dataesr/scientific-paragraphs-categorization huggingface dataset:
Abstract. A wide variety of observation data sets are used to assess long-term simulations provided by chemistry-climate models (CCMs) and chemistry-transport models (CTMs). However, the upper troposphere-lower stratosphere (UTLS) has hardly been assessed in these modelling exercises yet. Observations performed in the framework of IAGOS (Inservice Aircraft for a Global Observing System) combine the advantages of in situ airborne measurements in the UTLS with an almost-global-scale sampling, a ∼ 20-year monitoring period and a high frequency. Even though a few model assessments have been made using the IAGOS database, none of them took advantage of the dense and high-resolution cruise data in their whole ensemble yet. The present study proposes a method to compare this large IAGOS data set to long-term simulations used for chemistry-climate studies.
As a first application, the REF-C1SD reference simulation generated by the MOCAGE (MOdèle de Chimie Atmosphérique à Grande Echelle) CTM in the framework of Chemistry-Climate Model Initiative (CCMI) phase I has been evaluated during the 1994-2013 period for ozone (O 3 ) and the 2002-2013 period for carbon monoxide (CO). The concept of the new comparison software proposed here (socalled Interpol-IAGOS) is to project all IAGOS data onto the 3-D grid of the model with a monthly resolution, since generally the 3-D outputs provided by chemistry-climate models for multi-model comparisons on multi-decadal timescales are archived as monthly means. This provides a new IA-GOS data set (IAGOS-DM) mapped onto the model's grid and time resolution. To get a model data set consistent with IAGOS-DM for the comparison, a subset of the model's outputs is created (MOCAGE-M) by applying a mask that retains only the model data at the available IAGOS-DM grid points.
However, their conclusion also highlighted the limitations in space and time of the in situ observations, especially in the upper troposphere. Among available observation data sets, the commercial aircraft measurements from the ongoing IAGOS (In-service Aircraft for a Global Observing System;  Petzold et al. , 2015, http ://www. iagos. org, last access: 7 May 2021) European re-search infrastructure are well designed to study ozone and CO in the long term, notably in the UTLS  (Cohen et al. , 2018). IAGOS observations started in August 1994 for ozone and in December 2001 for CO. They are characterized by a high spatiotemporal resolution and a wide coverage with most data gathered at cruise levels (9-12 km above sea level). Thus, the IAGOS database is suited to assess long-term simulations in this altitude range.
Recently, its ozone data have been used to evaluate simulations from the CESM1 CAM4-Chem  (Tilmes et al. , 2016)  and GEOS-Chem models  (Hu et al. , 2017)  during the periods  1995 -2010 and 2012 -2013 , respectively. Tilmes et al. (2016) )  used the IAGOS measurements gathered in the vicinity of Narita airport (Japan) only, and the comparison made by  Hu et al. (2017)  only spread over 2 years, while IAGOS ozone data have been available since 1994 and covered a wide area, especially in the northern midlatitudes from western North America to East Asia. Brunner et al. (2003 Brunner et al. ( , 2005) )  combined research aircraft measurements with the first years of the IAGOS-MOZAIC database  (1995) (1996) (1997) (1998)  to assess five CTMs and two CCMs. Gaudel et al. (2015)  performed an evaluation of the MACC (Monitoring Atmospheric Composition and Climate) reanalysis over Europe during 2003-2010, using IAGOS O 3 and CO measurements.
Several gridding methods already exist for in situ measurements. Some of them consist of calculating a linear combination from the neighbouring measurements points onto each grid point (e. g. New et al. , 2000). However, it requires us to store the information of all the measurement locations and during a whole month simultaneously. It is thus convenient for measurements with regular locations such as surface stations, whereas their use on the IAGOS database would be expensive computationally as well. Variational methods are also widely employed (e. g. Bourassa et al. , 2005)  but they concern data assimilation, which is not our purpose. The present study aims at providing a new methodology designed to generate a gridded monthly data set from the IA-GOS measurements, in order to evaluate REF-C1SD types of simulations.
In Sect. 2, we describe briefly the IAGOS observations, the CCMI model intercomparison project, the MOCAGE CTM that we use in this study and its configuration for the REF-C1SD simulation. In Sect. 3, we present the methodology proposed to map the IAGOS data set onto the model grid on a monthly resolution, the chosen statistical metrics for models' evaluation and the different assessment diagnostics. In Sect. 4, we present a first application of this methodology on the evaluation of the MOCAGE REF-C1SD simulation. Strengths and weaknesses of the methodology and the chosen diagnostics are discussed. Conclusions are given in Sect. 5.
The IAGOS observations (referring to the IAGOS-Core database hereafter) frequently sample the whole troposphere near airports, measuring vertical profiles during ascent and descent phases, and the UTLS during the cruise phases, mostly in the northern midlatitudes where most of the flight observations are gathered. In these latitudes, a recent analysis of O 3 and CO climatologies and trends based on almost two decades of IAGOS cruise measurements has been performed in  Cohen et al. (2018). In addition to global climatologies, the same analysis also focused on eight wellhttps://doi. org/10. 5194/gmd-14-2659-2021 Geosci. Model Dev. , 14, 2659-2689, 2021 sampled regions in the UT and the LS separately. In order to generate results comparable with the latter, this study focuses on the same time period  (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013)  and, where relevant, on the same regions.
The CCMI project and the REF-C1SD experiment The CCMI project is a common initiative from the IGAC and SPARC programmes. CCMI phase 1 gathers a community of 18 CCMs and two CTMs, whose description is given in the review of  Morgenstern et al. (2017). A series of experiments has been designed to model tropospheric and stratospheric air compositions for past, present and future climates. For each experiment, a common protocol is recommended to all participating models. Amongst the CCMI simulations, the REF-C1SD reference experiment aims at modelling as realistically as possible the day-to-day tropospheric and stratospheric compositions in a recent climate, using SD. For this purpose, as described in  Eyring et al. (2013) , the simulations are driven by (or nudged towards) dynamical reanalysis data sets (typically ERA-Interim or MERRA) and extending from 1980 to 2010.
The MOCAGE model and the simulation setup The MOCAGE model  (Josse et al. , 2004; Guth et al. , 2016)  is an offline global CTM. The chemical scheme is composed by the coupling of the RACM (Regional Atmospheric Chemistry Mechanism;  Stockwell et al. , 1997)  and the REPROBUS (REactive Processing Ruling the Ozone BUdget in the Stratosphere;  Lefèvre et al. , 1994)  schemes, corresponding to tropospheric and stratospheric chemistry, respectively. The MOCAGE REF-C1SD simulation is run using a global domain at a 2 • ×2 • horizontal resolution, and 47 vertical levels, in hybrid σ -pressure levels, distributed from the surface up to ∼ 5 hPa. The simulation is driven by the meteorological fields from the ERA-Interim reanalysis. The biomass burning and anthropogenic emissions come from the Global Fire Emissions Database version 2 (GFEDv2) and MACC/CityZEN EU project (MACCity) inventories, respectively.
The latter is characterized by a 10-year resolution, and a linear interpolation is applied to derive yearly emissions. The period spreads from August 1994 to December 2013, consistent with  Cohen et al. (2018). The first 14 years come from the MOCAGE REF-C1SD simulation originally produced for the CCMI project. For the years out of the period covered by the experiment, the MOCAGE REF-C1SD run has been extended to 31 December 2013 using the same code and inputs as in the original MOCAGE CCMI REF-C1SD simulation.
Methodology The objective of the proposed methodology is to make possible the comparison between the whole IAGOS database and the 3-D monthly mean volume mixing ratios from CTMs and CCMs simulations. Our approach consists of distributing the IAGOS observations, performed every 4 s, on a given model grid. A first application is proposed on the MOCAGE REF-C1SD run, characterized by a ∼ 200 km horizontal resolution in the midlatitudes and a ∼ 800 m vertical resolution in the UTLS. In order to account for the distance of the measurements from the centre within one given cell, we chose a reverse linear interpolation at the first order, as described in Sect. 3. 1 and illustrated in Fig. 1. The subsequent gridded monthly means are derived using weighted averages, as described in Sect. 3. 2, and are directly comparable to the model monthly mean outputs.
Although the surface pressure can show an important intramonthly variability, we calculated that a 30 hPa change at surface would cause a variation weaker than 2 hPa on a given vertical grid level in the UTLS. Although caution is needed while treating low-altitude measurements, the monthly resolution on the surface pressure field thus has a negligible impact on the distribution of the IAGOS data from the cruise altitudes onto the model vertical grid.
Hereafter, we refer to it as N eq. In the end, this method yields monthly fields of IAGOS O 3 and CO mixing ratios (or any other variable measured by IAGOS, e. g. water vapour) projected on the MOCAGE grid points where IAGOS data are available. This data set is named IAGOS-DM hereafter, the suffix DM referring to the distribution on the model grid. With this method, the cruise observation data are distributed onto the MOCAGE vertical levels spanning from level 28 up to level 22 and corresponding to the ∼ 360-175 hPa interval. Note that the measurement points on the MOCAGE vertical levels below level 28 (∼ 360 hPa) are considered as corresponding to ascent or descent phases of the flights. These measurements are not processed since they are only available in small areas close to airports. Levels 27 and 28 generally correspond to these phases too but include cruise measurements above elevated lands, since hybrid σ -pressure levels tend to follow land elevation.
In order to compare the observations and the model at the same locations and months, we apply a mask on the MOCAGE REF-C1SD simulation outputs that allows us to account only for the IAGOS-DM sampled grid points. The subsequent data set is named MOCAGE-M, the letter "M" referring to the mask. Thus, IAGOS-DM and MOCAGE-M data sets are spatially consistent and can be used to make grid-point-to-grid-point comparisons on climatological timescales, as long as we assume the gridded IA-GOS data to be representative of the measurement period. The latter point has been tested on a 5-year subsample using the simulation daily outputs instead of monthly outputs. It consisted of comparing MOCAGE-M to a test product derived by calculating monthly averages from the daily outputs and applying a mask based on the IAGOS daily sampling.
The results from this test are briefly presented in Sect. 4. In order to test the advantages of the linear interpolation involving weighting factors, we also derive another product from the IAGOS database using a simplified method, i. e. by solely averaging the measurement points into the grid cells where they are located. This control product is named IAGOS-DM-noW hereafter, the -noW suffix standing for "no weighting". Since it changes the spatial sampling distribution, a new subsequent mask has to be applied to the MOCAGE grid to be consistent with the IAGOS-DM-noW product, called MOCAGE-M-noW hereafter.
Filtering conditions For the climatological part of this study, we chose to perform a seasonal and a yearly analysis. Avoiding sampling biases where and when IAGOS-DM data (counted as N eq ) are not numerous enough requires that the seasonal sample N eq reaches a minimum threshold to be selected (noted N thres ). We chose to set this N thres limit depending on latitude to account for the varying grid-box area linked to the 2 • × 2 • grid and on the chemical tracer to account for the shorter period for CO measurements compared to O 3. N thres therefore decreases with latitude following a cosine function, similarly to the model horizontal grid cell areas. The reference threshold N thres,ref corresponds to O 3 measurements for gridbox areas during a given season, over the whole period (December 1994-November 2013 for O 3 and December 2001-November 2013 for CO).
It has been set to N thres,ref = 100 as a compromise between sampling robustness and a largeenough amount of data in IAGOS-DM sample. Accounting for the shorter CO measurement period compared to O 3 (∼ 60 % of the O 3 period), the same threshold applied to the CO climatologies would result in a greater proportion of filtered-out grid cells. Thus, the corresponding N thres threshold for this species is derived by applying a factor of 0. 6, leading to 60. Note that this reference threshold is defined seasonally. Therefore, the N thres,ref used for yearly climatologies is multiplied by a factor of 4.
Summary and conclusions We developed a methodology that makes the IAGOS database ready to assess chemistry-climate long-term model simulations for recent decades and particularly the REF-C1SD experiment produced in the frame of the CCMI phase 1 project. The current paper describes this methodology and its application on a chosen simulation (the REF-C1SD simulation from MOCAGE-CTM), assessing modelled ozone and carbon monoxide monthly fields during August 1994-December 2013 and December 2001-December 2013, respectively. The first step consists of generating a gridded monthly IA-GOS data set (IAGOS-DM), firstly by a linear-distanced reverse interpolation on the chosen model grid on a monthly basis and then by deriving weighted monthly means on each grid cell. The second step consists of deriving seasonal and annual climatologies for the well-sampled vertical grid lev-Table  3.
Particularly, the use of the IAGOS-DM product instead of IAGOS-HR systematically reduced the biases characterizing the simulation, thus avoiding an underestimation of the model abilities to reproduce the chemical composition of the UT and the LS in a recent climate time period. The present methodology could be easily applied to CCMI REF-C1SD simulations from other models, both for an intermodel comparison and for assessing CCMI products against the IAGOS database, notably intermodel-averaged fields. To a greater extent, it can be used on a wide range of long-term simulations including both CCMs nudged and free runs in order to perform climatological comparisons. Precaution must be taken while extending this work to the specified-dynamics simulations from CCMs, regarding the loss of consistency between chemical and dynamical variables that is introduced by nudging, as highlighted in  Orbe et al.
A2 Carbon monoxide Figure  A5. As Fig. 4 for boreal winter. https://doi. org/10. 5194/gmd-14-2659-2021 Geosci. Model Dev. , 14, 2659-2689, 2021 Acknowledgements. The authors acknowledge the strong support of the  European Commission, Airbus  and the airlines (Lufthansa,  Air France ,  Austrian ,  Air Namibia ,  Cathay Pacific , Iberia and China Airlines so far) who have carried the MOZAIC or IAGOS equipment and performed the maintenance since 1994. In its last 10 years of operation, MOZAIC has been funded by  INSU-CNRS (France) ,  Météo-France ,  Université Paul Sabatier (Toulouse, France)  and  Research Center Jülich (FZJ, Jülich, Germany ). IAGOS has been additionally funded by the  EU projects IAGOS-DS and IAGOS-ERI. We also wish to acknowledge our colleagues from the IAGOS team at  FZJ  for useful discussions.
The MOZAIC-IAGOS database is supported by  AERIS  ( CNES  and  INSU-CNRS ). Data are also available via the AERIS website https://www. aeris-data. fr(last access: 6 September 2018). Yann Cohen  acknowledges the  University of Toulouse  for providing administrative support for his PhD. The authors acknowledge the reviewers for their fruitful comments. Financial support. This research has been supported by  Météo-France  and the Occitanie region. Review statement. This paper was edited by  Juan Antonio Añel  and reviewed by two anonymous referees.
Code and data availability. The IAGOS ozone and carbon monoxide data are available at https://doi. org/10. 25326/20 (Petzold et al. ,  2015), and more precisely, the time series data can be found at https://doi. org/10. 25326/06 (Bundke et al. , 2018). The IAGOS-DM product adapted to the MOCAGE model grid (named IAGOS-DM-MOCAGE) is available at https://doi. org/10. 25326/80 (Cohen et al. ,  2020), and the software (named Interpol-IAGOS) is available at https://doi. org/10. 25326/81 (Cohen et al. , 2020).
Study design and population The study population was extracted from the ovarian cancer database of the FRANCOGYN study group, a retrospective multicentric cohort from 11 referral centers in France (Tenon, Jean Verdier, Creteil, Poissy, La PitieSalpêtrière, Lariboisière, Lille, Rennes, Tours, Strasbourg and Dijon) including all patients managed for ovarian cancer from January 2000 through December 2017. This study reviewed records of all consecutive patients who underwent surgery and had histologically confirmed AEOC of stages IIB to IV according to the FIGO classification, but included only those considered suitable for primary complete resection of their disease on initial assessment and who underwent pelvic and/or paraaortic lymphadenectomy with the removal of 10 or more lymph nodes. The Ethics Committee for Research in Obstetrics and Gynecology approved the research protocol (CEROG 2019-GYN-605).
Data collection The following clinical and paraclinical items were collected: age at diagnosis, body mass index (BMI), personal or family history of gynecological cancers, presence or absence of identified genetic mutations, the American society of anesthesiologists (ASA) score  [14] , preoperative CA125, preoperative radiological characteristics (computed tomography (CT) and positron tomography emission/ computed tomography (PET/CT)). The tumor histology was detailed: histological type and tumor grade.
05). Adjusted DORs (aDOR) were calculated. Missing data were treated as a distinct category. The predictive accuracy of the model was assessed in terms of its discrimination and calibration. Discrimination is the ability to differentiate patients with no LNM from patients with LNM. It was studied using the receiver operating characteristic (ROC) curve and summarized by the area under the curve (AUC)  [15]. Calibration is the agreement between the observed outcome frequencies and the predicted probabilities. It was studied using graphical representation of the relationship between these two results (calibration curve). We also evaluated the average and maximal errors between the prediction and observation, obtained from the calibration curve. Internal validation of the prediction model used leave-one-out cross-validation to correct for overoptimism in the predictive performance of the model  [16].
This method consists of splitting the data set randomly into n partitions. For each of the n-th iterations, n -1 partitions served as the training set and the left-out sample as the test set  [17]. We created risk groups of pelvic and/or para-aortic LNM by choosing threshold values of the prediction model equation that maximised classification rates  [18]. From this, we proposed simple lymphadenectomy decision rules associated with a user-friendly free interactive web app, called shinyLNM, that determines the risk group and their predicted probability of LNM for individual patients. Differences were considered significant at a level of p<0. 05. Statistical analyses were performed using STATA 13. 0 (Stata Corp. ; College Station, TX, USA). The shinyLNM app was programmed with the package Shiny from R Studio.
Patient tumors were obtained after approval of the protocol by the institutional review board and ethics committee, with fully informed patient consent (French Ministry of Research agreement number: AC-2013-1871 and AC-2019-3426). Cell lines. Breast medullary adenocarcinoma cell lines E0771(CVCL_GR23) and melanoma B16-F10 (B16) (CRL_6475) were obtained from ATCC. B16-shTGF-β1 cells were generated by the introduction of shTGF-β1 RNA in B16. Briefly, lentiviral vectors encoding shRNA pLKO. 1 puro Tgf-β1: NM-011577. 1-1753s1c1 and empty vectors were kindly given by Prof. D. Klatzmann (Paris). Infected B16 were selected on puromycin (Sigma, P8833). MLEC cells  39  , with luciferase activity reporting TGF-β signaling, were used. All cell lines were maintained in DMEM (Gibco, 31966-021) supplemented with 10% FCS (LifeTechnologies, 10270-106), 1% HEPES (LifeTechnologies, 15630-056), 1% penicillin/streptomycin (Life-Technologies, 15140-122).
Graphs demonstrate the mean of fluorescence intensity (MFI) ± SD in T cells (a). Data are representative of two independent experiments with 4-6 mice per group. c-g Purified Tregs from either Foxp3 Ctrl mice or Foxp3 ΔItgβ8 mice were adoptively transferred with Foxp3 neg T cells (CD3 pos GFP neg ) from either CD4-Cre;Stop fl/fl tgfbr1 CA ; Foxp3 GFP mice (TGFβRI CA ) or their littermate controls (TGFβRI WT ) in CD3ε KO mice. Ten days later, recipient mice were injected i. d. with B16 cells and their tumors were analyzed 16 days after their implantation by flow cytometry. d Representative contour plots of CD107 cell surface expression and GzB production by tumor-infiltrating CD8 pos T cells are illustrated and percentages of each population mentioned populations are on the counterplots. e Graphs illustrate the percentage of GzB pos CD107 pos cells and of CD107 pos cells among CD8 pos T cells infiltrating the tumors.
All samples were acquired on BD Fortessa and data were analyzed with FlowJo Software version X. Mouse tumor TUNEL and immune-fluorescence staining. Tumors were embedded in Tissue tek OCT compound (Sakura Finetek) and snap-frozen; 10-µmthick sections were cut with CryoStar NX50 (Microm Microtech France). For TUNEL staining, sections were permeabilized with 0. 2% Triton (Sigma, T9284) and digested with Proteinase K (ThermoFisher, K182001). For positive control, sections were incubated with DNAse I at 1 mg/ml (Sigma, 11284932001). Sections were then incubated with biotin-16-dUTP (Sigma, 11093070910) and TUNEL enzyme (Sigma, 11767305001) in deoxynucleotidyltransferase buffer (1 mM CoCl 2 (Sigma, 15862-1ml-F), Tris-HCl, 200 mM sodium cacodylate (Sigma, C0250-25G), 0. 125% BSA (Sigma, A7906-500G)) at 37 °C for 60 min.
Sections were washed in stop buffer (300 mM NaCl (Sigma, S3014-1KG), 30 mM Sodium Citrate (Sigma, 71406-500 G)) and blocked with 2% BSA. Sections were then labeled with streptavidin-phycoerythrin (eBiosciences, 12-4317-87), CD8-A488 (53-6. 7, BD biosciences). For immunostaining, tumor sections were fixed in 4% PFA (Sigma) and stained with rabbit anti-mouse GP100 1/100 (ab137078 Abcam) was detected with goat anti-rabbit A647 1/100 (LifeTechnologies; A31573) and or LAP-PE 1/100 (TW7-16B4, Biolegend, 141404). All sections were stained with DAPI (Euromedex, 1050-A) and mounted with Fluoromount (Sigma, F4680-25ml). All samples were acquired on Upright microscope Zeiss Axioimager (SIP 60549) and data were analyzed with Zen 2 (blue edition). Quantitative real-time PCR. mRNAs were isolated with RNeasy mini kit (Qiagen) and reverse-transcribed with iScript cDNA synthesis kit (Biorad).