dataesr/scientific-paragraphs-categorization huggingface dataset:
More precisely, 8% of the identifications for the rbcL marker (52 taxa amongst 34 genera) and 6% for the 18S marker (58 taxa amongst 45 genera) were not included. To track the origin (link to specific sites and ecology) of the observed discrepancies in the species lists, we used Canonical Correspondence Analysis (CCA) on the occurrences across the 180 samples, for each mismatch code against our five environmental parameters. The CCA anal-ysis showed that low pH and high TOC explained most of the variability observed when a species is not identified with the molecular technique due to a lack in the DNA reference database (NR), in agreement with the fact that many of the species lacking from the databases are acidophilic diatoms. None of the tested environmental variables explained the occurrence of abundances mismatch (AB1 or AB2) with either of the markers.
When looking back at the mismatch codes, the majority of these species were represented in both reference data-Figure  3. Average probability, per analysis, of a correct match or mismatch for a species identification and abundance assessment when comparing molecular and morphological methods. The code G represents a reference barcode stopping at the genus level; the yellow portion represents a species (H) or a genus (G-H) not expected in Fennoscandia; the orange portion represents the case when no DNA was detected for a species (ND) or a genus (G-ND) despite having a reference barcode in the database; the red portion represents the case when the database lacked a reference barcode for a species (NR) or a genus (G-NR). The green portion represents a good match both in presence and abundance of species, between the morphological and molecular assessments.
bases, but showed significant discrepancy in their relative abundance when assessed by morphological or by molecular techniques (AB1 and AB2). Achnanthidium minutissimum was found occurring with a higher abundance with the morphological assessments when using either markers but also, in some cases, found by molecular assessment only. Fragilaria gracilis was found with higher abundance with the morphological method than when using metabarcoding with the rbcL marker. With the 18S marker, it was only identified below the threshold we used for our analysis (< 10 reads). Amphora pediculus was found with a higher abundance in the morphological method than by metabarcoding with the rbcL marker but with higher abundance in metabarcoding when using the 18S marker. Tabellaria flocculosa was always found with higher abundance with the morphological method when using the rbcL marker and was not found at all by the molecular technique when using the 18S marker (despite being represented in the reference database).
Comparative use of the rbcL and 18S markers The gaps in the two reference databases were overlapping: out of 416 species and 409 species missing across 74 genera for the 18S and the rbcL databases, respectively, 388 species were missing in both databases (across 69 genera). The IPS scores obtained with the two markers were correlated (r 2 = 0. 30, p < 0. 001, Fig. 4 ). However, our anal-yses still confirmed significant discrepancies between the results produced using the two markers. The IPS scores were significantly different from one another (Student paired t-test, p < 0. 01). The rbcL marker gave more results of "Poor" and "Bad" water quality class, none of them being the same sites as with the 18S marker (Fig. 4 , 9 dots versus 4 triangles). Student's paired t-test on the Shannon scores also revealed a significant difference in the species communities obtained with the rbcL marker and the ones obtained with the 18S marker (p < 0.
The reference database incompleteness leads both to non-identification and misidentification of species Even with the current effort to complete and curate the diatom reference databases, many species are still lacking barcode information. Visco et al. (2015)  estimated that no more than 30% of European species are currently represented in reference databases. The main species coverage in the Rsyst::diatom database is for temperate species and lack an important number of species from the Fennoscandian communities. For example, our results showed that species list discrepancies from a lack of reference barcodes were strongly correlated to the low pH levels and high TOC values, which are common in Northern European waters. Similarly, the highest IPS deviations, found in oligotrophic sites, highlight a lack of representation of acidophilic species in the reference database.
This is also supported by the CCA results, showing that samples, not affected by the databases gaps, were mostly correlated to a high pH, in agreement with the fact that many of the species lacking from the databases are acidophilic diatoms, such as Eunotia incisa and Encyonema neogracile. With more than half of the Fennoscandian taxa missing a reference barcode, the database is the main reason for the non-identification, with metabarcoding, of species otherwise identified with the conventional morphological method. There have been efforts to replace missing species information using phylogenetic information derived from higher taxonomical level. However, this approach is not refined yet and might lead to wrong indicator values of ecologically different but closely related species  (Keck et al.
2018). Incorrect identification of the reference barcodes can, of course, lead to important discrepancies, hence the importance of a continuous database verification and curation. However, even with the benefit of being constantly updated and curated, some taxonomic discrepancies were also detected when we used using Rsyst::diatom. Some taxonomic names differed from the ones used for the same taxon in morphological assessment leading to an artificial mismatch between morphological and metabarcoding species inventories. Sometimes, taxonomic information came on different taxonomic levels which required the merging of taxa for comparison (e. g. Ulnaria ulna var. acus and Ulnaria acus). The Rsyst::diatom version v5 for the rbcL barcode also included some sequences without a reliable taxonomic name assigned (e.
g. "Nitzschia aff. dissipata"), although those mainly concerned reference barcodes included in Rsyst from the NCBI nucleotide database. Sequences assigned to such reference barcodes were removed from our dataset to avoid incorrect identification. Completion and curation efforts of the rbcL marker database is now being undertaken at European level and the latest Rsyst::diatom version v7 (in February 2018, renamed "Diat. barcode") was released with most of those taxonomic discrepancies removed. On the other hand, the curation of the Rsyst::diatom database for the 18S-V4 barcode is still only partial.
2016 ). In the Diagnosyst process, a simple sequences filtering on size is followed by a strict taxonomic assignment without heuristics: every query is compared against the each barcode in the reference database. While this strict assignment might produce a more precise and clean taxonomic assignment than most currently used pipelines, it in turn requires the computer power for dealing with a massive amount of molecular data. The strict taxonomic assignment should remove chimera sequences (artificial read created by two distinct portions of the genome) and prevent most misidentifications of taxa. However, a strict assignment also leads to a massive proportion of discarded reads and thus, to the loss of sequencing data. It is also possible that the strict size selection of sequences might have removed valuable genetic data (specific species may produce shorter sequences than 300 bp (for the rbcL) or 320 bp (for the 18S)).
The only reliable way to assess the effects of different clustering and different taxonomic assignment processes is to compare the species list generated by different pipelines when using the same reference database. This has yet to be done for the variety of pipelines used in molecular assessment of diatom communities in Europe. Finally, the limitation of taxonomic resolution (when identification at species level was not possible) can also lower the quality of the ecological assessment using the IPS index because the genus level includes the pooling of closely related species, some of which can exhibit very different ecological preferences  (Keck et al. 2018). However, even if the Rsyst::diatom database (rbcL and 18S V4) contains a number of reference barcodes identified only at genus level, we judge that this factor had only a relatively small impact on the ecological assessment because only about 10% of our taxa were assigned to genus level (rbcL and 18S markers).
2008). Another source of abundance discrepancy is the possible assignation to a similar reference barcode when the correct taxon is not represented in the database. As mentioned previously, closely related species can have different ecological preferences, such as shown for Halamphora veneta and H. oligotraphenta by  Keck et al. (2018). However, the strictness of Diagnosyst taxonomic assignment should limit this type of error: a sequence assigned to several reference taxa will be discarded, thus, an erroneous assignation could only happen in the event of a very close match to a single similar reference barcode. Additionally, the cryptic diversity can create abundance disparities coupled with presence/absence discrepancies: where limited morphological identification under a light-microscope may result in assignment to a single taxon, metabarcoding works at a finer taxonomic level and can split the specimen into several taxa.
In that case, we obtain a higher richness of species, at lower abundance, with the molecular technique. A similar problem arises if one of the method's identification stops at genus level, when the other method splits the identification into several species. With the morphological assessment under a light-microscope, a genus level taxonomic identifi-cation may result from lack of taxonomic expertise or too small specimens. When assessed with the metabarcoding method, a limited taxonomic level may be due to a lack of higher level reference barcodes or to low primer specificity. The former case was more represented in our dataset but still rarely occurring.
Different markers give different ecological status assessment The DNA barcodes rbcL and 18S-V4 were chosen in this study because of their power to discriminate diatom communities, covering the three major diatom divisions and for their balance between variability and conservation of the primer binding sites  (Kermarrec et al. 2014; Zimmermann et al. 2011). The V4 region is the largest and most complex of the highly variables region in the 18S locus  (Zimmermann et al. 2011 ) but the rbcL barcode has a higher polymorphism than the 18S one. When looking at the species lists, the Shannon scores show greater deviations from the one calculated on morphological communities when using the 18S marker than when using the rbcL marker. The presence of green algae barcodes in the 18S dataset, which are completely absent from the rbcL dataset, is most likely responsible for that trend and a greater difference was found between the Shannon scores of the two markers in the molecular analysis, rather than between the two methods.
For example, T. flocculosa failed to be detected when using the 18S marker, despite the fact that this species is represented by two reference barcodes in the 18S-V4 database. The species is detected by the rbcL marker, so one could speculate that the Nordic T. flocculosa is not well amplified when using the 18S-V4 or that its 18S-V4 DNA fragment does not match the reference barcodes available. While this study confirms the strong impact of the reference barcodes available, as much in quantity as in quality and some marker-specific difficulties, we cannot efficiently recommend one or the other marker. The 18S-V4 seems to have promising efficiency in ecological assessment and covered more of the Fennoscandian taxa morphologically identified at the time this study was undertaken, whereas the rbcL marker generated species lists more similar to the ones generated by the morphological approach.
Conclusion Overall, our findings that the metabarcoding method in general is also suitable for Northern European conditions are promising. However, based on our results, we are convinced that there is a need for further development of this method for the use for environmental assessment in Fennoscandia. The limitations of both techniques are multiple and correlated, making them difficult to isolate and properly quantify. Still, based on our results, we would recommend focusing first on the completion and maintenance of the reference databases, adding important missing species and carefully curating them to remove ambiguous barcodes and widen their use to broader ecosystems. Next, the abundance discrepancies were not the most common error source but clearly the one that mostly affected the ecological assessment.
Thus, it would be interesting to adapt the  Vasselon et al. (2018)  biovolume correction factor to Fennoscandian diatoms' biovolumes and apply it to our dataset for the rbcL marker, to see if we could achieve an improvement for the ecological assessment. Additionally, the great diversity of bioinformatics pipelines, currently available for diatoms' metabarcoding, poses another challenge. Which pipelines are currently being used by the different research groups dealing with diatom metabarcoding development and the way they affect the molecular data and ecological assessment need to be evaluated, perhaps as a first step toward a standardisation of the molecular process. Finally, the current Fennoscandian way to set an ecological status class is based on the morphological method and the next step should be to focus on the integration of metabarcoding data into current ecological assessment methods, as recommended by  Pawlowski et al.
Acknowledgements This project was funded by  Stiftelsen Oscar och Lili Lamms Minne  (http://www. stiftelsenlamm. a. se/) and by  The Swedish Agency for Marine and Water management. We would like to thank  Jón S. Ólafsson , from the  Marine and Freshwater Resarch Institute (Iceland) , for the collection of data from Iceland and for his helpful feedback. We also thank the  Norwegian Institute for Water Research (NIVA) , which provided the water chemistry data for all Norwegian samples. We would like to thank  Teofana Chonova ,  Meline Corniquel  and  Sonia Lacroix  who performed the preparation of DNA libraries for all samples at the  molecular laboratory of INRA CARRTEL in Thonon (France)  and  The Plateforme Genome Transcriptome of INRA BIOGECO in Pierroton (France)  for the HTS sequencing.
Methods: The relationship between daily changes in mould (25 species) concentrations and daily SABA sales within a population of patients aged 6 to 39 years in a middle-size town of central France (approximately 127,000) was obtained from social security database and analysed with generalized additive models, taking into account confounding factors (air pollution, weather conditions, pollen counts and trend). Results: Daily SABA sales (mean, SD) rose from 17. 3 (9. 7) in 2010 to 22. 7 (12) in 2015. The relative risk (RR [95% CI]) of SABA sales associated with an interquartile increase in mould concentration was significant in the whole population for Alternaria 1. 06 [1. 002-1. 12]. When the influence of age and sex was accounted for, the relationship was significant only in 6-12 years old males for Alternaria 1.
Methods Records of all SABA treatments prescribed for people living in the demographically stable Clermont-Ferrand area (approximately 285,000 inhabitants) were provided for the study period  (2010-2012; 2014-2015)  from the public health insurance database, which covers 80% of the French urban population. Owing to missing data due to technical reasons, 2013 was omitted. The health outcome was defined as the number of "cases" per day, whereby a case is defined as the reimbursement of a SABA treatment. SABA sales were excluded for children < 6 years because of the uncertainty of asthma diagnosis  [2]  and for adults > 39 years, because they are also used for acute.
The tape representing 1 week of sampling was cut into 7 fragments, each representing 1 day, and transferred to a glass slide. After application of a reactive colouring solution, the mean spore concentration, expressed as total number of spores per cubic meter of air (spores/m 3 ), was determined using 400× optical microscopy along 1 longitudinal sweep per slide. Spores were identified at the genus level only, since in most cases it was not possible to identify spore species. Cladosporium and Alternaria taxa collected for 5 years, other mould taxa for 3 years. Data on influenza epidemics was obtained from the national network for transmissible disease surveillance. Air pollution (PM 10 , O 3 , NO 2 ), meteorological data, age and sex were available from the database. Data were analyzed by overdispersed Poisson regression with generalized additive models (GAMs).
The main strength of the present study lies in the extremely large regionally representative dataset comprising subjects between 6 and 39 years of age, and living within 15 km of the spore trap. The concentrations of outdoor spores, with Cladosporium predominating, are in line with other studies performed in European temperate climates. We used appropriate statistical tools to reveal the shape of the curve for every mould spore studied, while adjusting for potentially confounding factors (such as air pollution, meteorology and pollens). The lack of knowledge about mould sensitization in our general urban population is a potential limitation. As the genera Aspergillus and Penicillium are morphologically indistinguishable, laboratories list them together as Aspergillus-Penicillium type. Outdoor mould effect will vary by climatic region  [9].
Molecular epidemiology The epidemiological relationship between the colistin resistant strains was determined by multilocus sequence typing (MLST). The seven housekeeping genes (adk, fumC, gyrB, icd, mdh, purA, recA) were amplified, sequenced and then blasted on the MLST database available on the Warwick web site (http://enterobase. warwick. ac. uk/species/ecoli/allele_st_search).
Funding source This work was supported by the  ANR   FloraStopInfectMRE project ,  InfecERA-ERANET-Acciones complementarias  ( PCIN-2015-094 ) to CU and by  IHU Méditerranée Infection, Marseille, France  and by the  French Government  under the «Investissements d'avenir» (Investments for the Future) proGram managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was supported by  Région Provence Alpes Côte d'Azur and European funding FEDER PRIMI.
3) The measurement of originality The concept of species' originality can be evaluated by different measures that rely on various types of data (see online Supporting information, Appendix S1). The first originality indices were developed on a tree structure and thus measured phylogenetic originality. However, they were applied to undated phylogenies ignoring the evolutionary time of change in taxa  (May, 1990; Vane-Wright et al. , 1991; Nixon & Wheeler, 1992). Those indices used the number of internal nodes or the number of branches descending from each node to compute species' originality. Unfortunately, they can hardly distinguish the originality between species from the same clade  (Huang et al. , 2011; Redding et al. , 2014). These indices are useful when the branch lengths on a phylogenetic tree cannot be estimated.
According to  Redding et al. (2014) , technically, all tree-based indices of species' originality combine two main aspects of originality: the average distance to another species on the tree [average phylogenetic distance (APD)  (Redding et al. , 2014) ] and the terminal branch lengths that connect the species to the rest of the tree [pendant edge (PE)  (Redding et al. , 2008) ]. Another framework of tree-based measures of species' originality has been proposed that uses the genome evolution model of species characters called 'character rarity' (CHR;  Huang et al. , 2011). Its advantage over other indices is that it incorporates models of dynamic processes, such as character changes and distribution along lineages during evolution. However, applications of this framework are still scarce. With the accumulation of species' trait information in large databases, a new type of study appeared that considered a limited number of traits  (Violle et al.
5 ng•L -1 and 4. 3 ng•L -1 in 2014 and 2015, respectively ("Agence de l'eau RMC"). Therefore, the PCB fluxes entering the lake through Terre-nue canal could be estimated at 24 ± 5 g•yr -1. The PCB (dissolved + particulate) concentrations were measured in the Leysse River by the "Agence de l'eau RMC" in water from 2011 to 2014 (n = 27)  24. Unfortunately, water PCB concentration data were not available for the period 2015-2017. PCB concentrations for 2015-2017 were estimated from the mean value of the data from 2011 to 2014 since a steady contamination was observed for this period. Nondetectable values in the "Agence de l'eau RMC" dataset were substituted by LQ/2, as generally accepted  38  , and were suitable for basic data processing  39. Therefore, the mean PCB concentration was estimated at 3.
Uncertainties related to the inputs from rivers and the atmosphere were rather restricted. Indeed, every parameter necessary for the flux calculation was measured, and the need for modeling or extrapolating data was limited. However, uncertainty regarding the inputs through the Leysse River remains nonnegligible. The quantification limits communicated by "Agence de l'eau RMC" for individual PCBs were elevated (i. e. , from 0. 3 to 1. 2 ng•L -1 ). Therefore, the substantial number of nondetectable values (replaced by LQ/2 in the calculations) in the dataset could have led to an overestimation of the PCB concentration, and consequently, to an overestimation of PCB inputs. However, the sediment concentration of the Leysse river sediments recorded by "Agence de l'eau RMC" (5 -33. 5 ng•gD•W -1 ) for the period 2013 to 2017 and the concentration measured in the Leysse River plume zone in Lake Bourget (75 ng•gD•W -1 ) in 2005  40  are important.
2016 (Arney et al. , 2017;; Lincowski et al. 2018; Meadows et al. 2018). The 1-D representation of the atmosphere is plane-parallel at hydrostatic equilibrium. The vertical transport takes into account molecular and eddy diffusion. Atmos includes molecules which have O, H, C, S, N and Cl atoms. For the modern Earth-like simulation, 309 reactions between 74 species have been considered while for the Archean Earthlike simulation, 459 reactions between 97 species have been used. Depending on the reaction, the JP-15  (Burkholder et al. 2015)  or the NIST 2005 (http://webbook. nist. gov/chemistry/fluid/) database was used. The initial conditions such as the gas mixing ratios, out gassing fluxes and/or surface deposition velocities can be set at the top and bottom of the model. Table  2  shows the boundary conditions used for the modern Earth-like and Archean Earth-like (from  Charnay et al.
s. l. This yielded a total of 44,295 pixels which fall into four elevation bands  (1350 m -1650 m, 1650 m -1950 m, 1950 m -2250 m, and 2250 m -2550 m)  and 23 SAFRAN massifs (Fig. 1  and Table  S1 ). Pixel-based estimates were averaged by elevation band and massif. This resulted in a table containing 87 combinations of elevation band and massif and 17 years  (2000 -2016). Annual standardized anomalies were calculated from this table. For meteorological data, we used the SAFRAN reanalysis that simulates surface conditions as a function of elevation in 300 m increments and for 23 massifs of French Alps. These massifs were defined for their climatological homogeneity in a previous spatial analysis of all weather data available for the French Alps  (Durand et al. , 2009a; 2009b). The SAFRAN reanalysis was evaluated against in-situ observations by  Durand et al.