dataesr/scientific-paragraphs-categorization huggingface dataset:
01 according to two-way ANOVA followed by Bonferroni post-test. (G) Glycerol release from in vitro differentiated 3T3F442A and OP9 cell lines or human BM-MSCs under basal conditions or after stimulation with isoprenaline. The data are the mean of 3 independent experiments (3 independent donors were used for human BM-MSCs) and were normalized to the total lipid quantity per sample. The histograms represent mean ± SEM; *p < 0. 05; **p < 0. 01 according to two-way ANOVA followed by Bonferroni post-test. See also Figure  S4. validated by the target-decoy approach using a reverse database with both peptide and protein FDR at 1%. For label-free relative quantification of the samples, the ''match between runs'' option of MaxQuant was enabled with a time window of 0. 7min, to allow cross-assignment of MS features detected in the different runs.
The ''label free quantification'' (LFQ) metric from the MaxQuant ''protein group. txt'' output was used to quantify proteins. Missing protein intensity values were replaced by a constant noise value determined independently for each sample as the lowest value of the total protein population. Only proteins identified in at least three samples in the same location (i. e. , SC-Ad or BM-Ad) were considered to be robustly detected and used for statistical and bioinformatic analyses. Protein involved in lipid and glucose metabolism were selected using Gene Analytics software (GeneCards Suite) based on their involvement in the following pathways: Regulation of lipid metabolism, Insulin signaling-generic cascades, Lipoprotein metabolism, Adipogenesis, Regulation of lipid metabolism by Peroxisome proliferator-activated receptor alpha, Glucose / Energy Metabolism, Peroxisomal lipid metabolism, Calcium (Ca), cyclic adenosine monophosphate (cAMP) and Lipid Signaling, Nuclear Receptors in Lipid Metabolism and Toxicity, SREBF (Sterol Regulatory Element Binding Protein) and miR33 in cholesterol and lipid homeostasis, Acyl chain remodeling of Phospho Ethanolamine (PE) Cholesterol and Sphingolipids transport / Distribution to the intracellular membrane compartments, Synthesis of substrates in N-glycan biosynthesis, Synthesis of PhosphatidylCholine (PC), Metabolism of steroid hormones, Glycerophospholipid biosynthesis, Glucose metabolism, Fat digestion and absorption, Regulation of cholesterol biosynthesis by SREBP (Sterol Regulatory Element Binding Protein), cholesterol biosynthesis III (via desmosterol), Cholesterol and Sphingolipids transport / Transport from Golgi and ER to the apical membrane, Aldosterone synthesis and secretion, Citrate cycle (Tricarboxylic Acid (TCA) cycle), Adipocytokine signaling pathway, Sphingolipid metabolism, Fatty acid metabolism, Pyruvate metabolism, Arachidonic acid metabolism, Linoleic acid metabolism, Ceramide Pathway, Sphingolipid signaling pathway, sphingomyelin metabolism/ceramide salvage, Pentose phosphate pathway, Regulation of lipolysis in adipocytes, Mitochondrial Long Chain-Fatty Acid, Beta-Oxidation SuperPath and Fatty acid biosynthesis.
Among the 1948 proteins retrieved by the database, we identified 612 proteins that belong to one or more of the above pathways. The LFQ intensity for each identified protein was log2 transformed and used to perform principal component analysis with the FactomineR package (v1. 42) within R (v3. 6). The statistical analysis of differentially expressed proteins was performed with the limma package (v3. 40. 2) where a linear model was fit using a two group (BM-Ad and SC-Ad) design matrix followed by empirical Bayes moderation (see Table  S1 ). Protein expression was considered significantly different if the p value was less than 0. 05.
STAR+METHODS KEY RESOURCES Pathway enrichment analysis was performed with Gene Analytics (GeneCards) using official gene symbols. Heatmaps with hierarchical clustering of samples were made with the gplots package (v3. 0. 1. 1). Network analyses were performed with the String database (v11) using the following queries: Regulation of lipolysis in adipocytes, statin and cholesterol biosynthesis and lipoprotein metabolism. Confidence scores above 0. 8 were considered as significant. Protein-protein interaction (PPI) networks were created using cytoscape (v3. 7. 1). Nodes were color-coded according to the log2 fold-change of protein expression detected by LC-MS/ MS in BM-Ad compared to SC-Ad. Node sizes were attributed according to the p value of differential expression analysis from the linear model used for differential expression analysis.
Lipidomic analysis One ml of the lipid phase, extracted with MTBE as described above, was evaporated under a nitrogen stream. Dried samples were sent to the Harvard mass spectrometry core and were analyzed by their untargeted lipidomics profiling platform. Lipids were resuspended with 100ml of 1:1 LC/MS grade isopropanol:acetonitrile methanol and 5 ml was injected onto the LC-MS/MS. Data acquisition was performed as previously described  (Breitkopf et al. , 2017). Briefly, each peak area was calculated in both positive and negative ionization mode. Peaks structurally resolving to the same lipid species were summed when obtained from the same ionization. Only lipid species detected in at least 3 samples from the same location were considered as robustly detected and used for the analysis. Missing values were imputed as the first percentile of the entire dataset.
1% Tween-20 and incubated with HRP conjugated secondary antibodies (1:5000, Santa Cruz Biotechnology). The immunoreactive protein bands were revealed by ECL prime western blotting detection reagent (Ammersham TM ) and detected using a ChemiDoc Imaging System (Biorad). Densitometry quantification was performed using Image lab software (v5. 2. 1; Bio-Rad). Signal intensities were normalized to b-Actin.
Nodes represent detected proteins (circle) and undetected proteins (square) by LC-MS/MS. The size of the circle is based on the p value of differentially detected proteins and is color-coded according to log2-fold change of protein expression in BM-Ads compared to SC-Ads. Edges represent protein-protein interactions with a combined score in string database above 0. 8. Edges are colored based on the type of interaction, as indicated in the figure. See also Figure S2.
The histograms represent mean ± SEM; ***p < 0. 001 according to two-way ANOVA followed by Bonferroni post-test. (D) Free fatty acids (FFAs) released from isolated SC-Ads and BM-Ads as a readout of lipolysis under basal conditions or after stimulation with isoprenaline. Data are the means of 7 independent experiments and are normalized to the total lipid quantity. The histograms represent mean ± SEM; **p < 0. 01 according to twoway ANOVA followed by Bonferroni post-test. (E) cAMP production in SC-AT and BM-AT after stimulation with isoprenaline for 30 min. Data are the mean of 8 independent experiments, normalized to protein content. Histograms represent the mean ± SEM; *p < 0. 05 according to two-way ANOVA followed by Bonferroni post-test. (F) TG content measured in cell lysates from 3T3F442A (3T3) and OP9 cell lines or human BM-MSCs (MSC) before and after adipogenic differentiation (d, differentiated; nd, non-differentiated).
)  (15). GINsim is a computational tool dedicated to the building and analysis of logical models, enabling the delineation of logical regulatory graphs, together with various dynamical analyses, through model simulations, but also with the support of efficient algorithms to identify the attractors (stable states and/or oscillatory behavior) of the system, for wild-type or mutant conditions  (14). The resulting model can be further analysed using the CoLoMoTo tool suite, an interactive toolbox integrating several logical modeling software tools, with a uniform interface to perform simulations and other analyses, which are easy to share, and reproduce through the use of notebooks  (16). The aim of our study was to integrate all the information gathered from scientific literature and high-throughput data (RNA-seq and ChIP-seq) into a logical model of the regulatory network underlying moDCs differentiation.
These regulatory regions were then used to predict binding sites for the fourteen TFs activated by CSF2 and IL4 pathways (Figure  1 ) using the position-weight matrices collected in the Jaspar database  (19)  with the pattern-matching tool matrix-scan  (20)  from the RSAT suite  (21). This led us to define novel regulatory interactions targeting specific gene markers for moDCs, monocytes, and macrophages (Table  1 ), thereby enabling us to complete the regulatory network controlling monocytes to moDCs differentiation. Table1. Cell type-specific gene markers selected to be added into the model. Based on the epigenome analysis we identified relevant regulatory interactions that helped select candidate genes to be added to the model. For thirteen out of 20 genes related to moDCs phenotype, we found putative binding sites for the TF IRF4 (Figure  3a ), corroborating a central role for IRF4 in the moDCs differentiation.
Model simulations correctly estimate cellular commitment to differentiation We imported our model into the CoLoMoTo Interactive Notebook, a digital notebook that enables integrated complementary analyses software (with PINT, BioLQM, and MaBOSS) and facilitates reproducibility  (24). The notebook is available as supplementary material. We used the tool Pint  (25)  to assess nine single gene mutants (IRF4, STAT6, PU. 1, IRF8, MAFB, NCOR2, AHR, JAK3, CEBPB) that have been reported in the literature to affect the differentiation process. We were able to replicate the behavior of each mutant (perturbations) with our model. Table  2  shows the summary of the results obtained for these perturbations, while Figure  6  shows the behavior of each node for each perturbation at the corresponding stable states.
GINsim implementation and simulations. Using the software GINsim version 3. 0 (  14 ), we integrated the previously described signalling pathways that are activated when monocytes are cultured with CSF2 and IL4 (studies reviews are inside GINsim model as annotations, and in the SuppFile1). We also performed a review of the available literature related to the process of monocyte to moDCs differentiation. The logical model was built using GINsim  (33) , where nodes represent genes or proteins, and edges represent the interactions between them, these interactions can be negative (red arrow) or positive (green arrow). In 2)  (37). IDR analysis (37) was performed to replicate control.
Search for TFBS using matrix-scan From manual curation of literature, we identified 22 TFs that participate after monocyte stimulation leading to the differentiation of macrophages or moDCs. We retrieved one PSSM (Position-Specific Scoring Matrix) for each of the 22 TFs (SuppTable1) from the JASPAR2018 database human collection  (19). We performed pattern-matching searches for TF motif instances using the 22 PSSMs in the selected chromatin regions (Active TSS, Repressed TSS, Active gene/enhancer, and Poised regulation) from ChromHMM results. For this task we used the tool matrix-scan (20) from the RSAT suite  (21)  with the following main parameters: background model of Markov order 1 and stringent thresholds of p-value ≤ 10 -5 and score 1 (-markov 1 -lth score 1 -uth pval 1e-5).
RNA-seq analyses Raw fastq files from RNA-seq experiments were retrieved from the Blueprint Consortium  (31)  data access portal (http://dcc. blueprint-epigenome. eu/#/datasets) with dataset identifiers: EGAD00001002308, EGAD00001001506, EGAD00001002526, EGAD00001002507, and EGAD00001001582. For this analysis, we used the methods described in Law et al 2016  (40). In brief, that is quality control with FastQC/0. 11. 3  (41) , pseudo-alignment and count determination with Kallisto 0. 43. 1 (42) using the release-90 from Ensembl (ftp://ftp. ensembl. org/pub/release-90/fasta/homo_sapiens/cdna/Homo_sapiens. GRCh38. cdna. all. fa. gz) to create our index with the following command: kallisto index -i index_kallisto_hsap_90_cdna --make-unique Homo_sapiens. GRCh38. cdna. all. fa. gz. Counts were assigned to genes using Tximport 1.
blueprint-epigenome. eu/#/datasets) with dataset identifiers EGAD00001001552, EGAD00001002484, EGAD00001002485, EGAD00001001576, EGAD00001002504. We processed six histone marks data (H3K4me1, H3K4me3, H3K27ac, H3K36me3, H3K9me3, and H3K27me3) with two biological replicates from human monocytes, macrophages, and moDCs. We performed quality control of read sequences with FastQC/0. 11. 3 tool (34), then we used Trimmomatic/0. 33 (35) to improve quality reads before mapping them with bowtie2-2. 2. 6 (36) to the human hg38 reference genome. Second quality control is required after alignment, for which we used ENCODE QC, which consists of three major tests: NRF (non- redundant fraction), PBC1 (PCR Bottleneck coefficient 1), and PBC2 (PCR Bottleneck coefficient.
Networks enabled the use of eddy covariance data beyond a single site or ecosystem for cross-site comparisons and regional-to-global studies  [8] [9] [10] [11] [12] [13] [14] [15]. These regional networks have evolved into long-term research infrastructures or monitoring activities, such as ICOS, AmeriFlux, NEON, AsiaFlux, ChinaFLUX, and TERN-OzFlux. FLUXNET was created as a global network of networks  [16] [17] [18]  , a joint effort among regional networks to harmonize and increase standardization of the data being collected. It made possible the creation of global eddy covariance datasets. The first gap-filled, global FLUXNET dataset, which included derived, partitioned fluxes like photosynthesis and respiration, was the Marconi dataset  19  in 2000, with 97 site-years of data, followed by the 2007 LaThuile dataset  20  with 965 site-years of data, and finally in 2015 the FLUXNET2015 dataset  18, 21  (hereafter FLUXNET2015) with 1532 site-years of data.
Two main factors limited the numbers of sites and years included in each dataset: data policy and data quality. Willingness to share data under the selected data policy is a major reason why FLUXNET2015 likely only includes between 10-20% of existing sites globally-the total number of existing sites is still unknown. Then there is the evolution of processing pipelines and quality controls, leading to new issues being identified in the data that, if not solved in time, led to leaving out that data. The LaThuile dataset had a more restrictive policy and, in a few cases, previously undiscovered data issues, leading to fewer sites being included in FLUXNET2015.
OPEN FLUXNET2015 includes for the first time sites with records over two decades long (Fig. 1 ). The dataset was created through collaborations among many regional networks, with data preparation efforts happening at site, regional network, and global network levels. The global coordination of data preparation activities and data processing was done by a team from the AmeriFlux Management Project (AMP), the European Ecosystem Fluxes Database, and the ICOS Ecosystem Thematic Centre (ICOS-ETC). This team was responsible for the coding efforts, quality checks, and execution of the data processing pipeline. These combined efforts led to a dataset that is standardized with respect to the (1) data products themselves, (2) data distribution formatting, and (3) data quality across sites. The wide application of these datasets in global synthesis and modeling activities highlights their value.
At the same time, however, heterogeneity in the data-caused mainly by differences in data collection, flux calculations, and data curation before submission-highlights the need for estimates of data uncertainty and uniform evaluation of data quality. The data processing pipeline uses well-established and published methods, with new code implemented for this release as well as code adapted from implementations by the community. The main products in this pipeline are: (1) thorough data quality control checks; (2) calculation of a range of friction velocity thresholds to filter low turbulence periods, allowing an estimate of the uncertainty from this filtering along with the random uncertainty; (3) gap-filling of meteorological and flux measurements, including the use of a downscaled reanalysis data product to fill long gaps in meteorological variables; (4) partitioning of CO 2 fluxes into respiration and photosynthesis (gross primary productivity) components using three distinct methods; and (5) calculation of a correction factor for energy fluxes estimating the deviation from energy balance closure for the site.
Two features of this pipeline are the ranges of friction velocity thresholds, and the multiple methods for partitioning CO 2 fluxes. Both features support a more thorough evaluation of the uncertainty introduced by the processing steps themselves. Our implementation of this pipeline is available as an open-source code package called ONEFlux (Open Network-Enabled Flux processing pipeline)  22. The goal of this paper is to describe FLUXNET2015 and additional products, present the details about this processing pipeline, and document the methods used to generate the dataset. Doing so will provide the community of FLUXNET end-users with the technical and practical knowledge necessary to harness the full potential of the FLUXNET data, including data from the FLUXNET2015 release, and data submitted to the network since.
Data Processing Methods The data contributed by site teams for inclusion in FLUXNET2015 encompassed fluxes, meteorological, environmental, and soil time series at half-hourly or hourly resolutions. Contributed data underwent a uniform data quality control process, with issues addressed in consultation with site teams. Data were then processed using the pipeline (Fig. 2 ) described in this section. The resulting data products were distributed through the FLUXNET-Fluxdata web portal  23  , where the usage of the dataset is tracked through a registration of all the requests and details about the user and the data use plan. This information is crucial to better understand the user needs and the impact of the dataset.
Starting from these data, we applied the same set of methods for all the remaining processing steps (from filtering to gap-filling and partitioning), increasing uniformity, and allowing quantification of the uncertainty introduced by the processing. The harmonization of data-especially of data quality-was a high priority while creating the dataset, and therefore extensive interaction with the site teams was necessary. Data were contributed through a regional network, and the formats provided by the networks were converted into a standard input for processing. This FLUXNET dataset led to the creation of a new cross-network specification for standard site data and metadata submission formats, now adopted by different regional networks. Data processing pipeline overview. The data processing pipeline (Fig.
The final step involved merging all the products generated at previous steps, and adding daily through yearly temporal aggregations of most of the variables in the dataset and related quality flags. At this step, automated checks were performed on all the variables to ensure consistency, and the final files with all the contributed and derived data products to be distributed are created. Supplementary Fig. SM2  shows the general steps involved in the processing in the sequence organized in the code, as available in the ONEFlux package  22. All the steps have been implemented in the shared code except the Sundown partitioning (see Implementation Approach section for details).
Ensemble of results. The adoption of multiple methods for the same step (e. g. , two methods for USTAR threshold calculation, or two or three methods for CO 2 flux partitioning, see below in this section for details) is motivated by the existence of different methods in the literature, using different assumptions and potentially Fig. 2  The logic of the data processing steps for FLUXNET2015 (details about the different steps and meaning of abbreviations in the text). having diverging results  26, 27. On the one hand, this lack of uniformity can represent a problem for synthesis studies. On the other hand, adopting a single method could lead to biases and underestimation of the uncertainty in the methodology. The approach taken here, which simultaneously adopts multiple methods, allows the creation of an ensemble, helping assess uncertainty, and also the suitability of individual methods to a site's conditions.
Data quality assurance and quality control. Prior to the processing that generated the derived data products (hereafter called post-processing), the data for each site went through QA/QC checks following Pastorello et al. 25. All variables included in the dataset underwent checks, and critical variables underwent further scrutiny. These additional checks targeted variables critical to the processing, e. g. , flux variables, meteorological variables used in gap-filling, and variables used by uncertainty estimation procedures. The processing did not proceed for sites with pending issues in critical variables. Critical metadata variables for the post-processing: • The site FluxID in the form CC-SSS (two character country code, three character site identifier within country) -e. g. , US-Ha1 • The latitude and longitude for the site in the WGS 84  File format standardization.
To process hundreds of sites, we needed consistent file formats that supported the input data and metadata. This led to multi-network agreements and creation of formats for data and metadata contribution to the regional networks  28, 29. These formats have now been adopted by networks in Europe and the Americas and by some instrument manufacturers, and are under consideration by other regional networks. In addition, automated extraction and conversion tools for direct format translation were implemented to work with data in older formats. Data QA/QC steps. Data quality was checked before the processing started. If issues were identified that could not be resolved by the network-level data team, the site team was asked to suggest a course of action or send a new version of the data addressing the quality issue identified.
Meteorological products. The main processing applied to meteorological data was gap-filling by two independent methods: Marginal Distribution Sampling  31  (MDS) and ERA-Interim  32. Data gap-filled by MDS (applied to all variables that are gap-filled) are identified by the _F_MDS suffix. Data gap-filled using ERA-Interim downscaling (six variables that are available in the reanalysis dataset) have a _F_ERA suffix. The final gap-filled time series for variables combines both of these methods (indicated by an unqualified _F suffix), following a data-quality-based selection approach (see below). For SW_IN, in case of gaps or in case the variable was not measured, we performed a calculation from PPFD_IN when available, calculating the conversion factor from the periods of overlap of the two measurements (and assuming a factor 0.
g. , coordinate or temporal mismatches). The linear relationships are built taking into account instantaneous and averaged variables, and then applied to the whole ERA-I record, generating the spatially (coordinate-based) and temporally (diurnal cycle-based) downscaled version of each variable. The method was applied as in the original implementation; additional details can be found in Vuichard and Papale  32. Final gap-filled product. Measured or high quality gap-filled records using MDS (_F_MDS_QC < 2) are used in the final gap-filled products (_F suffixed variables, without _MDS or _ERA). If the variable has a low quality gap-fill flag (2 or 3), the ERA-I product is used instead. The final quality flag (_F_QC) is 0 for measured, 1 for high quality fill using MDS, and 2 for data gap-filled with the ERA-I downscaled product.