dataesr/scientific-paragraphs-categorization huggingface dataset:
We then performed clonogenic assay in FOXN3 or FOXM1 silenced GSLC to evaluate their respective roles in the control of intrinsic cellular radiosensitivity. The survival after irradiation of GSLC (GC1 and GC2) was significantly increased after FOXN3 silencing (Figure  3B ) while FOXM1 inhibition significantly radiosensitizes GC1 and GC2 cells (Figure  3C ). These data demonstrated that at least two members of the FOX family regulate GSLC radiosensitivity in an opposite manner, depending on their respective FGFR1-dependent regulation of expression. In cancer cells, FOXM1 forms a protein complex with MELK  [12]. MELK-regulated phosphorylation of FOXM1 transcriptional activity and induces the expression of various mitotic regulators such as survivin, Aurora B and CDC25B. Based on the evidence that FOXM1 directly interacts with MELK in glioblastoma stem-like cells, MELK may orchestrate the priming event of the complex  signaling toward p53, VEGF, and Wnt/β-catenin in cancers including GBM  [13].
FGFR1/FOXM1/EMT genes is predictive of glioblastoma relapse for patients treated with chemo-radiotherapy protocol To determine whether this previously defined new pathway of GSLC radioresistance involving FGFR1, FOXM1, MELK, GLI2, TWIST1 and ZEB1 may be crucial for the in vivo GBM response to the standard treatment associating surgery and radio-chemotherapy, we investigated whether the associated expression of these six genes may be predictive of overall survival for patients treated with chemo-radiotherapy protocol. Using TCGA cohort (n = 184), we calculated a risk score from a Cox model including the six genes for each patient in the database and divide them into a high-risk group and a low-risk group by taking the mean value of risk score. Univariate analysis showed that our risk score and risk groups were significantly associated with overall survival (risk score: HR = 2.
72 [1. 66; 4. 46], p = 7. 7e-05 and High versus Low risk: HR = 1. 85 [1. 28; 2. 68]; p = 0. 00119 respectively) (Figure  5A ). The median overall survival in the low-risk group was 18. 4 months versus 14. 0 months for the high-risk group (Figure  5A ). Multivariate analysis showed that our six genes association remains a strong prognostic factor, independently of GBM common clinical and biological parameters as MGMT methylation status (HR = 2. 82; p = 6. 9e-05) (Table  2 ). Good prognostic ability was also found in the Rembrandt dataset (risk score: HR = 2. 72  [1. 69; 4. 38] ; p = 3. 86e-05 and High versus Low risk: HR = 1. 59 [1. 17; 2. 16]; p = 0. 00336 respectively) (Figure  5B ). These data led us to identify a six genes set defined from our in vitro results, involved in GSLC Cells derived from one GBM biopsy specimen (GC2) were transfected with siFGFR1(11) (GC2FGFR1(-)) or scramble control (Ctl).
Other mesenchymal transition genes have been described in modulating glioma stem cells radioresistance  [29]. Also, it has been recently shown that FGFR1 functions as a driver of EMT-associated drug resistance  [30]. Our present results demonstrate that the FGFR1 pathway regulating GSLC radioresistance also affect GSLC migration in vitro strongly suggesting that FGFR1-dependent glial-mesenchymal transition may be linked to FGFR1-mediated GSLC cellular response to ionizing radiation through FOXM1, GLI2, ZEB1 and TWIST1. To confirm the implication of FGFR1 pathway in GBM radioresistance in vivo, we raised the hypothesis that FOXM1, MELK, GLI2, TWIST1, ZEB1 could be involved in the worse survival prognosis of patients treated for GBM with chemo-radiotherapy. Our analysis of patients included in TCGA database confirmed that the combined expression of FGFR1/FOXM1/MELK/GLI2/ZEB1/TWIST1 was significantly associated with overall survival of patients treated with chemo-radiotherapy.
The same conclusion has been drawn from Rembrandt database confirming that FGFR1-dependent pathway has a fundamental role in the GBM response to the treatment. Furthermore, these 6 genes set gathered three qualities: it was discovered from bench data obtained from GSLC established from patient samples; it involves a short list of genes; and it remains a solid prognostic factor, independently of GBM clinical or already known biological parameters. It could reasonably be hypothesized, even if complementally analyses should be performed, that this genes set might be useful to discriminate GBM patients whose tumor will respond to an association of a specific FGFR1 inhibitor with radiotherapy. In conclusion, this work associated with our previous one  [7] , fully establishes that FGFR1 is a key target to inhibit in the aim to increase the sensitivity of GBM to the radiotherapy.
Affymetrix analysis The affymetrix chips were standardized by the RMA method (R software version 3. 3. 2, Bioconductor version 3. 4). The differences between the different conditions were tested using an ANOVA (R software version 3. 3. 2) corrected for multiple tests using the Benjamini & Hochberg method. The gene lists were compared to different databases GeneOntology C5  [36] , Reactome  [37] ) using the ZE Autocompare software based on an exact Zelen test  [38]. All data are available at the National Center of Biotechnology Information (NCBI) GEO Database under the series number GSE116414.
Statistical analysis Student's test was performed to compare the means of values from different experiments. Differences were considered statistically significant at p < 0. 05. Correlations between FGFR1 expression and other genes in the TCGA dataset were assessed using Spearman's rank correlation coefficient. For survival analysis, using the glioblastoma database of TCGA (http://genome-cancer. ucsc. edu/), we focused on patients treated with standard chemoradiotherapy for primary GBM, excluding patients with prior glioma history (n = 184 patients). Overall survival rates were estimated using Kaplan-Meier method and univariate analysis were performed using Cox proportional-hazards model or log-rank test. Risk score was created from the linear predictor Xβ given by the multivariate Cox model including six genes (FGFR1/FOXM1/MELK/GLI2/ZEB1/TWIST1), where X is the expression matrix and β is the vector of coefficient estimates (Supplementary Table  3 ).
Risk groups (poor versus good prognostic) were obtained by taking the median value of the risk score. A multivariate Cox regression analysis was used to adjust on standard clinical parameters. To confirm the prognostic ability of this gene set, we have fit a new Cox model on the Rembrandt dataset (n = 178) and built a new risk score for this cohort. Two-sided p-values of less than 0. 05 were considered statistically significant. Statistical analyses were performed using R 3. 4. 0 software.
The lowest metric values reported are in Australia, New Zealand, southern parts of South America and some northern parts of Europe, Canada and the USA. Regional-scale assessments showed, for example, significantly higher AOT40 and W126 values in East Asia (EAS) than Europe (EUR) in wheat growing areas (p < 0. 05), but not in rice growing areas. In NAM, the dominant trend during 1995-2014 was a significant decrease in ozone, whilst in EUR it was no change and in EAS it was a significant increase. TOAR-Vegetation provides recommendations to facilitate a more complete global assessment of ozone impacts on vegetation in the future, including: an increase in monitoring of ozone and collation of field evidence of the damaging effects on vegetation; an investigation of the effects on peri-urban agriculture and in mountain/upland areas; inclusion of additional pollutant, meteorological and inlet height data in the TOAR dataset; where not already in existence, establishing new region-specific thresholds for vegetation damage and an innovative integration of observations and modelling including stomatal uptake of the pollutant.
igacproject. org/TOAR). Initiated in 2014, TOAR's mission is to provide the research community with an up-to-date scientific assessment of the global distribution and trends for tropospheric ozone, from the surface to the tropopause. TOAR's primary goals are: i) produce the first tropospheric ozone assessment report based on all available surface observations, the peerreviewed literature and new analyses, and ii) generate easily accessible, ozone exposure metrics at thousands of measurement sites worldwide. Through the TOAR Surface Ozone Database (https://join. fz-juelich. de), these ozone metrics are freely accessible for research on the globalscale impact of ozone on climate, human health and crop/ecosystem productivity  (Schultz et al. , 2017 , hereinafter referred to as TOAR-Surface Ozone Database).
The assessment report is organized as a series of papers in a Special Feature of Elementa: Science of the Anthropocene. In this paper, TOAR-Vegetation, we provide an overview of the vegetation metrics available through the TOAR database, focussing on global and regional distributions and trends for three widely used metrics. Ozone levels can be high in rural and semi-rural areas where ecosystems provide essential services such as agricultural production, forest production and water supply  (Mills et al. , 2013). Here, ozone impacts on ecosystems will include direct plant toxicity and cell damage, indirect effects mediated by changes in individual organisms and their ecological interactions, and changes in the rate and nature of chemical and biological processes  (Ainsworth, 2016; Ainsworth et al.
The stomatal flux of ozone is calculated at the leaf level at the top of the canopy using the DO 3 SE (Deposition of Ozone for Stomatal Exchange) model which considers the varying effects of plant phenology, climate and soil moisture on stomatal aperture  (Emberson et al. , 2000; Mills et al. , 2011a, b). The flux method is considered to be more biologically relevant than concentration-based methods  (CLRTAP, 2017; Mills et al. , 2011a, b) , and provides a better fit to field evidence of effects  (Braun et al. , 2014; Mills et al. , 2011a). Currently, the TOAR database only contains ozone metrics based on ambient observations, and thus it is not possible to include global maps of the stomatal uptake of ozone in this paper. Instead, we present a first global analysis of the distribution of ozone metrics based on ambient observations to give an indication of the potential damaging effects of ozone.
These results are presented for different global regions and climatic zones on the understanding that data coverage varies and in some of the driest areas, there may be substantial stomatal limitation of ozone uptake in C3 plants such as wheat, rice and many perennial species, as plants reduce stomatal apertures to lower water loss. In such areas, the effects on vegetation may not be as high as expected based solely on the magnitude of an ozone metric based on ambient observations. An extensive range of ozone metrics relevant to effects on vegetation has been collated in the TOAR database (see Table  1 ), including those averaged or accumulated over a range of time-periods. In this paper, we focus on three vegetation metrics that are used in national and international standards for protecting vegetation and/or in global or regional impact modelling.
Note 1: The timing of monthly periods are specified by climatic zone for each metric (see Section 2. 3). Note 2: Information on how to access the TOAR database is provided in the Data Accessibility Statement. Note 3: Whilst CLRTAP (2017) defines daylight hours as the period when clear sky radiation > 50 W m -2 , TOAR uses a threshold of 5° solar elevation angle as a surrogate for defining daylight hours. the cumulative indices AOT40 and W126, the third metric included in TOAR-Vegetation is M12, the mean ozone value for the hours 08:00-19:59 (local time), averaged over 3-or 6-months. M12 is not included in air quality standards but was selected for inclusion here for other reasons. Firstly, M12 has been used in global and regional model-based assessments of crop losses (e. g. Avnery et al. , 2011; Hollaway et al.
The main aims of this paper are to: • Summarize the present-day global distribution of ozone using vegetation metrics of interest to researchers and policy makers, including identifying areas where current ambient ozone standards are exceeded and vegetation is likely to be at risk of damage; • Inform the vegetation research and policy community of the regions of the world where ozone air quality is improving and where it is declining; • Identify areas of the world where there is limited or no ozone data currently available for assessing risk to vegetation; • Consider sources of uncertainty in the TOAR-Vegetation data and make recommendations for further development of the TOAR database.
Definition of vegetation metrics To facilitate comparison of published studies with the new analyses in TOAR-Vegetation, based on the ozone metrics in the TOAR Surface Ozone Database, we briefly describe the choice of ozone units reported in this paper. When referencing an observation in ambient air, TOAR follows World Meteorological Organization guidelines  (Galbally et al. , 2013)  and uses the mole fraction of ozone in air, expressed in SI units of nmol mol -1. Ozone metrics for veg-etation (e. g. AOT40 and W126) have typically been developed using the mixing ratio unit of parts per million (ppm) or parts per billion (ppb) which, in the case of ozone, refers to the number of ozone molecules per million or billion moist, ambient air molecules in a fixed volume. In reference to units of nmol mol -1 and ppb,  Galbally et al.
Many vegetation metrics are included in the TOAR database (Table  1 ), covering a range of time-periods. Results presented in this paper are restricted to the following three metrics: M12 is the 12-h mean at ozone monitor inlet height, averaged over the period 08:00-19:59h (local time) for 3-months (wheat, rice) or 6-months (perennial vegetation), unit: ppb. AOT40 presented here uses the CLRTAP definition and is the sum of the difference between the hourly mean ozone value and 40 ppb for all daylight hours over a specified time-period (3-months for wheat and rice, 6-months for perennial vegetation), unit: ppb h. Furthermore, the ozone value should be representative of the top of the plant canopy, usually considered to be 1 m for crops and grassland, and 20 m for trees  (CLRTAP, 2017). As TOAR does not include information on the height at which ozone is measured, AOT40 has been calculated in TOAR from the ozone observations at the height of the ozone monitor inlet and not from the canopy height.
W126 is the sigmoidally-weighted sum of all hourly ozone values observed during a specified daily and seasonal time window, where each hourly ozone value is given a weight that increases from zero to one with increasing values, units: ppb-hrs (TOAR-Metrics,  US Federal Register, 2015). The strongest weight is given to values over 80 ppb. W126 is calculated using hourly mean ozone values at ozone monitor inlet height. In this paper, W126 is accumulated over the hours 08:00-19:59 (local time) for 3-months for wheat and rice, and 6-months for perennial vegetation. The algorithm for calculating W126 is provided in TOAR-Metrics. Specification of the ozone monitor inlet height was not requested when populating the TOAR database with hourly ozone observations. As the ozone monitor inlet height at monitoring stations varies globally, this variation will provide some uncertainty in comparisons of M12, AOT40 and W126.
, 2007a; Mills and Harmens, 2011). Although less sensitive to ozone than wheat, rice is an equally important staple food crop. Its inclusion in TOAR together with wheat, allows wide representation of the agricultural areas of the world and extends coverage to tropical climates. Together, wheat and rice are grown on nearly 400 million hectares globally (year 2016, FAOSTAT), and cover almost all of the nonurban sites that meet the TOAR Vegetation metric inclusion criteria. Other staple food crops such as soybean and maize were considered for inclusion. However, their distribution largely overlaps with that of wheat (particularly for soybean) and/or they are widely grown in areas of Africa or South America that are very sparsely covered by the TOAR dataset. The third vegetation category is more general and consists of long-living or perennial vegetation, including grasslands, forested areas, wetlands and heaths, and long-lived crops.
Timing of averaging and accumulation windows The three vegetation ozone metrics are accumulated over 3-months (for wheat and rice) or 6-months (for perennial species) during daylight hours (AOT40) or 12 h (08:00-19:59, M12 and W126). These time-periods reflect the main growing seasons or sensitivity windows for the different types of vegetation. Metrics are accumulated or averaged during daylight or 12 h as these are the usual hours in most climate regions when the stomatal pores on the leaf surface are open and taking up ozone. For some vegetation types and climatic regions, there can be some stomatal conductance at night (e. g. Matyssek et al. , 1995). Night-time values are also included in the TOAR database but are not considered in this analysis. For the climatic zones in the northern and southern hemispheres (NH and SH, respectively), the 3-month periods when wheat and rice are likely to be actively growing and most sensitive to ozone are identified per climatic zone whilst for perennial species, 6-month growth seasons are identified by hemisphere (Table  3 ).
TOAR has taken a climatic zone approach for 3-month metrics, using hemisphere-specific timings for non-polar climatic zones taken from a spatially available (grid size 5 by 5 arc minutes) dataset (EUSOILS, 2015), illustrated in Figure  1. This classification has been applied by  IPCC (2006) , and the gridded data allowed identification of the climatic zone for each TOAR monitoring site. Threemonth intervals have been allocated to each climatic zone relevant for wheat and/or rice ending two weeks before the mean harvest date, with the time-period reflecting the most common growth cycle for that climate (e. g. for winter rather than spring wheat in W Europe). The harvest date per climatic zone has been established by overlaying maps produced by the USDA Major World Crop Areas and Climate Profiles (MWCACP) and cross-checking data with  Sacks et al.
g. Anav et al. , 2017). The TOAR community has agreed to use hemisphere-specific rather than climatic-zone specific periods for this vegetation type ensuring that metrics are accumulated or averaged over a fixed month range in all areas. The 6-month growth seasons selected are April-September for the NH, and October-March for the SH, with these time intervals described as "summer" in the TOAR database. Although the "summer" period represents a compromise, it is expected that these 6-month time intervals would capture the main growing period for long-living vegetation and trees in most areas of the world.
Site selection Full information on the TOAR dataset, including data selection and quality assurance is provided elsewhere in this special feature (TOAR-Surface Ozone Database). All data indicating the current or "present-day" values for ozone metrics are a mean of the values per site for 2010-2014, with a minimum inclusion criteria of at least 3 years of data (3 "growing seasons") with data capture > 75%. Trend analysis covers the periods 1995-2014 and 2000-2014, with any site required to have at least 16 or 11 years of data, respectively, and not more than 2 years missing at either end of the interval. For inclusion in TOAR-Vegetation, vegetation-specific site selection has been conducted as follows: Perennial species (6-month metrics): Includes all non-polar sites that do not meet the criteria for the TOAR category "urban", including rurallow elevation, rural-high elevation and unclassified sites (see site criteria in TOAR-Surface Ozone Database).
Wheat and rice (3-month metrics): Sites where wheat or rice are grown have been identified by first extracting production data in tonnes (t) for irrigated and non-irrigated crops for the year 2000 from the GAEZ data portal v. 3. on a 5 × 5 arc minute grid, and then selecting those sites with rice or wheat production >0. 099 thousand t year -1 (per grid cell). Whilst acknowledging that there will be some inaccuracies introduced by the data being from the year 2000, this is to our knowledge the most recent year for which a sufficiently detailed and suitable spatial dataset on crop distribution is available at the global scale. In some areas of the world, sites classified as urban have a mixture of urban and agricultural areas and meet the inclusion criteria for rice or wheat based on the amount of production.
Statistical analysis of regional differences in metrics To investigate differences between the three regions with the most data (EAS, EUR, NAM), general linear mixed models (with normal error) were run using the lme4 package  (Bates et al. 2015)  of the statistical software R (R Core Team 2018). As there were multiple sites per country per region, 'Country' was included as a random effect in the model. Model residual plots were examined and transformations were carried out where necessary, in order to meet assumptions on normality and heteroscedasticity. Likelihood Ratio Testing (using the ' drop1' command) was used to determine the best model and derive a p-value for the categorical variable 'Region. ' Post hoc testing (using a Tukey test) was carried out with the 'multcomp' R package  (Hothorn et al.
Data availability Overall, 3324 global sites met the criteria for inclusion for perennial vegetation for present-day ozone (mean for 2010-2014) (Table  4 ), 2269 sites for wheat (Table  5 ) and 991 for rice (Table  6 ). For the perennial vegetation metrics, 38. 5% have a full 5 years of data, 22. 6% have 4 years of data and 38. 9% have three years of data available for derivation of the mean. The corresponding values for wheat are 29. 2% (5 years), 20. 5% (4 years) and 50. 2% (3 years), whilst data availability for rice is 32. 6% (5 years), 34% (4 years) and 33. 4% (3 years). Data representation is greatest for NAM, EUR and EAS (see Table  4  for region definitions), with sites from these regions making up 97% of the global dataset for the three vegetation metrics. The highest proportion of the metric data for perennial vegetation (43.
W126 for perennial vegetation W126 is similar in concept to AOT40, but provides stronger weighting to the highest ozone values (see TOAR-Metrics). As a consequence, the spatial distributions are similar for the two metrics even though the values in ppb h of AOT40 cannot be directly compared with the ppb-hrs of W126 (compare Figures  4  and 5 , see Section 3. 2. 5). The higher weighting to the highest ozone levels in W126 results in a larger contrast between areas having higher and lower exposure for W126 than indicated for AOT40. In the three regions with greatest data representation, the mean values of W126 are highest for EAS (18572 +/-8153 ppbhrs, N = 612), followed by NAM (13832 +/-9755 ppb-hrs, N = 1165) and then EUR (12295 +/-8552 ppb-hrs, N = 1462, Table  4 ). These regional differences are significant (p = 0.
04, Text S-1), with means for EUR being significantly lower than for EAS (p = 0. 04). These regions are ranked in the same order as for AOT40 but with a larger difference between EAS and EUR, even though the mean M12 values are relatively similar in all three regions (39. 5-41. 3 ppb). Frequency distributions reflect the regional differences in W126, with the highest proportion of occurrences for EAS in the 15000-20000 ppb-hrs class (28. 1%) compared to 10000-15000 ppb-hrs for NAM (25. 0%) and EUR (23. 4% of sites, Figure  2 ). In other regions of the world less well represented in the dataset such as MDE and MCA, W126 is also relatively high (32937 +/-16058 ppb-hrs, N = 7, and 7375 +/-10304 ppb-hrs, N = 13 respectively, Table  4 ). The lowest mean W126 values are found in the SH in SEA and PAN (5767 and 1917 ppb-hrs for 2 and 36 sites for the two regions, respectively).