dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The  International Atomic Energy Agency  is grateful to the  Government of the Principality of Monaco  for the support provided to its  Environment Laboratories. This study was conducted as part of the  Malina Scientific Programme  funded by  ANR  ( Agence Nationale de la Recherche ),  INSU-CNRS  ( Institut National des Sciences de l'Univers -Centre National de la Recherche Scientifique ) and  CNES  ( Centre National d'Etudes Spatiales ) from  France , and  ESA  ( European Space Agency). Additional support from  ArcticNet  (a  Network of Centres of Excellence of Canada ) was welcomed and appreciated. We thank the captain and crew of the  Canadian Coast Guard research vessel Amundsen  for their assistance at sea. We are particularly indebted to  Nicolas Evensen  (faecal pellets),  Josephine Ras  (pigments),  Fernando Gomez  (phytoplankton identification),  Makoto Sampei  (trap analyses) and  Cyril Aubry  (swimmers identification) for their laboratory contribution.
Two sets of results are given, where the π + π -S-wave components are modelled with the Isobar  [16] [17] [18]  and K-matrix  [19]  formalisms. Dalitz plot analyses of the decay B 0 → D 0 π + π -have already been performed by Belle  [20, 21]  and BaBar  [22]. Similar studies for the charged B decays B -→ D ( * )+ π -π -have been published by the Bfactories  [23, 24]. The LHCb dataset offers a larger and almost pure signal sample. Feynman diagrams of the dominant tree level amplitudes contributing to the decay B 0 → D 0 π + π - are shown in Fig. 1. In addition to the interest for the CKM parameter measurements, the analysis of the Dalitz plot of the B 0 → D 0 π + π -decay is motivated by its rich resonant structure. The decay B 0 → D 0 π + π -contains information about excited D mesons decaying to Dπ, with natural spin and parity J P = 0 + , 1 -, 2 + ,.
Material Sixty-two specimens comprising 48 species belonging to 25 genera and representing seven of eight onchocercid subfamilies were analysed (Table  1  and S1 Table)   [8, 9,. Due to a lack of material, the Lemdaninae could not be included in this study. All procedures were conducted in compliance with the rules and regulations of the respective competent national ethical bodies (S2 Table ). Nematode specimens and DNA samples were deposited in the National Nematode collection of the Muséum National d'Histoire Naturelle (MNHN), Paris, France; accession numbers are recorded in Tables  1  and S1. Below we briefly list the specimens used in this study, followed by the source/collector in parentheses. For the author(s) and year of parasite and host (authors only) species, the reader is referred to Table  1  and S1 Appendix.
Phylogenetic analyses Sequences generated during the current study and previously published sequences from draft/ complete genomes were aligned using MAFFT  [60]. To check for the absence of stop codons, the alignment of coding genes was translated using EMBOSS Transeq  [61] , and a comparison with available transcript sequences made. Using the corrected version of the Akaike Information Criterion (AICc), JModelTest analysis  [62]  was performed to establish the evolution model best adapted to the sequences alignment for each individual gene and for the concatenation of all genes. The General time-reversible plus Invariant sites plus Gamma distributed model (GTR+I+Γ) offered the best fit for coxI, hsp70, 12S rRNA and 28S rRNA sequences as well as the concatenated alignment; the General Time-Reversible plus Gamma distributed model (GTR+Γ) for MyoHC and rbp1 sequences alignment; and the Kimura 2-parameter Invariant sites plus Gamma distributed model (K80+I+Γ) for 18S rRNA sequences alignment.
To root the trees, two species were included as outgroups: Filaria latala (Spirurida: Filariidae) and Protospirura muricola (Spirurida: Spiruridae). Phylogenetic relationships between onchocercid taxa, based on the concatenated dataset, were performed by Bayesian inference using MrBayes  [63]. A partitioned model was implemented to estimate evolution parameters separately for each gene. Two runs were performed using five millions steps with four chains, with tree sampling every 1,000 generations; the first 1,250 points were discarded as burn-in and Posterior Probabilities were calculated from these post-burning trees. In addition, Maximum Likelihood (ML) was used to infer a phylogenetic tree based on the partitioned concatenated dataset, and was executed with 1000 slow bootstrap replicates using RaxML  [64] ; presented in Supplementary data (S1 Fig).
Accession numbers All new sequences generated in this study were deposited in GenBank (http://www. ncbi. nlm. nih. gov/genbank) under the accession numbers: KP760168 to KP760211 for coxI, KP760314 to KP760357 for 12S rDNA, KP760116 to KP760167 for 18S rRNA, KP760212 to KP760262 for MyoHC, KP760410 to KP760460 for hsp70, KP760263 to KP760313 for rbp1, KP760358 to KP760409 for 28S rRNA.
and S1 Fig). To support this result, the average genetic divergence between the outgroup and ingroup taxa was about 20% (21% for P. muricola; 25% for F. latala), and only about 14% between onchocercid specimens themselves. It was, therefore, concluded that both P. muricola and F. latala are appropriate outgroups for the present multi-gene dataset. In congruence with previous systematic analyses  [5, 6] , the chosen rooting clearly supported the monophyly of the Onchocercidae.
Acknowledgments The authors wish to thank the following collaborators for the material they provided:  Dr. M. Casiraghi  (DNA of Aproctella sp. , R. andersoni, D. gracile, M. martini, F. candezei),  Dr. S. Uni  (DNA of O. eberhardi, O. d. japonica, O. skrjabini, L. caprini),  Dr. S. Nikander  (S. tundra),  Dr. D. Otranto  (C. bainae),  Dr. M. Diagne  (M. martini),  Dr. S. Wanji  (O. armillata, O. gutturosa, O. ochengi),  Dr. B. Makepeace  (S. labiatopapillosa),  Dr. Alexis Ribas  (B. (B. ) jittapalapongi),  Dr. J. Dupouy-Camet  ( Loa loa ),  Dr. S. Michalski  from  FR3  center (B. malayi, B. pahangi, A. viteae),  S. Landi  ( B. Timori), Bayer Animal Health GmbH (Dirofilaria immitis ).
Soil contamination could also affect the same soils close to urban and industrial areas, but other contaminants, particular airborne ones, could diffuse to affect soils distant from the source. Continued development and exploitation of soil and land-use spatial databases (e. g. Mackney et al. , 1983; Proctor et al. , 1998; Morton et al. , 2011)  should help to identify soils at risk from particular degradation threats. This kind of approach can then be used to review current land management practices in order to minimize degradation impacts.
Conclusions With the UK as an example, we have summarized some key data on the impact of degradation threats on soil properties and have sought to link changes in soil properties to important soil functions. The key next stage will be to continue to add to the database with field-based monitoring of broad changes to soil properties following degradation supported by controlled laboratory experimentation to improve understanding of the mechanisms involved. Outputs from such an approach, together with refinement of soil and land-use spatial databases, should aid the development of models capable of extrapolating up to the national scale in order to link soil degradations to soil functions. We may also be able to predict any effect that climate change is likely to have. Armed with this, we may revise our policies so that soils are truly managed sustainably.
Foster, C. , Pennington, C. V. L. , Culshaw, M. G. & Lawrie, K. 2012. The national landslide database of Great Britain: development, evolution and applications. Environmental Earth Sciences, 66, 941- 953. Foulds, S. A. & Warburton, J. 2007. Wind erosion of blanket peat during a short period of surface desiccation (North Pennines, Northern England). Earth Surface Processes and Landforms, 32, 481-488.
MATERIALS AND METHODS The updated list of Iberian brachyuran crabs was drawn up in the context of the MEGALOPADN research project, which is focused on the use of morphological and molecular techniques for identifying planktonic larval stages. For the compilation of this list, all publications since 1968 about the distribution of brachyuran crabs were checked, including previous lists for Iberian or European regions, data from Internet databases such as WoRMS (http://www. marinespecies. org/), GBIF (http://www. gbif. org/species) and Observadores del Mar (http://www. observadoresdelmar. es/), systematic data, new records, and unpublished or in preparation data. Several contributions need to be highlighted here, mainly the works on European decapods carried out by d'Udekem d'  Acoz (1999)  and  Türkay (2001) , but also several specific works on Iberian carcinofauna (García  Raso 1984 , 1985 , 1989 , 1993 , 1996 , García Raso et al.
ACKNOWLEDGEMENTS Thanks are due to  Félix Pérez  (ICM),  Enrique Macpherson  (CEAB),  Paul Clark  (NHM),  Laure Corbari  (MNHN),  Paula Martin-Lefevre  (MNHN) and to  J. -C. Dauvin  ( Univ. Caen Basse-Normandie ) for facilitating access to key specimens to complete the present study. Thanks are also due to  Cedric d'Udekem d'Acoz ,  Pierre Nöel  and  Carlo Froglia  for criticism and suggestions that clearly improved the manuscript. This research was carried out within the framework of the  MEGALOPADN  project ( CGL2009-11225 ) funded by the  "Ministerio de Economía y Competividad (MI-NECO)" Spanish Plan R+D+I  and  FEDER. Marco-Herrero  acknowledges the pre-doctoral  FPI Fellowship  ( BES-2010-033297 ), and the present study was carried out as part of her PhD Thesis.
Finally, lobSTR aggregates aligned reads and employs a model of STR-specific sequencing errors to report the maximum likelihood genotype at each locus. lobSTR recovered most (r 2 =0. 71) of the variation in STR locus lengths in the 1000 Genomes datasets based on large-scale validation using 5,000 STR genotype calls obtained by capillary electrophoresis, the gold standard for STR genotyping  42. The majority of genotype errors were from dropout of one allele at heterozygote sites due to low sequencing coverage. We simulated the performance of STR associations using lobSTR calls compared to the capillary calls. This process showed that STR genotype errors reduce the power to detect eSTRs by 30-50% but importantly do not create spurious associations (Supplementary Note and Supplementary Fig. 1 ).
To detect eSTR associations, we regressed gene expression on STR dosage, defined as the sum of the two STR allele lengths in each individual. We opted to use this measure based on previous findings that reported a linear trend between STR length and gene expression  19, 34, 36  or disease phenotypes  44, 45. As covariates, we included sex, population structure, and other technical parameters (Fig. 1a  and Supplementary Methods). We employed this process on 15,000 coding genes whose expression profiles were detected in the RNA-sequencing data. For each gene, we considered all polymorphic STR variations that passed our quality criteria (Online Methods) and were within 100kb of the transcription start and end sites of the gene transcripts as annotated by Ensembl  46. On average, 13 STR loci were tested for each gene (Supplementary Fig.
3 ). Both negative controls produced uniform p-value distributions expected under the null hypothesis. This provides support for the absence of spurious associations due to inflation of the test statistic or the presence of uncorrected population structure. To assess the effect of low sequencing coverage on our results, we generated high coverage targeted sequencing of 2,472 promoter STRs and repeated the eSTR analysis (Online Methods). We found that association results were largely reproducible across datasets, with 80% of tested eSTRs showing the same direction of effect (p=9. 9×10 -12 ; n=126) (Supplementary Note and Supplementary Fig. 4 ). Three previous studies described candidate gene studies of expression STRs and involved STRs that were tested in our framework  19, 36, 47. Our genome-wide approach was able to replicate the association between PIG3 and the pentanucleotide STR in the 5'UTR of the gene and showed the same direction of effect.
However, the other two candidate genes did not meet the multiple hypothesis p-value threshold (Supplementary Table  6 ). The initial discovery set of eSTRs was largely reproducible in an independent set of individuals using an orthogonal expression assay technology. We obtained an additional set of over 200 individuals whose genomes were also sequenced as part of the 1000 Genomes Project and whose LCL expression profiles were measured by Illumina expression array  48. These individuals belong to cohorts with African, Asian, European, and Mexican ancestry, enabling testing of the associations in a largely distinct set of populations. The Illumina expression array allowed us to test 882 eSTRs out of the 2,060 identified above. The association signals of 734 of the 882 (83%) tested eSTRs showed the same direction of effect in both datasets (sign test p=2.
The effect of eSTRs in the context of individual SNP eQTLs To further assess the contribution of eSTRs in the context of other variants, we also inspected the relationship between eSTRs and individual cis-SNP eQTLs (eSNPs). We performed a traditional eQTL analysis using the whole genome sequencing data for 311 individuals that were part of the discovery set to identify common eSNPs [minor allele frequency (MAF) ≥5%] within 100kb of each gene. This process identified 4,290 genes with an eSNP (gene-level FDR≤5%). We then re-analyzed the eSTR association signals while conditioning on the genotype of the most significant eSNP (Fig. 3a ). For each eSTR, we ascertained the subset of individuals that were homozygous for the major allele of the lead eSNP in the region. If the eSTR simply tags this eSNP, its conditioned effect should be randomly distributed compared to the unconditioned effect.
Conservation and epigenetics signals support the functional role of eSTRs To provide further evidence of their regulatory role, we analyzed eSTRs in the context of functional genomics data. First, we assessed the potential functionality of STR regions by measuring signatures of purifying selection, since previous studies reported that putatively causal eSNPs are slightly enriched in conserved regions  55. We inspected the sequence conservation  56  across 46 vertebrates in the sequence upstream and downstream of the eSTRs in our discovery dataset (Fig. 4a ). To tune the null expectation, we matched each tested eSTR to a random STR that did not reach significance in the association analysis but had a similar distance to the nearest transcription start site (TSS). The average conservation level of a ±500bp window around eSTRs was slightly but significantly higher (p<0.
In total, we obtained 499 eSTRs after applying this condition and excluding eSTRs that were genotyped in <1000 individuals. We identified 12 significant associations (FDR per phenotype<10%) between eSTRs and the clinical phenotypes in the TwinUK data (Figure  5b  and Supplementary Table  10 ). Only one association overlapped a known GWAS hit: an AAAC repeat on 4p16 was associated with decreased expression of SLC2A9 and increased uric acid in serum samples of the TwinsUK, which matches previous studies with SNPs  [62] [63] [64] [65]. The other 11 associations involved changes in blood metabolites such as albumin and C-reactive protein and physical traits such as diastolic blood pressure and FEV1 lung function and have yet to be described before in GWAS catalogs, suggesting novel loci. We caution that full validation of each of these associations will require replication in additional cohorts.
Online Methods Genotype datasets lobSTR genotypes were generated for the phase 1 individuals from the 1000 Genomes Project as described in  42. Variants from the 1000 Genomes Project phase 1 release were downloaded in VCF format from the project website. HapMap genotypes were used to correct association tests for population structure. Genotypes for 1. 3 million SNPs were downloaded for draft release 3 from the HapMap Consortium webpage. SNPs were converted to hg19 coordinates using the liftOver tool and filtered using Plink  69  to contain only the individuals for which both expression array data and STR calls were available. Throughout this manuscript, all coordinates and genomic data are referenced according to hg19.
Expression datasets RNA-sequencing datasets from 311 HapMap lymphoblastoid cell lines for which STR and SNP genotypes were also available were obtained from the gEUVADIS Consortium. Raw FASTQ files containing paired end 100bp Illumina reads were downloaded from the EBI website. The hg19 Ensembl transcriptome annotation was downloaded as a GTF file from the UCSC Genome Browser  71, 72  ensGene table. The RNA-sequencing reads were mapped to the Ensembl transcriptome using Tophat v2. 0. 7  73  with default parameters. Gene expression levels were quantified using Cufflinks v2. 0. 2  74  with default parameters and supplied with the GTF file for the Ensembl reference version 71. Genes with median FPKM of 0 were removed, leaving 23,803 genes. We restricted analysis to protein coding genes, giving 15,304 unique Ensembl genes.
Expression values were quantile-normalized to a standard normal distribution for each gene. The replication set consisted of Illumina Human-6 v2 Expression BeadChip data from 730 HapMap lymphoblastoid cell lines from the EBI website. These datasets contain two replicates each for 730 unrelated individuals from 8 HapMap populations (YRI, CEU, CHB, JPT, GIH, MEX, MKK, LWK) and were generated as described by Stranger et al. 75. Background corrected and summarized probeset intensities (by Illumina software) contained values for 7,655 probes. Additionally, probes containing common SNPs were removed  76. Only probes with a one-to-one correspondence with Ensembl gene identifiers were retained. We removed probes with low concordance across replicates (Spearman correlation ≤ 0. 5). In total we obtained 5,388 probes for downstream analysis.
eQTL association testing Expression values were adjusted for individual sex, individual membership, gene expression heterogeneity, and population structure (Supplementary Methods). Adjusted expression values were used as input to the eSTR analysis. To restrict to STR loci with high quality calls, we filtered the call set to contain only loci where at least 50 of the 311 samples had a genotype call. To avoid outlier genotypes that could skew the association analysis, we removed any genotypes seen less than three times. If only a single genotype was seen more than three times, the locus was discarded. To increase our power, we further restricted analysis to the most polymorphic loci with heterozygosity of at least 0. 3. This left 80,980 STRs within 100kb of a gene expressed in our LCL dataset.
A major caveat is that the eSNP dataset has significantly more power to detect associations than the eSTR dataset due to the lower quality of the STR genotype panel (Supplementary Note), and this analysis is therefore likely to underestimate the true contribution of STRs to gene expression. We used ANOVA to test whether the joint model performs significantly better than the SNP-only method. We obtained the ANOVA p-value for each gene and used the qvalue package to determine the FDR.
For each eSTR in our dataset, we generated sequences representing each observed allele. We filtered STRs with interruptions in the repeat motif, since the sequence for different allele lengths is ambiguous for these loci. For each mark, we used the model to predict the read count for each allele in a window of ±M bp from the STR boundaries, where M was set to 1,000 for all marks except p300, for which M was set to 200. Previous findings of GERV showed that these values of M give the best correlation between predicted and real ChIP-seq signals using cross validation. For each alternate allele, we generated a score as the sum of differences in read counts from the reference allele at each position in this window. We regressed the number of repeats for each allele on this score and took the absolute value of the slope for each locus.
eSTR associations with human traits To generate STR genotypes for each of the individuals in the UK10K TwinsUK dataset, we ran lobSTR v2. 0. 3 on each BAM using the options fft-windowsize=16, fft-window-step=4 and bwaq=15. The resulting BAM files were analyzed using v2. 0. 3 of the lobSTR allelotyper using default options, resulting in STR genotypes for 1,685 individuals. We then performed an association test between each STR and each phenotype. To control for population structure, we adjusted STR dosages and phenotypes for the top 10 ancestry principal components based on common SNPs (MAF>=5%) after LD-pruning. Principal components were computed using EIGENSTRAT 78 v5. 0. 1. Phenotypes were further adjusted for the age at which the phenotype was measured. Association tests were performed between the adjusted dosages and the quantile-normalized adjusted phenotypes.
We then examined the association in the 666 eSTR loci that contained an eSTR that significantly improved the gene expression variance when combined with the lead eSNP   Under the null expectation, the original association (red line) comes from mere tagging of eSNPs. Thus, the eSTR effect disappears when restricting to a group of individuals (dots) with the same eSNP genotype (colored patches). Under the alternative hypothesis, the effect is concordant between the original and conditioned associations (b) The original eSTR effect versus the conditioned eSTR effect. Red points denote eSTRs whose direction of effect was concordant in both datasets and gray points denote eSTRs with discordant directions (c) Quantile-quantile plot of p-values from ANOVA testing of the explanatory value of eSTRs beyond that of eSNPs (d) STK33 is an example of a gene for which the eSTR (red rectangle) has a strong explanatory value beyond the lead eSNP (blue circle) based on ANVOA.
Acknowledgments M. G. is supported by the  National Defense Science & Engineering Graduate  Fellowship. Y. E. holds a  Career Award at the Scientific Interface  from the  Burroughs Wellcome Fund. This study was supported by a gift from  Andria and Paul Heafy (Y. E) ,  NIJ  grant  2014-DN-BX-K089  (Y. E, T. W), and  NIH  grants  1U01HG007037  (H. Z), R01MH084703(J. P),  R01HG006399  (A. L. P),  HG006696  (A. J. S),  DA033660  (A. J. S), and  MH097018  (A. J. S), and a research grant  6-FY13-92  from the  March of Dimes Foundation  (A. J. S). We thank  Tuuli Lappalainen ,  Alon Goren ,  Tatsu Hashimoto , and  Dina Zielinksi  for useful comments and discussions.
Genomic and EST data Genomic data was collected from 37 Basidiomycota species from public databases, as well as from 9 Ascomycota species to use as outgroups. This data set included the complete genome of three rust fungi (Melampsora larici-populina, Puccinia graminis f. sp. tritici and P. triticina). Additionally, EST data from 65 Basidiomycota species was gathered from the NCBI public repository, except for Hemileia vastatrix  [30]. In total, 753,848 EST sequences were compiled. The full list of species and their corresponding genomic sources and citations is provided in S1 Table.
Processing of EST data Before processing the EST data, species' sequences for which complete genome sequences were available in alternative, were removed. Twenty one species were thus excluded, resulting in an initial data set with 44 species and 259,704 sequences. ESTs were screened for vector contaminants and trimmed using SeqClean (http://seqclean. sourceforge. net/) [31]  against the UniVec database (http://www. ncbi. nlm. nih. gov/VecScreen/UniVec. html), and sequences with less than 100 bp were discarded. Interspersed repeats and low complexity regions in the ESTs were then masked with RepeatMasker (A. F. A. Smit, R. Hubley & P. Green RepeatMasker at http:// repeatmasker. org). To this end, the RMBlast search engine and the repeat database RepBase, which is optimized for RepeatMasker, were used.