dataesr/scientific-paragraphs-categorization huggingface dataset:
Among the 184 mRNAs tested, 98 genes were detectable (Ct values < 32) in THP-1, including 14 interleukins, 11 interleukin receptors, 23 chemokines, 12 chemokine receptors, 6 interferons, 2 interferon receptors, 11 tumor necrosis factor (TNF) superfamily members, 12 TNF receptors and 7 toll-like receptors (Table  S2  in Supplementary Material). We used principal component analysis (PCA) and unsupervised hierarchical clustering to visualize the relationships between the three sample groups, based on the expression of these genes. PCA analysis displayed 60. 5% of the dataset on the two principal components (Figure  1A ), which was representative of the sample distribution. Interestingly, PCA showed that expression profiles obtained with M. marinum infection and HKMT stimulation were similar, as their distribution overlapped, and that both were very different from H37Rv infection.
This was confirmed by the unsupervised hierarchical clustering analysis (Figure  1B ). H37Rv-infected samples were found further from controls compared to the other two conditions on the PCA and heatmap (Figures  1A, B ), showing that infection with H37Rv induced more drastic changes than the other two experimental conditions. First, we assessed which genes are differentially expressed between uninfected and infected conditions. The UpSet diagram  (49)  represents the intersection between the three sets of differentially expressed genes (DEGs) from the three infections (Figure  1C ). The data indicate that H37Rv infection significantly deregulated 71 DEGs, whereas both other conditions only modulated 48 DEGs. While 36 DEGs were shared between the three conditions, 25 DEGs were specific for H37Rv infection and only 5 and 6 DEGs for M.
We also observed that the miR-132/212 cluster was significantly increased. Using a miRNome published dataset of CD14 + isolated from the blood of TB patients (GSE70425), we compared the expression levels of the 37 H37Rv-related miRNAs between Mtb-infected THP-1 and primary CD14 +. Results showed that 80% of miRNAs displayed comparable expression levels between both conditions (Figure  S1  in Supplementary Material).
mRNA/miRNA Inflammatory Pathways Specific for H37Rv To identify predicted target genes and related pathways and to provide experimentally verified information on miRNA-target interactions, we performed an automated text-mining search analysis. The thirty seven miRNAs identified as dysregulated in H37Rv-infected macrophages were examined using miRWalk database  (50). We found between 11 and 699 putative target genes for each of the 37 miRNAs. We applied a filter consisting in identifying genes targeted by at least 3 miRNAs and found 320 genes (Table  S3  in Supplementary Material). Using the Enrich database  (51) , gene ontology analysis revealed significant pathways with high Enrich combined score level such as cell cycle, FoxO signaling and P53 pathway with adjusted p-values 3. 710 -7 , 1.
110 -5 and 6. 210 -5 , respectively (Table  S4  in Supplementary Material). Interestingly, we found that 28 genes were targeted by at least five miRNAs (Table  S3  in grey color) and almost 70% of them have already been described as involved in Mtb infection (Table  S3  in Supplementary Material). The HSPA1B gene, also called HSP72, is targeted by 9 miRNAs, and the two genes LDLR and NUFIP2 are targeted by 7 miRNAs. The integrative analysis of the 30 and 37 H37Rvspecific host inflammatory response genes and miRNAs, respectively, has been carried out to find possible relationships. In this attempt, we used the Ingenuity Pathway Analysis software program, associated with a miRNA target filter analysis, to identify miRNA-target genes and mRNA-mRNA interactions (Figure  4 ). miR-939-5p, miR-1184, miR-20a-3p, miR-193a-3p, miR-345-p, miR-26b-3p, miR146a-5p, miR-941, miR589-5p, miR-130b-5p, miR-27b-3p, miR-221-3p, miR-744-3p, miR-425-3p, miR-132-3p, miR-181a-1-3p, miR-181a-2-3p, miR-19b-1-5p, miR-30a-5p, miR-1179, miR-455-3p, miR-199b-5p and miR-21-3p, all targeted at least one inflammatory response genes related to Mtb virulence.
Input data, basic corrections, and PMa computation We adopted the Hipparcos catalog at epoch J1991. 25 (van Leeuwen 2007, hereafter 'Hip2', 117 955 sources)  and the Gaia EDR3 catalog  (Gaia Collaboration et al. 2016 , 2021a,b; Gaia Collaboration 2020)  at epoch J2016. 0. For the collection of most of the data used in the present work, we made extensive use of the astroquery library  (Ginsburg et al. 2017 ) distributed as part of the Astropy library (Astropy  Collaboration et al. 2013 Collaboration et al. , 2018) )  to access the ViZieR online database  (Ochsenbein et al. 2000)  at the CDS. For the cross-identification of the Hip2 stars in the EDR3 catalog, we started from the gaiaedr3. hipparcos2_best_neighbour list provided as part of the EDR3  (Marrese et al. 2017 (Marrese et al. , 2019)) , which has 99 525 records (84.
Acknowledgements. The authors gratefully thank the referee,  Dr Andrei A. Tokovinin , for valuable comments and suggestions that led to significant improvements of this paper. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (http://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The authors acknowledge the support of the  French Agence Nationale de la Recherche (ANR) , under grant  ANR-15-CE31-0012-01  (project  UnlockCepheids ). The research leading to these results has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (project  CepBin , grant agreement No  695099 ).
This research made use of Astropy 4 , a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018), of the Numpy library (Harris et al. 2020) and of the Matplotlib graphics environment (Hunter 2007). This research has made use of the Washington Double Star Catalog maintained at the  U. S. Naval Observatory. We used the SIMBAD and VizieR databases and catalogue access tool at the  CDS, Strasbourg (France) , and  NASA 's Astrophysics Data System Bibliographic Services. The original description of the VizieR service was published in Ochsenbein et al. (2000). The Digitized Sky Surveys were produced at the  Space Telescope Science Institute  under  U. S. Government  grant  NAG W-2166. The images of these surveys are based on photographic data obtained using the  Oschin Schmidt Telescope on Palomar Mountain  and the  UK Schmidt Telescope.
Cohort composition Key model parameters are shown in Table  1 , with a full description in Additional file 1: Supplemental Methods  [7, 8, 13, 21,. Primary clinical data, together with published systematic reviews, were used to estimate the number of latent progressors and subclinical progressors (i. e. , people with TB infections or asymptomatic disease, respectively, that would later progress to active disease) within each cohort (Additional file 1: Supplemental Methods 1). The PWH cohort is based on primary data from adults presenting for outpatient HIV testing in Kwa-Zulu Natal, South Africa, from September 2013 through February 2019  [75]. Enrollees were screened for TB based on symptoms, then followed for 12 months for incident TB. Patients who were initially asymptomatic but received diagnoses of TB within 3 months were assumed to have had progressive subclinical TB at enrollment; sputum culture of a representative subset of patients at enrollment  [76]  (Additional file 1: Table  S6 ) supported this baseline prevalence estimate in a population among whom spontaneous resolution of subclinical TB is uncommon  [77].
Coverage of the substrate was monitored by atomic force microscopy imaging (Figure  S1 ). Height histograms show a peak centered at 4. 8 ± 0. 9 nm after the incubation with the hOR1A1, which was attributed to the receptor, in agreement with the sequence homology data obtained for hOR1A1. 22 mple potential (US) and initial probe potential (initial UP) were set at 0. 25 V and 0. 45 V, respectively. Faradaic leakage current was maintained below a few pA through probe insulation. 23 To obtain each I-V plot, the probe was positioned over the sample at a current set point of 0. 4 nA, the feedback loop disconnected and the probe current recorded while a voltage ramp is applied to the EC-STM probe. Up to 150 I-V curves were recorded at different sample positions by sweeping the probe potential back and forth from 0.
ACKNOWLEDGMENT This work was supported by the  Biomedical Research Networking Center (CIBER), Spain. CIBER  is an initiative funded by the  VI National R&D&i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program ,  CIBER Actions , and the  Instituto de Salud Carlos III  ( RD16/0006/0012 ;  RD16/0011/0022 ), with the support of the  European Regional Development Fund (ERDF). This work was funded by the  CERCA Program  and by the  Commission for Universities and Research of the Department of Innovation, Universities, and Enterprise of the Generalitat de Catalunya  ( 2017-SGR-1079 ,  2017-SGR-1442  and  2017-SGR-00465 ). This work was supported by grants from the  Conseil Régional Bourgogne Franche-Comté  ( PARI grant ) and the  FEDER (European Funding for Regional Economical Development).
Confidence intervals were calculated with the ggpredict function of the ggeffects package  68. The model R squared statistic (R 2 ) was computed to quantify goodness-of-fit with the r2glmm package using the Nakagawa and Schielzeth approach  69. Assumptions underlying the models were checked with diagnostic plots of residuals  70. Shapiro-Wilk tests were used to test for deviations from normality in the residuals. A third-degree polynomial regression was used to fit the deviation scores on the number of lines presented (R 2 = 0. 28). The random effect structure included random slopes and random intercepts for each subject. As our main stimulus of interest is the line triplet (Experiment 1) for which redundancy masking was expected to be most pronounced, the analyses in Experiment 2 and 3 focus on the line triplet as well, allowing direct comparisons between the similar and dissimilar contextual conditions.
Motivating example To illustrate the proposed work, we consider surface wind data from a previous work  (Bessac et al. , 2018). Time series of ground measurements from the NOAA Automated Surface Observing System (ASOS) network are available at ftp://ftp. ncdc. noaa. gov/pub/data/ asos-onemin and are extracted at 1 minute resolution. We focus on January 2012 in this study, the data are filtered via moving-average procedure and considered at an hourly level leading to 744 data-points. These ground-station data are considered as verification data in the following. Outputs from numerical weather prediction (NWP) forecasts are generated by using WRF v3. 6  (Skamarock et al. , 2008) , a state-of-the-art numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs.
The NWP forecasts are initialized by using the North American Regional Reanalysis fields dataset that covers the North American continent with a resolution of 10 minutes of a degree, 29 pressure levels (1000-100 hPa, excluding the surface), every 3 hours from the year 1979 until the present. Simulations are started every day during January 2012 with a forecast leadtime of 24 hours and cover the continental United States on a grid of 25x25 km with a time resolution of 10 minutes. Only one trajectory is simulated in this study. As observed in Figure  2 , the uncertainty associated with observation data can affect the evaluation of the forecast. As an extension, if two forecasts were to fall within the uncertainty range of the observations, it would require a non-trivial choice from the forecaster to rank forecasts.
Imperfect verification data and underlying physical state In verification and recalibration of ensemble forecasts, an essential verification step is to find data that precisely identify the forecast events of interest, the so-called verification dataset (see, e. g. ,  Jolliffe and Stephenson, 2004). Jolliffe and Stephenson (2004) , in Section 1. 3 of their book, discussed the uncertainty associated with verification data such as sampling uncertainty, direct measurement uncertainty, or changes in locations in the verification dataset. Verification data arise from various sources and consequently present various types of errors and uncertainties such as measurement error  (Dirkson et al. , 2019) , design and quality: e. g. , gridded data versus weather stations, homogeneization problems, sub-sampling variability  (Mittermaier and Stephenson, 2015) , or mis-matching resolutions.
Related literature Uncertainty and errors arise from various sources such as the forecast and-or the verification data (predictability quality, uncertainty, errors, dependence and correlation, time-varying distribution) and the computational approximation of scores. A distribution-based verification approach was initially proposed by  Murphy and Winkler (1987) , where joint distributions for forecast and observation account for the information and interaction of both datasets. Wilks (2010)  studied the effect of serial correlation of forecasts and observations on the sampling distributions of Brier score and in particular serially correlated forecasts inflate its variance. Bolin and Wallin (2019)  discussed the misleading use of average scores, in particular for the continuous ranked probability score (CRPS) that is shown to be scaledependent, when forecasts have varying predictability such as in non-stationary cases or exhibit outliers.
Hidden state and scoring Scores used in practice are evaluated on verification data y, s 0 (F, y). We define the "ideal" score as s 0 (F, x), where x is the realization of the hidden underlying "true" state that gives rise to the verification data y. Ideally, one would use x to provide the best assessment of forecast F quality through s 0 (F, x); however since x is not available, we consider the best approximation of s 0 (F, x) given the observation y in terms of L 2 -norm via the conditional expectation. For a given score s 0 , we propose the following score version s ∨ (. , y): s ∨ (F, y) = E (s 0 (F, X)|Y = y) (1) where X is the true hidden state, Y is the random variable representing the available observation used as verification data, and F is the forecast cdf to be evaluated. One can view this scoring framework incorporating information about the discrepancy between the true state and the verification data in terms of errors and uncertainty in a Bayesian setting.
Codes and data Codes and data used for this study can be found at https://github. com/jbessac/uncertainty_ scoring. In our case, the variable U = s o (f, X) and s ∨ (f, y) = E [U |Y = y]. This gives E [s ∨ (f, Y )] = E [s o (f, X)]. To show inequality (2), we use the classical variance decomposition V [U ] = V [E [U |Y = y]] + E [V [U |Y = y]]. With our notations, we have V [s o (f, X)] = V [E [s o (f, X)|Y = y]] + E [V [s o (f, X)|Y = y]] , = V [s ∨ (f, Y )] + a non-negative term. This leads to V [s o (f, X)] ≥ V [s ∨ (f, Y )].
Acknowledgements We thank  Aurélien Ribes  for his helpful comments and discussions. We also thank the  Office of Science and Technology of the Embassy of France in the United States, Washington, DC , for supporting our collaboration through the  initiative Make Our Planet Great Again. The effort of  Julie Bessac  is based in part on work supported by the  U. S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR)  under Contract  DE-AC02-06CH11347. Part of  P. Naveau 's work was supported by the  European DAMOCLES-COST-ACTION  on compound events, and he also benefited from  French national programs , in particular  FRAISE-LEFE/INSU, MELODY-ANR ,  ANR-11-IDEX-0004 -17-EURE-0006  and  ANR T-REX AAP CE40.
In addition, lignin-derived phenols  [10] [11] [12]  , photoactivated pigments such as chlorophyllin  9, 13  and protein partners like cellobiose dehydrogenases (CDH)  14  , are all possible electron donors for LPMO in vivo activity  15. Widely distributed across the entire tree of life  16  , the LPMOs are particularly abundant in bacterial and fungal kingdoms  17. According to the carbohydrate-active enzyme database (www. cazy. org), LPMOs are classified into seven "auxiliary activity" (AA) families: AA9-11 till AA13-16. The genome of several organisms (especially phytopathogenic fungi) features multiple LPMO gene copies. For example, Fusarium graminearum and Botrytis cinerea count, respectively, 13 and 10 AA9 isoforms  18  , a family generally active against cellulose and xyloglucans 8.
b Sequence-based comparison of BcAA9s with AA9 enzymes biochemically and structurally characterized. The phylogenetic tree was created based on the alignment of catalytic domain. Colors indicated the oxidative regiospecificity: C1, blue; C4, green; C1/C4, red; not characterized, black. The most expressed BcAA9s during B. cinerea-Arabidopsis interaction are labeled with arrows. c Structural comparison between TtAA9E and BCIN_12g03920 was built using the Swiss-Model Server and the TtAA9E crystal structure was obtained from the protein data bank (PDB:3EII). The structural models are shown in ribbon representation and copper ion is shown as a blue sphere. were proportional to time, acting stronger at 24 h than at 6 h (Fig. 2d , e and Supplementary Fig. 5a ). The transient and rapid generation of ROS is a hallmark of the response to microbe-associated molecular patterns (MAMPs) and chemical elicitor treatments  26, 39, 40.
3a ). Conversely, cellobiose treatment modulated 352 genes, of which 283 were upregulated and 69 were downregulated compared to mock-treated plants (Fig. 3a ). A principal component analysis explained 53% of the combined variance and clearly separate mock-, cellobiose-, and AA9_COStreated samples (Fig. 3b ). To assess the microarray data reproducibility, quantitative real-time PCR (qRT-PCR) analysis was performed on ten different genes (Supplementary Fig. 6 ). Microarray and qRT-PCR data showed a high and significant correlation (R 2 = 0. 89, false discovery rate (FDR) = 3. 9e -8 ; Supplementary Fig. 6 ). To identify the biological processes in which the AA9_COS-responsive genes were involved, Gene Ontology (GO) and pathway enrichment analyses were performed (Supplementary Data 1). Most of the detected GO terms were related to plant defense, including responses to bacterial and fungal attacks (Fig.
c, d Gene Ontology (GO) enrichment analysis related to biological processes of 482 upregulated by AA9_COS (c) and 283 genes upregulated by cellobiose treatments (d). The GO analysis was performed by using the g:Profiler software (Benjamin-Hochberg FDR < 0. 05) and the EnrichmentMap function of Cytoscape  60. Each node represents a pathway, edges represent overlapped genes between nodes, and circle size represents the number of genes encompassed. the MYB TFs family were either upregulated or downregulated by AA9_COS treatment (Fig. 5  and Supplementary Fig. 8 ). Among the MYBs upregulated by AA9_COS, we found MYB51 gene, a major regulator of the MAMP-dependent indole glucosinolate accumulation in Arabidopsis  42. Besides the canonical defenseassociated TFs, we also observed members of bHLH, C2H2, and HSF families being modulated by AA9_COS (Fig.
Genomic DNA from groundfrozen samples was extracted and fungal biomass was quantified by qRT-PCR analysis using primers specific for B. cinerea (BcTub) and Arabidopsis (AtSK11)  37. Three independent pools (n = 3) were so replicated. The B. cinerea symptomatology was performed in 5 detached leaves collected from three 5-week-old Arabidopsis or 2-month-old tomato plants (n = 3) by inoculating 10 μL droplet (5 × 10 5 spores mL -1 ) per leaf. Three days after fungus inoculation, pictures were taken with a photo camera and the areas of the developing necrotic lesions were analyzed by the ImageJ software. This experiment was repeated three times. The B. cinerea B05. 10 strain was grown on potato dextrose agar medium at 21 °C, with a photoperiod of 12 h light (100 µmol photons m -2 s -1 )/12 h darkness.
Sequence alignment, phylogenetic analysis, and structural model. The annotated AA9s from B. cinerea BCIN_15g03140, BCIN_05g08230, BCIN_06g00480, BCIN_12g03920, BCIN_03g05890, BCIN_11g05360, BCIN_09g06750, BCIN_09g06730, BCIN_02g02040, and BCIN_06g07050 were aligned with the AA9s biochemically and structurally characterized AfAA9B (PDBid 5X6A), CvAA9 (PDBid 5NLT), HjAA9A (PDBid 5O2W), HiAA9B (PDBid 2VTC), LsAA9A (PDBid 5ACF), MtAA9D (PDBid 5UFV), NcAA9A (PDBid 5FOH), NcAA9C (PDBid 4D7U), NcAA9F (PDBid 4QI8), NcAA9M (PDBid 4EIS), PcAA9D (PDBid 4B5Q), TaAA9A (PDBid 2YET), and TtAA9E (PDBid 3EII) using MUSCLE  57. Phylogenetic analysis was performed with the MEGA software 58 version 10. 1. 7 using the neighbor-joining method. The consensus tree was inferred using a bootstrap of 1000 replicates.
The structural model of BCIN_12g03920 was generated using the Swiss-Model Automated Comparative Protein Server and using LsAA9A (PBDid 5ACF) as template. Protein structures were visualized with PyMol Molecular Graphics Systems (Version 1. 5. 0. 4 Schrödinger, LLC, New York, NY, USA). RNA extraction, cDNA preparation, and qRT-PCR analysis. Total RNA was extracted from 100 mg of ground-frozen samples using the Spectrum™ Plant Total RNA Kit (Sigma-Aldrich), and the RNA purity was determined through NanoDrop 2000 UV-Vis Spectrophotometer (Thermo Scientific, Loughborough, UK). Firststrand cDNA was synthesized by using SOLIScript RT cDNA synthesis MIC (Solis Biodyne) and qRT-PCRs were performed using SYBR Selected MasterMix 2× (HOT FIREPol® SolisGreen® qPCR Mix, Solis Biodyne), following the manufacturer's protocol in a MyiQ real-time PCR detection system (Biorad).
The cycling conditions consisted of an initial 7 min at 95 °C, followed by 40 two-step cycles at 95 °C for 5 s and 60 °C for 30 s. Melting curve analysis was performed after cycle completion to validate amplicon identity. Relative expression levels for plant gene expression were calculated following the standard curve-based method by using ACTIN2 and Elongation Factor-1α as reference genes  59. Relative expression levels for B. cinerea AA9 genes in planta and in PDA medium growth were calculated following the standard curve-based method by using BcTUB as reference gene. Gene-specific primers used in this work are listed in Supplementary Table  3. All the qRT-PCRs were measured twice from 3 independent (n = 3) pools each consisting of 15 leaves. Microarray hybridization and data analysis. RNA sample preparation for microarray hybridization was carried out as described in the Applied Biosystems TM GeneChip TM Whole Transcript (WT) PLUS Reagent Kit User Guide (Thermo Fisher Scientific, Waltham, MA, USA).
4 software) and the DEGs were selected using two criteria: log 2 FC ≥1 or ≤-1 and Student's t test p value ≤0. 05. Average linkage hierarchical clusters of DEGs were generated by using Cluster 3. 0 and visualized by Java Treeview. GO, pathway enrichment analysis, and network analysis. GO analysis related to the biological process was performed by the Cytoscape software using upregulated genes (log 2 FC >1, p < 0. 05) through the g:Profiler software using Benjamini and Hochberg FDR (<0. 05) as filtering step accordingly to refs. 60, 61. In addition, to better visualize the results of the GO analysis, terms were clustered by using the "Autoannotate" in Cytoscape tool, thus obtaining 10 clusters associated with defense responses. The network analysis was performed using the "Expression Correlation" app of Cytoscape software 62.