dataesr/scientific-paragraphs-categorization huggingface dataset:
( 1 )Figure 3. 13 Figure 3. General workflow applied on Sentinel 2 imagery to assess the potential contribution of the temporal dimension to map UF 2. 3. 2. Data sampling It consists of subdividing the image into 256 × 256 pixel patches (Figure 3-Step1). The size should be sufficiently large as not to lose spatial context but should also take into account the available computational resources. Images with no spectral information are then removed. We perform a random shuffling of the dataset before splitting it into training and validation sets using a 10% ratio for validation and 90% for training. Data augmentation on the training set is also applied by random rotations, translations, and zooms to make our models more robust to variations and prevent overfitting. Table4shows the number of image patches obtained for each model.
The dynamic transcriptional changes during the first 4 h are a distinct feature of Tr1 cell differentiation compared with Th17 cells, which manifest a relatively stable profile during the early phase from 0-2 h  (Yosef et al. , 2013). To identify TFs that drive the distinct transcriptional waves, we hypothesized that genes co-expressed in a cluster (Figure  1A ) are likely to share regulators that are active at the relevant time point. We predicted regulator-target associations in the IL-27-driven transcriptional programs based on significant overlap between genes in a specific cluster and a regulator's putative targets in a regulator-target association database  (Yosef et al. , 2013). This generated a predictive network containing 79 TFs that were putative regulators of the gene clusters induced by IL-27 (Figure  1C ).
Role of Prdm1 and Maf in Regulating IL-10 in Different T Helper Cells We validated the association of Prdm1 and Maf expression with Il10 in various contexts by qPCR. The IL-10 + compartment of Th1, Th2, Th17, Tr1, and Treg cells expressed higher levels of Prdm1 and Maf than their IL-10 À counterparts (Figure  6A ). Moreover, analysis of public ChIP-seq datasets confirmed binding of Prdm1 and Maf at accessible chromatin regions in the Il10 locus (Figure  6B ). These findings further support the hypothesis that Prdm1 and Maf may be critical regulators of IL-10 in multiple settings. We tested the interaction between Prdm1 or Maf in regulating Il10 using luciferase assays. Although Prdm1 alone had very limited capability to transactivate Il10, it significantly enhanced transactivation of Il10 by Maf (Figure  6C ), suggesting that cooperation between Prdm1 and Maf is required for optimal IL-10 production.
, 2007; Pot et al. , 2009)  and co-inhibitory receptors (A and C) TFs enriched in the IL-10 + compartments compared with their IL-10 À compartments in (A) in-vitro-generated T helper cell subsets, (C) 10 in vivo/ex vivo conditions where a direct comparison between the transcriptome of IL-10 + and IL-10 À cells was made in public data (Table  S1 ). TFs that are enriched under at least 3 conditions were magnified. (B and D) A different display of same data in (A) and (C), respectively, showing TFs that are enriched under at least 3 conditions and the conditions where their expression is enriched in the IL-10 + compartment. SI, small intestine. (E) A ranking scheme (STAR Methods) for all potential regulators of IL-10, taking into account network centrality (Figure  3B ), enrichment in in vitro conditions (Figure  5A ), and enrichment in public datasets (Figure  5C ).
Computational Methods Microarray data pre-processing and analysis Individual. CEL files were RMA normalized and merged to an expression matrix using the ExpressionFileCreator of GenePattern with default parameters  (Reich et al. , 2006). Gene-specific intensities were then computed by taking for each gene j and sample i the maximal probe value observed for that gene. Samples were then transferred to log-space by taking log 2 (intensity). Differentially expressed genes (comparing to the Th0 control) were found using a method we previously described  (Yosef et al. , 2013). Briefly, genes that were detected in two of the four methods used were defined as differentially expressed: (1) Fold change. Requiring a 2-fold change (up or down) during at least two time points. (2) Polynomial fit. We used the EDGE software  (Leek et al.
, 2006; Storey et al. , 2005) , designed to identify differential expression in time course data, with a threshold of q-value % 0. 01. (3) Sigmoidal fit. We used an algorithm similar to EDGE while replacing the polynomials with a sigmoid function, which is often more adequate for modeling time course gene expression data  (Chechik and Koller, 2009). We used a threshold of q-value % 0. 01. (4) ANOVA. Gene expression was modeled by time (using only time points for which we have more than one replicate) and treatment. The model takes into account each variable independently, as well as their interaction. We report cases in which the P value assigned with the treatment parameter or the interaction parameter passed an FDR threshold of 0. 01. To associate the regulation activity of a differentially expressed transcription factor with the three phases of IL-10 expression (latency, induction and maintenance) we segmented our time course dataset into three corresponding time windows: 0-20h, 25-48h and 54-72h.
TFs were assigned to specific phases if they were differential expressed (> 1. 8 Fold change) anytime during this time window. Prediction of TFs regulating the IL-27 network Using approaches as we previously described  (Yosef et al. , 2013) , we identified potential regulators of Tr1 differentiation by computing overlaps between their putative targets and sets of differentially expressed genes grouped by k-means clustering. For every TF in our database, we computed the statistical significance of the overlap between its putative targets and each of the groups defined above using Fisher's exact test. We included cases where p < 5 3 10 À5 and the fold enrichment > 1. 5. Population RNA-seq data pre-processing and analysis RNA-seq reads were aligned using Tophat  (Trapnell et al. , 2009)  and RSEM-based quantification  (Li and Dewey, 2011)  using known transcripts (mm9), followed by further processing using the Bioconductor package DESeq2 in R  (Anders and Huber, 2010).
The data was normalized using TMM normalization. The TMM method estimates scale factors between samples that can be incorporated into currently used statistical methods for DE analysis. Post-processing and statistical analysis was carried out in R  (Li and Dewey, 2011). For the analysis of the effect of different regulator KOs, differentially expressed genes were defined as genes with abs (logFC between control and KO) > 1. For comparison between IL-10 + and IL-10 -cells, differentially expressed genes were defined based on the raw counts with a single call to the function DESeq2  (Love et al. , 2014)  (FDR-adjusted P value < 0. 05). Heatmap figures were generated using pheatmap package (https://cran. r-project. org/web/packages/pheatmap/index. html). ATAC-seq analysis Generation and analysis of ATAC-seq data for in-vitro differentiated Tr1 cells at 24 h and 72 h were performed in our previously published study  (Karwacz et al.
, 2017). A publicly available ATAC-seq pipeline  (Lee et al. , 2016)  was used for the processing of ATAC-seq on Prdm1 cKO, Maf cKO and DKO Tr1 cells. Briefly, reads were aligned to the mm10 genome using Bowtie2 and filtered to remove duplicates and mitochondrial reads. Biological replicate for each group were merged peaking-calling using MACS2  (Zhang et al. , 2008). Integrative Genomics Viewer (IGV) was used for visualization of ATAC-seq peaks. Single cell RNA-seq analysis Data preprocessing. De-multiplexing, alignment to the mm10 mouse transcriptome and UMI-collapsing were performed using the Cellranger toolkit (version 2. 1. 0, 10X Genomics). Subsequent analysis was performed with R package Seurat v3  (Butler et al. , 2018). For downstream processing we filtered out low quality cells that had (1) a low number (< 500) of unique detected genes, and (2) a high mitochondrial content (15%) determined by the ratio of reads mapping to the mitochondria.
ACKNOWLEDGMENTS The authors thank  Christophe Benoist ,  Arlene Sharpe ,  Mikael Pittet ,  Mary Collins , and  Karen Dixon  for constructive criticism and discussions and  Deneen Kozoriz ,  Rajesh K. Krishnan ,  Leslie Gaffney ,  Sarah Zaghouani ,  Haoxin Li ,  Ruihan Tang ,  Qianxia Zhang ,  Yu Hou ,  Jingwen Shi ,  Danyang He ,  Sheng Xiao , and  Ido Amit  for technical assistance. This work was supported by  NIH  grants  R01NS30843 ,  R01AI144166 ,  P01AI073748 ,  P01AI039671 ,  P01AI056299 , and  P01AI129880  (to V. K. K. ) and the  Klarman Cell Observatory  (to A. R. ). A. R. is an Investigator of the  Howard Hughes Medical Institute. A. C. A. is a recipient of the  Brigham and Women's   President's Scholar Award  and is supported by  NIH  grant  CA229400. A. M. was supported by the  Alon Fellowship for Outstanding Young Scientists ,  Israel Council for Higher Education.
Cell type assignment To identify which T cell subtype each cluster represents, we identified markers of each cluster using Seurat's FindAllMarkers function with min. pct at 0. 25. The top 200 genes of each cluster were then used as input for My Geneset module of Immgen (immgen. org). We assigned identity to each cluster based on the cell population in the Immgen database that display highest expression of its marker genes and confirmed the designation by expression of known marker genes (Figure  S5A ).
RimB functions as a specific protease for the modified C-terminal of RpsF The above observations suggest an enzymatic role for RimB. RimB shows modest secondary structural similarity to Pfam class PF05618 (ATP-dependent zinc proteases), leading us to hypothesize that the apparent suppression of RpsF band-shifting may represent cleavage of Cterminal poly-glutamate (poly-E) tails, following their addition by RimK. This would potentially explain how relatively low amounts of RimB are able to effectively prevent RpsF bandshifting by RimK. To test this, we first examined the effect of adding purified RimB to RpsF samples that had been previously glutamated by RimK addition (Fig  4A ). Addition of RimB to RimK-modified samples restored the RpsF bands to their original gel positions, consistent with cleavage of the poly-E tail.
N/D = Not Determined; N/I = Nil Identified. The positions of RimK, RimA and RpsF are indicated. https://doi. org/10. 1371/journal. pgen. 1008837. g004 experiment using isobaric labelling (iTRAQ) to examine how these conditions affected protein abundance in the WT and mutant backgrounds. When a 2D scatter plot was constructed for this data ( shown, with no dependency of the chain extension and termination probabilities, the chains follow an exponential distribution (purple curve, no RimB protease activity). Introducing RimB protease activity results in longer chains being reduced in length and shifting the distribution towards to minimal length at which RimB is active (four units). Increasing RimB:RimK activity further pushes the chain length distribution to peak sharply around a length of four.
RimABK exerts direct and indirect effects on mRNA translation through RpsF glutamation Our data suggests that a large proportion of the observed rimK regulon is controlled through RimK-induced shifts in the abundance of other translational regulators, particularly Hfq  [19]. To understand the contribution made by RimK glutamation to ribosome function, we must first understand the relationship between Hfq abundance and RimK. To assess the contribution of Hfq to the RimK phenotype, we produced rimK mutant strains in which the chromosomal hfq gene was flag-tagged at the C-terminus. We then quantified Hfq abundance using a Wes Simple Western system (Bio-Techne) in different media conditions. As expected based on previous results  [19] , Hfq abundance decreased markedly in the ΔrimK background for cells grown in M9-pyruvate media.
To test this, we conducted a Ribo-seq analysis on SBW25 WT and ΔrimBK cells grown to early exponential phase in M9-pyr-Cas media. The translational activity of 806 genes was significantly (log 2 >1) affected by rimBK deletion (S3 Table ), with more affected genes showing reduced (564) than increased (242) mRNA abundance in the ΔrimBK mutant background. The upregulated rimBK regulon was dominated by ribosomal proteins, while the downregulated genes contained a large proportion of ABC amino-acid transporter subunits. We observed a large degree of overlap between this experiment and our published data for the rimK proteome  [19] , with many upregulated genes in the initial (M9-pyr, stationary phase) experiment downregulated in our Ribo-seq dataset and vice versa (S3 Table ). Our data indicate that RimK produces an indirect, nutrient-dependent effect on the SBW25 translatome by changing the intracellular abundance of Hfq.
These genes comprised the majority of the rimBK regulon, and overlapped substantially with our previously published Hfq translatome dataset  [7]. Translation of the second set of genes (Class 2, Fig  9C ) was shifted by RpsF glutamation and rimBK deletion in the same direction (usually downregulated). This set contained numerous small, uncharacterised proteins alongside several stress response proteins and amino-acid ABC transporters/ metabolic genes (S5 Table ). We observed a large degree of overlap with the equivalent dataset for RpsfF 10glu , suggesting that both mutations produce similar effects on this gene class. The third set of genes (Class 3, Fig 9D ); those inversely affected by RpsF glutamation and rimBK deletion, are candidates for the direct RpsF-glutamation regulon. For RpsF 4glu , these genes predominantly split into four main classes (S5 Table ): ABC transporters for fatty/aminoacids, metabolic pathways for metabolism of amino acids, and genes associated with the initial stages of attachment to surfaces (e.
The resulting vectors were transformed into the target strain, and single crossovers were selected on Tet and re-streaked for single colonies. Cultures from single crossovers were grown overnight in LB medium, then a dilution series plated onto LB plates containing 5% w/ v sucrose to enable sacB counterselection. Individual sucrose-resistant colonies were patched onto LB plates ± Tet, and Tet-sensitive colonies tested for successful gene modification by colony PCR followed by Sanger sequencing. Reverse transcriptase PCR (RT-PCR) and 5' RACE were conducted using primers (1/2 and 3/4 respectively). qRT-PCR was performed as described in  [19]  using primers 5/6. qRT-PCR experiments were repeated three times independently and data are presented +/-standard error.
Growth assays Bacterial growth was measured in a microplate spectrometer (BMG Labtech FLUOstar Omega) using 2 biological duplicates each providing 2 experimental replicates and are presented as mean +/-standard error. 150 μL of the indicated growth medium in each case was added to the internal 60 wells of clear-bottomed, black-walled 96-well microplates (Sigma). Carbon sources were added to a concentration of 0. 4% w/v in each case. Growth was initiated by the addition of 5 μL of overnight cell culture (LB media, 28˚C, shaking) normalized to an OD 600 of 0. 01. Plates were incubated statically at 28˚C and agitated immediately prior to each data acquisition step. Optical density was measured at 600nm and fluorescence was monitored at 460nm with an excitation wavelength of 355nm. Experiments were conducted at least twice independently.
Data dependent analysis was performed using HCD fragmentation with the following parameters: positive ion mode, orbitrap MS resolution = 120k, mass range (quadrupole) = 300-1800 m/z, MS2 top20 in ion trap, threshold 1. 5e 4 , isolation window 1. 6 Da, charge states 2-7, AGC target 2e 4 , max inject time 35 ms, dynamic exclusion 1 count, 15 s exclusion, exclusion mass window ±5 ppm. MS scans were saved in profile mode and MS2 scans were saved in centroid mode. To generate recalibrated peaklists, MaxQuant 1. 6. 0. 16 was used, and the database search was performed with the generated HCD peak lists using Mascot 2. 4. 1 (Matrixscience, UK). The search was performed on a Pseudomonas fluorescens SBW25 protein database (Uniprot, 20140727, 21,935 sequences) to which copies of the RpsF sequence with 1-10 additional glutamate residues (E) at the C-terminus was added.
For the search a precursor tolerance of 6 ppm and a fragment tolerance of 0. 6 Da was used. The enzyme was set to trypsin/P with a maximum of 2 allowed missed cleavages. Oxidation (M), deamidation (N, Q), and acetylation (N-terminus) were set as variable modifications, carbamido-methylation (CAM) of cysteine as fixed modification. The Mascot search results were imported into Scaffold 4. 4. 1. 1 (www. proteomsoftware. com) using identification probabilities of 99% for proteins and 95% or 0% for peptides, as discussed in the results. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [41]  partner repository with the dataset identifier PXD017233.
4 (Thermo) with the following main parameters: protein sequence database: P. fluorescens SBW25 (Uniprot, Feb/2016, 6388 entries); variable modifications: oxidation (M), deamidation (N,Q); Percolator strict FDR target: 0. 01; reporter ion quantifier: most confident centroid, 20 ppm, HCD, MS3; consensus workflow for statistical analysis: replicates with nested design; use unique peptides for protein groups only, co-isolation threshold 50%, imputation: low abundance resampling, ratio based on pairwise ratios, hypothesis test: ttest (background based) generating adjusted p-values according to Benjamini-Hochberg. The protein results table was exported from Proteome Discoverer and used to generate the final protein expression tables and plots (.
Ribosomal profiling (Ribo-Seq) analysis Ribosomal profiling was conducted as described in  [7]. SBW25 cultures were grown at 28˚C in M9-pyr-CAS medium to the late exponential phase, then cells were harvested by rapid filtration as described in  [42]. Collected cells were flash frozen in liquid nitrogen and cryogenically pulverized by mixer milling (Retsch), then thawed and clarified by centrifugation. Resulting lysates were digested with MNase, quenched with EGTA and resolved by sucrose density gradient ultracentrifugation. Ribosome-protected mRNA footprints were processed as previously described  [7, 42]  and sequenced by Illumina HiSeq2000. Sequencing reads in fastq files were adaptor trimmed using a Perl script that implemented the procedure described in  [43]. Next, ribosomal RNA sequences were filtered out by aligning them against a Bowtie2 index of the SBW25 rRNAs.
The remaining reads were then aligned to the SBW25 genome to produce SAM files, which were used to calculate the centre-weighted coverage at each nucleotide position in the genome. For this, we used a Perl script to select alignments of between 23 and 41 nucleotides in length and counted for nucleotide positions after trimming 11 nucleotide positions from either end of the alignment. This was done separately for reads aligning to the forward and reverse genome strands and the centre-weighted coverage was stored in separate files for each strand. A separate Perl script was used to calculate RPKM values for each gene based on strand specific centre-weighted coverages along the genome. The limma function plotMDS was then used to make PCA plots. The ribosome profiling data has been submitted to ArrayExpress, with accession number E-MTAB-5408.
Kinetic modelling For each model, the chemical equations were translated into ordinary differential equations using mass action kinetics (S1 Table ). Thermodynamic constraints were considered to ensure consistency between reaction rates via different routes, thereby reducing the number of free kinetic parameters. Kinetic forward rate constants were set to 10 9 M -1 s -1 (diffusion limited) and equilibrium dissociation constants, K d , were-based on previous estimates-set initially to 1 μM and then optimized to fit with experimental data (see below). We used the ODE solver Vern7 from the Julia Differential Equations library to solve the chemical kinetics equations, which is suitable for high accuracy, non-stiff systems. Initial concentrations for cdG, RimK, RimA, and RimB were set to 1 μM unless specified otherwise, for instance when mimicking the conditions of a specific experiment as in S2 Fig.
where  [cdG] was 25 μM. Solution of the ODE allowed us to determine steady state values for all RimK species for each model. These steady state concentrations were used to calculate the enzymatic modification of RpsF. K m and k cat values for each species of RimK and K d values between Rim proteins were optimized to fit the model simulations to available experimental data, S2 Fig. , using the Julia BlackBoxOptim library. Contributions of each RimK species to the kinetics with multiple species present were determined via optimization using all available data set simultaneously using Michaelis-Menton equations for the enzymatic reactions. Glutamation of the population was computed using a stochastic simulation that consisted of the addition of glutamate unit, the removal of a unit, or doing nothing.
, 2020; Lu, 2020). Clinical trials have been recently registered to evaluate a combination of lopinavir/ritonavir and IFNα2b (ChiCTR2000029387) or a combination of lopinavir/ritonavir with ribavirin and IFNβ1b administered subcutanously (NCT04276688) for the treatment of COVID-19. The administration by vapor inhalation currently performed in China offers the advantage of targeting specifically the respiratory tract; however, to the best of our knowledge, the pharmacodynamics and pharmacokinetics of this mode of administration have never been assessed. On the contrary, the intravenous and subcutaneous modes of administration are well-described, have already proven safe in several clinical trials, and have similar pharmacodynamics and pharmacokinetics  (Mager and Jusko, 2002). The combination of IFN-I with lopinavir/ritonavir, ribavirin or remdesivir could improve its efficacy, because of the efficiency of such combinations observed in vitro in other coronaviruses  (Sheahan et al.
, 2020). It might also be relevant to evaluate type III IFN for the treatment of COVID-19  (Lokugamage et al. , 2020) , because of the protective effects of this interferon type in the respiratory tract. Subcutaneous IFNβ1a in combination with lopinavir/ritonavir is compared to lopinavir/ritonavir alone, hydroxychloroquine, and remdesivir in the DisCoVeRy trial (NCT04315948), which is the first clinical trial of the WHO Solidarity consortium of clinical trials. In conclusion, IFNβ1 may account for a safe and easy to upscale treatment against COVID-19 in the early stages of infection. Similar treatments had a mixed efficiency against MERS-CoV and SARS-CoV viruses, but in vitro studies suggest that SARS-CoV-2 could be substantially more sensitive to IFN-I than other coronaviruses. The current lack of animal model for COVID-19 should not prevent the clinical evaluation of IFN-I treatment, since its safety has already been assessed in numerous independent clinical trials.
Acknowledgments The DisCoVeRy French Trial Management Committee includes the following members: Principal Investigator:  Florence Ader. Scientific coordinator:  Yazdan Yazdanpanah. Chief  methodologist: France Mentre. Infectious diseases specialists:  François-Xavier Lescure ,  Nathan Peiffer-Smadja. Intensivists:  Lila Bouadma ,  Julien Poissy ,  Jean-François Timsit. Virologists:  Bruno Lina ,  Florence Morfin-Sherpa. Pharmacologist:  Gilles Peytavin. Genetic: Laurent Abel. Viral modeling:  Jeremie Guedj. Clinical Trial Unit: Methodologists:  Charles Burdet ,  Cedric Laouenan. Statisticians:  Drifa Belhadi ,  Axelle Dupont. E-CRF designers and data managers:  Basma Basli ,  Anissa Chair ,  Samira Laribi ,  Julie Level Marion Schneider ,  Marie-Capucine Tellier. Project manager:  Aline Dechanet.