dataesr/scientific-paragraphs-categorization huggingface dataset:
A single representative sequence for each OTU was aligned and a phylogenetic tree was built using FastTree  54. The phylogenetic tree was used for computing the weighted and unweighted UniFrac distances between samples  18, 55  , rarefaction were performed and used to compare abundances of OTUs across samples. Principal coordinates analysis plots were used to assess the variation between experimental group (beta diversity). Alpha diversity curves were determined for all samples using the chao1 index. LEfSe (LDA Effect Size) was used to investigate bacterial members that drive differences between groups  56. Unprocessed sequencing data are deposited in the European Nucleotide Archive under accession number PRJEB35012 (http://www. ebi. ac. uk/ena/data/view/PRJEB35012). Sample preparation for the identification of IgA-coated bacteria.
9%NaCl/0. 1 M HEPES buffer (pH 7. 2) containing a 1:4000 dilution of SytoBC (Invitrogen, S34855). Data acquisition was performed on a Sony Cell Sorter SH800Z. Samples were gated on appropriate SSC-A/FSC-A gates prior to being selected for SytoBC + events. For each sample, 100,000 events were collected from the IgA -and IgA + population into sterile tubes. Each fraction was stored at -20 °C prior to DNA extraction and sequencing of bacterial 16 S rRNA genes, as described above. Hematoxylin & eosin staining and histopathologic analysis. Following euthanasia, colons (proximal colon, 2 first cm from the cecum) were placed in methanol-Carnoy's fixative solution (60% methanol, 30% chloroform, 10% glacial acetic acid) for a minimum of 3 h at room temperature. Tissues were then washed in methanol 2 × 30 min, ethanol 2 × 15 min, ethanol/xylene (1:1) 15 min, and xylene 2 × 15 min, followed by embedding in paraffin with a vertical orientation.
Statistical analysis. Significance was determined using t test or one-way ANOVA with Bonferroni multiple comparisons test (GraphPad Prism software, version 8. 2. 0). Differences were noted as significant *p ≤ 0. 05 **p ≤ 0. 01 ***p ≤ 0. 001 ****p ≤ 0. 0001 for t test and # p ≤ 0. 05 ## p ≤ 0. 01 ### p ≤ 0. 001 #### p ≤ 0. 0001 for one-way ANOVA. For clustering analyzing on principal coordinate plots, categories were compared and statistical significance of clustering were determined via Permanova. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  Tyler Cullender  for his help. B. C. is recipient of a  Career Development Award  from the  Crohn's and Colitis Foundation , an  Innovator Award  from the  Kenneth Rainin Foundation , a  Starting Grant Award  from the  European Research Council (INVADERS)  and a  Chaire d'Excellence  from the  IdEx Université de Paris ,  ANR-18-IDEX-0001. A. T. G. is recipient of  NIH  grants  DK099071  and  DK083890. TwinsUK is funded by the  Wellcome Trust, Medical Research Council, European Union , the  National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's, and St Thomas' NHS Foundation Trust  in partnership with King's  College London.
Precipitation Driver Response Model Intercomparison Project (PDRMIP) All the PDRMIP models include aerosol-radiation interaction (direct aerosol effect) and associated cloud changes through changes in humidity and stability (rapid adjustments). Most models (except GISS-E2-R and NCAR-CESM1-CAM4) include aerosol-cloud interactions (indirect aerosol effects), which involve aerosol influences on cloud microphysics and are connected with large uncertainties  (Boucher et al. , 2013). Data All model data have been regridded to T42 horizontal resolution, and, in the case of 3-D data, to 60 vertical layers stretching from the surface to 0. 1 hPa. In Fig. S1  in the Supplement, IWV in the PDRMIP base experiment has been compared with observations from MODIS Aqua and Terra level 3 data (downloaded from https://giovanni.
Fast Slow Total Slow Total (%) (%) (%) (% K -1 ) (% K -1 ) CO2x2 0. 8 (-2. 5) 16. 8 (6. 1) 17. 6 (3. 6) 7. 7 (2. 8) 7. 2 (1. 4) CH4x3 0. 4 (-0. 5) 4. 6 (1. 9) 5. 0 (1. 4) 7. 4 (3. 1) 7. 3 (1. 8) Sol+2% 1. 2 (-0. 7) 18. 7 (6. 7) 19. 9 (6. 0) 8. 1 (3. 0) 8. 2 (2. 5) BCx10 2. 1 (-3. 0) 4. 4 (1. 5) 6. 5 (-1. 5) 7. 5 (2. 8) 9. 8 (-3. 4) SO4x5 -0. 4 (-0. 1) -13. 3 (-6. 1) -13. 7 (-6. 1) 6. 5 (2. 9) 6. 4 (2. 8) riod than for the future, with model-mean values of 0. 55±0. 1 and 0. 47±0. 06 d K -1 , respectively, and a paired sample t test shows that the two values are significantly different. The present-day lifetime of 8. 2 ± 0. 5 d from CMIP5 is close to, but slightly lower than, a recent assessment using reanalysis data of 8. 9 ± 0. 4 d (van der Ent and Tuinenburg, 2017). To further understand these differences, it is instructive to investigate WVLS for each of the fast and slow responses, and for each of the five climate drivers studied in PDRMIP.
This means that the uncertainty is larger if a decrease in convective mass fluxes is a major reason for the increase in WVL. Compared with present-day WVL from reanalysis, the climate models have too short WVLs  (Trenberth et al. , 2011 ; see also Sect. 3. 3). Kao et al. (2018)  compared trends in precipitation and column water vapour data from 13 CMIP5 models with observational datasets and also found differences in the moisture recycling rate between observations and the CMIP5 models, and concluded that this discrepancy was caused by relatively poor simulations of precipitation. However, the long-term trend and inter-annual variability of column water vapour was very well captured by nearly all models. • × 1. 4 • (MIROC-SPRINTARS) to 2. 8 • × 2. 8 • ( In conclusion, diagnosing changes in the water vapour lifetime reveals important changes in the hydrological cycle, and specifically changes in precipitation patterns since these are directly affected by how long water resides in the atmosphere.
Estimating WVL could become more important in the future as inclusion of isotopes in GCMs becomes more common, and this may lead to more robust projections of the hydrological cycle and precipitation. Data availability. The PDRMIP model results are available at http: //cicero. uio. no/en/PDRMIP (last access: 8 August 2019)  (Myhre et al. , 2017). The CMIP5 data are available at http://pcmdi9. llnl. gov/ (last access: 24 June 2019)  (Taylor et al. , 2011). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-19-12887-2019-supplement. Author contributions. GM and ØH designed the study. ØH performed the analysis with contributions from KA on the analysis of CMIP5 data. ØH was the primary writer and received input from all authors, especially GM and KPS.
Acknowledgements. Supercomputer facilities were provided by  NOTUR  ( NCAR-CESM1-CAM4  and NorESM1 simulations), the  Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory , sponsored by the  National Science Foundation  and other agencies (ark: /85065/d7wd3xhc ;  NCAR-CESM1-CAM5  simulations), the  NASA   High-End Computing Program  through the  NASA Center for Climate Simulation at Goddard Space Flight Center  (GISS-E2-R simulations), the MON-sooN system (HadGEM2/3 simulations), the  German Climate Computing Center (DKRZ)  in Hamburg (MPI-ESM simulations), the  ARCHER UK National Supercomputing Service  (ECHAM-HAM simulations), by  GENCI  at the TGCC under allocation gen2201 (IPSL-CM5A simulations), and the supercomputer system of the  National Institute for Environmental Studies, Japan , the  Environment Research and Technology Development Fund  ( S-12-3 ) of the  Ministry of the Environment, Japan  (MIROC-SPRINTARS simu-Financial support.
This research has been supported by the  Research Council of Norway  (grant nos. 229778 ,  207711 , and  229771 ), the  Natural Environment Research Council  (grant nos. NE/K500872/1 ,  NE/K007483/1 ,  NE/L01355X/1 , and  NE/J022624/1 ), the  JSPS KAKENHI  (grant nos. JP15H01728  and  JP15K12190 ), the  Horizon 2020  (grant nos. CRESCENDO (641816 ) and  RECAP (724602) ), the  FP7  (grant no. 603445 ), and the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  (grant no. GA01101 ). Review statement. This paper was edited by  Martin Dameris  and reviewed by three anonymous referees.
CAM5. 3 is run with the CFMIP Observation Simulator Package (COSP) to diagnose cloud properties from the model as if observed from satellite sensors  (Bodas-Salcedo et al. 2011) , and comparable to data from the International Satellite Cloud Climatology Project (ISCCP;  Schiffer and Rossow 1983). The atmosphere is coupled to a slab ocean model (SOM) to include the thermodynamic effects of the ocean mixed layer. Its spatially varying depth is based on observations of the annual-mean mixed-layer depth  (Kiehl et al. 2006). The SOM treats the ocean as motionless and perfectly mixed throughout its depth. To maintain a realistic mean climate, an annually periodic, monthly varying heat flux convergence (q flux) is prescribed in the mixed-layer ocean. The q flux is an added internal heat source that represents deep ocean exchange and lateral heat transport.
6) as the smaller signal can be explained by rising anvil tops due to a warmer surface temperature. For the same reason, the longwave CRE is lowest in IRIS10, as the reduced high clouds now trap less outgoing longwave radiation. Interestingly, there is a dipole-like structure in the bias between CESM and ISCCP (Fig. 5b ) for the thin cirrus (0. 3 # t # 1. 3; 310 # CTP # 50). The present analysis suggests that the thin cirrus between 310 and 180 hPa in CESM could be too low, which could account for the deficiency in simulating enough greenhouse effect of these clouds in CESM (Figs. 4d-f ). However, we note that there are limitations to the ISCCP dataset for the thinnest clouds (t , 1. 3;  Pincus et al. 2012) , which weakens our confidence of the dipolelike bias. If convective precipitation efficiency increases in a warmer climate, the expectation is that less cloud condensate would be available for upper-level detrainment.
Acknowledgments. This work was funded by the  National Science Foundation  through Grant  1352417  and the  European Research Council  through Grant  758005. Federov acknowledges support from  NSF  through Grant  AGS-0163807  and from  NASA  Grant  NNX17AH21G , and Choi was supported by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  (Grant  2018R1A6A1A08025520 ). We acknowledge computational support from the  Yale University Faculty of Arts and Sciences High Performance Computing Facility. We also thank three anonymous reviewers for their helpful comments and suggestions.
). Gel electrophoresis (1% agarose formaldehyde) was used as an RNA integrity test. One microgram total RNA with a RIN (RNA integrity number) value above seven was used for library preparation. The ribosomal depleted mRNA was then fragmented, reverse-transcribed and the sequences were processed and analyzed by GENEWIZ (Suzhou, China). Transcriptome sequencing (RNA-seq) was performed on an Illumina HiSeq 4000 platform. The raw data was quality trimmed, assembled, and annotated as detailed in Additional file 1: Extended Materials and Methods. Detailed statistics of clean data and sample transcripts from six microcosms are shown in Additional file 2: Dataset 1.
Data analysis We used FPKM (expected number of fragments per kilobase of transcript sequence per millions base pairs sequenced) to calculate relative transcript level of genes. All experiments were repeated three times independently. Data are presented as means ± standard errors. Significant differences among treatments were tested with one-way ANOVAs (StatView 5. 0). The ANOVA assumptions of normality and homogeneity of variance of residuals were validated with the Kolmogorov-Smirnov one-sample test and Levene's test, respectively (Stat-View 5. 0). Differences were considered statistically significant when p < 0. 05.
Transcripts proportions variation after AZ exposure in the whole plankton community Meta-transcriptomic sequencing was carried out to investigate the changes in transcription of the whole plankton community after AZ exposure. A summary of the meta-transcriptomic sequencing results is provided in Additional file 1: Extended Results. Taxonomic proportions of transcripts in two groups at different taxonomic levels are shown in Additional file 2: Dataset 2, which were represented by the relative abundance of taxonomically annotated transcripts (RAT). The RAT value does not represent microbial biomass, but rather the changes in transcriptional activity among species, The relative abundance of RAT in control microcosms after 7 days of culture was mainly controlled by Monoraphidium sp. (a Chlorophyta genus) (Fig.
Metabolic pathways in eukaryota and bacteria in response to AZ treatment Since overall transcriptomic annotation information is limited in the genus Chlorella and other Chlorophytes  [41] , meta-transcriptomic analyses of the entire microbial community were helpful to further understand the interactions between planktonic organisms and cyanobacteria. A summary of the functional annotation is provided in Additional file 1: Extended Results, and the detailed functional variation is provided in Additional file 2: Dataset 3, 4. We distinguished sequences of eukaryota and bacteria and dissected them at KEGG (Kyoto Encyclopedia of Genes and Genomes) level 3. Figure  5  shows the 40 most important pathways based on relative abundance of transcripts (belonging to four metabolic systems mentioned in the previous section) in eukaryota and bacteria.
Even though the composing proportion of eukaryota was not heavily affected by AZ (Additional file 1: Figure  S5a ), the KEGG level 3 pathways were often appreciably affected (Additional file 2: Dataset 4). As shown in Fig. 5a , the most highly over-expressed pathways due to the presence of AZ in eukaryota were plant hormone signal transduction, MAPK signaling pathway, nitrogen metabolism, ubiquitin-mediated proteolysis, and glycerophospholipid metabolism, and the most under-expressed were porphyrin and chlorophyll metabolism, oxidative phosphorylation, and peroxisome, indicating that AZ modulates functional gene expression in eukaryota. In response to the cyanobacterial bloom, the bacterial community structure and the relative abundance of metabolic pathways changed dramatically (Fig. 5b  and Additional file 1: Figure  S5b ).
As shown in Fig. 5b , pathways closely related to cyanobacteria were significantly over-expressed (p < 0. 05), including carotenoid biosynthesis (+ 1326% compared with control), photosynthesisantenna proteins (+ 1305%) and photosynthesis (+ 758%). Table  1  Variations of biotic factors potentially affecting cyanobacteria growth in the microcosms exposed to azoxystrobin for 7 days (AZ) as inferred from fold change in relative abundance of taxonomic transcripts of selected organisms in the control group (Con) or the AZ-treated microcosms (AZ). We also noted that many pathways with a low relative abundance (< 0. 05) related to antibiosis, vitamin metabolism, and polysaccharide synthesis were differentially expressed in response to AZ (Additional file 2: Dataset 4). These functions are discussed below.
In bacteria, genes involved in phycobilisome (e. g. , apcD, apcE, cpeC, cpeZ) were strongly over-expressed (by around 4-32-fold), while in eukaryota, the transcription of genes coding for proteins of the light-harvesting complex (e. g. , LHCB2 and LHCA1) remained mostly unchanged (Additional file 2: Dataset 5). This is in line with the rise in cyanobacteria in the AZ-treated Fig. 4  Schematic overview of the main biotic factors modulating cyanobacteria relative abundance after AZ treatment for 7 days. Biotic factors mainly include Chlorophyta, viruses, fungi, zooplankton and heterotrophic bacteria. Red or blue font indicates that the relative abundance of the organisms increased or decreased, respectively, in response to the AZ treatment relative to that of the control. Plus sign indicates potentiated effect; minus sign indicates weakened effect.
Interestingly, in bacteria, the relative abundance of two genes involved in vitamin B 12 (cobalamin) transport (i. e. , btuB or K16092: vitamin B 12 transporter and btuF or K06858: vitamin B 12 transport system substrate-binding protein) decreased by 88% and 57%, respectively, in the AZ-treated microcosms relative to the control (see Additional file 2: Dataset 5). Since several eukaryotic species (including fungi and many Chlorophytes) cannot synthesize cobalamin de novo and rely on mutualistic bacteria to acquire this vitamin  [52, 53] , our results support the hypothesis that the rise in cyanobacteria in the presence of AZ may be, at least in part, explained by a decrease in vitamin B 12 mutualistic exchange between bacteria and eukaryotes.
Availability of data and materials All data generated in this study are included in the article (and its supplementary information). The bacterial community composition of water samples derived from Taihu was provided in Additional file 1: Extended Materials and Methods. The raw meta-transcriptomic sequence data have been submitted to the NCBI Sequence Read Archive (SRA) database with accession numbers SAMN10362734 to SAMN10362736, and SAMN10644312 to SAMN10644314.
Floods are still a significant threat to people, despite of the considerable developments in forecasting, management, defensive, and rescue works. In the near future, climate and societal changes as both urbanization of flood prone areas and individual dangerous behaviors could increase flood fatalities. This paper analyzes flood mortality in eight countries using a 39-year database  named EUFF (EUropean Flood Fatalities), which was built using documentary sources. The narratives of fatalities were investigated and standardized in the database reporting the details of the events. The entire dataset shows a stable trend on flood fatalities, despite the existence of individual increasing (Greece, Italy, and South France) and decreasing (Turkey and Catalonia) trends. The 2466 fatalities were mainly males, aged between 30-49 years and the majority of them happened outdoor.
The employing of data on real situations for people impacted by floods, supply tangible data to improve people-flood interaction models based on laboratory tests  [12]. The majority of papers reporting food fatalities catalogues on large territories throughout long periods, essentially report the number of victims, focusing on flood seasonality and water speed  [13, 14]. Even exploiting information sources as the first responders, the characterization of FF in demographic and behavioral terms is not presented  [15]. Some scholars investigated behavioral choices leading to fatal events  [16] , especially if related to the use of motor vehicles  [17]. Nevertheless, there are no cases of databases collecting altogether data on FF occurrence in different countries, at a 'supra national scale', as in the present paper (except the previous paper of this research,  [18] ).
The present research compares the series of FF for the 39-year period (1980-2018) in eight European countries (Czech Republic, France, Greece, Israel, Italy, Portugal, Spain, and Turkey). The aim of the paper is to detect vulnerability and behavioral features leading to fatal events, and to identify changes in individual flood vulnerability throughout the years. Section 2 of this paper describes regions studied, database structure and the methodological approach. Section 3 presents data analysis at national levels. Section 4 discusses the results obtained using the whole dataset and compares them with results obtained in the regions studied. Section 5 outlines the features characterizing flood-victim interaction and its evolution throughout the time, highlighting how to use the results and what can be the most fruitful future research directions.
Materials and Methods The present research is the second phase of a project that started in 2017 aiming to create MEFF (MEditerranean Flood Fatalities) database, including flood fatalities that occurred on a 36-year period  in five study areas located in the Mediterranean area  [18]. Regions analyzed in MEFF were the following: (1) Calabria (Italy); (2) Languedoc-Roussillon, and Provence-Alpes-Cote d'Azur (France); (3) Catalonia (Spain); (4) Balearic Islands (Spain); and (5) Greece. The focus was on flood fatalities, defined as people who lost their life due to floods. The methodological approach was based on the systematic collection of fatal events descriptions from documentary sources and the disaggregation and systematization of all the available information in the fields of MEFF. Data analysis, on one hand supplied a series of results  [18] , and on the other hand, suggested further clues to be investigated.
Thus, to enlarge the database, we searched for new study areas where inventories of flood damage were available. We identified four new countries (Table  1 ): (1) Czech Republic, (2) Israel, (3) Portugal, and (4) Turkey, that provided data on flood fatalities. We sent to each one of the new partners an empty template of MEFF to fill FF for their study area in the 1980-2018 period. Simultaneously, the original study areas in MEFF were extended by fatalities to 2018 and the Calabria was substituted by entire Italy. By these activities, the new EUFF (EUropean Flood Fatalities) database was created (see Section 2. 2). #EV: number of events of fatal floods; #FF: number of flood fatalities.
The mean age of population is 42 years. Data comes from the INUNGAMA database  [30]  that contains all the flood events that have produced socioeconomic impact between 1981 and nowadays. This database contains information such as the date, the counties and municipalities affected, the main rivers or basins involved, the impacts produced, and the type of flood. Information regarding victims has been complemented with newspaper data, mainly La Vanguardia, and official reports.
9) Balearic Islands (BAL) The Balearic Islands archipelago (Spain) is situated off the eastern coast of Spain. It consists from five islands (Mallorca, Menorca, Eivissa, Formentera, and Cabrera) with adjacent islets. Precipitation expresses a clear Mediterranean pattern, with a maximum during autumn and a minimum in summer. Mean annual totals range from 1000 mm, in Mallorca, to 300 mm, in the southern islands of Eivissa and Formentera. BAL, with 4492 km 2 , accounts only to 0. 3% of the area and to 0. 6% of population for TOT-A. However, the population density achieves 258 inhabitants per km 2 , 50. 3% of which are females. Data were obtained from a PhD thesis  [31] , and complemented by research in regional newspapers, such as Diario de Mallorca and Ultima Hora, and by data gathered for the implementation of the Flood Prevention Plan.
EUFF Database The Czech Republic (until 31 December 1992 the western part of Czechoslovakia) is located in Central Europe. It has an indented morphology represented by lowlands, highlands and mountains with altitudes between the lowest point at the north-west in Hřensko (115 m a. s. l. ), and the highest point in Sněžka Mount (1603 m a. s. l. ). The annual precipitation has a maximum in summer and a minimum in winter, and totals fluctuate between 400 mm and 1450 mm. CZE represents 5. 3% of the surface, and 5. 3% of population of TOT-A, with a population density of 133. 8 inhabitants per km 2. The average age of population is 43 years and 50. 9% of the population is made of females. Data of flood victims comes from historical-climatological database of the Institute of Geography, Faculty of Science, Masaryk University in Brno, collected from different documentary evidence.