dataesr/scientific-paragraphs-categorization huggingface dataset:
pl algorithm (with parameterssize given -cpg) to search for significant enrichment against a background sequence adjusted to have similar CpG and GC contents. Transcription factor binding analysis was performed interrogating the overlap between the different sets of DMRs with ChIP-seq data for transcription factors available for GM12878 lymphoblastoid cell line from the ENCODE Project  (31). The enrichment factor was calculated against random regions as a background, and P values were calculated using Fisher's exact test. Finally, the transcription factors downstream of the BCR signaling pathway were manually annotated from a curated database  (32). Chromatin states and histone mark enrichments analysis for NBC, GC B cells and ncsMBC were assessed using a custom adaptation of the EpiAnnotator R package  (33)  using BLUEPRINT data  (21).
DMRs were converted to hg38 assembly with the liftOver function in the rtracklayer v1. 42 R package. Replication timing data in the GM12878 lymphoblastoid cell line were obtained from the UW Repli-seq track of the UCSC Genome Browser. Genomic replication timing values were binned in deciles where the first decile contained the regions of latest replication and the last decile the regions of earliest replication. Identified DMRs were then overlapped to these regions by genomic location. DMR annotation for genetic context location was performed using the annotatePeaks. pl algorithm in the HOMER software v4. 10. 3. To determine the location relative to a CpG island (CGI), we used 'hg19 cpgs' annotation in the annotatr v1. 8 R package. GREAT software  (34)  was used to enrich downstream pathways and gene ontologies.
We used the single nearest gene option for the association between genomic regions with genes. Genes obtained with HOMER annotation software were used to study correlations between their associated DMRs and gene expression data from healthy naïve, memory and GC B cells from BLUEPRINT database, as well as with the AID off-target genes dataset obtained from Álvarez-Prado et al. (35). All statistical analysis (excluding T-WGBS and ChIPseq analyses) were done in R v3. 5. 1. Data distributions were tested for normality. Normal data were tested using two-tailed unpaired Student's t-tests; non-normal data were analyzed with the appropriate non-parametric statistical test. Levels of significance are indicated as: *P < 0. 05; **P < 0. 01; ***P < 0. 001; ****P < 0. 0001. Nonsignificance (P ≥ 0. 05) was indicated as 'ns'.
Public RRBS of B cell activation. Data of EBV and CD40/IL4 B cell activation were downloaded from the NCBI Gene Expression Omnibus (GSE49629)  (36). Methylation calls from RRBS data were filtered so that only those CpGs with a minimum of five reads per position in all samples were retained. Since RRBS genomic coverage is significantly lower than T-WGBS we only tested the methylation status of positions common to two datasets.
RNA-seq data analysis. For bioinformatics analyses, raw sequence reads were initially processed using FastQC (Babraham Institute, Cambridge, UK) for quality control, and then adapter sequences and poor quality reads were removed using TrimGalore that uses Cutadapt for trimming. Quality-filtered reads were then mapped to the human genome (hg19) using STAR, and only the uniquely mapped reads were kept. Read counts were calculated using HTSeq-count. Differentially expressed genes were identified using R package DESeq2 (fold change ≥ 2 and adjusted P-value < 0. 05). Inference of TF activities from expression values were calculated using DoRothEA  (37). Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to perform functional enrichment analysis of differentially expressed genes. Nucleic Acids Research, 2021, Vol.
49, No. 9 5061 ChIP-PCR. Cell-sorter isolated naïve B cells from healthy donors were activated as described above for 24 h. 1 × 10 6 unstimulated and stimulated B cells were crosslinked with 1% methanol-free formaldehyde (Thermo Fisher) for 10 min subjected to 15 min of sonication utilizing Covaris M220 Focused-ultrasonicator (Covaris, MA, USA). ChIP experiments were performed using the LowCell# ChIP kit™ protein A, according to manufacturer's instructions (Diagenode, Liege, Belgium). Anti-BATF antibody (#8638) and corresponding rabbit IgG (#2729) were both obtained from Cell Signaling. Protein binding was analyzed by realtime quantitative PCR, and data are represented as a ratio of the enriched fraction with respect to input. ChIP primers are shown in Supplementary Table  S2.
In relation to chromatin states, we observed that DMRs occurring in PMDs were mainly associated with heterochromatic regions, while non-PMD-DMRs were highly enriched at enhancers and active promoters, in concordance to what was previously described as generally associated with replication-independent demethylation (43) (Figure  2D ). This was also confirmed by overlapping DMRs with ChIP-seq data of healthy GC B cells obtained from the BLUEPRINT database, in which non-PMD-DMRs were enriched in enhancer marks, including H3K4me1, H3K4me3 and H3K27ac (Figure  2E ). It has been previously described that PMDs are characterized by their association with late-replication domains (  40 ), hence we analyzed DMRs based on their association with late or early phase replication. First, genomic density data obtained from GM12878 Repli-seq (UW Repli-seq, UCSC Genome Browser) were divided into deciles by their replication timing and then overlapped by genomic location with identified DMRs.
HIGM2-associated defects in DNA methylation in the transition from naïve to memory B cells do not have the features of AID targets Given all the previous considerations, including the removal of DNA methylation changes related to DNA replication (PMD-DMRs), our model allows us to examine whether AID has a direct role in mediating demethylation in the B cell lineage. In this context, GC B cells, in the transition from naïve to memory, displayed the highest AICDA mRNA levels (Supplementary Figure  S2A ) (as obtained from the BLUEPRINT database). Hence, it is plausible that defective AID in HIGM2 patients could play a role in aberrant DNA demethylation occurring during the transition from naïve to memory B cells within the GC. To explore this possibility, we first identified DMRs whose DNA demethylation could potentially be driven by AID (P-AID DMRs).
(Figure  5A  and Supplementary Table  S6 ). We then performed Gene Ontology analysis of DEGs using GREAT software which revealed enrichment of categories related to EBV activation and BCR signaling pathway (Figure  5B ), consistent with our previous findings related to DNA methylation alterations in naïve B cells of HIGM2. Interestingly, we also observed differences in gene expression in memory B cells (2199 upregulated genes, 2384 downregulated) (Supplementary Figure  S6A  and Supplementary Table  S6 ) which were also enriched in the aforemen-tioned categories (Supplementary Figure  S6B ). Utilizing DoRothEA TF analysis, we observed that DEGs identified in naïve B cells of HIGM2 patients also displayed increased TF activity compared to controls (Figure  5C ), where similar results were observed for memory B cells (Supplementary Figure  S6C ).
Integration of the DNA methylation and expression datasets in naïve B cells did not show a clear correlation when comparing HIGM2 versus controls (Figure  5D ). Interestingly, hypomethylated DMRs in HIGM2 naïve B cells showed slightly increased expression levels in HIGM2 naïve B cells in respect to control naïve B cells. The naïve B cell expression levels of hypomethylated DMRs of HIGM2 patients were more similar to those of memory B cells, reinforcing the notion of a premature activation of these cells in HIGM2 (Figure  5E ).
Color scale represents the logarithmic fold change, circle size indicates the percentage of DMRs in the chromatin state, and the edge color indicates the statistical significance of the enrichment (black: significant, none: not significant; q < 0. 01). (E) ChIP-seq data of the represented histone marks in healthy GC B cells were downloaded from the BLUEPRINT database and mean coverage was calculated for 10bp bins ±5 kb centered around the midpoints of PMD-DMRs (left) and non-PMD-DMRs (right). (F) Circular representation of the proportion of PMD-(outer circle) and non-PMD-DMRs (inner circle) in deciles of replication timing data of GM12878 cell line. Color scale represents the proportion of DMRs in each decile. The first and last deciles correspond to the regions of latest and earliest replication, respectively.
(D) Bubble plot depicting the enrichment (red) or depletion (blue) of chromatin states. Color represents the logarithmic-fold change, size shows the percentage of DMRs in the chromatin state, and the edge indicates the statistical significance of the enrichment (black: significant, none: not significant, q-value < 0. 01). Chromatin state data was obtained from ChromHMM, generated in GC B cells. (E) Circular representation of proportion of DMRs in deciles of genes whose expression is ranked from low to high expression in GC B cells. The first and last deciles correspond to the least and most highly expressed genes, respectively. Color scale represents the proportion of DMRs in each decile. Gene expression data was obtained from the BLUEPRINT database. (F) Venn diagram of the overlap between P-AID and AID off-target DMRs with super-enhancers in GC B cells.
Super-enhancer identification is described in the Materials section utilizing H3K27ac ChIP-seq data from the BLUEPRINT database. (G) Box plots showing H3K27ac ChIP-seq peaks of super-enhancers associated with AID off-target (red) and P-AID (green) DMRs. One-sided unpaired Wilcoxon's test was used to examine signal intensity differences. (H) DNA methylation and H3K27ac profiles in the vicinity of two representative genes. DMR color indicates the type of DMR, either P-AID (green) or AID off-target DMRs (red). H3K27ac ChIP-seq signals from GC B cells are shown in dark orange. Super-enhancers and enhancers are depicted. Statistical tests: two-tailed unpaired Wilcoxon's (C) and Fisher's exact (D) tests (* P-value < 0. 05, ** P-value < 0. 01; ns is not significant).
-(red) B cells. Gene expression data obtained from BLUEPRINT database. FPKM refers to fragments per kilobase of transcript per million mapped reads. (C) Results of gene set enrichment analysis using GREAT software. The plot depicts the top five enriched terms for five annotation databases ranked by P-values from the binominal distribution. All the depicted enriched terms passed a significance threshold of adjusted P-value < 0. 05. (D) Smoothed DNA methylation data of BATF-associated DMRs in HIGM2 naïve B cells and H3K27ac ChIP-seq signal, super-enhancer and enhancer location in GC B cells. H3K27ac ChIP-seq data obtained from BLUEPRINT database. (E) Bubble scatter plot of transcription factor ChIP-seq peaks determined in GM12878 lymphoblastoid cell line in regions that correspond to DMRs identified in HIGM2 naïve B cells.
ACKNOWLEDGEMENTS We thank  CERCA Programme/Generalitat de Catalunya  and the  Josep Carreras Foundation  for institutional support. This study makes use of data generated by the  BLUEPRINT Consortium (Funding  for the  BLUEPRINT  project provided by the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no  282510--BLUEPRINT ). A full list of the investigators who contributed to the generation of the data is available from www. blueprint-epigenome. eu. We are also very grateful to  Dr Javier di Noia ,  Dr Almudena Ramiro ,  Dr Javier Carmona  and  Dr Biola M. Javierre  for useful feedback.
FUNDING Spanish Ministry of Science, Innovation and Universities  [ SAF2017-88086-R  to E. B. ]; cofunded by  FEDER funds/European Regional Development Fund (ERDF) --a way to build Europe. E. B is supported by  Instituto de Salud Carlos III (ISCIII) ,  Ref. AC18/00057 , associated with i- PAD  project ( ERARE European Union program ); P. L. and C. P. are supported by the  German Cancer Aid  project  CO-CLL [70113869 ]; B. G. is funded by the  Deutsche Forschungsgemeinschaft  [ GR1617/14-1/iPAD ,  SFB1160/2 B5 ,  RESIST-EXC 2155-Project ID 390874280 ,  CIBSS-EXC-2189-Project ID 390939984]; BMBF [GAIN 01GM1910A ]. Funding for open access charge:  Spanish Ministry of Science, Innovation and Universities  [ SAF2017-88086-R].
Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture and vegetation water content, which are related to the Earth water and carbon cycles. Only two spaceborne L-band sensors are currently operating: SMOS, launched end of 2009 and thus providing now a 10-year global data set and SMAP, launched beginning of 2015. This study provides a state-of-the-art scientific overview of the SMOS-IC retrieval data set based on the SMOS L-band observations. This SMOS product aims at improved performance and independence of auxiliary data, key features for robust applications. The SMOS-IC product includes both a soil moisture (SM) and a L-band vegetation optical depth (L-VOD) data set which are currently at the basis of several studies evaluating the impact of climate and anthropogenic activities on aboveground carbon stocks.
, 2020). Globbiomass is a recent AGB data set for the year 2010 which is obtained from multiple remote sensing and in situ observations at 100 m spatial resolution. Forest growing stock volume (GSV) is first estimated from the remote sensing observations and GSV is then converted to AGB with spatially explicit estimates of wood density and stem-to-total biomass expansion functions derived from forest inventory data sets  (Yang et al. , 2020). The ESA CCI AGB data set was developed within the ESA's Climate Change Initiative Biomass project which considers AGB, as SM, as an Essential Climate Variable (ECV). Global maps of AGB (Mg ha -1 ) were developed at 500 m to 1 km spatial resolution with a relative error of less than 20% where AGB exceeds 50 Mg. ha -1 and for four epochs  (mid 1990s, 2010, 2017 and 2018).
We used here the CCI AGB data set for 2017 at a resolution of 100 m (CCI AGB  D4. 3, 2020). All the three AGB estimates used here are based on optical, lidar and radar observations from multiple Earth observation satellites and inventory data sets. They are thus completely independent of SMOS-IC L-VOD, as SMOS-IC does not use ancillary information on vegetation as presented above.
Precipitation Precipitation data from 2014 to 2017 were obtained from NASA's Global Precipitation Measurement (GPM) IMERG Late Precipitation L3 1 day 0. 1 × 0. 1 • (version 06)  (Huffman et al. , 2019). These data were used here to help interpreting the impact of rainfalls on the time series of the SMOS-IC SM and L-VOD data. The different datasets (AGB, MODIS NDVI, precipitation) described here were rescaled to the SMOS 25 km × 25 km spatial resolution by simple averaging.
Features and parameterizations The SMOS-IC product which was developed during  2015 -2017 (Fernandez-Moran et al. , 2017a) , relied on the foundation of the L 2P approach presented above. In SMOS-IC, as for SMOS Level 2 (L2) and Level 3 (L3), L-VOD and SM are retrieved simultaneously from a twoparameter inversion of the L-MEB model from the multi-angular and dual-polarized SMOS observations. The initial released product (V105) provided global daily L-VOD and SM data from the descending and ascending orbits at a spatial resolution of 25 km over the period from 12 January 2010 to 31 December 2017. SMOS-IC differs from L2 and L3 in several respects (we refer the reader to  Fernandez-Moran et al. (2017a)  for more details), but the main one is that SMOS-IC assumes the pixel to be homogeneous: L-VOD and SM are retrieved over the whole pixel rather than over a fraction of it.
, 2017)  used a strong filtering procedure on sun-glint effects, which led to delete T B observations in the central part of the SMOS FOV, exhibiting characteristic central stripes in the orbital L3 T B maps. This filtering was estimated to be too strict for SMOS-IC as it deleted many data in the central region of the FOV, a region which is the richest in terms of ranges of multi-angular T B data. SMOS-IC used its own filtering procedure to filter out the non-filtered L3 T B data as described in the following sections. Note that the official ESA/SMOS L2 algorithm has not been affected by this issue as it is directly based on Level 1 T B products. In the near future, this issue should no longer affects the official L3 T B products since as a new L3 T B product is being released.
Data filtering Data filtering is a key component associated with the SMOS-IC algorithm. This filtering has been progressively improved since the initial paper by  Fernandez-Moran et al. (2017a)  to support the development of applications. Data filtering is essential because data errors would affect the SMOS SM and L-VOD retrievals: (i). errors associated with the viewing angle geometry of the interferometric antenna of SMOS; in SMOS-IC, only observations for incidence angles >20 • and < 55 • are kept in the retrievals, to avoid observations which are relatively noisy and inaccuracies in L-MEB for large angles. Note that this chosen range of angles corresponds relatively well to the alias free field of view (AF-FOV): most of the observations for incidence lower than 20   , as a small range of angular variations correspond with observations made at the edge of the SMOS FOV which are associated with a higher uncertainty  (Wigneron et al.
(2012) , the Root Mean Square Error (RMSE) value between the measured and the L-MEB modelled T B data (referred to as TB-RMSE) is used as an index to estimate the impact of RFI on the SMOS T B data. The rationale behind the choice of this index is that L-MEB is able to fit very well the multi-angular and dualpolarization signatures of the SMOS observations over a large variety of land surface scenes (covered by soil, vegetation, water, ice, snow, rocks etc. for low, medium or high topography). So, in the absence of RFI, L-MEB will be able to fit very well the SMOS TB observations and the TB-RMSE index will be low, as it will determined primarily by the standard deviation associated with the SMOS T B data (typically close to 3 to 4 K). Most of the differences between this lower limit value (TB-RMSE ~3-4 K) and the actual value of TB-RMSE are due to RFI effects.
-for L-VOD, as a criterion, we used the spatial correlation between yearly averages of L-VOD and Saatchi AGB. The evaluation was made here using classical statistical metrics as in  Li et al. (2020a) : correlation in terms of coefficient of correlation (R), Root Mean Square Error (RMSE, m 3 /m 3 ), unbiased Root Mean Square Error (ubRMSE, m 3 /m 3 ) and bias (m 3 /m 3 ). To obtain best scores in the SM and L-VOD retrievals considering the above criteria, we evaluated possible values of N, σ VOD and TB R. Based on previous studies  (Wigneron et al. , 2007 (Wigneron et al. , , 2012)) , we evaluated only a limited number of cases corresponding to N = 10 or 20 days, σ VOD = 0. 05 or 0. 1 and TB R = 6 or 8 K. So, 8 "retrieval algorithms" corresponding to 8 cases corresponding to different values of N, σ VOD and TB R were evaluated here by comparison to the ancillary data sets (ERA5-Land SM for SM and Saatchi AGB for L-VOD).
Based on this analysis, the differences in the performances of IC for these 8 different algorithms were generally small (Table  S1-5 ). We selected for V2 the algorithm providing best results (i. e. N = 10 days, σ VOD = 0. 05 and TB R = 6 K; very similar results obtained for N = 20 days and σ VOD = 0. 1). Based on this approach, circularity that may happen when considering the same ancillary data for the "calibration" and "evaluation" steps is very limited in this study. Moreover, ECMWF ERA-Interim SM was used for calibration and ECMWF ERA5-Land SM was used for validation and even though these two SM datasets are very similar, they are not the same.
For instance, (i) in situ data are very interesting  (Dong et al. , 2020) , but are at point scales and not representative of microwave satellite scales  (Crow and Wood, 2002)  (ii) AGB and NDVI are limited because they only provide biomass proxies and water content changes that can be out of phase with the LAI and NDVI metrics  (Tian et al. , 2018). So, the evaluation made in this work used several criteria for both the SM and VOD retrievals; a multi-criteria evaluation being more able to reveal strengths and weaknesses of the updated SMOS-IC version. Moreover, in the following, the term "improvement" will not be used. We will use instead terms as "closer to benchmarks/proxies" or "better scores" as the evaluation data sets used in this study have errors and do not represent integrated satellite data at a resolution of 25 km × 25 km.
taraexpeditions. org) has revealed 37,470 species of Bacteria and Archaea  (Sunagawa et al. 2015). Analysis of sequence datasets has also revealed that we are still far from capturing the whole picture of the total pro karyotic diversity in the oceans. A considerable fraction of this diversity belongs to the "rare biosphere", an immense reservoir of species with low abundances  (Overmann and Lepleux 2016 ). Moreover, recent studies revealed that some marine niches, like marine biofilms, are even more diverse than the pelagic waters and still constitute a substantial source of hidden diversity  ( Zhang et al. 2019). The recovery of large metagenomic datasets from oceanic samples has also provided evidence for the extraordinary functional diver sity of marine prokaryotes; in their 193 samples, the Tara Ocean datasets revealed the presence of 39,246 different orthologous groups.
The oceanic metagenomic datasets were enriched in functional categories related to transport of sol utes (coenzymes, lipids, amino acids, secondary metabolites) and energy production (including photosynthesis)  (Sunagawa et al. 2015). Marine bacteria are also known to produce many types of bioactive compounds that are of interest for indus trial applications, including active enzymes and molecules with anticancer, antimicrobial and anti-infl ammatory prop erties  ( Zeaiter et al. 2018).
The genomic comparison of strains representative of these pigment types revealed that most genes involved in the biosynthesis of phycobilisome rods are located in a large (up to 30 kb) specialized region of the genome, generally predicted to be a genomic island. The gene content and orga nization of this region is specific to each pigment type, inde pendently from the strain phylogenetic position, and shows a tremendous increase in phycobilisome gene complexity from pigment types 1 to 3, the latter type being a more recent structure and the most sophisticated phycobilisome known so far. Together with the presence of phycobilisome genes in metaviriome datasets, this suggests that genes related to the phycobilisome rod region can be laterally transferred between Synechococcus lineages and that this might be a key mechanism facilitating adaptation of these lineages to new light niches.
2013). The recent implementation of methods for plating and genetic manipu lations such as the disruption and/or overexpression of CA4 genes in marine Synechococcus has allowed us to start deci phering the regulation of the CA4-A process. Thus, in the model strain Synechococcus sp. RS9916, isolated in the Red Sea, the CA-4 process involves chromophore switch systems at three phycoerythrin cysteines, which are regulated by the two transcription factors FciA and FciB  (Sanfilippo et al. 2019). Thanks to the setup of genetic transformation meth ods, CA is one of the physiological processes that has been more closely studied in the laboratory in picocyanobacteria. Using phycobiliprotein and CA4 genetic markers, the study of the extensive metagenomic Tara Oceans dataset allowed us to determine that, globally, CA4-A and CA4-B strains account for 23% and 19% of all Synechococcus, respectively  (Grébert et al.