dataesr/scientific-paragraphs-categorization huggingface dataset:
Regional and global emission database At the global and the regional scales, several emission inventories were developed over the past few years in order to provide the distribution of surface emissions of different gaseous and particulate compounds. The EMEP regional emission inventory covers the period from 1980 to 2020 at a 0. 5 • resolution. It contains national total emissions for Europe and sector data and gridded emission data for modelling purposes. The sectors follow the SNAP categorization (Selected Nomenclature for reporting of Air Pollutants). Only the emissions regarding road transport (SNAP 07) are included in this study. Since there is no global emissions dataset available after 2000, the MACCity emissions have been developed within two European Commission funded projects (MACC and CityZen) as an extension of the ACCMIP and the RCP 8.
5 emissions dataset. The emissions for each compound were then linearly interpolated for each sector and each year between 2000 and 2010. A seasonal cycle was applied for each sector, and the NMVOC species anthropogenic emissions were lumped into 13 species (excluding acetylene) using the same methodology for the NMVOC speciation as used in the ACCMIP dataset. A large number of emission inventories only provide the distribution of total NMVOC emissions by lumping organic species (alkanes, alkenes, alkynes, alcohols, aldehydes, ketones, and aromatic compounds) for chemistry modelling purposes (EDGARv4. 2, ECLIPSE-GAINS-4a). We rely on the ACCMIP inventory from the ECCAD (Emissions of Atmospheric Compounds and Compilation of Ancillary Data) database, which provides a detailed speciation of VOCs, including acetylene.
The ACCMIP global emission inventory covers the historical period  in decadal increments at a horizontal resolution of 0. 5 • in latitude and longitude, based on the combination of the best global and regional available datasets. ACCMIP was developed mainly to provide consistent gridded emissions of reactive gases and aerosols for use in chemistry model simulations needed by climate models for the Climate Model Intercomparison Program 5 (CMIP5) in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment report (AR5)  (Lamarque et al. , 2010). To combine the various sources within this inventory, 40 regions and 12 sectors were used, and a set of species including CO, NO x , and total and speciated NMVOCs is provided. Speciation of NMVOC emissions is performed using the RETRO (REanalysis of the TROpospheric chemical composition)  (Schultz et al.
, 2007)  inventory and is kept constant for the whole historical period because of the lack of additional information. For all NMVOC species of the RETRO inventory, a factor was calculated only for the year 2000 by dividing the emission of each individual species by the total NMVOC anthropogenic emissions for each sector in each grid cell. For comparison with observations, we extracted the emission fluxes (kg m -2 s -1 ) of 11 individual NMVOCs as well as the CO of eight grids covering Lebanon surface area (EC-CAD database). Target sectors cover all anthropogenic sectors and the transport sector for 2000. Since the flux of the eight grids is spatially homogeneous for all compounds, we calculated a mean flux for each NMVOC to derive the emission ratio from the inventory. Comparing emission ratios from the inventory to observations collected at one measurement site stays relevant.
Determination of urban enhancement emission ratios The emission ratio for NMVOC species is the ratio of a selected VOC to a reference compound in fresh emissions without undergoing photochemical processing. We applied two methods to determine ERs for each VOC species relative to CO and acetylene. The first method consists of linear regression fits (LRFs) on data applied to summer and winter datasets, and the other method relies on extrapolating the photochemical age to zero, which was applied to the summer data (photochemical removal is negligible in winter).
For the latter, NMVOC : CO and NMVOC : acetylene ERs were derived from slopes of LRF for measurements made between 23:00 and 07:00 LT in summer when chemistry can be neglected. For isoprene, anthropogenic emissions can be derived from night-time data in the absence of its light-dependent biogenic origin  (Borbon et al. , 2001). The LRF approach assumes that the composition of urban emissions relative to CO and to acetylene does not change between day and night. For other species, the emission ratios were derived from slopes of LRF for all the data in winter and summer since there is no effect of photochemistry even during the day in summer and in order to cover the urban mixing of emissions from all the sources. The results are detailed in Sect. 5. 4 and summarized in Table  2.
The photochemical age method The photochemical method takes into consideration the photochemical processing of measured ratios of NMVOC with acetylene. We applied this method to the entire summer dataset. Following de Gouw et al. (  2005 ),  Warneke et al. (2007) , and  Borbon et al. (2013) , we assume that the photochemical removal of NMVOC species is dominated by a reaction with the OH radical. In this method, the ratio of NMVOC to acetylene is plotted vs. the photochemical age, as shown in Fig. 4. The photochemical age of air masses, t, is estimated by using the NMVOC ratios using the following equation: t = 1 [OH] (k 135TMB -k benzene ) × ln [135TMB] [benzene] t=0 -ln [135TMB] [benzene] , (1) where  [OH]  is the concentration of hydroxyl radicals, fixed to 5 × 10 6 molec cm -3 ; k 135TMB (56.
Performance of the different methods Figure  5  illustrates the performance of the ER determination methods for the summer data where ER from the photochemical age and LRF methods are compared. There is an overall very good agreement between the two methods (slope of the linear fit in red = 0. 90 and a correlation coefficient of 0. 99), showing the robustness of the methods and confirming that the selection of all the data for less-reactive species and night-time data for more reactive species for summer in the LRF method does not bias the results. The calculated determination coefficients R 2 with acetylene and CO ranged from 0. 3 for some species like ethane to 0. 9 for the majority of the species in winter, while they ranged from 0. 5 to 0. 9 with acetylene in summer, showing the importance of combustion-related sources during both seasons.
Emission ratios and seasonal variability The ER derived from all winter and summer (night-time dataset for most reactive species) datasets using the LRF method as well as the ER from the road transport established from near-field measurements  (Salameh et al. , 2014)  are summarized in Table  2. ERs relative to CO were not calculated in summer because CO data were not available for the whole campaign. The ER derived from the observations (summer and winter) is comparable to the ratios of the road transport sector at ±50 %, during at least one season, for more than 80 % of the species. This shows that urban emission fingerprint is mainly driven by traffic emissions, including ethane, which does not have any urban natural gas usage contribution in Lebanon. For other species like, C4 to C6 alkenes, styrene, and a few alkanes, in winter the differences stay within a factor of 2, except for propane where they are within a factor of 5 in summer and 3 in winter.
Evaluation of global emission inventories for anthropogenic speciated VOC emissions The emission fluxes (kg m -2 s -1 ) extracted from the EC-CAD database are converted to mole emission fluxes, and then the NMVOC ratios relative to CO and relative to acetylene are determined. When possible, the comparison held here will consider the common compounds present in the ACCMIP and MACCity global emission inventories and measured during ECOCEM campaigns, which are listed in Table  3. Important NMVOC species are present within these inventories as tracers of many anthropogenic sources, reactive species, and important precursors of ozone and SOA (Table  3 ). We consider the speciation as "reasonable" when there is an agreement within a factor of 2 between observations and emission inventory.
Emission inventory vs. observations: all anthropogenic sectors To analyse the consistency of the speciation of NMVOCs in the ACCMIP and MACCity global emission inventories, we compared the individual NMVOC relative to CO as well as acetylene ratios during summer and winter obtained by the LRF method. Figure  8  displays the ratios from the anthropogenic emissions data (ACCMIP and MACCity) and the observations in a logarithmic scale for 11 individual NMVOC species, colour coded by the NMVOC groups. Except benzene, xylenes, and long-lived alkanes, ER relative to acetylene agree within a factor of 2 between observations and inventory, suggesting that the VOC speciation in ACCMIP is better represented for more reactive VOCs such as alkenes and some aromatics. Regarding ER relative to CO, differences with ACCMIP are significant.
Conclusions Detailed measurements of NMVOCs collected at a suburban site in Beirut, Lebanon, were used to evaluate regional and global emission inventories (ACCMIP, MACCity, and EMEP) downscaled to the studied domain. These data were  The emission ratios (ERs) of individual NMVOC species relative to CO and acetylene were successfully derived from ambient measurements in summer and in winter as well as from near-field measurements for the road transport sector by applying two independent methods: the linear regression fit method and the photochemical age method. Emission ratios from both methods show a very good agreement at ±10 %. The ERs derived from the observations (summer and winter) are comparable to the ratios of the road transport sector for more than 80 % of the species. There is generally no significant seasonality in ER regardless of the species, unlike the seasonality usually observed in other cities.
To improve the quality of future VOC emission estimates, more efforts should be made toward refinement of source classification, development of representative local emission factors, comprehensive collection of activity data, and more accurate spatio-temporal characterization. Additionally, comparison of available datasets will allow a quantification of the uncertainties on emissions. At a regional and global level, long term and continuous studies integrating more than one measuring site and more specific tracers are of great interest in order to provide more reliable information, and the use of surface observations from monitoring stations could help define better speciations.
Acknowledgements. Funding for this study was obtained from  Mines Douai Institution , the  Lebanese National Council for Scientific Research, Saint Joseph University (Faculty of Sciences and the Research Council) ,  CEDRE (Coopération pour l'Évaluation et le Développement de la Recherche) , and  PICS  no. 5630  ( Programme Interorganismes de Coopération Scientifique du CNRS ). This work is also part of the  ChArMEx program. ChArMEx is the atmospheric component of the  French multidisciplinary program MISTRALS (Mediterranean Integrated Studies aT Regional And Local Scales ). ChArMEx-France was principally funded by  INSU, ADEME ,  ANR ,  CNES ,  CTC  ( Corsica region ),  EU/FEDER ,  Météo-France , and  CEA. Thérèse Salameh 's postdoc is supported by DIM  R2DS  from  Région Ile-de-France  ( 2014-09 ).
Compounds present in both ACCMIP and MACCity emission inventories. (Absence of * means that the compounds are only present in the ACCMIP emission inventory. ) ACCMIP nomenclature Compounds considered from k OH (10 -12 cm 3 y SOA MACCity  * ECOCEM database molecule -1 s -1 ) Ethane  * Ethane 0. 25 0. 1 Propane  * Propane 1. 09 0 Butanes Butane and isobutane 2. 36 and 2. 12 0. 3 and 0 Pentanes Pentane and isopentane 3. 8 and 3. 6 0. 3 and 0. 2 Ethene  * Ethene 8. 52 1. 3 Propene  * Propene 26. 3 1. 6 Acetylene Acetylene 0. 9 0. 1 Benzene Benzene 1. 22 92. 9 Trimethylbenzene Sum of 1,2,3-1,2,4-,1,3,5-trimethylbenzene 32. 7; 32. 5; 56. 7 43. 9; 20. 6; 13. 5 Toluene Toluene 5. 63 100 Xylenes Sum of m,p-xylenes and o-xylene 13. 6; 23. 1; 14. 3 95. 5; 84. 5;67. 1 CO * CO *.
Microscopy system STORM imaging was performed on a custom-built microscopy system featuring a Nikon Ti-E eclipse microscope body, a 647 nm laser (MPB Communications) and a 405 nm laser (Oxxius) for wide-field illumination and an EMCCD camera (Andor IXON 897 ultra). The microscope is equipped with a Perfect Focus System (PFS; Nikon) to prevent axial drift of the sample. Micromanager is used to control the microscope and the camera during image acquisition. The laser power is adjusted during image acquisition by controlling the AOTF (AA Optoelectronic) using a Python program  [12].
Imaging Because the polarized cells were grown on the filter placed between coverslips, the apical surface of cells is not always close to the coverslip, making STORM imaging more difficult. To find polarized cells and avoid the axial drift during STORM acquisition, the coverslip is scanned along the xand y-axes with the PFS turned on until cells are found sufficiently close to the coverslip and in the offset range of the PFS. PALM/STORM imaging is performed as previously described  [12]. Image acquisition parameters are as follows: EM camera gain 300; exposure time 100 ms; binning 1 : 1; in each experiment, a sequence of 30 000-50 000 full-sized raw images (512 × 512 pixels) was acquired. Raw images were processed to compute molecular localizations using PALMTT, a modified version of the Matlab-based single particle tracking software MTT  [14].
g. Figure  1C ). The two distributions were then compared using a Kolmogorov-Smirnov test in order to determine if the clustered model fits the data significantly better than the random model. If this difference was not significant, we categorized the molecular distributions as random and further analyzed the distribution of fitted σ s (see Supplementary Figure  S1 ). Otherwise, we categorized it as clustered and used the fitted parameters ξ to determine the cluster sizes. Simulation of random and clustered molecular distribution is shown in Supplementary Figure  S2. For each experiment, the P-value was computed on the residuals of the fits of more than 100 4 × 4 μm regions. Moreover, the analysis was performed at the same labelling densities and the comparable number of localizations was considered for each STORM dataset.
These data are in line with previous analyses showing the lack of sensitivity to cholesterol depletion of GPI-AP homoclusters in polarized epithelial cells  [5, 29]. As a positive control, we monitored the behaviour of PLAP in CHO cells upon cholesterol depletion. Although after mevinolin treatment we detected comparable reduction in total cholesterol in MDCK and CHO cells (40-45%, Supplementary Figure  S5 ), unlike MDCK cells, PLAP no longer formed clusters and assumed a random organization in CHO cells upon this  treatment (Figure  3A ). When fitting the PC-PALM curves for PLAP in cholesterol-depleted CHO cell curves with a random model, we obtained a distribution of localization precision σ s similar to that for p75-NTR in MDCK cells when using diluted fab-GFP (Figure  3B  and Supplementary Figure  S1 ), consistent with a random organization of these molecules on the membrane.
4. The transcription factor Steroidogenic Factor-1 has a pivotal role in regulating. adrenocortical cell proliferation and differentiation  (20). Its overexpression is associated to. adrenocortical tumorigenesis through regulation of a specific set of SF-1 dosage-. dependent target genes  (21, 22). One of these genes encodes VAV2, a guanine. nucleotide exchange factor (GEF) for small GTPases of the Rho family (23). We have. recently shown that VAV2 overexpression induced by an increased SF-1 dosage in ACC. is an essential factor driving tumor cell invasion (24). Herein, we present the results of a. (25). All clinical data were collected through the ENS@T database (registry. ensat. org). All. patients gave informed consent and the study was approved by ethical committees from. all participating institutions.
blinded to the clinical data (S. S. and I. S. ). Cytoplasmic staining intensity was evaluated. with a grading score of 0, 1, 2 or 3, corresponding to negative, weak, moderate and strong. intensity, respectively. The proportion of positive tumor cells was calculated for each. specimen and set up to be scored 0, 0. 1, 0. 5 or 1, if 0%, 1-9%, 10-49% or >50% of the. tumor cells were positive for VAV2, respectively. A semiquantitative H-score was then. calculated by multiplying the staining intensity grade by the proportion score  (12, 24). In all. cases analyzed, the proportion of VAV2 positive cells was always >50%, so all intensity. values were multiplied by a factor equal to 1 to yield the H-score. The cut-off point to. separate samples in high or low VAV2 expression was between H-scores <2 and ≥2.
Meier method and differences between groups were assessed with log-rank and Cox. proportional hazards statistics, using the SPSS software package (version 23. 0. 0 for Mac),. after adjustment for sex, age and tumor stage. Progressionfree survival (PFS) was. defined as time elapsed from primary resection of ACC to the first recurrence, loco-. regional or systemic. Overall survival (OS) was defined as time elapsed from primary. resection of ACC to disease-related death or last follow-up visit. In the group of patients. the same tumors was limited, with a residual standard deviation σ=0. 14 and an intra-class. correlation coefficient α=0. 95 (95%CI: 0. 92-0. 97) (Fig. S1 ).
The. PFS and OS compared to patients with concordant high-risk parameters. In our study,. intra-and interobserver variability, although further work is needed to specifically address. this question on an even larger number of cases. These results show that. fellowship from the "Novartis-Stiftung für theraputische Forschung" to S. S; Italian. Association for Cancer Research (AIRC) grants no. IG/14820/2013 to M. P. , 14411 to M. T. and IG/17691/2015 to M. L. ; ERA-NET "E-Rare" (http://www. erare. eu/) (grant 01GM1407B). to M. F. ; French National Research Agency (ANR) through the LOCALDO (ANR-15-CE14-. 0017-01) and "Investments for the Future" LABEX SIGNALIFE (ANR-11-LABX-0028-01). grants to E. L. C. R. was a recipient from Ville de Nice and Fondation de France (grant. Table 1. multivariate analysis.
Data sources We extracted data on breast cancer mortality for the Netherlands for 1987 to 2013 (international classification of diseases, 10th revision (ICD-10) code C50) from the World Health Organization mortality database. 14 n January 2015, the Integraal Kankercentrum Nederland provided the data on the incidence of in situ and invasive breast cancer by size, stage, and five year age group from 1989 to 2012 (data for all the Netherlands before 1989 are not available). The threshold diameter size for distinguishing cancer stage 1 and 2 is 20 mm. 15 16  We therefore considered in situ and stage 1 cancers as early cancers and stage 2-4 cancers as advanced cancers. This delineation between early and advanced breast cancers is common and was used in mammography screening trials (for example  5  ).
Moreover, even in the era of efficient adjuvant treatements, the survival of women with stage 1 breast cancer is close to that of women without breast cancer, whereas the transition to stage 2 cancer is associated with lower survival. 17  We obtained data on age groups invited to screening by period, the type of mammography materials, and numbers of screen detected breast cancers in the reports of the Dutch National Evaluation Team. 18 19  Trend analyses Using the European Standard Population we age standardised the crude incidence and mortality rates. 20  We computed incidence trends of all breast cancers and advanced breast cancers for women of all ages, women aged less than 50, and women aged 50 or more. We did not split trends of stage 2-4 cancers by narrower age groups because screen detected cancers among women invited to screening could affect the rate of stage 2-4 cancers in older women not invited to screening.
For estimating the influence of screening on the incidence of early cancers, we computed incidence rates for in situ and stage 1 cancers for age groups invited and not invited to screening-that is, women aged less than 50 (not invited), 50-69 (invited since 1989), 70-74 (invited since 1997), and 75 or more (not invited). For quantifying changes in breast cancer incidence and mortality over time, we computed an overall percentage change using the annual percentage change derived from a linear regression of the log transformed age adjusted rates. Analysis of changes in temporal trends was done by joinpoint regression. 21 he joinpoint regression identifies periods when distinct linear or log linear trends are separated by inflexion points (joinpoints). Joinpoint regression was performed on data from the earliest available data until the last year of available data.
Funding: This work was part of internally supported research projects of the International Prevention Research Institute. No external funding was received for this project. The International Prevention Research Institute had no influence on the methods, data analyses, or discussion. Competing interests: All authors have completed the ICMJE form and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work. Ethical approval: Not required. Data sharing: Data on breast cancer incidence by stage can be requested from the Integraal Kankercentrum Nederland. Data on breast cancer mortality are available from the WHO mortality database (www.
Illumina Sequencing In total, 2 µg total RNA per sample were used for transcriptome analysis. Library preparation and Illumina sequencing were performed by GENterprise Genomics (Mainz, Germany). For library preparation the TruSeq RNA Library Preparation Kit (Illumina, San Diego, CA) was used. All RNA samples had a RIN ≥ 6. 9. The samples were sequenced by Illumina NextSeq 500 with a calculated output of 50 million paired-end reads (2 × 150 bp) per sample. The raw Illumina data are available at the NCBI SRA database under the accession numbers biosample: SAMN062002211 to SAMN06200226 (Bioproject PRJNA360070). Since currently no annotated Phodopus sungorus genome is available, the reads were mapped against the genome of the Chinese hamster (Cricetulus griseus), that showed best compliance, using the CLC-Genomics Workbench 7.
5. 1 (Qiagen, Hilden, Germany). Only reads with intact pairs mapping with an 85% read identity and 85% read length were used for RPKM (reads per kilobase per million mapped reads) calculation  (Mortazavi et al. , 2008). Supplementary Table  1  shows the total number of reads and the number of reads mapped in pairs for each sample. Statistically significant expression changes between normothermic and torpid hamsters were calculated by an empirical analysis of digital gene expression (DGE) statistics, performing an "Exact Test"  (Robinson and Smyth, 2008). This tool is implemented in CLC-Genomics Workbench 7. 5. 1. To correct for multiple testing, a false discovery rate (FDR) correction of p-values was applied (see Supplementary Table  2 ). Transcripts with an RPKM-value ≥ 0. 1 in one of the samples were chosen for further analysis.