dataesr/scientific-paragraphs-categorization huggingface dataset:
Smoothed conditional mean of the signal was computed using the LOESS smoothing method with bin width span 0. 1 and plotted using R  47. A comparison of the ATAC-seq data generated in this study with selected published ATAC-seq datasets is provided in Supplementary Fig. 11. Whole genome bisulfite sequencing. Genomic DNA was extracted from the aerial portions of 10-day old seedlings using the Wizard ® Genomic DNA Purification Kit (Promega) following manufacturer's instructions. For WT and h2a. w-2 replicates 1 and 2, sodium bisulfite conversion, library preparation, and sequencing on a Hiseq 4000 were performed at the Beijing Genomics Institute (Hong Kong) from 1 µg DNA, producing paired 100-bp (replicate 1) or 150-bp (replicate 2) paired-end reads. For remaining samples, methylated adapters were ligated prior to bisulfite treatment using the ZYMO EZ DNA methylation Gold kit, libraries were prepared using the Nugen ultralow methyl-seq kit and sequenced on NovaSeq 6000.
Reads were trimmed, filtered, and aligned using bowtie2, and multi-mapping reads and PCR duplicates were removed, all as indicated above (see 'ATAC-seq' section). Alignment statistics for each library are available in Supplementary Table  6. Sample tracks and metaplots over genomic regions were obtained using deeptools 58 bamCoverage (-normalizeUsing CPM). All samples except for H3 were normalized to their matched H3 sample using deeptools bamCompare. Plots over entire chromosomes were obtained from average ChIP-seq signal over 1 kb nonoverlapping bins tiled genome-wide and smoothed using the same approach as the ATAC-seq data. H2A. W ChIP-seq data were re-analyzed from ref. 22. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  James Watson  from  GMI  for editing and comments on the manuscript. We thank the  Vienna Biocenter Core Facility Next Generation Sequencing  and we thank  IMBA/IMP/GMI BioOptics  facility for FACS sorting. Work in the Mathieu laboratory was supported by  CNRS ,  Inserm ,  Université Clermont Auvergne ,  Young Researcher  grants from the  Auvergne Regional Council  (to O. M. ), an  EMBO Young Investigator award  (to O. M. ), and a grant from the  European Research Council  ( ERC ,  I2ST 260742  to O. M. ). P. B. was supported by a PhD studentship from the  Ministère de l'éducation nationale , de l'enseignement supérieur et de la recherche. Work in the Berger laboratory was supported by the  Gregor Mendel Institute  core funding from the  Austrian Academy of Sciences  and the  Austrian Science Fund (FWF) :  I2303 ,  P32054 ,  P28320 , and  P26887.
H 2 O 2 measurements H 2 O 2 production by the cells was assayed using the Amplex Ultra-Red® fluorochrome (Molecular Probes, USA). For this, a 50-μL aliquot of algal suspension was collected from the culture vessel and immediately placed in a 96-well plate; 10 μL of 50 μmol solution of Amplex Ultra-Red® in 0. 05 M HEPES buffer (pH 7. 0) were added, and fluorescence was measured using the spectrofluorometer Varioskan Flash-Spectral Scanning Multimode Microplate Reader (Thermo Fisher Scientific, USA) run by the Varioskan Flash SkanIt Software, according to the method described in  Aksmann et al. (2014). To calculate the content of H 2 O 2 produced by algal cells, a standard curve was prepared using stabilized H 2 O 2. The results were given in nmol of produced H 2 O 2 and.
All PCR reactions were performed using the CFX96 Touch™ Real-Time PCR Detection System and the included CFX Maestro™ Software (Bio-Rad Laboratories, Inc. ) for experimental design and data analysis. For analysis, genes encoding for chloroplastic isoform of manganese superoxide dismutase ( chl Mn-SOD), mitochondrial isoform of manganese superoxide dismutase ( mt Mn-SOD) and chloroplastic isoform of iron superoxide dismutase (Fe-SOD) were chosen. In the case of isoforms of catalase and ascorbate peroxidase, the genes: CAT 1 and APX 1 were chosen as the ones, that exhibit the highest expression in C. reinhardtii, overlapping the expression of other CAT-and APX-encoding genes  (Blaby et al. , 2015). The names of the analyzed genes and the design of primers for the RT-PCR reactions are presented in Table  1.
Statistical analysis Statistical analysis was performed using MS Excel 2010 (Microsoft) and Statistica 9. 0 (StatSoft). All data were expressed as the mean of at least three independent experiments with at least 4 replications ± SE. Statistica 9. 0 (StatSoft) was used to calculate a correlation matrix to measure the strength of associations between the different variables. The same software was applied to compute a basic statistic and nonparametric Mann-Whitney U test when the population could not be assumed to be normally distributed. A p-value < 0. 05 was considered significant. For the integrated assessment of the metabolic effects of the studied compounds, a discriminant analysis was performed.
Statistical analysis For the chemical and energy composition of the diets, milk production, N2O, and CO2 were analyzed using the General Lineal Models, using the SAS program (1990), according to the following model: Yijk = μ + Txi+ Pj + Ck + εijk where Yijk = is each treatment observation; μ is the overall average; Txi is the treatment effect (i = 4), Pj is the effect due to the period (j=3), Ck is the effect due to the animal (k=12) and εijk is experimental error. The CH4 production data were analyzed by the GLM procedure, with the SAS program  (SAS 1990 ) according to the model: Yij=μ + Mi + Txj + (Mi x Txj) + εij where: Yijk=CH4 output, μ= is the overall average, M (i=2) is the effect of the model used, Tx (j =4) is the treatment effect, M (i=2) x Tx (j=4) is the effect of the interaction between the model and the treatment used; and εij is experimental error.
The basic cycling conditions of the PCRs were set as follows: initial denaturation at 94°C/2 min, 35 cycles of 94°C/ 30 s, gene-specific annealing for 45 s and 72°C/60 s and the final extension at 72°C for 7 min. PCR products were sequenced as described above. The coding sequence of the two X. laevis urp2 cDNAs has been deposited in the GenBank database under the accession numbers MZ054702 and MZ054703 for urp2L and urp2S, respectively. Data from X. laevis were used to identify the full-length X. tropicalis urp2 cDNA and to decipher organization of the corresponding gene.
Phylogeny and synteny analyses A set of 47 vertebrate UII and Urp precursor sequences was collected from NCBI and Ensembl databases and supplemented by the sequences characterized in the present study. All sequences were aligned using the Clustal algorithm  [24]  then manually adjusted. Molecular phylogenetic relationships were analysed using the neighbour-joining (NJ) algorithm  [25]  with MEGA6 software  [26]. The reliability of phylogenetic trees was assessed with bootstrapping (1000 iterations). The accession numbers of the sequences used in the phylogenetic analysis are shown in the electronic supplementary material, figure S1. Ensembl database (http:// www. ensembl. org/index. html) and Genomicus  [27]  (version 100. 1, https://www. genomicus. biologie. ens. fr/genomicus-100. 01/cgi-bin/search.
Phenotype analysis of utr4 crispants 2. 8. 1. Body curvature analysis Xenopus laevis utr4 crispant and control tadpoles (from stages NF46 to 52) were euthanized, fixed in 4% PFA, stored in 100% methanol and then re-hydrated in PBS. Tadpoles were imaged in a bright field with a Lumenera Infinity 3-6UR microscope camera attached to an Olympus Microscope operated with Infinity Analyze software in an agarose plate submerged in PBS laterally with the head pointing to the left. To define the body curvature, the images were analysed with the Fiji Angle tool. The line was drawn starting from the posterior half of the tail mid line to the head (figure  10 ), and the value of the angle formed in between the two lines was defined by the Angle tool for each tadpole. To evaluate the statistical difference in body curvature in utr4 crispant and control tadpoles, the measured angles were applied to Student's t-test (function t.
Quantification of locomotor activity To record the locomotion of tadpoles, we used a Phantom Miro M110 camera (Vision Research, Elancourt, France) set at 700 or 1000 frames per second (s). Stage NF53 tadpoles were placed in a large Petri dish (30 cm diameter with 3 cm of water depth), stimulated to swim and recorded for 3 s. We recorded five utr4 crispant and three control tadpoles. Three videos per individual were recorded, saved and analysed using ProAnalyst (Xcitex, Cambridge, MA, USA) software. For each frame, the eye was digitized either manually or using the auto-tracking routine implemented in ProAnalyst. Raw coordinates were exported to Excel and smoothed using a zero phase-shift low-pass Butterworth filter with a cut-off frequency set at 50 Hz. Next, we calculated the cumulative distance swum along the path.
Instantaneous velocities and accelerations were calculated by numerical differentiation of the smoothed cumulative displacement profile. We extracted peak velocity, acceleration and deceleration from the kinematic data and further calculated the straight-line distance between the beginning and the end of the video recording. We calculated the sinuosity of the trajectory by dividing the cumulative distance by the straight-line distance. All variables were log 10 -transformed and analysed using IBMSPSS statistics (v. 26). We used univariate analyses of variance to test for differences in swimming kinematics between utr4 crispant and control tadpoles for each variable tested.
Synteny of genes in the urp2 locus in five selected osteichthyan species: human (H. sapiens), chicken (G. gallus), western clawed frog (X. tropicalis), spotted gar (L. oculatus) and zebrafish (D. rerio). Genes are represented by block arrows. The position of the genes (in megabases, Mb) is displayed below each box, according to the Ensembl database. The detailed chromosomal locations of genes displayed in this map are included in the electronic supplementary material, tableS3.
Acknowledgements. We thank  Céline Maurice ,  Jean-Paul Chaumeil  and  Philippe Durand  ( PhyMA, MNHN ) for animal care, and  Anne De Cian  and  Jean-Paul Concordet  ( StrInG, MNHN ) for preparing sgRNA and providing with Cas9 protein. We are grateful to  Thierry Decamps  (Mecadev, MNHN) and  Romane Braun  ( PhyMA, MNHN ) for technical assistance. We also thank  Nicolas Buisine  and  Fabrice Girardot  (PhyMA, MNHN) for helpful discussions.
Synteny of genes in the urp2 locus in five selected osteichthyan species: human (H. sapiens), chicken (G. gallus), western clawed frog (X. tropicalis), spotted gar (L. oculatus) and zebrafish (D. rerio). Genes are represented by block arrows. The position of the genes (in megabases, Mb) is displayed below each box, according to the Ensembl database. The detailed chromosomal locations of genes displayed in this map are included in the electronic supplementary material, tableS3. Urp2 10,4 27,7 1,5 X. tropicalis URP2 10,1 10,2 10,3 10,3 10,4 107,3 107,2 106,9 106,9 33,9 34,0 34,4 34,5 34,6 25,6 27,6 1,6 35,8 Cryz1 Istn1 Atp5po Mrps6 Kcne1 Rcan1 Clic6 10,0 10,0 106,8 106,7 33,5 33,6 81 64 32 17 22 51 32 85 92 64 97 75 68 71 46 87 72 40 100 60 69 11 18 human URP macaque URP chimpanzee URP cat URP cow URP dog URP horse URP squirrel URP tree-shrew URP chicken URP X.
• Studying populations with a history of cardiovascular events is vital as more patients with cardiac conditions are surviving because of healthcare improvements. AMI events were obtained from data in the electronic health record, including data recorded in inpatient and outpatient settings, which increases the sensitivity of capturing adults with underlying CVD; available inpatient diagnosis dates before study start date were collected as well. The Northern California study region included 35 counties where KPNC members reside (Figure  1 ). Study follow-up began on January 1, 2007, with a follow-up period of up to 10 years, to December 31, 2016. Each subject began the study on his or her index date, defined as the first day that all inclusion criteria were met. Follow-up continued to the first of the following dates: death, end of KPNC membership, moving to an address out of the study region or to an address that cannot be successfully geocoded, or were trained on all predictor variables, and predicted PM 2.
5 was obtained from each learner, after which a weighted generalized additive model was used to get a combined PM 2. 5 estimation. Then, spatially and temporally lagged PM 2. 5 predictions from nearby monitoring sites and neighboring days were calculated and used as input variables, along with the existing predictor variables, and fit to the same models described above to get a final PM 2. 5 estimation. The ensemble model was validated with 10-fold cross-validation, and had outstanding predictive performance, with an R 2 of 0. 86 for daily PM 2. 5 predictions and an R 2 of 0. 89 for 1-year PM 2. 5 predictions. Complete residential address histories of all cohort members from 1 year before the index date to the end of follow-up were obtained from the KPNC historical and current residential address databases.
Cardiovascular Event Outcomes We examined 3 primary cardiovascular event end points: AMI, stroke, and cardiovascular mortality. AMI and stroke events were defined using hospitalization records and ICD-9 and ICD-10 codes. An AMI event was defined as an inpatient hospitalization with a principal discharge diagnosis of AMI (ICD-9 code 410. x; ICD-10 codes I21. x-I23. x). A stroke event was defined as an inpatient hospitalization with a principal discharge diagnosis of stroke (ICD-9 codes 430. x-432. x, 433. x1, 434. x1, and 436. x; ICD-10 codes I60. x-I63. x). 1] [22]  As secondary end points, we analyzed AMI and stroke subtypes: ST-segment-elevation myocardial infarction, non-ST-segment-elevation myocardial infarction, ischemic stroke, and hemorrhagic stroke (Data S1). Mortality data were compiled from several sources: official state of California death certificate data, health plan in-hospital mortality, Social Security Administration Death Files, and data from the National Death Index.
Covariates We measured multiple covariates at baseline, including age, sex, race/ethnicity, comorbidities, body mass index (BMI), smoking, revascularization procedures, medication use, and indicators of socioeconomic status (SES). Age, sex, race/ethnicity, BMI, and smoking status were obtained from KPNC electronic health records. Comorbidities included hypertension, hyperlipidemia, and diabetes mellitus: hypertension was determined from diagnosis codes (ICD-9 codes 401. x-404. x; ICD-10 codes I10. x-I15. x); hyperlipidemia was determined from diagnosis codes (ICD-9 code 272. x; ICD-10 code E78. x); and diabetes mellitus was determined from the KPNC Diabetes Registry. Medication use data were extracted for statins and for use of any hypertension medications (angiotensin-converting enzyme inhibitors, angiotensin antagonists, antiadrenergics, β adrenergic blockers, calcium channel blockers, diuretics, vasodilators, and combination antihypertensive medications); hypertension medication use was only included for those individuals with a hypertension diagnosis because some medications can be used for other clinical purposes and many patients with AMI are often treated with β-blockers.
We addressed death as a competing risk for all 3 main outcomes. Last, we fit a Cox regression model with mixed effects (ie, a frailty model) using random intercepts for each county to account for county-level clustering. 34 For all statistical tests, a level of α=0. 05 was used to determine statistical significance. Analyses were conducted using SAS software, version 9. 4 (SAS Institute) and R software, version 3. 5. 1 (The R Foundation for Statistical Computing).
Current data suggest that there is a strong clinical and immunophenotypic overlap between secondary HLH and MAS  (2). Key molecules or pathways that drive HLH/MAS, such as interleukin-1β (IL-1β), IL-6, IL-18, interferon-γ (IFNγ), or JAK/ STAT, can be targeted by state-of-the-art therapies, and ever since the proposal regarding an overlap of (critical) COVID-19 with classic cytokine storm conditions was put forward  (1, 3) , those types of conditions have been considered therapeutic targets in COVID-19 or have already been studied in respective clinical trials (ClinicalTrials. gov identifiers: NCT04372186, NCT04317092, NCT04324021, and NCT04338958). Yet, at the same time, studies addressing the relevance of cytokine storm conditions in COV-ID-19 are frequently limited to discussions focused on the effects of IL-6 (4) and tend to draw conclusions based on comparisons with many different critical clinical conditions or even with healthy controls.
Specific reagents (all purchased from R&D Systems) and the sera were prepared according to the manufacturer's instructions. Data acquisition and analysis were performed on a MAGPIX instrument (Merck Millipore) using xPO-NENT version 4. 2 software (Luminex). Concentrations of S100A12 in the subjects' sera were quantified by sandwich enzyme-linked immunosorbent assay using in-house monoclonal antibodies. Data analysis. Serum marker data were assessed using unsupervised clustering analysis, including correlation distance and ward. D linkage in the pheatmap R package and RStudio platforms (RStudio Team 2015 and RStudio: Integrated Development for R; http://www. rstud io. com/). Principal components analysis (PCA)  of the serum marker expression data was performed using the ggfortify and autoplot R packages and RStudio software.
Multiple serum analytes were assessed for correlations with severe COV-ID-19 using Spearman's rank correlation analyses, with the data plotted using the corrplot R package and RStudio or GraphPad Prism software (version 8. 0 for Mac OS X; GraphPad Software). Data on individual serum markers were analyzed for normality distribution with the D'Agostino-Pearson normality test, using GraphPad Prism software. The majority of the data did not pass this test, and therefore the nonparametric data were tested using a Kruskal-Wallis test followed by Dunn's test for multiple comparisons (GraphPad Prism version 8. 0). Receiver operating characteristic (ROC) curve analyses were performed using GraphPad Prism software.
Elevated serum levels of these markers can indicate general inflammatory activity. However, with respect to the specific clinical presentation of critical COVID-19, increased IL-8 serum concentrations may indeed reflect the pathologic features of ARDS. In patients with ARDS, IL-8 has been shown to enable both neutrophil influx and survival in lung tissue  (18). Correspondingly, the therapeutic efficacy of IL-8 blockade is currently being tested in patients with COVID-19 (ClinicalTrials. gov identifier: NCT04347226). While our analyses suggest that we may identify particular axes of inflammation to contrast COVID-19 from inflammation-or infection-induced cytokine storm as in MAS or secondary HLH, we are well aware of 3 limitations of our study. First, the study is descriptive and limited to a rather small number of patients.
Second, even though we were able to significantly extend our findings beyond those previously reported with regard to associations with IL-6 (4), our serum marker panel still comprises comparably few analytes, but covers those with reported relevance in classic cytokine storm conditions  (2). Third, we carried out our comparisons of serum marker signatures between clinical conditions with a predominant lung involvement (COVID-19) and those with systemic pathology (secondary HLH/MAS). Yet, despite these limitations, we believe our data provide important insights into the proposed overlap between SARS-CoV-2-induced immune dysregulation and classic cytokine storm conditions (3), and raise questions regarding the significance of systemic hyperinflammation in COVID-19  (19). Furthermore, our analyses may raise doubt about the efficacy of clinical trials targeting key molecules and pathways associated with secondary HLH and/or MAS in the treatment of COVID-19.
Therapeutic blockade of IFNγ, which appears to be a promising therapeutic option in the treatment of HLH  (20)  and potentially also MAS  (21) , may be less effective in COVID-19 (ClinicalTrials. gov identifier: NCT04324021) as the overall activation of the IL-18-IFNγ axis seems far less pronounced in the context of SARS-CoV-2 infection. In contrast to IL-18 and IFNγ, the serum levels of IL-1Ra in patients with COVID-19 are substantially elevated. This observation may point to a limited utility of therapeutic IL-1 blockade in patients with COVID-19  (22, 23) , since high endogenous levels of IL-1Ra have been reported to indicate a rather poor response to treatment with drugs neutralizing IL-1β or IL-1 signaling  (24). However, elevated circulating concentrations of IL-1Ra usually reflect an IL-1 signature, and the correct timing of IL-1 blockade in COVID-19 may be critical and likely complicates the interpretation of the present data  (22).
Thus, early intervention upon the development of acute hyperinflammatory respiratory failure in patients with COVID-19 can have a therapeutic effect  (25) (26) (27). Furthermore, albeit at a different level compared to that in patients with secondary HLH/MAS, the IL-18-IFNγ axis is certainly active in patients with critical COVID-19, and targeting this and IL-1 simultaneously may constitute a rescue treatment for extremely ill patients  (28). A corresponding randomized controlled trial is ongoing. Indeed, our data may further support the use of combined medications directed against different targets or the use of medications with broader immunoregulatory effects, such as glucocorticoids or dexamethasone  (29) , and may suggest strategies to bypass low sFasL expression or block IL-8 signaling in the treatment of patients with COVID-19 (ClinicalTrials.
Supported by the  German Research Foundation (DFG)  (grant  FO 354/14-1 ) and the  European Union Horizon 2020 Programme  (grant  779295  [ImmunAID]). research grants from  AB2 Bio , and owns stock or stock options in AB2 Bio. Dr. Foell  has received consulting fees, speaking fees, and/or honoraria from  Chugai-Roche ,  Novartis , and  Sobi  (less than $10,000 each) and research support from  Novartis ,  Pfizer , and  Sobi.
Identification of PF00545 ribonucleases from diverse fungi Hmmscan was run against the Pfam 27. 0 database using the default settings to identify proteins with the PF00545 ribonuclease domain  (Finn et al. , 2014; Eddy, 2011). Searches were run against 32 genomes from diverse fungi (Supporting Information Table  S2 )  (Goffeau et al. , 1996; Galagan et al. , 2003; Loftus et al. , 2005; Dean et al. , 2005; Kämper et al. , 2006; Cuomo et al. , 2007; Espagne et al. , 2008; Coleman et al. , 2009; Martin et al. , 2010; Spanu et al. , 2010; Rouxel et al. , 2011; Kubicek et al. , 2011; Duplessis et al. , 2011; Goodwin et al. , 2011; Klosterman et al. , 2011; Amselem et al. , 2011; Yang et al. , 2011; Berka et al. , 2011; Arnaud et al. , 2012; O'Connell et al. , 2012; Gan et al. , 2013 Gan et al. , , 2016 Gan et al.
, , 2017;; Blanco-Ulate et al. , 2013; Cissé et al. , 2013; Tisserant et al. , 2013; Baroncelli et al. , 2014; Hu et al. , 2014; Gazis et al. , 2016; Zampounis et al. , 2016). Proteins identified with the PF00545 domain were aligned by MAFFT and trimmed using trimAl  (Katoh et al. , 2002; Capella-Gutiérrez et al. , 2009)  using the default automated settings in both programs. The trimmed alignment was then used to construct a maximum likelihood tree with RAxML using the PROTAUTOGAMMA setting and 1,000 bootstrap replicates  (Stamatakis, 2006). The conservation of active sites was assessed by checking for residues corresponding to Aspergillus oryzae ribonuclease T1 (RNase T1) Y64, H66, E84, R103, and H118 in the conserved domain cd00606 (NCBI's conserved domain database)  (Marchler-Bauer et al.