dataesr/scientific-paragraphs-categorization huggingface dataset:
Taxonomic Assignation Scaffolds were submitted to a custom Java pipeline retrieving their closest homologs in several NCBI databases. First, low complexity regions (simple sequence repeats, SSRs), were de novo predicted and masked with RepeatMasker using default settings (Tarailo-Graovac and Chen 2009) and any scaffolds with greater than 50% masked were discarded to avoid BLAST search slowdown. Then, each scaffold was categorized based on the best-hit sequence identified by discontiguous-megablast in the NCBI complete genome database (ftp. ncbi. nlm. nih. gov/ blast/db/other_genomic; 2 September 2013; sequence identity greater than 70%, alignment length >150 bp, E<1eÀ9), to either "Hexapoda" (best hit to a Hexapoda genome), "non-Hexapoda" (best hit to a genome other than Hexapoda), and "unidentified" (no hit at the selected threshold).
Such classification is strongly biased by the phylogenetic content of the database, for example, only two complete beetle genomes were available but dozens of Diptera and Hymenoptera genomes. Consequently, supplementary BLAST identifications were performed with the nt (nucleotides) and EST (expressed sequence tags) databases  (Benson et al. 2013). Non-Hexapoda or unidentified scaffolds with better hits in these databases were reassigned. Mitochondrial scaffolds were identified against a reference database (ftp://ftp. ncbi. nlm. nih. gov/genomes/MITOCHONDRIA/; 4 September 2014). A final category, "potential symbiont," included scaffolds with similarities to genomes of bacterial genera with known symbiotic interactions  (Werren et al. 2008; Duron and Hurst 2013). Scaffolds with best hit to any species of the bacterial genera Wolbachia, Rickettsia, Spiroplasma, Arsenophonus, Cardinium, Hamiltonella, Blattabacterium, Midichloria, or Rickettsiella belonged to this category.
Hexapoda Repeats Identifications Hexapod retroelements, satellites and DNA transposons were identified via a RepeatMasker analysis using the Hexapoda repeat definitions of RepBase  (Jurka et al. 2005)  and DFam  (Wheeler et al. 2013 ) (April 22, 2013 updates). Identifications were complemented by a comparative approach aligning all Hexapoda scaffolds to the Tc and Dp genomes. Scaffolds with BLAST hits matching 30 regions and >30 regions of the Tc or Dp reference genomes were designated as "low-copy number" (LCN) and "HCN" repeats, respectively (dc-megablast, E-value threshold 1e-9, alignment >150 bp). hcn repeats were also annotated as "similar to a transcript" when they matched a NCBI EST database transcript.
rRNA and Histone Gene Identifications Histone-related scaffolds were extracted via protein BLAST (BLASTp) to Drosophila melanogaster histones and open reading frames (ORFs) greater than 250 bp in the six frames of each extracted scaffold were aligned to the NCBI Conserved Domain Database (CDD, v3. 11) via CD-search  (Marchler-Bauer et al. 2011). All ORFs matching a histone domain signature at E < 1e-9 were then annotated with the corresponding histone gene annotation. rRNA-containing scaffolds were identified with discontiguous-megablast alignments to the content of the SILVA database  (Quast et al. 2013 ; E > 1e-9, alignment length >150 bp and sequence identity >85%). The database contained both curated large subunit (LSU) and small subunit (SSU) rRNA genes, representing about 3,000,000 bacteria, 150,000 archaea, and 250,000 eukaryote sequences.
2007). A surprisingly high proportion of scaffolds showed homology to EST databases; 21. 3% in Canopy and 29. 3% in Weevil (table 2). A noticeable proportion of the latter exhibited high similarity (>90%) to the Dp transcriptome sequences generated by  Chan et al. (2012)  (data not shown). Many Hexapoda scaffolds had multiple mapping sides, which identified them as dispersed repeat regions. An example of Hexapoda repeat is illustrated in figure 4B. This conserved 200 bp motif showed high similarity to a single scaffold from the Canopy_Long library and was found in 461 Dp genome regions and in 9 different mRNAs in the Dp transcriptome. A core fragment of this repeat was similar (85% identity) to an mRNA of Hypothenemus hamperi, a member of the same weevil subfamily (Scolytinae). Despite the similarity to an mRNA, many indels among copies suggest that this hcn repeat is not a coding region.
The high lineage specificity of scaffolds captured here suggests that this large unidentified portion is derived from lineages that are not represented by available reference sequences. Lineage specificity applies equally to those identified as Hexapoda and to all others, as the proportions of scaffolds shared among libraries were similar for the Hexapoda and unidentified scaffolds (fig. 2 ), that is, the unidentified fraction was not generally more clade specific, but probably was more distant from an available reference genome, which include a limited of the Coleoptera tree in the infraorder Cucujiformia within one of the four recognized suborders. Approximately 20% of all scaffolds matched known repeat sequences deposited in the RepBase database that includes sequences derived from the Tc genome (fig.
4A ). Genomic repeats are copiously sampled during GS  (Straub al. 2012 ) and scaffolds captured here through MGS, but those remaining unidentified are probably repeats specific to particular lineages of Coleoptera without available reference sequences. In support of this interpretation, many more repeats are identified in Weevil when mapped to the closely related Dp genome compared with Tc (table 2), and while around 20% of the identified Hexapoda DNA contained known repeat sequences in the database (fig. 4A ), this is a low estimate as only 93 coleopteran repeat definitions were available in the repeat databases interrogated by RepeatMasker (compared with >5,000 for Diptera). Interestingly, the scaffolds built here appear to maintain some correlation between sequencing depth and low complexity repeats (SSRs) sampling, whereas other categories of identifiable repeats are obtained already at lower sequencing depth (see Canopy_Next in fig.
Skimming Environmental Interactions The detection of non-Hexapod DNA (up to 8% of scaffolds) may reflect the interactions of the sequenced specimens with their environment. Our sequencing libraries were obtained by extracting DNA from isolated specimens. Hence, non-Hexapoda reads mostly constitute either internal fauna, ingested material or genes recently acquired by horizontal gene transfer (increasingly observed in insects; see  (Nakabachi 2015). For instance, plant DNA decay is slow enough in Coleoptera guts for PCR amplification of chloroplasts up to 72 h after feeding  (Wallinger et al. 2013 ). Short cpDNA scaffolds are insufficient for unequivocal species-level identification, in particular in areas where reference data are lacking, although the presence of Theobroma cacao in Canopy is plausible as smallholder plantations are common in the sampling region.
Toward Integrative Analysis of Specimen Pools The metagenomes were based on a complex mixture of specimens varying by their genome structure, phyletic composition and, in the case of the Canopy sample, by variable biomass and intraspecific variation. These factors determine the abundance and sampling probability of DNA motifs in the pool and hence the MGS outcome. The chance for assembling a scaffold depends on the copy number of sequence in the genome and its evolutionary conservation, given the phylogeproximity of species in the pool. We are only beginning to disentangle these complex sets of parameters, and more targeted sampling would be needed to confirm these conclusions (the current pools were initially designed for answering different questions). The demonstrate that MGS of insect specimen pools extracts data relevant for phylogenomics, ecology or simply to explore the black box of arthropod genome evolution through their multicopy and most conserved elements.
Acknowledgments The authors thank  Carmelo Andujar ,  Paula Arribas Blazquez ,  Kirsten Miller ,  Rosli Kasah ,  Steven Dodsworth , and  Chris Barton  for their data contribution and many fruitful discussions. They also thank  Peter Foster  for help with server computing and computational support. The work was developed within the  NHM Biodiversity Initiative. Additional funding was from The  Leverhulme Trust  ( F/969/P  to A. P. V. ); an  NHM/UCL  PhD studentship (to A. C. P. ); and a  NERC   Postdoctoral Fellowship  ( NE/I021578/1  to M. J. T. N. T. ).
2A, right). A total of 5,174 scaf- folds were shared by both samples (5. 8% of Weevil scaffolds; 4. 7% of Canopy scaffolds), showing that thousands of similar scaffolds can also be recovered between pools of different species composition. Phyletic Composition The scaffolds were assigned to four categories based on their top-hits in sequence databases (see Materials and Methods, and supplementary file S2, Supplementary Material online): Hexapoda, non-Hexapoda, unidentified and potential symbi- onts. Between 15% (Weevil) and 23% (Canopy_Next) of scaf- Truseq libraries (Weevil, Canopy_Long, Canopy_Short) produced 17. 3-23. 9MreadspairsandtheNexteralibrary(Canopy_Next)produced7. 3 M reads. Following trimming, 30% of reads were discarded in the three Canopy libraries and 5% in the Weevil library (table1).
readnumbersfromequimolarDNAsamplesmayhaveaidedtheassembly. We determined intersections of library contents with pairwise alignments of the scaffolds (fig. 2A). The scaffolds of the three Canopy libraries were aligned with a stringent threshold of sequence identity >90%, E < 1e-18, alignment length >250 bp. In total, 19,297 scaffolds were shared by at least two Canopy libraries, and the tripartite folds showed similarity to a known Hexapoda sequence (fig. 2B). Despite variation in sequencing procedures, the libraries exhibited fairly similar identification profiles and mainly differed in the absolute number of scaffolds in each category. Two-thirds of Hexapoda identifications resulted from sequence homology with complete genomes and one-third was based on shorter nucleotide fragments and transcriptome data form the NCBI nt and EST databases (supplementary file S2, Supplementary Material online).
falciparum multidrug-resistance gene 1 [pfmdr1]) were amplified by nested PCR, as described in previous studies  [20] [21] [22]. PCR products were sequenced using the Sanger dideoxy method. Nucleotide sequences were analysed using Chromas 2. 4. 1 software for DNA sequencing (Technelysium Pty Ltd. , South Brisbane, Australia) and aligned using MultAlin software to identify mutations  [23].
Data interpretation and statistical calculations A total of 576 children aged less than 15 years old were enrolled in the present household survey. The sample size was calculated on the basis of an expected smearpositive rate of 25% determined in an earlier pilot study in Balombo, risk of error of 5%, and 5% precision. The sequences of the following key codons were determined: Ala16Val, Asn51Ile, Cys59Arg, Ser108Asn or Ser108Thr, and Ile164Leu for pfdhfr; Ser436Ala, Ala437Gly, Lys540Glu, Ala581Gly, and Ala613Thr or Ala613Ser for pfdhps; codons Cys72Ser, Val73 (invariable), Met74Ile, Asn75Glu, Lys76Thr for pfcrt; and Asn86Tyr, Tyr184Phe, Ser1034Cys, Asn1042Asp, and Asp1246Tyr for pfmdr1. Based on these codons, different haplotypes were determined and compared. Wildtype pfdhfr haplotype was defined as ANCSI.
Study sites Weekly during February to September 2008, I estimated FIDs for birds, using a standardised technique developed by  Blumstein (2006). All estimates were collected blindly with respect to the hypothesis being tested, because the present study was not envisaged when the data were collected, preventing any conscious or unconscious bias. These recordings were made in Ile-de-France, France, and Northern Jutland, Denmark. I obtained data on 931 FIDs for individuals involved in eating and loafing for 55 species of birds.
Whilst recording these FIDs, I also recorded the date and time of day and the sex of the individual if external characteristics allowed sexing with binoculars. FID was estimated as the square root of the sum of the squared horizontal distance and the squared height above ground level  (Blumstein 2006). All recordings were made during the breeding season when most individuals are sedentary, thus preventing the same individual from being recorded in different sites. Only adults were included to avoid problems caused by juveniles having shorter FIDs than adults (A. P. Møller unpublished data). If there was doubt about age, an individual was not recorded. I avoided pseudo-replication by only recording individuals of a given sex and species at a given site. FID was consistent for the same species in different studies, as shown by a comparison of data from previous studies  (Møller et al.
Comparative analyses Analyses of comparative data based on single species may provide misleading conclusions if sister taxa are more similar with respect to the variables under investigation than randomly chosen species. Therefore, I analysed statistically independent, standardised linear contrasts  (Felsenstein 1985) , which controls for similarity in phenotype amongst species because of common descent. The contrasts were calculated using the software of  Purvis and Rambaut (1995) , implemented in the software CAIC. All regressions were forced through the origin  (Felsenstein 1985) , because the dependent variable is not assumed to have changed, when the predictor variable has not evolved. Standardisation of contrast values was checked by examination of absolute values of standardised contrasts versus their standard deviations  (Garland 1992; Garland et al.
(2015). Table  1  presents the list of sampled rivers with the main physico-geographical parameters of the watersheds. The mean multi-annual monthly discharges of WSL rivers are available from systematic surveys of the Russian Hydrological Survey (Hydrological Yearbooks of RHS), generalized in  Nikitin and Zemtsov (1986)  and also compiled in the R-AcricNET database  (www. r-arcticnet. sr. unh. edu). In this study, due to the limited number of observations over the year, the river discharge for each river was averaged for each of the three seasons of sampling (May to June, July to September, and October to April). In addition, a systematic hydrological study of the State Hydrological Institute in 1973-1992 in the northern part of western Siberia allowed reliable evaluation of small and medium rivers' discharges  (Novikov et al.
45 µm. The first 20 to 50 mL of filtrate was discarded. Filtered solutions for trace analyses were acidified (pH ∼ 2) with ultrapure double-distilled HNO 3 and stored in the refrigerator. The preparation of bottles for sample storage was performed in a clean bench room (ISO A 10 000). Blanks of Milli-Q water were processed in the field in parallel to samples in order to control the level of pollution induced by sampling and filtration. For most TEs except Zn, these blanks were less than 10 % of the element concentration in the sample. For several rivers in winter, the Zn blanks were 30 to 50 % of their sample concentration; these data were not used in the discussion. Analyses of DOC, pH, major cations and anions and their uncertainties are described in detail in a previous publication  (Pokrovsky et al.
Statistical treatment of the data and element speciation in the river water The concentrations of carbon and major elements in rivers were treated using the least-squares method, Pearson correlation and one-way ANOVA (SigmaPlot version 11. 0, Systat Software, Inc. ). Regressions and power functions were used to examine the relationships between TE concentration and the watershed area, latitude, and seasons. TE concentrations in rivers of (1) three main permafrost zones (continuous, discontinuous and permafrost-free regions), (  2 ) six latitudinal classes of the watershed (56-58, 58-60, 60-62, 62-64, 64-66 and 66-68 • N), (3) during three main seasons and (4) four watershed size classes (< 100, 100-1000, 1000-10 000, and > 10 000 km 2 ) were processed using nonparametric H -criterion Kruskal-Wallis test.
This test is suitable for evaluation of difference of each TE among several samplings simultaneously. It is considered statistically significant at p < 0. 05. However, we found that a p level of < 0. 0001 corresponding to H > 30 indicated more significant differences and was thus also used in assessing the relative effect of season, latitude and watershed size. Principal component analysis (PCA) was used to compute and interpret the spatial structures of TEs in rivers using the STATISTICA package (http://www. statsoft. com). This treatment was used for both the full set of sampled rivers for all seasons simultaneously and for each season individually. Both log-transformed and non-transformed data were used for analyses. Here, we considered the average latitude of the watershed and its watershed area, pH, and all major and trace element concentration as numerical variables.
, 2004). These enhancing factors are tightly linked to the nature of colloidal carriers of TEs (organic, organo-ferric or organo-aluminum species) and the efficiency of metal leaching from the organic topsoil and plant litter. A comprehensive database of rivers of various sizes across the full gradient of permafrost investigated during the main hydrological seasons in this study allows testing of the abovementioned environmental factors. DOC and Fe are not correlated in rivers (R < 0. 40; p > 0. 05), and this is consistent with decoupling of Fe and DOC during the size separation procedure as two independent colloidal pools, already demonstrated for European boreal rivers  (Lyvén et al. , 2003; Neubauer et al. , 2013; Vasyukova et al. , 2010)  and other Siberian rivers and WSL thermokarst lakes  (Pokrovsky et al.
, 2015) , the percentage of organic complexes of TEs remained quite similar among all three permafrost zones. Among metals available in the vMinteq database, Mg, Ca, Sr, Ba, and Mn are complexed to DOM at 5 to 15 %; Co, Cd and Zn are complexed from 20 to 40 %; Ni is complexed at 55-60 %; and Al, Pb, Cu, Fe(III), La, Ce and other REEs are bound to DOM by 90 to 98 % (Fig. 12 ). Only U(VI) exhibited contrasting speciation between permafrost-free and permafrost-bearing zones. From 10 to 70 % of U is present as organic complexes in HCO - 3rich, circumneutral solutions of southern rivers but U(VI) remained > 90 % DOM-complexed in acidic, DIC-poor northern rivers. The annual TE fluxes of WSL rivers averaged over the full latitudinal profile (Table  2 ) can be compared with available data of TE fluxes in other subarctic rivers.
Mechanisms of TE mobilization from the soil to the river Together with a comprehensive database of concentration, colloidal status and fluxes of TEs in the Kalix River (i. e. ,  Ingri et al. , 2000 Ingri et al. , , 2005;; Andersson et al. , 2001; Dahlqvist et al. , 2007) , the Kryckland watershed  (Björkvald et al. , 2008; Laudon et al. , 2013) , and Alaskan rivers  (Sugai and Burrell, 1984; Rember and Trefry, 2004) , the present study contributes to our understanding of the nature and magnitude of element transport in boreal rivers. The main peculiarity of the WSL region is the presence of permafrost in almost half of its area. This permafrost likely acts as a very strong barrier between surface organic and underlying mineral soil horizon, thus regulating the degree of mineral vs. peat leaching depending on latitude and season, as is known for other subarctic environments  (Bagard et al.
Acknowledgements. We acknowledge support from a  BIO-GEO-CLIM  grant (no. 14. B25. 31. 000 ) from the  Ministry of Education and Science of the Russian Federation  and  Tomsk State University. O. S. Pokrovsky  and  R. M. Manasypov  were also supported (50 %) by  RSF  grant no. 15-17-10009 , "Evolution of thermokarst ecosystems in the context of climate change". Edited by:  M. Sarin.
Although the authors used the ULF waves onset as a qualitative criterion for boundary crossing identification, they did not include any quantitative limit on the amplitude of the fluctuations. In another study based on magnetometer data from ISEE 1 and 2, Le and Russell 33 examined 373 bow shock outbound crossings and recorded whether or not ULF fluctuations are present immediately upstream. They found that ULF waves are present only for θ Bn less than ∼ 50 •. Given the association of foreshock particle distributions with the ULF waves, Meziane and D'Uston 39 (MD98) presented a statistical investigation of the location of the onset of intermediate and gyrating ion populations in the Earth foreshock based on the Fixed Voltage Analyzer data from ISEE 1. They found that for θ Bx = 45 • , the spatial location for intermediate ions coincides with the UWFB reported by GB86.
This determination will require a stationary condition for the magnetic field direction, which will be discussed in the following Sections. A precise determination of the UWFB puts strong constraints on models involving wave-particle interactions occurring upstream of the bow shock, and at the same time it sheds light on the particle emission mechanisms at the Earth bow shock. In-situ observations made by the flux gate magnetometer (FGM) and the Cluster Ion Spectrometer (CIS) are presented in Section II. Observations of ULF boundary crossings are presented in Section III. After, we introduce the Solar Foreshock system of coordinates in Section IV, the statistical results are presented in Section V. An attempt of an interpretation of the data is presented in Section VI followed by a short conclusions in Section VII.
II. DATA MEASUREMENTS The observations used for the present paper consist of magnetic field and solar wind velocity vectors and plasma density measured by Cluster spacecraft obtained upstream from the Earth bow shock during the first three years of Cluster's orbital data, i. e. from February 2001 through December 2003. We have used a cadence of 5 s -1 of magnetic field data from the flux gate magnetometer (FGM) on-board Cluster to investigate the presence of ULF waves upstream from the Earth bow shock. The particle data used in the present study are from the Cluster Ion Spectrometer (CIS) experiment, which includes a top-hat electrostatic analyzer (HIA) and a mass spectrometer (CODIF), which combines a top-hat electrostatic analyzer with a time-of-flight section to measure the major species, i.
e. H + , He + , He ++ and O + over an energy range 0. 02-38 keV/q. The HIA detector operates according to several modes. The velocity distributions are obtained from both instruments, which accumulate full 3-D distribution functions within one spin period (4 seconds), with an angular resolution of 22. 5 • ×22. 5 •. For a more extensive description of FGM and CIS/CODIF Cluster experiments see 3 and 49 , respectively. If a boundary crossing occurs when the HIA instrument is not operating in the solar wind mode, the plasma measurements from Cluster-CIS are not reliable. In this case, we estimated the solar wind density and velocity from plasma data taken by WIND/3DP experiment. For some events, the WIND spacecraft is located upstream as far as ∼250 R E , and therefore for these cases a time delay between WIND and Cluster spacecraft was taken into account.
III. IDENTIFICATION OF UWFB CROSSINGS During Cluster excursions into the solar wind, we looked for intervals displaying patterns of ULF waves in the magnetic field components. For the determination of these crossings, we made no distinction regarding the type of magnetic field fluctuations, requiring only that the transition from (or to) the wave region would be clearly apparent. For each identified crossing, we extracted B, v sw and n sw data from the Cluster Active Archive (CAA) and the CLWeb data base (http : //clweb. cesr. f r/). The ULF wave foreshock boundary (UWFB) is a surface embedded in the foreshock region. By definition, the region downstream from the UWFB is the one that displays ULF wave activity, while the region upstream does not. For the purpose of the present study, we find that the optimal time interval to compute mean values of the quantities listed above lies between three and six wave periods at each side of the boundary.