dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene-protein integration Among the 89 unique proteins identified, 81 corresponding genes were available in the muscle transcriptome dataset  [18]. When multiple probes mapped to the same gene, the highest differentially expressed probe according to the interaction between gestational time points and fetal genotypes was retained. Transcriptomic and proteomic analyses were carried out using the same 64 muscle samples, but 60 fetuses were available in both proteomic and transcriptomic datasets. To identify proteins with possible transcriptional regulation, for each protein within each fetal genotype, a Pearson's correlation was computed between mRNA (from the gene expression dataset  [18] ) and protein expression levels. A test was also run to assess if the correlation was significantly non-null (p < 0.
01). Multiple testing was controlled using the FDR approach  [49]. From a biological point of view, the Ingenuity Pathway Analysis (IPA, QIAGEN Redwood City, www. qiagen. com/ingenuity) software was used to check enrichment analysis (biological functions and canonical pathways), to construct bibliographic networks and regulation networks based on the identification of potential upstream regulators. A focus was on sub-network 2 at 110 dg, because of its relevant spatial correlation with two biological phenotypes of interest and a high number of proteins with a possible transcriptional regulation. Briefly, IPA constructed two separated networks. The first was based on bibliographic data in which the edges were obtained from biological links such as receptor-ligand interactions, enzyme activity on another protein, or a transcriptional factor activating the expression of targeted genes.
Identification of mRNAs correlated with proteome networks For each protein (with a corresponding gene in the transcriptomic dataset) within each extreme fetal genotype, a Pearson's correlation was computed between mRNA and protein expression levels in order to identify proteins with possible transcriptional regulation. The transcriptomic dataset was previously used in  [18]  and 60 fetuses were represented in both the transcriptomic and proteomic datasets. figure  11  shows the correlation between transcriptome and proteome in MS (x axis) and LW (y axis) genotypes. Among the 81 proteins available in the transcriptomic dataset, 31 proteins exhibited a significant positive correlation between mRNA and protein expression levels in both extreme fetal genotypes, 8 proteins in LW only (mostly involved in muscle filament sliding and located in cytosol) and 13 proteins in MS only (with no specific biological enrichment) (Figure  11  and Additional File 13).
An IPA analysis of this sub-network (using all nodes) identified possible transcription factors (TFs) affecting these proteins, in particular PPARGC1A (peroxisome proliferator-activated receptor gamma coactivator 1-alpha) with an effect on CKM (creatine kinase, muscle, cytoplasm), ATP5A1, CKMT2 and ACADVL (very long-chain specific acyl-CoA dehydrogenase, mitochondrial -involved in fatty acid beta-oxidation) (Figure  12 ). The PPARGC1A gene (available in the transcriptomic dataset) was over-expressed at 110 dg compared to 90 dg. It is interesting to note that at 90 dg, the expression of PPARGC1A was lower in LW than in MS. This difference between genotypes, which was only visible at 90 dg, suggests that the increase in PPARGC1A expression was delayed at 90 dg in LW, which could have subsequently reduced protein expression of CKM, ATP5A1, CKMT2 and ACADVL at 110 dg in LW vs.
MS fetuses. ESR1 (Estrogen receptor alpha) was also seen to affect targets mostly involved in proliferation and differentiation of muscle cells such as MACROD1 (MACRO Domain Containing 1), PDIA3 (isoform 2), SEPT2 (Septin 2), ANXA2 (Annexin A2) and GSN (Figure  12 ). The ESR1 gene (also available in the transcriptomic dataset) was over-expressed at 110 dg with a higher expression in MS than in LW at both 90 and 110 dg. IPA identified KCNJ11, a subunit of the ATP-sensitive K+ channel (KATP), as a regulator of many proteins involved in energy metabolism that were up-regulated between 90 and 110 dg and over-expressed in MS compared to LW at 110 dg. Expression of KCNJ11 at the mRNA level increased between 90 and 110 dg but did not differ between genotypes. However, KCNJ11 is not a transcription factor but is involved in the regulation of K+ ions in response to the ATP/ADP ratio  [54]  and cannot be considered as a potential upstream transcriptional regulator of muscle maturity.
It is important to note that, at both 90 and 110 dg, a large number of biological processes and cellular components overlapped between sub-networks and, within each sub-network, the enriched biological analysis often highlighted a mixture of GO terms. Indeed, it is sometimes difficult to assign a single biological function to a protein because they often play several roles in interdependent metabolic pathways. Several isoforms were also found in the proteomic dataset, and were diversely intercorrelated meaning two isoforms of the same protein could be found in two different sub-networks. The GO terms over-expressed at 90 dg were mostly involved in the actin cytoskeleton, muscle development and mRNA processing (Figure  4 ). As already highlighted in a previous transcriptomic study using the same individuals  [18] , these results are consistent with the second phase of myofiber genesis known to occur between 55 and 90 dg in pigs  [56].
scriptional regulator of muscle maturity at birth To gain further insight into the molecular machinery underlying the muscle maturation process in pigs, we undertook a guided and integrated analysis of the transcriptomic and proteomic datasets. Among the 89 unique proteins identified, 81 were available in the transcriptomic dataset. Using Pearson's correlations, mRNA and protein expression levels were analyzed to find proteins with possible transcriptional regulation. Thirty-one proteins were identified with a significant positive correlation between mRNA and protein expression levels in both extreme fetal genotypes (Pearson's correlation |r| > 0. 7) (Figure  11 ). A number of transcriptome and proteome data integration studies have already been reported e. g. in cell lines  [66] [67] [68] , plants  [69] , and mammal models  [6, [70] [71] [72] [73].
Conclusion In this study, we developed an innovative strategy combining state-of-the-art statistical and computational methods to integrate multi-omics datasets to gain a deeper insight into the late fetal maturation process. Three biological phenotypes of interest (i. e. adult fast and embryonic MyHC and muscle glycogen content) were identified as good descriptors of maturity. Some proteins, involved in mitochondrial oxidative metabolism and with a possible transcriptional regulation, were also emphasized as possible biomarkers of the maturation process. As one of the main sources of energy during late fetal life, muscle oxidative metabolism is very important at birth  [56] , and likely strongly influences muscle and whole body metabolic maturity at the time of birth. In pigs, a higher rate of death at birth has already been observed in genotypes with a lower percentage of oxidative fibers  [15, 57].
Acknowledgments We are grateful to  P. Ecolan  and  S. Tacher  for 2D gel analyses,  C. Tréfeu  and  S. Daré  for RT-PCR analyses and glycogen determination, respectively,  D. Viala  for LC-MS/MS protein identification and  Annie Vincent  for validation of CKMT2 differential expression by western blot. We also wish to thank D. Goodfellow for English revision. We also thank  Kim-Anh Lê Cao  for her relevant comments.
Funding  Animal Genetics Division (INRA , http://www. ga. inra. fr/en/), Animal Physiology and Livestock Systems (INRA , http://www. phase. inra. fr/en/)and  Région Languedoc Roussillon Midi Pyrénées  (http://www. regionlrmp. fr/)(to V. V. );  French 'Agence Nationale de la Recherche '  Porcinet  grant [ ANR-09-GENM005 , http://www. agence-nationale-recherche. fr/]. Funding for open access:  INRA ,  UMR1388 Génétique, Physiologie et Systèmes d' Élevage, Castanet-Tolosan ,  F-31326 , France.
Secondary antibodies: antirabbit or anti-mouse Alexa Fluor 647 (Invitrogen), 1 mg/ml in 5% BSA/5% goat serum/PBS. DAPI (Sigma) was used at 0. 1 mg/ml in PBS. Sections were visualized on an Axioscope with Axiovision software (Zeiss, Germany). Images were imported into Fiji/ImageJ, areas of staining were calculated from binary images by calculating pixel numbers from injected and non-injected sides, when appropriate neural tube staining was removed. A minimum of 10 sections from 3 embryos were analysed for each experiment. Statistical analysis used GraphPad Prism (version 6) software. Mann-Whitney non-parametric two-tail testing was applied to determine P values.
RNA sequencing Sequencing libraries were built according to Illumina Standard Protocols (Earlham Institute, Norwich, UK), each sample contained pooled, injected somites from 10 embryos. Sequencing was performed on one lane of a flow cell on a Illumina HiSeq2500 platform. cDNA was end-paired, Atailed and adapter-ligated before amplification and size selection. Library QC used a gel and a bioanalyser. Transcript abundances for each sample were estimated with  Kallisto (Bray et al. , 2016)  using Gallus gallus reference cDNA set (Galgal5) downloaded from Ensembl  (Yates et al. , 2016). Differential expression (DE) between antagomir-133 (AM133) and scrambled (AMscr) injected samples was calculated using DESeq  (Anders and Huber, 2010)  with an adjusted p-value significance threshold of 0. 05. The data has been uploaded to the NCBI SRA, accession number PRJNA384007.
, , 2017)). Facing the large pollutant increase since the 1990s, China started implementing stringent air quality controls in 2006 with the reduction of SO 2 emissions which was followed by the more recent successful reduction of NO x emissions (e. g. ,  van der A et al. , 2017; Li et al. , 2017; Ma et al. , 2016). Only a few studies evaluate the contemporary trends of ozone concentrations for period encompassing the recent changes in NO x emissions. Ma et al. (2016)  used ozone data collected at the Shangdianzi background station, which are representative of the NCP, to derive trends over the 2003-2015 period. They did not find any significant correlation between ozone and NO 2 trends. They also stated that the changes in VOC emissions and the VOC / NO x ratio might play a more important role in the observed increase of ozone than reduced NO titration induced by NO x emission reductions, which was in agreement with conclusions from  Sun et al.
, 2017). In order to avoid the potential impact of "versioning" of the auxiliary parameters (e. g. , the temperature profile and clouds screening) on the ozone retrieval, we apply a self-consistent procedure. Surface temperature and temperature profiles are retrieved before the ozone retrieval. A data screening procedure is applied to filter cloudy scenes and to ensure the data quality  (Dufour et al. , 2010 (Dufour et al. , , 2012;; Eremenko et al. , 2008).
We also tested different dynamical variables similar to  Wespes et al. (2017)  in the regression model, including those related to the solar activity, the dynamical processes leading to a modulation of the stratospheric circulation and of the stratospheric-tropospheric exchanges (STE), and those influencing the tropospheric ozone such as the quasibiennial oscillation (QBO) and the El Niño-Southern Oscillation (ENSO). The tested variables were as follows: the 10. 7 cm solar radio flux. The monthly means were calculated from daily data from the NOAA National Weather Service Climate Prediction center: ftp://ftp. ngdc. noaa. gov/STP/space-weather/ solar-data/solar-features/solar-radio/noontime-flux/ penticton/penticton_adjusted/listings/listing_drao_ noontime-flux-adjusted_daily. txt (last access: February 2018).
-The QBO at 10 and 30 hPa was considered and summed up. These data were sourced from http://www. geo. fu-berlin. de/met/ag/strat/produkte/qbo/singapore. dat (last access: February 2018). -The Multivariate ENSO index (MEI) was taken from https://www. esrl. noaa. gov/psd/enso/mei/table. html (last access: February 2018). -The tropopause height, given by the geopotential height for 2 PVU and the potential vorticity at 300 hPa. These data were taken from the ERA-Interim reanalysis at http://apps. ecmwf. int/datasets/ data/interim-full-daily/ (last access: February 2018). In addition to these dynamical variables, some chemistryrelated variables used as proxies for emission changes in the NCP were also tested. In particular, we used the tropospheric NO 2 columns derived from OMI  (Boersma et al.
, 2007 , available from TEMIS database http://www. temis. nl, last access: August 2018), the tropospheric HCHO columns derived from OMI  (De Smedt et al. , 2015 , 2018 , available from TEMIS database http://www. temis. nl, last access: August 2018), and the total CO columns derived from IASI  (George et al. , 2009 , available from the AERIS database http://www. aeris-data. fr/, last access: August 2018). 3 Evaluation of the reliability of the IASI derived trends.
Indeed, as mentioned in Sect. 2, different constraints and a priori profiles are used depending on the tropopause height. Trends in the tropopause height may then influence the retrieval. Moreover, depending on the depth of the troposphere, the LT ozone column calculated up to a fixed altitude (6 km) is more or less influenced by upper tropospheric and lower stratospheric air. We consider both the tropopause height derived from the IASI temperature profiles (lapse rate method) and the tropopause given by the 2 PVU geopotential from the ERA-Interim reanalysis to evaluate the evolution of the tropopause height during the 2008-2016 period. Both datasets lead to similar monthly time series with a calculated trend of 0. 02 km yr -1 , although this trend is not significant at the p>0. 05 level (p = 0.
The calculated linear trend (-0. 19 ± 0. 04 DU yr -1 ) is similar to the trend derived for data without aerosol filters (see Sect. 4), and is significant (p<10 -3 ). In addition, we based our evaluation of the retrieval stability on the analysis of the averaging kernels (AK). This is due to the fact that they integrate and translate the retrieval sensitivity to the atmospheric conditions (e. g. , temperature, pressure changes). We consider two related variables: the degrees of freedom (DOF) of the retrieval, calculated as the trace of the AK matrix, and the altitude of the maximum sensitivity of the retrieval. The DOF and the altitude of maximum sensitivity are calculated for the LT ozone column. The resulting monthly time series averaged over the NCP are displayed in Fig. 2c, d. The linear trends derived for these two variables are increasing (0.
Comparison with the independent IASI-B ozone observations The trends derived in this study are computed from the IASI-A instrument as it covers the entire 2008-2016 period. Since February 2013, the second IASI instrument aboard the Metop-B satellite has also been providing data. In this section, we use the IASI-B instrument for the 2013-2016 period and compare the monthly time series and trends to those derived from the IASI-A instrument. For the comparison of the two instruments, the monthly averages are calcu-lated from daily (morning overpass) gridded data at a resolution of 0. 25 •. The grid cells considered in the average are those for which data are available for the two instruments. Figure  4  shows the results of the comparison. A positive bias of +0. 41 DU (+2 %) is observed between IASI-B and IASI-A on average.
Looking at the results in detail, it can be noted that the winter period was not sampled (no coincidence) for 3 out of the 4 years during the beginning of the period  (2008) (2009) (2010) (2011). Filtering out the winter period (DJF) in the IASI-sonde comparison leads to a much better agreement with a small bias (-0. 12 %) and a good correlation (r = 0. 68), in addition to no significant degradation of the comparison between the beginning and the end of the period (Table  1 , last row). However, removing the winter season in the comparison for the Asian sondes does not improve the comparison, except slightly for Sapporo. We also compared IASI-B and IASI-A LT ozone columns to the ozonesonde measurements for the 2013-2016 period, using only days for which observations were available for the three datasets.
g. ,  Ebojie et al. , 2016; Heue et al. , 2016; Oman et al. , 2013; Wespes et al. , 2016 Wespes et al. , , 2017 Wespes et al. , , 2018)). The long-range transport of ozone or precursors could also   et al. , 2017; Li et al. , 2017). The emission reduction of NO x species, which are ozone precursors, has been observed in the satellite NO 2 columns since 2013, as shown in Fig. 8  and reported in very recent inventories  (Zheng et al. , 2018). On the contrary, the emissions of anthropogenic volatile organic compounds (VOCs) are not regulated and do not show any decrease in recent years. Zheng et al. (2018)   2015) , available from the TEMIS database, a continuous increase is observed starting in 2013 and extending to 2016 (Fig. 8 ). It is worth noting that the increase is less observable in a more recent version of the HCHO product, except for the last year  (De Smedt et al.
A recent study shows that the downward mixing of free tropospheric ozone may largely impact the morning level of ozone in the surface layer, with the surface ozone level on one day likely being related to ozone at higher levels the day before  (Wang et al. , 2017a). In that case, the calculated trend is even more negative but still poorly significant (Fig. 9h ). These results illustrate the sensitivity of the trend calculation to the sampling (day/night, clear-sky conditions) and stress the need to compare datasets with different temporal sampling frequencies over subsets of data with consistent sampling before drawing conclusions. In addition, the answers given by the surface and satellite observations are inconsistent: surface measurements show positive and/or non-significant trends and satellite observations show a significant negative trend.
Comparisons with Asian ozonesondes show a bias ranging from -10 % to -15 %. The limited sampling and changes in the instrumentation of three out of five sondes during the study period do not allow one to clearly evaluate and make firm conclusions concerning the reliability of IASI trends compared with those of the sondes. In general, comparisons with independent measurements (sondes or surface in situ) performed in this study show the importance of the sampling regarding the conclusions drawn. Differences in the sampling can significantly affect the trends calculated and thus the conclusions. One recommendation when comparing datasets with different sampling parameters would be to perform the comparison over subsets of data with similar sampling methods. According to the evaluation carried out, the trends derived from the IASI-A observations seem fairly reliable and can be used to study the LT ozone trend over the NCP.
, 2015). Code and data availability. The IASI observations (level 1C) are available from the AERIS data infrastructure (http://www. aeris-data. fr/, last access: November 2018). The IASI ozone product retrieved from the level 1C data is available, upon requested from Gaëlle Dufour (dufour@lisa. u-pec. fr), for the Asian domain considered in this study between 2008 and 2016. The NO 2 and HCHO tropospheric columns from OMI are available at http://www. temis. nl (last access: November 2018). The ozonesonde data are available from the WOUDC database (https://woudc. org/, last access: November 2018), except for the Beijing station. These data can be requested directly from the PI of the station, Yi Liu (liuyi@mail. iap. ac. cn). The surface measurements at the SDZ station are available upon request from the PIs of the station, Xiaobin Xu (13311298991@189.
Acknowledgements. The authors are grateful for the essential support from the  Agence Nationale de la Recherche (ANR)  through the  PolEASIA  project ( ANR-15-CE04-0005 ). The IASI mission is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). This study was financially supported by the  French Space Agency -CNES  (project " IASI/TOSCA "). The authors acknowledge the AERIS data infrastructure (https://www. aeris-data. fr,last access: August 2018) for providing access to the IASI Level 1C data, distributed in near-real-time by  EUMETSAT  through the EUMETCast system distribution. The authors acknowledge the dataset producers and providers of data used in this study: the  LATMOS/ULB  for the provision of the IASI CO total columns through the AERIS database; the ozonesonde data used in this study were mainly provided by the  World Ozone and Ultraviolet Data Centre (WOUDC)  and are publicly available (see http://www.
Carbon dioxide (CO 2 ) and methane (CH 4 ) are important atmospheric greenhouse gases (GHG) and, therefore, classified as essential climate variables (ECVs). Previously, satellite-derived atmospheric CO 2 and methane CH 4 ECV data sets have been generated and made available via the GHG-CCI project of the European Space Agency's (ESA) Climate Change Initiative (CCI, http://www. esa-ghg-cci. org/). The latest GHG-CCI data set, Climate Research Data Package No. 4 (CRDP 4), covers the time period 2003-2015 and is available since February 2017. Currently, the production and provision of these data sets is being continued (pre-)operationally via the Copernicus Climate Change Service (C3S, https://climate. copernic us. eu/), which is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission.
The C3S satellite GHG sub-project (C3S_312a_Lot6) is led by University of Bremen supported by University of Leicester (UK), SRON (The Netherlands) and CNRS-LMD (France). The first Climate Data Record (CDR) data set produced and delivered within the C3S framework covers the time period 2003-2016 and consists of column-average dryair mole fraction CO 2 and CH 4 products, i. e. , XCO 2 and XCH 4 , from SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. Furthermore, mid-tropospheric CO 2 and CH 4 mixing ratios from IASI Metop-A and Metop-B are part of this data set. It is planned to extend this data set each year by one additional year. The data products are available via the Climate Data Store (CDS) of C3S. Here a short overview about this new Earth Observation data set is presented.
Data Products The C3S satellite-derived CO 2 and CH 4 data set consists of three types of data products (see  [7]  for details): and XCH 4 products (at 5°× 5°spatial resolution) in Obs4MIPs format (see  [7] ) as generated from the merged L2 products (see Figs. 1, 2 ). The products are listed in Table  1. Each individual product has a product ID indicating if it is a CO 2 or CH 4 product, the corresponding satellite (SCI SCIAMACHY, GOS GOSAT, IASA/IASB IASI Metop-A or B, AIR AIRS) and the used retrieval algorithm.
Data Quality The data quality of the XCO 2 and XCH 4 data products has been estimated by comparisons with the corresponding ground-based data products of the Total Carbon Column Observing Network (TCCON)  [12]. The overall L2 products comparison results are shown in Fig. 3  for the XCO 2 and in Fig. 4  for XCH 4. As can be seen, the single footprint random error is about 2 ppm for XCO 2 and 50-90 ppb for SCIAMACHY XCH 4 and around 20 ppb for GOSAT XCH 4. The relative accuracy is around 0. 5 ppm for XCO 2 and around 10 ppb for SCIAMACHY XCH 4 and around 5 ppb for GOSAT XCH 4. Stability in terms of linear bias drift is high for all products. The IASI and AIRS mid-tropospheric products have been compared with aircraft observations  [8]  and it has been estimated that the single footprint random errors are around 1 ppm for IASI CO 2 , around 1.