dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to  Dr. Nathan W. Luedtke  ( Chemistry Department, University of Zurich ) for kind help with EdU staining and to  Dr. Christoph Englert  ( Leibniz Age Research, Jena, Germany ) for sharing the Tg(wt1b:EGFP) line. We also acknowledge  Zhiyong Chen ,  Yuya Sugano ,  Ruxandra Bachmann-Gagescu  and  Matthias Gesemann  for support and helpful advices,  Benoit Palancade  for critical reading of the manuscript and other members of our labs for helpful advices. The authors acknowledge the assistance and support of the  Center for Microscopy and Image Analysis, University of Zurich  for support with electron microscopy experiments. We also acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , member of IBISA and of the  France-Bioimaging  ( ANR-10-INBS-04 ) infrastructures.
Work in the laboratory of VD is supported by the  Centre National de la Recherche Scientifique (CNRS) , the  "Fondation pour la Recherche Médicale" (Foundation for Medical Research)  under grant No  DEQ20150734355 , "Equipe FRM 2015" and the  Labex Who Am I?  ( ANR-11-LABX-0071 ; Idex  ANR-11-IDEX-0005-02 ). AB received PhD fellowships from the  "Ministère de l'Enseignement Supérieur et de la Recherche" and the "Ligue Nationale contre le Cancer " and a "transition post-doc" grant from the  Labex Who Am  I?. Work in the laboratory of SN was supported by project grants from the  Swiss National Science foundation  (to SCFN and JL) and the  RiMED foundation in Palermo  (to CCC). CCC received a fellowship from the  RiMED foundation.
Bacteria Process for identification of a new prokaryote To confirm the novelty of a bacterial species, 16S rRNA gene sequencing is performed along with a BLAST analysis keyed to the National Center for Biotechnology Information nucleotide database for phylogenetic analyses. A threshold of 98. 7% of 16S rRNA gene sequence similarity with the phylogenetically closest species with standing in nomenclature was suggested by Stackebrandt and Ebers  [8]  to classify a new bacterial species. The 16S rRNA gene sequence of each isolated new bacterial species is submitted to the GenBank database, and its strain type is deposited in a strain collection institute. However, DNA-DNA hybridization, a technique previously considered to be the reference standard to classify a new bacterial species, should be considered as outdated, as it is not reproducible among laboratories and is not cost-effective  [9].
With the advent of both matrix-assisted desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) and genome sequencing, we recently proposed describing new isolated species by using the taxonogenomic approach  [9]. The determination of digital DNA-DNA hybridization as well as the average of genomic identity of orthologous gene sequences are also included  [9]. Unfortunately, this process is time-consuming-sometimes several years. Accordingly, we proposed a new format (new species announcement) that reports the 16S GenBank accession number, phylogenetic tree, strain deposit accession numbers and main phenotypic characteristics of the new isolated species  [10]. Finally, according to the rules of the International Committee on Systematics of Prokaryotes, the name of the new bacterial species must be officially recognized  [6].
On the other hand, Paenibacillus provencensis, first isolated from the urine sample of a patient with a urinary infection  [24] , has been isolated from a stool sample by culturomics. Similarly, Paenibacillus massiliensis, initially isolated from a blood culture bottle in a clinical microbiology laboratory  [25] , has since been isolated in a stool sample analysed by culturomics. Indeed, the boundary between commensal and pathogenic bacteria remains indistinct, so efforts must be sustained by both clinical microbiology laboratories and culturomics studies in order to increase the identification of new microorganisms. This also highlights the need to update and share MALDI-TOF MS databases for the optimal identification of bacterial species during clinical or research studies  [26].
Acknowledgements We thank all the contributors involved in culture and those who work in our laboratory. This work has benefited from the support of the French state, managed by the  'Agence Nationale de la Recherche ,' including the ' Programme d'Investissement d'avenir'  under the reference Méditerranée Infection  10-IAHU-03. This work was supported by  Région Provence Alpes Côte d'Azur and European funding FEDER PRIMI.
Gene fragment Primers 5' region of the mitochondrial cytochrome c oxidase subunit I (COI)gene jgLCO1490+jgHCO1490  [44] , Dm_FL+RL  [45] , ZooplF+R  [42] , COI-F+R  [43]  5' region of the mitochondrial rDNA 16S gene 16Sin-F + 16Sin-R  [17]  5' region of the nuclear rDNA 18S gene 18a1 +700R  [46]  5' region of the nuclear heat shock protein 90 kDa gene HSP-90_F+R  [47]  Region of the nuclear histone H-3 gene H3F+R  [48]  https://doi. org/10. 1371/journal. pone. 0194045. t001 sequence was assembled based on the forward and reverse sequences using CodonCode Aligner v. 6. 0. 2 (CodonCode Corp, USA). DNA sequences were submitted to the NCBI Gen-Bank database (S1 Table ).
As a consequence, sequences from GenBank had been amplified with varying primers, explaining the variation in fragment lengths. Hence, all COI sequences were cropped to the minimal overlapping length of 563 bp. To analyze the genetic variation among samples, we estimated the following parameters for each gene fragment and for each species separately: number of haplotypes (Nh), number of variable (polymorphic) sites (Nv), number of parsimony informative sites (Np), haplotype diversity (Hd), nucleotide diversity (Pi), and average number of nucleotide differences (k)  [63, 64]. We also carried out neutrality tests and assessed mismatch distributions. All analyses were performed in DnaSP v. 5. 1  [65]  and MEGA v. 7  [66]. The best-fitting model of nucleotide substitution was selected using the ModelFinder web application  [67] , based on likelihood scores for 154 different models and the Bayesian information criterion  [68].
The first 50% of the generations were discarded as burn-in. A 50% majority rule consensus tree was generated from the remaining trees, and the posterior probability of each node was estimated as the percentage of trees recovering any particular node. MP and BI analyses were performed on the computer cluster CIPRES Science Gateway v. 3. 3  [74]. To establish whether there was any evidence for cryptic species, we performed a Bayesian generalized mixed Yule coalescent (bGMYC) analysis  [75]  with a threshold of 0. 5, using the last 100 trees of the BEAST MCMC file and the GMYC-web server  [76]. Furthermore, we used our haplotypes to construct a network in the program PopART  [77]  to show relationships among the individuals sampled from different locations. The TCS algorithm was selected for this network, based on the implemented statistical parsimony  [78].
Second, the Network package (version 5, http://www. fluxus-technology. com) was used to construct a median-joining haplotype network with Steiner maximum parsimony post-processing  [79] , putting equal weight on each variable nucleotide site. Before constructing this network, data were processed using the star contraction algorithm  [80]  to reduce of number of similar haplotypes and hence to reduce the complexity of the network. As an additional test for presence of distinct clades, we used the program ABGD  [81]  to assess p-distances  [69]  with 100 replicate steps in this reduced-complexity network. The reduced-complexity network was also used for a nested clade analysis, which identifies significant phylogroups, and to test for historical demographic processes within the major phylogroups.
The nested clade phylogeographic analysis (NCPA) was performed in the program ANeCA v. 1. 2 [82]  with integrated module GeoDis v. 2. 6 for calculation based on a new approach to the interpretation of biological processes according to  [83]. Subsequently, to test for neutrality within individual clades, Tajima's D tests  [84]  and Fu's FS tests  [85]  were performed in Arlequin v. 3. 5  [86]  with 1000 permutations. We also performed R2-tests  [87]  in DnaSP, again with 1000 permutations. Mismatch distributions were investigated for some groups to evaluate a model of exponential population growth  [88]. For the latter, a goodness of fit test was performed, using a parametric bootstrap approach based on the sum of squared deviations (SSD) between the observed and simulated mismatch distributions  [89].
The demographic parameter Tau was estimated using a generalized nonlinear least square approach, and the confidence interval of this parameter was computed using parametric bootstrap with 1000 replicates in Arlequin. Divergence times were estimated using a relaxed molecular clock approach with uncorrelated lognormal distributions of branch rates in the program BEST v. 1. 6. 1, following methods and calibrations of  [90]. A second variant of this analysis was done using the additional calibration point of  [91]  for the Dapnia/Ctenodaphnia split (145 MYA), which suggests that the speed of nucleotide substitutions is significantly lower than suggested by  [90].
Genetic diversity We obtained 174 original COI sequences of three Daphnia species (D. magna, D. similis, D. sinensis) from different regions of the Palaearctic (S1 Table  , Fig 1). They were analyzed together with 609 COI sequences obtained from Genbank (S2 Table ). As we found that these haplotypes belong to two strongly divergent clades ("super-clades" A and B, see below), we analyzed genetic diversity in the whole sample as well as within each of the two super-clades (S3 Table ) and carried out neutrality tests and assessed mismatch distributions also for each super-clade separately (S4 Table ). The genetic diversity at COI was high in the entire sample as well as in super-clades A and B. Haplotypic diversity was higher in super-clade A (mainly European populations) than in super-clade B (mainly Asian populations), possibly explained by the wider geographic distribution of super-clade A.
COI phylogenetic tree The best-fit model  [92]  for the whole COI data set (our sequences and GenBank sequences combined) was HKY with four categories of gamma (+G4, shape alpha 1. 61) and a proportion of invariable sites (+I, 0. 602) with a relative BIC weight of 0. 3648. Based on the cumulative relative weight, two other models were also within the 95% confidence interval. One best ML tree was found, with an overall likelihood score of -ln L = -2775. 1574. These analyses revealed the existence of two highly divergent and well-supported main super-clades within Dapnia magna The super clade A includes clade A1, which was found in samples from Europe, the Mediterranean region (including North Africa), the Middle East, Turkey and Caucasus, as well as in a few populations in Western Siberia. Within this clade, there are several sequences from the Genbank labeled as originating from North America (Mexico, USA, Canada), but in reality most of these may have originated from laboratory cultures, presumable of European origin (S6 Table ).
Few others are very likely cases of recent human-mediated invasions (see Discussion). The American individuals with A1 haplotypes were therefore excluded from further analyses and are not represented on any of the figures, except for proven laboratory clones, which are included in S4 Table. The super clade also includes clade A2, which is present exclusively in samples from North America. Unfortunately, there is no exact information on the sampling localities of the sequences deposited to the GenBank, only stated "Manitoba, Canada"  [51] , and only names of sampling sites ("Round Lake" and "Sue") with unclear exact geographic locations are provided in  [14]. We hence decided to omit all North American samples from the map (Fig  1).
Invasions Cladocerans, and particularly some species of Daphnia, are very popular laboratory animals and are often used as food in aquaculture. Moreover, their resting stages are long-lived, which favors dispersal by human activities (e. g. , the ephippia of D. magna of super-clade A were found in ballast water  [56] ). Many COI sequences of D. magna are marked on GenBank as belonging to "Mexican" or "Canadian" populations (S6 Table ). However, closer investigation led to the conclusion that these contain many specimens that originated from laboratory cultures in those countries. The genetic variability among specimens with known laboratory origin ("cultures" in S6 Table) is very low. Possibly, all these specimens derive from commercial clones (or perhaps from a single clone) from Aquatic BioSystems Inc.
Acknowledgments Field collection in Russia was carried out by our team or by colleagues as part of a governmental project " Ecology and biodiversity of aquatic ecosystems and invasions of alien species " (no. 0109-2014-0008 ), with governmental permission to collect samples from public property. Sampling in the natural reserves of Russia was conducted with special permissions of their Directors ( M. A. Kirgintsev ,  Belozersky Natural Reserve; Yu. P. Suschitsky, State Khankaisky Biosphere Reserve ), we also thank the Administration of these nature reserves for assistance during the sampling. Verbal permission to collect in private farm ponds was obtained from local owners. Mongolian samples were collected by the  Joint Russian-Mongolian Complex Biological Expedition with permission of the Ministry of Nature, Environment and Tourism of Mongolia.
This work was supported by the  European Union  ( Marie Curie Career Integration Grant DAMANMP for CRH ) and the  Russian Government Program of Competitive Growth of Kazan Federal University  (for AAK), and the  Russian Foundation for Basic Research  (grant  15-29-02-447  -for TVN). The part concerning biological invasions was funded by the  Russian Foundation for Basic Research  (project  17-05-00782 -a for DPK), the part concerning nuclear loci was supported by the  Russian Science Foundation  (grant  16-14-10173  for TVN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Hydrometric monitoring of small water bodies (1-10 ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to support mapping of small water bodies but the influence of their limited surface areas, vegetation growth and rapid flood dynamics on long term surface water monitoring remains unquantified. A semi-automated method is developed here to assess and optimise the potential of multi-sensor Landsat time series to monitor surface water extent and mean water availability in these smallest water bodies. Extensive hydrometric field data (1999-2014) for 7 small reservoirs within the Merguellil catchment in Central Tunisia are used to calibrate the method and explore its limits.
Landsat preprocessing Landsat Level 1 Terrain Corrected (L1T) (orthorectified) products from USGS Earth Explorer were used here. Level 2A products corrected to land surface reflectance using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) or MACCS treatment chain were not available for Landsat 8 or before 2002 for our catchment at the time of research. The required radiometric (including atmospheric & topographic) corrections were therefore derived for each sensor and band combination. These are detailed in the Appendix. Based on data availability of Landsat images and field limnimetric data for the seven lakes, 546 Landsat 5-8 images over the period 1999-2014 were acquired and treated (Fig. 2 ).
Errors from clouds, shadows and SLC-off pixels were then assessed and minimised in post-processing by removing images for each lake with interferences above a determined threshold. This optimal threshold was determined by minimising the root mean squared error (RMSE) on surface area aggregated over time (15 years) in order to give importance to both the number and quality of the Landsat observations over time, and therefore compromise between maintaining high temporal resolution and minimising spatial errors from clouds, shadows and SLC-off interferences. Thresholds were varied in 5% increments and the quality of fit in terms of R 2 and RMSE between remotely sensed surface area and field surface area was calculated for decreasing subsets of images. Gouazine lake which possessed both the longest time series (over 15 years) and the most accurate data and rating curves (updated 6 times) was used to optimise the thresholds.
Remote monitoring of surface water dynamics and water availability The complete method was applied to all 546 images and surface water extents were assessed for the 7 small reservoirs over 1999-2014. Values affected by excessive cloud & shadow & SLC presence over each lake were removed and remaining observations were linear interpolated over time into continuous time series of flood dynamics. Alternate smoothing and interpolation of discrete observations were shown not to improve the results  (Ogilvie, 2015) , considering the abrupt flood dynamics in small water bodies. These were then converted to volumes using the site-specific updated surface-volume rating curves. The mean water availability per year was calculated to assess the suitability of the approach to derive long term water availability data for stakeholders.
Comparison with published global water data sets Results were compared to those obtained when using the global surface water data sets produced by  Pekel et al. (2016)   were extracted from the raster files for the 13 lakes used previously. Confusion matrices between three JRC images and the DGPS contours on 7 lakes were first calculated to provide pixel-based comparisons of the method's ability to detect water in the smallest reservoirs. Monthly surface water values extracted from JRC datasets over 1999-2014 for 7 small reservoirs were then compared with mean monthly surface areas based on in situ monitoring. RMSE and RSE values on monthly surface water dynamics as well as mean annual water availability were again calculated to compare with results from our approach. In parallel, the number of NA values in monthly water history rasters within each lake grid cell were collected for each image to reduce their detrimental influence on surface water area estimates.
Reduced surface water amplitudes On the largest lake studied here (Morra), GPS contours revealed the obsolescence of the stage-surface rating curve, which has not been updated over the past 20 years due to the lake remaining flooded through the year and the access difficulties to perform bathymetric surveys. This partly explains the low R 2 calculated but results also display difficulties in extracting consistent flood trends from the remote sensing data (Fig. 12 ). These can be explained by the morphology of this large deep lake, where the uncertainties of the remote sensing method are significant compared to the amplitude of the surface area variations (80 000 -100 000 m 2 on Morra against 0 -90 000 m 2 on Gouazine). Accordingly, it is not only the spatial resolution and number of pixels but the amplitude of variations in the flooded surface area over time (e.
Comparison with published global data sets Analysis of available monthly surface water datasets  (Pekel et al. , 2016)  against field data for 7 lakes over up to 15 years  (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014)  highlighted firstly the incidence of NA pixels and the necessity to assess and remove NA pixels above individual lakes to reduce underestimations (Fig. 15 ) and reproduce coherent flood dynamics (Fig. 12 ). Filtering out images with over 25% NA values per lake cell led to optimal RMSE errors and is coherent with the SLC threshold determined with our approach. After NA removal, NSE across all 7 lakes rose from 0. 39 to 0. 78 but Fig. 16  confirms the residual difficulties, consistent with the omission errors identified on single JRC water history rasters (Fig.
6 ). Mean RMSE reaches 22 500 m 2 and removing all NAs only improves RMSE further to 21 800 m 2 compared to 9 300 m 2 with the MNDWI approach. On small lakes under 5 ha which present greater shallow waters and flooded vegetation, PDAI errors are proportionally more important and reach 61% on average. On Fidh Ali (Fig. 12 ), difficulties due to the significant vegetation in the lake lead to vast underestimations and fail to detect certain floods completely. On surface areas over 5 ha, PDAI errors remain high around 35% and interestingly continue to underestimate surface water areas. These result from omission errors during summer months due to vegetation growth in permanent lakes as well as undetected vegetated areas on the fringes inundated during large floods. Figure  12  highlights the valuable insights into the general flood dynamics that can be extracted from the JRC datasets on larger lakes such as Gouazine and Morra, though omission errors remain apparent in the underestimated flood peaks.
These classification inaccuracies are further exacerbated by the limited number of observations. After NA removal, JRC monthly water history sets provide for instance 99 exploitable images over 1999-2014 on Gouazine, compared to 269 when Remotely sensed surface areas with global surface water data sets  (Pekel et al. , 2016)  versus field data over 1999-2014 across all 7 lakes. Percentage of NA pixels was calculated over each lake cell   (Pekel et al. , 2016)  against field data for 7 lakes using the complete L5, L7 and L8 archives filtered for clouds, shadows and SLC failure (Fig. 8  and Fig. 9 ). The greater temporal resolution allows better detection of dry spells, multiple flood peaks, timing of flood peaks and more coherent flood rises and declines as seen on Gouazine, while on the small Hoshas lake two floods in 2013-2014 are otherwise undetected with JRC datasets (Fig.
12 ). These difficulties also have implications on the accuracy of maximum extent estimates, and though median underestimation is 41%, the spread in the error (Fig. 16 ) even for larger lakes, prevents systematic corrections. Surface area inaccuracies on small lakes in JRC datasets also degrade water resource assessments for farmers or water balances for hydrologists, with for instance Gouazine RMSE on mean water availability increasing by 24% to 17 800 m 3. Validated against significant hydrometric field data, this paper demonstrates the potential and limitations of Landsat imagery to monitor surface water extent dynamics and water availability within the smallest reservoirs (1-10 ha). Results confirmed the superiority of MNDWI out of six widely used band ratio indices to detect flooded areas in seven reservoirs of varying sizes and over three flood stages.
Residual errors from cloud uncertainties, due to undetected cirrus clouds and shadows, as well as greater vegetation, shallow water and associated mixed pixels led to a number of outliers which can be detrimental notably on smaller lakes. Filtering images with excessive cloud and SLC presence reduced temporal resolution to 1. 5 images/month which can lead to minor floods and those subject to high infiltration to be overlooked by Landsat time series. With a mean RMSE of 9 300 m 2 and R 2 reaching 0. 9, the method however confirmed the operational potential of Landsat imagery to study surface water dynamics of floods over 3 ha, as well as long term water availability (R 2 = 0. 82). Comparisons with global water data sets  (Pekel et al. , 2016)  notably confirmed the benefits and relevance of specific, complementary approaches to improve classification in smaller water bodies.