dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion DNA metabarcoding from bulk zooplankton samples is effective to detect benthic NIS. Overall our results suggested that the use of a customdesigned database combined to a BLAST®-based alignment was the more efficient approach for NIS detection and to avoid false assignments. This pinpoints the need for NIS-dedicated databases as advocated by  Darling et al. (2017)  or  Dias et al. (2017) , especially since using metabarcoding as a tool to detect NIS is becoming more popular. Focusing on the larval stage allowed us to go beyond the simple presence of a given NIS and helped us to, firstly, evaluate the reproductive success of the identified species and, secondly, to show that one notorious and conspicuous NIS (C. gigas) apparently do not produce larvae in the study area. In addition, for the long dispersers where enough reads were obtained, a reproductive window could be defined.
Figure1. A. Proportion of reads assigned to each of the target classes with the three tested assignment methods, namely BLAST ® against a custom-designed database (Bc), BLAST ® against the GenBank nt database (Bnt), and the ecotag tool from the OBITools suite (E). The number of reads assigned to Ascidiacea and Gymnolaemata are too low to be noticeable. B. Number of species identified with each method for the four target classes (Ascidiacea: As, Bivalvia: Bi, Gastropoda: Ga, Gymnolaemata: Gy). The proportion of NIS (dark colour) and native species (light colour) is indicated.
Acknowledgements We would like to thank  John Bishop ,  Charlotte Roby ,  François Rigal ,  Fanny Leroy , and  Sophie Delerue-Ricard , for making their samples, sequences and/or data available. We are also thankful to the crew of the Neomysis and to the Diving and Marine core service, at the  Station Biologique of Roscoff , for their help during sampling. We also would like to thank the  ABIMS Platform  for providing access to the calculation resources needed to perform the data analyses. This project was supported jointly by the  Interreg IVa Marinexus programme  and the  Fondation TOTAL  (project  Aquanis2. 0 ). The European project  Assemble Plus  ( H2020-INFRAIA-1-2016-2017 ; grant no. 730984 ) contributed to the production of reference sequences. The sequencing of individual larvae was conducted at the  Centre National de Séquençage (Genoscope, Evry, France) in the framework of the programme Bibliothèque du Vivant , jointly supported by the  CNRS , the  MNHN  and the  INRA.
Data availability Reference sequences locally produced were deposited in GenBank (accession nos. MN066416 -MN066544 and MN073504-MN073507 for 18S and accession nos. MN064582-MN064634 for COI). In addition, the custom-designed database (fasta format), a file providing the species list and their Genbank accession number and the metabarcoding pipeline are available on Dryad Digital Repository at https://doi. org/10. 5061/dryad. hj53jd8.
Simulation setup and fitting The simulations are performed with the PyXspec interface to the XSPEC spectral-fitting program  (Arnaud 1996). We use here a build in version of XSPEC 12. 10. 1. For simulations intended to sample the spin parameter space, we consider 50 values ranging from 0 to 0. 995 in regular spacing. For all the other free physical model parameters, they are drawn from a uniform distribution bounded by their allowed interval of variations (listed in Table  1 ). The overall model is first normalized to correspond to an absorbed flux equivalent to a 1 mCrab source in the 2-10 keV range (or an absorbed flux of 2 × 10 -11 ergs/cm 2 /s). The sample size of 50 is commensurate with the number of known unabsorbed (typically type 1) AGN of similar brightness, which currently populate the Athena mock observing plan 2 , and as can be found in common catalogues such as the 3XMM-DR8  (Rosen et al.
Comparison with other instruments and simulations Feasibility studies have so far been carried out, considering Xray spectra with limited spectral resolution, ∼ 100 eV at 6-7 keV, e. g. as provided by XMM-Newton EPIC instruments  (Strüder et al. 2001) , combined with hard X-ray data enabling to sample the smooth Compton reflection bump above 10 keV, e. g. as provided by NuSTAR  (Harrison et al. 2013 ). We briefly discuss here how these studies compare to those presented in this paper. Kammoun et al. (2018)  have conducted a similar analysis to ours, simulating XMM-Newton EPIC-PN and NuSTAR spectra in the range of 1 to 3 mCrab fluxes. Beside relativistic reflection, they considered a warm absorber and two layers of partially covering neutral absorbers, cold reflection and thermal emission from the galaxy, thus introducing complexity in their spectral model similar to ours.
Acknowledgements. The authors wish to thank the anonymous referee for useful comments. DB acknowledges useful discussions with  Thomas Dauser ,  Edoardo Cucchetti ,  Chris Done ,  Jeremy Drake  and  Etienne Pointecouteau. DB and MC thank  Laura Brenneman ,  Francisco Carrera ,  Mauro Dadina ,  Javier Garcia ,  Matteo Guainazzi ,  Jelle Kaastra ,  Elias Kammoun ,  Giovanni Miniutti ,  Jon Miller , and  Jiri Svoboda  for their useful suggestions and comments on an earlier version of the paper. Special thanks to  Edoardo Cucchetti  and the computer support team at  IRAP  ( Elodie Bourrec and Cédric Hillembrand ) for providing DB with the cluster resources required to carry out these time consuming simulations. MC acknowledges financial support from the  Italian Space Agency  under agreement ASI-INAF n.
Acknowledgements This work has been partially funded by the  ANR JCJC  grant  COCA HOLA  ( ANR-16-CE40-004-01 ) and by the  EPSRC  grant  EP/R029121/1  " Typed Lambda-Calculi with Sharing and Unsharing ". The authors are grateful to  Claudio Sacerdoti Coen  and  Andrea Condoluci  for valuable discussions, in particular about the renaming on β.
Excluding PNG, 90% of Pacific Islanders live within 5 km of the coast. All of the population in the coral atoll nations of Tokelau and Tuvalu live within a km of the ocean. Results using two global datasets, the SEDAC-CIESIN Gridded Population of the World v4 (GPWv4) and the Oak Ridge National Laboratory Landscan differed: Landscan under-dispersed population, overestimating numbers in urban centres and underestimating population in rural areas and GPWv4 over-dispersed the population. In addition to errors introduced by the allocation models of the two methods, errors were introduced as artefacts of allocating households to 1 km x 1 km grid cell data (30 arc-seconds) to polygons. The limited utility of LandScan and GPWv4 in advancing this analysis may be overcome with more spatially resolved census data and the inclusion of elevation above sea level as an important dimension of vulnerability.
Firstly, such analyses provide the foundation of integrated analysis of risk and exposure to sea-based threats, including chronic risks associated with climate change (e. g. [2, 8, 9, 10, 11, 12] ), and acute risks from extreme weather events  [2, 13, 14, 15, 16]. Distance from the coast is an important dimension of such analyses and, in the absence of elevation and other variables, is a proxy for vulnerability to sea-based threats. Another broad category of need is in the management of the coastal zone in face of multiple conflicting uses, including tourism, urban development and, increasingly, the alienation of small-scale fishing communities as more people migrate to the coastal zone  [17, 18, 19, 20, 21]. Information required to map and analyze the spatial arrangement of people is usually derived from national population censuses and from global datasets.
In this paper we estimate the numbers and proportions of populations living close to the coast in the 22 PICTs, and to stratify these estimates by distance from the coast. We use a combination of national census data and two global datasets, the SEDAC-CIESIN GPWv4  [28, 29]  and the Oak Ridge National Laboratory LandScan  [30, 31]. National census data vary in detail among PICTs: the recently completed national census in Tonga gathered georeferenced household data, but the vast majority of PICTs have older, less reliable and precise information. Our analysis explores differences in population estimates using census data and global models, and develops a decision tree to guide future analyses based on the availability and method of national surveys.
The two global datasets used to spatially distribute populations were the SEDAC-CIESIN Gridded Population of the World v4 (hereafter GPWv4; http://sedac. ciesin. columbia. edu/ data/collection/gpw-v4) and the Oak Ridge National Laboratory LandScan™ (2015) (hereafter LandScan; http://web. ornl. gov/sci/landscan/index. shtml). GPWv4  [28, 29]  is a minimally modelled population dataset that uniformly distributes census data into 30 arc-second (approximately 1 km resolution at the equator) grid cells. Population data for censuses occurring between 2005 and 2014 were collected at the most detailed spatial resolutions available and then extrapolated to produce population estimates for 2015. LandScan  [30, 31]  allocates population census data using dasymetric cartographic techniques where ancillary data such as land cover, roads, terrain slope, urban extent, and accessibility are used to model population density at 30 arc-second grid resolution within administrative boundaries.
Land and administrative boundaries Land administrative boundary and coastline data were extracted from the FAO Global Administrative Unit Layers (GAUL) network (www. fao. org/geonetwork/srv/en/-metadata. show?id= 12691). For both global datasets, we overlaid the coastline boundaries of GAUL onto population data when defining the zones because LandScan geographic outlines were too coarse with respect to coastline boundaries for small islands (Fig  2 ). In particular, this meant that houses near inlet bays or on narrow isthmuses that misalign with the 30 arc-second grid would have been excluded by LandScan, but are included by coastal zones drawn using GAUL data (Fig  2 ). Enumeration areas that crossed zone boundaries were assigned a single zone based on the centroid of the enumeration area.
In each zone, we estimated the number of residents using the latest national census and from two global databases. Both global datasets had population estimates for 2015 and censuses were done between 2000 and 2015 (Table  1 ). Each nation was in control of its own census protocol, though often guided by the Statistics for Development Division of the Pacific Community, and the two global databases were curated by independent agencies. In general, national population data could not be projected to 2015 at subnational level, as we lacked spatial resolution for vital statistics and within-country migration patterns. We therefore normalised population estimates to the total coastal population of each dataset.
Allocation of census data Population data collected from national censuses were collected using different methods and so varied in spatial precision. Countries were analysed with one of four methods depending on the availability of data: • Method 0. In five of the smallest PICTs the land area of all populated islands fell within the 1 km zone. No analyses or assumptions were required. This method was used in the following PICTs: Kiribati, Marshall Islands, Pitcairn Islands, Tokelau, and Tuvalu. All except Pitcairn Islands are comprised solely of coral atolls and coral reef islands (defined as low-lying accumulations of unconsolidated, or poorly lithified, carbonate sands and gravel deposited on coral reef platforms  [35]. • Method 1. Households were GPS-located and each household had specific census data, allowing the population in each coastal zone to be counted directly and hence this is the 'gold standard' for mapping population distribution.
Data were geo-rectified using local coordinate reference systems. This method assumed that both GPS and census data were accurate and precise. PICTs: Tonga and French Polynesia. • Method 2. Households were GPS-located. Population data was only available down to enumeration area, extracted from census datasets. The population data and number of households were used to calculate an average mean household size in each enumeration area, which was allocated to each GPS-located house. This method resulted in all households in a given enumeration area approximating the same household size. Coastal zone populations were calculated by selecting all households falling within each of them Data were geo-rectified using local coordinate reference systems. PICTS: Federated States of Micronesia (FSM), Fiji, Commonwealth of the Northern Mariana Islands (CNMI), Palau, Samoa, Solomon Islands, Vanuatu, and Wallis and Futuna.
• Method 3. Population data were available for each enumeration area with no GPS household location data available. The population was averaged by unit area across a given enumeration area. Vector analysis allowed us to precisely determine the proportion of each enumeration area, and hence each enumeration area's population, within each zone. Data were georectified using the ESRI World Cylindrical Equal Area (WKID: 54034) projection to preserve area. This method assumed the population was uniformly distributed within the enumeration area. PICTs: American Samoa, Cook Islands, Guam, New Caledonia, Niue, Nauru, and PNG. Nauru was unique as only three enumeration areas fell outside the 1 km zone, and these were manually allocated to the 5 km Coastal zone. Population estimates in the grid cells from the LandScan and GPWv4 data sets that bridged zone boundaries were allocated to a Coastal zone based on the location of the centre of the cell.
All data are presented as raster images on a 30 arc-second grid which is the native resolution of the LandScan and GPWv4 datasets. The logic tree used to generate population estimates from methods 1-3 is summarized in S1 Fig. More detail about how the coastal buffers were created and population figures calculated can be found at the protocols. io repository (http://dx. doi. org/10. 17504/protocols. io. 59yg97w).
Effect of differences in census date We conducted an analysis, using Fiji, to assess the impact of such differences in the year of census. We used data from 2007 to project a population for 2015 using birth/death rate data at the scale of the tikina, the local meso-level administrative unit. We calculated mean household population for each tikina, and allocated this value to household GPS data from the 2007 census. We then used these data to measure the number of people in each zone as if they were any other Method 2 data. We also had projected 2015 populations for both of the global databases to compare 2007 census, 2015 projected, and the two (LandScan and GPWv4) 2015 globalprojected estimates. This comparison allowed us to quantify the effect of using census data from a range of dates. Secondly, by overlaying the global datasets with our projected 2015 population, we were also able to visualize where global projections over-or under-estimated population density.
Comparison of disaggregation methods Differences in allocation of households and people to zones using Method 1 and 2, and estimates derived from LandScan or GPWv4 were consistent with the nature of the algorithms used to allocate population within grids (  The effect of the different methods were most apparent in the 1 km Coastal zone in all PICTS (Fig 5A ), as well as highly apparent in the 5 km Coastal zone in the Melanesian PICTs (Fig  5B ). When comparing the global databases with national census data (S1 Table ) we found that, for the 1km zone, LandScan overestimated the proportion of the population by more than 5% for 8 out of 22 countries (Fig  5 ). As the area of zones increased, LandScan and GPWv4 proportions converged towards National Census data estimates. In addition, different estimates of the proportion of people living within 10 km of the coast converged (Fig 5C, S1 Table ).
Comparison of census methods Only for Tonga were we able to compare the results from Method 1 and Method 2 data. In this case, the models agreed very closely. For other countries, we were able to compare Method 2 and Method 3. In general, models of Method 3 data underestimated populations in the 1km and 5km zones (Fiji, Solomon Islands, Vanuatu, Palau, and Samoa). For three PICTs, this was untrue: Federated States of Micronesia, Northern Mariana Islands (where Method 2 was very similar to Method 3), and Wallis and Futuna (Method 3 > Method 2). Generally, GPWv4 underestimated the proportion of people living in the 1 km and 5 km zones, though this was not true for Fiji or Niue. By contrast, the LandScan model tended to slightly overestimate proportions of populations in those zones.
Clearly, national population census data georeferenced at the household level (including number of occupants), combined with ancillary data on elevation above sea level provides the best platform for future analyses. Recent Population censuses in Tonga and Vanuatu completed in 2016 have georeferenced households, as will the Kiribati household and income and expenditure survey in 2019. In the meantime, for most countries in the region and possibly for other developing countries, analyses will rely on stitching together data of varying resolution and quality, including Landscan and GPWv4, among other sources. LandScan and GPWv4 data provide a significant source of information about where people live. Both sources are, however, publically available only at 30 arc-second scale and have inaccuracies that are predictable consequences of the models used.
Unsurprisingly, differences among data sources diminished and converged on estimates from the census as greater proportions of the population were captured in the 1, 5 and 10 km zones. Most islands in the PICTs are less than 20 km across so most residents cannot be farther than 10 km from the coast. Global data sets such as LandScan and GPWv4 provide invaluable sources of information, particularly in the developing world where there are limited alternatives. Nevertheless, at the scale of analysis needed to advance this work within the particular geographies of small Pacific islands they will be of limited utility. The LandScan model concentrated population into urban areas and where infrastructure (roads, night-time lights and so forth) was most developed-in all instances this coincided with development in the coastal zone.
This model was not well suited to rural coastal villages, many of which do not have roads or electricity. Conversely, census method 3 and GPWv4 averaged populations across the grid cells and so over-dispersed population in rural areas. Errors from both GPWv4 and LandScan were greatest in the 1 km zone where finer scale accuracy was most needed. Other global datasets, such as WorldPop  [27, 28]  have finer grids but were not available for PICTs at the time of writing. In addition to allocation errors introduced by the global models, errors were introduced as an artefact of allocating 1 km x 1 km grid cell data (30 arc-seconds) to polygonal zones. The population in each grid cell was estimated, and each grid cell was then classified as being within the 1, 5, or 10 km zone based on the location of the center of the grid cell.
National Laboratory website (http://web. ornl. gov/sci/landscan/index. shtml). The administrative boundaries and coastline data were extracted from  FAO Global Administrative Layers  (GAUL) through their website (http://www. fao. org/geonetwork/srv/en/main. home). The national household location datasets can be obtained through the  respective National Statistics Offices (Federated States of Micronesia : http://www. fsmstatistics. fm/,Fiji: https://www. statsfiji. gov. fj/,French Polynesia: http://www. ispf. pf/,Northern Mariana Islands: http://ver1. cnmicommerce. com/divisions/central-statistics/, Palau: https://www. palaugov. pw/budgetandplanning,Samoa: http:// www. sbs. gov. ws/,Solomon Islands: http://This work was funded by  The Pacific Community  and the  Australian Aid Program  through  ACIAR  projects  FIS/2012/074  and  FIS/ 2016/300.
Identification of coding variants in SLC22A12 by whole-exome sequencing. WES analysis was performed in 31 individuals with hypouricemia of the KoGES cohort (Fig. 1 ). The average depth coverage for these individuals was 85-fold. We performed variant calling and downstream filtering analyses assuming an autosomal recessive inheritance model. Coding variants in SLC22A12 were observed in 87. 1% (27/31) of the individuals (Table  2 ). One subject was a compound heterozygote for SLC2A9 variants. In the remaining three individuals, variants within other genes were identified that appeared to have disease-causing potential and will be further investigated. 76% (24/31) individuals had variants previously reported in the Human Gene Mutation Database (HGMD) and the remaining 9. 7% (3/31) exhibited novel missense mutations that had not been previously reported (Supplementary Table  3 ).
Image analysis and base calling were performed with the pipeline software using default parameters. Mapping was done using the human reference genome assembly (GRCh37/hg19), and all variants were called and annotated using CLC Genomic Workbench (version 9. 0. 1) software (QIAGEN bioinformatics, Redwood city, CA, USA). WES variant filtering analysis. We performed variant-filtering analysis assuming an autosomal recessive or X-linked recessive pattern according to the predominantly observed inheritance mode in hereditary RHUC  31. First, we systematically excluded variants with minor allele frequency (MAF) > 1%, which has been the conventional threshold for a rare variant, using dbSNP database (version 150), 1000 Genomes Projects phase 3 data (2,504 individuals), Exome Aggregation Consortium (ExAC, http://exac.
broadinstitute. org), and Genome Aggregation Database (gnomAD, http://gnomad. broadinstitute. org/)  29. Second, variants present in the homozygous or hemizygous state in in-house database consisting of 46 healthy Koreans without hypouricemia were excluded. Third, non-synonymous variants, small insertion/deletion (indel) or splice-site variants were selected. In the further analysis, we excluded single heterozygous variants so that only bi-allelic variants (homozygous, compound heterozygous, hemizygous for male) finally remained Direct Sanger sequencing. Confirmation of called variants was conducted via direct Sanger sequencing. The DNA sequences spanning the variants were amplified using specific primers (Supplementary Table  1 ) and sequenced using an Applied Biosystems 3500xl genetic analyzer3500XL (Applied Biosystems, Foster City, CA, USA).
Snapshot method. The SNaPshot assay of rs121907896 (p. Arg90His) and rs121907892 (p. Trp258*) was performed according to the manufacturer's instructions (ABI PRISM SNaPshot Multiplex kit, Foster City, CA, USA). The analysis was carried out using GeneMapper software (version 4. 0; Applied Biosystems). The primer sets for the SNaPshot assay are described in Supplementary Table  1. In silico analysis of novel missense variants. Prior to the analysis, known pathogenic variants of SLC22A12 were screened in the Human Gene Mutation Database (HGMD ® ) as a public reference. For the newly discovered missense SLC22A12 variants, we checked if the mutated amino acid resides are highly conserved across the vertebrate orthologs using the UCSC Genome Browser (https://genome. ucsc. edu/). Given the role of the nitrogen excretion function in the evolutionary process, we identified amino acid sequences in several mammals (Rhesus macaque, Mus musculus, Canis lupus familiaris, and Loxodonta africana) that share the urea cycle rather than direct UA excretion.