dataesr/scientific-paragraphs-categorization huggingface dataset:
The set of peaks was verified at an irreproducible discovery rate (IDR) of 0. 05 %  [46]  to confirm that the samples were sufficiently similar to be used in the analysis. The number of mapped reads was multiplied by a scale factor to normalize the total number of reads in different samples. To compare the H3K4me3 CHIP datasets of ATZ-treated and control samples, differential peak calling was performed using several steps. First, from all the peaks called above, we selected the peaks that were reproducible in both datasets in the same condition. From the dataset of selected peaks, we kept only peaks with average expression values above the 5 % quantile; last, we selected the peaks with fold changes above 1. 5 and FDR <5 %. Statistical significance was calculated using a Limma test  [89]. Annotation of significant differential peaks was performed by CEAS  [83].
All sequencing data from this study are publicly available and have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus. The following samples from ChIP seq data were deposited at GEO: Gene Ontology (GO) term analyses Gene Ontology (GO) term analyses were performed using DAVID v6. 7  [33]  with enrichment thresholds ease 0. 05, kappa index 3. Functional classification was performed with the same subset of the gene list with parameter kappa similarity 5 and initial group number 5.
Meme-ChiP motif search For the analysis, we used the summits of 100-bp sequences from differential peaks, excluding DSB-associated peaks. Motif finding was performed with MEME-ChIP  [49]  following default parameters. Identified motifs were compared with known motifs using TomTom  [93]  with a q -value less than 0. 003. All sequencing and Gene-Chips data from this study are publicly available and have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus, GO number: GSE 64037.
Acknowledgements The authors would like to thank  Stephanie Kica ,  Marc Aubry  and  Florent Denoual  for genome-wide sequencing at the genomic platform at University Rennes 1,  Pierre-Yves Kernanec  for animal care and help with dissections, and  Bertrand Evard  for help with production of the microarray data. This work was supported by the  Chair of Excellence program  from the  European University of Brittany ,  Atip -Avenir program  from INSERM; CH was supported by a fellowship from the  China Scholarship Council (CSC).
The main OH transition lines at 1665 and 1667 MHz and the satellite lines at 1612 and 1720 MHz were observed in June 1986 with the VLA in the BnA wide array configuration. The main transition lines were also observed with the VLA in its DnC compact array configuration in October 1989. The initialand post-calibration of the observational data were done with the National Radio Astronomy Observatory (NRAO), Astronomical Image Processing System (AIPS) at the VLA site and at the Array Operations Center in Socorro, NM. The two sets of (u, v) data for each of the main lines were subsequently concatenated into one data set to improve sensitivity and reduce the effects of missing zero-spacing observations. The concatenation was done with the NRAO AIPS DBCON program package, and baselines were subtracted with the NRAO AIPS UVLSF program package in the (u, v)-plane.
Throughout this paper we define "OH absorption" as "continuum minus line flux" (= -T L ), in units of mJy/beam. The angular resolution is 7 × 5 , and the velocity resolution is 8. 8 km s -1. Typical noise levels in the concatenated spectral line maps and line profiles are about 25 mJy/beam, and about 5 mJy/beam for the original 4. 0 × 2. 8 angular resolution BnA 1667 MHz data. The concatenated data are used for the main part of this paper, but the original BnA 1667 MHz data are used as a complement. The subsequent processing of the data was done with both the NRAO AIPS and the XS 1 program packages. Projected distances correspond to about 0. 04 pc/ assuming a distance to the GC of 8 kpc. The position of Sgr A * is (RA, Dec) = 17 h 45 m 40 s. 03, -29 • 00 27. 6 (J2000. 0). For further information about the observations, see  Karlsson et al.
Acknowledgements. The authors acknowledge the open policy for the use of NRAOs VLA. The  National Radio Astronomy Observatory (NRAO)  is operated by  Associated Universities Inc. , under cooperative agreement with the  National Science Foundation. Kambiz Fathi  acknowledges support from the  Swedish Royal Academy of Sciences' Crafoord Foundation. Furthermore , the extensive support from the  Swedish National Space Board (Rymdstyrelsen)  is gratefully acknowledged by  Aage Sandqvist. We also acknowledge with gratitude the support from  Miller Goss  with concatenation of the observations with the  VLA  in its BnA and DnC configurations.
5 g cm -3 ) and residue (> 2. 6 g cm -3 ) were washed with Milli-Q. The latter should contain heavy minerals, and only a limited amount of glass shards, which should instead be concentrated within the 2. 3 to 2. 5 g cm -3 bracket. All residues were microscopically checked for their purity. The separation only rarely resulted in high-purity end products. Additional ad hoc sample specific treatments were conducted to improve the separation. In the case of a high concentration of low-density shards (e. g. pumice) within the biogenic sample (< 2. 3 g cm -3 ), a wet-sieving step was used to separate the biogenic siliceous bodies from shards. Size distributions for each fraction were determined using light microscopy (Nikon SMZ1500, ×16) and the NIS-Elements software for size measurements. The selected mesh size corresponded with the point of minimum overlap.
A shard signature Group 1 represents relatively pure tephra samples (Fig. 2a, d ) where dissolution of glass shards dominates. Our data suggest that glass shards release the majority of Si and Al at a rapid and a constant rate during the time period (ca. 30 min) we monitor dissolution in NaOH. In contrast to Na 2 CO 3 , the stronger NaOH seems to obtain apparent linear dissolution within the course of the experiment, after an initial non-linear release. This initial decrease in reactivity followed by a substantial constant Si and Al release corroborates previous observations describing the stoichiometric dissolution of glass shards  (Oelkers and Gislason, 2001; Stephens and Hering, 2004). Si : Al ratios of the non-linear Si Alk and linear fraction coincide with Si : Al ratios from unweathered glass shards (Si : Al solid ).
3 ). Grouped by dominant fraction: shards (solid circles), minerals (open circles, i. e. nanocrystalline and clay minerals) or biogenic Si (triangles). No data are available for Reclus R 1 sample. come rounded. The rounding of edges is responsible for the observed Si Alk content when glass shards are dissolved. Afterwards, glass shards will continue to release Si and Al at a steady stoichiometric rate (see also  Hodder et al. , 1990). Hence, the dissolution pattern reflects the continuous but incomplete dissolution of glass shards. This process makes the dissolution of glass shards distinct from the dissolution of other non-biogenic (e. g. nanocrystalline minerals) or biogenic fractions and adsorbed Si and Al release. These processes occur rapidly at the onset of NaOH extraction but do not lead to a fast constant release after unspecified time  (Barão et al.
Acknowledgements. The following people are thanked for their invaluable assistance:  S. R. Gíslason  for providing fresh tephra (Eyjafjallajökull);  A. Cools  and  D. U. Delmonte  for assistance during the continuous extractions;  A. Gosh  for helping with microscopic analysis; and  G. Fontorbe ,  P. Abbott  and  S. Davies  for advice on sample preparation. Comments by  P. Frings ,  G. Fontorbe ,  H. Alfredsson ,  B. Alvarez de Glasby  and  J. Stadmark  help in improving the manuscript. Likewise, the editor, anonymous reviewers and  D. J. Lowe  are thanked for their constructive comments and suggestions. This research was funded by the  Knut & Alice Wallenberg Foundation, FWO en Belspo (SOGLO). L. Baråo  thanks the  Special Research Foundation of the University of Antwerp (BOF-UA and NOI)  for the PhD fellowship funding.
Each site provided serum, DNA and clinical data. Standardized methods were used to genotype 202 polymorphisms (with previous malaria or antibody associations) and four IgG antibody levels to the malaria antigens AMA1, MSP1, MSP2, (NANP) 4 , plus total IgE levels. Despite the challenges of combining data from these heterogeneous study designs and backgrounds, a resource of 13,299 individuals was created to look for consistent genetic effects on anti-malarial antibodies.
Study designs Studies were conducted at ten sites in seven countries across West Africa, East Africa and Asia; details of study design at individual sites and local epidemiological conditions including malaria endemicity are described elsewhere (see  [12] [13] [14] [15] [16] [17] [18] ). Each site provided a DNA sample, serum sample and clinical data for participants collected at a single time point; at several sites these were provided as part of an ongoing study, while at others these were provided from a new study or as an extension to an earlier study. The clinical parameters measured at each site were influenced by study design, which varied considerably (Additional files 1 and 2). Sites either collected cross-sectional data only, longitudinal data only or a combination of both. account variations in study design and implementation, and agreed on methods for recording these clinical measurements to encourage uniformity across the data.
DNA extraction and genotyping DNA extraction was performed at each site using the local method of choice; either Nucleon ™ BACC Genomic Extraction Kits (GE Lifesciences, [19]) or Qiagen DNeasy Blood Kits (Qiagen, Crawley, UK, [20]), and DNA samples were then shipped frozen to the Malaria-GEN resource centre in Oxford. Sequenom iPLEX Gold (Agena Bioscience, CA, USA) was chosen for genotyping because of its high-throughput capacity, flexibility for assay design and ability to genotype up to 40 SNPs (Single Nucloetide Polymorphisms) in one reaction. Multiplexes were designed using MassARRAY ® Assay Design software (Agena Bioscience) and assays with poor performance or concordance were removed from the multiplex during testing. A total of 202 SNP-assays were designed. These included a set of 65 that have been analysed in a severe malaria case-control study across 11 countries  [21]  plus a further 137 autosomal SNPs selected in genes associated with or described as playing a role in malaria and antibody production (Additional file 3 contains details of the gene regions and SNPs assay multiplexes; Additional file 4 contains further details of the genotyping methodology).
Data cleaning To create the final dataset for analysis the clinical, genetic and antibody data were merged based on the unique ID code assigned to each participant at the time of sampling. Entries that did not contain all three data components after merging were removed. Duplicated ID codes were resolved if the correct entry could be identified, otherwise both entries were removed. This gave a dataset with 15,216 individuals. Records where gender missmatched between clinical and genetic gender or DNA samples with <90 % pass rate across 65 core SNP assays  [21]  were also removed (540 miss-matched gender, 1,237 <90 % pass-rate and 140 both miss-matched gender and pass-rate), leaving 13,299 individuals in the merged dataset eligible for analysis. This dataset was then used to quality control the remaining 137 SNP-assays, removing assays with pass rates <80 % across all samples (n = 3).
Assays for SNPs monomorphic across all sites (n = 18) or Amelogenin [gender determining (n = 3)] were also removed from further analysis leaving a total of 178 SNPs for analysis (65 core and 113 extra). Antibody titres were log base 10 transformed to obtain approximate normal distributions for parametric analyses. Once logged all anti-malarial antibodies showed some evidence of a bimodal distribution, while IgE levels showed a negatively skewed distribution (Additional file 5). In the MSP-1 data (Additional file 5) a number of individuals were identified (log 10 titre of 4. 85, n = 216) that were not measured correctly due to technical difficulties. These were removed and excluded from further analyses. Plots of the residuals from the regression analyses (see below) were made (Additional file 6) in order to check that the antibody data were normally distributed.
Data from Senegal, Mali (Pongonon), Sudan and Sri Lanka were not included in this analysis as their study populations were either entirely microscopically negative or entirely microscopically positive for malaria at the time of sampling. Linear regression was used to investigate the effect of SNPs with significant SNP-antibody associations on parasite density, adjusted for relevant clinical parameters (Additional file 7). Data from Senegal, Kenya, Sudan, and Sri Lanka were not included in this analysis as parasite density was not recorded at these sites. In all regression models, age was included as a grouped rather than continuous variable to allow for a non-linear relationship with antibody levels. Sample month was included as a covariate in regression models as a proxy for rainy season, the timing of which varied between sites.
Epidemiological factors A number of study designs were used across sites; Mali (Manteourou), Burkina Faso and Tanzania studies collected data during their malaria transmission season, Mali (Pongonon) included only individuals positive for malaria, Senegal and Sudan based data collection on active case detection, Kenya collected data as part of a birth cohort study at aged 7 years-old, and Sri Lanka followed up individuals that had been malaria positive in 1992/93. Further details can be found in Table  1 , Additional file 1 and the MalariaGEN Website  [18]. After data curation and quality control (see "Methods"), 13,299 individuals were included for analysis (Table  1 ) for five antibodies and 178 SNPs. At all sites the ratio of males to females was approximately 1:1. The number of ethnic groups within a site varied, and individuals belonging to the Fulani ethnic group (referred to here as Peulh) were collected in Senegal, Manteourou in Mali and Burkina Faso.
Discussion This dataset highlights considerable differences in malaria prevalence, antibody titres and study design between the sites that provided data. There are challenges associated with combining heterogeneous data; however, after quality control, a large dataset was compiled with 13,299 individuals typed for 178 SNPs, measures for five antibody levels and a range of clinical covariates. Of the ten sites in the study two found a 0 % prevalence (Sudan and Sri Lanka) at the time of sampling as measured by microscopic positivity and two had an ascertainment design not allowing an analysis of effect of age on malaria prevalence (Mali-Pongonon and Kenya). Across the remaining studies, there was a significant increase in the odds of having microscopically positive malaria from the 1 year-old to 5-15 years-old age groups (OR 3.
Firstly, a large dataset was generated with over 13,000 individuals, 12,380 of whom were HbAA and 868 HbAS. Secondly, a similar effect size was found for HbS in association with anti-AMA1, anti-MSP1 and anti-MSP2 levels, using the heterozygote model. Thirdly, the p-values for each HbAS-antibody association were similar and highly significant even after adjusting for multiple testing. Finally, not including an interaction term between SNP and study in the model meant that all significant outcomes represented effects that were consistent across sites, as demonstrated in the Forest plots for HbAS with the merozoite antibodies. The only exceptions were the Manteourou study in Mali and the Sri Lanka study, which had opposite directions of effect to the other sites for anti-AMA1. Neither of these associations were significant and were likely due to relatively small sample sizes (Mali = 643, Sri Lanka = 497) combined with low derived allele frequencies for HbAS (Mali = 1.
Pictures were taken with a Leica DFC425 C camera. For Aphanomyces euteiches, zoospores of the strain ATCC 201684, a pea isolate, were produced as described by  [39]  and adjusted to 10 5 spores/ml. Seedlings were inoculated one day after transfer onto M medium  [40]  with a 5 μl droplet of spore suspension deposited in the middle of the root. To assess mutant resistance, three independent in vitro infection assays were performed with 10 to 20 inoculated plants/genotype in each repeat. Relative length of tissues displaying symptoms (14 days post-inoculation, or dpi), along with percentages of dead plants (21 dpi), which have been shown to be the most significant parameters to assess the level of resistance of M. truncatula to A. euteiches, were recorded, as described by  [41]. Symptoms were quantified with the ImagePro image analysis software.
Gene expression analysis Affymetrix microarray data available on the M. truncatula Gene Expression Atlas (MtGEA; http://mtgea. noble. org/v3/) were used to analyze expression of cytokinin signaling genes: Histidine Kinases (HKs), Histidine Phosphotransfer proteins (HPs), and Response Regulators (RRs). Correspondence between genome (v. 3. 5. 1) and Affymetrix IDs was determined using the "Nickname" tool from the "Legoo" website (http://www. legoo. org/). Hierarchical clustering was performed on logarithmic gene expression ratio between the different conditions and their respective controls, based on Euclidean distance and average gene clustering using the MeV software (http://www. tm4. org/mev/). For real time RT-PCR, total RNA was extracted from frozen roots using the RNeasy plant mini kit (Qiagen, http://www.
qiagen. com/). First-strand cDNA was synthesized from 1. 5 μg of total RNA using the Superscript II first strand synthesis system (Invitrogen, www. invitrogen. com/). Primer design was performed using Primer3 software (http://frodo. wi. mit. edu/cgi-bin/ primer3/). Primer combinations showing a minimum amplification efficiency of 90% were used in real-time RT-PCR experiments (S1 Table ). Real-time RT-PCR reactions were performed using the Light Cycler Fast Start DNA Master SYBR Green I kit on a Light Cycler apparatus according to manufacturer's instructions (Roche, http://www. roche. com/). Cycling conditions were as follows: 95C for 10 min, 40 cycles at 95C for 15 sec, 60C for 15 sec, and 72C for 15 sec. PCR amplification specificity was verified using a dissociation curve (55-95C). A negative control without cDNA template was always included for each primer combination.
Technical replicates (on two independent syntheses of cDNA derived from the same RNA sample) and two independent biological experiments were performed. Ratios were done with constitutive controls for gene expression to normalize the data between different biological conditions. MtRBP1 and MtH3L were selected using the Genorm software (http://medgen. ugent. be/~jvdesomp/genorm/  [42] ) as reference genes (primers shown in S1 Table ), and the value of the experimental control condition was set up to 1 as a reference to determine fold changes.
truncatula Gene Expression Atlas (MtGEA) database: "myc. ", mycorrhized roots  [70, 71] ; salt stress, two independent experiments (exp. 1 and exp. 2  [72] ); Phymatotrichopsis omnivora and Aphanomyces euteiches pathogens (respectively  [68, 73] ). All probes corresponding to cytokinin signaling genes were included in the heat map, which was constructed with logarithmic gene expression ratio between the different conditions and their respective controls, based on Euclidean distance and average clustering probes across the experimental conditions included, using the MeV software. Color scale ranges from eight time fold-repression in green (log2 = -3) to eight time fold-induction in red (log2 = 3). Accession numbers correspond to Affymetrix probes (correspondence with gene ID in S2 Table ), and multiple probes corresponding to a single gene are indicated by a vertical black bar on the right.
Figure1. Heat map of Medicago truncatula cytokinin signaling gene expression in roots exposed to various environmental conditions. Selected Affymetrix array data corresponding to roots under various abiotic and biotic conditions were retrieved from the M. truncatula Gene Expression Atlas (MtGEA) database: "myc. ", mycorrhized roots [70, 71] ; salt stress, two independent experiments (exp. 1 and exp. 2 [72] ); Phymatotrichopsis omnivora and Aphanomyces euteiches pathogens (respectively [68, 73] ). All probes corresponding to cytokinin signaling genes were included in the heat map, which was constructed with logarithmic gene expression ratio between the different conditions and their respective controls, based on Euclidean distance and average clustering probes across the experimental conditions included, using the MeV software.
Color scale ranges from eight time fold-repression in green (log2 = -3) to eight time fold-induction in red (log2 = 3). Accession numbers correspond to Affymetrix probes (correspondence with gene ID in S2 Table), and multiple probes corresponding to a single gene are indicated by a vertical black bar on the right. Colors indicate cytokinin signaling gene families: in blue, CHKs (CHASE domain containing Histidine Kinases); in green, HPTs (Histidine PhosphoTranfert proteins); in orange, RRBs (Type-B Response Regulators); in violet, RRAs (Type-A Response Regulators). MtCRE1, MtCHK2/HK2, MtCHK3/HK3, MtRR1 to MtRR5 gene names were defined in [13] ; MtRR8 in [46] ; MtRR11 in [45]. For other IDs, no gene name is available in the literature.
Work in F. F. 's laboratory was supported by the  LEGUROOT French ANR  project,  CNRS , and the  "Ministère de l'Enseignement Supérieur et de la Recherche " (PhD grant to T. K. ). Work in C. J. 's laboratory (LRSV) was funded by the  "Université Paul Sabatier", CNRS , and the " Immunit-Ae "  French ANR  project ( ANR-10-GENM-0007 ; PhD grant to O. A. ). Work in P. B. 's laboratory was funded by the  Italian  project  PRO-ROOT (Call Prin 2010 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Methods Trypanosome genomes used for searches of TcPRAC homologous genes Searches for TcPRAC homologs were performed by BLAST against draft and annotated genomes of trypanosomatids freely available in TriTrypDB, geneDB and NCBI data banks. Sequences of TcPRAC and TvPRAC  [10]  were used as queries; full-length sequences and specific motifs from PRAC-like gene family were used as baits for the genome analyses of T. cruzi CL Brener, Silvio X10 plus other strains of T. cruzi (Table  1 ) sequenced by the Kinetoplastid Genome Sequencing and Analysis Consortium NIH/NHGRI/NIAID, T. brucei ssp. , T. evansi, T. congolense, T. vivax, T. c. marinkellei, T. grayi, and species of Leishmania (Table  1 ). In addition, we examined the freely available genomes from Crithidia acanthocephali, Angomonas desouzai, Angomonas deanei, Strigomonas culicis, Strigomonas oncopelti and Herpetomonas muscarum, all generated in our laboratories, plus genomes from Phytomonas sp.
, Crithidia fasciculata and Endotrypanum schaudinni (Table  1 ). We also searched for PRAC genes in genomes that have been generated in our laboratories for a large number of euglenozoans within the ATOL (Assembling the Tree of Life, NSF-USA) and TCC-USP (Brazil) projects aiming highly comprehensive phylogenomic inferences. The following ongoing genomes were analyzed: T. cruzi (G and Tcbat), T. cruzi marinkellei (TCC344), T. dionisii (TCC211), T. erneyi (TCC1946), T. rangeli (AM80), T. lewisi (TCC34), T. conorhini (TCC025E), T. serpentis   1 ). The draft genomes generated in our laboratories were sequenced using standard pyrosequencing shotgun methodology according to Roche 454 protocols and assembled by Roche's Newbler software (version 2. 3) as previously described  [32, 33]. The ongoing genomes from trypanosomes of toad, snake, lizard and bird were obtained using the MiSeq Illumina plataform (mate-pair reads), and assembled using Newbler (version 2.