dataesr/scientific-paragraphs-categorization huggingface dataset:
Total number of sites by sediment and measurement type. The sediment types are lacustrine (LACU), bog (BOGM), unknown (NOTK), soil (SOIL), coastal (COAS), and marine (MARI). The measurement types stored in the database are concentration (CONC), influx, (INFL), proportions (C0P0; e. g. , ratio of charcoal particles to pollen grains), and other (OTHE).
Acknowledgements. We thank PAGES working group support for the GPWG. This research and paper was initiated during the GPWG workshop 2013 held in Franche-Comté and supported by the  UMR Chrono-Environnement , the  OREAS  project, the  University of Franche-Comté , and the  Région Franche-Comté. Jennifer R. Marlon  is supported by  NSF  grants  BCS-1437074  and  EF-1241870. Patrick Bartlein  and  Brian Magi  are supported by  NSF  grant  BCS-1437074. Anne-Laure Daniau  is supported by the project  PICS CNRS 06484. Philip Higuera  was supported by  NSF  grant  IIA-0966472. Boris Vannière is supported by the project  MISTRALS-PaleoMEX. Edited by:  A. Ito.
Data availability The complete GCDv1, v2, and v3 (this paper) Microsoft Access database with all available metadata is stored and available at paleofire. org. Supporting information about the Global Charcoal Database and the Global Palaeofire Working Group is also available at paleofire. org. Site metadata and the charcoal data are accessible through the paleofire package  (Blarquez et al. , 2014)  for R (R Development Core Team,  2013). The Supplement related to this article is available online at doi:10. 5194/bg-13-3225-2016-supplement.
gov; URL: http://www. auger. org tic IMMs (Lorentz factor γ ≈ 10 11 ) is reported by the ANITA-II experiment at ≈ 10 -19 (cm 2 sr s) -1  [13]. These upper limits are below the Parker bound  [15]  of ∼ 10 -15 (cm 2 sr s) -1 , which represents the largest possible magnetic-monopole flux consistent with survival of the Galactic magnetic field. However, the original Parker bound does not take into account the current knowledge of the Galactic magnetic field and its almost chaotic nature, with domain lengths in the range 1 -10 kpc. The so-called "extended Parker bound"  [16]  becomes massdependent with Φ ∼ 10 -16 M/(10 26 eV) (cm 2 sr s) -1 with M the monopole mass, and is well below current experimental sensitivities (for relativistic and ultrarelativistic monopoles). In this paper, we report a search for ultrarelativistic IMMs with data collected with the Pierre Auger Observatory between 1 December 2004 and 31 December 2012.
Details of the Observatory are given in Section II. The search is motivated by the large energy deposited by ultrarelativistic IMMs along their path in the atmosphere, comparable to that of UHECRs, with a distinctive longitudinal development well-suited for detection by the fluorescence detector. The characteristics of air showers induced by IMMs are described in Section III. Simulations and event reconstruction procedures are presented in Section IV. The event selection criteria are described in Section V. The exposure, i. e. , the time-integrated aperture, for the IMM search is evaluated in Section VI. Details of the data analysis and results are presented in Section VII. Conclusions are drawn in Section VIII.
This energy will be dissipated by the IMM through production of secondary showers initiated by photo-nuclear effects and pair productions along its path. In order to study the characteristics of IMM-induced showers, we implemented magnetic-monopole interactions in the CORSIKA air-shower simulation software  [26]. Specifically, existing subroutines for muonic collisional loss, e + e --pair production and photo-nuclear interaction were appropriately modified in CONEX  [27] , which can be used within CORSIKA to perform a combination of stochastic particle production and numeric integration of particle cascades. We used  [28, 29]  to parameterize the differential cross section for e + e --pair production and the Bezrukov-Bugaev parameterization  [30, 31]  for the photo-nuclear interaction model.
Events were simulated according to an E -1 p energy spectrum, to ensure sufficient Monte Carlo statistics at the highest energy, and then appropriately weighted to reproduce the energy spectrum measured by the Pierre Auger Observatory  [33]. For both the IMM and UHECR simulations, we used the CORSIKA package  [26]  to generate an isotropic distribution of showers above the horizon, and the Auger Off line software  [34]  to produce the corresponding FD and SD events. We found that the standard event reconstruction, which is optimized for UHECRs, provides equally accurate direction and longitudinal profile for ultrarelativistic IMM showers. An example of reconstructed longitudinal profile for a simulated magnetic monopole of energy 10 25 eV and γ = 10 11 is shown in Figure  3  indicating the profile of the generated COR-SIKA shower (blue line) and the result of a fit of the reconstructed profile with a Gaisser-Hillas function  [35]  (red line).
On the other hand, the reconstructed X max will always be outside the FD field of view for ultrarelativistic IMM showers, independently of the shower's X up. This is apparent in Figure  5 , where the correlation of dE/dX| Xup with X up is shown for ultrarelativistic IMM simulated events. The background from UHECRs is almost eliminated by excluding an appropriate region of the (X up , dE/dX| Xup ) plane. We optimized the selection to achieve less than 0. 1 background event expected in the data set of this search. The final requirement, X up > 1080 g/cm 2 or dE/dX| Xup > 150 PeV/(g/cm 2 ), is shown in Figure  4 (b) and Figure  5  as dashed boxes, and results in an expected background of 0. 07 event in the search-period data set. The selection criteria used for this search are summarized in Table  I   for ultrarelativistic IMMs ranges from 3% for γ = 10 9 to 91% for γ = 10 12 (see Table  II ).
In general, the detection efficiency ǫ changes over time, which must be taken into account in the calculation of the exposure. In fact, the effective area of the SD array and the number of operating FD telescopes grew during the Observatory installation from 2004 to 2008, and then varied due to occasional failures of the SD stations or FD telescopes. Sometimes weather conditions (e. g. , wind, rain) introduced down-time in the operation of the FD. Also, the night-sky background and atmospheric conditions, such as aerosol concentration and cloud coverage, changed during data taking, which affected the sensitivity of the FD telescopes. These effects were properly taken into account with a time-dependent detector simulation  [36] , which makes use of slow-control information and atmospheric measurements recorded during data taking.
The detection efficiency estimated through the time-dependent detector simulation depends on the fluorescence yield assumed in the simulation, on the FD shower-reconstruction methods and on the atmospheric parameters and FD calibration constants recorded during data taking. Following the procedures of  [36] , the corresponding uncertainty on the exposure was estimated to be 18%. To estimate the uncertainty associated with the event selection, we changed the size of the (X up , dE/dX| Xup ) selection box according to the uncertainty on the two selection variables. X up was changed by ±10 g/cm 2 , corresponding to the uncertainty on X max  [23] , and dE/dX| Xup was changed by the uncertainty on the FD energy scale  [33]. The number of selected IMM events changed by 9%, which was taken as an estimate of the uncertainty on the exposure.
VII. DATA ANALYSIS AND RESULTS The search for ultrarelativistic IMMs was performed following a blind procedure. The selection criteria described in Section V were optimized using Monte Carlo simulations and a small fraction (10%) of the data. This training data set was excluded from the final search period. Then the selection was applied to the full sample of data collected between 1 December 2004 and 31 December 2012. The number of events passing each of the selection criteria is reported in Table  I. The correlation of dE/dX| Xup with X up for events passing the showerquality criteria and X max > X up is shown in Figure  6. The corresponding distributions of dE/dX| Xup and X up are compared in Figure  7  with Monte Carlo expectations for a pure UHECR proton background, showing a reasonable agreement between data and simulations.
Acknowledgments The successful installation, commissioning, and operation of the  Pierre Auger Observatory  would not have been possible without the strong commitment and effort from the technical and administrative staff in Malargüe. We are very grateful to the following agencies and organizations for financial support: Comisión  Nacional de Energía Atómica ,  Agencia Nacional de Promoción Científica y Tecnológica (AN-PCyT) ,  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) ,  Gobierno de la Provincia de Mendoza ,  Municipalidad de Malargüe ,  NDM Holdings  and  Valle Las Leñas , in gratitude for their continuing cooperation over land access, Argentina; the  Australian Research Council ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (FINEP) ,  Fundação de Amparo à Pesquisa do Estado de Rio de Janeiro (FAPERJ) ,  São Paulo Research Foundation (FAPESP)  Grants No.
2010/07359-6  and No. 1999/05404-3 ,  Ministério de Ciência e Tecnologia (MCT), Brazil ; Grant No. MSMT CR LG15014 ,  LO1305  and  LM2015038  and the  Czech Science Foundation  Grant No. 14-17501S ,  Czech Republic ;  Centre de Calcul IN2P3/CNRS ,  Centre National de la Recherche Scientifique (CNRS) ,  Conseil Régional Ile-de-France ,  Département Physique Nucléaire et Corpusculaire  ( PNC-IN2P3/CNRS ),  Département Sciences de l'Univers (SDU-INSU/CNRS) ,  Institut Lagrange de Paris (ILP)  Grant No. LABEX ANR-10-LABX-63 , within the  Investissements d'Avenir Programme  Grant No. ANR-11-IDEX-0004-02 , France;  Bundesministerium für Bildung und Forschung (BMBF) ,  Deutsche Forschungsgemeinschaft (DFG) ,  Finanzministerium Baden-Württemberg ,  Helmholtz Alliance for Astroparticle Physics (HAP) ,  Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) ,  Ministerium für Wissenschaft und Forschung ,  Nordrhein Westfalen, Ministerium für Wissenschaft, Forschung und Kunst, Baden-Württemberg, Germany ;  Istituto Nazionale di Fisica Nucleare (INFN) , Istituto Nazionale di Astrofisica (INAF), Ministero dell'Istruzione, dell'Universitá e della Ricerca (MIUR) ,  Gran Sasso Center for Astroparticle Physics (CFA) ,  CETEMPS Center of Excellence, Ministero degli Affari Esteri (MAE), Italy ;  Consejo Nacional de Ciencia y Tecnología (CONACYT)  No.
This revolutionary tool provides unprecedented precision in the measurement of transcript levels  [31]. The aim of the present study was to detect differentially expressed genes that play a role in the domiciliation process and sexual behavior of T. brasiliensis bugs sampled in different ecotopes (sylvatic, peridomiciliary, domiciliary). We first evaluated the diversity of OBPs and CSPs through the analyses of a T. brasiliensis reference transcriptome  [32]  and compared this diversity within the super-order Paraneoptera by building protein phylogenetic trees. We then evaluated contigs that were significantly differentially expressed (DE) in different environmental conditions and searched for contig clusters that show similar expression patterns using HTSCluster. We evidenced genes significantly differentially expressed between sexes and ecotopes including genes belonging to the chemosensory system (especially OBP and CSP genes), genes encoding takeout proteins involved in adult feeding and male courtship behavior, or genes encoding for proteins involved in detoxification or in preventing toxins from penetrating the cuticle.
De novo assembly of the reference transcriptome We assembled the reference transcriptome from datasets consisting of one chemosensory library of a sylvatic female sequenced via Illumina paired-end and of eight chemosensory libraries from the males and females of the four samples described above pooled and sequenced with 454 technology. We followed the procedure 10c published in a previous study  [32]  with the following modifications: we cleaned paired-ends using prinseq version 0. 20. 4 [34]  and we added the step of selecting the longest isoform per transcript cluster (as defined in Trinity) from the Trinity output. Transcriptome completeness was assessed using the CEGMA (Core Eukaryotic Genes Mapping Approach) pipeline that searches for the presence of sequences belonging to a set of ultra-conserved eukaryotic proteins  [35].
Annotation We look at contigs that encode for a set of proteins of interest (CSPs, OBPs, ORs, ionotropic receptors (IRs), gustatory receptors (GRs), transient receptor potential channels (TRPs), sensory neuron membrane proteins (SNMPs), takeout proteins, cytochrome P450, gluthatione Stransferases, cuticular proteins) in the reference transcriptome. We queried insect amino acid sequences retrieved from GenBank against our reference transcriptome using Tblastn searches with an e-value threshold of 10 -6. The selected contigs were aligned to the non-redundant protein database (Blastx with an e-value threshold of 10 -6 ). Only contigs with at least one protein match from the families cited above were kept. For the OBP and CSP protein families, contigs were translated and checked for the following: conservation of 6 (OBPs) and 4 (CSPs) cystein positions, the presence of α helices using PSIPRED  [36] , and the presence of a signal peptide using SignalP  [37].
Differential expression analysis Two different statistical methods were used for differential expression analysis: DESeq2 package v. 1. 6. 3 [54]  and EdgeR v. 3. 8. 5  [55]. For both packages, we filtered contigs with a low coverage using HTSfilter  [56]. We used the DESeq normalization method and a threshold of five mapped reads on average. DESeq2 automatically evaluates normalization and dispersion but we turned off the independent filtering. We chose the RLE model for normalization and a robust estimation method to evaluate dispersion with EdgeR. In both cases, count data was modeled with a negative binomial distribution in a Generalized Linear Models (GLM) with two factors: environment and sex. For both methods, we controlled the factor "sex" when testing the factor "environment". We compared the differential expression for the three modalities (sylvatic, peridomiciliary, domiciliary) of the "environment" factor and for the two modalities (male, female) of the "sex" factor.
Our two-factor model (sex and environment) allowed R packages to assess changes that would be due to one or the other factor despite the absence of biological replicates per sex for domiciliary and peridomiciliary conditions. We applied the Benjamini-Hochberg correction  [57]  for multiple tests. The contigs were considered to be differentially expressed (DE) when the Padj was below 0. 05. We selected contigs that were differentially expressed with both the EdgeR and DESeq2 methods. These contigs were annotated using blastx (BLAST 2. 2. 29+) with the non-redundant protein database (version May 2015) and we selected only the best matches that had an e-value below 10 -6. The heatmap was built from chemosensory contig expression with DESeq2.
Gene clustering based on transcriptomic data Clusters of co-expressed contigs were searched using HTSCluster  [58]. This R package implements a Poisson mixture model to cluster expression observations. Despite the use of TMM normalization, HTSCluster is sensitive to library size. The sample DFB-U was excluded from the analysis because of the lack of a technical replicate for this sample otherwise there would have been one quarter fewer reads mapped than other samples for which technical replicates were collapsed. The count table was first filtered to remove contigs with a normalized average below 5. We selected the slope heuristic DDSE method.
Reference transcriptome We selected only the biggest isoforms from Trinity "genes" using the assembly 10c workflow established by  [32] , and obtained a reference transcriptome of 48,290 contigs with an N50 of around 1,160 bp and a total length of 56,014,905 bp. The transcriptome appeared to be quite complete, with a CEGMA value of 94. 35%. Males and females sampled from several samples were pooled to generate the 454 sequencing data so that the reference transcriptome represented all environmental conditions and sexes.
prolixus (for example, TbraCSP3 and 4 were close to RproCSP3 and 4, two paralogs of R. prolixus). These proteins could be orthologs that arose from gene duplication in recent common ancestry of Triatominae. In such hypothesis (paralogy), each contig is supposed to represent a specific gene. However, as the T. brasiliensis chemosensory transcriptome was built from RNA pooled from several individuals, these proteins could also be encoded by allelic variants or isoforms from the same gene. Under these allelic or isoform hypothesis, all clustered contigs derive from the same gene. Therefore, the numbers of OBP/CSP genes expressed in antennae and rostra vary from 25 to 19 OBPs and 16 to 14 CSPs, depending on the hypothesis (paralogs versus alleles/isoforms). Hereafter, with the aim to limit overestimation of OBP/CSP gene numbers based on RNAseq database, we will only reference the lower and more stringent estimate of gene numbers (allelic/isoform hypothesis).
Differential expression analysis We used both DESeq2 and EdgeR software, which differ in dispersion estimation methods (see Fig  2 for DESeq2 ). For both software, dispersion decreases when expression increases, which is typical for RNAseq data among biological replicates. We used a two-factor general linear model (GLM) with both packages, allowing differential analysis despite the lack of biological replicates for some conditions. DESeq2 and EdgeR presented similar p-value distributions for all but the comparison between the sylvatic and peridomiciliary habitats. The peak was close to 0, corresponding to differentially expressed contigs between modalities and a uniform distribution for larger p-values, indicating a good fit to models generated by both types of software (Fig  3 ). However, the number of DE contigs differed between the two types of software.
Annotation of differentially expressed genes We were only able to annotate 50% of the DE contigs with the non-redundant proteins database. Three CSPs (CSP14, 15 and 16) and one OBP (OBP4) were differentially expressed between sexes, and all were over-expressed in males. A significant proportion of OBP and CSP contigs were differentially expressed between environments, particularly in the  ). Most were under-expressed in the domiciliary samples. However, no expression difference was observed for contigs annotated as ORs. We used a heatmap to compare chemosensory contig expression (Fig  4 ), revealing two distinct groups: one merged sylvatic and peridomiciliary samples and the other merged domiciliary samples plus the sylvatic SFC sample. The similarity between gene expression in domiciliary samples and this peculiar sylvatic sample could explain why few DE contigs were detected in the sylvatic versus domiciliary comparison.
"+" indicates number of overexpressed contigs in the first modality listed in each condition comparison (example, 148 contigs are found as over-expressed in males compared with females using DESeq2). "-" indicates the number of under-expressed contigs. We annotated differentially expressed contigs found in both DESeq2 and EdgeR and we indicated the number of transcripts encoding odorant-binding proteins (OBPs), chemosensory proteins (CSPs), takeout proteins (TOs), transient receptor potential channels (TRPs), sensory neuron membrane protein (SNMP), cuticular proteins (CPs), gluthatione S-transferases (GSTs) and Cytochrome P450 (P450s). D = domiciliary, P = peridomiciliary and S = sylvatic; M = male and F = female. doi:10. 1371/journal. pntd. 0005067. t003 was too slight to be detected in the DE study.
Differential gene expression To maximize support for our results, we selected contigs that the EdgeR and DESeq2 techniques both identified as differentially expressed. Recent studies suggest that EdgeR and DESeq2 R packages are the best statistical methods for normalization and differential expression analysis of multi-factored experiments without a reference genome  [58, 59, 65, 66]. They are currently the only methods able to maintain a reasonable false-positive rate without decreasing the power of differentially expressed gene detection. They are stable under different sequencing depths. However, DESeq2 and EdgeR did not provide the same results with our dataset. EdegR found more differentially expressed contigs than did DESeq2. Moreover, EdgeR found a higher log2-fold change than did DESeq2.
Nevertheless, RNAseq is a recent technology and statistical research into how best to use differential expression analysis software with this kind of data is ongoing. Recently, a gene clustering methodology based on transcriptomic data has been developed to highlight co-expressed contigs (HTSCluster,  [58] ). We found similar results using differential expression analysis and the HTSCluster gene clustering methodology. Furthermore, gene clustering provides additional detail about the dynamics of gene expression, especially for chemosensory contigs. We were able to annotate OBP or CSP contigs with very similar sequences to different clusters that reflect different expression patterns, suggesting they represent recent paralogs or isoforms rather than allelic variants. This could support the idea that these sequences play a role in different functions, for instance they could target different odorant molecules.
Conclusion Our aim was to better understand the domiciliation process in T. brasiliensis. We focused on the chemosensory transcriptome because chemosensory genes are implicated in adaptation to changing environments. In the absence of a reference genome for T. brasiliensis, we used the methods of Marchant et al. [32]  to build a reference transcriptome. RNAseq is a recent technology, so differential expression analysis software is still not optimized. Therefore, to generate results with strong support, we selected congruent results from two different programs: EdgeR and DESeq2. We then processed an independent clustering analysis based on the same expression pattern data. We characterized numerous DE transcripts from bugs of different environments and of different sexes. The domiciliary sample showed different expression profiles than those of sylvatic and peridomiciliary samples.
Acknowledgments We thank to all members of the communities in which this study was carried out for allowing and helping in the field work. We would like to thank  Andrea Rau  ( GABI, INRA, France ) and  Marie-Laure Martin-Magniette  ( IPS2 ,  INRA Paris-Saclay) , who helped us to develop the statistical strategy of differential expression analysis and gene clustering. We thank  Marie-Christine François  ( iEES, INRA Versailles, France ) for help with the T. brasiliensis RNA extractions. The authors are also very grateful to the engineers of the bioinformatics platforms Genouest ( Université de Rennes 1 ) and eBio ( Université Paris Sud)  for technical support. We also thank  Sharilynn Wardrop  for the comprehensive review of the English text.
Financial support was provided by  Conselho Nacional de Desenvolvimento Cientı ´fico e Tecnologico (CNPq)  and  Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP , process numbers  2010/17027-0 ,  2011/22378-0  and  2016/08176-9 ) This study was funded by the role in the chemosensory system. project,  ANR-097-PEXT-009 ) and supported by the  labex BASC (University Paris Saclay, France ). AM was funded by the  Idex Paris Saclay, France. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.