dataesr/scientific-paragraphs-categorization huggingface dataset:
The phylogenetic tree obtained with these sequences globally fits the tree obtained with 16S rRNA gene sequences, suggesting little horizontal gene transfer between these species (Fig. 4 ; also see Fig. S8 ). In a search against the Tara Oceans data collection, a unique significant hit was obtained with contig TARA_076_MES_0. 45-0. 8_C13306631_1. This sequence was acquired from a water sample obtained at a depth of 800 m from a mesopelagic zone in the South Atlantic Ocean (Tara station 076). In a search of metagenome data FIG 3 Three-dimensional model generated by Swiss-Model for FP0506 with PDB 3C37 as a template in a standard orientation  (42). Regions with local QMEAN scores below 0. 6 (local reliability threshold) are shown in black. The active site cleft is in the center. Features characteristic of gluzincin, including the active helix (cyan), the glutamate helix (magenta), the backing helix (orange), and the three beta sheets (yellow), are highlighted.
e. , FPC 840, KU 060626-4, and KU 060626-59), as all 3 of the strains were proficient in elastin degra-FIG  4  Phylogenetic tree of FP0506-homologous proteins. For the genera Chryseobacterium and Elizabethkingia, the branches have been collapsed for better reading. A complete phylogenetic tree, including accession numbers, is available in Fig. S8  in the supplemental material. dation. Conversely, the 2 rainbow trout-associated strains belonging to ST90 (i. e. , IT2 and FRGDSA 1882/11) and the 3 Coho salmon-associated strains (i. e. , OSU THCO2-90, FPC 831, and NCIMB 1947 T ) were devoid of elastinolytic activity. Therefore, it seems that there is a general tendency linking elastinolytic ability and strain genotype, as suggested previously  (21). It would be of interest to extend this analysis to additional isolates in order to perform sound statistical correlations among strain genotype, host fish, and virulence.
This element is conserved upstream of the trp operon in F. psychrophilum, indicating that cells facing amino acid starvation may conjointly express the biosynthetic pathway for Trp, a rare amino acid in nature, and the elastinolytic protease that could liberate amino acids and peptides to be used as bacterial nutrients. FP0506 does not display any significant homologies with known elastases. Because of remote similarities with several MPs belonging to different families, discrepancies regarding the prediction results were noticed, as follows: (i) BLASTp results were poorly informative, even with the MEROPS database dedicated to peptidases; (ii) InterProScan predicted only the lipoprotein secretion signal (positions 1 to 22), in perfect agreement with the results obtained with LipoP; and (iii) the COGnitor unique prediction was COG2321, a member of the Zn peptidase superfamily cl19825.
In addition, the latter homology encompassed only 101 amino acid residues, corresponding to one-third of the full-length protein. Therefore, we used HHPred, which is based on pairwise comparisons of HMM profiles and is presumed to be more sensitive than the previously mentioned tools  (40, 41). Indeed, HHPred was run using the FP0506 protein as a query (after signal peptide removal) with the Pfam-A (version 31. 0) and PDB_mmCIF70 (version 28) databases. Significant results were obtained using both databases, suggesting that the FP0506 protein belongs to the zincin tribe and gluzincin clan of MPs  (33). Regardless of the prediction tool used (Swiss-Model, Robetta, RaptorX, or I-Tasser), the only slightly similar 3D structure was PDB 3C37, which corresponds to a putative Zn-dependent peptidase (UniProtKB accession no.
(43). Alternatively, these proteins may correspond to a zymogen with a fourth zinc-blocking residue, as reported for PDB 3KHI of Klebsiella pneumoniae  (44). Within the gluzincin clan, three families harbor the HExxH and ExxA motifs. Accordingly, FP0506 might belong to the M48/M56 intramembrane MP family, the anthrax lethal factor family, or the neprilysin family or to an undescribed protein family. All FP0506-homologous proteins identified using the NCBI nonredundant database were members of the family Flavobacteriaceae. Most were identified in marine members of the family, while others were retrieved from pathogenic representatives. Intriguingly, some FP0506-homologous proteins were identified in alga-degrading bacteria, such as some Zobellia species. It is possible that these bacteria experience living stages during which they are associated with elastin-possessing organisms (e.
g. , marine invertebrates) instead of algae, and FP0506-homologous proteins (such as ZGAL_1019) might be required for elastin degradation in this context. A single, unique, FP0506 homologue was identified in the Tara Oceans data collection. This unique hit was rather surprising, because this collection encompasses 243 ocean microbiome whole-genome sequencing assemblies and 111,530,851 predicted genes. One possible explanation could be that FP0506-homologous genes belonging to bacteria that do not occur in the water column but are attached to some marine animals have passed unnoticed, since such ecological niches were not sampled during the Tara expedition. Intriguingly, in searches using other metagenome data sets, only very limited numbers of FP0506-homologous proteins were identified. The best hit using the MGnify tool also corresponded to a deep sea metagenome sample.
The sequence was 97% identical to that from the Tara Oceans data collection, suggesting that the two belong to the same or highly related taxonomic groups of bacteria. Finally, the next three significant hits corresponded to human microbiome samples (i. e. , stool, skin, and oral cavity samples), suggesting that human-associated bacteria, as observed previously for some Elizabethkingia and Chryseobacterium species, do possess this gene. Therefore, we propose that FP0506 and homologous proteins are likely required by some pathogenic bacteria for efficient tissue penetration and dissemination, acting as spreading factors.
MATERIALS AND METHODS Bacterial strains and growth conditions. The strains, plasmids, and primers used in this study are listed in Table  1. Detailed data on the F. psychrophilum isolates (i. e. , fish host, country and year of isolation, and culture collection) are available in Table  S1  in the supplemental material. Escherichia coli strains S17-1 and MFDpir  (45)   Elastin degradation assay. Proteolytic activity on solid medium was visualized by plating 10 l of stationary-phase (48-h) bacterial culture on TYES agar supplemented with 0. 75% (wt/vol) elastin from bovine neck ligament (product no. E1625; Sigma-Aldrich). The ability of F. psychrophilum strains to hydrolyze elastin was recorded after 4 to 10 days of incubation at 18°C, by observation of a zone of clearing surrounding the bacterial growth.
Protein homology search and phylogenetic tree reconstruction. Searches for elastase-homologous proteins were performed using FP0506 from strain JIP02/86 to query the nonredundant protein sequence database, the Tara Oceans data collection (http://bioinfo. szn. it/tara-blast-server), and the MGnify data collection (https://www. ebi. ac. uk/metagenomics) for microbiome data sets. Proteins with at least 40% sequence identity and 80% query sequence coverage were retrieved. Sequence alignment was performed using the MUSCLE tool (version 3. 8. 31) implemented in UGENE software (version 1. 31) (47), and the resulting alignment was visualized. Sequence alignment was manually checked, and sequences displaying inconsistencies (i. e. , errors in start site prediction) were discarded. Phylogenetic tree reconstruction was performed after peptide signal removal (to increase the signal/noise ratio), using neighbor joining and the Jones-Taylor-Thornton distance matrix model.
UGENE software  (47)  was used for sequence alignment, curation, and tree building. Tree accuracy was measured by bootstrap resampling of 100 replicates. The resulting tree was drawn using FigTree (version 1. 4. 3) (http://tree. bio. ed. ac. uk/ software/figtree). A cgMLST tree (Fig. 1 ) was built using 1,549 conserved single-copy genes. Unique identifiers were attributed to the distinct allele types defined on the basis of the multiple DNA sequence alignments, and a tree was built with the neighbor-joining approach, as reported by Duchaud et al. (12). Bioinformatic tools for protein sequence analysis, classification, and prediction of structural characteristics. Lipoprotein signal prediction was performed using the LipoP 1. 0 server  (48). Inter-ProScan 5  (49)  was used to search the InterPro database  (50).
ACKNOWLEDGMENTS This study was funded by the  EU EMIDA  and by the  Agence National de la Recherche  (grants  ANR-14-CE19-0020-01  and  ANR-17-CE20-0020-01 ). We are very grateful to  Brigitte Kerouault  and  Robert Larocque  for skillful technical assistance and to  Gurvan Michel  and  F. Xavier Gomis-Rüth  for fruitful discussions.
The code is available on Github (github. com/JeanDanielJulien/auxinTransport). Simulations results were analyzed and figures were made with Matlab. Table  1  summarizes the values of the parameters used in the present work. Table  1  Values of the parameters used in the simulation: Parameters were estimated from previous models  [22, 44, 17]. Sti↵ness dependence on auxin concentration was chosen to display 5-fold variations, in the range of realistic values  [29]. The tension resulting from hydrostatic pressure was then chosen to yield a typical deformation of 4%, in the range estimated from osmotic treatments  [32]. Finally, PIN1 insertion was tuned so that the fraction of transporters inserted in the membrane is around 0. 7, similar to experimental estimates  [32]. The transporters pointing toward this cell are also removed.
Participants' data From a total of 334 recruited participants, 44 participants (13%) gave up participation between the learning phase and the final test, four participants were excluded because they did not complete the learning phase entirely, and one was excluded for completing the learning phase twice in a day, so we had a total of 285 participants that were included in the analysis. Demographic data are indicated in Table  1. An independent-samples t-test revealed no significant differences between the three groups in terms of age and education level (ps > 0. 05). The sex ratio did not differ either (χ 2 (2) = 0. 32 and p = 0. 85). We also asked participants to estimate their degree of knowledge on DNA on a scale from 1 ("I do not have any knowledge on DNA") to 5 ("I have extensive knowledge on DNA").
Furthermore, 11 generalisation questions were also created to measure indirect benefit of the retrieval practice. The answers were not literally included in the learning material, but required inferring or synthesising information across several texts (e. g. , placing cell constituents correctly on a picture of a cell that was not included in the learning material). Finally, nine "catch" questions were interspersed throughout the training quizzes and the final test to ensure participants were paying proper attention and were not answering randomly (e. g. , What is the colour of Henri IVth's white horse?), and to be used as exclusion criteria (data from participants who correctly answered fewer than seven of the nine "catch" questions during the training phase were excluded). The final test therefore included a total of 52 questions: 26 randomly selected from the set of the training phase's questions (i.
DATA AVAILABILITY The preregistration for this experiment can be accessed at http://aspredicted. org/ blind. php?x=wg7nj6. An initial version of the preregistration planned final test at both 7 and 28 days post training. However, during the preparation of the experiment, we decided to drop the 28-day test, hence the new version of the preregistration. Deidentified data, and materials are posted on Dataverse website with the link:  35  https://doi. org/10. 7910/DVN/XPYPMF.
e. , including solar forcings and volcanic aerosols). The ensembles are carried out using coupled (oceanatmosphere) climate models or atmosphere-only models, which are forced by observed sea-surface temperature (SST) and sea-ice concentrations  7. The atmosphere-only approach has been the dominant approach up to date leading to several large data bases designed for extreme event attribution  21, 23, 24. Both types of model approaches (coupled or atmosphere-only) simulate a nonstationary climate given that the radiative and the marine boundary forcing (in the atmosphere-only experiments) change over time. A reliable ensemble response to these forcings is fundamental as illustrated in the following example. The example uses the case of high temperatures during Northern Hemisphere summers (June-to-August (JJA)) and warm Southern Hemisphere winters, respectively, which have been the subject of many event attribution  [2] [3] [4]  and physical process studies  [25] [26] [27].
The reliability is here expressed as 1 minus the reliability component to obtain a positive orientation of the metric (a value of 1 denoting perfect reliability 32 ) r ¼ 1 À X K k¼1 n k N o k n k À p k 2 : ð1Þ The reliability is computed for the two observational datasets and then averaged to account for observational uncertainties. Ensemble probabilities and the FAR. FAR compares the probability that an extreme event occurs in the ensemble using all radiative forcings (P ALL ) and the counterfactual ensemble comprising of natural forcings alone (P NAT ) as FAR ¼ 1 À P NAT P ALL. The probabilities are here estimated by fitting a Normal distribution to all ensemble members. FAR is calculated in each year for the period 2001-2010 and then averaged in order to obtain a more robust signal. The difference between the calibrated and raw FAR shown in Fig.
t o and t x are respectively the linear trend slopes of the observations and the ensemble mean over the calibration period. The calibration factors are estimated from both alternative observational datasets and then averaged. We note that linear trend slopes are a relatively simple (though easily understood and therefore frequently used) measure to estimate long-term changes. However, the assumption of linear changes may not always be justified, and more sophisticated approaches  11  to evaluate long-term changes may be useful to refine the outlined calibration approach. Also note that ideally the calibration should take into account the difference between the current and a "natural" climatology. However, in the absence of observations from a natural climate the evaluation of long-term transient changes over a period with reasonably trustworthy observations appears reasonable to calibrate the long-term climate response.
Acknowledgements This work has been supported by the  EUropean CLimate  and weather Events:  Interpretation and Attribution  ( EUCLEIA ) project, funded by the  European Union 's  Seventh Framework Programme  [ FP7/2007-2013 ] under Grant agreement no. 607085  as well as the  Horizon 2020 EUCP EUropean Climate Prediction system  under Grant agreement no. 776613  and by the  Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017  grant reference  RYC-2017-22964. We thank  Antje Weisheimer  and  Tim Palmer  from the  University of Oxford  for their support to conduct this research. We are further indepted to  Fraser Lott ,  Jonas Bhend ,  Stefan Siegert ,  Karsten Haustein , and.
Data availability Model data that supports the findings of this study are available on the following repositories: HadGEM3-A, https://catalogue. ceda. ac. uk/uuid/99b29b4bfeae470599fb962 ?43e90cde3, C20C+: http://www. happimip. org/happi_data/, weather@home: Please consult under https://www. climateprediction. net/. Observational data sources are available on the following repositories: CRU T3. 23. GHCN: ftp/ftp. ncdc. noaa. gov/pub/ data/ghcn/daily/. GPCC.
Maximum chromatin accessibility for IFNB1, IFNL1 and several ISGs was detected at 24 h post infection in cells expressing the GFP reporter (Figures 2D; S2C-D). For IFN-related genes and the NF-κB family members, RELA, RELB, REL, and the related TF, HIVEP1, chromatin accessibility was higher in HIV-infected, CD86-low DCs that were not fully mature as compared to CD86-high, mature DCs (Figure  2D ), suggesting that the chromatin state is linked to DC maturation status. To determine whether mapping open chromatin using ATAC-seq in HIV-infected DCs offered specific advantages over publicly available datasets, we compared our ATAC-seq peaks to steady-state open chromatin data from CD14+ monocytes (Figure  S2C-D ). Several ISGs displayed high chromatin accessibility at baseline (STAT1, STAT2, IRF1, IRF7, IRF9, OASL, HLA-C, and ISG20), with peaks detected in both DC ATAC-seq samples and CD14+ monocyte data available from ENCODE (Figure  S2C ).
, 2015; Bonneau et al. , 2006; Ciofani et al. , 2012; Greenfield et al. , 2010; Madar et al. , 2010)  by integrating results obtained from RNA-seq and ATAC-Seq experiments performed at the bulk level in a time-course fashion (Figure  3A ). Here, prior information on network architecture is derived from the 88k peaks of chromatin accessibility data generated by our ATAC-seq experiments that tracked DC responses to HIV infection  (Figures 2A & 3A). Using curated TF binding motif databases, we established possible TF-gene target relationships by searching ATAC-seq peaks for TF motifs that we located within or up to 1 kb upstream of gene bodies (Figure  3B ). This information was used to build a prior matrix that connected each gene with its possible TF regulators (Table  S4 ). We then estimated TF activity over the course of stimulation based on the combined expression of a TF's predicted gene targets (Figure  3C ).
To improve the overall performance and stability of network inference, several aspects of the method (and key inputs) were tested, such as: different sources of priors (publicly available ENCODE data vs our ATAC-seq data), different TF binding motif databases  (HOCOMOCO (Kulakovskiy et al. , 2013)  vs CisBp 2. 0  (Weirauch et al. , 2014) ), and different model selection methods (Bayesian Best Subset Regression (BBSR) vs Elastic Net (EN)). Additionally, since every computational run is subject to stochasticity in the inference procedure, we evaluated whether network performance could be improved by combining hundreds of individual computation runs (selecting model components such as TF activity estimates that were stable across random subsamples of the structure prior; see STAR Methods). To evaluate the performance of networks inferred using the parameters described above, we used area under precision-recall curves  (Madar et al.
, 2010)  to compare the prediction and ranking of our TF-to-gene target regulatory edges against TRRUSTv2, a goldstandard reference database of human transcriptional regulatory events  (Han et al. , 2018) , and well-known lists of ISGs  (Kane et al. , 2016; Schoggins et al. , 2011; Shaw et al. , 2017). We found that the EN regression model outperformed BBSR when ATAC-seq based networks were benchmarked against TRRUSTv2 (Figures  3E-F ). We further improved precision-recall on TRRUST and known ISGs by bootstrapping 400 individual EN-ATAC inference runs into a converged network (EN-ATAC x400) (Figures  3E-H ; S3C-D). This final EN-ATAC network correctly predicted 90 out of 97 "core" mammalian ISGs  (Shaw et al. , 2017)  to be downstream of IRF and NF-κB family members (Figure  S3E ) and emphasized a role for IRF3 (Figure  S3F ).
Network clustering, differential gene expression analysis, and TF enrichment tests define key subregulatory groups. We have used this DC transcriptional response network to group the expression changes observed into sets of co-regulated genes with high confidence regulatory subnetworks. We first applied a cutoff to visualize the top 75k regulatory edges that displayed the highest confidence beta scores (Figure  4A ) and then partitioned the network using Louvain-modularity clustering into 10 major "neighborhoods" of TF and target gene communities, with each cluster encompassing at least 1% of the total number of genes in the network (DC network topology can be freely explored using Gephi software by downloading the Gephi-formatted supplemental file, available in the STAR Methods section). We were able to assign putative biological functions to 7 out of 10 clusters based on pathway enrichment scores (see STAR Methods).
(Figure  6B  and S6A-B). Thus, an IFN response is not sufficient to explain the differences in DC maturation status following HIV infection. Consistent with our "DC Maturation" gene set results, the NF-κB family members (RELB, REL, NFKB1, RELA, and NFKB2) showed stronger enrichment in CD86-high than in CD86-low populations (Figure  6D -F) as did additional, unexpected, TFs (CLOCK, THRB, SRF, and HIVEP1) found in clusters 2 (Regulation of Cell Activation) and 8 (NF-κB & Inflammation). Interestingly, pathway analysis of differentially accessible chromatin in CD86-high vs CD86-low conditions suggested that Retinoic Acid Receptor Alpha (RARA), PPARD, NCOR1, and the canonical NF-κB family member RELA (among other TFs) influenced the transition between immature and mature DCs (Figure  S6C-E ). When differentially expressed genes were assessed in CD86-high vs CD86-low conditions in similar fashion, pathway analysis predicted strong enrichment of protein-protein interactions with the RARA partner molecule, RXRA, pathways associated with nuclear receptor transcription, MyD88 signaling, and gene expression changes resembling published datasets from RARA perturbation (highest among other inflammatory activators) (Figures  6G-I ).
Many, but not all of these connections were detected using the Search Tool for the Retrieval of Interacting Genes (STRING) database  (Szklarczyk et al. , 2015) , highlighting the position of RARA in close proximity to TFs involved in controlling cell fate, lipid & sterol metabolism, interferon signaling, and inflammation (Figure  7B ). Knockdown of cGAS or STAT2 led to a pronounced loss of DC maturation during infection with HIV-1-GFP (Figure  7C-D ). In contrast, knockdown of RARA upregulated CD86 at baseline and potentiated DC maturation during infection, suggesting RARA acts as a negative regulator of inflammation. Consistent with this notion, expression levels of many predicted targets of RARA increased in response to LPS and pIC treatment and to a lesser degree in response to HIV-1-GFP (Figure  S7A ).
, 2007) ). Collective analyses of these data will help to further decipher the mechanisms of innate immune regulation that span initiation, feed-forward amplification, and the resolution of aberrant hyperactivation. Understanding DC maturation and IFN production will likely inform future antiviral strategies, since the robust activation of T cells, which is facilitated by mature DCs, is crucial for immunological control of HIV  (Walker and McMichael, 2012). We anticipate that the quality of network inference will further improve as motif databases are refined and large-scale genetic perturbations become tractable in primary human DCs. The network presented herein highlights how the innate response depends on the coordinated action of multiple TFs and serves as a resource for further exploration of pathways that govern HIV replication and innate immunity.
See also Figure  S2  and Table  S3. (B) The prior matrix denotes a possible connection between a TF and gene X if that TF's binding motif is found in accessible chromatin upstream (-1kb) or within gene X. (C) Schematic illustrating how TF activity is estimated by summing predicted target expression over time (red = increase; blue = decrease). (D) TF and gene target connections in the prior network are pruned using an Elastic Net regression to yield the final inferred network. (E) Precision/Recall plots and area under precision/recall (AUPR) curves (F) for the indicated networks scored against the TRRUST database (see STAR Methods). (G) Precision/Recall plots and AUPR curves (H) for the indicated networks scored against known ISGs  (Schoggins et al. , 2011). See also Figure  S3  and Table  S4.