dataesr/scientific-paragraphs-categorization huggingface dataset:
Flood Extraction The method's accuracy is assessed for two entities: ponds and grasslands. The former are well suited to the IW mode of Sentinel-1 images. They consist of connected regions with a water depth higher than 50 cm that are easily detected from radar images. Consequently, the detection limits are evaluated on their edges where the vegetation is flooded. Detection of flooded areas within grasslands is more difficult, since they consist of narrow features that are intertwined at the intra-field scale. The results confirm that partial polarimetric modes can distinguish open water from flooded vegetation, as previously observed  [20] , as well fully polarimetric C-band data can  [18, 49]. However, due to the difficulty in quantitatively evaluating the accuracy between Cow and Cfv, the resulting validation focuses on the two classes for which it is possible to assess accuracy quantitatively: flooded (CFl = Cow ∪ Cfv) and non-flooded areas (CN_Fl).
The confusion matrix for temporary ponds for all 14 classifications (Table  2 ) had an OA (Overall Accuracy) of 82% and a κ (Kappa) value of 0. 65, indicating good results overall. Errors are due to isolated pixels or those located at pond edges. Better results (OA = 91%, κ= 0. 83) are obtained for a wider and deeper permanent pond (area ≈ 150 ha). This is due to it being a large connected patch with no isolated pixels. Moreover, it has sharp edges, without flooded vegetation as a transition between open water and grassland. Errors occurred only at the edge of the pond, leading to an underestimate of open water areas. To identify pond areas that can be detected by the Sentinel-1 IW mode, permanent ponds detected in radar images are compared to those in the BD ORTHO ® database. The results are shown in Figure  6 , which presents the percentage of ponds detected in function relation to of their size.
This is due to it being a large connected patch with no isolated pixels. Moreover, it has sharp edges, without flooded vegetation as a transition between open water and grassland. Errors occurred only at the edge of the pond, leading to an underestimate of open water areas. To identify pond areas that can be detected by the Sentinel-1 IW mode, permanent ponds detected in radar images are compared to those in the BD ORTHO ® database. The results are shown in Figure  6 , which presents the percentage of ponds detected in function relation to of their size. The diagram corresponds to the average percentage obtained for the 14 radar acquisitions.
In this case, C-band radar data cannot detect the flooded area (which appears flooded in the present validation dataset) because they cannot reach the underlying water. Detection based on optical Sentinel-2 data (not shown here) corresponds well with radar detection, as it also fails to detect these flooded areas. In addition, low omission errors (e. g. , 30% over the corresponding area of Figure  7 ) are due to the spatial resolution of Sentinel-1 data, which is larger than the size of flooded features. The accuracy results presented here are interpreted as being more representative of surface states observed during ground surveys. The two main advantages of this method are the good detection of the edges of the flooded areas and the prevention of isolated pixel misdetection. Future works will focus on ground surveys to assess the potential of this method to detect as well the flooded grassland.
e. , similar to RADARSAT, ERS or ASAR configurations). The influence of spatial resolution on flood monitoring will be assessed in future work by comparing Sentinel-1 data to RADARSAT-2 data, which has a spatial resolution of 1 m. These results illustrate the potential of the IW acquisition mode of Sentinel-1 SAR data to monitor dynamic hydrological processes at a regional scale. The potential of Sentinel-1A data to monitor floods will be strengthened in the near future with the launch of the Sentinel-1B satellite, carrying a SAR sensor similar to that of Sentinel-1A, which will allow a six-day acquisition frequency. Flood duration is one of the most useful types of information about marshlands, since it drives both natural habitat types and their conservation value, as well as agronomic constraints on land use.
Acknowledgments: The  Etablissement Public du Marais Poitevin (EPMP)  funded purchase of the automatic piezometric probes. We are grateful to  Jérome Mansons (EPMP) and  Olivier Gore  ( UMR ECOBIO ) for the information they provided about wetlands, and to  Guillaume Bouger  ( OSU Rennes ),  Olivier Jambon  and  Olivier Gore  ( UMR EcoBio ), who set up the water level monitoring designs. This research was co-funded by the  CarHab project (French Ministry of Ecology and Sustainable Development). Radar data were provided by the  European Space Agency (ESA)  through to the  Copernicus program. The authors would like to thank  Arnaud Le Bris  for his help with presenting classification results. We are grateful to  Michelle Corson , from  Editor du Jour , for her English correction and to the reviewers for their detailed and constructive suggestions.
This assay involves formaldehyde-cross-linking and chromatin fragmentation to mononucleosomes using MNase followed by ChIP. DNA fragments that immunoprecipitate with a H3 antibody were then detected by deep sequencing. To take into account the underlying intrinsic nucleosome occupancy levels, we first measured the overall histone levels by immunoblotting  (Appendix Fig S1B). These data showed that the overall histone levels between the wild type and nap1D strain were similar. Therefore, we normalized the total tag counts in both samples to be equal. In these single-read sequencing experiments, nucleosome sizes (including subnucleosomes) were deduced by pairing adjacent peaks located on opposite strands and in the 3 0 direction. Confirming the in-gel observation, these structures were approximately hexasomal in size in the nap1D strain (Fig 1B, ~100-130 bp range, compared to ~150 bp for wild type).
The unique N-terminal helix aN of H2A (residues 16-21) complements this binding interface by packing against E310 of yNap1, which is located in the region C-terminal to the disordered b-hairpin (b5-b6). The second DNA binding interface of H2A-H2B, containing binding loops L1 of H2B and L2 of H2A, packs against histone binding region 2 (HBR2), which comprises the N-terminus of helix a8 of the second yNap1  The EMBO Journal Structure of the Nap1-H2A-H2B complex Carmen Aguilar-Gurrieri et al To assess the contribution of the HBR interfaces to yNap1-H2A-H2B complex formation, we created mutants targeting conserved, surface-exposed residues in the histone binding interfaces and tested H2A-H2B binding using a GST-pulldown assay (data are summarized in Appendix Table  S2 ). Single, double, or triple (D201R, D205R, E310R) mutations in the HBR1 interface did not abolish H2A-H2B binding (Fig 3D  , lanes 1-6 ).
Non-denaturing mass spectrometry Prior to non-denaturing MS analysis, 30 ll of sample was bufferexchanged into 150 mM ammonium acetate, pH 7. 5 using micro Bio-Spin6 columns (Bio-Rad) or 10 kDa Vivaspin concentrators (Sartorius). For all measurements, 2 ll of sample was loaded into a capillary needle and spectra were recorded. The data were acquired on a quadrupole time-of-flight mass spectrometer (QTof II) modified for the transmission of large macromolecular assemblies  (Sobott et al, 2002). Typical instrument conditions used for non-denaturing MS conditions were as follows: capillary voltages 1. 7 kV and cone voltage up to 120 V; for collision-induced dissociation experiments, Carmen Aguilar-Gurrieri et al Structure of the Nap1-H2A-H2B complex The EMBO Journal the collision voltage was increased up to 180-200 V, extractor voltage 5 V, analyzer pressure 8.
Acknowledgements We thank the staff of  ESRF  and  EMBL-Grenoble  for assistance and support in using beamlines ID14-4, 23-1, and  23-2. We thank  F. Mietton  and  J. Govin  for in vivo analysis. We thank  K. W. Muir  for critical reading of the manuscript. We thank  K. T. Simons  and  K. Luger  for providing yNap1 and histone expression plasmids, respectively. Atomic coordinates and structure factors of the reported crystal structure have been deposited in the Protein Data Bank under accession code  5G2E. This work used the platforms of the Grenoble Instruct center (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB). The EM platform is supported by the  FRM, IBISA , the  Région Rhône-Alpes  and the  Fond Feder.
girinst. org/censor (Jurka et al. , 2005) , Gypsy Database 2. 0 at http://gydb. org/index. php/Blast (Llorens et al. , 2011) , and the NCBI databases (https://www. ncbi. nlm. nih. gov/). These same raw reads and assemblies were used to check for the presence of typical satellite DNA motifs using the RepFind (http://cagt. bu. edu/page/ REPFIND_submit) and Tandem Repeats Finder (http://tandem. bu. edu/trf/trf. html) tools. Primers for PCR amplification of these repeats were designed using Primer3 (http://www. bioinformatics. nl/ cgi-bin/primer3plus/primer3plus. cgi/). For fluorescent in situ hybridization (FISH), slides were prepared by dissection in a drop of 45% acetic acid of anthers previously fixed in ethanol:glacial acetic acid (3:1, v/v) and digested in enzymatic solution as previously described.
Fifty thousand nuclei were measured in each of the five rounds. For TEM, anthers were processed using modified Karnovsky's solution  (Karnovsky, 1965)  and embedded in Araldite resin ® as described above. Ultra-thin sections (~70 nm) of 10 anthers were collected in a copper grid and stained with 9% uranyl acetate and Reynold's lead citrate solution  (Reynolds, 1963). Sections were analyzed using a FEI Tecnai 12 transmission electron microscope at 60 kV. Images were acquired with Analysis FEI software.
A careful examination of the effect of the eight model parameters onto the dynamics enabled the quantitative estimation of six of them from our experimental datasets (Table  1 ). With these estimated values, numerical simulations of the model  (Fig 3C)  are in good agreement with experimental observations on the first two days, before invasion of malT mutants, suggesting it captures most of the relevant information contained in our data. The main discrepancy observed is in the initial velocity of the temporal evolutions, faster in the model than   1 : the lines represent the temporal evolution of population densities S+S L (blue), L (red), S L (green) and V (purple), with different initial conditions: L 0 = 1x10 6 cfu/g and, from left to right, S 0 = L 0 , S 0 = 10×L 0 , S 0 = 100×L 0. doi:10.
Quantitative estimation of mathematical model's parameters To calibrate the mathematical model, we used different datasets and versions of the model, aiming at the disentanglement of the eight parameters (see S1 Text for details). We identified four groups of parameters, which were estimated sequentially. The first group consists of parameters r, k and d that directly control the general growth of bacterial populations. As a global parameter, independent of the bacterial strains, the dilution rate was fixed to d = 0. 25, in line with  [77] [78] [79]. Subsequently, parameters r and k were estimated from single species experiments in mice, using a mono-dimensional logistic equation and nonlinear least square minimization. The second group consists of parameters x and l. A closer look at the equations points out the central role of these two parameters on the dynamics of latent cells Q.
Model-independent estimation of induction rate Since induction rate is the sole parameter differentiating growth of S and L lamB lineages, the induction rate (x) was determined from the evolution of the L/S ratio between days 0 and 9 in function of the number of generations. The number of generations was estimated by assuming that growth rate is equal to r, 1. 1 h -1 , during the first 24 hours, and after equal to excretion rate d, 0. 25 h -1. A linear regression on data from 6 mice (lm function in R software) gave x = 0. 0170 ± 0. 0046.
Acknowledgments We are thankful to  Colin Tinsley  for careful reading of the manuscript and his help with the experiments;  Sébastien Blugeon  for his help to start mouse experiments; and to the Anaxem platform, and in particular to  Anne Foussier  and  Fatima Joly , for their help with mouse and isolator handling. We also thank  A. Gruss  for critical reading of the manuscript and  V. Fromion  for fruitful discussions about the mathematical model.
Phylogenetic and genotypic analysis Data used for phylogenetic analyses comprised the novel whole genome sequences generated by this study, as well as additional publicly available sequences of closely related viruses identified by Blastn analysis of NCBI and GISAID databases. Full datasets for each gene segment were aligned using the MUSCLE algorithm in Mega software version 6. 06 (www. megasoftware. net). Alignments were trimmed to yield the maximal sequence coverage per segment as follows: 260-2289 for PB2; 78-2242 for PB1; 232-1981 for PA; 160-1599 for HA; 49-1507 for NP; 24-1386 for NA; 26-996 for MP; and 56-859 for NS. Phylogenetic trees were created using the neighbor-joining algorithm and the Tajima-Nei model, with 1000 bootstrap replications. Bootstrap values of b 70 were excluded from trees.
Phylogenetic analysis and genotyping We utilized next generation sequencing using the Illumina Nextera XT Sample Preparation Kit and the MiSeq platform as previously described  (Zaraket et al. , 2015)  using both direct sequencing of pooled surveillance swabs, as well as egg-grown virus isolates. Amplification enrichment for influenza genomes was performed on a total of 96 samples (19 egg isolates, 77 pooled surveillance swabs), from which 55 were selected for Nextera XT library preparations, and 50 were included in the run. Next generation sequencing identified 17 H9N2 complete genomes, 2 H5N6 complete genomes, 11 partial genomes. The HPAI H5 genomes detected from LBM will be presented within a separate publication and are only briefly discussed here. To determine relationships among the novel A/H9N2 sequences, we performed Blastn on each gene segment to identify closely related viruses within public datasets, and included a number of additional references.
5 to 9 years of evolution  (Fusaro et al. , 2011). Of the 18 surveillance swabs initially identified by RT-PCR as containing both H5 and H9, only 8 were subjected to MiSeq (the other 10 samples were excluded during quality control steps), and 1 yielded sequence results indicating presence of both subtypes (H7F-CB4-35). Additionally, there was one sample that tested positive for H5 but not H9 on initial RTPCR screening (H7F-HG4-604), however it yielded H9HA, H5HA, H9-like PB2, PA, NP, NA, MP and NS genes, and an H5-like PB1. The status of these samples as intersubtypic reassortants cannot be established as they represented pooled swabs from multiple birds. Of the samples designated as 'influenza positive, unknown subtype' by RT-PCR, 3 of 9 samples sequenced by MiSeq yielded partial sequence data of H9N2, and the remaining did not yield sequences of sufficient coverage for analysis.
FUNDiNG This study was financially supported by  URMITE ,  IHU Méditerranée Infection, Marseille, France ; and by the A* MIDEX  project (n°  ANR-11-IDEX-0001-02 ) funded by the  «Investissements d' Avenir» French Government program , managed by the  French National Research Agency (ANR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
ACKNOWLEDGMENTS This research was supported by grants from  Universidad Nacional Autónoma de México  ( DGAPA-PAPIIT grant IA201815 ), the  European Leukodystrophy Association (ELA) Research Foundation , the  Agence nationale de la recherche  ( ANR-05-BLAN-0385-01 ), the  Centre National de la Recherche Scientifique  and the  Université Paris-Sud  ( UMR 8621 ). We are much indebted to  Fernando Suaste-Olmos  ( IFC, UNAM ) for technical assistance and to  Véronique Berteaux-Lecellier  ( CRIOBE, CNRS ) and  Robert Debuchy  ( I2BC ,  Université Paris-Sud ) for assistance and valuable discussions.
Statistical analyses The dynamics of infection intensity (based on EPG or worm counts) were analysed using the non-parametric Kruskal-Wallis test. Additionally, a logarithmic transformation for an analysis of variance (ANOVA) was carried out, followed by a post hoc Newman-Keuls test to classify the months into high or low infection periods based on EPG in faeces, and adults or larvae recovered at necropsy. All statistical analyses were performed using SPSS 11. 5 software. The percentages of L4s and juveniles were calculated by the following formulae:.
Data analysis We tested whether skinks and geckos avoided a control scent (i. e. , the odor of P. pacificus) unrelated to any predator/aggressor. Then, we compared lizard avoidance ratings when exposed to predator or aggressor odors, with two different controls: (i) no effect of odor (i. e. , random choice corresponding to an avoidance rating of 50%) and (ii) control scent (P. pacificus odor). These two ways of analyzing the data yield complementary information. The first reveals whether a predator/aggressor odor is attractive/repulsive. The second reveals whether one particular predator/aggressor odor is more attractive/ repulsive than an unknown odor unrelated to any predator/aggressor. Making this comparison should distinguish the strategy "avoid any odor" from the strategy "avoid the odor of a recognized predator".
Acknowledgments This study was funded by the  CNRS, Institut Ecologie et Environnement (INEE)  ( PEPS Programme ) and by  Province Sud in New Caledonia. A. G. was funded by a  Ph. D. scholarship (EDSE251). We are grateful to  H. de Meringo ,  F. Rigault ,  L. Debar ,  E. Bourguet ,  Y. Pillon ,  R. Taputuarai ,  R. Pouteau  for their help in the field,  C. Gomez ,  J. Anso  for the two maps in this manuscript, and to the  Parc Zoologique et Forestier  for the use of their boxes for experiments. Our thanks to  M. Sweetko  for English language revision.
Molecular analysis of DNA from rodent spleen A 1024-bp fragment of the CNM groEL gene was amplified from three out of six tested rodent spleen DNA   the numbers of screened organs are lower than the number of spleens as not all organs were available (one A. flavicollis and two M. glareolus, all captured at Fúgelka in 2012). The three DNA sequences were 100 % identical compared to each other and shared 100 % identity with the CNM groEL gene amplified from urban hedgehogs in Hungary (KF803997. 1), from I. ricinus ticks from Poland (KF312363) and from a patient with severe febrile illness in Germany (EU810406). The sequence from M. glareolus was submitted to the NCBI database (accession number KR912350).
The CNM infection rate was within the range published other European sites, although in particular locations considerably higher prevalence was determined  [13, 32, 40]. We assume that the observed spatial variations in CNM prevalence were associated with the diversity of the vertebrate host communities and the presence and abundance of competent reservoirs, such as rodents, in this case  [20, 23]. This assumption is supported by the 0. 1 % CNM prevalence in ticks from transect B1 (the SAS campus), where the lowest rodent density (0. 7 individuals/100 trap nights) was determined. In contrast, CNM prevalence in ticks ranged from 1. 5 to 3. 6 % in locations with denser rodent populations, i. e. 19. 8 and 16. 1 individuals/100 trap nights in Bratislava forest park and Fúgelka, respectively. However, prevalence data from different studies should be compared with caution due to the sensitivity of the molecular methods used for CNM detection (e.
Acknowledgments The study was funded by  EU  grant  FP7-261504  EDENext and is catalogued by the EDENext Steering Committee as EDENext394 (http://www. edenext. eu). The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the  European Commission. Financial support by grant  APVV DO7RP-0014-11  is also acknowledged. The work of ZHS, MK and LB was supported by  German Academic Exchange Service. The authors thank  Dr. Cornelia Silaghi  and  Dr. Anna Obiegala  for their help in methodology and for providing CNM-positive control samples. The help of  Renáta Szalayová  and students ( Monika Bednáriková ,  Katarína Miťková ,  Lenka Burdová  and  Erika Vrbová ) with tick collections, rodent trappings and technical assistance is also acknowledged.
Phylogenetic and amino acid alignment analyses We obtained 45 amino acid sequences of ChREBP (also called 'Mondo B', 'WBSCR14', 'MLXIPL' or 'MLX interacting protein-like') for two vertebrates (human and mouse), one mollusc and 42 arthropods (wasps/sawflies, ants, bees, beetles, bugs, flies, moths, butterflies and a horseshoe crab) and amino acid sequences of Mondo A for two vertebrates (human and mouse) from the database at NCBI. Sequence searches were performed using 'ChREBP alpha', 'WBSCR14', 'MLXIPL', 'MLX interacting protein-like', 'MondoB', 'Mondo', 'dMondo', 'dChREBP' and 'Mio' keywords. Amino acid sequences from the termites Zootermopsis nevadensis  [30]  and Macrotermes natalensis  [31]  and from the NCBI protein database were aligned using two different algorithms, Muscle and ClustalW2, with the SEAVIEW software  [69] , and the alignment was checked manually.
ChREBP is a multi-domain protein and some of its regions are highly conserved among the Mondo proteins, such as the Mondo conserved region (MCR) and the glucose-sensing module (GSM) that is the most important region in terms of glucose sensing and regulation  [16, 32]. Owing to its high conservation and structural role in the glucose response, only the GSM region was used in the phylogenetic study and alignment was trimmed according to the Mus musculus GSM region boundaries (accession no. NP067430)  [16]. The best model of evolution was selected using PROTTEST v. 3. 3 software  [70]  and following the corrected Akaike Information Criterion. The best model of evolution identied by PROTTEST was JTT þ G (Jones Taylor Thornton model  [71] ). ML analyses were performed by PHYML  [72]  using an input tree generated by bioNJ, the JTT þ G model of amino acid substitution and 1000 repetitions of bootstraps.
Acknowledgements. The authors would like to thank  C. Bordereau  and  C. Rouland-Lefe  `vre for critical comments on the manuscript and  A. Robert ,  A. Herengt Darmon ,  A. Gastineau  and  C. Espinasse  for assistance;  J. Vuillemin , J. S ˇobotnı ´k and  A. Buc  ˇek for field assistance; and our laboratory members for feedback. We are very grateful to the referees for their comments.
Data Data used to assess the status of tunas and billfishes were obtained from stock assessment outputs compiled in the RAM Legacy Stock Assessment Database  (Ricard et al. 2012). Most reference points time series available from assessments were current through 2012. We found data for 40 stocks of 13 species, 7 species of major commercial tunas and 6 species of billfishes (Fig. S1-S2 ) from at least 48 stocks defined globally (Table  1 ). Data for management variables were compiled from information available on the websites and reports of different tRFMOs and through personal communication with their staff. Only regulations that existed during the 5-10 year period leading up to the last stock assessment were considered for each stock, although in some cases new management measures are currently in place.