dataesr/scientific-paragraphs-categorization huggingface dataset:
Metagenome analysis We studied a number of publically-available shotgun metagenomic datasets from diverse environments to determine the global distribution of the three NSAP classes (Table  1 ). Shotgun metagenome sequences generated using Illumina® sequencing technology containing at least 10 million reads were downloaded in FASTQ format from the European Nucleotide Archive (ENA), the DNA Data Bank of Japan and Sequence Read Archive. The specific datasets used were chosen because they contain the largest number of reads in each respective collection. We used three marine datasets, a bathypelagic sediment from the Gulf of Mexico (SRR4027974), sediment from the Columbia River estuary coastal margin, Washington State, US (ERR864075), and sediment from the Noosa River estuary in south east Queensland, Australia (ERR688352) and four terrestrial soil datasets, a tallgrass prairie soil from Fricke Cemetery, NE (ERR346662), a peat bog hydric soil from the Marcell Experimental forest, MN (SRR1157608), a rice paddy hydric soil from southeast China (SRR1190306), and an arid soil from Uluṟu, Northern Territory, Australia (ERR671923).
53. For each gene, a pHMM of the resulting MSA was generated using HMMbuild (HMMER ver 3. 1b1) resulting in a 799 nucleotide (nt) Class A pHMM based upon 426 sequences, a 714 nt Class B pHMM based upon 319 sequences and an 836 nt Class C pHMM based upon 479 sequences. These pHMMs were used to search the unassembled metagenome datasets using HMMsearch, employing an E < 1 × 10 -5 along the full sequence length. The probability thresholds for the multiple segment Viterbi, Viterbi and Forward filters were 0. 02, 0. 001 and 1 × 10 -5 respectively. PHMMER was used to compare the retrieved metagenome sequences, following six-frame translation using EMBOSS Transeq  (Rice et al. , 2000) , to the UniprotKB protein sequence database to confirm that the sequences represented the correct protein family.
Only those metagenome sequences for which one of the six frame translations elicited a UniprotKB hit of the appropriate protein family (E < 1 × 10 -5 ) was included in the subsequent analysis. Phylogenetic placement and visualization of the recovered metagenome sequences upon reference Maximum-Likelihood phylogenetic trees was performed using pplacer ver 1. 1  (Matsen et al. , 2010). To allow meaningful comparison between metagenomic datasets, gene abundance was expressed as a proportion of the estimated total number of genomes in each dataset, assessed by estimating the abundance of the ubiquitous, single-copy genes rpoB, recA, gyrB  (Santos & Ochman, 2004)  and atpD  (Gaunt et al. , 2001). Nucleotide sequencebased pHMMs were developed for each gene as described above. Metagenome-derived homologue counts for each single-copy gene were sizenormalized to the length of the shortest gene, recA accounting for differences in length between the genes.
Signal peptides were identified in all but 20 of the proteins, suggesting that most Class A proteins are transported at least out of the cytoplasm. However, it is clear that PSORTb was unable to determine a clear location for the proteins, possibly because NSAPs are a relatively poorly studied group and there may be few Class A NSAP proteins in the training datasets as a consequence. The proteins were found in a range of free-living bacteria including Caulobacter, Stenotrophomonas, Methylobacterium, Sphingomonas, Xanthomonas and Pseudomonas. Class B In all, 319 unique class B proteins were identified. The proteins were found predominantly in eukaryote-associating Gram-negative bacteria, for example Aeromonas, Aggrigatibacter, Citrobacter, Enterobacter, Escherichia, Klebsiella, Photobacterium, Salmonella, and Xenorhabdus.
The proteins were identified in a broad range of bacteria including Bacillus, Clostridium, Enterobacter, Erwinia, Lysobacter, Pedobacter, Pseudomonas, Rhodobacter and Serratia among others. read placements is shown on the phylogenetic trees. In the case of class A genes, there was a clear association between gene phylogeny and environment (Fig. 3 ). For example, the dominant class A gene in the Columbia River estuary shows high homology to that of marine Gammaproteobacteria HTCC2080 and HTCC2148, originally isolated from nearby coastal waters off Oregon  (Thrash et al. 2010). A second placement, with high homology to the cyanobacterium Synechoccocus sp. RS9916, is also abundant in the Columbia estuary. In contrast, sequences from the Noosa River estuary generally have weaker homology to the sequence database and the class A sequences appear more diverse than those from the Columbia estuary.
Biogeography of NSAPs For class C NSAPs there was again a distinct separation in marine and terrestrial environments in the placement of metagenome reads on the reference ML phylogenetic tree (Fig. 3 ). The Columbia estuary was dominated by a group of closely-related reads with high homology to NSAP from a SAR86 cluster bacterium and lesser numbers of reads with high homology to class C NSAPs from OM182 (oligotrophic marine) bacterium B A C L 3 , B re v i b a c i l l u s c h o s h i n e n s i s a n d metagenome reads were identified typically showed limited homology to reference genes. In contrast, the datasets from paddy soil, Fricke prairie and Uluṟu contained greater numbers of class C homologous reads: those identified exhibited greater homology to reference genes. Reads from paddy soil displayed homology to a group of Alphaproteobacteria including Azospirillum and Hyphomonas and there were also placements of small numbers of reads distributed across the reference tree.
Discussion NSAPs are a phylogenetically diverse group of enzymes found predominantly in Gram-negative bacteria. They remain relatively poorly studied and so the significance of NSAPs to the turnover of organic P in the environment is unclear. Searches of the UniprotKB protein sequence database identified a large number of proteins from each of the three NSAP classes. From these, a number of conserved amino acid motifs characteristic of each class have been identified (Fig. 1 ): although these are not novel we have expanded the number of sequences described previously by  Rossolini et al. (1998)  and  Gandhi and Chandra (2012)  to include 1900 non-redundant enzymes across the three classes, with class C being the most numerous. The three classes appear to have distinct sub-cellular localization profiles with enzymes that can be classified from classes A and B being periplasmic and associated with the cytoplasmic membrane while those from class C are associated with the outer membrane and thus able to catalyse the hydrolysis of large organic compounds.
In comparison, homologues from terrestrial systems appear more diverse, particularly those of class A genes, the dominant ecotype and number of ecotypes varying from hydric peat bog and paddy soils to tallgrass prairie and hot arid desert environments. The hydric peat bog from the Marcell Experimental Forest is unique among the metagenomes studied here in that class A gene homologues comprise a higher proportion than class C. Previous studies have demonstrated that acid phosphatases (Pfam family PF01451. 16) in general are more abundant than alkaline phosphatases (PF00245. 15) in this acid (pH 3. 5-4. 0) oligotrophic soil  (Lin et al. 2014). In contrast, Fricke Cemetery prairie (pH = 6. 5,  Fierer et al. 2013) , Uluṟu (pH = 6. 8, https://downloads. bioplatforms. com/base/contextual/sample/102.
100. 100. 8160) and Rothamsted Highfield (pH 5. 1-6. 0, Gregory et al. 2016 ) soils are less acidic and in each case class C homologues are more abundant than class A. This is also the case with the other four environments (unfortunately, pH is not consistently reported for the datasets used in this study) where the pH of submerged paddy soils is often in the range of 6-7  (Yu 1991 ) and marine sediments are typically of circumneutral to alkaline pH  (Gaillard et al. 1989; Zhu et al. 2006). Niche separation of class A and C NSAPs is evident from comparison of Highfield soils (Figs. 5  and 6 ) where three class A ecotypes showing homology to Caulobacter crescentus, a Brevundimonas clade and a group of reads placed deep on the cladogram, are much more abundant in grassland and arable soils compared to bare fallow soil.
Shotgun metagenome datasets from eight diverse marine and terrestrial environments were used to establish the distribution of NSAPs. Gene abundance was normalized and expressed as % genome equivalents (%GE) to allow meaningful comparison between metagenome datasets of different read numbers. A census of the environments (Fig. 2) indicated that classes A and C are relatively much more abundant in both marine and terrestrial environments than class B. Given the association of class B with pathogens and other microorganisms found in close association with eukaryotes this is perhaps unsurprising. Comparisons of the normalized abundance of classes A and C indicated that although both are relatively more abundant in marine than terrestrial environments, there was no significant difference in abundance (F 1,12 = 0.
Acknowledgements We are grateful to the  DeepSoil consortium including Illumina® ,  Rothamsted Research  and  Janet Jansson  at the  Lawrence Berkeley National Laboratory/Pacific Northwest National Laboratory U. S. A  for providing the Highfield metagenomic data and reviewers whose comments helped improve the quality of the final manuscript. The authors are indebted to  Tony Hooper  for collecting 31 P NMR spectra from Highfield soils. Rothamsted Research receives strategic funding from the  UK Biotechnology and Biological Science Research Council  which supported this work (grant  BBS/E/C/00005196 ).
2014 bathypelagic (>1. 5 km depth) Columbia River costal ERR864075 a Illumina, HiSeq 1000 51,801,990 7528 Fortunato and Crump 2015 margin, Washington, USA (33 ‰ salinity, 16 m depth) Noosa River estuary, ERR688352 a Illumina, HiSeq 2000 129,525,367 10,405 Queensland, Australia (0. 34 m depth) a European Nucleotide Archive b DNA Data Bank of Japan c Sequence Read Archive.
In the infective morphotype conidia, the cell wall is covered by a melanin layer and an outer layer with rodlet morphology consisting of amyloid fibers composed of the protein RodA, which belongs to the hydrophobin family. The capacity of airborne conidia to reach alveoli is due to the highly hydrophobic layer of rodlets that facilitates air dispersion  [6]. Furthermore, the rodlet layer formed by RodA masks conidial recognition by the human innate immune system  [7]. In addition to RODA, (AFUA_5G09580; AFUB_057130) (Aspergillus Genome Database) identified five more hydrophobin genes in A. fumigatus, RODB (AFUA_1G175250, AFUB_016640), RODC (AFUA_8G07060, AFUB_080740), RODD (AFUA_5G01490, AFUB_050030), RODE (AFUA_8G05890, AFUB_081650), and RODF (AFUA_5G03280, AFUB_051810)  [8]. RODG (AFUA_2G14661) was identified later, due to mis-annotation in the A.
fumigatus strain Af293 database  [2]. RODG is located on the A. fumigatus Af1163 scaffold scf_000002, on the opposite strand from an annotated gene (AFUB_030300). A search for new hydrophobins in filamentous fungi using successive blasts on the Aspergillus comparative database led to the identification of three more proteins after the sixth blast (AFUA_5G09960, AFUA_7G00970 and AFUA_8G01770)  [4]. However, these new proteins were not taken into account herein, because these proteins are predicted as adhesins by the FungalRV adhesin predictor and, hence, they do not show the hallmarks of hydrophobins  [8, 9]. Sequence analysis in silico placed RodA, RodB, and RodC in Class I, and, thus, as competent to form rodlet layers. RodF and RodG belong to Class III, which contains hydrophobins with intermediate (between classes I and II) or atypical characteristics  [2, 4].
Gene Expression Quantification RNA-Seq data obtained previously to compare the transcriptomes of A. fumigatus growing under biofilm and liquid planktonic conditions  [22] , sporulating cultures  [23] , and in vivo data obtained here with mice were used to quantify the expression of the different hydrophobins as described previously  [23]. For each dataset raw Illumina reads were quality and adapter trimmed using Trim Galore  [24]  with a quality cut-off of 20 and a minimum length cut-off of 30 bp. Quality trimmed reads were then mapped against the reference A. fumigatus Af293 genome using TopHat2 with the default setting  [25]. Read counts and RPKM (Reads Per Kilo bases per Million reads) expression values for each gene were calculated using GFOLD  [26]. For in vivo RNA-Seq data, eight-week-old OF1 male mice weighing approximately 28 g (Charles River Laboratory, L'Arbresle, France) were used.
3. 1. 0). To allow for comparison of shell growth rates published elsewhere, the estimates of intercept and slope were used to calculate the growth parameter k and the asymptotic length at which growth is zero (L 1 ) of the Von Bertalanffy growth equation: L t ¼ L 1 ð1 À e À kðtÀ t 0 Þ Þ ðeq: 5Þ At intra-lagoon scale, instantaneous mortality rates (M) were square-root transformed and expressed as a function of seasons, stations, depth and interactions between these factors on the basis of a linear model. Shell growth rates (G) calculated over each monitoring period were expressed as a function of shell length and sampling-structuring factors (stations and season for Tubuai; station, season and depth for Tatakoto) using a linear mixed model (function lme of nlme package in R. 3. 1. 0. ). We could not test for interactions between factors, but a random effect considered the existence of dissimilarities between individuals (i.
This work was supported by a  PhD fellowship  from the  "Ministère de l'Enseignement Superieur et de la Recherche " to Universitede la Polynesie francaise and additional funding provided to Labex CORAIL under the TEKE- TEKE  Project (www. labex-corail. fr). Sensors were sponsored by the SITADEL 2012 project, GOPS (www. observatoire-gops. org). The funders provided support in the form of salaries [SVW, MM], but did not have any role in study design, data collection or analyses performed, decision to publish, or preparation of the manuscript. The specific roles of individual authors are articulated in the 'author contributions' section.
Sequencing analysis of the vp60 gene obtained from two specimens showed identical vp60 sequences. Comparison with other known RHDV2 strains from public databases through BLAST analysis revealed a closer similarity with strains from Alentejo collected during 2013. Maximum Likelihood and Bayesian phylogenetic analysis showed that the 2016 strains from the archipelago have a higher resemblance with a group of strains mostly collected in the South of Portugal between 2013 and 2014.
Nucleotide sequencing analysis Amplification of the complete vp60 sequences of RHDV2 strains was accomplished with two pairs of primers, 27F (5′-CCATGCCAGACTTGCGTCCC-3′) and 986R (5′-AACCATCTGGAGCAATTTGGG-3′), 717F (5′-CGCA GATCTCCTCACAACCC-3′)  [24] , and RC10R  [62]  enabling the obtainment of two overlapping fragments. Both 717F and 986R are specific for RHDV2. The One Step (Qiagen, Hilden, Germany) kit was used, under the manufacturers' recommendations. Sequencing was accomplished using the BigDyeTM Terminator cycle sequencing kit (Applied Biosystems, Foster City, CA, USA). The complete vp60 nucleotide sequences of two RHDV2 strains (GenBank accession numbers KY247124 and KY247125) were determined on an automated 3130 Genetic Analyzer system (Applied Biosystems, Foster City, CA, USA).
Phylogenetic analysis The phylogenetic relationships between two RHDV2 vp60 sequences from the Berlenga Island with other RHDV2 sequences originated in Portugal mainland, Azores and worldwide, were investigated. Sequence KC345614 (from a classical RHDV strain belonging to genogroup G5) was chosen as outgroup to root the trees. Multiple alignments of the nucleotide sequences were generated by CLUSTAL Omega  [58]. Phylogenetic inference was performed by Maximum Likelihood (ML) and Bayesian methods. For ML analysis, the appropriated substitution model was determined resourcing to R software (R Development Core Team, 2011). The GTR model  [61]  with gamma-distributed rate variation across sites (GTR + G) showed the lowest BIC and AICc values and was subsequently used to infer phylogenetic relationships.
Robustness of the tree nodes was assessed by bootstrapping 1000 times. For the Bayesian analysis, the CLUSTAL Omega results were converted to the NEXUS format using Mesquite software  [42]  The phylogenetic tree was obtained with a Bayesian inference of phylogeny throughout the MrBayes version 3. 1. 2 software that uses the Markov chain Monte Carlo simulation technique to approximate the posterior probabilities (pp) of trees  [33, 56]. MrBayes analysis was performed using the GTR model (nst = 6) with gamma-shaped rate variation with a proportion of invariable sites (rates = invgamma). The analysis was run for 10 6 generations (ngen = 10 6 ) with four chains of temperature (nchains = 4), and each chain was sampled every 10th generations (samplefreq = 10). The graphical representation and edition of the phylogenetic trees were performed with FigTree v1.
Acknowledgements We are thankful to  João Teixeira ,  Fátima Cordeiro ,  Sandra Carvalho  and  Rosário Luís  for excellent technical assistance. We also present our acknowledgments to Berlengas Natural Reserve guards ( Paulo Crisóstomo ,  Eduardo Mourato ,  Filipe Correia ,  Tiago Menino  and  António Figueiredo ), all volunteers and SPEA staff (namely  Isabel Fagundes and Pedro Luís Geraldes)  who supported the entire field work. Thanks to the  National Maritime Authority  and  Peniche Captaincy  for the transport to and from the  Berlenga Island. Finally , thank you to the  Instituto de Conservação da Natureza e das Florestas , namely to  Maria de Jesus Fernandes  and  Maria de Lurdes Morais  for their full support to this study.
Funding Most of the field and laboratory work refered to in this manuscript was supported by the  Interdisciplinary Research Centre on Animal Health, Faculty of Veterinary Medicine, University of Lisbon  (CIISA, FMV, UL) (Portugal) (Project  UID/CVT/00276/2013 ). Molecular analysis was partially funded by the  Fundação para a Ciência e Tecnologia (FCT)  (grant  SFRH/79225/2011 ) and and  Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM)  (Project  UID/AGR/00115/2013 ). Field work was partially supported by the  LIFE + Berlengas  ( LIFE13 NAT/PT/ 000458 ), a contribution of the LIFE financial instrument of the  European Union  and the " Fundo de Biodiversidade " funded from the  Portuguese Government. Funding bodies played no direct role in the design or conclusion of the study.
The PCR products were purified with ExoSap-IT ® (USB Corporation, Cleveland, OH, USA) and sequenced in both directions. Sequencing reactions were performed using the "BigDye ® Terminator v3. 1 Cycle Sequencing Ready Reaction" and sequencing was performed on the automatic sequencer ABI 3130 DNA Analyzer (Applied Biosystems). The sequences and collateral information have been deposited in BOLD  (Ratnasingham and Hebert, 2007)  in the projects BIFGA and BIFG in the container 'Barcoding Indonesian Fishes' of the 'Barcoding Fish (FishBOL)' campaign and DNA sequences were submitted to GenBank (accession numbers are accessible directly at the individual records in BOLD).
Population genetic structure (Fig. 1, step 1a) We examined the distribution of molecular variance through an additive partitioning across increasing spatial scales as implemented in AMOVA  (Excoffier et al. , 1992; Excoffier and Smouse, 1994). We opted for the SAMOVA version that both defines groups of populations without a priori partitioning scheme and estimates molecular variance within populations, among populations within groups and among groups, based on a simulated annealing approach  (Dupanloup et al. , 2002). SAMOVA is not a decision-based method pointing to the optimal number of groups, we hence applied an empirical threshold and stopped increasing the number of groups once the partitioning scheme produced at least one group consisting of a single sampling site. The SAMOVA were performed for each species using the software SAMOVA 2.
0  (Dupanloup et al. , 2002). SAMOVA partitioning schemes were further compared to the results of a hierarchical clustering based on genetic distances. Mean genetic K2P distances were computed among sampling sites using MEGA 6  (Tamura et al. , 2013)  and used to produce hierarchical clusters derived from the complete linkage algorithm as implemented in hclust function of the R Stats ver. 3. 1. 2 package  (R_Core_Team, 2014). Finally, traditional parameters of population genetic diversity including the number of haplotypes, nucleotidic diversity and mean pairwise K2P distance for each groups were computed using ARLEQUIN 3. 5  (Excoffier et al. , 2005). 2. 4. Delineating mitochondrial lineages and inferring their diversification (Fig. 1 , step 1b) Several alternative methods have been proposed for delineating molecular lineages  (Schloss and Handelsman, 2005; Pons et al.
At the end, each cluster of sequence is assigned to a Barcode Index Number (BIN) in BOLD  (Ratnasingham and Hebert, 2013). Once BINs were delineated (Table  S2 ), their timing of diversification was explored through the Bayesian approach implemented in BEAST 1. 8. 1  (Drummond et al. , 2012). In order to establish robust prior, the best-fit substitution model was selected through the Bayesian Information Criterion (BIC) as implemented in JMODELTEST 2. 1. 7 (Darriba et al. , 2012)  and further used as a prior for the joint reconstruction of tree topology and divergence times. The initial tree topology was obtained with an UPGMA starting tree and a Coalescent model was used as a tree prior  (Kingman, 1982). We used the canonical fish substitution rate of 1. 2% of genetic divergence per Millions years for mitochondrial protein coding gene  (Bermingham et al.
Delineating ESU (Fig. 1, steps 2 & 3) We examined the relationship between the genetic groups delineated by SAMOVA and the BINs (Fig. 1,  Step 2) by testing the independence of each partitioning scheme using the Pearson chi-square test of independence as implemented in the R Stats ver. 3. 1. 2 package  (R_Core_Team, 2014). The objective was to examine to what extent the SAMOVA groups of populations were determined by the BINs delineated by the RESL algorithm (Fig. 1,  Step 2). We further examined the concordance of the BINs spatial distribution among species by performing a general hierarchical cluster analysis based on the average phylogenetic distance of the BINs among sites as implemented in hclust function of the R Stats ver. 3. 1. 2 package  (R_Core_Team, 2014). The matrix of phylogenetic distance among sites was computed based on the composite chronogram of the three grafted maximum credibility trees of Barbodes binotatus, Channa gachua and Glyptothorax platypogon and BINs occurrence data using the R package PICANTE  (Kembel et al.
Acknowledgements The study was designed by R. H. , B. S. , P. K. , R. K. H. and N. H. These results were obtained in the framework of AH master degree under the supervision of N. H and  B. S. Fieldwork  was carried out by A. H. , H. D. , S. S. , F. B. , R. K. H. , P. K. and N. H. S. S and H. D. prepared and deposited the specimens in museum collection, N. H. and  R. K. H. identified the specimens and R. H. supervised the sequencing. A. H. and N. H. analyzed the data and wrote the manuscript, and all authors contributed to revisions. The authors wish to thank  Siti Nuramaliati Prijono ,  Witjaksono ,  Mohammad Irham ,  Marlina Adriyani ,  Ruliyana Susanti ,  Hari Sutrisno  and  Sri Sulandari , at  Research Centre for Biology (RCB-LIPI) ;  Jean-Paul Toutain ,  Robert Arfi  and  Jean-François Agn  ese from the  'Institut de Recherche pour le D eveloppement ';  Joel Le Bail  and  Nicolas Gascoin  at the  French embassy in Jakarta  for their continuous support.