dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017). This selection algorithm was applied to objectively identify the most highly urbanized sites, with approximately one quarter of all sites in the database classified as urban. The non-urban sites considered in this paper include suburban sites as well as rural sites surrounded by heavily urbanized areas. At the other extreme, some of the sites are considered to be remote, either located in unpopulated coastal regions, on islands, on top of high mountains, or in low-elevation, land-locked areas remote from anthropogenic emissions. Another subset of sites was selected to explore long term trends in air masses characteristic of the lower free troposphere. This subset consists of eight mountaintop or very high elevation sites in the Northern Hemisphere. One of these sites, Mauna Loa Observatory (MLO) on the Big Island of Hawaii in the central North Pacific Ocean (19.
The UTLS is not highlyresolved by satellite instruments either. However, ozonesonde data are temporally sparse and unevenly distributed, with only about 60 sites worldwide making regular soundings, most only once per week. Therefore TOAR-Climate uses a derived product that addresses these issues by taking advantage of the long lifetime of ozone in the free troposphere. This product is known as the Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) and is described in Section 2. 3. 2. Ozone observations at individual ozonesonde sites, for example, Lauder, New Zealand, are only assessed by TOAR-Climate for the purposes of evaluating remotely sensed TCO.
TOST The Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) is a 3-dimensional, longterm ozone dataset derived from ozone soundings using a trajectory-based ozone mapping methodology  (Tarasick et al. , 2010; Liu, G. et al. , 2013; Liu, J. et al. , 2013). TOST is derived from over 67,000 ozonesonde profiles at over 100 stations from the 1960s to 2010s. Locations of these stations for the period 2008-2012 are shown in Figure S-1. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model  (Draxler and Hess, 1998) , driven by the National Centers for Environmental Prediction (NCEP) reanalysis data, is applied to extend each ozone record along its trajectory path forward and backward for four days, as ozone lifetime in the free troposphere is a few weeks.
Then, all ozone values along these trajectory paths are binned into grids of 5° × 5° × 1 km (latitude, longitude, and altitude), from sea level or ground level up to 26 km. TCO is integrated from the ozone concentrations below the tropopause, which is defined by the NCEP reanalysis data (Table  1 ). TOST provides a more accurate ozone distribution than simple linear or polynomial interpolation of ozonesonde data. It depends on neither a priori data nor photochemical modeling and reveals ozone variations in three dimensions. It covers a longer term and higher latitudes than some satellite-derived tropospheric ozone data. TOST has been evaluated by comparing with individual ozonesondes (removed from TOST one by one). The agreement is generally quite good. Biases are larger near the tropopause, over mountainous regions and in areas with sparse soundings.
, 1998). As the successor to MOZAIC, with the objective of long-term sustainable operations, the first IAGOS aircraft became operational in July 2011  (Petzold et al. , 2015; Nédélec et al. , 2015). As of 2017, eight IAGOS aircraft from six airlines (Air France, Lufthansa, China Airlines, Cathay Pacific, Iberia and Hawaiian Airlines) are in operation. The 4-year overlap of MOZAIC and IAGOS has demonstrated that the new system provides data with the same quality as the former, permitting the reliable calculation of temporal trends from 1994 to the present  (Nédélec et al. , 2015). The MOZAIC-IAGOS data record (referred to as IAGOS hereafter) now contains over 50,000 flights, freely available through the open-access central database (http://www. iagos. org). In this study, we use IAGOS data averaged in 5° × 5° grids in the UT and profiles over USA, Europe and Asia (Table  1 ).
Lidar Differential Absorption Lidar (DIAL) is a well-known technique to measure tropospheric and stratospheric ozone. The two DIAL systems described below contribute routine measurements 2-4 times per week to the Network for the Detection of Atmospheric Composition Change (NDACC). The DIAL system located at the Observatoire de Haute Provence, France (OHP, 44°N, 6°E, 690 m) has operated since 1991  (Ancellet et al. , 1997). The instrument measures ozone between 3 and 14 km above sea level (a. s. l. ) with a vertical resolution ranging from 200 m at 2 km to 1000 m at 12 km. Precision remains within 9% at all altitudes, and accuracy is 5 ± 5 nmol mol -1. For this analysis, the lidar data set is combined with data from Electrochemical Concentration Cell (ECC) ozonesondes launched weekly from OHP.
Therefore, attribution of the lowest Umkehr layer information to TCO (below the tropopause) variability is not well-defined and can be influenced by ozone variability in the lower stratosphere. The bias between Umkehr and other measurements, including ozonesondes and lidar has been identified  (Komhyr et al, 1995; Fioletov et al. , 2006; Nair et al. , 2011). The bias between ozonesondes and Umkehr in the troposphere ( ~10-20%) is reduced by almost half when the ozonesonde profiles are smoothed with the Umkehr AKs. Correction for the out-of-band stray light error reduces the bias by about 5%  (Petropavlovskikh et al. , 2011) , and the Umkehr tropospheric ozone data for this study are treated for stray light error (ftp://aftp. cmdl. noaa. gov/data/ozwv/ DobsonUmkehr/Stray%20light%20corrected/).
4), satellite data rely on retrieval algorithms that model the expected measured radiance with a forward model and then invert this model using the measurement, usually with optimal estimation  (Rodgers et al. , 2000) , to produce an estimated vertical distribution of abundance (nmol mol -1 ) or sub-columns (DU) along with a posteriori error covariance and averaging kernel (AK) matrices. The AK quantifies the relative sensitivity of the radiance and retrieval to the "true state" for vertical retrieval layers and varies with observation type (land/ocean, day/night), the spectral range being measured (thermal infrared or UV), spectral resolution, measurement noise and choice of a priori covariance. For example, OMI/MLS, OMI-SOA, IASI-FORLI and IASI-SOFRID are more sensitive to the UT, while OMI-RAL is more sensitive to the lower half of the troposphere (Figures S-2, S-3 and S-4).
To generate monthly GOME and OMI data to a common grid of 1° latitude × 1. 25° longitude, only retrievals with good quality flags under near clear-sky conditions (with effective cloud fraction < 0. 3) were used. The individual retrieval TCO errors due to precision and smoothing errors are typically within 2-5 DU (14% on average), and average total errors including other systematic and forward model errors are estimated to be ~21%. Both GOME and OMI TCOs typically show good agreement with ozonesonde TCO to within 3 DU. However, accurate radiometric calibration of Level 1b data as a function of time is critical to producing a long-term consistent data record. The degradation correction in GOME data might cause time-dependent systematic biases in the retrievals and no time-dependent correction is applied to OMI data even during the occurrence of the serious row anomaly since 2009.
OMI-RAL Global height-resolved ozone distributions spanning the stratosphere and troposphere are retrieved from satellite UV nadir sounders by the RAL's optimal estimation scheme  (Miles et al. , 2015). Data sets spanning 1995-2016 are being produced from a series of five instruments for ESA's Climate Change Initiative (CCI) and will be updated in coming years for the EU's Copernicus Climate Change Service (C3S). RAL's scheme was the first to demonstrate tropospheric sensitivity  (Munro et al. , 1998). This is achieved through a three-step approach: firstly, the strong wavelength dependence of ozone absorption in the Hart-ley band (260-307 nm) is exploited in fitting the ratio of backscattered to direct-sun spectra to retrieve heightresolved information principally in the stratosphere; secondly, an effective surface albedo is retrieved in the 335-340 nm interval and, thirdly, temperature dependent ozone absorption in the Huggins bands (323-334 nm) is fitted to high precision (<0.
Negative ozone trends in the troposphere at mid-high northern latitudes have been reported, especially in summer  (Wespes et al. , 2018). For the present study, the daily tropopause height used to generate the IASI-FORLI TCO dataset relies on the WMO definition applied to the IASI level 2 temperature profiles provided through the Eumetcast operational processing system  (August et al. , 2012). Only daytime and clear-sky measurements characterized by a good spectral fit have been considered. Similar to other products, the IASI data were mapped on a daily basis to a grid of 5° latitude × 5° longitude, and then averaged to produce seasonal and annual means. IASI-SOFRID: SOFRID retrieves global ozone  (Barret et al. , 2011)  and  CO (De Wachter et al. , 2012)  profiles from IASI radiances. SOFRID is built on the RTTOV (Radiative Transfer for TOVS) operational radiative transfer model  (Saunders et al.
, 1999 , Matricardi et al. , 2004)  jointly developed by ECMWF, Meteo-France, UKMO and KNMI within the NWPSAF. The RTTOV regression coefficients are based on line-by-line computations performed using the HITRAN2004 spectroscopic database  (Rothman et al. , 2005)  and the land surface emissivity is computed with the RTTOV UW-IRemis module  (Borbas et al. , 2010). We use the IASI-L2 temperature profiles from EUMETSAT for radiative transfer computation. The retrievals are performed with the UKMO 1D-Var algorithm  (Pavelin, et al. , 2008)  based on the optimal estimation method  (Rodgers, 2000). The results presented here are based on the IASI-SOFRID v1. 5 ozone product  (Barret et al. , 2011). In this data version, SOFRID uses a single a priori ozone profile and associated covariance matrix based on one year (2008) of ozonesondes from the WOUDC network.
5 km a. s. l. over land (where thermal contrast is positive). Further details are provided in TOAR-Observations  (Tarasick et al. , 2018). The multispectral synergism of IASI and GOME-2 enhances the vertical resolution of the retrieval so as to consistently resolve ozone levels in the lower/middle troposphere, the middle/upper troposphere and lower stratosphere. Since January 2017, global scale IASI+GOME-2 observations are routinely produced at the ESPRI French National data center (http://cds-espri. ipsl. fr) of the AERIS data center (http://www. aeris-data. fr) and will be available soon for the scientific community. For the current paper, the new capability of the recent IASI+GOME2 product is illustrated with the dataset available before 2017 (the year 2010 for East Asia and August 2009 for Europe).
Satellite observations of tropospheric ozone as a greenhouse gas TES (Tropospheric Emission Spectrometer) operated on the NASA EOS-Aura satellite from 2004 to 2018 and measured vertical ozone profiles using thermal infrared spectra, similar to IASI, using Fourier Transform Spectrometry  (Beer et al. , 2001). TES obtained global observations from 2004 to 2009 but shifted to a smaller latitude range following instrument failures associated with continuous operation. Seasonal mean TCO as measured by TES is shown for the years 2004-2008 in Figure S-6. Since IASI observations will continue into the next decade with identical IASI instruments on Metop-B and -C, there have been efforts to combine the TES and IASI data records by accounting for differences in spatial coverage, spectral resolution, and a priori information in the optimal estimation retrievals  (Oetjen et al.
As shown in Figure S-18. a, the analysis using all available sites finds overall decreasing trends of summertime (April-September) daytime average ozone across eastern North America and Europe, but increasing ozone over East Asia. The rate of change for rural sites in these three regions is -0. 42, -0. 17 and + 0. 23 nmol mol -1 yr -1 , respectively. A regional increase of +0. 20 nmol mol -1 yr -1 was also found for southeast Asia, using all available sites. Further details on this methodology are described by  Chang et al. (2017). Because East Asia is a major ozone precursor emission region, we also highlight ozone trends from two recent studies at sites that were not included in the TOAR database or used for the main TOAR analysis. Mt. Tai at 1. 5 km above the North China Plain is ideally situated to monitor regional scale ozone levels.
1), Western Europe (Section 5. 2), East Asia (Section 5. 3), the NH tropics (0°-30°N) (Section 5. 4) and the SH tropics (0°-30°S) (Section 5. 5). We also highlight the region across the Mediterranean and Middle East, not because we have firm evidence that ozone in this region is changing, but because it contains a strong summertime TCO maximum that has not been fully explored or monitored with in situ observations, especially across the Middle East (Section 5. 6). The section concludes with an assessment of the tropospheric ozone burden (Section 5. 7), and ozone's long-wave radiative effect (Section 5. 8), followed by information for accessing the ozone datasets discussed by TOAR-Climate (Section 5. 9). In the discussion that follows, the peer-reviewed literature is relied upon to briefly place the observed trends in the context of current understanding of the processes that control ozone in each region.
As ozone trends are strongly impacted by changes in precursor emissions, we summarize the latest findings on global emissions of the key ozone precursor, nitrogen oxides (NO x ). The Community Emissions Database System (CEDS) global bottom-up emission inventory  (Hoesly et al. , 2018)  shows an increase in global anthropogenic NO x emissions of roughly 17% from 2000 to 2010 with nearly constant emissions from 2010-2014. Over this period, emissions decreased in North America and western Europe and increased in Asia. The net result is a small decrease of about 5% in the 30°-90°N latitude band, and large increases of 60% in the NH tropics (0°-30°N) and a doubling of emissions in the SH tropics, with SH tropical emissions being only a quarter of NH tropical emissions. A recent top-down emission inventory using OMI-detected tropospheric column NO 2 indicates no net change in global NO 2 emissions from 2005 to 2014, but with decreases of NO x emissions in North America and Western Europe and increases in India and China, although Chinese emissions have decreased since 2011  (Miyazaki et al.
Europe As with mid-latitude North America, extensive air quality monitoring and analysis have shown that reductions in ozone precursor emissions have reduced extreme ozone levels across much of Europe at both rural and urban sites  (Derwent et al. , 2010; Simpson et al. , 2014; EEA, 2016). A recent update to the baseline ozone record on the west coast of Ireland shows ozone increased during the 1980s and 1990s and has remained constant or even begun to decline since 2000  (Derwent et al. , 2018). Focusing on just the annual mean of the maximum daily average 8-h ozone values, rural background sites across Europe were generally characterized by decreasing ozone while heavily urbanized sites showed ozone increases over  2000 -2014 (EEA, 2016)). Analysis of the surface sites from the TOAR database found similar results, but seasonal trends of daytime average ozone revealed that non-urban sites only showed broad decreases across Europe during summer months while increasing and decreasing trends varied widely across the region during other seasons (Figures  14  and 15 ).
Availability of all data sets presented in TOAR-Climate. DOI: https://doi. org/10. 1525/elementa. 291. t6Surfaceozoneobservations The surface ozone values and trends shown inFigures 1, 2, 3, 13, 14, 15 (as wellas the figures themselves) can be downloaded from: Schultz, MG; Schröder, S; Lyapina, O et al. (2017): Tropospheric Ozone Assessment Report, links to datasets https://doi. pangaea. de/10. 1594/PANGAEA. 876108 TOST ozonesonde product The global monthly gridded product is available from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC): http://woudc. org/archive/products/ozone/vertical-ozone-profile/ozonesonde/1. 0/tost/ IAGOS commercial aircraft High resolution IAGOS observations can be downloaded from the IAGOS Data Portal: http://iagos. sedoo. fr/ Mauna Loa Observatory ozone and meteorology Hourly ozone and meteorological data shown in Figure 12 are available from NOAA's Global Monitoring Division: https://www.
esrl. noaa. gov/gmd/dv/ftpdata. html Lauder, NZ ozonesondes The ozonesonde data at Lauder are part of WOUDC and the Network for the Detection of Atmospheric Composition Change (NDACC) and are publicly available: http://woudc. org/data/explore. php, ftp://ftp. cpc. ncep. noaa. gov/ndacc/station/lauder/ames/o3sonde/. Table Mountain lidar Data used in this publication are archived with NDACC and are publicly available (see http://www. ndacc. org; Leblanc, 2016). For additional data or information please contact the authors. Leblanc, T. : TMO lidar data at NDACC database, available at: ftp://ftp. cpc. ncep. noaa. gov/ndacc/station/tmo/ames/lidar/, last access: 25 July 2016. OHP ozonesondes and lidar Data used in this publication are archived with NDACC and are publicly available: http://www. ndacc.
org FTIR Profiles with 40-50 levels archived at NDACC database: http://www. ndsc. ncep. noaa. gov/data/ , https://www2. acom. ucar. edu/irwg Umkehr http://www. woudc. org/, stray light corrected monthly averaged data from six Umkehr stations are provided by NOAA/ESRL/GMD at ftp://aftp. cmdl. noaa. gov/data/ozwv/DobsonUmkehr/Stray%20 light%20corrected/monthlymean/ at 1° × 1. 25° resolution available from NASA Goddard Space Flight Center: https://acd-ext. gsfc. nasa. gov/Data_services/cloud_slice/ GOME and OMI from SAO Monthly GOME gridded data are available at https://www. cfa. harvard. edu/ ~xliu/res/gmtrop. htm. Monthly gridded data at 1° × 1. 25° resolution are derived from OMI OMPROFOZ data. OMI OMPROFOZ data are available from Aura Validation Data Center, NASA Goddard Space Flight Center: https://avdc.
Acknowledgements and Funding information The TOST ozonesonde product relies on observations made by many institutions, including  NASA 's  Southern Hemisphere ADditional OZonesondes (SHADOZ) network , with data made available by  Anne Thompson  and  Jacquie Witte , NASA GSFC: https://tropo. gsfc. nasa. gov/shadoz/. The TOST team gratefully acknowledges the  NOAA Air Resources Laboratory  for the provision of the  HYSPLIT trajectory model (Hybrid Single Particle Lagrangian Integrated Trajectory Model)  (http://www. arl. noaa. gov/ready. html), and the  NOAA Physical Sciences Division  for the NCEP/NCAR reanalysis data (www. esrl. noaa. gov/psd/data/reanalysis/reanalysis. shtml). Jack Fishman , St. Louis University, provided the 1979-1983 Tropospheric Ozone Residual product. The  MLS, OMI  and  TES  projects are supported by the  National Aero-nautics and Space Administration (NASA) Earth Observing System (EOS) Aura Program.
IASI is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). The IASI L1 data are distributed in nearreal time by  EUMETSAT  through the EumetCast distribution system. LATMOS and ULB acknowledge support from the  C3S-O 3  project funded by  ECMWF  and from the  AC-SAF  project funded by  EUMETSAT. LISA acknowledges the support from  CNES (Centre National des Etudes Spatiales)/TOSCA (Terre Océan Surface Continentale Atmosphère) ,  PNTS  ( Programme National de Télédétection Spatiale)  and  ANR (Agence Nationale de la Recherche  -project:  ANR-15-CE04-0005 ) for the development and production of ozone observations from IASI+GOME-2 and IASI. CMIP6 global NO x emissions data were provided by  CEDS  (Community Emissions Data System): http://www. globalchange.
08 (-0. 30, 0. 14) 0. 32 -0. 08 (-0. 30, 0. 14) 0. 32 a Boldface indicates trend values that are statistically significant at p-value < 0. 05. Trend values in parentheses indicate the high and low trend values that bound the 95% confidence interval. b The Mt. Bachelor data were updated in the TOAR Surface Ozone Database on February 22, 2018 to remove the hourly observations for Sept 1-Dec 31, 2006 and Sept 1-Dec 31, 2010 due to newly identified instrument problems during those time periods. The trends in this table were updated accordingly.
Population structure The final dataset for analysis comprised 173 strains with clinical designations of non-atrophic gastritis, progressive to cancer and gastric cancer. These strains were obtained from a larger collection of 565 H. pylori genomes after excluding strains that either did not have an appropriate clinical designation or were not assigned to the hpEurope population in a fineSTRUCTURE  [11]  analysis (Additional file 1: Figure  S1 ). There is substantial population structure within the 173 isolates. GC, Prog and NAG isolates were found in multiple places on the tree, and isolates from Northern Europe clustered at one end of the tree and those from Southern Europe and South America at the other (Fig. 1 ). The first principal component is 2. 2% of the total genetic variance and basically corresponds to the difference between hspEuropeN and others.
02 and therefore not independent, based upon Pearson's correlation (Fig. 3 ). To limit the weight of these correlated genes, an average of the 3 was used in the calculation of the risk score. As expected, the distribution of risk scores in our dataset was significantly associated with disease progression (ANOVA, p value < 0. 0001) (Fig. 4 ). Specifically, patients from which H. pylori isolates presented a risk score below -25 may be unlikely to develop gastric cancer, as no isolate from such patients had a risk score below -24. 37. Seventeen patients have a risk score under this limit and on this basis might be considered lower priority for H. pylori eradication, depending on other factors. However, it should be emphasized that we have calculated the risk score for the same isolates used to perform the GWAS rather than with an independent validation panel.
3  Prevalence of genes highlighted by GWAS in H. pylori genomes. a Prevalence of genes containing a GWAS hit with p value < 10 -5 in three groups of isolates: non-atrophic gastritis isolates (red, n = 55 genomes), progressive toward cancer isolates (grey, n = 49) and gastric cancer isolates (black, n = 39), and defined as the ratio of number of isolates in each group harbouring the gene and the total number of isolates in each group. b Matrix of correlation of pairs of gene prevalence patterns in 143 H. pylori genomes. Red indicates that two genes have a high positive correlation of their patterns of presence and absence in all genomes examined and blue indicates a negative correlation. White indicates core genes that did not vary in prevalence in the dataset and for which correlations could not be calculated CagA through a type IV secretion system into host epithelial cells  [20].
This protein is considered as a target for vaccine development  [31, 32]. Other H. pylori genes, in which a highly significant association was found with gastric cancer included trmB (HP0747), and the less well-annotated HP0709 and HP0468. trmB, homologous to E. coli Yggh, encodes a predicted S-adenosylmethionine-dependent methyltransferase regulated by the H. pylori orphan response regulator HP1021  [33] , presumably involved in the regulation of acetone metabolism. It has also been identified as a gene with overrepresented radical substitutions in fast-evolving regions  [34]. HP0709 encodes an enzyme that is involved in either methylation of DNA and proteins or in the synthesis of branched amino acids valine, leucine and isoleucine. However, the exact function is not certain and conflicting annotations  [35]  make protein structure prediction problematic, making it difficult to compare alleles in our dataset beyond the identification of cancer-associated SNPs.