dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Processing. Since gene expression in EVCTs and VCTs was detected using different microarray platforms, different procedures were followed for data processing. For EVCT gene expression, which was quantified using the GeneChip (U133A 2. 0, Affymetrix) application, data processing used the following filter thresholds: (i) percentage of missing data was no more than 50%, (ii) threshold to identify up-and downregulated genes for statistical comparison was set to a fold change of 1. 5, (iii) maximum false discovery rate (FDR) was set to 5%, and (iv) fold change of one gene was equal to mean of treated groups minus mean of control groups and then divided by the minimum value from all (fold change = ðmean ðtreatedÞmean ðcontrolÞÞ/minimum ðtreated, controlÞ)  [22]. For VCT gene expression, which was measured using the SHDZ GeneChip/Stanford University (GPL21609) application, data processing used the following filter thresholds: (i) background-corrected data were log2transformed and subjected to the Loess normalization method  [11] , (ii) differentially expressed genes (DEGs) were determined via the significance analysis of microarrays (SAM) method  [26] , and (iii) the maximum false discovery rate (FDR) was set to 1%, without a fold change threshold imposed  [27].
GO and Pathway Enrichment Analyses. ClueGO is a Cytoscape plug-in application for the functional classification of genes  [28]. Our analysis used Cytoscape version 3. 7. 1 (The Cytoscape Consortium, New York, NY) and ClueGO version 2. 5. 4 (released 28 Feb 2019), with the simultaneous update of gene ontology (GO) terms. Using ClueGO, we recovered the GO terms associated with the dataset of all DEGs as well as of up-or downregulated DEGs only; this same application was also used for KEGG and Reactome pathway analysis. GO terms were compared between EVCTs and VCTs using the R package clusterProfiler (version 3. 9, synced to latest GO terms and pathways)  [29]. For term comparison in clusterProfiler, 10 category terms for each group were selected for inclusion in charts. Instead, ClueGO analyses were based on approximately 30 terms per group in order to generate more detailed visualizations.
P values lower than 0. 05 identified significant enrichment. 2. 6. Protein-Protein Interaction (PPI) Network. The STRING database (https://string-db. org) was used to analyze the interactions of DEG-encoded proteins and construct a PPI network. For this, the significant confidence score was set to greater than 0. 4. Cytoscape was used to visualize and organize the PPI network. Proteins interacting with PPARγ or RXRα were indicated by different colors, and shapes were used to represent different groups. Binding site interactions between transcription factors and target genes were predicted by the Cytoscape plug-in iRegulon (based on the TRANS-FAC database; version 1. 3). Putative regulatory regions were defined as 10 kb around transcription starting sites. The FDR was set to 0. 1% to verify the interaction.
Gene Expression Profiling of RT-EVCTs and RT-VCTs. Microarrays were used to characterize gene expression in EVCTs and VCTs with or without rosiglitazone treatment. Our microarray data have been deposited in the Gene Expression Omnibus public repository (https://www. ncbi. nlm. nih. gov/geo/; EVCT microarray data under accession number GSE28426, VCT microarray data under accession number GSE137434). Gene expression profiles of the rosiglitazone-treated (TRT) samples of EVCTs and VCTs were normalized (Figure  2(a) ). Four of the five independent RT-EVCT samples yielded consistent results, with one sample appearing slightly different; instead, all five independent RT-VCT samples yielded similar results. Next, DEGs were detected based on thresholds for both fold change in expression levels and FDR.
Gene Ontology and Pathway Terms of all DEGs from RT-EVCTs and RT-VCTs. The entire set of DEGs from RT-EVCTs and RT-VCTs was separated by cell type of origin and submitted independently to ClueGO with the default parameters. GO and pathway enrichment were set up for analysis. DEGs were classified by three ways: by GO biological process, GO molecular function, and GO cellular component. Enriched pathways were identified through a search of the KEGG and Reactome databases. The results are visualized in Figure  3. Among the DEGs identified in RT-EVCTs, the main GO biological processes represented were "negative regulation of epithelial cell apoptotic process," "long-chain fatty-acyl-CoA biosynthetic process," and "phosphatidylcholine biosynthetic process. " For the same group of DEGs, the GO molecular functions were mainly classified as "alpha-tubulin binding," "wide pore channel activity," "positive regulation of cold-induced thermogenesis," "glutathione transferase activity," "long-chain fatty acid binding," "regulation of cell adhesion mediated by integrin," and "positive regulation of nonmotile cilium assembly.
" Finally, the GO cellular component that was most associated with these DEGs was "desmosome. " In the pathway enrichment analysis of RT-EVCTs, DEGs were mainly associated with the terms "HIF-1 signaling pathway," "p53 signaling pathway," "glutathione metabolism," "NRAGE signals death through JNK," "PPAR signaling pathway," "plasma lipoprotein assembly," and "remodeling and clearance". In the analysis of GO terms associated with the RT-VCT dataset, DEGs were mainly involved in the following biological processes: "regulation of receptor biosynthetic process," "negative regulation of nucleotide metabolic process," "cyclic nucleotide biosynthetic process," and "negative regulation of B cell apoptotic process. " The molecular functions of this : GO and pathway terms associated with all DEGs in RT-EVCTs and RT-VCTs.
All DEGs were submitted separately according to their cell type of origin to ClueGO with the default parameters. GO and pathway enrichment were set up for analysis. DEGs were classified by three ways: by GO biological process, GO molecular function, and GO cellular component. The KEGG and Reactome database was consulted to determine pathway enrichment. An exhaustive list of all terms (including those not shown above) can be found in supplementary materials (Tables  S1-S8 ). DEGs: differentially expressed genes; RT-EVCTs: rosiglitazone-treated extravillous cytotrophoblasts; RT-VCTs: rosiglitazonetreated villous cytotrophoblasts; GO: gene ontology; KEGG: Kyoto encyclopedia of genes and genomes.
GO and Pathway Terms Associated with Downregulated DEGs in RT-EVCTs and RT-VCTs. DEGs that were downregulated in RT-EVCTs and RT-VCTs with respect to controls were submitted to ClueGO using the same procedure as described above. Results are visualized in Figure  5. In RT-EVCTs, downregulated DEGs were mainly associated with the GO biological process "positive regulation of small molecular metabolic process;" the GO molecular functions "membrane fusion," "regulation of epithelial cell migration," "response to estriol," and "protein kinase binding;" and the GO cellular component "phosphorylase kinase complex. " From the analysis of pathway enrichment based on the KEGG and Reactome database, downregulated DEGs in RT-EVCTs appeared to be mainly associated with pathways linked with "glycogen breakdown," "influenza infection," "protein processing in endoplasmic reticulum," and "regulation of actin cytoskeleton".
Comparison of GO Terms Associated with Tissue-Specific or Tissue-Generalist DEGs. Next, we wanted to determine the extent to which the cellular processes affected by rosiglitazone treatment were specific to either EVCTs or VCTs, and which instead were present in both tissue types. To do this, we characterized the up-and downregulated DEGs of RT-EVCTs and RT-VCTs separately using clusterProfiler, using information from the GO and KEGG databases, as well as the Disease Ontology (DO) and Disease Gene Network (DisGeNET) databases. Terms appearing in at least three columns were thought important in both, while terms appearing only in the RT-EVCT or RT-VCT dataset were labelled tissue-specific; significance was determined by P values less than 0. 05. In both RT-EVCTs and RT-VCTs, the GO biological processes "regulation of endothelial cell migration," "noncanonical Wnt signaling pathway," "receptor metabolic process," "negative regulation of protein phosphorylation," and "metabolism process" appeared to play important roles.
Both tissue types shared the significant GO cellular components "smooth endoplasmic reticulum," "ruffle," "transcription factor complex," "apical plasma membrane," "lumen," and "cell-cell junction. " Instead, the component terms "betacatenin destruction complex," "M band," "integral component of lumenal side of endoplasmic reticulum membrane," and "A band" were found only in RT-EVCTs, while "spliceosomal complex," "Wnt signalosome," "pronucleus," "microtubule end," and "autophagosome membrane" appeared to be specific to RT-VCTs (Figure  6(c) ). Through a search of the KEGG database, the following pathways appeared to be important in both tissue types: "protein processing in endoplasmic reticulum," "glucagon signaling pathway," "Epstein-Barr virus infection," "PPAR signaling pathway," "HIF-1 signaling pathway," "progesterone-mediated oocyte maturation," and "mTOR signaling pathway.
" Pathway terms specific to RT-EVCTs included "primary immunodeficiency" and "fatty acid metabolism," while those specific to RT-VCTs were linked with "bacterial invasion of epithelial cells" and "parathyroid hormone synthesis, secretion, and action" (Figure  6(d) ). From the Disease Ontology database, the terms "preeclampsia," "HELLP syndrome," "spinocerebellar ataxia," "familial hyperlipidemia," "lipid metabolism disorder," and "musculoskeletal system cancer" were important in both EVCTs and VCTs. Terms specific to RT-EVCTs included "breast benign neoplasm," "thoracic benign neoplasm,".
PPAR Research "lipomatous cancer," "amyloidosis," and "vein disease," while those specific to RT-VCTs were "alveolar rhabdomyosarcoma," "osteopetrosis," "giant cell tumor," and "germ cell and embryonal cancer" (Figure  6(e) ). From a search of the DisGeNET database, the terms "preeclampsia," "hypertrophic cardiomyopathy," "immunologic deficiency syndromes," "diabetes mellitus," "vascular inflammations," "hematopoietic neoplasms," "non-alcoholic fatty liver disease," "vascular disease," "ischemic cardiomyopathy," and "triploidy syndrome" were significant for both tissue types. Instead, "chronic neutrophilic leukemia," "glycogen storage disease," and "myeloid, chronic, atypical, and BCR-ABL negative leukemia" were specific to RT-EVCTs, and "alport syndrome" and "aggressive non-Hodgkin lymphoma" were specific to RT-VCTs (Figure  6 (f)).
The PPARγ and RXRα (heterodimeric nuclear receptor partner of PPARγ) complex, together with the DEGs recovered in this study, were submitted to the STRING online tool. Visualizations were modified in Cytoscope to depict hierarchical interactions and gene expression. For hierarchical protein-protein interactions, red text represents upregulated genes, blue text represents downregulated genes, blue circles represent direct interactions with the PPARγ complex, red circles represent second-level interactions, and grey circles represent plus-level interactions. TF-TG interactions of the PPARγ complex with DEG promoters were predicted by iRegulon based on the TRANSFAC database. Red circles represent upregulated DEGs and blue circles represent downregulated DEGs. PPARγ: peroxisome proliferator-activated receptor-γ; RXRα: retinoid x receptor-α; RT-EVCTs: rosiglitazone-treated extravillous cytotrophoblasts; RT-VCTs: rosiglitazone-treated villous cytotrophoblasts; DEGs: differentially expressed genes; PPI: protein-proteininteraction; TF-TG: transcription factor-target gene.
Such regulatory changes might be represented here by the terms "regulation of endothelial cell migration," "Wnt signaling pathway," "negative regulation of protein phosphorylation," "transcription factor activity," "PPAR signaling pathway," and "HIF-1 signaling pathway". In general, our datasets revealed an abundance of biological processes or pathways affected by PPARγ, many of which are consistent with previous reports. This concordance should increase confidence in our results and indicate avenues for further study. However, because this study relied on DNA microarray technology, it was inherently limited by the probe set used; it is possible that some unknown genes may not have been detected effectively and certain biological processes or pathways may have been missed. Further exploration with the application of advanced technology such as RNAseq would be helpful to identify and fill in any missing gaps in our dataset.
Figure3: GO and pathway terms associated with all DEGs in RT-EVCTs and RT-VCTs. All DEGs were submitted separately according to their cell type of origin to ClueGO with the default parameters. GO and pathway enrichment were set up for analysis. DEGs were classified by three ways: by GO biological process, GO molecular function, and GO cellular component. The KEGG and Reactome database was consulted to determine pathway enrichment. An exhaustive list of all terms (including those not shown above) can be found in supplementary materials (TablesS1-S8). DEGs: differentially expressed genes; RT-EVCTs: rosiglitazone-treated extravillous cytotrophoblasts; RT-VCTs: rosiglitazonetreated villous cytotrophoblasts; GO: gene ontology; KEGG: Kyoto encyclopedia of genes and genomes.
Figure4: GO and pathway terms associated with DEGs that were upregulated in RT-EVCTs and RT-VCTs. These DEGs were submitted separately to ClueGO by their cell type of origin with the default parameters. GO and pathway enrichment were set up for analysis. Upregulated DEGs were classified by three ways: by their GO biological process, GO molecular function, and GO cellular component. The KEGG and Reactome databases were consulted to determine pathway enrichment. All additional enrichment terms (not shown above) can be found in supplementary materials (TablesS1-S8). DEGs: differentially expressed genes; RT-EVCTs: rosiglitazone-treated extravillous cytotrophoblasts; RT-VCTs: rosiglitazone-treated villous cytotrophoblasts; GO: gene ontology; KEGG: Kyoto encyclopedia of genes and genomes.
Targeted by PPARγ in RT-EVCTs and RT-VCTs. We next filtered our datasets to examine only the DEGs targeted directly by the PPARγ complex, based on the TF-TG predictions described above. The filtered RT-EVCT database contained26 upregulated and d d d d d d d degr d d d d d d d d d d d d d d d d d dd adation PEX14 Nucl l cl l l l cl l l cl l l l l cl cl c cl cl l cl cl c c c c c c c c ea ea ea a a a ar a a a a a a a a a a a a a a rece e e e e e e e e e e e.
TF-TG interactions of the PPARγ complex with DEG promoters were predicted by iRegulon based on the TRANSFAC database. Red circles represent upregulated DEGs and blue circles represent downregulated DEGs. PPARγ: peroxisome proliferator-activated receptor-γ; RXRα: retinoid x receptor-α; RT-EVCTs: rosiglitazone-treated extravillous cytotrophoblasts; RT-VCTs: rosiglitazone-treated villous cytotrophoblasts; DEGs: differentially expressed genes; PPI: protein-proteininteraction; TF-TG: transcription factor-target gene.
Acknowledgments The authors wish to thank the consenting patients and the clinical staff midwives of  Cochin Port Royal  for providing placental tissues and  Dr. Danièle Evain-Brion , former director of the Unit, for her support, as well as  Lindsay Higgins  for English editing (http://www. englishservicesforscientists. com). This work was carried out with the funding support of  INSERM, University of Paris Descartes , and  RSI Professions Libérales Provinces, 44 boulevard de la Bastille ,  75578 Paris Cedex 12 , as well as the  China Scholarship Council (CSC) in Chegongzhuang Avenue, Beijing 100044 ,  P. R China.
4 RORCFormation 4 Targeted by PPARγ in RT-EVCTs and RT-VCTs. We next filtered our datasets to examine only the DEGs targeted directly by the PPARγ complex, based on the TF-TG predictions described above. The filtered RT-EVCT database contained26 upregulated and d d d d d d d degr d d d d d d d d d d d d d d d d d dd adation PEX14 Nucl l cl l l l cl l l cl l l l l cl cl c cl cl l cl cl c c c c c c c c ea ea ea a a a ar a a a a a a a a a a a a a a rece e e e e e e e e e e e.
Material and methods We tracked imported biomass and value of 330 crop products from the main exporting countries and their allocation to feed protein use in China, using different sub-databases module from FAOSTAT (Table  1 ). The impacts of feed protein import on domestic livestock production was estimated based on the feed protein intake, and impacts on land use in exporting countries were evaluated according to average crop yield in each exporting country. Import biomass and value of livestock and aquatic products from the main exporting countries was derived from database of Ministry of Agricultural and Rural Affairs in 2019 (Table  1 ), with dairy products corrected to a fresh-milk basis. Manure nitrogen (N) excretions and livestock N use efficiency of each livestock category has been estimated to show environmental impacts of export livestock production to China in the main exporting countries based on parameters provided by FAOSTAT.
Import of crop products and associated value and resources use Information about crop products import and trade matrix was collected from FAOSTAT (Table  1 ), data refers to 2017 due to the availability of data across different sub-database of FAOSTAT. We selected 330 crop species to track their export amount, final use and trade value from the main exporters to China, such as EU-28, Argentina, Brazil, Canada, Indonesia, Malaysia, Australia, Russia, and United States. The protein content of each product was derived from  Lassaletta et al. (2014)  and FAO Food Balance sub-database (Table  1 ). The feed use of each product was obtained from FAOSTAT (Table  1 ), considering both the direct feed use and indirect use of residues from processing, as most of protein was retained in the residues after processing.
This is important because soybean, rapeseed and barley contribute over 95% to China's total crop import. Import value of crop products was collected from FAOSTAT (Table  1 ). External land use was calculated based on the amount of exported crop product and average yield of each crop in each export country. Note that import and re-export of certain products was not included in the calculations. The total traded value of crop products was collected from the detailed trade matrix database of FAOSTAT (Table  1 ). We also tracked the land-usechange related GHG emission in 2017 from the GHG emission dataset of FAOSTAT (Table  1 ).
Import of livestock products and associated value and resources use Information about the historical changes of livestock products import from 1961 to 2019 was collected from FAOSTAT and MORAR database (Table  1 ). MORAR database provides information of most recent trade matrix data. Six livestock categories were selected: bovine meat, dairy products, sheep&goat mutton, eggs, chicken and pork. In addition, all the imported dairy products were converted to fresh milk for comparison among products. The conversion ratio was 8. 0 for bulk milk powder packages, 7. 0 for infant formula, 10. 0 for cheese, 11. 0 for cream, 0. 1 for whey, 2. 5 for condensed milk, 1. 0 for packaged fresh milk and 1. 0 for yoghurt  (Bai et al. , 2018b). Livestock manure N excretion per kilogram product was based on the amount of excreted N in manure divided by livestock production per animal category per year (Table  1 ), and assumed no changes between 2017 and 2019.
The feed protein intake of each livestock category was sum of the protein content of different products and N content in the manure multiplied by a factor of 6. 25, following the conversion rate between protein and total N content. Then, N use efficiency of livestock production was calculated, based on the N content in the products and total feed N intake. Manure N production related to export of livestock products to China was calculated based on the average livestock manure N excretion per kilogram product and export of different livestock products to China. Traded value was derived from the trade matrix data from MORAR database (Table  1 ). Reduction of livestock products supply under a no-feed-protein import scenario was calculated based on proportion of feed protein import to total feed protein consumption.
Acknowledgments This work was supported by the  National Key R&D Program of China  ( 2016YFD0800106 ); the  National Natural Science Foundation of China  ( 31572210 ,  31872403 ,  71961137011 );  Key Research Program of Frontier Sciences-CAS  ( QYZDY-SSW-SMC014 );  Key Laboratory of Agricultural Water Resources-CAS  ( ZD201802 ); the  Key Research Program-CAS  ( KFJ-STS-ZDTP-053 );  Hebei Dairy Cattle Innovation Team of Modern Agro-industry Technology Research System, China  ( HBCT2018120206 ); the  Youth Innovation Promotion Association, CAS, China  ( 2019101 ) and  Outstanding Young Scientists Project of Natural Science Foundation of Hebei, China  ( C2019503054 ). Zhaohai would like thanks to  Food Agricultural Biodiversity, Land and Energy (FABLE) Consortium  and the  New Food and Land Use (FOLU) Coalition.
, 2016) ; nitrate, sulfate, and ammonium aerosol measurements in July 2009  (Zhang et al. , 2013) ; or atmospheric values based on numerical simulations  (Skyllakou et al. , 2014). Our study uses a data-driven regional approach and considers a longer time period in order to study the seasonal and interannual variabilities in NH 3 and its impact on PM 2. 5 formation over the Paris megacity. Specifically in this paper we study concentrations and spatiotemporal variability in atmospheric NH 3 from the agricultural sector to gain insights on the effect of NH 3 on megacity air quality using the following: (1) longterm satellite observations derived from IASI (10 years from 2008 to 2017) and CrIS (5 years from 2013 to 2017) at the regional scale (400 km radius circle around the Paris city centre); (2) spatiotemporal patterns of the CHIMERE model evaluated against the IASI and CrIS datasets for 2014 and 2015; and (3) analysis of the main meteorological parameters that favour the formation of secondary PM 2.
, 2009). The NH 3 total columns used here are derived from IASI observations using an artificial neural network (NN) reanalysed with ERA-interim data (ANNI-NH3-v2. 1R, Van Damme et al. , 2017). This dataset is consistent in time and suitable for investigating interannual variability, which is one purpose of this study. Note that we have considered only morning measurements (09:30 LT) here since the evening ones (21:30 LT) are associated with larger relative errors  (Van Damme et al. , 2017). IASI retrievals provide a robust error estimate for each IASI-NH3 observation, allowing us to take the variable sensitivity into account when comparing the IASI dataset with independent measurements. Finally, no filter on relative errors of the IASI datasets has been applied, following recommendations from  Van Damme et al.
g. thermal contrast and cloud cover). IASI NH 3 has been evaluated using the LOTOS-EUROS model over  Europe (Van Damme et al. , 2014b)  and ground-based and airborne measurements  (Van Damme et al. , 2015b) , which show consistency between the IASI NH 3 and available datasets. When comparing IASI NH 3 (the previous IASI NN version) with ground-based Fourier transform infrared (FTIR) observations, a correlation of 0. 8 and a slope of 0. 73, with a mean relative difference of -32. 4 ± (56. 3) %, and an underestima-  tion by IASI ranging from 10 % to 50 % have been found  (Dammers et al. , 2016).