dataesr/scientific-paragraphs-categorization huggingface dataset:
The procedures adopted to construct these maps typically consider the data associated with vent position of past eruptions and, sometimes, major fault structures. Thus, they require the availability of a detailed dataset of geological and volcanological information  (Marzocchi and Bebbington, 2012; Connor et al. , 2015; Németh and Kereszturi, 2015). Vent opening maps have been developed for a growing number of volcanoes, such as Campi Flegrei  (Alberico et al. , 2002; Selva et al. , 2012; Bevilacqua et al. , 2015 Bevilacqua et al. , , 2017b)) , Somma-Vesuvius  (Tadini et al. , 2017a, b) , Etna  (Cappello et al. , 2012; Del Negro et al. , 2020) , the Yucca Mountain region  (Connor and Hill, 1995; Connor et al. , 2000) , the Main Ethiopian Rift  (Mazzarini et al. , 2013 (Mazzarini et al. , , 2016)) , the Auckland volcanic field  (Bebbington, 2013 (Bebbington, , 2015;; Ang et al.
, 2001; DeMets, 2001)  and pose particularly critical volcanic risks due to the presence of densely inhabited communities in the surrounding areas, including the metropolitan areas of San Salvador (∼ 2. 4 million people) and Managua (∼ 1. 4 million people), respectively (Fig. 1a  and b ). This implies that any future volcanic activity could threaten the life of hundreds of thousands of people. Thus, in addition to being an interesting application of our new methodology, the availability of vent opening maps becomes relevant for improving hazard assessment in these regions. In this paper, after a description of the geological framework of the SSVC and NCVC (Sect. 2), we present new thematic vent opening maps for the two studied volcanic complexes (Sect. 3). Then we present invasion probability maps of small-scale pyroclastic density currents (PDCs), which are derived from the application of the branching energy cone model (program ECMapProb) and the adoption of the thematic vent opening maps described previously (Sect.
Geological framework and volcanological dataset We performed a detailed literature analysis in order to collect volcanological data and structural information on the SSVC and NCVC (Fig. 1a  and b ). First, we collected information about past vent locations and their uncertainty areas, which were defined using polygons (Fig. 1a  and b ). Inside these polygons the probability is distributed uniformly. Small polygons are associated with well-constrained vent locations (e. g. , by the existence of a well-preserved crater), while large polygons are related to significant uncertainty in vent position. Figure  1c  and d  presents the eruptive sequence of the two volcanic complexes addressed in this work, where yellow symbols represent their well-documented eruptions (see Tables  1  and 2 ) and black symbols represent regional stratigraphic markers.
Acknowledgements. The work is dedicated to the memory of  Eduardo Gutierrez , who suddenly passed away before having the opportunity to receive the final version of the manuscript. We are all deeply indebted to him; without his enthusiasm and continuous stimulus to initiate and enforce our collaboration, we would have never obtained these results. We lost a precious teammate and a good friend. Rest in peace, Eduardo!This research was supported by the  RIESCA  project, funded by the  Italian Agency for Development Cooperation  and aimed at promoting applied training in risk scenarios in Central America. The constant coordination and support to the project by  Giuseppe Giunta  is greatly acknowledged. Alvaro Aravena  was also financed by the  French government IDEX-ISITE  initiative  16-IDEX-0001.
Code and data availability. The program ECMapProb used to apply the branching energy cone model is available at https://doi. org/10. 5281/zenodo. 3755086 (Aravena, 2020). The vent opening maps and derived data are also available upon request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/nhess-21-1639-2021-supplement.
Methods QRICH1 variants were identified in the probands using massively parallel sequencing (next generation sequencing, NGS) based technologies (exome/genome sequencing with or without employing virtual gene panels) in either clinical diagnostic or research settings, and parental testing for the identified variant was performed in most cases. Pathogenicity of the identified QRICH1 variants was established according to ACMG/AMP (American College of Medical Genetics and Genomics / Association for Molecular Pathology) criteria  (Richards et al. , 2015). The Genome Aggregation Database (gnomAD v. 2. 1. 1; https://gnomad. broadinstitute. org/) was used to check the presence of variants in control populations. NMDEscPredictor tool (https://nmdprediction. shinyapps. io/nmdescpredictor/) was employed to predict whether the truncating variants escaped nonsense-mediated decay (NMD).
Accepted Article tool was used to annotate the variants for their predicted effect on splicing. All the variants are described using the NM_017730. 3 (GRCh37/hg19) transcript of QRICH1 according to HGVS (Human Genome Variation Society) recommendations (https://varnomen. hgvs. org/) and confirmed by Mutalyzer (https://mutalyzer. nl/). To explore genotype-phenotype relationships, the frequency of specific clinical features in individuals with missense variants was compared to those with LoF variants using Fisher's exact test, and two-tailed p values were calculated (https://www. graphpad. com). To model the 3D structure of QRICH1 and to identify its possible interactions with DNA "homology modelling" and "sequence-structure relationship" methods were employed. For homology modelling multiple sequence alignments including structures present in the Protein Data Bank database (https://www.
rcsb. org/) and the SwissModel server (https://swissmodel. expasy. org/) were employed as previously described  (Marcos-Alcalde et al. , 2017). The sequence-structure relationship search was performed using the Phyre2 server (http://www. sbg. bio. ic. ac. uk/phyre2), based the construction of Hidden Markov models (HMMs) using multiple alignments and subsequent search using these HMMs in an internal database of solved structures  (Kelley et al. , 2015).
Only one nonsense variant, c. 2216G>A, located in the last exon was predicted to escape nonsense-mediated decay (NMD), while all the other protein truncating variants were predicted to be subject to degradation by NMD. Only one splice site variant (c. 310-2A>C, identified in Individual 5) was reported in the gnomAD database (https://gnomad. broadinstitute. org/) with an allele frequency of 0. 000003997 (1/250,190 alleles). This variant was familial (verified in the father and paternal grandmother and is possibly present in the paternal grandaunt) and the carriers had some mild symptoms as described above. This variant was initially classified as VUS (variant of unknown significance). However, targeted RNA sequencing revealed skipping of exon 2 both in the proband and the father (Supplementary figure  3 ), and this variant is now classified as pathogen.
Accepted Article features between those with missense and LoF variants, we were able to review facial photographs of only three individuals with missense variants, compared to ten individuals with LoF variants. This limits our conclusions as sample size is inadequate to determine whether true differences exist between individuals with missense and LoF variants. Although predominant expression of Qrich1 has been demonstrated in the maxilla and mandible of mouse embryos  (Lui et al. , 2019) , none of the individuals had striking lower craniofacial or palatal malformations. Elevated creatine phosphokinase (CK) was reported in three individuals, but most individuals in this series did not have CK level tested. Furthermore, two individuals with large chromosomal deletions, including QRICH1 (Decipher database (https://www.
Structural cardiac anomalies were noted in five individuals. Homozygous knock out Qrich1 mice develop congenital heart disease, cleft palate and renal anomalies  (San Agustin et al. , 2016). One of five individuals reported in the Decipher database with a heterozygous QRICH1 deletion is reported to have congenital heart disease (transposition of the great arteries and atrial septal defect). It is possible that heterozygous loss of function variants have a morphological impact on the development of midline structures. Hypertelorism and pineal cysts are other features that support this hypothesis. Formal evaluation of pituitary function is needed to determine whether defects in the pituitaryhypothalamic axis, frequently associated with midline defects, contribute to the short stature.
Sequence-structure relationships (Phyre2  (Kelley et al. , 2015) ) predicted the existence of secondary structure from residues 1 to 64 (CARD domain) as well as from residues 460 to the end of the protein (including the DUF3504 domain) (Figure  1 ). The confidence score for the sequencestructure homology between residues 460-600, harboring three missense variants, with several other DNA-binding proteins was significant (more than 80%). It has been previously suggested that the DUF3504 domain could belong to a family of "domesticated" tyrosine-recombinase transposons present in jawed vertebrates  (Kojima & Jurka, 2011). The results of our sequence-structure relationship analysis suggested a high similarity between this domain and the N-terminal domain of the yeast Ndc10 protein (protein data bank code 4ACO, https://www.
Acknowledgements We thank  Frederic Tran-Mau-Them  ( Inserm UMR1231 GAD, Génétique des Anomalies du Développement, Université de Bourgogne, Dijon, France ) for the RNA splicing experiments performed for individual 5. Jette Bune Rasmussen  ( Department of Clinical Genetics, Copenhagen University Hospital -Rigshospitalet ) is acknowledged for her graphical support. Kathleen Pope (MD, Division of Genetics, Nemours Children's Hospital, USA)  is acknowledged for the clinical information of individual 6 and  Eléonore Viora-Dupont  ( Medical student, Centre de Génétique Clinique, CHU de Dijon ) for the further clinical information of individuals 5 and 11. Part of this work has been generated within the European Reference Network on Rare Congenital Malformations and  Rare Intellectual Disability (ERNITHACA) [EU Framework Partnership Agreement ID :  3HP-HP-FPA ERN-01-2016/739516 ].
This study was supported by the  National Human Genome Research Institute (NHGRI)  and  National Heart, Lung, and Blood Institute Accepted Article GraphPad: https://www. graphpad. comGTEx portal: https://www. gtexportal. org/home/HGVS: https://varnomen. hgvs. org/LOVD, Leiden Open Variation Database: https://databases. lovd. nl/shared/genes/QRICH1Mutalyzer: https://mutalyzer. nl/NCBI: https://www. ncbi. nlm. nih. gov/gene/54870NMDEscPredictor tool: https://nmdprediction. shinyapps. io/nmdescpredictor/Phyre2 server: http://www. sbg. bio. ic. ac. uk/phyre2RCSB Protein data bank: https://www. rcsb. org/SpliceAI: https://github. com/Illumina/SpliceAISwissModel server: https://swissmodel. expasy. org/.
Availability of data and material The variant and phenotype data compiled are included in the tables or supplementary material and variant data is submitted to LOVD-database (https://databases. lovd. nl/shared/genes/QRICH1). All data which are not already included in the supplementary material are available upon request. Data curation -genetic analyses: JP, HG, LB, BCX, ADP, AF, RSF, MJGS, TLH, SVM, RSM, HO, MPM, KP, JEP, ARP, YX, NJB, ZT.
While systems biology approaches have been applied successfully to study sulfur metabolism in Escherichia coli  (21, 22) , Pseudomonas spp. (23, 24) , and Bacillus subtilis  (25, 26) , such studies are rare for fuel-biodesulfurizing bacteria and provided insights only into details of dibenzothiophene and benzothiophene desulfurization  (27, 28). To fill these knowledge gaps, we performed proteomics and metabolomics studies with R. qingshengii IGTS8 and compared cultures grown on either dibenzothiophene or inorganic sulfate. The data enabled us to build an experimentally supported model for sulfur metabolism in R. qingshengii IGTS8 and unveil biodesulfurization-driven adaptive responses. We show how the biodesulfurization-associated sulfate starvation cues provoke sulfur metabolism remodeling and suggest systems for dibenzothiophene uptake and efflux of the biodesulfurization product 2-hydroxybiphenyl.
The genome of the IGTS8 strain does not encode homologs of the known global regulators of sulfur metabolism such as the LysR-type CysB and Cbl of Gram-negative bacteria or the LysRtype CmbR and the TetR-type McbR of some Gram-positive bacteria  (29, 36). However, the proteome revealed many transcriptional regulators, some of which are related to sulfur metabolism and are divergently encoded in operons for transport and utilization of organosulfur compounds (Fig. 3 ). One potential candidate as a global regulator of sulfur metabolism in the IGTS8 strain is the product of IGTS8_peg394, annotated as predicted transcriptional regulator of sulfate adenylyltransferase Rrf2 family that was uniquely detected in the dibenzothiophene culture throughout its life span. This annotation suggests a regulatory role for this protein in sulfur metabolism that was backed by BLAST  FIG 7 Legend (Continued)  represent the label-free quantification (LFQ) values showing the abundance profile of the proteins in both the DBT (dibenzothiophene) and IS (inorganic sulfate) cultures.
Monitoring protein identification rates and coefficients of variation (CV) of this QC sample revealed very good stability of the system: 2,105 of the 2,228 identified proteins, namely, 94%, showed a CV value lower than 20% considering all 4 injections. Data interpretation and statistical analyses. Raw MS data processing was performed using MaxQuant software v1. 6. 0. 16  (70). Peak lists were searched against an in-house generated database from the sequencing of the IGTS8 genome (6,734 sequences) using the RAST pipeline  (71). The annotated genome is accessible via FigShare (https://dx. doi. org/10. 6084/m9. figshare. 14547426). MaxQuant parameters were set as follows: MS tolerance set to 20 ppm for the first search and 5 ppm for the main search, MS/MS tolerance set to 40 ppm, maximum number of missed cleavages set to 2, carbamidomethyl (C) set as fixed modification, oxidation (M) and acetylation (protein N-term) set as variable modifications.
False-discovery rates (FDR) were estimated based on the number of hits after searching a reverse database and were set to 1% at both peptide spectrum match and protein levels. Data normalization and protein quantification were performed using the LFQ (label-free quantification) option implemented in MaxQuant using a "minimal ratio count" of one. The "match between runs" option was enabled using a 2-min time window after retention time alignment. All other MaxQuant parameters were set as default. To be considered for differential analysis, proteins must be identified in at least three out of the four replicates in both dibenzothiophene and inorganic sulfate cultures. The imputation of missing values and differential data analysis were performed using the open-source ProStaR software  (72). A Welch moderated t test was applied on the data set to perform differential analysis.
Proteins were considered differential with a P value lower than 0. 05 and a log 2 fold change higher than 2 or lower than 22 with a minimum of 5 unique peptides. Proteins uniquely identified in one culture condition with a minimum of five unique peptides were also kept in a separate excel file. A complete data set has been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  (73)  with the data set identifier PXD021362. Data were analyzed with principal-component analysis (PCA) on R (version 3. 6. 3) using the MetaboAnalyst package. The PCA was built considering the different proteins as individuals and the growth phases as variables. The PCA results indicate that the first two axes represent 94. 6% of the data set inertia (variability of the data cloud). Metabolomics analyses.
The spectrometer was calibrated before the injections from 50 to 1,000 daltons (Da) using a mixture of 50 ml of isopropanol (Fisher Chemicals)/water (50/50, vol/vol), 500 ml of 1 M NaOH (Agilent Technologies), 75 ml of acetic acid, and 25 ml of formic acid (Sigma-Aldrich). The calibration mixture was injected at the beginning of each run for recalibration at the processing step. The data sets obtained were processed in Metaboscape 3. 0 to investigate the nontargeted metabolome and perform the statistical analysis. Metabolite identification was made using analyte lists that were created from open access databases as described by Villette et al. (76). The following databases were interrogated: E. coli metabolome database (ECMDB, https://ecmdb. ca/), FooDB (https://foodb. ca/), Lipid Maps (https://www.
lipidmaps. org/), Plant Cyc (https://plantcyc. org/), KNaspSAcK (http://www. knapsackfamily. com/KNApSAcK/), and Swiss Lipids (https://www. swisslipids. org/#/). Annotations were performed as described in reference 77, and metabolites were annotated to the level 3 of this classification. The metabolome coverage was estimated by dividing the number of putatively identified metabolites by the total number of m/z obtained. Statistical analyses. Statistical analysis of the metabolomics data set was performed in Metaboscape 3. 0 using the area of the peaks as the unit of reference. To comply with the small number of samples, a Wilcoxon rank sum test was performed for comparison of the samples by pairs (e. g. , early log dibenzothiophene versus early log MgSO 4 ). A log 2 fold change threshold of 1 and 21 was used to determine the differential metabolites, with a P value of ,0.
05. The annotated metabolites were also analyzed using PCA on R (4. 0). The PCA was built considering the different metabolites as individuals and the growth phases as variables. The PCA results indicate that the first two axes represent 97. 3% of the data set inertia (variability of the data cloud). Therefore, the main metabolites identified were placed in this PCA plot according to the two axes defined. Finally, the metabolites intensities were also graphically presented for each growth phase using boxplots drawn in the software R (4. 0). Functional analyses of the detected proteins and metabolites. Sulfur metabolism genes were identified in the genome of the IGTS8 strain using the Seed Viewer  (78). Similarity search was performed using the BLAST program at the NCBI and UniProt databases with default settings.
Mapping of the proteins and metabolites to metabolic pathways was performed with the KEGG mapper-annotate sequence by BlastKOALA (https://www. kegg. jp/kegg/tool/annotate_sequence. html) using the genus Rhodococcus as a data set and the MetaCyc databases (https://metacyc. org). The sequences of well-characterized CymR proteins from Bacillus subtilis strain 108 (UniProt accession number: O34527) and Rhodococcus sp. strain AD45 (UniProt accession number: A0A0D8I4J9) were obtained from the UniProt database (https:// www. uniprot. org/) and aligned to IGTS8_peg394 using Clustal omega (79), followed by manual curation. The active domains including a-helices and b-sheets were mapped from Shepard et al. (80). The folding and 3D structure of the proteins were analyzed using Phyre 2  (81). Sulfur-sparing analysis.
Regulary network and pathway network 2. 1. 1. miRNA datasets Experimentally validated MTIs were downloaded from miRTar-Base v7. 0 human database, which is a manually curated database of experimentally validated MTIs (based on luciferase assays, qPCRs, micro-arrays and high-throughput RNA sequencing approaches such as CLASH)  [17]. To ensure that sufficient numbers of MTIs were available, we restricted our analyses to the use of miRNAs with a minimum of 75 MTIs, encompassing 660 miRNAs and 6,578 genes (representing a matrix of 99,375 interactions -Supplementary Table  S1 ). Predicted MTIs were sourced from miRDIP 4. 1  [5]  and Targetscan 7. 2  [7]. miRDIP integrates MTI predictions across 30 different sources, including Targetscan, and ranks the confidence of those interactions into classes ranging from ''very high" to ''low" based on an integrative score.
Both databases were first filtered to keep only the 660 miRNA present in the validation dataset, miRTarBase (Supplementary Table  S1 ). miRDIP high MTIs resulted from the merging of rows marked ''very high" and ''high" confidence MTIs from miRDIP (82,199 MTIs for 639 miRNAs), while ''low" confidence MTIs were taken from rows marked ''low" from miRDIP. For step 3 of the miRSTATION analyses presented below we filtered total miRDIP MTIs supported by 5 sources and with an integrative score >0. 1, to generate a dataset of 2. 3 million MTI predictions, referred to as miR-DIP* predictions herein. For Targetscan, the MTIs used were derived from both the conserved and non-conserved human MTIs, which represented respectively 184 and 453 miRNAs. In Figs. 1  and 3  where miRDIP high/low or Targetscan MTIs were compared with miRTarBase MTIs, both predictive sets were filtered to have the same number of targets as in miRTarBase, keeping the top predictions (e.
Transcription factor (TF) datasets For the initial studies shown in Fig. 1A  and 1B, experimentally validated TF interactions from the ENCODE project  [18]  were retrieved for the human genome as ''. bigwig" files for hg19 and hg38. The files were transformed into ''. bed" files relying on the bedGraph algorithm (https://genome. ucsc. edu/goldenpath/help/ bedgraph. html). TF sites were restricted to those present in the 1500 base-pair region upstream of transcriptional start sites. For this purpose genome coordinates were normalised using Perl scripts and CrossMap (http://crossmap. sourceforge. net/) to convert genome coordinates from one assembly to another. The method resulted in the use of 124 TFs targeting a total of 15,653 genes for a total of 59,306 interactions (Supplementary Table  S2 ).
For all other analyses, experimentally validated TF interactions were retrieved from the ''TFtargets" R package (https://github. com/ slowkow/tftargets) that aggregates multiple validated TF interactions datasets. We selected four comprehensive TF-target interaction datasets: ENCODE  [18] , ITFP  [19] , TRED  [20]  and TRRUST  [21]. The aggregation of these TF target datasets resulted in the use of 1249 TFs targeting a total of 13,500 genes for a total of 86,070 interactions (Supplementary Table  S3 ).
Pathway datasets For the pathway dataset, we used MSIGdb  [22] , a collection of annotated gene sets utilized for gene set analyses. Only the most general and functionally informative categories of pathways were selected : ''C2_curated", ''C5_GO" and ''Hallmark". Additionally, we arbitrarily filtered out gene sets with low (below 30) or high (above 1000) gene numbers, reasoning that small gene sets would miss overlaps of TF and miRNA regulons, while the too large gene sets would be too unspecific for TF and miRNAs regulons. This gave us 21,319 pathways with 1. 3 M interactions in total, with gene clustered into biological processes informing on their high-level functions. The database was retrieved through the MSIGdb API accessible through R with the package ''msigdbr". Step 1: TF enrichment analyses.
TF target and miRNA target from miRTarBase, miRDIP high or low matrices were built with genes as row and regulators (i. e. TF or miRNA) in columns. Multiplication of the matrices was used to identify the number of common targets in each miRNA-TF combination. We then compared the proportion of TF targets in the miRNA-regulated gene sets to the proportion of the TF targets in the ''genome background", which is the number of unique genes in our dataset (i. e. 25, 367). The Fisher's exact test was used with the following alternative hypothesis, for each miRNA set of genes: the proportion of genes with both miRNA and TF sites is greater than the proportion of sites for this TF in the genome background. Associations having a pvalue lower than 0. 05 and with 5 or more common targets were considered as significantly enriched (Supplementary Table  S4  for miRDIP high).