,p) 12 B reaction is highly uncertain, as shown in Fig. 1 , where experimental data available  [2] [3] [4] [5] [6] [7]  and various evaluated libraries are reported. Large discrepancies, as large as a factor two or more in some case, are present both for the width and the height of the cross section peak, as well as for the position of the reaction threshold. In addition, an integral measurement of this reaction from the threshold energy (∼13. 6 MeV) up to 10 GeV has been recently performed at the neutron time-of-flight facility n_TOF at CERN  [8]  providing an integral value significantly higher than suggested by the available datasets and by most of the evaluated nuclear data libraries  [9, 10].
Conclusions The energy differential cross-sections for the reactions 12 C(n,p) 12 B and 12 C(n,d) 11 B have been measured at the n_TOF facility at CERN, with the aim of clarifying the discrepancies in the few scarce datasets already existing in literature. The measurement has been performed by means of two silicon strips telescopes capable of providing also information on the energy differential angular distributions of the two reactions. The expected final uncertainty of the measurement is ∼10-15%, mostly depending on the correction for excited states of the residue nuclei. Thanks to the well-suited features of the n_TOF facility, the preliminary results show the excellent performances of the telescope, in particular in terms of resolving power and wide neutron energy coverage, hinting at the fact that new accurate data on 12 C(n,p) 12 B and 12 C(n,d) 11 B reaction cross-sections can be provided by this experiment.
Not all users (international organizations, national governments, civil societies, researchers) have the capabilities to readjust such maps  (Saah et al. , 2020). To accommodate diverse user profiles, a common processing scheme is employed. The resulting datasets can be utilized through various platforms and systems. This work adopts the Land Cover Classification Scheme of the Food and Agriculture Organization  (FAO LCCS, DiGregorio 2005) , an internationally approved ISO standard approach. The presented datasets in this paper are produced within the Copernicus High Resolution Hot Spot Monitoring (HSM) activity of the Copernicus Global Land Service. All HSM products feature the same thematic land cover legend and geometric accuracy, and were processed and validated following the same methodology.
In addition, as all Copernicus products, including the HSM data, are free and open to any user with guaranteed long-term maintenance and availability. Copernicus serves as an operational program where data production takes place on a continuous basis. This paper presents twelve KLC land cover  [change]  datasets that cover up to 560,442km 2 terrestrial land area in Sub-Saharan Africa (SSA) mapped under the first phase (Phase 1) of the HSM activity. The datasets are based on freely available medium spatial resolution data. Each of the KLCs were individually validated for both present (~2016) and change (~2000) dates. The developed processing chain always consists of preliminary data assessment for availability, pre and post processing as well as fully independent quality verification and validation steps.
For the latter, a second dataset called validation data is presented as well. Several recent studies call for the sharing of product validation datasets  (Fritz et al. , 2017; Tsendbazar et al. , 2018) , especially if a collection received financial support from government grants  (Szantoi et al. , accepted, 2020C). Accordingly, the validation datasets (LC/LCC) associated with each of the KLCs are also shared.
Study Area The provided thematic datasets concentrate on Sub-Saharan Africa, a region on the frontline of natural and human induced changes. The selection of areas were conducted based on present and future pressures envisioned and predicted  (MacKinnon et al. , 2015). In this first phase (Phase 1) 12 large areas totalling 560442km 2 in SSA were selected, mapped and validated (Figure  1 ). These areas cover various ecosystems and generally reside in transboundary regions (Table  1 , Figure  1 ).
Validation dataset production The validation datasets (Table  3 , Figure  2 ) were individually created for each KLCs. The validation datasets (points) were generated using a stratified random sampling procedure. This assured a sufficient estimation for all land cover and land cover change classes according to their frequency of occurrence. The following formula  (Gallaun et al. , 2015)  was used to determine the minimum number of validation points (per class per KLC): 𝑛 𝑐 = 𝑝 𝑐(1-𝑝 𝑐 ) 𝜎 𝑐 2 , 𝑐 = 1, … , 𝐿 (4)   number of sampling units for class c   estimated error rate for class c   accepted standard error of the error of commission for class c.
The presented KLC datasets can be used for continuous land cover/use monitoring, evaluation of management practices/effectiveness, endowment for scientific counsel, habitat modelling, information dissemination and capacity building in their corresponding countries and to manage natural resources such as forests, soil, biodiversity, ecosystem services and agriculture  (Tolessa et al. , 2017). Furthermore, regional climate change, biogeochemical and hydrologic models are currently capable of using high resolution LC data for predictions in general  (Nissan et al. , 2019)  and spatially focused (i. e. Africa)  (Sylla et al. , 2016; Vondou and Haensler, 2017). Earth System Science Data Discussions Preprint. Earth System Science Data Discussions Preprint. Earth System Science Data Discussions Preprint.
The validation datasets are independently collected and verified through a robust procedure. Validation datasets can then be used for additional land cover mapping, creating spectral libraries, and for the validation of other local, regional and global datasets. It is important that various land cover products can be used or compared against one another regardless of their geographic locations. Here, twelve land cover maps for different areas in Sub-Saharan Africa where quality land cover products are missing  (Marshall et al. , 2017)  were introduced. These products come with land cover change information as well, generally dating back to year 2000 (±3 years). All data were produced using the unified Land Cover Classification System. The LCCS's modular level can be applied to local scales through its very detailed classes (here 32).
Datasets current and future use The C-HSM datasets have been widely used by policy makers (African and European partners) to help identify areas prone to change due to human activities. For example, COFED (Support Unit for the [DRC] National Authorizing Officer of the European Development Fund) the EEAS (European External Action Service) of the DRC manage an envelope of EUR120m, allocated for five protected areas in the DRC (Virunga, Garamba, Salonga, Upemba and the Yangambi biosphere), where they use the C-HSM products for planning and for investment strategies (i. e. hydropower). Another example comes from West Africa, where NGOs (i. e. Wild Chimpanzee Foundation), public-benefit enterprises (i. e. German Society for International Cooperation -GIZ) as well as national authorities (i. e. l'Office Ivoirien des Parcs et Réserves -OIPR) use the data to identify areas under pressure for agriculture (cocoa, oil palm, rubber, coconut) and human-wildlife conflicts in Cote d'Ivoire, Ghana and Liberia.
Acknowledgements. The development of the thematic maps have been made possible thanks to the effort of  ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action ) and Telespazio -a LEONARDO And THALES company; and their quality evaluations by  IGNFI (France) ,  Joanneum Research (Austria) ,  EOXPLORE (Germany) ,  GISBOX (Romania) ,  Space4environment  (Luxembourg),  ONFI (France) and LuxSpace (Luxembourg). The authors also thank  Mr. A. McKinnon  ( EC/JRC ) for proofreading the paper.
Conclusions and Outlook The C-HSM service component is part of the Copernicus Global Land Service, which produces near real time biophysical variables at medium scale, globally. In contrast, the C-HSM activity is an on demand component addressing specific user requests in the field of sustainable management of natural resources. The products presented here provide the first set of standardized land cover and land cover change datasets for 12 KLCs with their corresponding validation datasets  (200X, and 201X)  in Sub-Saharan Africa. The geographic distribution covers the tropical and subtropical regions of West, Central and South-Eastern Africa. The next release will also include countries in the Caribbean and Pacific areas of the ACP region and some areas beyond these regions may be mapped depending on user demands.
Genomic libraries were prepared using the Nextera DNA Flex library prep kit (Illumina, San Diego, CA, USA), and sequencing was performed on an Illumina MiniSeq platform with a 2 ϫ 150 paired-end protocol  (1). Default parameters were used for all software except where otherwise noted. Raw reads were quality filtered and adapter trimmed with Trimmomatic v. 0. 36  (4). An Oxford Nanopore Technologies (ONT) sequencing library was prepared using the manufacturer's 1D genomic DNA by ligation kit (SQK-LSK 108), and sequencing was carried out on a MinION device using flow cell type R9. 4. 1 (FLO-MIN106D). Porechop v. 0. 2. 1 (5) was used for adaptor trimming, and NanoFilt v. 2. 2. 0 (6) was used to remove reads of Ͻ500 bp or with average quality scores of Ͻ10. Thus, we used a robust pipeline relying on a combination of Oxford Nanopore long-read (681,890; N 50 value, 15,543 bp; 9.
A blastn search of the sequence of this plasmid against the NCBI database showed 79% query coverage and 94. 56% identity with that of Campylobacter lari pCL2100 (GenBank accession number CP000933). pCA639-2 carried several conjugative transfer genes and shared 82% query coverage and 95. 06%   9 ) and QUAST v. 5. 0. 0  (10) , respectively. First, we constructed MiniSeq assemblies (illumina) using SPAdes v. 3. 12. 0  (11)  or Unicycler v. 0. 4. 7  (12). Second, MinION assemblies (minion) were achieved using Canu v. 1. 5 (13) , Flye v. 2. 4 (  14 ), or Unicycler. These three assemblies were aligned to MinION reads using Minimap2 v. 2. 17  (15)  and SAMtools v. 1. 9 (  16 ) and then polished using Nanopolish v. 0. 11. 0  (17). An additional round of Nanopolish did not improve their accuracy. Moreover, the Canu assembly was polished using Pilon v.
Circle 3 shows GϩC contents higher and lower than the average GϩC content (black). Circle 4 shows GϩC skew, with positive values in green and negative values in purple. identity with pGMI16-001 (GenBank accession number CP028188) carried by Campylobacter coli strain CFSAN054106, suggesting an intraspecies dissemination. This study highlights the value of combining short-and long-read sequencing data for high-quality genome assemblies and annotation of repetitive genomic regions. The complete genome sequence of C. armoricus CA639 comprises essential data for taxonomic and comparative genomic studies within a One Health approach, a concept which recognizes that the health of people is connected to the health of animals and the environment. Data availability. The sequencing data have been deposited in the DDBJ/EMBL/ GenBank databases under accession numbers CP044262 for the chromosome and CP044261 and CP044263 for plasmids pCA639-1 and pCA639-2, respectively.
ACKNOWLEDGMENTS We acknowledge the  Pôle de Calcul et de Données Marines  (PCDM; http://www. ifremer. fr/pcdm)for providing DATARMOR (storage, data access, computational resources, visualization, Web services, consultation, and support services). This work was supported by funding from the  European Commission 's  Horizon 2020 Research and Innovation program  under the  COMPARE  project (grant agreement  643476 ).
The climate sensitivity of the terrestrial carbon cycle can be benchmarked using ratios between these fluxes across a range of climates. We focus here on the ratio of NPP to GPP, the so called carbon use efficiency (CUE = NPP/GPP) and of BP to GPP, called biomass production efficiency (BPE = BP/GPP). The two concepts are close, but not identical  4, 5. BPE is substantially easier to obtain, because the additional fluxes that constitute NPP are notoriously difficult to measure. For this reason, there are far more data available on BPE, while uncertainties associated with both BP and NPP measurement make it impossible to distinguish them in large data compilations. Therefore, we assessed estimates of both BPE and CUE as a single metric, hereafter called forest production efficiency (FPE), but making distinctions between them when needed and when possible.
Results FPE is not a universal constant. Results show that both CUE (0. 47 ± 0. 13; range 0. 24-0. 71; n = 47) and BPE (0. 46 ± 0. 12; range 0. 22-0. 79; n = 197) have large variability; therefore, neither can be assumed to be uniform (Fig. 1  and Supplementary Fig. 2 ). CUE and BPE are statistically indistinguishable in our dataset because of uncertainties associated with both quantities (±0. 39 for CUE and ±0. 16 for BPE: see Methods). Overall, the average FPE in our dataset (0. 46 ± 0. 12; range 0. 22-0. 79; n = 244) is statistically indistinguishable from that provided by Waring et al. 6  , but its standard deviation is three times larger (Methods and ref. 5  ). Different GPP estimation methods produced slightly different distributions (Fig. 2a ), with median values ranging from 0. 42 (scaling; upscaling of chamber-based measurements) through 0.
4 ). The modelled CUE values agree well with the data only in temperate regions (MAT 5-15 °C, n = 156), but differ greatly in boreal (MAT < 5 °C, n = 35) and tropical (MAT > 15 °C, n = 40) regions. Fig. 3  Comparison of the present work dataset vs. Waring et al. 6. Scatter plot of net primary production (NPP, gC m -2 yr -1 ) or biomass production (BP, gC m -2 yr -1 ) versus gross primary production (GPP, gC m -2 yr -1 ) (n = 228). Open circles: BP, filled circles: NPP. Stars represent data points from Waring et al. 6. The line marked with W98 represents a CUE (i. e. NPP/ GPP) of 0. 47. Age classes are marked by colours (see top left of the figure); NA stands for 'age not available'. The uncertainty (gC m -2 yr -1 ) of the data points is indicated by bars (for data uncertainty see Methods).