dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions Here, we conducted a comprehensive analysis of citylevel fossil fuel and industrial process-related CO 2 emissions from cities in the Beijing-Tianjin-Hebei region. We showed their temporal dynamics, spatial distributions, and urban and non-urban emissions fractions. We recognized the similarities and differences in emissions and thus improved the understanding of current inventories and provided useful information for policy making in terms of reducing city emissions and monitoring CO 2. The results showed that compared to city-data-based inventories, provincial-data-based inventories were 6%  Spatially, all datasets agreed with high emissions in the triangular spatial distribution pattern of Beijing-Tianjin-Tangshan and low emissions in the northern parts of Zhangjiakou and Chengde.
We used annual CO 2 emissions data from eight emission inventories, including the China High Resolution Emission Database (CHRED); China Emission Accounts and Datasets (CEADs); Multi-resolution Emission Inventory for China (MEIC), version 1. 3; the Nanjing University CO 2 emission inventory (NJU); the Peking University CO 2 emission inventory (PKU), version 2 (PKU-CO 2 -V2); the Open-source Data Inventory for Anthropogenic CO 2 , version 2018 (ODIAC2018); the Emissions Database for Global Atmospheric Research, version 5. 0 (EDGARv5. 0); and the Fossil Fuel Data Assimilation System, version 2. 2.
Acknowledgements This work was supported by the  National Key R&D Program of China  (No. 2017YFB0504000 ). Tomohiro Oda is supported by the  NASA   Carbon Cycle Science program  (grant no. NNX14AM76G ). We thank  Dr. Dabo Gun 's team,  Dr. Qiang Zhang ,  Dr. Shu Tao  and  Dr. Bofeng Cai  for kindly providing CEADs, MEIC,  PKU-CO2  and CHRED data.
Availability of data and materials The data sets of ODIAC, EDGAR, PKU, CEADs and FFDAS are freely available from http://db. cger. nies. go. jp/datas et/ODIAC /DL_odiac 2018. html, https :// edgar. jrc. ec. europ a. eu/overv iew. php?v=50_GHG, http://inven tory. pku. edu. cn/downl oad/downl oad. htmlhttp://www. ceads. net/, and http://ffdas. rc. nau. edu/Data. html, respectively. CHRED, MEIC and NJU are available from the data developers upon request.
Data analysis and signature identification Raw intensity data from microarray experiment were normalized using the robust multichip average (RMA) method and then filtered according to the median expression and standard deviation in all of the samples. Specifically, genes with a median expression value higher than 6 and/or standard deviation less than 0. 5 were retained for downstream analysis. After data preprocessing, two feature selection algorithms, mRMR and Lasso, were combined with the support vector machine (SVM) classification model to identify signatures with the best performance in cancer and noncancer discrimination. Genes selected via the two strategies were combined and validated using the qPCR method. Five candidate reference genes (CSNK1G2, PPIB, FPGS, DECR1, and CRY2) that were reported to be stably expressed in human whole blood were evaluated  (13).
Four statistical approaches were used for the evaluation: geNorm, normFinder, bestKeeper, and delta-Ct (dCt). Three genes (CSNK1G2, PPIB, and FPGS) were finally selected as the reference gene set for data normalization. The Ct geometric mean of the three reference genes was used for normalization and subtracted by Ct of each gene that was validated. The same Lasso-SVM algorithm implementation was used for the qPCR validation study. A 10-fold crossvalidation accuracy metric was used for model selection. Differentially expressed genes were identified via the significant analysis of microarray (SAM) method. Annotation and function analysis were performed via The Database for Annotation, Visualization, and Integrated Discovery (DAVID), which is an online annotation tool for transcriptome study.
The SVM classification model was subsequently used for signature identification. For the mRMR-SVM process, the detailed implementation was as follows: (I) The whole process began with an external iterative Leave One Out Cross Validation (LOOCV) procedure. In each iteration, only one sample was left out as an external validation sample, and all of the remaining samples were used in a training dataset. (II) In each iteration, 30 runs of gene selection and model training were performed, each with a different number (ranging from 1 to 50) of genes to be selected. Each run consisted of two steps: mRMR gene selection and SVM model training with a 10-fold cross-validation procedure both applied on the training set. (III) For gene selection, the mRMR algorithm was applied to the external training dataset to search for subsets of n genes that had a maximum relevance with the clinical status and a minimum redundancy within the gene sets.
Once gene selection was completed, the external training set was further split into 10 folds to initiate an internal 10fold cross-validation procedure to train an SVM classification model using the selected genes as input features. The trained SVM model was then used to classify the external validation sample. (IV) The external LOOCV procedure was repeated in such a way that each sample function was an external validation sample only once. The performance of the SVM models with certain numbers of genes and parameters was reported as the external LOOCV validation metric of accuracy, which was then used to determine the optimal number of gene numbers. The best model was thus selected and applied to the whole dataset, and the resulting signature was deemed to be the final gene signature (Figure  1A ).
qPCR validation and signature identification For the qPCR validation experiment, 43 target genes and five reference genes (CSNK1G2, PPIB, FPGS, DECR1, and CRY2) were tested. Three genes (CSNK1G2, PPIB, and FPGS) were finally selected to be used as reference genes for data normalization according to the algorithms described in methods. The Lasso-SVM procedure was applied to the normalized qPCR data. We achieved the optimal classification performance when lambda equaled 0. 01, which corresponded to a signature of 22 genes (Table  2 ). In the end, six genes existed in the results of both model training procedures for the microarray; 12 genes were from mRMR-SVM alone, and 4 genes were from Lasso-SVM (Figure  1C ).
Function and pathway analysis of Differential Expressed Genes and Diagnostic Signature Differential Expressed Genes (DEGs) were identified using the limma package of R. Probe sets, which fulfilled the criteria of LogFC >1 or <-1 and P value lower than 0. 05. They were selected and submitted to DAVID for further analysis. Nighty three probe sets representing 64 genes were found to be up-regulated, and 422 probe sets associated with 284 genes were down-regulated in the HCC group. The most significant biological process among the upregulated genes was platelet degranulation (10 genes), with adjusted P value =2. 49E-08 (Benjamini), and blood coagulation (7 genes), with adjusted P value =7. 9E-3. In the KEGG pathway analysis, platelet activation was also listed as one of the top most significant pathways, with five genes enriched (adjusted P value =0.
Paraffin blocks were kept cold and 5 µm sections were generated. For alveolar morphometry, lungs were flushed subsequently with PBS and 4% paraformaldehyde in phosphate-buffered saline (pH 7. 0) at a vascular pressure of 20 cm H 2 O. Then PBS was infused via the trachea at a pressure of 20 cm H 2 O and fixed with 4% paraformaldehyde in phosphate-buffered saline (pH 7. 0) via the trachea at a pressure of 20 cm H 2 O. Investigations were performed using 5 µm sections of the paraffin-embedded left lobe of the lungs. The mean linear intercept, mean air space, and mean septal wall thickness were measured after staining with hematoxylin and eosin (HE). Total scans from the left lobe were analyzed using a Leica DM6000B microscope with an automated stage according to the procedure previously described  [26, 27] , which was implemented into the Qwin V3 software (Leica, Wetzlar, Germany).
Hybridization and subsequent washing and drying of the slides were performed following the Agilent hybridization protocol. The dried slides were scanned at 2 µm/pixel resolution using the InnoScan is 900 (Innopsys, Carbonne, France). Image analysis was performed with Mapix 6. 5. 0 software (Innopsys, 31,390 Carbonne, France), and calculated values for all spots were saved as GenePix results files. Stored data were evaluated using the R software and the limma package from BioConductor (free accessible software, www. r-project. org) [29]. Log mean spot signals were taken for further analysis. Data was quantile-normalized before averaging. Genes were ranked for differential expression using a moderated t-statistic  [30]. Pathway analyses were done using gene set tests on the ranks of the t-values.
We also carried out a gene microarray between experimental [Rosa26 rtTA)/rtTA ; Tg(tet(o)miR154)/+] and control [Rosa26 rtTA)/rtTA ; +/+] lungs (exposed to Dox food from E7. 5 to E18) for the selection of potential mRNA targets after the pull-down assay. The data of the microarray experiment are deposited in GEO and are available through the accession number GSE141300 (please note that this SuperSerie is composed of three SubSeries).
Fluorescence activated Cell Sorting Whole lungs were isolated in ice-cold Hank's balanced salt solution (HBSS). Then, lobes were chopped finely using sterile razor blades, digested in a 10 mL solution of 0. 5% collagenase in HBSS on a heating plate (40 • C) with stirring at 700 rpm for 60 min. Once the homogenate was dissociated, the cell suspension was successively passed through 20G, 24G, and 26G needles, then strained on 70 µm and 40 µm filters. One volume HBSS was added to dilute collagenase and cell suspensions were centrifuged at 1500 rpm for 5 min to remove the enzyme solution. Cells were then resuspended in 500 µL 10% FCS in DMEM and stained with fluorochrome-labeled anti-mouse antibodies for 20 min at 4 • C (please see supplementary data), followed by washing and flow cytometric analysis with LSR Fortessa equipped with FACSDiva™ software (BD Bioscience, San Jose, CA, USA).
5, 1 mM EDTA, 1 mg/mL yeast tRNA and 1 mg/mL BSA) and washed twice with 1 mL washing buffer (10 mM Tris-HCl pH 6. 5, 1 mM EDTA 0. 5 M NaCl). Beads were resuspended in 0. 5 mL washing buffer. Cytoplasmic extract was then added to the beads and incubated for 1 h at 4 • C with slow rotation. The beads were then washed five times with 1 mL washing buffer. RNA bound to the beads (pull-down RNA) or from 10% of the extract (input RNA), was isolated using Trizol reagent LS (Invitrogen). The level of mRNA in the Bio-miR-154 or Bio-scramble control pull-down was quantified by qPCR. mRNA levels were normalized to a housekeeping gene (Gapdh, H4). The enrichment ratio of the control-normalized pull-down RNA to the control-normalized input levels was then calculated. The data for each cell line are representative of three independent experiments.
Quantification by qPCR of the expression of miR-154 isoforms (miR-154-3p and miR-154-5p) confirmed that at P2, the 3p isoform was significantly increased upon HOX treatment. A trend towards an increase was also observed for this isoform at P5 and P8. No difference at the level of the whole lung was detected for the 5p isoform (Figure  1D ). Finally, we isolated alveolar type 2 (AT2) cells by FACS and detected by qPCR an increase in the expression of both isoforms in HOX versus NOX (p = 0. 0015 and 0. 02 for the 3p isoform and 5p isoform, respectively). These data suggest that down-regulation of miR-154 in AT2 cells could be important to facilitate alveologenesis. expression of both isoforms in HOX versus NOX (p = 0. 0015 and 0. 02 for the 3p isoform and 5p isoform, respectively). These data suggest that down-regulation of miR-154 in AT2 cells could be important to facilitate alveologenesis.
Identification of Potential miR-154 Targets Using a Biotinylated Pull-Down Assay Followed by Gene Arrays Next, we wanted to identify the miR-154-3p mRNA target genes. For this purpose, we performed a pull-down experiment using a biotinylated miR-154-3p followed by gene array to identify these targets (Figure  2E ). We used scrambled biotinylated miR as a control and identified 338 potential targets (deposited in GEO, accession number GSE141300). As a next step, we also identified in the miR-154 overexpressing lungs (compared to control lungs), the downregulated gene products, which we propose may contain miR-154 targets (Figure  2F ). We overexpressed miR-154 ubiquitously in the developing lung by exposing the pregnant females carrying both experimental [Rosa26 rtTA)/rtTA ; Tg(tet(o)miR154)/+] and control [Rosa26 rtTA)/rtTA ; +/+] embyos to Dox food from E7.
More specifically, it is important to identify the pathways involved in TAM formation and to understand to what extent the macrophage plasticity facilitates this process in a TME. On the other hand, despite the wealth of quantitative information from bulk and single-cell sequencing datasets, the inference of regulatory networks based on experimental data remains a difficult challenge, with most approaches proposing a combination of both literature-and data-driven methods  [28] [29] [30]. In Chronic Lymphocytic Leukemia (CLL), a B-cell malignancy in which patients accumulate large quantities of malignant CLL cells in their lymph nodes, an interesting ecology of cancer cells and immune cells is established. CLL cells are able to educate surrounding monocytes, through direct contact and cytokine signals, turning them into TAMs, which in this disease are referred to as Nurse Like Cells (NLCs)  [31].
Reconstruction of the regulatory network leading to NLC formation To reconstruct the gene regulatory network (GRN) governing the formation of NLCs, we started from a previous macrophage polarization GRN  [26]  and extended it in order to include specific extracellular signals found in the Chronic Lymphocytic Leukaemia (CLL) context and other intra-cellular components involved in NLC formation. The network extension was based on extensive literature review and transcription factor (TF) activities estimation for each phenotype. Briefly, we used transcriptomics data for monocytes, M1, M2 and NLCs to calculate the TF activities in each condition, and chose the TF with the highest activities in each phenotype (see Methods, Section 5. 3). For NLCs, we identified specific TFs using a set of 17 microarray expression profiles  [33] , which interestingly have higher activities than in M1 and M2.
Calculating the transcription factor activities Microarray data used in this publication were downloaded from the NCBI repository Gene Expression Omnibus (GEO) database. M1 and M2 Macrophages microarray data accession number is GSE5099. Our previously published NLC microarray dataset can be found under accession number GSE87813 and was processed as described in  [33]. Raw microarray datasets were then normalized using the RMA (Robust Multi-arrays Average) normalization method and batch corrected. Transcription factors activities were estimated using the Dorothea R package. Dorothea is a TF-regulon interaction database giving each interaction a confidence level. Here, levels of confidence of interactions from A to E were taken into account. The VIPER algorithm was used to estimate TF activities based on Dorothea interactions and our expression data  [65, 66].
BV421-labeled PBS-57-loaded CD1d tetramer was provided by National Institutes of Health Tetramer facility. To exclude dead cells, cells were stained with Fixable Viability Dye (LD; eBioscience) before fixation. For intranuclear staining, cells were fixed with transcription factor staining buffer set (BD Bioscience) and stained with PE-Vio615 anti-mouse T-bet (REA102), Alexa Fluor 647 anti-mouse T-bet (4B10), PE anti-mouse RORγt (Q31-378), BV421 anti-mouse RORγt (Q31-378), and PE anti-mouse FoxP3 (FJK-16s). Unless otherwise stated, all antibodies were purchased at Thermo Fisher Scientific, BioLegend UK, Miltenyi Biotec, or BD Biosciences. Cells were acquired on an LSR Fortessa and BD FACSymphony flow cytometer (BD Biosciences). Data were further analyzed with FlowJo Software v10 and SPICE  (Roederer et al.
Statistical analysis Two-tailed Wilcoxon signed rank, two-tailed Mann-Whitney U test, Kruskal-Wallis, and Friedman's test with multiple comparison corrections were employed for statistical analysis of datasets. Statistical analyses were performed with Prism 8 (GraphPad Software) using a confidence level of 0. 95, and P values >0. 05 were considered not significant. Online supplemental material Fig. S1  shows the gating strategy for the identification of NKG2D + CD4 + T cell population in different tissues of C57BL/6 mice at steady state and its phenotype analysis. Fig. S2  shows kinetics of NKG2D expression in Th cultures generated in different polarizing conditions, dependence of expression on IL-12-STAT4-T-bet axis and antigen dose, and expression of Tbx21 and Rorc in in vitro-generated NKG2D + and Klrk1 -/-Th subsets.
Acknowledgments We thank  J. Kirsch  and  T. Kaiser  ( Flow Cytometry Core Facility, Deutsches Rheuma-Forschungszentrum, Berlin ) for the expert cell sorting services;  A. Kühl  ( iPATH, Charité ) for histological scoring of arthritic mice;  K. Lehmann  ( Deutsches Rheuma-Forschungszentrum ) for technical help;  M. Colonna  ( University of Washington, St. Louis, MO ) for providing Hcst -/-mice;  Lewis L. Lanier  ( University of California, San Francisco, San Francisco, CA ) for providing Tyrobp -/-mice; and  S. Jonjic  ( University of Rijeka, Croatia ) and  Daniela Hernández-Torres  ( Deutsches Rheuma-Forschungszentrum ) for critically reading the manuscript. This research was supported by the  European Union Seventh Framework Programme ,  Marie Curie Intra-European Fellowship   PIEF-GA-2012-327643 , and  Deutsche Forschungsgemeinschaft  grant  BA5642/1-1  to  M.
In the past years, many methodologies and tools have been developed to assess the FAIRness of research data. These different methodologies and tools have been based on various interpretations of the FAIR principles, which makes comparison of the results of the assessments difficult. The work in the RDA FAIR Data Maturity Model Working Group reported here has delivered a set of indicators with priorities and guidelines that provide a 'lingua franca' that can be used to make the results of the assessment using those methodologies and tools comparable. The model can act as a tool that can be used by various stakeholders, including researchers, data stewards, policy makers and funding agencies, to gain insight into the current FAIRness of data as well as into the aspects that can be improved to increase the potential for reuse of research data.
Approach The Working Group used a methodology to develop a FAIR data maturity model in four phases (Figure  1 ). In the first phase, the definition phase, a scoping, planning and landscaping exercise were carried out. The landscaping exercise was published  (Bahim, Dekkers, & Wyns, 2019)  and served as a basis for the next phase. Once the baseline was established, the second phase consisted in building the model. To this end, all aspects mentioned in the FAIR principles that could be assessed were identified. All input received from the Working Group members was clarified, standardised, and consolidated in a draft set of indicators, together with proposed priorities and draft guidelines for the application of the indicators in practical situations. The purpose of the third phase, the testing phase, was to determine the soundness, completeness and usability of the indicators.
After a pilot test, testers used a test plan with a template to record results and feedback. The results of the testing phase allowed the Working Group to revise the model and present a stable version as a draft RDA Recommendation. Issues that were brought up during the testing phase are included in the Discussion section further on. The fourth and last phase was the delivery phase, to finalise and publish the FAIR Data Maturity Model (FAIR Data Maturity Model Working Group, 2020) as an RDA Recommendation. Throughout these four phases, nine working meetings 2  were held to develop the model. True to the spirit of any RDA Working Group, during the meetings of this Working Group, consensus among our members was sought. The consensus process used several tools: in addition to discussion on GitHub, 3  a number of surveys were conducted to gather opinions on proposed decisions, and draft versions of the recommendation were made available as a Google document in which the members of the Working Group could make comments and suggest improvements.
Results The landscaping exercise brought many interesting points to light. For example, there were notable differences in the formats used in the approaches that were analysed: some used checklists, others used questionnaires. Not all the initiatives assessed the same type of object: some focused-on datasets and their use, while others assessed the data management plan. Some elements of the FAIR principles were covered more often than others; for example, the area of findability was the most covered whereas the area of accessibility received less attention. Furthermore, some approaches included questions that did not directly refer to any of the FAIR principles. For example, there were questions about the data repository and curation practices, which are not explicitly mentioned in the FAIR principles.
Another important difference was the way in which answers were to be provided, with some methodologies asking for yes/no answers and others giving a set of options to choose from. The Working Group used the landscaping exercise as base material to develop the set of indicators and maturity levels -with respect to the level of FAIRness -which were discussed on GitHub. The indicators that are used in the FAIR Data Maturity Model are derived from the FAIR principles and aim to formulate measurable aspects of each principle that can be used by evaluation approaches (Table  1 ). In order to determine the relative importance of the indicators, the Working Group defined a set of priorities as shown in Table  2. The indicators defined in the FAIR Data Maturity Model can be used to evaluate data resources and their metadata.
The model may be used during the development of Data Management Plans for research data, before any data resources have been produced, to specify the level of FAIRness that the resources are expected to achieve. It can also be used after the production of data resources to test what is the achieved level of FAIRness of the resources. Data producers, e. g. researchers, and data publishers can use the model to determine how to improve the FAIRness of their data, while project managers and funding agencies can use the model to determine whether the data resources achieve a pre-defined, expected level of FAIRness.
Observations It is important to note that the Working Group agreed that assessment should not be conceived as a value judgment but rather as a means to encourage improvement of the level of data FAIRness. It also needs to be noted that the FAIR principles are aspirational in nature, focusing on a long-term view of improving the potential for reuse of research data. As such, they should not be interpreted as strict rules. Communities have different practices and therefore there should be some flexibility on how the FAIR principles should be implemented within a certain community, i. e. a professional domain or grouping of related organisations. Such flexibility allows different communities to adapt the speed of their 'journey' towards greater FAIRness of their data. Although the word "community" is mentioned only once in the FAIR Principles (R1.