dataesr/scientific-paragraphs-categorization huggingface dataset:
This protein displays the catalytic HxHxDH motive indicating it belongs to the zinc metallo-β-lactamase superfamily, as previously suggested by Melino and coworkers  [66]. Except for the catalytic residues, AtsA possesses very limited sequence identity with the alkylsulfohydrolases (~13%). However, AtsA shows about 30% similarity with the members of the ElaC family (ribonuclease Z family) within the zinc metallo-β-lactamase superfamily. This observation was confirmed by our phylogenetic analysis of the zinc metallo-β-lactamase superfamily in which AtsA and four related proteins constitute a clade close to the ribonuclease Z clade (  Fig 7). The other putative arylsulfohydrolases present in the UniProt databank were identified by BLAST search, using AtsA as query sequence. Only fifteen sequences could be new putative arylsulfohydrolases.
Proposition of nomenclature and classification for sulfatases With the increasing number of completely sequenced genomes, new sulfatase genes and their corresponding proteins have been regularly released into sequence databases, but their functional annotation is often prone to inaccuracies and misinterpretations due to several reasons. The formylglycine-dependent sulfatases are frequently considered as the only family of sulfatases, even in recent articles or reviews, and are thus annotated as "sulfatases" or "arylsulfatases" without any other precisions. This error is erroneously propagated by two popular web sites, PROSITE and PFAM, which provide protein profiles reducing the sulfatases to FGly-sulfatases (http://www. expasy. ch/prosite/PDOC00117 and http://pfam. xfam. org/family/PF00884, respectively).
These signatures also correspond to the profiles IPR000917 (http://www. ebi. ac. uk/interpro/entry/IPR000917) and IPR024607 (http://www. ebi. ac. uk/interpro/entry/ IPR024607) in the Interpro database  [80]. More surprisingly, the "seed" on which is based the PFAM profile PF00884 comprises numerous uncharacterized sequences which do not feature the catalytic signature of FGly-sulfatases! For instance, eleven sequences homologous to a putative protein from Streptococcus mutans (trEMBL accession: Q840W2) contain a conserved TXNXE motif instead of the canonical (C/S)xPxR pattern. Among the 59 sequences composing the PFAM seed, 30 putative proteins featured a threonine in place of the catalytic cysteine or serine. To the best of our knowledge, oxidation of a threonine residue, in a similar manner to serine or cysteine, would give the corresponding ketone, not formylglycine residue.
It is probably the reason why it has never been shown that the formylglycine residue can be generated from a threonine. Nonetheless none of the TXNXE-containing proteins have been characterized yet, and they cannot be considered as functional sulfatases in absence of experimental evidences. Therefore, the profile PF00884 is incorrect and has already introduced numerous false annotations in sequence databases. Another problem is the inaccurate use of the term "arylsulfatase". Artificial aryl compounds such as 4MUF-S, p-nitrophenyl-sulfate (PNP-S) and pnitrocatechol sulfate (PNC-S) are conveniently used to test the activity of new sulfatases, but are not the true substrates of these enzymes. For instance, the so-called "arylsulfatases" ARSA and ARSB are specific for cerebroside-sulfate and N-acetylgalactosamine-4-sulfate, respectively, which are not phenolic compounds (Table  1 ).
Finally, the number of sulfatases with known substrate specificity is limited in comparison to the huge diversity of sulfated compounds. Moreover, most of these enzymes were characterized in animals and only in a few bacterial phyla. Since genome annotations are generally based on best BlastP hits against sequence databases, new sulfatases are often given substrate specificities which are not always relevant for non-model organisms. The presence of such inexact annotations in databases creates a snowball effect propagating assignment errors  [81]. A classification system reflecting the catalytic machinery, allowing for a better prediction of substrate specificity and for setting the limit of functional annotations, is therefore urgently needed for sulfatases. We propose to classify the sulfatases according to the principles used for the classification of carbohydrate-active enzymes (http://www.
For instance, the numerous FGly-sulfatases of R. baltica and Z. galactanivorans are likely involved in the utilization of these various sulfated compounds as carbon sources. Z. galactanivorans Dsij T is already known for its capacity to degrade agars  [85, 86] , porphyrans  [87]  and carrageenans  [88, 89]. Moreover, we have demonstrated that R. baltica SH1 T also degrades κand ι-carrageenans  [90]. These marine proteins likely cover an unprecedented panel of substrate specificities and constitute a significant fraction of FGly-sulfatases in sequence databases. The correct annotation of these enzymes is thus essential to avoid error propagation in sequence databases and to define substrate specific subfamilies of FGly-sulfatases. The phylogenetic tree of the FGly-sulfatases is divided into 73 different clades (S2 Fig).
If one day two FGly-sulfatases from the same subfamily are experimentally demonstrated to have different activities, the subfamily will be split on the basis of the deepest reliable node resulting into two monospecific subfamilies. To avoid instability in the classification, the subfamily with the first demonstrated activity will keep the number of the original subfamily, while the second subfamily will be given a new, sequential number. To provide this classification system to the scientific community, we have built a free web accessible database, called SulfAtlas, available at the following address: http:// abims. sb-roscoff. fr/sulfatlas/. The home page of the SulfAtlas website summarizes information about the different families of sulfatases, giving the number of sulfatases in each of them.
Clicking on a family name (e. g S1) displays the family page with information about the family, the list of its subfamilies and the list of EC numbers found in these subfamilies (Fig  8 ). The subfamily page, accessed by clicking on a subfamily name, shows some subfamily descriptors (known enzymatic activities, catalytic residues and available 3D structures) and a table with all the Uni-Prot accession numbers of sulfatases belonging to this subfamily with, for each enzyme, the protein or locus name, the EC number, the taxonomic name of organism and the PDB accession number when it exists. All these fields are linked to the matching databases: UniProtKB from UniProt, the enzyme database ExplorEnz, the Taxonomy database from NCBI and the Protein Data Bank from RCSB PDB. Selected sulfatase sequences can also be exported in fasta format.
Moreover, it is possible to search the database using keywords: the family or subfamily number, the taxonomy ID number, the organism name, the locus or gene name, the full or short UniProt accession number (ex. G0L000_ZOBGA or G0L000 respectively) or the EC number and the PDB accession number. Finally, it is possible to query SulfAtlas by single BLAST or multiple BLAST with one sequence or with an entire proteome. Updating of SulfAtlas will be facilitated by the use of different consensus patterns (used alone or in combination) identified in multiple alignments (Figs  4  and 6 ).
In contrast Asp14 is less conserved (76% of conservation). The multi-alignment shows that this residue can be replaced by a large number of amino acids (S1C Fig). The insertions "x(0,16)" and "x(0,6)" are due to the sequences B3T1C6 from the uncultured marine microorganism HF4000_009G21 and A8HPB7 from Chlamydomonas reinhardtii, respectively. These two sequences have been excluded in order to build a conserved signature useful to identify the FGly-sulfatases. Thus, we propose the following consensus pattern, referred to as Cabinding 1 pattern (Fig 4C), [PM]-x(0,1)-[NHD]-[IVFL]-[LIV]-[FLVIY]-[IVLF]-[LMVFI-TYW]-[ATVSLI]-[DE]-[DQ]-[LMQVH]-[GRANTDS]. The corresponding sequence logo is shown in Fig 4C. This consensus pattern was used to query the TREMBL database via the PROSITE website and recovered 9355 sequences (July 2016), mainly annotated sulfatases or arylsulfatases, type I phosphodiesterase/nucleotide pyrophosphatase family protein or uncharacterized protein.
Among these sequences, 145 (1. 55%) were identified as false positive sequences and 9210 (98. 45%) were true FGly-sulfatases. These results suggest that this new consensus pattern will be useful to recover sequences of putative sulfatases in order to assist in the updating of a dedicated database to sulfatases. The residues Asp317 and Asn318 are also involved in calcium ion coordination (Fig 1B). They are included (in bold) in the conserved signature N 72 -T 94 -x(0,2)-I 40 -V 43 -I 37 -F 57 -x(0,1)-T 42 -S 65 -x(0,1)-D 99 -N 61 -G 99 (S1D Fig). The amino acid Asp317, conserved at 99% (S1D Fig), is most frequently replaced by a glutamate in 19 sequences only. Also, some rare amino acids can replace it as threonine, alanine, arginine and tyrosine (S1D Fig). Surprisingly, Asn318 is poorly conserved (61%), although this residue is involved in the calcium coordination and the activation of the FGly residue.
The sequence logo corresponding to this consensus pattern is shown in Fig 4D. From interrogation of TREMBL database using the Cabinding 2 consensus pattern, we have obtained 9299 sequences that included only 7525 sulfatases (81%), a lower efficiency than the Ca-binding 1 consensus pattern. An additional consensus sequence is P 93 -F 75 -F 66 -L 55 -x(0,1)-Y 39 -x(0,34)-L 29 -A 30 -x(0,1)-F 26 -T 20 -x(0,5)-A 39 -P 62 -H 99 (S1E Fig). This motif corresponds to the sequence PFFAYLPFSAPH in the reference sequence P51691. Pro200 and His211 are conserved in 93 and 99% of sequences respectively (S1E Fig) suggesting that these amino acids are essential for FGly-sulfatases. Pro200 is structurally important, facilitating the direction change between the α-helix D and the β-strand 10, while His211 is located in the active site (Fig 1B).
The value 0% means that the amino acid is present in less than 1% of sequences. The accession numbers of sequences responsible of insertions in the consensus sequence or their number is indicated at positions "x". (PDF) S2 Fig. Phylogenetic tree of FGly-sulfatases. The tree was obtained by maximum likelihood with RaxML using MTMAMF as a substitution matrix from an alignment of 4005 sequences and 329 positions. The clades represent the subfamilies according the proposed nomenclature. For the subfamilies with a characterized activity, the activity name and the corresponding EC number are indicated. All resolved three-dimensional structures are indicated. Orphean sequences (which were not include in a clade) are annotated S1_N. C. (for non-classified). Only bootstrap values above 60% are shown.
(PDF) S3 Fig. Consensus sequences extracted from the global multi-alignment of Alkylsulfodioxygenases. The alkylsulfodioxygenases consensus sequence was deduced from an alignment of 111 sequences, extracted from the global multi-alignment of 469 dioxygenases. This latter alignment was obtained using the MAFFT program with the L-INS-i algorithm as the iterative refinement method. The consensus sequence appears in bold. The blue numbers indicate the position of amino acids in the reference sequence AtsK (Q9WWU5). For each position, the amino acids present and the percentage of sequence that they represent in the multi-alignment are indicated. The value 0% means that the amino acid is present in less than 1% of sequences. The accession numbers of sequences responsible of insertions in the consensus sequence or their number is indicated at positions "x".
Acknowledgments We thank  Jan Karlseder  ( Salk Institute ),  Eros Denchi  ( Scripps La Jolla ),  Roger Greenberg  (UPenn),  Alan Lehmann  ( University of Sussex ),  Cyrus Vaziri  ( UNC Chapel Hill ),  Tony Huang  ( NYU ), and  Bing Xia  ( Rutgers ) for sharing published reagents and generous suggestions given for this project. We are indebted to  Richard Jones (MS Bioworks ,  Ann Arbor ,  MI ) for technical assistance with mass spectrometry and data analysis. Funding was provided by the  CMRF  (R. J. O. ) and SPRIG (R. J. O. and P. L. O. ) at the  University of Pittsburgh School of Medicine  and the  National Institute of Environmental Health Sciences (NIEHS)   R01 ES022944  (P. L. O. ). Work in J. S. H. 's laboratory is supported by funding from  INCa -PLBIO2016-11 ,  ANR PRC 2016 ,  Labex Toucan , and  La Ligue contre le Cancer (Equipe Labellise´ e) , of which E.
Secondary outcome parameters were time to definite result of BC and SF, number of antimicrobial therapy changes according to BC and SF results, and the predictive power for positive SF of additional biomarkers such as C-reactive protein, leukocyte count and procalcitonin. Additionally, we documented the treatment status at day 30 according to the following categories: 1) death, 2) still on pediatric intensive care unit, 3) transferred to or still staying on peripheral ward, 4) discharged from hospital. Study protocol: In children with SIRS and antibiotic pretreatment, who were enrolled in the study, personal data including duration of antibiotic therapy and its modification were documented. Vital parameters (heart rate, respiratory rate, body temperature) and infection biomarkers from blood (C-reactive protein CRP, leukocyte count and procalcitonin PCT) were recorded on day 0, day 4-6 and day 30, if available.
Statistical analysis According to prior data  [8]  of a retrospective study on the comparison of BC and SF, we performed a sample calculation with 90 % power of a two-sided exact test on a significance level of 0. 05 for the superiority of the rate of pathogen detection by SF over the rate of pathogen detection by BC. The estimated number of patients was 61. As enrolment was much slower than expected we repeated sample size calculation after two years with 80 % power and revealed a number of 40 patients. Therefore we stopped recruitment after three years and 39 patients. All data are presented as median and range. Analysis of categorical variables was performed using Chi-square test. Two independent groups were tested by Mann-Whitney-U test. Predictive power of biomarkers for positive SF was assessed by receiver-operating characteristic curves (ROC curves) and calculation of the area under the curve (AUC).
Literature search Web of Knowledge and PubMed were searched for age-stratified incidence data since 1980 as we were interested in contemporary dengue transmission and wanted to be consistent with our previous study where we collated age-stratified seroprevalence data  [13]. Search terms used were 'dengue' and 'age' and ('incidence' or 'cases' or 'notifications' or 'notified cases') with inclusion criteria mapped to subject headings. Additional web-based searches were performed to augment the primary literature search. Data were extracted from published datasets where authors reported age-stratified incidence data with corresponding population age-structure data.
Here B is a baseline risk of disease used to represent any non-dengue related illnesses that are misdiagnosed as dengue, and was only estimated when fitting suspected dengue incidence data where laboratory confirmation was lacking. We assumed that secondary infections were more likely to be symptomatic than primary infections  [7, 26]  and that post-secondary infections were even less likely to be symptomatic than primary infections, i. e. ρ>γ 1 >γ 3. Single values of γ 1 and γ 3 were estimated per country. For datasets that reported DHF only, we assumed that DHF cases only arose from secondary infections and set γ 1 and γ 3 to zero  [27, 28]. Where fewer than four serotypes were in circulation, we adjusted our calculation of the expected incidence accordingly-full details are given in the S1 Text.
Comparing force of infection estimates by data type We used weighted regression to assess how comparable force of infection estimates obtained from cumulative incidence data were with those derived from seroprevalence data described previously  [13]  and from four additional seroprevalence datasets (see Table  S1  in S1 Text). Location-and time-matched incidence and serology data were not available, so we matched datasets by country, region, and survey year. Since seroprevalence data represent all past infections, we compared force of infection estimates with those obtained from cumulative incidence data rather than yearly incidence data where possible (see Table  S2  in S1 Text for full details on pairings). We used the weighted regression method described by Ripley and Thompson  [31]  which explicitly accounts for measurement errors in both force of infection estimates from seroprevalence data (y-axis) and incidence data (x-axis) to estimate the maximum likelihood estimate (MLE) line.
Results We identified 23 papers reporting incidence data. Fig 1  describes  the search process and Table  1  summarises the studies identified. Seven papers reported age-stratified incidence data from multiple years, one paper reported data where the number of serotypes in circulation had changed over the survey years, six papers reported cumulative age-stratified incidence data, eight papers reported age-stratified incidence data from a single year, and two papers reported age-stratified incidence data from multiple countries. The identified studies provided a total of 34 datasets from 13 countries. The years included ranged from 1978 to 2011. The dataset reporting incidence data from 1978 was included since data were presented for the eleven-year time period of 1978-1988  [33]. Of the 23 papers reporting incidence data, ten reported dengue incidence at the national level and only two studies reported cases detected via active as well as passive surveillance.
This is consistent with our previous results analysing seroprevalence data  [13]. Some force of infection estimates in Cambodia were very high, perhaps as a result of the active surveillance undertaken as part of the study by Vong et al. [38]  (for all parameter estimates see S1 Text). The baseline reporting rate (ρ), defined as the probability of detecting a secondary infection, was less than 15% when averaged across all studies (Fig  3 ). The median probability of detecting a primary infection relative to that of detecting a secondary infection (γ 1 ) was less than 25% for the majority of datasets. However, the credible intervals for some γ 1 estimates were wide. The data proved uninformative about the contribution of post-secondary infections to disease incidence, as our estimates of γ 3 (Fig  3 ) reflected the prior distribution assumed for that parameter (uniform from 0 to 1).
Discussion From a literature search we selected 23 papers reporting age-stratified case notification data in 13 countries from 1978-2010. For each dataset we estimated dengue transmission intensity as quantified by the force of infection (λ) and the basic reproduction number (R 0 ). Where possible we fitted to the cumulative incidence data as fitting to yearly incidence data gave less stable estimates (model fits to yearly incidence data are given in the S1 Text) The total force of infection (λ total ) estimated from cumulative incidence data were then compared with previous λ estimates from seroprevalence data. The incidence model presented in this paper provides a method for estimating dengue transmission intensity in areas where seroprevalence data are not available. Force of infection estimates and corresponding basic reproduction numbers varied widely across and within countries as expected, highlighting the heterogeneous nature of dengue transmission spatially and temporally.
However, given the available data it was not possible to estimate serotype-specific or time-varying forces of infection. Multiple cross-sectional surveys or cohort studies are required to estimate how forces of infection have changed by age over time, and serotype-specific data are needed to resolve differences between serotypes. Due to the lack of incidence and serology data collected in the same year and region, we matched cumulative incidence and serology datasets according to the year or region (see S1 Text). While overall estimates from incidence data were comparable with those derived from seroprevalence data, it would nonetheless be beneficial to validate this model with more incidence and serology datasets collected simultaneously in the same geographical location. Generally, estimated reporting rates (ρ) in the Americas were higher than those in South East Asia with Singapore having the highest rate within South East Asia, consistent with their well-established dengue surveillance program  [56].
Finally, all the amplicons were mixed together stoichiometrically before their sequencing in one run of MiSeq PE 2×300 Illumina (CNRS-UMR8199, Lille). The sequences were processed using the MOTHUR v1. 34. 0 software  (Schloss et al. , 2009)  following the standard operating procedure  (Schloss, Gevers & Westcott, 2011). Only reads above 492 bp for eukaryotes and 465 bp for bacteria, with homopolymers shorter than 8 bp were kept in the analysis. Eukaryotes and Bacteria sequences were dereplicated to the unique sequences and aligned independently against the SILVA 108 database  (Pruesse et al. , 2007)  containing only the targeted region (V2-V3 for eukaryotes and V3-V4 for bacteria) and matching the universal primers used in this study. Subsequently, around 130 sequences for eukaryotes and 8.
956 sequences for bacteria suspected of being chimeras were removed using the UCHIME software  (Edgar, 2010). The remaining sequences were clustered into operational taxonomical units (OTUs) at 97% similarity. Single singletons (sequences present only once in one sample) were removed from downstream analysis. Finally, the dataset containing both eukaryote and bacterial sequences was normalized according to the lowest number of reads in a sample (55,102 reads). Rarefaction curves calculated for all the sampling dates approached a plateau in most cases when ≥97% levels of sequence similarities were applied (Fig. S1 ). Sequencing data from this study have been submitted to the Sequence Read Archive (SRA) (http://www. ncbi. nlm. nih. gov/sra) with accession number SRP061259. Taxonomic classification was assigned using BLASTN  (Altschul et al.
, 1990) , against the sequences (PR2 database containing 23. 003 protist sequences  (Guillou et al. , 2013) ) for eukaryotes and against the SILVA database for Bacteria containing 530. 946 bacterial sequences  (Pruesse et al. , 2007). The OTUs identified as metazoans (69 OTUs) were removed from analysis. For each date, OTU relative abundances between the C and T tanks were compared with the Wilcoxon test. For the whole experiment Chi2 test was used to compare C and T tanks for OTU abundance frequency distributions of eukaryotes taxonomic and trophic groups. Statistical analyses were done using the PAST 3c software  (Hammer, Harper & Ryan, 2001). 2014 ) using mussel mean wet weight (modified from  Delegrange et al. , 2015).
In this study, mussel filtration impact was considered regarding changes in species richness and relative abundance of planktonic unicellular eukaryotes and particle-attached bacteria. However, our dataset and experimental design did not allow assessing whether these modifications resulted from partial or total digestion by mussels. Although, absolute numbers of one species can remain the same and still show a decrease in relative abundance if other species increase (making absolute numbers of organisms more important in the context of infection or toxicological potential), in the present study only the relative abundance of OTUs was taken into consideration. This was because of biases of deep-sequencing, which involve pyrosequencing errors and copy-number variations among taxa  (Kunin et al.