dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Atomic models for three crystal forms of FcpA, and for FcpB, have been deposited in the Protein Data Bank under accession numbers 6NQW, 6NQX, 6NQY, and 6NQZ (respectively). Cryo-EM maps have been deposited in the Electron Microscopy Data Bank (EMDB) under accession number EMD-20504. A pseudo-atomic model consisting of main chain FlaB, FcpA and FcpB docked into the wildtype filament reconstruction has been deposited in the Protein Data Bank (PDB) under the accession number 6PWB. The following datasets were generated:.
Field data collection During the 2016, 2017 and 2018 breeding seasons, artificial nest boxes were used in four research areas to attract breeding cinereous tits, and we regularly checked nest boxes to monitor reproductive activities of breeding birds. In Xianrendong, a total of 332 wooden nest boxes were put up in 2016 and in 2018 the total number of nest boxes was 426, at a height of 1. 3e4. 4 m in pine trees (Pinus densiflora), poplar trees (Alnus japonica) and Chinese ashes (Pterocarya stenoptera). These nest boxes had an entrance-hole diameter of 3. 5e4. 5 cm and a large chamber (outside 14 cm Â 16 cm Â 34 cm, inside 10 cm Â 12 cm Â 32 cm) and were checked by LZ. In Saihanba, 195 wooden nest boxes were put up at a height of 3e4 m on Mongolia scotch pines (Pinus sylvestris var. mongolica) and telegraph poles in 2018.
The individuals who did not emit hissing calls within 10 s never did give hissing calls. For clutch sizes, if a female bird flew off the nest, we were able to count the eggs. If we were not successful, we counted the number of nestlings and unhatched eggs 2e3 days after hatching. During each breeding season we caught both parents and nestlings before fledging and ringed them for individual identification. In this way, we ensured that each individual bird only contributed data from one of the years in analyses.
Data analysis Statistical analysis was performed in SPSS 20. 0 for Windows (IBM Inc. USA). Normality of distributions was determined using the Kolmogorov-Smirnov test. For comparison, non-normally distributed data were examined using nonparametric tests. When comparing the differences in clutch sizes at the four locations, we used the Kruskal-Wallis test because the data did not conform to a normal distribution. Fisher's exact test or Chi-squared test was used to compare the differences in nest defense behavior of the cinereous tit at the four locations. All tests were two-tailed, with a significance level of P < 0. 05. Our data were pooled over years because no yearly differences were found in clutch size (t ¼ À1. 32, df ¼ 80, P¼0. 191) or propensity to give hissing calls (Fisher's exact test, c 2 ¼ 2.
Acknowledgments We would like to thank  Prof. Corlett  and four anonymous reviewers for their helpful suggestions. We are grateful to  Prof. Oliver Krüger  and two anonymous referees for their constructive comments on an early version of this manuscript. We thank  Saihanba National Forest Park , and  Xianrendong, Dongzhai and Diaoluoshan National Nature Reserves  for their help and cooperation, and  Li Zhang ,  Weirong Wang  and  Lingzhi Chong  for their assistance with field work.
Data analysis The number of survived animals belonging to the tagged fish group and the untagged controls group, both after the implantation of the microchip and at the end of the experiment, were compared using a χ 2 test. The standard length of the tagged fish and the untagged controls in each tagging group and at each biometric measurement were analyzed for normality using the Shapiro-Wilk test and for homoscedasticity using Bartlett's test. These tests indicated that in general the data did not conform to the assumption of normality or homoscedasticity, even after transformation (log or square root). The Wilcoxon-Mann-Whitney nonparametric test was then used to compare the standard length of the tagged fish and of the untagged controls at each biometric measurement and for each tagging group (one test per tagging group at each biometric measurement).
The cDNA resulting from reverse transcription was diluted 25× and used as template. PCR amplifications were carried out in CFX96 qPCR System (BioRad) as described previously  (Fan et al. , 2014). The primers used in the quantitative-PCR experiments are listed in Table  3. The primers used for analyzing pkn22 gene expression were chosen downstream to the insertion site of the Spectinomycin resistance cassette used to construct the pkn22 mutant  (Xu et al. , 2003). All measurements were carried out in triplicate. The data were analyzed using Software Bio-Rad CFX manager 3. 0 (BioRad), and the delta Ct method. Only reactions with over 80% efficiency were considered. Imidazol was removed from purified proteins using PD10 columns (Healthcare, Orsay, France). Proteins were concentrated on Vivaspin columns and used for subsequent analyses.
Phylogenetic Analysis The genomic set analyzed in this study included 160 genomes present in the NCBI database 2. The complete list of the genomes selected to build the tree is given in Supplementary Table  S1. To build the phylogenetic tree of HetR, the genomes cited above were analyzed with BlastP  (Altschul et al. , 1990)  using the sequence form Nostoc as a query and an e-value < e -95. Multiple alignments of the proteins were generated using the Constraintbased multiple Alignment tool (COBALT)  (Papadopoulos and Agarwala, 2007). The phylogenetic tree was constructed using the tree generator tool available in the NCBI database. The sequence of Synechococcus served to root the tree.
, 2015)  partially restored the ability of the mutant to grow in the presence of N 2 as the sole nitrogen source (Figure  1A ). These data indicate that the growth of Nostoc requires the presence of the Pkn22 kinase under nitrogen starvation. The heterocyst differentiation process was then analyzed in the pkn22 mutant in comparison with the wild type strain. Interestingly, the heterocyst formation process was delayed in comparison with what occurred in the wild type strain. No heterocysts were observed during the first 24 h after the nitrogen step-down (Figure  1B  and Supplementary Figure  S2 ) in the mutant strain, and a few heterocysts began to appear only 96 h after the step-down (Supplementary Figure  S2  and Table  2 ). These heterocysts did not allow the strain to grow in the absence of combined nitrogen starvation, since the growth of the mutant did not resume after several days in BG11 0.
Acknowledgements The authors thank  Grimaud Pillet  for preliminary experiments and  Sylvain Joubaud  and  Philippe Odier  for insightful discussions. This work was supported by the  Labex iMUST  ( ANR-10-LABX-0064 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ), operated by the  French National Research Agency (ANR), France , and by the  ANR, France  through grant  ANR-17-CE30-0003  (DisET). One of the authors (TRA) also acknowledges support from the  US National Science Foundation  through grant  DMS-1512925. This work was made possible thanks to the resources of PSMN from  ENS de Lyon.
, 2009; Davison et al. , 2013; Giering et al. , 2014; Ariza et al. , 2015). However, there is a lack of comprehensive dataset at global scale to properly estimate micronekton biomass and composition. The few existing estimates of global biomass of mesopelagic micronekton vary considerably between less than 1 and ∼ 20 Gt  (Gjosaeter and  Observations and biomass estimations of micronekton rely traditionally on net sampling and active acoustic sampling (e. g. ,  Handegard et al. , 2009; Davison, 2011). Each method has limitations. Micronekton species can detect approaching fishing gears and part of them can move away to avoid the net. This phenomenon leads to biomass underestimation from net trawling  (Kaartvedt et al. , 2012). Conversely, acoustic signal intensity may overestimate biomass due to presence of organisms with strong acoustic target strength, e.
The lower velocities are coupled with lower temperatures and the higher velocities with higher temperatures. Therefore, it seems here that the difference observed in the temperature values of the two datasets has a stronger impact on the parameter estimation than the regime of currents. In the following, although distribution along secondary variables are not always shown, they have always been used in the analysis to check that the results of twin experiments are not biased by this type of difference between the distributions of randomly selected datasets. Experiments with such cross-correlation between indicator variables are not presented.
Acknowledgements. This publication has been developed in cooperation with the European Union's  Horizon 2020 research and innovation project AtlantOS  ( 633211 ). The authors thank the  Groupe Mission Mercator Coriolis (Mercator Ocean)  for providing the ocean general circulation model FREEGLORYS2V4 reanalysis and  Jacques Stum  and  Benoit Tranchant at Collecte Localisation Satellite  for processing satellite primary production and ocean reanalysis data. We also thank  Arnaud Bertrand  and  Jérémie Habasque  from the  Institut de Recherche.
, 2013). NKCC1 paralogs are expressed in SW-type ionocytes in numerous teleost species including the European sea bass  (Lorin-Nebel et al. , 2006; Inokuchi et al. , 2008; Buhariwalla et al. , 2012; Breves et al. , 2014). NKCC2 was detected in tilapia and in D. labrax intestine  (Hiroi et al. , 2008; Alves et al. , 2019) , but there is no data available on its localisation in the gills and in the posterior kidney of D. labrax. European sea bass exhibiting a FW intolerant phenotype face a severe hydromineral imbalance and we will analyse if this failure originates from a failure in ion uptake at the kidney and/or gill levels. The involvement of the gill in differential ion uptake capacity of D. labrax will be analysed by comparing the expression profile of the main ion transporters in tolerant and intolerant fish to FW.
Phylogenetic analysis and protein comparisons Phylogenetic analyses of slc26a6 and nkcc (slc12a1 and slc12a2) have been performed (Figs 1, 2). Nucleotide sequences from different species were obtained at Ensembl or NCBI (Tables  2, 3 ). D. labrax sequences were obtained from the European sea bass genome project  (Tine et al. , 2014). Multiple nucleotide alignments were performed with MUSCLE V3. 8. 31  (Edgar, 2004)  and ambiguous regions were removed with Gblocks V0. 91b  (Talavera and Castresana, 2007). The phylogenetic trees were inferred using the maximum likelihood (ML) method implemented in the PhyML program (v3. 1/3. 0 aLRT)  (Guindon and Gascuel, 2003). Best model of evolution was selected using Modelgenerator V. 85  (Keane et al. , 2006)  following the corrected Akaike Information Criterion (with four discrete gamma categories) and used to construct a phylogenetic tree.
Bayesian posterior probabilities were computed with MrBayes 3. 2. 1  (Huelsenbeck and Ronquist, 2001; Ronquist and Huelsenbeck, 2003). Two different runs with four incrementally heated simultaneous Monte Carlo Markov chains were conducted over one million generations, applying respective substitution models determined for each partition in PartitionFinder2  (Lanfear et al. , 2017). Trees were sampled every 100 generations to produce 10,000 trees. In order to estimate posterior probabilities, 25% of the trees were discarded as a burn-in stage, observing when average standard deviation of split frequency (ASDSF) values dropped below 0. 01. Tree were generated and robustness of the phylogeny assumption was evaluated by bootstrapping procedure from 1000 data set replicates and with posterior probabilities (PP).
The phylogenetic tree of slc26a6 was rooted with the slc26a6 sequences of two reptilians, the Chinese soft-shelled turtle (Pelodiscus sinensis) and the painted turtle (Chrysemys picta bellii). The phylogenetic tree of nkcc (slc12a1 and slc12a2) was rooted with the ncc3 sequences of the tropical frog (Xenopus tropicalis) and the lizard (Anolis carolinensis)  (Hartmann et al. , 2013). Amino acid identities (in %) were performed with MUSCLE V3. 8. 31  (Edgar, 2004)  implemented in Geneious® 9. 1. 8 software (Biomatters).
Statistical analysis Analyses were performed using GraphPad Prism (version 6, GraphPad Software Incorporated, La Jolla, CA 268, USA). When parametric conditions were verified, one-way analyses of variances were performed followed by a Tukey's post-hoc test. In the case of non-parametric conditions, a Kruskal-Wallis test was performed followed by a Dunn's post-hoc test. Data are represented as box and whisker plots (from first quartile to third quartile) showing median, minimum and maximum values. Statistical differences were accepted from P < 0. 05.
Acknowledgments We would like to thank  Philippe Clair  from the qPCR CEMEB platform for his help, and technicians of Ifremer Palavas-les-flots for the maintenance of the fish. This research was partially granted from  CeMEB  ( Centre Méditerranéen Environnement et Biodiversité ) labex project SALSA, granted to  C. Lorin-Nebel & C. Cosseau.
Acknowledgement This work is part of the  SMS  project " Micropollutant Source Separation  for health risks control and preservation of the environment" funded by the  Adour-Garonne Water Agency  and the  French Agency for Biodiversity  and of the project  ANR Design  ( ANR-17-CE22-0017 ). We acknowledge  C. Claparols  from  ICT  for the UHPLC-MS-MS analysis.
Data Daily minimum and maximum temperatures are extracted from 14 CMIP5 models (Table  1 ), including historical simulation for the period 1861e2005 as well as future projections under the RCP8. 5 (Representative Concentration Pathways) scenario for the period 2006e2100. We selected these 14 models by only considering the availability of their outputs. As results are quite independent of precise RCP scenarios leading to the global warming levels at 1. 5 C or 2 C (not shown), only the results derived from RCP8. 5 are shown in the present study. The historical simulation was realized with both natural (solar irradiance and volcanic activity) and anthropogenic forcings. RCP8. 5 represents the highest emission scenario and it is actually the most representative of global CO 2 emissions from 2005 to present  (Peters et al.
CsI and Cdl2 aérosol décomposition rate under irradiation has been quantified at 80°C and 120°C in presence of humidity and on different substrate (stainless steel, quartz and Epoxy paint). A model has been developed for the ASTEC-SOPHAEROS code to reproduce the data and help the identification of the gaps remaining in the understanding of iodine volatility in a severe accident of a Nuclear Power Plant (NPP). The current model applied to model the gaseous iodine behaviour in the containment of PHEBUS-FP tests does not fit with the experimental data probably because the nuclear aerosol reaching the containment are much more complex than pure CsI aerosols. It has been clearly shown than the radiolytic oxidation of metallic iodide aerosol into molecular iodine can significantly impact the source term evaluation even if additional experimental data area required to cover the variety and complexity of nuclear iodide aerosols.
3). During the washing, CsI is expected to be washed whereas adsorbed I2 is expected to remain on the paint. The paint rinsing solutions was analyzed and showed that ~ 13 % of the initial iodine inventory was washed. As the global volatilization is 77. 5%, it means that ~ 10 % could have been adsorbed on the paint (this could not be verified by a gamma-counting as the coupon washing was performed several months after the test so that the activity of the Iodine tracer was too low to be detected). Both organic and inorganic species releases are quite well modelled and even if some discrepancies exist, they are reasonable as the uncertainties of the experimental data are close to 30%. In the long term, the discrepancies between the experimental data and the modeling might increase according to Fig.
Acknowledgments Authors acknowledge the OECD hosting the  STEM  project, the STEM partners for their support ( Electricité De France , the  Atomic Energy of Canada Limited , the Teknologian tutkimuskeskus  VTT (Finland) ,  Ustav Jaderného  vyzkumu Rez a. s ( Czech Republic ),  The Gesellschaft für Anlagen -und Reaktorsicherheit (Germany) , The  Korea Atomic Energy Research Institute , The  Korea Institute for Nuclear Safety and The US Nuclear Regulatory Commission) Fig. 1: Simplified view of the experimental EPICUR loop.
Moreover, a camera can be easily "carried", while a LiDAR is heavier. It is however important to consider the lighting condition. Too much light produces shadows that interfere with the detection of points of interest and therefore homologous points  (Gómez-Gutiérrez et al. , 2014). A weak light has the same effect. According to  James and Robson (2012) , diffuse light is recommended for an optimal processing of 3D point cloud. 3D point cloud production by photogrammetry is relatively easy using free or paid software. In  Eltner et al. (2016) , the authors provide a complete benchmark of these softwares. Metashape  (Metashape, 2019)  is used for this article.
Methods for discontinuity planes extraction The extraction of coplanar point sets from a 3D topographic model has an interest to geoscientists. Over the past twenty years, several methods has been proposed to extract discontinuity planes from solid images  (Assali et al. , 2014) , meshes  (Slob et al. , 2005; Lato and Vöge, 2012)  or point clouds  (Jaboyedoff et al. , 2007; Riquelme et al. , 2014; Gigli and Casagli, 2011; Dewez et al. , 2016). All of them deserve to be detailed in this paper. Nevertheless, only the methods proposed by  Dewez et al. (2016)  are succinctly described. The first reason is their robustness and efficiency to extract discontinuity planes from large dense clouds (billion points). The second reason is their easy access as a plugin named Facets, available in the free software CloudCompare (Girardeau-Montaut, 2019).
If the 3D point cloud covered a whole outcrop, the virtual scanline, cannot be directly compares to a real in situ scan line, but the comparison is possible by reducing the 3D point cloud to a narrow band corresponding to the neighbourhood of a real in situ scan line. To perform our approach, we develop a software named DiscontinuityLab that uses as input data the csv Facets file. The software DiscontinuityLab was developed in Python 3. 7 language especially for the backend algorithm and PyQt5 Designer was used for the creation of the Human Machine Interface (HMI). The main open source modules used are: numpy v1. 17. 4, pandas v0. 25. 3, scikit-learn v0. 22, scipy v1. 4. 1, mplstereonet v0. 5, matplotlib v3. 1. 2 and h5py v2. 10. 0. The digital analysis perform can be saved in the hdf5 format and can be reopened whenever the user wants, thereby the treatment is still reproductible.
So, the first distance equalizes 1. 218 m = [(0. 1+1. 49)*cos(90-50)] and the second distance equalizes 1. 609 m = [(0. 11+1. 99)*cos(90-50)]. The Table  2  displays the orientations used to extract discontinuities from the point cloud the virtual outcrop points cloud with the interactive stereonet of the Facets plugin. It also displays the Fisher constant K  (Fisher, 1953)  that defined the deviation angle around the mean orientation of a discontinuity family. The higher is K, the lower is the deviation angle around the mean orientation which means that discontinuity planes tend to be perfectly parallels. In the opposite, the lower is K, the more scattered is the distribution around the mean orientation. Here, K is calculated with the software Stereonet (2019) and a K upper than 10 4 is considered as infinite.
It is important to note that the orientation of F1-Synt and F2-Synt should be 90/090 and 90/140. Yet, during the point cloud generation from the STL format, CloudCompare change some orientation by its opposite because of a limit case as a dip of 90°. The choice of the authors is to keep intact the export Facets file and use the option of DiscontinuityLab to set a specific orientation for the calculation. Finally, the number of discontinuities and the RMS mean given by the DiscontinuityLab software are displayed in Table  2. Table  2. Discontinuity families characteristics for the synthetic outcrop (K upper than 10 4 is considered as infinite).
Additionnal information on the different families is displayed in this Table. In the case of Saulges, the discontinuities with a surface lower than 10 cm² were rejected thanks to the associated option in DiscontinuityLab. The Fisher constant K is calculated with the software Stereonet (2019). It is important to note that the segmentation orientation intervals in the Table  4 , are chosen from the CloudCompare stereonet, are consistent with the orientations measured on the field (Table  3 ).
Digital analysis of the synthetic outcrop For the DiscontinuityLab results validation, the synthetic outcrop data are used. The aim is to compare the software results with the real dimensions (section 4. 1): the results are supposed to be equals. The results obtained are displayed in the Table  5. After the classification, the family S0-Synt contains six planes merged in three Super-Discontinuity planes as expected. Two distances are provided by the software: 0. 75 and 1. 0 m. They correspond with the expected values (section 4. 1). For the F1-Synt family the expected results are also consistent with the DiscontinuityLab results. Indeed, seven main planes have a constant spacing of 25 cm and represent 46. 8 % of the total surface. It is important to note that the number of seven clusters in Table  5  is due to the exclusion of the narrow bands by a surface threshold in DiscontinuityLab.
This problem has been solved by clustering these planes into one. Future works must certainly focalise on the minimum spacing to take into account, considering the precision of the points of the 3D cloud. The proposed approach has also shown its usefulness for the characterization of fracturing corridors. Indeed, it also provides a complete statistic on the internal distances in these corridors. We demonstrated the pertinence of our approach by the comparison of extracted information of a synthetic outcrop to set values and, in the case of a natural outcrop the extraction of several useful information for geoscientists. To complete this work, a userfriendly software, named DiscontinuityLab, has been developed in Python to manage the KDE segmentation manually. Moreover, the settings and the results can be saved as a project to ensure the repeatability and the transferability of results.
ACKNOWLEDGEMENTS The authors would like to acknowledge the financial support of the  Brgm and the region of "Pays de la Loire ". The authors would also like to thank  E. Labergerie  for Saulges data acquisition and  C. Charlet ,  G. Ferré  and  S. Durand  of  Le Cnam Esgt  for providing a support for georeferencing the Saulges data.
X-ray single-crystal structural data were collected on a Bruker D8 VENTURE equipped with a PHOTON II CPAD detector and a microfocus X-ray source with Cu-Kα radiation (λ = 1. 54184 Å) operating at 50 kV and 1 mA. A suitable crystal was chosen and mounted on a 150 µm aperture Dual-Thickness Microloop. The data collection was performed at -173 °C and at ambient temperature. The asobtained unit cell and space group showed that the structures were identical. We therefore decided to report the data collected at -173 °C. The single crystal X-ray diffraction allowed collecting a total of 1496 frames. The total exposure time was 8. 31 h. The frames were integrated with the Bruker SAINT Software package using a narrow-frame algorithm. The integration of the data using a monoclinic unit cell yielded a total of 4196 reflections to a maximum θ angle of 55.