dataesr/scientific-paragraphs-categorization huggingface dataset:
Many water treatments methods have been developed to remove organic pollutants from contaminated waters. Their efficiency is usually assessed by removal measurements and the effect of operational factors on the processe is rarely explored. To bridge this gap, an experimental design methodology has been applied to optimize an advance oxidation process combining adsorption and photocatalysis, using AZURAD® software. This approach was applied to the removing of carbamazepine (CBZ) in aqueous solution by a bio sourced activated carbon and TiO 2 composite material under light exposure. Two experimental designs were performed considering three quantitative factors (pH, catalyst dose and temperature) and one qualitative factor (dissolved oxygen concentration). From the response surface methodology (RSM), the optimized operational factors for CBZ adsorption and degradation were independently determined.
Experimental design The main objective of the experimenters is to estimate the current levels of factors that are near to optimum. Indeed, the study is designed to model the percentage of adsorption and degradation (that one seeks to maximize) of carbamazepine in the whole experimental design. For this purpose, two experimental designs (A and B) were performed using AZURAD ® software. In a preliminary approach, the first experimental reached by the oxygenation of the solution through bubbling with oxygen (50 mL solution is oxygenated for 10 min before experiment and all along the experiment)  [37]. design.
, 2020; Meyer et al. , 2020; Rudrappa et al. , 2016; Samuel and Shulman, 2012). As such, metabolic stress is a priority research for human space exploration  (Bergouignan et al. , 2016). The roles of the liver and the muscle are often interlinked, and muscle-liver crosstalk are described in metabolic diseases NAFLD, NASH, IR, and diabetes  (Chakravarthy et al. , 2020; Soeters and Soeters, 2012). However, the relationship of the two organs in the response to space environment has not been yet elucidated. In this study, we describe for the first time the relationship of the response of both the liver and the muscle of mice during spaceflight using the R software package mixOmics widely used in the omic field. The mixOmics package enables datasets comparison, correlations, and associations visualization in various research areas  (Durufleet al.
RESULTS Lipid metabolic processes are the most common biological processes affected in mice quadriceps and liver during spaceflight Previous studies showed that the muscle and the liver are differentially affected by metabolic changes during spaceflight (da  Silveira et al. , 2020). Here, we first investigated the commonalities between the two organs. We chose to focus on the quadriceps, which is the most important in term of size and as such potentially in terms of effects. Transcriptomic data from C57BL/6J mouse liver and quadriceps from the NASA Rodent Research 1 (RR1) protocol were analyzed by the Gene Set Enrichment Analysis (GSEA) method and visualized with Cytoscape software (Figure  1A ). In the quadriceps, ''fatty coA process'' and ''axon extension negative'' were the most enriched processes.
Hypoglycemic signaling pathways genes are the most significantly downregulated in mice quadriceps during spaceflight In the quadriceps, 972 genes were significantly differentially expressed, among which 562 were downregulated and 410 were upregulated. KEGG pathways over representation analysis (ORA) in the online software Webgesalt reported 30 most enriched pathways (Figure  2A ). Pathways involved in metabolism are particularly enriched, especially those regulating glycemia: HIF1, ErbB, FoxO, AMPK, Insulin, and PI3K-Akt signaling pathways. Hypoglycemic signaling pathway gene expression was decreased in flight mice compared to ground control mice (Figure  2B ). Interestingly, glucose transport processes were among the most negatively enriched in GSEA reports, and glucose transport process gene expression was decreased in flight mice compared to ground control mice (Figures  2C  and 2D ).
We investigated the relationships between the downregulation of glucose transport process genes and the downregulation of hypoglycemic signaling pathway genes using the supervised method sPLS-DA (sparse partial least square-differential analysis) with the R-mixOmics package. We compared the reported genes by the sPLS-DA performed at 100% (22657), 75% (16992), 50% (11328), and 25% (5664) (according to the process suggested in  Durufleet al. , 2021)  transcript input in a Venn diagram, and we found that 3463 genes were commonly reported with the strongest importance for discrimination (''discriminating genes'') (Figure S2A, Related to Figure  2 ). KEGG pathways ORA on these specific genes reported several enriched pathways among which were the previously mentioned hypoglycemic signaling pathways.
RNAseq data processing Raw FASTQ files were proceed using the ''GeneLab RNAseq data processing protocol'' as reported at https://genelab-data. ndc. nasa. gov/genelab/accession/GLDS-103/ and https://genelab-data. ndc. nasa. gov/genelab/accession/GLDS-168. On this work we used the mentioned protocol up to the step ''Quantification data was imported to R (version 3. 6. 0) with tximport''.
R Differential Expression (DE) analysis has been performed using the R studio software Version 1. 2. 5001 and the DESeq2 package version 1. 30. 1 that uses shrinkage estimation for dispersion and fold change. RSEM expected counts were extracted and rounded up to the next integer and used as input for DE analysis. Statistical significance of differentially expressed genes (FDR %0. 05) were assessed using Benjamini-Hochberg multiple testing adjustment procedure.
Biological process, pathways, networks and clusters analysis For biological process, pathways, networks and clusters analysis, normalized count from RR1 mice quadriceps and liver were processed by the DEseq2 package and assessed for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways Over Representation Analysis on the online Webgesalt platform, and for Gene Ontology Biological Process (GOBP) and KEGG pathways GeneSets Enrichment by the Gene Set Enrichment Analysis (GSEA) software. The Cytoscape software was used to build geneset networks from GSEA results. GSEAs were run with a ''Flight vs Ground Control'' comparison setting. The ranked list of genes was defined by the signal-to-noise metric, and the statistical significance were determined by 1000 permutations of the gene sets. Enrichment importance were sorted according to the enrichment score calculated by GSEA  (Mu ¨ller, 2019).
ACKNOWLEDGMENTS W. A. d. S. acknowledge this work was partially funded by the  ESA  grant/contract  4000131202/20/NL/PG/pt  ''Space Omics: Toward an integrated ESA/NASA -omics database for spaceflight and ground facilities experiments'' awarded to  Raul Herranz. V. W. acknowledges this work was partially funded by the  ESA  grant/ contract  4000134990/21/UK/AL. This work was allowed by the free access online repository data resources NASA genelab. The Rodent Research 1 data collection is supervised by  Jonathan Galazka , Project  Scientist, NASA GeneLab  and  Ruth Globus ,  RR1  Project  Scientist NASA ARC.
METHOD DETAILS Full experimental procedures are available at: https://genelab-data. ndc. nasa. gov/genelab/accession/ GLDS-103/ and https://genelab-data. ndc. nasa. gov/genelab/accession/GLDS-168. The omics data of mice liver and muscle were obtained from the NASA Rodent Research 1 (RR1) protocol accessible on the NASA's GeneLab public omics repository. Also, all animal procedures, ethics, and tissues.
Acknowledgements. We thank  Dr. Pavel Sakov  for helpful discussions and improvement regarding the EnKF-C code and  Dr. Jiping Xie  for contributing the TOPAZ interface to sea ice observations. We thank the  EUMETSAT OSI-SAF centre  for providing the sea ice concentration and SID data, https://osi-saf. eumetsat. int/. The production of the merged CryoSat2-SMOS sea ice thickness data was funded by the  ESA  project  SMOS & CryoSat-2 Sea Ice Data Product Processing and Dissemination Service , and data from 9/2019 to 4/2020 were obtained from  AWI. The work is funded by the  DASIM-II  grant from  ONR  (grant no. N00014-18-1-2493  and  N00014-18-1-2204 ). AC, CJ, AA, and PR acknowledge the support of the project SASIP funded by Schmidt Futures -a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies.
Tips aligned with stars indicate evidence of prokaryotic host-CRISPR spacer match in blue and bacteriolytic domain in red. Green arcs indicate clades with an alternative genetic code in R50% of the sequences. Orange arcs indicate clades with motif permutation in R50% of the RdRPs. The 5 established phyla and the proposed candidate phylum p. 0002 are color coded in both the text and the bar-plot in the outermost ring, which represents the maximum genome length observed for each RCR90 cluster (i. e. , tree tip). Key taxa are labeled directly on the tree. Additional visualizations of the tree are available in the project's Zenodo repository (see data and code availability). See also Figure  S2. broke away from their respective phyla, although the consensus tree placed Picobirnaviridae and Cystoviridae confidently within Pisuviricota (see below).
Clades were considered as likely composed primarily of phages if R50% of the predicted ORFs were associated with an SD motif, after excluding genes predicted on the edges of contigs. Ltase, lytic transglycosylase, lysozyme superfamily fold; SGL, ''single-gene lysis'' (cell wall synthesis inhibitors); PRO-M15, Zn-DD-carboxypeptidase (sensu PF08291. 13); PRO-M35, M35 family zinc metalloendopeptidase; PRO-M23, M23-family metallopeptidases; Amidase, N-acetylmuramoyl-L-alanine amidase; Endopep, L-alanyl-D-glutamate endopeptidase. (C) CRISPR spacer landscape of Roseiflexus sp. RS-1 in Yellowstone hot springs, including spacers matching genPartiti. 0019 genomes. Left panel displays the total number of spacers identified for each type of Roseiflexus sp. RS-1 CRISPR arrays (see Figure  S3 ). The right panel presents phage type (dsDNA, ssDNA, or RNA) for which hits to CRISPR spacers were identified for each CRISPR type.
(D) Example of a predicted pair of RdRP and capsid-encoding segments from a genPartiti. 0019 phage. Top panel: CRISPR spacer matches are indicated alongside a genomic map for each segment. The number of mismatches is shown on the y axis, and the position of the hit is indicated on the x axis. The bottom panel displays the relative abundance of both segments across a metatranscriptome time series. (E) Relative abundance of different prokaryotic RNA virus groups across biomes. Only datasets dominated by prokaryotic sequences (''P-dominated'') containing at least 10 prokaryotic RNA viruses were considered. The right panel shows a breakdown of the biome distribution for each group, calculated from a balanced dataset composed of random subsamples of 50 samples per environment (random subsampling was performed 100 times, and the mean values were plotted).
0278 strongly supports bacterial host assignment. Finally, RvANI90_0011770, a putative phylum of RNA bacteriophages identified by the RdRP searches not included in the present phylogeny, showed similar conservation of M23-family metallopeptidases. The final line of evidence for prokaryotic host assignment was the detection of matches between RNA viruses and CRISPR spacers. Although most known CRISPR systems target DNA templates, a large subset of type III CRISPR systems encode RT and can protect bacteria against RNA bacteriophages  (Makarova et al. , 2020; Silas et al. , 2017). We compared all identified RNA virus genomes with the IMG database of R50 million spacers (see STAR Methods), detecting spacer matches for 161 RNA viruses from 23 RvANI90 clusters, across two clades: Leviviricetes, and genPartiti.
9) to at least one gen-Partiti. 0019 RdRP-encoding sequence, we identified 88 potential capsid-encoding contigs (Figure  3D ; Table  S3 ), of which 86 encoded proteins with best alignment to HMM profiles of known partitiviruses capsids (Figure  S3 ). Thus, genPartiti. 0019 members are most likely segmented RNA phages infecting Roseiflexus sp. RS-1. Interestingly, in datasets dominated by prokaryotic hosts (''P-dominated,'' see below), most potential RNA phages were detected across a broad range of biomes, where Leviviricetes was by far the most abundant group of prokaryotic RNA viruses, except in some Yellowstone hot springs dominated by genPartiti. 0019 (Figure  3E ).
Differential distribution of RNA viruses across samples and habitats Our RNA virus survey spanned the entire globe, reflecting the ubiquity of RNA viruses on Earth (Figure  4A ). Metagenomic studies have shown that DNA virus distribution is shaped by the environment type and host community composition  (Gregory et al. , 2019; Martinez-Hernandez et al. , 2017; Roux et al. , 2016) , and the same factors likely determine the RNA virus distribution. For metatranscriptomes, the sample processing protocol can be another factor, namely, whether the total RNA was sequenced or whether any specific preprocessing was used (such as mRNAs enrichment via poly(A) amplification, or rRNAs depletion)  (Gann et al. , 2021). Here, most of the datasets analyzed were rRNAdepleted (67%, Figure  S4 ). Although the poly(A)-enriched and total RNA datasets were dominated by eukaryotic sequences, the rRNA-depleted datasets consisted mostly of sequences from prokaryotes (Figure  S4 ).
The datasets were separated into three groups: ''Eukaryote(E)-dominated'' (811), ''Prokaryote(P)dominated''  (2, 706) , and ''Mixed'' (452), based on the taxonomic composition of non-viral contigs. Most RNA virus classes showed clear distribution patterns across dataset types and environments, likely reflecting the distribution of their primary host groups (Figures  4B  and 4E ). For instance, Leviviricetes were consistently enriched in P-dominated samples from engineered, rhizosphere, and soil habitats (Figure  4B ). This implies an uneven global ecological distribution of RNA phages, supporting previous findings  (Callanan et al. , 2020). Also among Lenarviricota, Miaviricetes which infect mostly fungi, invertebrates, and plants were associated with E-dominated and Mixed datasets, whereas Howeltoviricetes members, including mitoviruses, were common in all sample types but found preferentially in plant-associated datasets also rich in fungi.
Although assigning specific eukaryote hosts to RNA viruses is a challenging task not addressed in this work, we suspect that many of the detected viruses infect diverse unicellular eukaryotes, as they utilize alternative genetic code (see below). Assuming that the broad host assignment (plants, animals, or fungi) of viruses can be extended over minor sequence dissimilarity (less than 10%), we identified only 1,038 metatranscriptomic contigs that belonged to the same RvANI90 cluster as viruses from  VirusHostDB (MIhara et al. , 2016)  assigned to plant or animal hosts, indicating low prevalence of viruses infecting these hosts in the analyzed datasets. Additionally, specific host assignment to plants can be made for 1,038 metatranscriptomic contigs (in 6 families: Tombusviridae, Virgaviridae, Betaflexiviridae, Alphaflexiviridae, Benyviridae, and Mayoviridae), encoding movement proteins (MPs), which enable viruses to pass through plasmodesmata.
, 2020). (B) Relative proportion of (proposed) RNA virus classes (x axis) detected across ecosystem types (y axis). To take into account the total number of genomes detected for each class and the total number of samples for each ecosystem type, the counts are represented as enrichments compared with the expected number of genomes assuming even distribution of all classes across all ecosystems. The datasets were divided into ''E-dominated'' (mostly composed of eukaryotic transcripts), ''P-dominated'' (mostly composed of prokaryotic transcripts), and ''Mixed'' (see Figure  S4 ). Enrichments are shown only for combinations of ecosystem and dataset type (e. g. , ''Marine P-dominated'') for which at least 20 metatranscriptomes with R1 RNA virus were detected. See also Figure  S4. Originally described in Permutotetraviridae and Birnaviridae, a distinct rearrangement (known as ''domain permutation'') occurs within the RdRP domain, where the order of the motifs (A, B, C.
In contrast, the basal position of Lenarviricota in the tree rooted with RT likely reflects the origin of the rest of the RNA viruses from a common ancestor with this phylum within the bacterial domain. This scenario appears particularly plausible considering the major expansion of the bacterial RNA virome in this work. Considering the size and diversity of the analyzed dataset, it appears likely that the information contained in the RdRP sequences is indeed insufficient to resolve the deepest relationships among RNA viruses. This problem will merit revisiting once sufficient diversity of RdRP structures accumulates, possibly, providing for a better phylogenetic resolution. The present analysis eliminates the long-standing bias in the RNA virome toward eukaryote-infecting viruses  (Koonin et al.
, 2022). In summary, the results greatly expand the diversity of the kingdom Orthornavira, in particular that of RNA viruses associated with bacteria, while introducing relatively minor changes into the latest taxonomic scheme, supporting its general robustness. Additionally, multiple protein functionalities were predicted in RNA viruses. The large amount of sequence and derivative data generated in this work is available through the companion website (riboviria. org) or via the Zenodo deposit. We expect this resource to enable researchers to gain meaningful and comprehensive context when describing new RNA viruses in future studies, for example, by offering insights into the ecological distribution of specific viral lineages or via the clade-specific protein domain annotations. Furthermore, this resource can help researchers identify key RNA virus genomes to be characterized experimentally.
Primary Filtering process For convenience, we summarized the final tools and cutoffs of the Primary and secondary filtration process in Table  S6  -Discovery pipeline search and filtration thresholds. Our initial criteria for contigs acquired from the IMG/M portal discarded sequences shorter than 1,000 nt or encoding rRNA genes (the remaining contigs were dereplicated at 99% sequence identity via mmseqs easy-linclust)  (Steinegger and So ¨ding, 2017). To filter out sequences that were highly unlikely to represent RNA viruses, we compared the obtained metatranscriptome contigs to a compendium of DNA sequences built from 1,831 metagenomes originated from the same studies as 1,306 of the metatranscriptomes. We selected metagenomes that shared the metadata attribute of ''Study_ID'' with the 5,510 metatranscriptomes in the Genomes OnLine Database (GOLD) portal  (Mukherjee et al.
, 2021)  as these DNA assemblies would cover a similar range of habitats as the analyzed metatranscriptomes. Using multiple sequence search tools (specifically, MMseqs2 (nucleic -nucleic (search type 3)  (Hauser et al. , 2016; Steinegger and So ¨ding, 2017) , DIAMOND (translated nucleotide versus the IMG sourced DNA metagenomic predicted ORFs (diamond blastx)  (Buchfink et al. , 2015) , and NCBI BLAST (nucleic -nucleic -blastn)  (Altschul et al. , 1997; Camacho et al. , 2009; Johnson et al. , 2008) ) in an iterative manner, we identified and excluded metatranscriptomic contigs that matched sequences in the DNA sequence dataset (Figure  1A ), based on the assumption that RNA viruses would not be present in DNA assemblies -which would be comprised of cellular organisms, DNA-based mobile elements, and integrated retroviruses.
Secondary Filtering process To further filter the contig set, we supplemented the above filtering process output with 5,954 RNA viral sequences from reference databases and performed an additional iterative filtering procedure using public databases (NCBI NT/NR and IMG/VR) as the DNA set. To prevent the exclusion of bona fide RNA virus sequences, we masked entries of the public databases that matched reference RNA viruses from subsequent iterations. All discarded contigs were aggregated and supplemented with manually identified DNA encoded contigs, creating a database of ''false positives'', that was used to further filter the metatranscriptome dataset through exclusion of sequences with producing passable matches to the ''false positive'' set. The procedure of collecting the discarded matches to further refine the working set was repeated three times.
RdRP identification Previously published multiple sequence alignments of RdRPs and reverse-transcriptases  (Wolf et al. , 2018 (Wolf et al. , , 2020) )  were formatted as tool-specific subject databases, and employed as queries to search a sequence database consisting of the 6-frame end-to-end translations of contigs passing the above-described filtering processes, using PSI-BLAST, hmmsearch, DIAMOND and MMseqs2. To estimate the desired search cutoffs, we supplemented the query set with non-RdRP sequences likely to produce false matches (termed ''true-negative'' set), constructed as follows: (1) using a large set of RdRPs as queries for an hhsearch (from the HH-Suite) against the PDB70 database (  2019 ), collecting all matches of bitscore R 20 that were not from RNA viruses, that aligned with at least 2 RdRPs; (2) fetching PDB entries clustered with those at 70% identity, (via ftp://resources.
rcsb. org/sequence/clusters/bc-70. out); (3) fetching Pfam entries relating the resulting PDB IDs, and sequences linked to the Pfam entries; (4) collapsing highly similar sequences to a single representative (MMseqs2 minimum coverage: 100%, minimum ident. : 90%). Subject RdRP profiles capable of producing alignments to any sequence from the ''true-negative'' set were discarded. Otherwise, acceptance criteria for the RdRP profiles searches were: profile coverage R 50%, E-value % 1e-10 and score R 70. These stringent parameters were then fine tuned to represent the best possible value a non-RdRP sequence was able to achieve. Subsequently, reliable RdRP matches were trimmed to the approximate core domain, which we operationally defined as motif A-D (see ''Motif A-D identification'' below). The extracted RdRP core sequences were pre-clustered (CD-HIT, coverage R 75%, % ID R 90)  (Fu et al.
, 2012) , passed to an all vs. all (DIAMOND BLASTp) run, formatted for use with MCL using mcxload (-stream-mirror -stream-neg-log 10 -stream-tf "ceil(  200 )"), clustered (MCL, Inflation value between 3. 6 and 2. 8), aligned (MUSCLE), and formatted as profile databases as described above  (Altschul et al. , 1997; Buchfink et al. , 2015; Edgar, 2021; Enright et al. , 2002; Steinegger and So ¨ding, 2017). This process was repeated twice. Subsequently, contigs with putative RdRPs were used to recover additional contigs from the entire metatranscriptomic collection, which were highly similar yet shorter than the initial search length criteria (see below ''Comprehensive identification'' for details). Of the resulting collection, sequences covering R 75% of an RdRP profile, or with identifiable motifs A-D, were considered sufficiently complete for downstream phylogenetic analysis.
Contig set augmentation with published genomes To assess the novelty of our findings in terms of the number and diversity of newly predicted viral genomes, and in order to avoid the exclusion of established viral lineages that may be underrepresented in environmental metatranscriptomes, we aggregated and compiled a collection of ''previously published'' viral genomes termed ''Reference Set''. These include RdRP-carrying sequences identified in NCBI's NT database  (NCBI Resource Coordinators, 2018) , as well as sequences not indexed (at the time of writing) in such public databases, that were identified in several previous large scale and notable RNA virus surveys and transcriptomic atlases. Our criteria for addition of these supplementary sequences required that they originate from peer reviewed publications, and that all underlying sequences were entirely publicly available, with no restrictions.