dataesr/scientific-paragraphs-categorization huggingface dataset:
Equal quantities (325 ng) of Cy3-and Cy5-labeled cRNA targets were mixed and incubated on microarray slides at 65°C for 17 h. After washing, slides were scanned using the Microarray Scanner (G2565CA, Agilent Technologies) at 5-µm resolution and at high and low photomultiplier voltages to optimize the dynamic range of image quantification. The data were extracted from these images using the Agilent Feature Extraction 10. 7. 3. 1. software.
Data processing and statistical analysis of microarray data Data were computed using the Genespring GX version 12. 6 (Agilent Technologies), a webbased tool that allows integrated analysis of two-colour microarrays. Arrays were background corrected using minimum method (any intensity which is zero or negative after background subtraction is set equal to half the minimum of the positive corrected intensities for that array), and a within-array loess normalization was performed. Finally, microarray data were normalized using quantile normalization. Log intensity ratios for each spot were obtained with background subtraction. Systematic biases were corrected by applying successively an intensity dependent print-tip loess normalization  (Smyth and Speed, 2003)  and a scale between-array normalization  (Yang et al.
Statistical analysis for RNA and RT-qPCR data Roundup-treated and control groups were compared for relative transcripts levels with GraphPad Prism ® by Student's t-test with a Fischer's test for variance comparison and Welch's correction if unequal variances were found between groups. P<0. 05 was considered significant. Fieller's theorem was used to calculate the 95% confidence interval (error bars) of the fold changes. Statistics of correlation between fold-changes obtained by RT-qPCR and microarrays were analyzed (http://vassarstats. net/).
This allowed to confirm differential expression for 20 (i. e. 71%) of these genes (Fig. 2A ). Only the catA gene displayed opposite results with the two methods. A highly significant correlation (r = 0. 96) between fold-changes measured by microarray analysis and RT-qPCR was observed (Fig. 2B ). Overall, the RT-qPCR experiment confirmed the results measured on the microarrays. We next conducted an ontology analysis of the 1816 genes deregulated by the Roundup treatment using the DAVID gene functional classification tool. A total of 1547 genes were recognized. An enrichment analysis was performed in order to reveal the most affected biological functions using annotations from KEGG, InterPro and Gene Ontology (GO) databases. Fold enrichments (FE) and p-values (p) for the most affected pathways are presented in Fig.
P450s and GSTs play a central role in xenobiotic metabolism, which includes 3 phases leading to the elimination of these foreign substances  (Guéguen et al. , 2006). While the phase I involves P450s enzymes which catalyze oxidation-reduction reactions, GSTs are part of the phase II and act by catalyzing the conjugation of reduced glutathione, via its thiol group, to electrophilic moieties of xenobiotic substrates. Phase III then consists of export of the resulting conjugated derivatives through the membranes using ABC transporters. Cytochrome P450 genes expression was markedly altered, as indicated by an heatmap (Fig. 6 ) which displayed a clear segregation between the two groups of cultures, untreated ("control") and R450-treated ("Roundup) with a small intragroup variation. Genes associated with the corresponding InterPro annotation ("IPR001128:Cytochrome P450") displayed large fold-changes.
Acknowledgements This work was supported by the non-governmental organization " Générations Futures " and the  Committee for Independent Research and Information on Genetic Engineering (CRIIGEN) , in the framework of a participatory research project. It was funded by the  Regional Council of Ile-de- France as part of a participatory research program  (grant " PICRI" n°   10016331 ) and the  Foundation "Un Monde Par Tous" (Grant n°   00081254 ).
Hierarchical clustering was carried out to cluster among the gene signal intensities and among the samples with Omics Explorer 3. 0 (Qlucore) from selected enriched gene clusters. Results leading to "hypothetical proteins" were submitted to the Broad Institute database Cerqueira et al. , 2014)ute. org)aswell as to the Aspergillus genome database (http://www. aspergillusgenome. org;Cerqueiraet al. , 2014). The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) and are accessible through GEO Series accession number GSE95241.
Models and Simulations This study evaluates long-term ozone and water vapor changes in 14 models which have performed the CMIP historical simulations and a subset of which have performed ScenarioMIP simulations. The treatment of stratospheric chemistry varies significantly across the 14 models evaluated in this study. We evaluate all models which have produced ozone and water vapor output, regardless of the complexity of the stratospheric chemistry used, as these models may be used in other studies to diagnose the impacts of stratospheric composition changes on radiative forcing and/or regional climate change. In this section, the models and simulations used in the subsequent analysis sections are described, along with the observational datasets used for evaluation. Several of the figures were created with the Earth System Model Evaluation Tool (ESMValTool) version 2.
Models At the time of the preparation of this manuscript, 14 models (BCC-CSM2-MR, BCC-ESM1, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-ESM2-1, E3SM-1-0, FGOALS-g3, GFDL-CM4, GFDL-ESM4, IPSL-CM6A-LR, MRI-ESM2-0, SAM0-UNICON, and UKESM1-0-LL) have provided ozone mixing ratios and 10 models (BCC-CSM2-MR, BCC-ESM1, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-ESM2-1, GFDL-CM4, IPSL-CM6A-LR, MRI-ESM2-0, and UKESM1-0-LL) have provided water vapor as diagnostics. Of the 14 models analysed in this study, five (CESM2-WACCM, CNRM-ESM2-1, GFDL-ESM4, MRI-ESM2-0, and UKESM1-0-LL) use fully coupled, online stratospheric chemistry, while two (CNRM-CM6-1 and E3SM-1-0) use a simple chemistry scheme. The remaining seven (BCC-CSM2-MR, BCC-ESM1, CESM2, FGOALS-g3, GFDL-CM4, IPSL-CM6A-LR and SAM0-UNICON) do not include an interactive chemistry scheme, and instead prescribe stratospheric ozone according to the CMIP6 ozone database (except in the case of CESM2, which prescribes ozone values from the CESM2-WACCM model).
Relevant details of each model are provided in the appendix, and a summary is provided in Table  1. The CMIP6 ozone dataset is designed to be used by those models without interactive chemistry, and was created using a different approach from the previous CMIP5 ozone database  (Cionni et al. , 2011). The  Cionni et al. (2011)  dataset was based on stratospheric ozone values from a combination of model and observational datasets between the 1970s and 2011, and extended into the past and future based on assumptions of changes to EESC and the 11-year solar cycle. Tropospheric ozone values were based on a mean field of two models with interactive chemistry. The CMIP6 ozone dataset was created using simulations from the CMAM and CESM-WACCM models which both performed the REF-C2 simulation as part of the Chemistry-Climate Model Initiative  (Eyring, et al.
BCC-CSM2-MR: The BCC-CSM2-MR model, developed by the Beijing Climate Center, is a coupled ocean-atmosphere model. Ozone in the stratosphere and troposphere is prescribed using monthly mean time-varying gridded data from the CMIP6 dataset. Other GHG concentrations including CO2, N2O, CH4, CFC11, CFC12 are monthly zonal-mean values using the CMIP6 datasets  (Meinshausen et al. , 2017 (Meinshausen et al. , , 2019)). Stratospheric water vapor concentrations are prognostic values calculated in a similar way to those in the troposphere. A full description and evaluation of the BCC-CSM2-MR model is provided by  Wu et al. (2019a).
BCC-ESM1: The BCC-ESM1 model, developed by the Beijing Climate Center, is a fully coupled global climate-chemistryaerosol model. Tropospheric ozone is modelled interactively using the MOZRT2 chemistry scheme, while stratospheric ozone is prescribed to the zonally averaged, monthly mean values from 1850 to 2014 derived from the CMIP6 data package in the top two model layers, and relaxed towards the CMIP6 dataset between these layers and the tropopause. GHG concentrations including CH4, N2O, CO2, CFC-11, and CFC-12 are prescribed using CMIP6 historical forcing data as suggested in the AerChemMIP protocol  (Collins et al. , 2017). Stratospheric water vapor is a prognostic variable without any special treatment of CH4 oxidation. A full description and evaluation of the BCC-ESM1 model is provided by  Wu et al.
CESM2: The Community Earth System Model version 2 (CESM2) is the latest generation of the coupled climate/Earth system models developed as a collaborative effort between scientists, software engineers, and students from the National Center for Atmospheric Research (NCAR), universities, and other research institutions. CESM2(CAM6) uses the Community Atmosphere Model version 6 as its atmosphere component, which has 32 vertical levels from the surface to 3. 6 hPa (about 40 km) and a horizontal resolution of 1. 25° longitude by 0. 95° latitude, and limited interactive chemistry for tropospheric aerosols. GHG concentrations including CH4, N2O, CO2, CFC11eq, and CFC12 are prescribed using CMIP6 historical forcing data. CESM2 uses datasets derived from previous runs of CESM2-WACCM6, which includes complete interactive chemistry, for tropospheric oxidants (O3, OH, NO3, and HO2; 3D monthly means), stratospheric water vapor production from CH4 oxidation (3D monthly means), stratospheric aerosol (zonal 5-day means), and O3 for use in radiative transfer calculations (zonal 5-day means).
CNRM-CM6-1: The CNRM-CM6-1 model, developed by the Centre National de Recherches Météorologiques, is a global climate model which uses a linearised scheme to model stratospheric ozone, in which ozone mixing ratios are treated as a prognostic variable with photochemical production and loss rates computed from its associated Earth System Model CNRM-ESM2-1. Details of the linearization of the net photochemical production in the ozone continuity equation are provided by  Michou et al. (2019). Tropospheric ozone mixing ratios are not calculated interactively, and are instead prescribed form the CMIP6 dataset. Methane oxidation is parameterized throughout the model domain by the introduction of a simple relaxation of the upper-stratospheric moisture source due to methane oxidation Untch (2011). A sink representing photolysis in the mesosphere is also included.
GFDL-CM4: The GFDL-CM4 model, developed by the National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory, is a coupled ocean-atmosphere model. Ozone is prescribed using the recommended CMIP6 dataset throughout the troposphere and stratosphere, while stratospheric water vapor is interactive, but does not include a source from methane oxidation. A full description and evaluation of the GFDL-CM4 model is provided by  Held et al. (2019).
IPSL-CM6A-LR: The IPSL-CM6A-LR model, developed by the Institut Pierre-Simon Laplace, is a coupled atmosphereland-ocean-sea ice model. Stratospheric and tropospheric ozone is prescribed using the CMIP6 dataset but implemented so that profiles are stretched in a thin region (few kilometres only) around the tropopause, ensuring that the tropopause of the ozone climatology and that of the model match. Differences in tropopauses heights would lead to spurious ozone transport between the upper troposphere and lower stratosphere, a region where the corresponding non-physical radiative impact would be particularly high (see e. g. ,  Hardiman et al. , 2019). Stratospheric methane oxidation is not included in the version of the model evaluated here. A full description and evaluation of the IPSL-CM6A-LR model is provided by  Servonnat et al.
Simulations To evaluate changes in stratospheric ozone and water vapor from 1850-2100, this study makes use of two types of simulations performed as part of the wider CMIP6 activity  (Eyring et al. , 2016) : the CMIP6 historical simulation and the ScenarioMIP future simulations  (O'Neill et al. , 2015). The CMIP6 historical simulation runs from 1850-2014, in which the models are forced by common datasets based on observations which include historical changes in short-lived species and long-lived GHGs, global land use, solar forcing, stratospheric aerosols from volcanic eruptions and, for models without ozone chemistry, prescribed time varying ozone concentrations. These simulations are initialised from the pre-industrial control (piControl) simulation, a time-slice simulation run with 1850 perpetual pre-industrial conditions performed by each model.
Observation datasets The evaluation of stratospheric ozone and water vapor makes use of two datasets: the NIWA-BS combined total column ozone database and SWOOSH zonal mean ozone and water vapor datasets. The NIWA-BS combined total column ozone (TCO) database takes daily gridded TCO fields from 17 different satellite-based instruments, bias corrects them against the global Dobson and Brewer spectrophotometer network, and merges them into a seamless homogeneous daily gridded (1. 25° longitude x 1. 0° latitude) TCO data record. First, overpass data from the TOMS instruments flown onboard Nimbus-7, Meteor-3, Earth Probe, and Adeos, and from the OMI instrument on Aura, are bias corrected against the ground-based TCO measurements. Those five bias-corrected datasets then provide the basis for correcting the remaining datasets i.
e. those from the GOME, GOME-2 and SCIAMACHY instruments, the SBUV instrument flown on Nimbus-7, and the SBUV-2 instruments flown on  11, 14. 16, 17, 18 and 19. The bias corrected measurements are then combined in a way that traces uncertainties from the source data through to the final merged data product. A filled version 3. 3 dataset was created by first filling missing data that could easily be interpolated between neighbouring values and then fitting a regression model, comprising an offset and trend basis function, expanded in spherical harmonics to account for latitudinal and longitudinal structure, to available TCO measurements from fields on day N-1, N, and N+1. The The source satellite measurements are homogenized by applying corrections that are calculated from data taken during time periods of instrument overlap.
The primary SWOOSH product is a merged multi-instrument monthly-mean zonal-mean (10°) dataset on the pressure grid of the Aura MLS satellite (12 levels per decade). Because the merged product contains missing data, a merged and filled product is also provided for studies requiring a continuous dataset. These merged and filled products for ozone and water vapor (combinedanomfillo3q and combineanomfillh2oq respectively) from SWOOSH version 2. 6 are used in this study for comparison with CMIP6 model fields.
Evaluation over recent decades Before investigating long-term changes in stratospheric ozone, we evaluate each model's performance, and the performance of the CMIP6 multi-model mean (MMM), against observations. In the following sections we evaluate the 2000-2014 climatological zonal mean distribution of ozone and the seasonal evolution of zonal mean total column ozone against observations, in the form of the combined zonal mean ozone dataset from SWOOSH and the TCO dataset from NIWA-BS.
2000-2014 climatological zonal mean and total column ozone Latitude-height cross sections of zonal mean ozone volume mixing ratios for each CMIP6 model, the CMIP6 MMM and the SWOOSH dataset, averaged over the years 2000-2014, are shown in Figure  1. There is generally good agreement between the individual CMIP6 models and the SWOOSH dataset. All models broadly capture tropospheric and stratospheric ozone gradients, with a clear peak in ozone mixing ratios in the tropical stratosphere at around 10 hPa, the downwards bending of the contour lines towards high latitudes in the lower stratosphere (e. g. Plumb, 2002)  and flat contour lines in the tropical upper stratosphere in the quasi-equilibrated photochemical regime (e. g. Haigh and Pyle, 1982; Meul et al. , 2014; Chiodo et al. , 2018; Nowack et al.
Gettelman et al. (2010)  documented the seasonal cycle of ozone at 100 hPa from 18 models involved in the CCMVal-2 inter-comparison project, and showed that while there is good agreement between the MMM and the observations, there is a large spread in ozone mixing ratios between individual models, and many models do not accurately capture the observed seasonal cycle. For the CMIP6 models investigated here, there is also good agreement between the tropical (15°S-15°N) MMM ozone mixing ratios at 70 hPa and the SWOOSH dataset (Figure  2 ). Further, many models accurately capture the seasonal cycle of ozone, with lower ozone mixing ratios simulated between February and April, and higher values in August and September. However, as for CCMVal-2 models, there is still a large spread in modelled ozone mixing ratios in the tropical tropopause region.
Both MRI-ESM2-0 and UKESM1-0-LL are high biased (both use interactive chemistry), while BCC-ESM1 is low biased compared to the observations and the CMIP6 MMM. Despite the differences between the individual CMIP6 models, there is generally good agreement between the zonal mean distribution of ozone in the CMIP6 MMM and the SWOOSH dataset throughout much of the stratosphere (Figure  4 ), with differences between 70 hPa and 3 hPa typically less than ±10%. Maximum ozone mixing ratios at ~10 hPa are slightly underestimated by the CMIP6 MMM, while ozone mixing ratios in the lower tropical stratosphere, and at ~1 hPa in the midlatitudes, are overestimated. The CMIP6 MMM also overestimates ozone mixing ratios at all latitudes in the upper troposphere between 200-100 hPa by ~20-40 %. Since upper tropospheric ozone is a particularly important climate forcing agent  (Lacis et al.
, 1990; Stevenson et al. , 2013; Young et al. , 2013; Nowack et al. , 2015) , this has important implications for the ozone radiative forcing estimated from climate model simulations. However, it should be noted that the uncertainties in the SWOOSH dataset are likely to be relatively large in the upper troposphere. in the NH polar regions (CNRM-CM6-1, CNRM-ESM2-1), and some models slightly overestimate TCO globally (MRI-ESM2-0, UKESM1-0-LL). Note that the NIWA-BS dataset statistically models high latitude values during the polar night to provide a global, gap-free data field (see Section 2. 3), and therefore uncertainties on the observations in these regions are higher than elsewhere. The lower row of Figure  4  shows the detailed differences between the TCO CMIP6 MMM and the observations.
Long-term evolution of Total Column Ozone from 1850-2014 The regional evolution of zonal mean, annual mean TCO for the CMIP6 MMM from 1850-2100 is shown in Figure  7. There is poor agreement in the ability of the individual CMIP6 models to simulation pre-industrial TCO, which vary between 275 and 340 DU (Figure  A2 ). The UKESM1-0-LL and MRI-ESM2-0 models are particularly high, while the GFDL-CM4 values are lowest. Surprisingly, there is a ~20 DU range in pre-industrial TCO values between those models prescribing the CMIP6 ozone dataset. When TCO values from each CMIP6 model are normalised to the 1960 annual mean value (Figure  A3 ), there is a smaller difference between the modelled pre-industrial TCO values, which cover ±5 DU around the MMM. When the models are normalised to the 1960 annual mean, it is also clear that, compared to the CMIP6 MMM, the CNRM-CM6-1, CNRM-ESM2-1 and UKESM1-0-LL models have much stronger ozone declines during the period of halogenated ODS emissions (globally for the UKESM1-0-LL model and in the NH for the CNRM-CM6-1 and CNRM-ESM2-1 models), while the MRI-ESM2-0 model has much weaker TCO declines during this time.
Long-term evolution from 2015-2100 From 2015 onwards, models follow the assumptions made in the various SSP scenarios. Most models which performed the historical simulations also provided ozone data from SSP1-2. 6, SSP2-4. 5, SSP3-7. 0 and SSP5-8. 5, while a much smaller number provided data from SSP1-1. 9, SSP4-3. 4 and SSP4-6. 0 (see Table  1  for an overview of which models performed which SSP scenarios). Due to the different numbers of models performing each scenario, the MMM for each SSP is normalised to the 2014 value to produce one smooth dataset and allow for comparison between the trajectories of TCO under each SSP scenario. Zonal mean, annual mean CMIP6 MMM TCO, averaged over 60°S-60°N, is projected in the simulations evaluated here to follow three main trajectories from 2015 to 2100 (Figure  7 ).
Evaluation of recent changes Ten of the models used in this study provide stratospheric water vapor output from the historical simulations, with a smaller subset providing water vapor from the SSP scenarios (see Table  1 ). Zonal mean water vapor volume mixing ratios for each CMIP6 model, the CMIP6 MMM and the SWOOSH dataset, average over the years 2000-2014, are shown in Figure  11. There is relatively poor agreement between the individual CMIP6 models and the observations. The CESM2, CESM2-WACCM and MRI-ESM2-0 models all capture the distribution of stratospheric water vapor mixing ratios reasonably well, with the largest percentage differences in the polar regions, most likely related to the formation and sedimentation of polar stratospheric cloud particles. Several models (BCC-CSM2-MR, BCC-ESM1, GFDL-CM4 and IPSL-CM6-LR) do not accurately capture the increase in water vapor with altitude throughout the stratosphere, as these models do not include a representation of water vapor produced from CH4 oxidation.
In contrast, CNRM-CM6-1 and CNRM-ESM2-1 simulate very large changes in stratospheric water vapor between the tropical lower stratosphere and upper stratosphere, consistent with an overestimate in the water vapor production from CH4 oxidation in the CNRM-ESM2-1 model (discussed below). Water vapor mixing ratios in the UKESM1-0-LL model are biased high throughout the stratosphere compared to the SWOOSH dataset. Differences between the individual models and the CMIP6 MMM are shown in Figure  A4. As for ozone mixing ratios, models have typically performed poorly in simulating water vapor mixing ratios in the tropical tropopause region. Gettelman et al. (2010) , show the seasonal cycle of water vapor at 80 hPa from 16 models involved in the CCMVal-2 inter-comparison project, and while there is good agreement between the CMIP6 MMM and the observations, there is a large spread in model mixing ratios, and many models do not accurately capture the seasonal cycle.
Climatological  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014)  tropical stratospheric water vapor mixing ratios (average over 15°S to 15°N) at 70hPa, which lies just above the cold point entry into the stratosphere, are shown for the CMIP6 models in Figure  13. There is reasonable agreement between the seasonality of the CMIP6 MMM and that calculated for the SWOOSH combined dataset, although the CMIP6 MMM is between 0. 5-1. 0 ppm lower than the observations throughout the annual cycle and the minima and maxima in the seasonal cycle both occur a few months earlier in the MMM than in the observations. However, individual models display a wide range of water vapor concentrations (between 1. 5-6 ppm). As seen in Figure  11 , the UKESM1-0-LL model has high stratospheric water vapor mixing ratios compared to the SWOOSH dataset but captures the seasonal cycle well, while the IPSL-CM6A-LR, CNRM-CM6-1 and CNRM-ESM2-1 models all have much lower stratospheric water vapor mixing ratios and muted seasonal cycles.