dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments To  Jean-Louis Martin  ( Registre du Rhone ) for his expertise in using RAAR files. To  Melanie D'Auria  (from the  French Ministry of the Interior)  for her help in accessing timely to the RAAR database. The Traumabase Group listed as contributors LEAD AUTHOR FOR THE GROUP :  Anatole Harrois, MD, PhD  ( UniversiteParis Sud and Department of Anaesthesiology and Critical Care, Hôpital Bicêtre, Groupement Hôpitaux Universitaires Paris Sud, AP-HP, Kremlin Bicêtre, France );  Paer Selim Abback MD, (Université Denis Diderot and Beaujon University Hospital, Hôpitaux Universitaires Paris Nord-Val-De-Seine, Clichy, AP-HP, France) Sylvain Ausset, MD (Anaesthesiology and Critical Care, Hôpital Interarmées Percy, Clamart, France) ;  Mathieu Boutonnet, MD  ( Anesthesiology and Critical Care, Hôpital Interarmées Percy, Clamart, France );  Thomas Geeraerts, MD, PhD  ( Department of Anesthesiology and Critical Care, Hospital, University Toulouse III Paul Sabatier, Toulouse, France );  Anne Godier, MD, PhD  ( Université Paris Descartes and Department of Anaesthesiology and Critical Care, Hôpital Européen Georges Pompidou, APHP, Paris, France );  Olivier Langeron, MD, PhD  ( Université Paris Est, Department of Anesthesiology and Critical Care, APHP, Hôpital Henri Mondor, 51 avenue du Marechal de Lattre de Tassigny 94010 Créteil, France ) ;  Eric Meaudre , ( Department of Anesthesiology and Critical Care, Military Teaching Hospital Sainte-Anne, Toulon, France, French Military Health Service Academy-Ecole du Val-de-Grâce, Paris, France );.
While HSA hi iCLPs expressed B-lineage associated genes (Ebf1, Vpreb, Pax5, Foxo1, Pou2af1, Ly6d and Rag1), HSA lo iCLPs expressed ILC-lineage associated genes (Tcf7, Gata3, Id2, Nfil3, Rorc, Cd7, Ccr9, Pira1 and Glis3) and some transcripts found in adult ETP (IMMGEN data base). However, they did not express known Notch1 signaling targets that are essential for T cell differentiation. Therefore, HSA lo iCLP although having partially lost B cell potential do not appear to be engaged in the T cell pathway of differentiation, only when they reach the thymus do we find signs of Notch activation (Hes1, Dtx)  [83]. This sequence of events leads to the conclusion that the establishment of B versus T cell identity occurred at sequential stages of differentiation and it is not a binary choice, and, importantly, the loss of B cell lineage potential may take place before colonizing the thymus (for further discussion on this topic, also refer to  Cumano et al.
Acknowledgments We thank to members of the A. C. laboratory for many fruitful discussions. This work was financed by the  Institut Pasteur, INSERM, ANR (grant Twothyme)  to A. C. ,  REVIVE Future Investment Program  to A. C. and  R. E. and Pasteur-Weizmann Foundation  through grants to A. C. J. Y. is part of the  Pasteur e Paris University (PPU)   International PhD program. This project has received funding from the  CNBG Company, China. r e f e r e n c e s.
Amplifications and quantifications were made using the PCR TaqMan QuantiNova Probe kit (Qiagen, Coutaboeuf, France). The two primers and the probe have already been used by authors  [62] [63] [64]. Five microliters of each extracted sample were added to 20 µL of the PCR mixture containing 12. 5 µL of PCR TaqMan QuantiNova mix (Table  3 ), followed by fluorescent data acquisition. Besides the specificity of probe for the detection of the P. aeruginosa targeted gene gyrB, preliminary tests were accomplished with a melting temperature ramp from 55 to 95 • C at 1 • C/5 s, and the amplicon's size was 220 bp. Additionally, for each amplification round, negative (RNase-free water) controls were included. Each amplification was performed at least twice per DNA sample. Standard curves. To generate a standard curve, DNA extracted from serially-diluted planktonic P.
In the case of 14. 1, the growth remained, however, at a lower rate compared to the hosts cell control grown in the absence of bacteriophages (Figure  4B, C ). Additional assays were performed by adding a second dose of the virus. No supplemental bactericidal effects were obtained (data not shown) suggesting the selection and multiplication of non-susceptible cells. Similar trends were also observed in earlier studies for P. aeruginosa infected with Podoviridae or Myoviridae phages  [20, 26]  and were associated with the emergence of phage-resistant cells. These phases of steady state bacteria-virus interaction can, to some extent, select natural resistant bacteria or provide the evolution of host cells by developing molecular mechanisms of resistance to bacteriophage infection. Amongst them, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), restriction-modification, abortive infection, or the mutation of surface receptors are the most encountered  [68].
However, when the cell multiplication of cultures became higher than the number of dead cells, the optical density proportionally increased according to the MOI used. In the case of 14. 1, the growth remained, however, at a lower rate compared to the hosts cell control grown in the absence of bacteriophages (Figure  4B, C ). Additional assays were performed by adding a second dose of the virus. No supplemental bactericidal effects were obtained (data not shown) suggesting the selection and multiplication of non-susceptible cells. Similar trends were also observed in earlier studies for P. aeruginosa infected with Podoviridae or Myoviridae phages  [20, 26]  and were associated with the emergence of phage-resistant cells. These phases of steady state bacteria-virus interaction can, to some extent, select natural resistant bacteria or provide the evolution of host cells by developing molecular mechanisms of resistance to bacteriophage infection.
Data obtained by qPCR and v-qPCR were not significantly different in the two cases and suggested a homogenous physiological state of cells within the juvenile biofilm. The SPC data were identical to the qPCR results and confirmed that they were mainly composed of viable cells. SPC was also applied to enumerate viable cells of the 24 h + 14 h biofilm, which corresponded to the untreated biofilms. The results show that the populations of the 24 h and 24 h + 14 h biofilms were not significantly different (Tables  7  and 8 ). Pires et al. [70]  also showed that the quantity of cells in a 24 h old biofilm of the PAO1 strain, growing in 24-well microplates, was identical to that of a 48 h biofilm and was close to 8 Log CFU/cm 2. Then, viable cells were assessed 14 h after the addition of phage 14.
2 Log equivalent CFU/cm 2 of the coupon (Figure  7A ). The exposure of 24 h old D1 contamination led to a reduction of 0. 72 Log with LUZ7 (Figure  6B ) and a reduction of 1. 7 Log equivalent CFU/cm 2 of coupon with 14. 1 (Figure  7B ). Then, viable cells were assessed 14 h after the addition of phage 14. 1 or LUZ7 against PAO1 and D1 24 h-old biofilms by v-qPCR. Data were then compared to the non-treated controls that received no phage, (Figures  6, 7 ) (Table  8 ). The four histograms also showed that there is no significant difference between the standard qPCR and v-qPCR results, for either treated or untreated biofilms. That means that all cells embedded to the biofilm matrix were metabolically active, infected or not. This result suggests that the application of phages in the biofilm environment does not impair the physiological state of all cells, but rather acts directly by killing and eliminating the most accessible cells.
Acknowledgments: The authors thank  Oliver Kaltz  and  Claire Gougat Barbera  at the  Evolutive Sciences Institute of Montpellier (Montpellier, France)  for providing the viruses of Pseudomonas aeruginosa and some technical tips and also for their hospitality for a few days. We thank  Christophe Gantzer  and his staff at  LCPME (University of Lorrain, Nancy, France ) for the phage purification protocol and  Joelle Veziers  from the  SC3M platform (Nantes, France ) for transmission electron microscopy observations and advice. Y. A. and V. M. were supported by the  Department of Energetic Systems  and the  Environment of Institute of Mines-Telecom Atlantic (Nantes, France ). V. M. and N. G. were supported by the  Scientific and Technical Center of buildings (Nantes, France).
We thank  P. Selenko  and  P. Theillet  for helpful discussions regarding the electroporation method. MEYS is also acknowledged for their support of access to research infrastructure ( CEITEC 2020 LQ1601 ;  CIISB-LM2015043 ;  Czech-BioImaging LM2015062 ;  EATRIS-CZ LM2015064 ;  CZ. 02. 1. 01/0. 0/0. 0/15003/0000441 ) and for their support of students' mobility ( 8J18FR001 ). This project was supported by grants from the  Czech Science Foundation  ( 16-10504S  and  19-26041X ), the  Grant Agency of Masaryk University  ( MUNI/E/ 0771/2018 ), iNEXT (grant agreement  653706 ) funded by the  Horizon 2020 Programme , the " IDI 2016 " project funded by the  French National Research Agency  ( ANR-11-IDEX-0003-02 ), and project  SYMBIT  ( CZ. 02. 1. 01/0. 0/15_003/0000477 )funded by the  European Regional Development Fund and Ministry of Education, Youth, and Sports (MEYS)  of the Czech Republic.
This multistep procedure partially explains the relatively long JSWSC review process. A regular manuscript requires about 210 days (median) from submission to online publication, with 255 days (median) for a manuscript belonging to a TI. These data concern the 256 published papers, including editorials (3), prefaces (5) and errata (1). The analysis shows that the review process itself is the main driver for such a long duration since the prescreening, while post-review discussions take only about 10% of the total handling time. The exceptionally long handling times for some manuscripts, sometimes exceeding two years duration (only a few cases), is due to the large number of revisions, to the difficulty in finding available reviewers and to the failure of the authors to respond to the requests for revisions within the allocated time frame.
Data An eight member ensemble of 15-year ICON simulations forms the data basis of this study. The lower boundary conditions are forced by monthly mean sea ice and sea surface temperatures from ERA-Interim for the period 2001 to 2015, which are linearly interpolated to yield slowly varying daily fields (following the established AMIP procedure according to  Gates, 1992). To mimic initial condition perturbations, individual members are initialized using a time-lagged, irregularly spaced series of starting dates ranging from 0000 UTC on 1 August 2000 to 1800 UTC on 8 August 2000. The model uses an approximate icosahedral horizontal grid resolution of 80 km with 90 vertical levels from the surface up to 75 km. The data is interpolated to a regular grid at 1°horizontal resolution, 19 pressure levels reaching from 1000 hPa to 1 hPa and is available at 6-hourly temporal resolution.
Acknowledgments We thank the two anonymous reviewers for their constructive comments on the manuscript and the  DWD  for providing the ICON model simulations. We acknowledge MeteoSwiss and ECMWF for access to the  ERA-Interim reanalysis data. The authors further thank  Heini Wernli  ( ETH Zurich ) for his valuable input on the dynamics governing blocking,  Stephan Pfahl  ( FU Berlin ) for his assistance with the statistical approach,  Daniel Steinfeld  ( ETH Zurich ) for the many fruitful discussions on the diabatic influence on blocking,  Richard Forbes  ( ECMWF ) for his help with obtaining the MLS satellite measurements, and  Mark Rodwell  ( ECMWF ) for the inspiring discussion of the findings. The data analysis and visualization was done using R (R Core Team 2013) and the  NCAR Command Language  ( UCAR/NCAR/CISL/VETS 2014 ).
RA acknowledges funding by the  Swiss National Science Foundation (SNSF)  under Project  165941. The contribution of JR is supported by  ETH Zurich Foundation  in collaboration with Coop ( ETH Research Grant   1014-1 ). The contribution of CMG was supported by the  SNSF  under grant  PZ00P2 141777/1  and finished under a  Helmholtz Young Investigator group grant  ( VH-NG-1243 ).
We present the largest comparative biogeographical analysis that has complete coverage of Australia's geography (20 phytogeographical subregions), using the most complete published molecular phylogenies to date of large Australian plant clades (Acacia, Banksia and the eucalypts). Two distinct sets of areas within the Australian flora were recovered, using distributional data from the Australasian Virtual Herbarium (AVH) and the Atlas of Living Australia (ALA): younger Temperate, Eremaean and Monsoonal biomes, and older southwest + west, southeast and northern historical biogeographical regions. The analyses showed that by partitioning the data into two sets, using either a Majority or a Frequency method to select taxon distributions, two equally valid results were found. The dataset that used a Frequency method discovered general area cladograms that resolved patterns of the Australian biomes, whereas if widespread taxa (Majority method, with >50% of occurrences outside a single subregion) were removed the analysis then recovered historical biogeographical regions.
(1995)  analysed 11 angiosperm taxa (i. e. Daviesia latifolia group, Leptosema, Lechenaultia sect. Latouria, Gossypium subgen. Sturtia, Eucalyptus stringybark group, Telopea + Albxylon, Lomatia, Persoonia sect. Pycnostylis, Banksia sect. Banksia, Banksia ser. Salicinae, Banksia ser. Spicigerae) using the tree mapping software of Page  (1993) , resulting in the first general area cladogram for the Australian flora. The study revealed three definitive groups, the Monsoon, the Southwest and the East/ south coast groups (sensu regions), which are analogous to the zoogeographical areas proposed by  Hedley (1894). However, a problem revealed itself in a reanalysis of a similar dataset by  Crisp et al. (1999). Unlike the  Crisp et al. (1995)  analysis, the 1999 study united biotic areas, possibly to reflect the concept of biomes, namely Central Australia (=Pilbara, Western Desert, Eastern Desert, Northern Desert), Tasmania, Monsoon Tropics (Kimberley, Arnhem, Cape York) and the Wet Tropics.
Given that different areas (regions) were used, it is not surprising that the results changed in the  Crisp et al. (1999)  analysis. Moreover, subtree analysis  (Nelson and Ladiges, 1996)  revealed only four subtrees in the data, of which two (Gossypium and Lomatia) did not geographically overlap by more than one area, meaning that the data were insufficient to find a general pattern. The lack of overlapping data may have been overcome by adding in taxa with overlapping distributions, but was not attempted to our knowledge. In the 1990s, gathering phylogenetic data, particularly of larger groups (e. g. Acacia or the eucalypts) was a monumental task, whereas finer scale datasets or phylogenetic analyses of smaller, more biogeographically restricted groups were not suitable for cladistic biogeographical studies on a continent-wide scale.
In addition, many biogeographical studies lacked reliable distributional data, as large-scale specimen databases for the national level did not yet exist and distribution maps for taxa were thus often incomplete. By the 2000s large specimen databases had become available, with the Australian botanical community leading the way with the advent of Australia's Virtual Herbarium (AVH), and later, the Atlas of Living Australia (ALA). Large datasets of the eucalypts (Gonz alez-Orozco et al. 2016), Acacia  (Mishler et al. , 2014)  and Banksia  (Cardillo and Pratt, 2013)  were then combined with large-scale phylogenies, which promulgated a revival in geospatial analyses of the Australian flora (e. g. Crisp et al. , 2001; Thornhill et al. , 2016)  and a means for area definitions based on models of taxic (e.
, 2012). While the methods of comparative biogeography (sensu  Parenti and Ebach, 2009)  have been developed for decades (e. g. Nelson and Platnick, 1981) , there has been limited application in recent years, and now the opportunity exists to use it on large continental-scale datasets. With biogeographers finally having access to large, well-sampled molecular phylogenies and herbarium records, it is now possible to conduct an initial, near-complete continental-scale comparative biogeography of three major Australian plant clades, the eucalypts, Acacia and Banksia. This allows an empirical.
Area selection Twenty subregions proposed by  Ebach et al. (2015)  were used in the analysis, excluding New Guinea (Fig. 3 ). The subregions are part of the Austral Bioregionalisation Atlas (http://aba. myspecies. info/) and are derived from a spatial overlap analysis by Gonz alez-Orozco et al. (2014a), based on the distribution of 6043 species. In addition, the biome names used by  Crisp et al. (2004)  were used. The combined 20 subregions span the whole continent, meaning that all distributional data can be used in the analysis. In previous studies (e. g. Weston and Crisp, 1994; Crisp et al. , 1995 Crisp et al. , , 1999) )  endemic areas only occupied parts of the continent. The distributional data for each taxon herein were derived from.
Assigning taxa to subregions and creating area cladograms Each record of the 1337 taxa (508 Acacia, 177 Banksia, 652 eucalypts) was assigned a subregion based on latitudinal and longitudinal distribution as indicated in AVH. Each subregion was represented as a polygon, which was used to allocate the distributional data into the areas in which they occur. Replacing the names of taxa in the original cladograms with the area(s) in which they occur generates an area cladogram. The analysis in total used three area cladograms based on the Acacia, eucalypt and Banksia phylogenies.
Removing widespread taxa: majority and frequency methods An ideal area cladogram has a single area on a single terminal branch, following the axiom that taxa are maximally informative at a scale of endemism that provides multiple taxa endemic to smaller single areas within the larger biogeographical area of interest  (Platnick, 1991). Widespread taxa (i. e. species that occur in more than one area) contribute ambiguity to cladistic biogeographical analyses, and therefore are a problem in comparative biogeography. In addition, the size of a dataset also increases the chance that widespread taxa will swamp any patterns and overall results. To remove widespread taxa from each area cladogram, a proportional distribution of taxa across subregions was examined, with the aim to identify a single subregion most representative of the distribution of each taxon.
Combining area cladograms: Paralogy-free Subtree method The Paralogy-free Subtree Method (PfS) was used to infer the relationship between the biotic areas  (Nelson and Ladiges, 1996). The PfS consists of three routines. The paralogy-free routine eliminates any geographical paralogy (i. e. redundant data), meaning that both extracted area cladograms will contain only a single occurrence of area A, namely ((A,((B,(C,D)))) and (A, ((E,(F,G)))). The subtree routine extracts any subtrees from the data. For example, the hypothetical area cladogram (A,((B,(C,D),(E,(F,G))), contains two unique relationships or subtrees,  (A, ((B, (C, D ))) and  (A, ((E, (F, G ))). The final dataset contains paralogy-free subtrees that represent precise relationships within the dataset (see  King and Ebach, 2017). At first, the taxa, areas and cladograms are combined into a primary data matrix (.
3ia file). The PfS is implemented using the phylogenetic software LisBeth v. 1. 3 3area Fig. 4. Schematic diagram of taxa distributions across subregions. (a) Endemic taxa are more likely to have a higher proportion of their distribution within a single subregion (location of theoretical observations in blue, overlayed onto a nine-subregion grid, distribution area outlined in dark blue). Taxa with >50% of observations distributed within a single subregion would be included in both the Majority and the Frequency method analyses (e. g. Acacia calcicola with proportional distributions of 83. 8%, 7. 0%, 5. 3%, 3. 6% and 0. 3%). (b) Widespread taxa are distributed across several subregions and are less likely to have >50% of their distribution within a single subregion. Taxa with <50% of observations within a single subregion would be excluded from the Majority method analysis, but included in the Frequency method analysis if a single subregion contained the majority of the distribution (e.
Results and discussion A GA cladogram was found for the Majority dataset that includes a Northern clade (A), an Eastern clade (B) and a Southwestern + Western clade (C) (Fig. 5 ; Table  1 ). Each clade includes parts of the Temperate/ Eremaean or Monsoonal Tropics/Eremaean biomes (sensu  Crisp et al. , 2004). The Frequency dataset resulted in a GA cladogram with four main clades: an Eremaean clade (D), a Monsoonal Tropics clade (E), a Southeastern Temperate clade (F) and the Southwestern Temperate clade (C), the last being recovered in both analyses and as sister to all other clades (Figs  5  and 6 ).
Widespread taxa recover biomes This study has shown that the inclusion of widespread data into the eucalypt-Acacia-Banksia dataset resolves patterns of the Australian biomes, rather than historical areas. The reasoning is that some taxa will undergo "recent" range expansion into similar environments. For example, hypotheses of niche conservatism state that a taxon that is endemic to the Eastern Desert will probably expand its range into another similar environment and ecological niche  (Wiens and Graham, 2005) , such as the Western Desert, rather than into a temperate area, such as the Southeastern (see Table  2 ). With this assumption in mind, widespread distributions will reflect the current ecology of a biota rather than historical boundaries. The opposite is true when we remove widespread areas (Table  3 ).
Conclusions The study empirically confirms previous hypotheses that Australia has two distributional patterns: a recent, non-phylogenetic and ecological pattern found in biomes (sensu  Crisp et al. , 2004) , and a phylogenetic historical biogeographical pattern (sensu  Ladiges et al. , 2012). The study has also confirmed that the use of large distributional datasets may lead to two independent and equally valid results, meaning that future studies that use the AVH or ALA and other large specimen datasets need to be wary of widespread distributions and how they can change area relationships within a phylogenetic context. The future of Australian phytogeography involves scrutiny of the data and assumptions underpinning alternative hypotheses. Doing so may distinguish between ecological and historical patterns, as demonstrated above in Acacia, the eucalypts and Banksia, thereby resolving disputes as to whether the current distributions are a result of ecological biomes (sensu  Crisp et al.
1. Global map showing the extent of the ABNJ (white) and EEZs (green,  [98] ). This dataset combines the boundaries of the world countries and the Exclusive Economic Zones of the world. It was created by combining the ESRI world country database and the EEZ V7 dataset. Red countries represent LDCs. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article. ) and plural usage) to cover both vertical distinctions. The term 'High Seas' will be used, as appropriate, when citing directly from an existing publication that uses that specific terminology or when the discussion is, indeed, referring only to the High Seas water column.