dataesr/scientific-paragraphs-categorization huggingface dataset:
halophilum presents a new genus and a new species. The comparison with the three closely related species shows that the genome size of P. halophilum is similar to that of P. fulgidum (3,36 Mb), K. eburnea (3,53 Mb) and slightly higher than M. thermohalophilus (3,19 Mb) (Table  S1 ). Besides, the GC% content of P. halophilum is comparable to the GC% of M. thermohalophilus (52,9%), while it is slightly lower compared with GC % of P. fulgidum (58,5%) and K. eburnean  (54,1%). In this database search, 3527 proteins were deduced from 3,702 CDSs (CoDing Sequence), which are more important than K. eburnean (3360) M. thermohalophilus (3074) and P. fulgidum (3223). Also, the tRNAScan-SE predicted that a total of 57 tRNAs in the genome of strain SMBg3, that are similar to K. eburnea (57 tRNAs), M. thermohalophilus (56 tRNAs) but more important than P.
Genes involved in ectoines biosynthesis and degradation The second objective of the study was to identify, based on the draft genome of P. halophilum, biosynthetic and catabolic pathways of ectoines under salt stress conditions. While the bacterial genome lacked all the genes for ectoines degradation, it contains the whole canonical ectABCD ectoine/hydroxyectoine biosynthetic gene cluster, a diaminobutyrate-2-oxoglutarate transaminase (ectB), a L-2,4-diaminobutyric acid acetyltransferase (ectA), an ectoine synthase (ectC), and an hydroxyectoine synthase (ectD) (Fig. 6 ). The nucleotide blast results extracted from NCBI database showed that genes ectA, ectB, ectC and ectD of P. halophilum shared 100% identity with those of Streptomyces chrysomallus. However, ectoine/hydroxyectoine biosynthetic gene clusters often contained other genes involved in either the transcriptional regulation (ectR) of the ect operon, the provision of the precursor L-ASA (asD) from aspartyl-P and the aspartyl-P from aspartate (asK_ect), or sometimes, even a gene for a mechanosensitive channel (mscS)  (Reshetnikov et   We found that P.
When the whole genome sequence of P. halophilum was sequenced and taxonomically analyzed using the Mauve software pipeline, it was found to be taxonomically clustered with K. eburnea, P. fulgidum and M. thermohalophilus as shown by construction of a phylogenomic tree (Fig. 2 ). To further infer the evolutionary relatedness of the strain relative to closely related phylogenetic species, we ran EDGAR analysis and results showed that P. halophilum harbor 43 distinct genes which were not found in the other three closely associated species (Fig. 5 ). The 43 distinct genes found in P. halophilum, made up approximately 1. 7% of the total genome size of the strain. These distinct gene determinants were likely salt resistance proteins, transcriptional regulators, transporter proteins, and for sporulation proteins (Table  S2 ).
Introduction The problem of time series forecasting is to estimate the future values of a process from a sequence of its observations. This task is important because it has many practical applications. Examples include predicting future stock prices and air temperature forecasting. Nowadays, there are many different approaches to solving this problem. Classical statistical models such as exponential smoothing and the autoregressive integrated moving average (ARIMA) model are very popular, highly accurate, and relatively easy to use. A detailed description of these methods can be found in  [1, 2]. Neural networks  [3] [4] [5]  are also widely used, especially on large datasets. However, there is no best method for all situations, and the development of new forecasting techniques remains relevant.
lcacomp (https://code. google. com/archive/p/lcacomp/)-a grammar-based data compressor proposed in  [22] ; 2. Re-Pair (https://github. com/nicolaprezza/Re-Pair)-a grammar-based compressor, the implementation is described in  [23] ; 3. zstd (https://github. com/facebook/zstd)-a fast lossless data compression algorithm, developed by Facebook; 4. bzip2 (https://www. sourceware. org/bzip2)-a data compressor based on the Burrows-Wheeler transform; 5. zlib (https://zlib. net/)-a data compression library that implements the DEFLATE algorithm; 6. ppmd (https://github. com/Shelwien/ppmd_sh)-an implementation of the prediction by partial matching (PPM) algorithm; 7. zpaq (https://github. com/zpaq/zpaq)-a journaling archiver optimized for userlevel incremental backup of directory trees; 8. automaton (https://github.
Experiments In this section, we consider sunspot numbers forecasting. Sunspots are temporary spots on the Sun with reduced surface temperature that appear darker than the surrounding areas. The sunspot number time series is provided by the WDC-SILSO (World Data Center-Sunspot Index and Long-term Solar Observations)  [26]. The daily, monthly, yearly and 13-month smoothed monthly total sunspot numbers can be found on the SILSO site http://www. sidc. be/silso/datafiles. Here we used the monthly mean total sunspot number series. We accessed the SILSO site on 20 June 2020, at that time the series had 3257 observations. The entire series is shown in Figure  2. It has an obvious cyclical component and it's known that the approximate cycle lengths is 11 years  [27]. The Space Weather Services (SWS) of the Australian Bureau of Meteorology issues forecasts for the WDC-SILSO sunspot numbers and maintains an archive of this forecasts (http://listserver.
4. For brevity, let us denote the combination of all 8 algorithms, obtained using (3), as joint method. Code lengths for different compressors, obtained by compressing the entire series (100% of values), are presented in Table  2. We can see that by code length zstd is the best compressor for this series and hence it will have the greatest contribution to the result of the joint method. For 10% trough 40% of the series values the best compressor by code lengths is ppmd, then for 50-100% zstd becomes the best compressor. The code length for ppmd and zstd are shown in Table  3. We ran the program 5 times using all 8 algorithms and 5 more times using the adaptive algorithm (50% of the values were used to select the best compressor). The average time required to compute one 4-step forecast was 9945.
Data Compressor Code Length (Bits) Data Compressor Code Length (Bits) zstd Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: https://novosibstat. gks. ru/folder/31734 (average number of unemployed persons in Novosibirsk region), https://novosibstat. gks. ru/folder/31847 (average annual monetary income of the population in Novosibirsk region), http://www. sidc. be/silso/datafiles (monthly mean total sunspot numbers), http://listserver. ips. gov. au/pipermail/ips-ssn-predictions/ (SWS observed and predicted solar indices).
Overexpression of inflammatory pathways, NF-kB signaling, and metabolic changes related to hypoxia in acute infection and postacute hyperinflammatory conditions To gain further insight into the mechanisms behind acute infection and postacute hyperinflammation driven by SARS-CoV-2 in children, we assessed pathways modulated in each group by looking at the differentially expressed genes obtained from the scRNA-seq dataset. In individuals with acute infection (Acute-Inf (CoV-2 + ) and Acute-Inf (CoV-2 À )), the numbers of differentially expressed genes were distributed homogeneously among monocytes/DCs and T and B cells (Figures  4A  and S4A ). Pathway enrichment analyses revealed a decrease in oxidative phosphorylation coupled with an increase in HMGB1 signaling, HIF-1a signaling, and hypoxia signaling (Figure  4B ).
All patient data are available in Table  S1. Introduction of specific treatments for MISC and KD cases was decided by the pediatrician in charge of the patient; it generally consisted of intravenous polyvalent immunoglobulins [IVIG] alone (2g/ kg in one or two infusions), or IVIG associated with methylprednisolone (2-10 mg/ kg/day for at least 3 days) as first or second-line therapy. Patients with negative initial serology testing were retested after an interval of at least 3 weeks (Architect SARS-CoV-2 chemiluminescent microparticle immunoassay; Abbott Core Laboratory). Healthy controls were recruited before the COVID-19 pandemic (before November 2019).
Samples For each patient and healthy donor, peripheral blood samples were collected on EDTA and lithium heparin tubes. After a centrifugation of the EDTA tube at 2300rpm for 10 minutes, plasma was taken and stored at À80 C before cytokine quantification. PBMCs were isolated from the lithium heparin samples, frozen as described below and stored at À80 C and were used for both bulk and single-cell RNaseq, as well as cell phenotyping by CyTOF. The number of samples included in each dataset is summarized in the metadata table (Data S1) and the workflow is summarized in Figure  1B.
Cells were washed and resuspended at a concentration of 1 million cells per mL in Maxpar Cell Acquisition Solution, a high-ionic-strength solution, and mixed with 10% of EQ Beads immediately before acquisition. Acquisition of the events was made on the Helios mass cytometer and CyTOF software version 6. 7. 1014 (Fluidigm, Inc Canada) at the ''Plateforme de Cytometrie de la Pitie-Salpetriere (CyPS). '' An average of 500,000 events were acquired per sample. Dual count calibration, noise reduction, cell length threshold between 10 and 150 pushes, and a lower convolution threshold equal to 10 were applied during acquisition. Mass cytometry standard files produced by the HELIOS were normalized using the CyTOF Software v. 6. 7. 1014. For data cleaning, 4 parameters (center, offset, residual and width) are used to resolve ion fusion events (doublets) from single events from the Gaussian distribution generated by each event.
80 After data cleaning, the program produces new FCS files consisting of only intact live singlet cells. These data were analyzed in FlowJo v10. 7. 1 using 3 plugins (DownSampleV3, UMAP and FlowSOM) with R v4. 0. 2. To increase efficiency of the analysis, samples were downsampled to 50,000 cells, using the DownSample V3 plugin. All samples were concatenated and analyzed in an unsupervised manner. Anti-CD127 antibody had to be excluded due to poor staining. Clustering was performed using FlowSOM. 70 The number of clusters was set to forty-five in order to overestimate the populations and detect smaller subpopulations. Grid size of the self-organizing map was set to 20x20. Resulting clusters were annotated as cell populations following the kit manufacturer's instruction. When several clusters were identified as the same cell types, they were concatenated into a single cell population.
Following fragmentation, end repair and A-tailing, sample indexes were added during index PCR. The purified libraries were sequenced on a Novaseq 6000 (Illumina) with 28 cycles of read 1, 8 cycles of i7 index and 91 cycles of read 2. Sequencing reads were demultiplexed and aligned to the human reference genome (GRCh38, release 98, built from Ensembl sources), using the CellRanger Pipeline v3. 1. Unfiltered RNA UMI counts were loaded into Seurat v3. 1  68  for quality control, data integration and downstream analyses. Apoptotic cells and empty sequencing capsules were excluded by filtering out cells with fewer than 500 features or a mitochondrial content higher than 20%. Data from each sample were log-normalized and scaled, before batch correction using Seurat's FindIntegratedAnchors. For computational efficiency, anchors for integration were determined using all control samples as reference and patient samples were projected onto the integrated controls space.
On this integrated dataset, we computed the principal component analysis on the 2000 most variable genes. UMAP was carried out using the 20 most significant principal components (PCs), and community detection was performed using the graphbased modularity-optimization Louvain algorithm from Seurat's FindClusters function with a 0. 8 resolution. Cell types labels were assigned to resulting clusters based on a manually curated list of marker genes as well as previously defined signatures of the well-known PBMCs subtypes. 81 Despite filtering for high quality cells, five clusters out of the twenty-six stood out as poor quality clusters and were removed from further analysis, namely: one erythroid-cell contamination; one low UMI cluster from a single control; two clusters of proliferating cells originating from a patient with EBV co-infection and one megakaryocytes cluster.
In total 152,201 cells were kept for further analysis. After extraction and reclustering of high-quality cells, differential expression was performed separately on all PBMCs, monocytes/DCs, T cells or B cells. Differential expression testing was conducted using the FindMarkers function of Seurat on the RNA assay with default parameters. Genes with log(FC) > 0. 25 and adjusted p values %0. 05 were selected as significant. Differential analysis results and links to the pathways analysis in EnrichR  34, 35  can be found in Data S2. Transcriptomic signatures for Type I and Type II interferon signaling were performed using Seurat's AddModule-Score function, based on interferon-stimulated gene lists extracted from Rosenberg et al. 82  and Reactome database respectively. Violin plots were performed using Seurat's VlnPlot function.
91 using Hisat2 (v2. 0. 4)  71  and counted using featureCounts from the Subread R package. The raw count matrix was analyzed using DESeq2 (version 1. 28. 1). 72 No prefiltering was applied to the data. Differential expression analysis was performed using the ''DESeq'' function with default parameters. For visualization and clustering, the data was normalized using the 'variant stabilizing transformation' method implemented in the ''vst'' function. Plots were generated using ggplot2 (version 3. 3. 2), and pheatmap (version 1. 0. 12). During exploratory analyses, it was noted that the clustering was mainly driven by the sex of the patients. To remove this effect, it was included in the regression formula for DESeq (~sex + groups), and then removed following vst transformation, using ''removeBatchEffect'' from the ''limma'' package (version 3.
Gene signature analysis To identify genes that could be used as markers of severe myocarditis in the SC-RNA-SEQ dataset, three initial strategies were used, all based on differential expression and selection of the upregulated genes. First, we performed the differential expression between MIS-C_MYO (CoV-2 + ) samples and all other samples. Second, differential analysis was computed between MIS-C_MYO (CoV-2 + ) and other samples with postacute hyperinflammatory illness. In the last strategy, we selected genes that were upregulated between the MIS-C_MYO (CoV-2 + ) and the CTL, but not upregulated in any other group compared to the CTL (Figure  7A ). These three strategies allowed us to identify 329 unique genes. To further explore whether these genes could be considered as markers of severe myocarditis, we analyzed their expression profile in our bulk RNA-SEQ dataset.
This dataset excluded samples from patients of the MIS-C_MYO (CoV-2 + ) that were included in the SC-RNA-SEQ cohort. Vst-transformed counts were log2normalized and converted to Z score using the scale function in R (v 4. 0. 2). A Gene-SCORE was computed for each group as the mean Z score of the samples of a group. Heatmaps representing this GeneSCORE group were performed using pheatmap. Hierarchical clustering of the 329 previously identified genes was performed using the complete method on the distance measured using Pearson's correlation, as implemented by pheatmap. The hierarchical clustering was divided into 15 main clusters, 4 of which had the expected pattern of expression: Clusters that had a higher expression in MIS-C_MYO (CoV-2 + ) than any other group were selected, resulting in 116 genes.
A signature score for each sample was performed on these genes, corresponding to the mean expression (Z score) of these N genes in each sample (SignatureSCORE). These genes were subsequently ranked based on the following equation: RankingSCORE = GeneSCORE MISÀC MYO ðCoV 2 + Þ À ðGeneSCORE MISÀC ðCoV 2 + Þ + GeneSCORE KD ðCoV 2ÀÞ Þ where the SCOREs represent the mean expression (Z score) in each disease groups, and the SignatureScore was computed on the top 25 genes. All gene lists and scores can be found in Data S3.
QUANTIFICATION AND STATISTICAL ANALYSIS Cytokine heatmaps were made with Qlucore OMICS explore (version 3. 5 (26)) and dot plots with GraphPad Prism (version 8). Differentially secreted cytokines were included in the heatmaps based on a 1. 5-Fold Change (FC) comparison between groups as indicated. Dot plot differences between each group were identified by Kruskal-Wallis tests followed by post hoc multiple comparison Dunn's test. Statistical tests for cellular composition analysis in both the CyTOF and SC-RNA-SEQ datasets were performed in R v3. 6. 1. Kruskal-Wallis test followed by post hoc multiple comparison Dunn's test was applied to assess differences in cell population proportions (*: p % 0. 05; **: p % 0. 01; ***: p % 0. 001). Differential expression testing in the SC-RNA-SEQ dataset was conducted using the Find-Markers function in Seurat, with default Wilcoxon testing.
p values were controlled using Bonferroni correction. Genes with an absolute log(fold-change) R 0. 25 and an adjusted p value % 0. 05 were selected as differentially expressed. Pathways analysis was performed using both the Ingenuity pathway analysis v57662101 software (IPA (QIAGEN Inc. ) and EnrichR. 34, 35 Heatmaps were extracted from the comparison module in IPA. Pathways with an absolute Z score lower than 2 or a Bonferroni-Hochberg corrected p values higher than 0. 05 were filtered out. Reactome 2016 and Molecular Signature DataBase Hallmark 2020 (MSigDB Hallmark 2020) pathway enrichment analysis was performed using En-richR. The TRRUST transcription factors 2019  21  used for the transcription factors enrichment analysis was performed using Enrich R. All differential analysis performed and the links to the EnrichR results are indicated in Data S2.
On the left, names of each group with their corresponding color are shown. (B) Heatmap of expression of the 116 of 329 genes with a higher expression in MIS-C_MYO (CoV-2 + ) than in other groups in the bulk dataset generated from PBMCs (7 CTL, 7 MIS-C (CoV-2 + ), 9 MIS-C_MYO (CoV-2 + ), and 9 KD (CoV-2 À ) donors). The color scale indicates the scaled GeneSCORE (mean Z score of the gene in all samples of a group), with red and blue representing the highest and lowest expression, respectively. Hierarchical clustering of the genes was computed with a Pearson's correlation as a distance. (C) Boxplot of the expression of the 116 genes validated in (C), calculated as a SignatureSCORE, which represents the mean Z score in each sample of the 116 genes selected in (B) in the bulk RNA-seq dataset (FigureS7A).
(D) Boxplot of the SignatureSCORE of the top 25 genes, as ranked in FigureS7B, in the bulk RNA-seq dataset. (E) Graphical representation based on cytokines,cellular, and transcriptomics analyses (above the black dotted line), combined with known literature (below the black dotted line), illustrating a putative model explaining the occurrence of myocarditis among children in the MIS-C (CoV-2) group. Black represents genes and functions modulated in the MIS-C (CoV-2 + ) and MIS-C_MYO (CoV-2 + ) groups compared with CTL, whereas red highlights genes and pathways differentially modulated in the MIS-C (CoV-2 + ) and MIS-C_MYO (CoV-2 + ) groups, respectively. See also FigureS7and Data S3. (C and D) Each mark represents a sample. Dots represent untreated samples, triangles represent IVIG-treated samples, and squares represent IVIG-and steroid-treated individuals.
ACKNOWLEDGMENTS The study was supported by the  Institut National de la Santeet de la Recherche Medicale (INSERM) , the '' URGENCE COVID-19 '' fundraising campaign of  Institut Pasteur, the Atip-Avenir  and  Emergence Ville de Paris programs and Fond de Dotation Janssen Horizon , government grants managed by the  Agence National de la Recherche  as part of the '' Investment for the Future'' program  ( Institut Hospitalo-Universitaire Imagine  grant  ANR-10-IAHU-01 ,  Recherche Hospitalo-Universitaire  grant  ANR-18-RHUS-0010 , and  Laboratoire d'Excellence ' ' Milieu Interieur' ' grant  ANR-10-LABX-69-01 ), the  Centre de Reference Deficits Immunitaires Hereditaires (CEREDIH) , the  Agence National de la Recherche  ( ANRflash Covid19 ''AIROCovid '' to F. R. L. and ''CoVarImm'' to D. D. and J.
D. S. ), and the  FAST Foundation (French Friends of Sheba Tel Hashomer Hospital). The LabTech Single-Cell@Imagine is supported by the  Paris Region  and the '' Investissements d'avenir'' program through 2019 ATF funding -Sesame Filieres PIA  (grant  3877871 ). C. d. C. is the recipient of a CIFRE PhD (Sanofi). L. B. was a recipient of an  Imagine Institute PhD international program  supported by the  Fondation Bettencourt Schueller. L. B. was also supported by  EUR G. E. N. E. (reference   ANR-17-EURE-0013 ) and is part of the Universitell Med 2,  1072-1092 , September 10, 2021 1089 Clinical and  Translational Article de Paris IdEx  # ANR-18-IDEX-0001  funded by the  French government  through  its ''Investments for the Future'' program. S. M. was a recipient of an  INSERM and Institut Imagine post-doctoral program  supported by the  Fondation pour la Recherche Medicale  ( FRM SPF20170938825 ).
grant  ANR-10-IAHU-01 ;  Recherche Hospitalo-Universitaire , grant  ANR-18-RHUS-0010 ;  Laboratoire d'Excellence ''Milieu Interieur,''  grant  ANR-10-LABX-69-01 ;  ANR-flash Covid19 ''AIROCovid'' and ''CoVarImm'') ,  Institut National de la Santeet de la Recherche Medicale (INSERM) , and the '' URGENCE COVID-19'' fundraising campaign of Institut Pasteur.
In these shallow nearshore environments, riverine inputs and high rates of sedimentation and sediment resuspension likely lead to a high degree of connectivity between coastal waters, rivers and sediments. Given these tight links between land, water and sediments, samples were also collected in several rivers feeding the fjord arms, and from sediments along the same gradients, providing important contextual data for the interpretation of our coastal water column observations. We hypothesized that terrestrial runoff would lead to spatial changes along the sampled environmental gradient, i. e. , from the estuaries to the outer fjord, either directly (via communities transported from rivers), or indirectly (shaped by physico-chemical variables affected by runoff).
Sequence Analysis Sequences were processed using a pipeline modified after  Hassenrück (2019). Demultiplexing and primer clipping were done using cutadapt  (Martin, 2011) (v2. 8) , and the resulting sequences were archived in the European Nucleotide Archive under project accession number PRJEB40446, run accessions ERR4653578-ERR4653672. The reads were quality trimmed with a sliding window of 4 and a quality threshold of 15 using trimmomatic  (Bolger et al. , 2014) (v0. 39)  and reads shorter than 100 bp were discarded. Remaining reads were merged using Pear  (Zhang et al. , 2014) (v0. 9. 6 ) with a minimum overlap of 10 bp, a minimum length of the merged reads of 200 bp (expected 292 bp), and a maximum of 500 bp. Successfully paired reads were quality controlled with FastQC (Babraham Bioinformatics, 2019) (v0.
11. 9), dereplicated using Swarm's dereplication code  (Mahé et al. , 2015) , and clustered using Swarm v2  (Mahé et al. , 2015)  (v3. 0. 0) with the fastidious option. An Operational Taxonomic Unit (OTU) contingency table was created and taxonomy of the representative amplicons assigned using SINA  (Pruesse et al. , 2012) (v1. 6. 0) against the SILVA SSU non-redundant (v138) reference database  (Quast et al. , 2013). Further analyses were performed in R (v3. 6. 3) (R Core Team, 2020), as referenced in GitHub  (Delpech, 2020). Sequences related to chloroplasts or mitochondria were removed, and only OTUs with more than one sequence in more than two samples were kept, removing 9% of the sequences. A negative control was included in the sequencing, and sequences identified in this sample were removed from all samples.