dataesr/scientific-paragraphs-categorization huggingface dataset:
We examined the 20 co-occurring phyla that occurred on each ARMS across both metabarcoding datasets. The model assessed the effect of marker choice (COI versus 18S) on the RRAs of each phylum. To tease apart the effects of the spatiotemporal variables, we incorporated treatment (year and site) as a random effect. We then ran a Bayesian mixed model to compare the taxonomic differences of the COI and 18S data from the sessile fraction of each ARMS to the visually annotated ARMS plate data, again using the packages brms  (Bürkner, 2017) , tidybayes  (Kay, 2019) , and rstan  (Stan Development Team, 2019). This analysis included the seven cooccurring phyla on each ARMS across all datasets: Annelida, Bryozoa, Chlorophyta, Chordata, Mollusca, Porifera, and Rhodophyta. We recalculated RRAs after subsetting each dataset to include these seven phyla.
The model examined the impact of quantification technique (COI, 18S, or visual annotation) on the RRAs of each phylum. We included treatment (year and site) as a random effect. For both models, we applied a log-transformation (log(x)) on the OTU relative abundances and fitted the model to a student t distribution to avoid outliers that would heavily impact the model  (Gelman et al. , 2013). The models were run with four chains for 2000 iterations, each containing 1000 warm-up samples. We specified weakly informative priors with a uniform distribution (b ~ uniform (-100,0)) given that each value, as a predicted relative abundance, should not exceed one, and our data were log-transformed. For each model, the posterior predictive distributions reflected the observed data, and all model parameters had stable trace plots.
| RE SULTS Following quality filtering and the removal of singletons, the final COI OTU table included 3,964,674 sequences and 31,900 OTUs; for 18S, the final OTU table included 3,696,915 sequences and 25,994 OTUs. In the COI dataset, an average of 41. 1% of the sequences across samples were unidentified to any taxonomic level, representing 49. 44% (15,770) of all OTUs. In contrast, in the 18S dataset, only an average of 0. 62% of the sequences across samples were unidentified to any taxonomic level, representing 0. 8% (209) of all OTUs. There was a higher phylum-level taxonomic diversity represented in the 18S dataset, which recovered 51 phyla as compared to 38 phyla in the COI dataset. Of these phyla, 20 phyla co-occurred across each ARMS in both datasets, and 65% of these co-occurring phyla were metazoans.
Of the 10 phyla with the highest average relative abundances across ARMS (across years and sites) in each dataset, 9 out of 10 of the phyla were co-occurring (Figure  4 ). Only the phyla with the 10th highest average relative abundance in the COI dataset (Ochrophyta) and 18S dataset (Platyhelminthes) did not occur in the top 10 phyla of the other dataset. Notably, the average relative abundance of Platyhelminthes was extremely low across the COI dataset as compared to the 18S dataset (COI = <0. 01%, 18S = 1. 47%). Overall, Porifera had the highest average relative abundance in the COI dataset, which was considerably higher than in the 18S dataset (COI = 17. 22%, 18S = 8. 98%), and Chordata had the highest average relative abundance in the 18S dataset, which was substantially higher than in the COI dataset (COI = 3.
94%, 18S = 18. 31%). Arthropoda had the second highest average relative abundance in both datasets and occurred at a similar frequency (COI = 16. 33%, 18S = 18. 19%); however, the rest of the phyla differed in rankings between the two markers. A higher proportion of the 18S data (6. 71%) as compared to the COI data (1. 61%) comprised of the "Other" category (i. e. , sum of all remaining taxa that were identified to phylum but did not rank in the top 10 phyla). Several strong differences emerged between the two markers upon modeling the relative abundances of the 20 co-occurring   5 ). The most notable differences occurred in the predicted relative abundances of Amoebozoa, Ascomycota, Bryozoa, Chlorophyta, Chordata, Entoprocta, and Nematoda. Of these seven phyla, five of them were rare across both datasets (see Table  S3  for means ±95% credible intervals), so the discordant results between the markers for these phyla are less relevant for community wide patterns.
However, the predicted relative abundances of Bryozoa and Chordata were comparatively high, with both ranking in the top 10 most abundant phyla in the COI and 18S datasets. Consistently across all spatiotemporal variables, Bryozoa exhibited a markedly higher predicted relative abundance in the COI versus 18S dataset, whereas Chordata had a substantially lower predicted relative abundance in the COI versus 18S dataset. The COI marker also predicted a slightly higher relative abundance for Porifera as compared to the 18S marker. F I G U R E 2 The spatiotemporal variables had a minimal impact on the distribution of phyla for both the COI and 18S markers (Figure  S3 ). Year had a larger effect size than site, as was also showcased in the nMDS ordination (Figure  3 ). The only notable trends were the slight increase of Mollusca and Entoprocta in 2013 as compared to 2012.
Importantly, time and space resulted in comparatively minimal differences in taxonomic composition between the two markers. Finally, notable differences emerged in the comparison the molecular markers and the image annotation from ARMS plates CoralNet. When the analysis to the sessile fraction in the molecular datasets, the marker continued to represent the highest phylum-level diversity, with 44 phyla appearing in the 18S dataset as compared to 32 phyla in the COI dataset. Of these phyla, 14 phyla co-occurred across each ARMS in both datasets. We only scored nine phyla across the annotated ARMS plates; this number was limited by the standardized, predefined categories used for ARMS annotations. Modeling the relative abundances of the seven co-occurring phyla among the COI dataset, 18S dataset, and the annotation of ARMS plates revealed marked differences across three phyla: Bryozoa, Chlorophyta, and Chordata (Figure  6 ).
The COI marker predicted similar relative abundances as the annotated ARMS plates for Bryozoa and Chordata, whereas the 18S marker predicted substantially lower relative abundances for Bryozoa and higher relative abundances for Chordata (see Table  S4  for means ±95% credible intervals). For Chlorophyta, the COI and 18S markers predicted markedly lower relative abundances than the annotated ARMS plates, but this phylum was comparatively rare across all datasets. The COI marker and the annotated ARMS plates also predicted similar relative abundances for Porifera, but the predictions were slightly lower for the 18S marker. The molecular markers and annotations provided remarkably similar predicted relative abundances for Mollusca and Rhodophyta.
| DISCUSS ION The standardized sampling provided by ARMS creates a unique opportunity to rigorously assess potential sources of biases in DNA metabarcoding of marine eukaryotic biodiversity. Although marker choice did not impact estimates of OTU richness and overall diversity patterns, the COI and 18S datasets provided distinct pictures of eukaryotic taxonomic diversity. 18S metabarcoding identified 25% more phyla and resulted in vastly higher identified OTUs than COI metabarcoding. However, COI more faithfully recovered diversity patterns of sessile organisms on ARMS plates, as supported by visual image analysis. In contrast to marker choice, year and location of ARMS deployment had a minimal impact on eukaryotic communities. Thus, consistently across small-scale spatial and temporal variations, the taxonomic representation of different "universal" metabarcoding markers provided skewed estimates of biodiversity patterns.
| Markers matter One of the major dissimilarities between the two markers was the more than 50-fold difference in unidentified OTUs in the COI (15,770 OTUs; 49. 44% of all OTUs) compared to the 18S (209 OTUs; 0. 8% of all OTUs) datasets. Importantly, COI failed to significantly represent (<0. 01% across the dataset) several taxa that had considerable occurrences across the 18S dataset, such as Entoprocta (1. 17%), Platyhelminthes (1. 47%), and Nematoda (0. 82%). Previous studies highlight that COI does not perform well in Platyhelminthes or Nematoda  (Andújar et al. , 2018; Leray & Knowlton, 2017; Prosser et al. , 2013). However, Platyhelminthes and Nematoda only represented ~2. 5% the total OTUs recovered by 18S, so the poor performance of COI for these two phyla does not account for the nearly 50% of unidentified OTUs in the COI dataset.
The mean posterior distributions (±95% highest density intervals (HDIs) designated by vertical black bars within each curve) are from a Bayesian mixed model. When the HDIs between the two curves do not overlap, this indicates strong differences between quantification techniques taxonomic precision, especially as sampling expands across biogeographic regions and reference databases become more complete  (DiBattista et al. , 2020; Gold et al. , 2020). An important caveat, however, is that the apparent consistency in the proportions of dominant sessile phyla between the COI marker and the annotated ARMS plates required the removal of many rare phyla from the metabarcoding datasets to perform this cross-technique comparison. The removal of rare taxa disproportionately masked the performance of the 18S marker, which recovered the highest phylum-level diversity with 45 phyla for 18S versus 33 phyla for COI.
| Moving forward with metabarcoding One of the main limiting factors in our comparison of the COI and 18S markers was the comparatively high number of unidentified OTUs in the COI dataset (49. 44% of OTUs and 41. 1% of sequences) as compared to the 18S (0. 8% of OTUs and 0. 62% of sequence). High rates of homoplasy in the hypervariable COI gene region make the assignment of OTUs difficult without the presence of reference sequences from closely related organisms  (Deagle et al. , 2014; Leray & Knowlton, 2015 , 2016). Despite the increasing number of COI records available in public databases  (Porter & Hajibabaei, 2018) , the lack of comprehensive COI reference barcode libraries across the wide taxonomic scope of hyperdiverse assemblages (such as coral reefs) remains the greatest challenge to harnessing the full capacity of COI metabarcoding, and the contrasting ability of the more slowly evolving, yet better sampled, 18S marker to identify the same community is striking.
To surmount these limitations, we encourage employing a multimarker approach to metabarcoding across a variety of marine habitats and regions (see also  van der Loos & Nijland, 2020). Such studies are essential to identify "dark" taxa that remain undescribed, even morphologically, and will help prioritize voucherbased sampling to advance catalogues of marine biodiversity and hasten the completion of reference databases. Currently, the scientific community considers taxonomic descriptions as lowimpact science, impeding taxonomists' access to research funding and stalling this fundamental characterization of biodiversity  (Agnarsson & Kuntner, 2007). Although taxonomy-free metabarcoding methods are an alternative, taxonomy often plays a critical role in metabarcoding marine communities, and increasing DNA barcoding library coverage is essential to accurately monitor biodiversity patterns with advanced molecular techniques.
initially scored images manually to train the program, then we used semi-automation for the remaining assignments, but we visually verified automatically scored plates to ensure accuracy. Following standard ARMS protocols (oceanarms. org), we used the following categories for ARMS annotations: bryozoan, sponge, colonial tunicate, red encrusting algae, green algae, foraminifera, calcareous worm tube, soft worm tube, bivalve, hard coral, solitary tunicate, soft coral, no recruitment, and unknown. For each plate, we employed a 15 × 15 point matrix, for a total of 225 annotated points. To facilitate comparison with the COI and 18S metabarcoding datasets, we summarized all annotations at the phylum level (Annelida, Bryozoa, Chlorophyta, Chordata, Cnidaria, Mollusca, Porifera, Protista, and Rhodophyta), and we calculated averages for each phylum across all plates (including tops and bottoms) from a single ARMS.
DATA AVA I L A B I L I T Y S TAT E M E N T Illumina MiSeq FASTA files and high-quality images of the ARMS plates are available on the California Digital Library (https://n2t. net/ ark:/21547/ DXy2). The final OTU tables, CoralNet data, metadata, and R scripts are available on Dryad (https://doi. org/10. 5061/dryad. rr4xg xd8r). O RCI D Jordan M. Casey.
Report 13 July 2021 https://doi. org/10. 5256/f1000research. 57398. r89096 © 2021 Papoutsi A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Athanasia Papoutsi Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology -Hellas, Heraklion, Greece.
Acknowledgements I used "we" as science is a collective endeavour. Discussions on this topic had begun as early as 2013 with my former PhD Advisor and collaborators from  Institut Pasteur Paris. I also want to acknowledge  M. Humphries ,  F Zeldenrust ,  A. Foust  for their valuable comments on the early draft and  Ms Marini-Audouard  for the proof-reading before submission. An earlier version of this article can be found on bioRxiv (doi: https://doi. org/10. 1101/2021. 04. 02. 438177 ).
Version 1 Reviewer 1 Report 13 July 2021 https://doi. org/10. 5256/f1000research. 57398. r89096 © 2021 Papoutsi A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Athanasia Papoutsi Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology -Hellas, Heraklion, Greece.
Study design PsABio (NCT02627768) is an international, prospective, observational, cohort study designed to evaluate the persistence, effectiveness and tolerability of ustekinumab versus TNFi as a first-line, second-line or third-line bDMARD in PsA. Each patient is followed biannually for up to 3 years, with a first analysis performed once all patients have reached the 6-month time point (figure  1 ). Outcomes are focused on achievement of cDAPSA low disease activity (LDA)/remission and MDA/VLDA and analysing predictors of reaching cDAPSA LDA or MDA.
Patients were excluded if they were treated beyond third line, had received an investigational drug, vaccine or invasive medical device within 30 days before study start, or were currently enrolled in an interventional study. Data were collected at baseline, then every 6 months with a window of ±3 months for flexibility with standard clinical practice. Data came from patients' medical records, including available patient-reported outcomes data, and were collected and entered into an electronic case report form, except for physicianreported and investigator-reported scales/assessments, which were recorded on paper forms. Patients who stopped/switched ustekinumab or TNFi were retained and followed up on their new treatment (another TNFi or bDMARD, or a csDMARD or tsDMARD, or no additional therapy).
Treatment effectiveness The following data were recorded for both ustekinumab and TNFi to allow comparison of effectiveness at 6 months. PsABio focused on the composite disease activity measures cDAPSA LDA and remission,  17 20  and achievement of MDA and VLDA. 21 DAPSA is based on the summation of four variables: tender joint count of 68 joints (TJC68), swollen joint count of 66 joints (SJC66), Patient Global Assessment (PtGA) visual analogue scale (VAS, in cm) and patient pain (PtP) VAS. cDAPSA LDA is defined as a score of ≤13, and cDAPSA remission as a score of ≤4. 17  The MDA/VLDA criteria assess seven domains (cut-offs): TJC68 (≤1); SJC66 (≤1); enthesitis (Leeds Enthesitis Index.
Statistical analyses Data validation, development of a detailed analysis plan and all statistical analyses were performed by or under the authority of the sponsor (Janssen Pharmaceutica NV, Beerse). In this analysis, the full analysis set (FAS) included patients who completed the 6-month initial treatment period, plus those who switched/ stopped their original treatment during the 6-month follow-up. The safety set included all patients with baseline and any available follow-up data. Partially missing data were imputed where required for analysis. For validated scales, missing items were imputed according to recommendations of the scale developers. Percentages were calculated over non-missing data. In addition to observed case analysis, endpoint analysis used the last observation carried forward (LOCF).
Patient disposition Of 991 enrolled participants, 930 were eligible and had baseline data (figure  1 ); 62 were not included in the FAS owing to unavailability of effectiveness data. The FAS comprised 868 patients for whom both baseline and follow-up data to month 6 were available (426 ustekinumab, 442 TNFi), including 28 (6. 6%) patients who switched/stopped ustekinumab and 44 (10. 0%) who switched/stopped TNFi during the first 6-month period. The completer analysis set comprised 796 patients (online supplemental table  S3 ). The safety analysis set comprised 455 patients in the ustekinumab group and 470 in the TNFi group (n=925 with follow-up data; figure  1 ).
GSK-343 accelerates inflammation resolution and prevents subsequent cardiac dysfunction development in mouse model of MI Next, we evaluated the effects of GSK-343 treatment on cardiac inflammation and cardiac function in a mouse model of MI induced by permanent left coronary ligation. As GSK-343 is a poorly soluble epigenetic drug, we used cyclodextrin-based Captisol ® , as previously described for daily intraperitoneal injections  25. Because our in vitro data indicated that EZH2 inhibition promotes immunomodulatory functions in monocytes, we first examined circulating and cardiac immune cell levels post-MI using flow cytometry. In line with previous reports, circulating as well as cardiac classical pro-inflammatory monocyte populations were found to be increased at 3 days after MI. Interestingly, GSK-343 treatment significantly reduced classical blood monocyte (Ly6C hi CXCR3 hi )/inflammatory (M1)-like levels while increasing cardiac levels of the same population in both infarct scar and border zone at 3 days post-MI (Fig.
Gene expression profiling by RNA-Sequencing For RNAseq, we used total RNA extracted from 3 independent non-coronary patients treated with 5 µM GSK-343 or vehicle for 72 h therefore representing three biological replicates. Total RNA was sent for library preparation, mRNA sequencing and bioinformatics analysis to Novogene Company (Novogene company Ltd, Cambridge, UK). 400 ng of total RNA with a concentration > 20 ng/ml and a RIN ratio > 8 were used for Poly-A mRNA-seq, sequencing length of 150 nt paired-end (PE150) with the Illumina NovaSeq 6000. Reads were aligned to the human genome build hg38 and the transcript assembly GRCh38 from Ensmbl using Hisat2 version 2. 0. 5  38. Transcript quantification was performed with featureCounts from the subread package  39. Differential analysis, including normalization, was performed with DESeq2 bioconductor package version 1.
22. 1  40  using R version 3. 5. 1. Genes significantly upor down-regulated upon epigenetic combined drug treatment (adjusted p-value < 0. 05) were submitted to DAVID Gene Ontology Analysis version 6. 7  41, 42. To generate heat maps, expression values in FPKM for genes within the selected GO Categories were retrieved. Heat maps were generated with Cluster version 3. 0  43  and JavaTree View version 1. 1. 6r4  44. RNA-Seq data are available into the public GEO database (GSE165543) with the following protected access for reviewers using the provided link and token: Link: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE165543 Token: mvmvwsogdhszlez.
ChIP-seq data analysis We downloaded the all sets from the ENCODE portal (Sloan et al. 2016) (https://www. encodeproject. org/) with the following identifiers: ENCSR267NWZ, and ENCSR000ASK respectively for human CD14 + monocytes H3K4me3 (GSM1003536) and H3K27me3 (GSM1003564) ChIP-seq data sets. Reads were aligned to the hg38 genome build using Bowtie2 version 2. 3. 4. 1  45, 46. Unmapped and duplicate reads were removed with Samtools version 1. 5  47. Peak calling was performed with MACS2  48, 49  using default parameters for H3K4me3 samples and with the addition -borad option with H3K27me3 samples. Overlapping peaks were identified with the intersect Bed tool from the bedtools package version 2. 26  50. Annotation of common H3K4me3 and H3K27me3 peaks was performed with PAVIS  51.
Cardiac functional analysis Non-invasive echocardiography was performed as described  37  3-and 7-days post-MI and GSK-343 treatment. Briefly, mice were anesthetized using Isovet -2% (Osalia, Cat#3248850) and echography was performed with VEVO 3100 ultrasound echograph equipped with a MX550D probe (Fujifilm VisualSonics Cat#51073-45). Short axis view of the left ventricular was performed at the level of papillary muscle and M-mode tracing was recorded. Doppler was used to calculate Velocity Time Integral (VTI) in pulmonary artery. Left ventricular diameter was measured by image analysis using software VEVOLAB V3. 1. 1.
Paulus Mulder,  Thomas Duflot ,  Nicolas Perzo  and  Jérémy Bellien  for critical commenting on experiments;  Gaëtan Riou  from CyFlow flow cytometry and cell analysis facility platform ( Institute for Research and Innovation in Biomedicine, IRIB ) for his help in performing flow cytometry experiments and analysis;  Manon Lefrançois ,  Florian Vallin  and  Paul Rouault MSc  students for experimental help. We thank the  ENCODE Consortium  and the  Bradley Bernstein, Broad Institute of MIT and Harvard ENCODE production laboratory  for generating the GSE29611 ChIP-seq data sets, and more particularly for generating respectively  GSM1003536  and  GSM1003564 human CD14+monocytes H3K4me3  and  H3K27me3 ChIP -seq data sets.
Data and Statistical Analysis The data was analyzed using the ThermoFisher Connect ™ online application (ThermoFisher). The mRNA content relative to the secreted proteins was normalized to GAPDH and GUSB expression, that of the receptors was normalized to GAPDH and TBP expressions and miRNA content was normalized using the global normalization method. Then, relative expression and expression fold changes were calculated following the DCt and 2 DDCt methods, respectively. Briefly: DCt = Ct gene -Ct endogenous control and DDCt = DCt treated sample -DCt untreated control. Heatmaps and principal components analysis (PCA) were generated with Clustvis online software  (48). All miRNA and gene Taqman Low Density Array data are available from the ncbi database: https://www. ncbi. nlm. nih. gov/geo/ query/acc.
cgi?acc=GSE165327. Genes and miRNAs interactions were further explored with the Ingenuity Systems ™ Pathways Analysis (IPA) tool (http://www. ingenuity. com). We used GraphPad Prism 8 for statistical analysis. Student's t-test was used on DCt to compare treated samples to their controls and on DDCt to compare H37Rv-infected PMA differentiated THP-1 to H37Rv-infected CD14 + (monocyte-derived macrophages) MDM responses. ANOVA with multiple comparisons were used on DDCt to compare H37Rv-infected macrophages to M. marinum-infected macrophages and to HKMT-stimulated macrophages. PCA and hierarchical or unsupervised clustering were performed using Clustvis online software with the correlation distance method and average linkage. Relative gene expression or gene expression fold changes as indicated were centered and unit variance scaled.