dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure3Top Ingenuity® canonical pathways enriched by genes that were significantly differentially expressed in resistant cell lines. The Ingenuity® canonical pathway analysis associates the 943 gene dataset with the canonical pathways in Ingenuity's Knowledge Base and returns two measures of association: (1) a ratio of the number of genes from the list that maps to the pathway divided by the total number of genes that map to the same pathway, and (2) a P value of the Fisher's exact test for each pathway. Ingenuity® canonical pathways associated with a P value <0. 01 are presented.
Acknowledgments We gratefully thank  Dr. Sébastien Jacques  and  Dr. Franck Letourneur  at the  Cochin Institute (Paris, France ) for performing the micro-array experiments. We thank the anonymous donors of the NLGIP biobank at  Tel Aviv University, Israel , whose altruism and trust in biomedical research have made this study possible. This study was funded by the  BioIntelligence collaborative program  for the  Institut National de la Recherche Médicale (INSERM).
, 2002; Pan et al. , 2007a Pan et al. , , 2007b;; Brioude et al. , 2008; Barre´et al. , 2013). This paper contributes to the set of various evaluation procedures of the MACC system with other independent networks of in-situ data (NDACC, GAW) as it presents evaluation with simultaneous ozone and CO data in the critical Ex-UTLS region. Our objective is also to 'pave the road' for further developments on a long-term survey for quantifying model improvements in the frame of the CAMS and the expansion of the IAGOS programme including additional sampled regions and additional measured compounds. Sections 2 and 3 introduce the model and observational data as well as the methodology applied to perform a quantitative assessment of the differences between observations and model outputs. Then, Section 4 presents the evaluation of seasonal and annual distributions, and mixing processes in the Ex-UTLS of the MACC reanalysis and its control run.
MOZAIC-IAGOS data The MOZAIC programme started in 1994 with five aircraft from European airlines  (Marenco et al. , 1998 and www. iagos. fr/mozaic) and was designed to measure ozone and water vapour from the beginning, and additionally CO (since 2001) and NOy (2001Á2005). Figure  1  shows the map of the flights between 2003 and 2010. IAGOS as successor programme started in July 2011 (www. iagos. org; WMO Bulletin, 2014; Petzold et al. , 2015)  and six aircraft have been flying so far. The data base is now called the IAGOS data base and includes former MOZAIC data. Ozone and CO have been measured simultaneously since December 2001 on board five aircraft. The measurements of ozone and CO use improved commercial analysers with UV photometer and IR absorption technique, respectively. Measurements of ozone are taken every 4 seconds from takeoff to landing and the measurement's total uncertainty is estimated at92 ppbv92%.
In total, 14 127 flights took place over Europe in the UTLS for the period 2003Á2010, which means 1766 per year, 147 per month and 4 per day on average (Fig. 2 ). Although the number of flights per month decreased with time because of the decrease of the number of aircraft (from 5 in 2003 to 2 in 2010), there is still at least one flight per day as the number of flights per month is never less than 30. However, between April and August 2010, there were no ozone and CO data because of technical problems in the transition phase between MOZAIC and IAGOS programmes. Therefore, 2010 will be excluded from our statistical evaluation.
MACC reanalysis The MACC Reanalysis (REAN) of atmospheric composition covers the years 2003Á2012 and was produced with ECMWF's Global IFS model and data assimilation system coupled to the CTM MOZART  (Stein et al. , 2012) , using the coupling software OASIS4 (Ocean Atmosphere Sea Ice Soil version 4). The resolution of IFS is T255 corresponding to 80 km horizontal resolution. The vertical resolution is 60 vertical levels. The resolution of MOZART is 1. 1258)1. 1258. Further details about the model configuration are given in  Flemming et al. (2009)  and  Inness et al. (2013). Satellite data from GOME (Global Ozone Monitoring Experiment), MIPAS (Michaelson Interferometer for Passive Atmospheric Sounding), MLS (Microwave Limb Sounder), OMI (Ozone Monitoring Instrument), SBUV/2 (Solar Backscatter UltraViolet), SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY) were used in the assimilation for ozone.
, 2013  for more details). Emission inventories include MACCity (MACC/CityZEN EU projects) for anthropogenic emissions  (Granier et al. , 2011) , GFAS (Global Fire Assimilation System,  Kaiser et al. , 2012)  and GFED (Global Fire Emission Database,   Van der Werf et al. , 2010)  for the biomass burning, MEGAN (Model of Emissions of Gases and Aerosols from Nature,  Guenther et al. , 2006)  for the biogenic emissions and POET (Precursors of Ozone and their Effects in the Troposphere) for other natural emissions  (Inness et al. , 2013). Figure  3  shows the time evolution of the assimilated ozone and CO data and the emissions inventories from 2003 to 2010. It shows, for example, that no ozone profile data were available in April, May and June 2004, and that the assimilation of IASI CO retrievals started in April 2008.
When multiple satellites observing the same component were assimilated in REAN a variation bias correction scheme was applied to some of the datasets while data from one instrument were used uncorrected per species, to act as an anchor for the bias correction  (Inness et al. , 2013). MOPITT was used as anchor for CO and SBUV/2 data were used as anchor for ozone. Unfortunately, using SBUV/2 as anchor could not stop the bias correction drifting for individual MLS layers which had a higher vertical resolution, and this led to a drift in the REAN ozone field. When this was discovered during the reanalysis production, the bias correction was turned off for MLS and MLS was used uncorrected in REAN from 1 January 2008 onwards.
MACC control run The MACC control run (CNTRL) is a MOZART-3 standalone simulation driven by IFS meteorology and applying the same settings as MOZART in REAN in terms of model code, resolution, and emissions, but without constraints on chemical composition from observations. It is suitable for assessing the impact of the data assimilation on the modelled distribution of ozone and CO from 2003 to 2010. More information about CNTRL can be found in  Inness et al. (2013). Because the control run only covered the years 2003Á 2010, the comparisons in this paper are limited to this period.
Observed and modelled data in the Ex-UTLS To make observational data and the model directly comparable, the model fields were first linearly interpolated to the locations of the observations in space (latitude, longitude and pressure) and time. Potential vorticity is calculated systematically for each measurement in the MOZAIC database using the Lagrangian particle dispersion model FLEXPART  (Stohl et al. , 2005)  to associate each measurement with PV from the ECMWF's operational analysis. We defined the Ex-UTLS relative to the dynamical tropopause as the pressure level of the potential vorticity equal to 2PVU in the mid-latitudes (Potential Vorticity Units: 1 PVU 0 10 (6 m 2 s (1 Kkg (1 )  (Holton et al. , 1995; Bethan et al. , 1996). The measurements and model data were then assigned to two bins as detailed in  Thouret et al.
, 2009). Indeed, Taylor diagram and MB yr are complementary information to summarise the performance of the model  (Taylor, 2001). 3. 1. 3. O 3 ÁCO PDF. As ozone and CO are measured simultaneously by MOZAIC-IAGOS, we can compare the relationships between both species simulated by models and observed by MOZAIC-IAGOS. The O 3 ÁCO PDF is displayed as the scatter density plot of ozone as a function of CO for the three datasets (both model systems and MOZAIC-IAGOS). For this purpose we divide the plot in small squares. Ozone ranges from 0 to 700 ppbv with ozone bins of 20 ppbv. CO ranges from 0 to 150 ppbv with CO bins of 10 ppbv. Then, we calculate the percentage of element inside each square. PDF in one square is calculated and displayed if there are 10 measurements at least in the square. In the LS (Fig.
4. 1. 3. Focus on the changes over the 8-yr period. This section investigates the ability of the models to reproduce the observed inter-annual variability of ozone and CO between 2003 and 2009. 2010 is excluded as there is a lack of MOZAIC data between April and August, which could lead to important uncertainties in the comparison with the models. The objective is here to present a measure of the changes of ozone and CO over the period, to facilitate the comparison between observations and model outputs. In its current version, because of the changes in the assimilated datasets, REAN is not an appropriate run for a trend analysis. However, analysis of the temporal evolution of model/observations differences is of interest. As a measure of these differences, we present and summarize the behaviours in terms of annual and seasonal anomalies as linear fits over the period 2003 to 2009.
Evaluation of mixing in the Ex-UTLS The aim of this section is to assess the ability of the model to reproduce mixing in the Ex-UTLS over Europe. Schematically, in an O 3 /CO scatter plot, the vertical branch is related to the stratospheric reservoir and the horizontal branch to the tropospheric reservoir with mixing between the two resulting in linear mixing lines  (Hoor et al. , 2002; Brioude et al. , 2008). Mixing lines in the Ex-UTLS over Europe can be assessed with in situ MOZAIC-IAGOS data as the two species are measured simultaneously. Figure  9  shows seasonal O 3 ÁCO scatter plots in the Ex-UTLS with MOZAIC-IAGOS, the reanalysis (REAN, with assimilation) and the control run (CNTRL, without assimilation) for the entire period 2003Á2010, over Europe. MOZAIC-IAGOS data have been averaged on a grid with the same horizontal resolution as the models (1.
1258)1. 1258) in order to allow for a fairer comparison. We have chosen seasonal ozone and CO thresholds to define three regions: 'Low CO', 'Low O 3 ' and the 'Extra tropical Transition Layer' (ExTL) (as defined by  Gettelman et al. , 2011)  in between, as marked in Fig. 9. The ozone threshold is the seasonal mean value of ozone in UT and the CO threshold is the seasonal mean value of CO in LS. UT and LS refer to the two layers defined in Section 3. 1. The mean values of ozone in UT (ozone threshold) calculated from MOZAIC-IAGOS data vary from 48/49 ppbv in winter/fall to 68/77 ppbv in spring/summer, whereas the mean values of CO in LS (CO threshold) vary from 50/48 ppbv in spring/summer to 52/51 ppbv in winter/fall. Thus the maximum of the ozone threshold is observed in summer but the maximum of the CO threshold is observed in winter.
Therefore in spring, when mixing is maximal, the ExTL ratio is overestimated by REAN, which is in agreement with other studies. Barre´et al. (2013)  showed that, although the location of the ExTL is improved by the assimilation of the satellite data (MLS and MOPITT in MOCAGE), the spread of the ExTL tends to be increased compared with a free-model run showing an overestimation of the mixing. It is worth noting that the differences between the thresholds between model and observations do not induce significant differences on percentage of air masses classified in the three regions 'Low CO', 'Low O 3 ' and 'Ex-TL' as they are equivalent to the percentages calculated with MOZAIC-IAGOS, except in spring. As a consequence, the impact of assimilation on the O 3 Á CO correlation in the Ex-UTLS is definitely linked to the impact of the vertical distribution of the two species around the tropopause as we show in Figs.
In conclusion, the shape of the O 3 /CO scatter plot is quite well reproduced by the models. In general, REAN is doing a better job than CNTRL. Nevertheless, the models still have difficulties in properly reproducing the lowest and the highest CO mixing ratios and the mixing lines that are observed by dataset of MOZAIC-IAGOS, especially in spring/summer. This is directly linked to the ability or the inability of the models to reproduce the ozone and CO gradients (e. g. Clark et al. , 2007). The CO gradient does not change significantly after assimilation. The weak vertical resolution of the models (500 m compared with 30 m for MOZAIC-IAGOS), the averaging kernel of MOPITT and IASI (CO measurement) applied to the MOZART-IFS system and background errors may limit the ability of REAN to reproduce the observed O 3 ÁCO relationships in the Ex-UTLS (between 300 and 200 hPa, a 3 km-layer).
Conclusions The MOZAIC-IAGOS programme provides high density and high resolution ozone and CO simultaneously recorded in-situ data. These data are used in this study to evaluate the MACC reanalysis  (Inness et al. , 2013)  with the overall objective of assessing the ability of the model to reproduce the observed temporal and spatial variability of ozone and CO in the Ex-UTLS region. The impact of data assimilation was investigated by comparing the results from the MACC reanalysis to a control run without data assimilation for the years 2003Á2010 focussing on Europe. In general, the reanalysis is closer to the observations than the control run, which underlines the significantly positive impact of data assimilation. This is encouraging and gives room for further improvement of the description of ozone and CO in the Ex-UTLS.
Problems also remain in reproducing the observed interannual variability. Observed CO is clearly decreasing in the UT but the MACC reanalysis shows this behaviour only in winter. The observed decrease is likely the result of the reduced North American emissions. For other seasons, deficiencies in transport, mixing processes and also emissions during extreme events may be important factors. In spring/summer 2007 a positive anomaly of CO is observed in the Ex-UTLS with MOZAIC-IAGOS data but not reproduced by either of the two models. North American and European anthropogenic and fire emissions would have an important impact on CO mixing ratios over Europe for this period, which models may not be able to capture. In the critical Ex-UTLS region, both REAN and CNTRL have difficulties in reproducing the wide range of ozone (20Á700 ppb) and CO (20Á150 ppb) mixing ratios.
The reanalysis performs better than the control run in reproducing the mixing lines in the Ex-UTLS. However, models have difficulties in reproducing low CO in LS and high CO in UT. This is directly linked to the fact that the ozone and CO gradients around the tropopause are not necessarily better reproduced by the model after assimilation. This illustrates the limitations of the current data assimilation system, and at the same time the impact of the coarse resolution in the Ex-UTLS region. Finally, MOZAIC-IAGOS is an on-going programme providing regular data of reactive gases (and soon aerosols and greenhouse gases). Its long-term participation in CAMS will allow us to pursue such evaluations in the future and help the understanding and quantification of the processes playing a role in the Ex-UTLS region.
and China Airlines so far) who carry the MOZAIC or IAGOS equipment and perform the maintenance since 1994. MOZAIC is presently funded by INSU-CNRS (France), Me´te´o-France, Universite´Paul Sabatier (Toulouse, France) and Research Center Ju¨lich (FZJ, Ju¨lich, Germany). IAGOS has been and is additionally funded by the EU projects IAGOS-DS, IAGOS-ERI, and IGAS. The MOZAIC-IAGOS database is supported by ETHER (CNES and INSU-CNRS). MACC-II was funded by the European Commission under the EU Seventh Research Framework Programme, contract number 283576. The research leading to these results has received funding from the European Community's Horizon 2020 Programme under grant agreement n8 633080.
Experimental design NEMO-PISCES was first spun up over 3000 years using constant preindustrial dynamical forcings fields from IPSL-CM5A-LR  (Dufresne et al. , 2013)  without activating the N 2 O parameterizations. This spin-up phase was followed by a 150-year-long simulation, forced by the same dynamical fields now with N 2 O production and N 2 O sea-to-air flux embedded. The N 2 O concentration at all grid points was prescribed initially as 20 nmol L -1 , which is consistent with the MEMENTO database average value of 18 nmol L -1 below 1500 m  (Bange et al. , 2009). During the 150-year spin-up, we diagnosed the total N 2 O production and N 2 O sea-to-air flux and adjusted the α, β, γ and θ parameters in order to achieve a total N 2 O sea-to-air flux in the two parameterizations at equilibrium close to 3.
In NEMO-PISCES, a decrease of 13 % in export leads to a maximum decrease in N 2 O emissions of 12 % in the P. OMZ scenario. Based on results by  Bopp et al. (2013) , changes in export of carbon span -7 to -18 % in the CMIP5 model ensemble at the end of the 21st century and for RCP8. 5. The spread would propagate to a similar range in projected N 2 O emissions across the CMIP5 model ensemble. When these values are applied to present N 2 O emissions of 3. 6 TgN yr -1 , uncertainties are then bracketed between -0. 25 and -0. 65 TgN yr -1. Regarding the low-O 2 pathway, a similar approach is not that straightforward. Zamora et al. (2012)  found that a linear relationship between AOU and N 2 O production might occur even at the OMZ of the eastern tropical Pacific. Zamora et al. (2012)  acknowledged the fact that the MEMENTO database includes N 2 O advected from other regions and that mixing could play a relevant role, smoothing the fit between N 2 O and AOU from exponential to linear.
(Bange et al. , 2009)nd correlation coefficients between P. TEMP and P. OMZ parameterizations with respect to MEMENTO database observations(Bange et al. , 2009). The model results at present day were evaluated against the MEMENTO database(Bange et al. , 2009), which contains about 25 000 measurements of colocated N 2 O and dissolved O 2 concentrations. Table1summarizes the standard deviation and correlation coefficients for P. TEMP and P. OMZ compared to MEMENTO. The standard deviation of the model output is very similar to MEMENTO, i. e. , around 16 nmol L -1 of N 2 O. However, the correlation coefficients between the sampled data points from MEMENTO and P. TEMP/P. OMZ are 0. 49 and 0. 42, respectively. Largest discrepancies are found mostly in the deep ocean and in the OMZs.
Acknowledgements. We thank  Cynthia Nevison  for providing us the N 2 O sea-to-air flux data set. We thank  Annette Kock  and  Herman Bange  for the availability of the MEMENTO database (https://memento. geomar. de). We thank  Christian Ethé  for help analyzing PISCES model drift. Comments by  Parvadha Suntharalingam  and the three anonymous reviewers significantly improved this paper. Nicolas Gruber  acknowledges the support of  ETH Zürich. This work was supported by the  European Union  via the Greencycles  II FP7-PEOPLE-ITN-2008 , number  238366. Edited by:  F. Joos.
Still, these density datasets only allow fine analysis of the latitudinal structure on a per orbit (about 95 min) basis, and from pole to pole. However, the density database is augmented for completeness with total densities inferred from: d Stella and Starlette precise orbit determination; d Densities inferred from energy dissipation rates (EDR densities). These densities are low-resolution observations, averaged over one day or longer, but their spatial and solar cycle coverage is good. Bruce Bowman has kindly made the EDR data available as additional validation data in the 200-500 km altitude range. The models evaluated in this paper are constructed by fitting to the underlying density database as good as possible in the least-squares sense (i. e. semi-empirical model). They represent mean climatology models of the thermosphere.
The total density data Essentially the same neutral density data that were used in the benchmark model DTM2009 and described in  Bruinsma et al. (2012)  are used in the construction and validation of DTM2013, with some notable exceptions. Only these new datasets will be described in more detail, as well as the only independent dataset. The two-line element dataset is not used anymore because of its low accuracy (20-30%), and the Deimos-1 data are not used because of its too short duration  (2010) (2011). All datasets assimilated in DTM2013 are listed in Table  1. Daily-mean densities inferred from satellite drag data of 18 satellites in elliptical orbits, with perigee heights in the 200-500 km range, were kindly provided by Bruce Bowman for this evaluation. Latitude coverage is from pole to pole taking all objects into account, and most satellites cover more than the last solar cycle  (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010).
The DTM2013 algorithm The algorithm has not been modified with respect to the description given in  Bruinsma et al. (2012). However, the 30 cm solar radio flux, F30 (described in detail Dudok de  Wit et al. 2014) , is presently used as solar proxy, whereas DTM2009 uses F10. 7. The F30 proxy, measured in Japan (Nobeyama) since 1957, is more representative of the thermospheric solar energetic input in the ultraviolet than F10. 7. Dudok de Wit et al. (  2014 ) tested both F10. 7 and F30 in DTM test models that were otherwise constructed identically with the same density datasets. The model driven by F30 reconstructed observed densities with higher fidelity, and the authors ascribed this to the five times higher contribution of thermal Bremsstrahlung that comes from plages and faculae in F30 compared with F10.
7 (50% vs. 10%, respectively). Being a terrestrial measurement, it is more robust and more accurately calibrated than satellite measurements of solar emissions. For convenience and practical reasons, F30 is used after scaling to F10. 7, so that both proxies can be used to drive the model. Figure  1  shows the different long-period (81-day average) evolution of F30 and F10. 7 for 2000-2013, and the shortperiod variations for 2010-2012. The dataset of solar radio  emissions (F30, F15, F10. 7, F8, and F3. 2 cm) is available for download from http://projects. pmodwrc. ch/solid/. The planetary geomagnetic index Km (based on the am index, which is described here: isgi. latmos. ipsl. fr/des_am. html) is used, as is the case of DTM2009, but Kp (based on the ap index) can also be used. The representation of the total density in the altitude range 120 to approximately 1500 km is achieved by summing the contributions of the main thermosphere constituents (N 2 , O 2 , O, He, H), under the hypothesis of independent static diffuse equilibrium.
Relevant information on data, DTM2009 and JB2008 DTM2009 employs F10. 7 and the Km or Kp index to model temperature and major and minor constituents. JB2008 is constructed using a combination of solar-and geomagnetic proxies and indices, which were not all available before 1998, to reconstruct temperature and total density. The GRACE densities were scaled to be consistent with the CHAMP densities (errors in the satellite macro model, e. g. cause scale effects). This was done by comparison of normalized densities (to the average altitude) for two periods  (in 2003 and 2005)  for which the orbits of both satellites were coplanar, and then scaling the GRACE to the CHAMP densities  (Bruinsma & Forbes 2010). The scale factor applied to the GRACE densities is 1. 23. The EDR densities are not scaled, and therefore offsets with the other datasets are to be expected.
However, scaling of the GOCE densities to HASDM should lead to good consistency with the EDR densities (both originate at US Air Force Space Command). A few years of CHAMP data were used in JB2008, but for storm-time modeling only. CHAMP, GRACE, GOCE, Starlette, and Stella data have been assimilated in DTM2013. The EDR densities were assimilated from 1997 to 2007 in JB2008.