dataesr/scientific-paragraphs-categorization huggingface dataset:
Systematic map The systematic map aims at organizing knowledge to guide subsequent analyses. The evidence database is provided in Additional file 5. As shown in Fig. 2 , the number of individual studies (n) is not balanced between the three sub-questions (detailed in Table  1 ), with a lower number in S1 (n = 59) compared to S2 (n = 781) and S3 (n = 476). Only one article addressed the three sub-questions (identifier UW004 in Additional file 5)  [24]. Figure  3  shows the only recent awareness of problems related to ATBR dissemination in the environment (increase in scientific publication starting in 2005). Research on solutions to control it has been growing since the 2010s and the number of studies (included in our synthesis) has almost doubled between 2015 and 2016. Studies were distributed across the five continents (Fig.
How to consider results Culture-dependent method: ARB Culture-independent method: ARG and MGE 60 articles also examined the consequences of treatment in downstream environments. Studies (n = 517) mostly corresponded to full-scale observations (e. g. , full-scale WWTP) or experiments (e. g. , mesocosms, pilot-scale experiments), whereas 264 were lab-scale studies (see evidence database in Additional file 5). Lab-scale experiments were identified either based on author's reporting or when conducted in conditions controlled by the authors. Among the 781 studies and as shown in Fig. 6 , the largest number of studies focused on wastewaters (n = 416, i. e. , 53% studies) from various origins: urban, hospital, residential, aquaculture, livestock, slaughterhouse and industrial (pharmaceutical manufacture or other).
The comparison was not possible in 19/781 studies since they only report qualitative results such as ATBR phenotypes of bacteria or presence/absence of ARG (Table  6 ). For the remaining 762/781 studies, ARG were more often measured (total n = 475) than ARB/MGE (total n = 383 and 244, respectively). As for S1, the effects of treatments can be assessed only if quantitative results are reported. We distinguished fully-and partially-quantitative results depending on how the measurement was conducted and the measurement units (Table  6 ). A quantitative comparison was possible with fully quantitative results to determine effects of treatment in 657/762 studies while only qualitative comparison was possible with partially quantitative results in 105/762 studies (metagenomic analyses in 21 studies, percentage detection of ARG/MGE in matrices in 65 studies and of bacteria in 19 studies; database in Additional file 5).
In the following paragraph, descriptive statistics are given from the dataset for meta-analysis on organic waste treatments (N = 98 articles, Additional file 6). As mentioned above, ARG are more frequently measured than MGE and ARB (N = 79 vs 50 and 23, respectively, Fig. 8 ). Concerning ARB (Fig. 8a ), antibiotic susceptibility of total culturable bacteria was the most measured, followed by ATBR of Escherichia coli, either naturally occurring in samples or introduced in samples by authors in laboratory (because known to carrying ARG or MGE). In the last case, ATBR was followed by measuring antibiotic susceptibility of introduced bacteria and/or detecting ARG/MGE. The most followed ARGs were resistance genes to tetracyclines (e. g. , tetM, tetX), sulfonamides (e. g. , sul1, sul2) and macrolides (e.
Studies on organic waste treatments Quantitative data were extracted from studies selected for this meta-analysis ("Supplementary selection of articles for the final syntheses" and "Data synthesis and presentation" sections) (N = 98 articles or n = 126 studies, Fig. 1 ). The resulting dataset included 9138 individual data extracted from 98 articles, as detailed in Additional file 6. Absolute and relative abundances of ARB, ARG and MGE in initial organic waste (before treatment) and final organic waste products (after treatment) are detailed in Additional file 7. Abundances obviously depend on ATBR marker, organic waste type and origin. Medians of absolute abundance before treatment were comprised between 1. 44 × 10 4 and 1. 17 × 10 8 CFU/g (dry weight) for ARB; 7. 83 × 10 6 and 1. 73 × 10 9 gene copies/g for ARGs; and 7.
27 × 10 7 and 5. 16 × 10 9 gene copies/g for MGE (Table  S2  in Additional file 7). Median abundance of 16S rRNA gene copies varied between 1. 51 × 10 9 and 3. 87 × 10 11 copies/g (Additional file 7: Table  S2 ). Median proportion of ARB before treatment was comprised between 2. 73 and 27. 1%; median relative abundance of ARG varied between 1. 0 × 10 -4 and 8. 4 × 10 -3 gene copies/16S rRNA, and 3. 80 × 10 -5 and 6. 2 × 10 -3 gene copies/16S rRNA for MGE (Additional file 7: Table  S3 ). According to study validity assessment (Additional file 4 and assessment detailed in Additional file 12), there were 56 high, 46 medium and 24 low validity studies, in which variable efficiencies of organic waste treatments were reported (before/after comparison). The following meta-analysis on relative abundance of ATBR markers was conducted on 89 studies in which such data were reported (dataset in Additional file 6).
Limitations due to the search strategy Concerning limitations due to the search strategy, it would be necessary to update this systematic review Fig. 14  Variability of reduction (%) of the relative abundance of antibiotic resistance genes (ARG) and mobile genetic elements (MGE) after a composting and b anaerobic digestion regarding antibiotic families and resistance mechanisms (n is the number of studies; * significant effect with p < 0. 05). Positive % means a reduction while negative % means an increase with eligible articles from Wiley Online Library (database we did not use due to time constraints), as well as from databases for which we encountered technical problems to save results Connect and AGRIS FAO). Although articles were collected from seven publication databases, the search was conducted only in English.
Limitations of the meta-analysis In this review, we conducted only one meta-analysis due to resource limitations. Other meta-analyses could be performed to determine the effect of treatments on ATBR, e. g. , in WWTPs, as well as to determine the variation of ATBR in natural environments after contamination (see implication for Research in "Implication for Research based on knowledge clusters" section). A dataset of 2495 measures of ARB, ARG and MGE relative abundance was selected for the meta-analysis. As the effect size was expressed as a ratio (RA after/ RA before, "Quantitative synthesis with meta-analysis" section), studies in which the initial relative abundance was undetectable, i. e. , below the limit fixed at < 1 × 10 -7 in our model, were discarded because dividing by "zero" was not possible.
Therefore, the overall effect could be biased toward a decrease in ATBR if such measures correspond to an increase of ATBR after treatment. However, this only concerned 5% of the dataset, i. e. , 126/2495 measures were excluded from statistical analyses (Additional file 7: Table  S6 ). From the 126 excluded studies, only 61 corresponded to RA before lower than 1 × 10 -7 (Additional file 7: Table  S6 ). A high level of heterogeneity was observed across studies, especially for the meta-analysis on ARB proportions. Confidence intervals for each "experimental condition" were derived from the empirical variances calculated based on the reported measures, which could lead sometimes to very broad intervals. Differences across studies such as the study type, the waste origin or the temperature could explain heterogeneity.
Sensitivity analyses on the effect of study validity obtained from critical appraisal were conducted to assess the robustness of the results: no major effect was identified. Limitation could be caused by the lack of open access repository of primary datasets. In our meta-analysis, the combination of antibiotic families and resistance mechanisms was necessary to limit convergence model problems, while different mechanisms of resistance can be promoted inside a same ARG family and a same mechanism of resistance can be shared between ARG families. For instance, protection mechanisms can be promoted by ARGs for different antibiotic families, i. e. , tetM, tetO, tetQ, tetW genes for tetracyclines but also qnr genes for quinolones. Therefore, complementary and specific analyses would be necessary to determine effects of treatments for each gene family/mechanism of resistance.
Implication for research Implication for research based on knowledge clusters Knowledge clusters were identified for both second and third sub-questions. Strategy 2) How effective are treatment options of liquid and solid matrices in controlling antibiotic resistance in the environment? This sub-question was characterized by a large number of articles and datasets. The efficiency of WWTPs to reduce ATBR must be confirmed by a meta-analysis. Investigations on efficiency of treatments should be made to consider the release of ARB, ARG and MGE in final effluents. Another meta-analysis would be possible with the dataset available on absolute abundance of ARB, ARG and MGE to bring complementary elements of understanding on effects of organic waste treatments. Strategy 3) How effective are environmental management options in controlling antibiotic resistance in the environment? Meta-analyses would be useful to see whether heterogeneity observed in our narrative synthesis on wildlife and aquatic environment is significant.
With regard to WWTPs, if a comparison is conducted between influent, effluent and the river receiving the effluent, estimation of the daily discharge of treated effluent to river flow are necessary to assess the load of ATBR in the environment. Too many articles conducted interesting experiments but never mentioned the processes used in WWTP, and this prevents further understanding of outcomes. Concerning the availability of data, extracting results from articles is not always feasible, e. g. , when authors only report heat maps with no associated datasets. More investigation is consequently needed to obtain quantitative data, by contacting authors and/or by lengthy handmade data extraction. Datasets must be available in repositories. Concerning research designs, control and baseline should be promoted in order to establish causal relationship between Intervention/Exposure and outcomes, and facilitate subsequent meta-analyses, recommendations and decisions.
Acknowledgements Anaïs GOULAS gratefully thanks  Anne-Marie POURCHER ,  Olivier CROUZET ,  Jérôme LABANOWSKI ,  Leslie MONDAMERT ,  Marie-Cécile PLOY ,  Christophe MERLIN ,  Jean-François LORET  for their time and the valuable discussions, as well as the working group of ANSES on antibiotic resistance in the environment. Many thanks to  Marie ALEXANDRE  for her searches of grey literature on websites and her help for the screening; and many thanks to  Ludivine BOURSIER  for her kind help in the screening of titles. A great thank to  Hélène SOUBELET  for her availability, valuable reading and suggestions. Authors are grateful to  Céline COUDERC-OBERT , the project officer for the  French Ministry of Ecological and Inclusive Transition (CGDD/SR)  who participated to all our meetings and guided the review's questions considering the request of the Ministry.
g. : antibiotics, metals, biocides organic waste spreading on agricultural soils, various municipal waste in landfills, aquaculture activities, agricultural and farming activities, urbanization and rejection of untreated wastewaters and to overcome the difference between matrices Environmental samples defined as liquid (e. g. wastewater) and solid matrices (e. g. sewage sludge, livestock manure), natural environment samples including water and sediment from aquatic envi- ronment, soil and biological samples from wildlife Eligible study types Inclusion: laboratory-scale and field-scale environmental studies; modelling studies only if a dataset was acquired from experiments or observations Exclusion: clinical studies, pure simulation studies, reviews and opinion papers even if considered as potential secondary information sources Any process on matrices contaminated by ATBR, e.
, 2019). MC-LR, des-MC-LR, cyanopeptolin A, and aerucyclamide A and D were quantified in this experiment using linear relationship between peak area (MC-LR and des-MC-LR at 238 nm, cyanopeptolin A (CP A) at 220 nm, and aerucyclamide A (AC A) at 237 nm and D (AC D) at 240 nm) and known concentrations of the toxin standards. MassLynx v4. 1 was used for both detection and quantification of the cyanobacterial peptides. The microcystin-LR standard was purified as previously described  (Edwards et al. , 1996). CP A and AC A and D standard were purified using preparative HPLC (high performance liquid chromatography) (Biotage Parallex Flex, Cardiff, UK) and Flex V3 software for instrument control and data acquisition. The separation was performed on Atlantis Prep C18 column (5 um particle size, 19 mm ID × 300 mm long; Waters, Elstree, UK) using a 30-min linear gradient from 60% to 100% methanol in MilliQ water.
Acknowledgments We are grateful to  D. Azam  and  M. Coke  from the  PEARL INRA   1036 U3E  system (The  National Infrastructure in Biology and Health, France ) for providing the Daphnia magna strain (originating from l'INERIS) and  B. Le Rouzic  ( University of Rennes 1 ) for providing us with the Scenedesmus communis culture (originating from the lake of Grand Lieu, France). We would like to acknowledge  L. Pitois  and  J. P. Caudal  for constructing the coculture chambers. We also thank  P. Legouar  for help with the statistics. We would like to thank the  Analytical Plateform (Ecochim)  and the  Experimental Platform (ECOLEX)  for the use of the equipment. The help during the experiments of master student Naoual Bendahhane is greatly acknowledged. GBS was supported by a  Presidential scholarship  from the  University of Rennes 1 , the project benefited from funding from the  Centre National de la Recherche Scientifique (CNRS) Initiative Structurante Ecosphère continentale et côtière  ( EC2CO ) under the  Interactions métaboliques entre cyanobactéries et daphnies (MICYDA)  project.
Fruit Growth Parameter Measurements The age of individual reproductive organs was assessed by noting the dates of full flower opening (anthesis). The fruit fresh weight was measured by sampling fruits throughout the whole period of fruit development from 8 to 55 days after anthesis (daa), regardless of their position on flower trusses. Fruit growth rates were estimated for the whole fruit growth period according to these data. Gompertz functions y = a*exp(-exp(-(xc)/b))  [18]  were fitted to fruit fresh weight measurements. Adjustment was performed on pooled data from five replicate plants per treatment (R 2 > 0. 95).
Histological Analyses For histological analyses, the first five fruit of the third and fifth trusses were harvested at  3, 5, 8, 10, 12, 20, 39, 42, 44 , and 55 daa. One half of the fruit pericarp was used for measuring the total cell number and mean cell size using the method of cell separation by pectinase solution described in Bertin et al. [11]. The other pericarp half was used for the histological analysis, and microslides were prepared according to Ruzin  [19]. Pericarp sections were observed with a Leica DMLS microscope and images were acquired with a Leica DC300 digital camera. For the mean and individual cell size measurements, we used the public domain Image J software  [20]. The individual size of the sections was estimated using the "analyze particles" tool of Image J, after manually adjusting the segmentation threshold.
Statistical Analysis A two-way repeated-measures ANOVA was used to determine the main effects of fruit age and irrigation treatment on the fruit traits for each genotype separately. For those traits that significantly varied in response to the treatment, comparisons were performed by the Tukey HSD test to detect significant differences from FI at p < 0. 05. To obtain more precise insight into the effects on the cell area (cross section), differences between genotypes and treatments observed at 20 daa and 55 daa were analyzed separately by the Unequal HSD test. Statistical analysis was carried out using the statistical package STATISTICA (StatSoft, Tulsa, OK, USA).
The DC was initially developed to estimate the water content of relatively deep and compact duff, and is related in the Mediterranean to longterm adjustments of the living shoots of plants  21. All meteorological data were extracted from the ECMWF ERA-Interim reanalysis  22  from 1985 to 2015 at 0. 75° spatial resolution across the Mediterranean Basin (details in "Methods"). Each wildfire in the database was then related to the daily values of the 5 variables observed at the closest ERA-Interim 0. 75° grid cell. The weather and climate conditions associated with wildfires were classified into five FWTs through a cluster analysis of the raw variables  17  (details in "Methods"). Then, we computed for each FWT, the lead lag composites of climate and weather variables on daily (from 8 days before to 2 days after the start of a wildfire for temperature, relative humidity and wind speed) and monthly (from 5 months before to 1 month after the start for DC and DMC) time scales (Fig.
2a ) in order to assess the role of instantaneous and short-term weather (i. e. daily atmospheric variables) versus antecedent long-term climate (i. e. monthly DC and DMC) conditions  23. Figure  1a  shows how FWTs are distributed over the broad range of weather and climate conditions observed during wildfires across the Mediterranean Basin. The five FWTs were named according to their main characteristics and grouped into broader categories to ease their description. The first category consists of two FWTs, Normal-and Normal+ (accounting for 19. 2 and 20. 3% of the wildfires, respectively; Fig. 2c ) characterized by little or no change in temperature, humidity or wind speed during The wildfire niche is the atmospheric space that contains all the wildfires in the dataset, the All Voxels space covers all days and grid cells (including non-fire voxels) in the summer fire season.
S9 ) throughout the Mediterranean Basin (Supplementary Fig. S10 ). Uncertainty remains, however, as to the extrapolation of FWTs outside the current wildfire niche (Fig. 1b ). Under the RCP8. 5 scenario, an average of 8. 5% of grid-cell*days fall outside the current wildfire niche by the end of the century. Here, these grid-cell*days were related to the closest FWT centroid, while being aware that this hypothesis (conservative) might hide other fire-weather conditions that are not captured by the FWTs defined from present-day climate conditions. Finally, to assess the potential impact of FWTs shifts on wildfire frequency, we determine the future wildfire frequency in the four countries for which fire datasets were available under the strong hypothesis that the ratio between wildfire frequency and FWT frequency would remain constant in the future (stationarity of the fire-FWT relationship).
Methods We extracted the location, date, and size of wildfires that occurred in Southern France, Greece, Portugal, and Tunisia (Supplementary Fig. S1 ) between 1985 and 2015 from national fire databases. To limit uncertainties related to the detection rate of the smallest wildfires and increase the fire-weather signal, wildfires smaller than 30-ha size were excluded. Only summer wildfires (from June to September) were analyzed because 84% of wildfires occurred during these months and accounted for more than 82% of annual burnt area (including of all wildfire sizes, Supplementary Fig. S2 ). The four countries were selected for two main reasons: (1) the availability of a database with daily information on the characteristics, including the location and size, of all fire events; and (2) the fact that together, they cover most of the biogeographic and socio-economic conditions found in the Mediterranean Basin  44  (Supplementary Figs.
S1-S3 ). For Southern France, fire statistics were extracted from the "Prométhée" database, managed by French forest services, and examined extensively in previous studies  45, 46. For Greece, fire data from before 1998 were obtained from the Greek forest service and from 2000 onwards, the data were obtained from the Greek fire brigade  47  , such that data for the years 1998 and 1999 are missing. While this combination of sources may have led to some minor inconsistencies, the potential consequences on the results were assumed to be negligible, as year of occurrence was not a factor in the analysis. Fire statistics for Portugal were extracted from the Portuguese rural fire database  48. For Tunisia, fire statistics were obtained from the Tunisian fire database  49  as compiled from the records of the Tunisian Directorate-General for Forests and curated from various remote sensing sources.
Wildfires from 2011 to 2015 were ignored because of the disruption in fire activity resulting from the Arab spring in December 2010  49. Fire statistics for the four studied databases are shown in Supplementary Figure  S2. The meteorological data used to determine wildfire variables in the studied period and as a bias correction reference for the climate simulations were taken from the ECMWF ERA-Interim reanalysis  22  of data from 1985 to 2015 across the Mediterranean Basin (see studied region in Supplementary Fig. S1 ). To project current and future frequencies of FWTs, we ignored grid cells that contained more than 90% of non-burnable area, as determined from the ESA CCI Land Cover maps for the year 2015 (ESA 2019) at a spatial resolution of 300 m and aggregated at the ERA-INTERIM reference grid cell level.
Daily variables were obtained from a 0. 75° resolution geographic grid. We used the mean daily 2-m air temperature, dew point temperature, total surface precipitation, surface pressure and 10-m wind speed. Relative humidity was calculated using the mean daily dew point temperature, surface pressure and the mean daily 2-m air temperature. The DMC and DC subcomponents of the Fire Weather Index  20  were respectively used as generic indicators of medium and long-term drought. The DMC and DC are calculated in theory from daily 24 h accumulated precipitation data, and the temperature, humidity, and wind speed at 12:00 local time. However, since obtaining modeled data at 12:00 local time was not possible, we used the daily mean temperature, mean humidity and accumulated precipitation. This may have biased the DC and DMC estimates, but time series calculated with both variables are highly correlated  50.
5, i. e. the highest concentration pathway  52. RCMs were selected based on the availability of daily values for mean temperature, relative humidity, wind speed and precipitation. For each simulation, data were extracted at a spatial resolution of 0. 44° in latitude and longitude for the historical (1970-2005) and future (2006-2099) periods in the studied regions (shown in Supplementary Fig. S1 ). Spatial and seasonal biases in projected climate variables can be problematic for the calculation of fire danger indices 53 so the data were corrected using univariate and multivariate bias correction based on daily summaries from ECMWF ERA-Interim data for the overlapping period between ECMWF ERA-Interim and climate simulation (1980-2005). The Euro-CORDEX data were interpolated to the regular 0.