dataesr/scientific-paragraphs-categorization huggingface dataset:
This may also explain why A. grandis has a low number of matched sequences. The A. grandis transcriptome was GO-annotated based on matches to Interpro proteins. In order to group the proteins with associated GO terms, the top level terms for each GO category "Molecular function", "Biological Process" and "Cellular component" were recorded at the different match levels. The dominant terms for Molecular function are clearly transporter activity and binding, while the dominant term for Biological process is pigmentation. Within Cellular component the dominant terms are evenly divided between organelle, cell part and organelle part (Figure  S3A, B  and C ). A more detailed classification of the contigs function can be obtained from the top 35 InterPro entries (Table  2 ). The most abundant entry is NAD(P)-binding domain (IPR016040).
Chaperones, nucleic acid binding and oxidative stress-related domains constitute the most part of InterPro entries, in accordance to the grouped GO top terms (Figure  S3A, B  and C ). We used WEGO  [44]  for visualizing and comparing our GO annotation to the T. castaneum genome annotation data (Figure  3 ). Similar number of genes was annotated for the same GO terms in both insects for a determined GO category and no significant differences were shown, which indicates that de novo annotation for A. grandis is comparable to the T. castaneum genome annotation. Hence, we consider that we accomplished the objective of generating a database describing a significant and representative portion of the A. grandis transcriptome. We performed a comparison of the A. grandis 454 Pfam entries to D. melanogaster and B.
mori Pfam transcript sects from Flybase  [53]  and Silkbase  [54]  (with tBLASTx, e < 10 -3 ) in order to establish a simplified genetic overlap between these species. The low number of A. grandis sequences, which do not match either D. melanogaster or B. mori (Figure  4 ) is probably due to the sum of new unique genes, poorly conserved genes, and erroneously sequenced reads. We noticed that the protein family similarity is higher to Drosophila (Diptera) than to Bombyx (Lepidoptera). This is significant because the number of sequence data in plant-insect pest interaction is greater for Lepidoptera than for Diptera, which normally lead to a probably erroneous biased search for ortholog sequences for coleopterans in lepidopteran databases.
Conclusions Here it is described the analysis of a new database of cotton boll weevil (A. grandis) nucleotide sequences obtained by pyrosequencing of the insect transcriptome. It is the largest number of sequences provided for this insect pest so far. These results provide a significant molecular biology dataset, which can be used, as an example, for molecular prospection in order to validate genes to be used in insect control. The silencing of a chitin synthase gene in larvae emerged from eggs laid by dsRNA-microinjected females proved that not only RNAi machinery is able to trigger RNAi silencing in A. grandis, but also to transfer its effect to the next generation. Since the main goal here was to generate and analyze data in silico, other experiments of gene expression quantitation, silencing via RNAi and gene sequencing in specific insect stages or submitted to certain conditions must be carried out.
Introduction The preservation of DNA in fossil specimens has opened new perspectives in evolutionary biology, providing access to genetic information from past individuals  [1, 2]  and extinct species  [3, 4]. Ancient DNA research has long been limited to the retrieval of short sequence information from organelle genomes  [5]  but recent developments in next-generation sequencing (NGS) technologies have circumvented intrinsic methodological limitations, providing megabase-scale datasets  [6] [7] [8]  to complete ancient genomes  [1] [2] [3] [4] [9] [10] [11]  ranging from a hundred of years  [2]  to several hundreds of thousands of years  [12]. Most ancient DNA extracts consist of a mixture of DNA templates originating from the fossil specimens themselves as well as environmental microbes that colonize fossils after death  [13].
DNA Sequencing Amplified libraries were quantified using the 2100 Bioanalyzer (Agilent) High-Sensitivity DNA Assay. In case a high amount of adapter dimer was present, size selection was performed using either an E-Gel SizeSelect 2% Agarose electrophoresis (Invitrogen), or a LabChip XT 750 DNA Assay (Caliper) following the manufacturer's instructions. Libraries were pooled with other indexed DNA libraries and Paired-End sequenced (26100) over 4 lanes on Illumina Hiseq 2000 platforms at the Danish National High-Throughput DNA Sequencing Centre (Table  1 ), except for one that was pooled with other unrelated DNA libraries and sequenced as Single-End (100 cycles). All sequence data generated in this study have been submitted to Sequence Read Archive under accession number (SRA099068).
Sequence analysis The overall sequence analysis procedure is based as previously described  [21, 25, 34]. Briefly, prior to sequence alignment against modern reference genomes using BWA  [35] , each set of single-end or paired-end reads were processed using AdapterRemoval  [36] ; this involved removing the known adapter sequence, allowing a mismatch rate of 1/3, trimming low quality bases (Phred score 2 or base called as N) at read termini. We collapsed paired-end reads into single reads where the sequencing ends were found to overlap for at least 11 bp  [3, 11]. Finally, processed reads shorter than 25 bp were discarded in order to limit noise resulting from misalignments. Following processing, reads were aligned to reference genomes (equids: EquCab2. 0 autosomes; E. coli: Accession number NC_010473) using BWA v0.
5. 9-r26-dev, with ancient sequences aligned without a seed region as recommended in  [34]. For ancient DNA, only paired-end reads collapsed into single reads were considered. Only high-quality hits (PHRED score $ 25) were retained. Finally, aligned single-end and pairedend reads for each sample were filtered for PCR duplicates using MarkDuplicates from the Picard toolkit [http://picard. sourceforge. net/], and using a script kindly provided by Martin Kircher (FilterUniqueBAM. py; for a SAM compatible version, see https://bioinf. eva. mpg. de/fastqProcessing/) for collapsed reads. Post-mortem damage patterns and DNA composition was estimated using mapDamage  [24]. To estimate the fraction of PCR duplicates, high-quality hits including high-quality PCR duplicates were tabulated.
This bias could admittedly be overcome by increasing sequencing efforts, something that becomes more feasible as sequencing costs decrease; however, our findings also reveal that heterogeneous datasets with non-optimal overlap will be obtained for low-coverage genomes especially when different library preparation procedures are used. This is of particular relevance for genomic projects involving limited amounts of DNA material, e. g. for non-invasive samples  [42]  or when genomes of single cells are targeted  [43]. In addition to the bias described in this study, other sources of bias in Illumina sequencing have been reported and could impact the outcome of genomic surveys. Those range from the size selection step (with high-melting temperatures favouring %GC-rich regions;  [29] ), the amplification step (with a full range of possible size-dependent and %GC-dependent biases depending on which Taq DNA polymerase is used  [44] ), to artifacts in base calling and image analysis  [45, 46].
0 genome. These rates are calculated using mapDamage output  [24]  by summing over positions where a C (G) is found in the reference genome but a T (A) is found in sequencing reads. doi:10. 1371/journal. pone. 0078575. g004 misincorporation rates at sequencing starts and ends, ancient DNA libraries prepared following the AT-overhang ligation procedure will systematically provide an under-estimation of the overall DNA damage levels compared to those prepared with the BE ligation procedure. This implies that quantitative comparative analyses of DNA damage levels across samples coming from temporal and/or environmental gradients must be precluded unless (i) sequence datasets have been generated using identical types of library preparation procedure or (ii) adequate DNA damage models, capturing the specificities of the molecular tools used during library preparation, are used.
Conclusion We have shown that library preparation procedures based on AT-overhang adapter ligation introduce significant bias in the base composition of Illumina libraries. Together with variations in mappability across regions, this bias results in an uneven representation of different genomic landscapes. This has important consequences in structuring the lateral coverage of genome datasets sequenced at low depth and should be considered upfront in any project involving single cell genome sequencing, non-invasive samples, museomics and ancient DNA where DNA material is scarce. It also significantly limits our ability to successfully integrate into libraries the whole molecular complexity of DNA templates preserved in ancient extracts. Finally, it substantially changes the shape of nucleotide misincorporation patterns that are often used as important authentication criteria in ancient DNA research, leading to a systematic under-estimation of the levels of post-mortem cytosine deamination.
Acknowledgments We thank the  laboratory technicians of the Centre for GeoGenetics  and the staff of the  danish National High-Throughput DNA Sequencing Centre  for technical assistance. We acknowledge the  DTU Multi-Assay Core  for giving us access to its Covaris equipment. We thank  Pr. M. Thomas P. Gilbert ,  Dr. Philip L. F. Johnson  and  Dr. Aurelien Ginolhac  for fruitful discussions and  Pr. Pr. M. Thomas P. Gilbert  for carefully editing the final version of the manuscript.
To quantify gene expression levels, SYBR-Green based qRT-PCR was performed using the 7900HT fast real-time PCR system from Applied Biosystems (Darmstadt, Germany). Data were normalized on 18S-rRNA expression as reference gene. Primers are listed in Supplementary Table  1. Melting curves were plotted to verify the specificity of the amplified products. All determinations were performed in triplicate. The relative expression of the target gene transcript and reference gene transcript was calculated as DDC t. Two independent experiments were done for each gene. CAM assay and primary GBM cultures. CAM assay was done as described previously. 28 Briefly, 1 Â 10 6 T98G cells were resuspended in 10 ml serum-free medium and 10 ml Matrigel matrix (BD Biosciences) after treatment with or without 2. 5 mM BV6 for 24 h and implanted on the CAM of fertilized chicken eggs on day 8 of incubation.
Four days after inoculation, tumors were excised with the surrounding CAM, fixed in 4% paraformaldehyde, embedded in paraffin, cut in 5 mm sections and stained with 1 : 1 hematoxylin and 0. 5% eosin solution for histological analysis. Images were digitally recorded with an SZ61 microscope (Olympus, Center Valley, PA, USA) and tumor area was analyzed using ImageJ digital imaging software (NIH). Primary GBM cultures (GBM1, GBM2) were established from surgical specimens obtained from two patients with grade 4 glioma and were characterized and cultured as described previously. 48 The study was approved by the ethics committee, Medical Faculty, University of Frankfurt. Statistical Analysis. Statistical significance was assessed by two-sided Student's t-test using Microsoft Excel (Microsoft Deutschland GmbH, Unterschleiheim, Germany).
Acknowledgements. We thank  D Vucic  ( Genentech, South San Francisco, CA, USA ) for providing  BV6 ,  D Bru  ¨cher for expert technical assistance and  C Hugenberg  for expert secretarial assistance. This work has been partially supported by grants from the  Deutsche Forschungsgemeinschaft, European Community  ( ApopTrain, APO-SYS ) and  IAP6/18  (to SF).
In the absence of experimental data confirming the handedness, we proposed a right-handed helix, which appeared to allow better preservation of the interactions between subunits  (Tawar et al. , 2009). Here we set out to determine unequivocally the handedness of the helical RSV NC by electron tomography of purified NC-like ribonucleoprotein complexes (RNPs). To provide a control for our methods, we also analysed tomograms of measles virus (MeV) RNPs, which are known to be lefthanded helices  (Schoehn et al. , 2004). RNPs were prepared by recombinant expression of the relevant N-protein in insect cells as previously described  (Bhella et al. , 2002). Tomography of purified RNPs embedded in negative stain was performed in a JEOL 2200 FSC TEM (for a full description of the methods, see the supplementary material, available in JGV Online).
Data deposition The subtomogram averages have been deposited in the EMDB, accession codes emd2369 (RSV) and emd2370 (MeV). The atomic model of the helix has been deposited in the PDB, accession code 4bkk. In the left-handed NC, the bulky NTD projects away from the polymerase complex, allowing simultaneous access to both sequence elements, whereas in the right-handed NC helix, it would project toward the polymerase, interfering with access to the gene-start element.
Data were collected from two independent experiments, each timepoint corresponds to 6 independent measurements, each on 3-5 individual plants (four leaves/plant). doi:10. 1371/journal. pgen. 1003766. g003 RKS1 confers resistance to multiple strains and races and pathovars of Xcc. Time course evaluation of disease index in lines differing only by the presence of RKS1 gene (rks1-1 mutant (blue), complemented rks1-1 mutant (purple) and the parental lines Col-0 (green) and Kas-1 (red) after inoculation with different strains of (A) Xcc belonging to races as defined by Vicente et al. [49]  and Fargier et al. [50] , and of (B) Xc pathovars raphani (Xcr), armoriaceae (Xca), vesicatoria (Xcv) and incanae (Xci). doi:10. 1371/journal. pgen. 1003766. g005 whereas the second one including Kas-1, contains several polymorphisms in complete LD that are scattered across the 5 kb region (Figure  4A , Table  S8 ).
Data were collected from two independent experiments, each time point corresponds to 6 independent measurements, each on 3-5 individual plants (four leaves/plant). At the inoculation site (basis of the inoculated leaves), bacterial growth was measured and found similar in the different lines (4. 960. 5 to 5. 160. 4 CFU/cm 2 at T0; 8. 960. 5 to 9. 260. 6 CFU/cm 2 at 7 dpi). For data reported in Figure  3D , and according to parametric and non parametric tests, respectively ANOVA (df = 3; mean squares = 47. 202 ; F = 53. 887 ; P,0. 001) and Kruskal-Wallis test (df = 3 ; Kruskal-Wallis test statistic = 34. 136 ; P,0. 001), the genetic background of the lines (Col-0, Kas-1, rks1-1 and complemented line) explained significantly (81%) the phenotypic variation observed at 7 dpi. The genetic background effect of lines analyzed by pairwise comparisons (Dwass-Steel-Chritchlow-Fligner test) was also significant with a p-value below 0.
RNA isolation and Q-RT-PCR Leaves from RNA extraction and Q-RT-PCR analysis were performed as described  [53]  using leaves from healthy plants or inoculated with the Xcc568 strain (28 hpi)  [52]. A gene (At2g28390, SAND family) whose expression has been shown to be extremely stable under different physiological conditions  [57]  was used as a control. Average DCp was calculated from 4 independent experiments with 3 individual plants (3 leaves/plant). Data are expressed as fold induction of each point as compared to the wild type. RNA isolation from Arabidopsis seeds imbibed for 16 h was performed using a protocol adapted from http://cotton genome center. ucdavis. edu/protocols/RNA. In order to study the relationship between disease index and expression level of RKS1 (or At3g57720) in different transgenic lines and natural accessions, linear and non-linear regressions were fitted using the 'lm' and 'nls' functions implemented in the R environment, respectively  [58].
Statistical analysis For QTL mapping, data were analyzed for each block and each experiment. Adjusted means of disease scores (LSmeans) of RILs in blocks were estimated from variance analysis (ANOVA). Broad sense heritabilities (H 2 ) were estimated from the mean square (MS) of ANOVA using the formula adapted from Gallais  [59]. Variance analysis of in planta bacterial growth data was performed using PROC GLM of SAS with random effects. QTL analysis was done using the R-qtl package  [60]. For GWA mapping, the following general linear model was used to analyze disease index (GLM procedure in SAS9. 1, SAS Institute Inc. , Cary, North Carolina, USA): disease index ij ~mzblock i zaccession j zcov Col{5 zcov Kas{1 ze ij : Where 'm' is the overall mean; 'block' accounts for differences among the four experimental blocks; 'accession' corresponds to the 384 natural accessions; cov Col-5 and cov Kas-1 are covariates accounting for mini-greenhouse effects; and 'e' is the residual term.
Normality of the residuals was not improved by transformation of the data. Least-square mean (LSmean) was obtained for each natural accession and was subsequently used for GWA mapping analyses. All the 384 accessions have been genotyped for 214,051 SNPs. In order to fine-map genomic regions associated with natural disease index variation, we ran a Wilcoxon rank-sum test  [61]  and a mixed-model approach implemented in the software EMMAX (Efficient Mixed-Model Association eXpedited  [62]. The latter model includes a genetic kinship matrix as a covariate to control for population structure. The percentage of quantitative disease resistance explained by the three allelic groups detected by nested GWA mapping was estimated in two polymorphic natural populations MIB and TOU (Table  S7 ).
Sequencing of natural accessions and molecular evolutionary genetics analysis Ninety-five natural accessions already sequenced for 876 short fragments of ,500 bp  [63]  were sequenced for a 5,111 bp region centered on At3g57710 (i. e. RKS1), using five pairs of primers described in Table  S2. Genbank accession numbers for the 5,111 bp sequences produced in this study are KF363545-KF363829. The average number of nucleotide substitutions per site between the two intergenic haplotypes and the Tajima test were computed across the 5,085 bp region (window size = 100 bp, step size = 10 bp; window size = 500 bp, step = 50 bp, respectively)  [64]. Note that nucleotide diversity and Tajima's D were estimated using nucleotide and insertion/deletion (indel) polymorphism data, the latter coded as single characters.
Acknowledgments We thank  Claudine Balague, Celine Remblie  `re,  Anne Genissel ,  Maurice Tronchet ,  Cedric Glorieux ,  Nathalie Faure  and  Benjamin Brachi  for technical assistance and helpful discussions, and  Jane Parker ,  Yves Marco ,  Laurent Nussaume  and  Olivier Loudet  for critical reading of the manuscript.
This work was supported by the  French Agence Nationale de la Recherche -ANR  blanc grant ( ANR-09-BLAN-0072 ),  Genoplante -Trilateral  project  France/Germany/Spain TRI 033 , the  National Science Foundation  ( MCB-0603515 ) and  National Institute of Health  ( GM083068 ). This work has been mainly done in the  LIPM, part of the ''Laboratoire d'Excellence'' (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Behavioral Observations Behavior at Loading. Behavior of pigs was recorded using 3 digital camcorders (DCR-HC48; Sony of Canada Ltd. , Toronto, Canada) installed using 3 camera mounts overhead of the loading ramp and the alley at the loading ramp. The cameras recorded all occurrences of pig behavior (Table  1 ) from a predetermined start gate at the farm alley to the trailer gate. The course was divided into 2 zones: zone 1 was the alley from the start gate to the farm door, and zone 2 was between the farm door and the end of the external ramp (trailer door). With the frequency of the behaviors observed being relatively low, data were calculated as the percentage of loading groups that showed the behavior in at least 1 of the 2 zones. Video recordings were watched in real time by a trained observer using a handheld Psion Workabout (HC-110; Psion Inc.
Physiological Measurements Gastrointestinal Tract Temperature. Approximately 8 h before loading, a total of 240 pigs (40/wk) were orally administered Thermocroni-Button data loggers (model DS1921H; Dallas Semiconductor, Maxim Integrated Products, Sunnyvale, CA) to monitor gastrointestinal tract (GIT) temperature using a snare, a heavy gauge metal "pig gag," and balling gun. Each data logger was programmed to begin recording from 1 h before loading until slaughter and to log temperature once per minute. The GIT temperature was measured in 0. 125°C increments with ±1°C accuracy in the range of 15°C to 46°C. Data loggers of selected pigs were recovered after gross dissection of the viscera during the slaughter process. Later, data from 135 pigs (56. 2% recovery rate) were downloaded onto a laptop computer.
Statistical Analysis Behavior data were analyzed by the FREQ procedure (SAS Inst. Inc. , Cary, NC) using the treatment group as the experimental unit. Categorical variables relative to the animals had low frequencies, and were transformed to binary variables, and were analyzed by the LOGISTIC procedure of SAS. Analyses of handler intervention data, load time, fighting events, and fighting duration were made through an adjustment to nonnormal distributions using the GLIMMIX procedure of SAS, except for vocal sound and posture during lairage, which presented normal distribution. In this case, the data were analyzed by the MIXED procedure of SAS. Severity of skin bruises was analyzed with a Cochran-Mantel-Haenszel (CMH) test and the LOGISTIC procedures of SAS to determinate interactions between factors.
The MIXED procedure of SAS was applied to analyze the physiological data using the treatment combination group as the experimental unit and carcass and meat quality data using the experimental individual animal as the experimental unit. Urinary hormone data were transformed into their logarithmic score (log 10 ) for data normalization before analysis. The model included treatment, week, and their interaction as fixed effects. Comparisons between treatment means were adjusted for multiple comparisons with a Tukey-Kramer correction. A probability level of P < 0. 05 was chosen as the limit for statistical significance in all tests, whereas probability levels of P £ 0. 10 were considered to be a tendency.
In nondenaturing conditions, the mass measurement of the protein was performed in 50 mM ammonium acetate with 10% isopropyl alcohol, to preserve the native conformation. The protein was injected at 2. 0 M. Clusters of Cs(nϩ1)In were used for the calibration of the extended masse range in the high m/z region. Mass data were acquired in the positive ion mode on the mass range 1000 -10,000 m/z. CD40 deglycosylation was performed using the deglycosylation mix from New England Biolabs following the manufacturer's instructions. Molecular Modeling-All procedures were performed with Discovery Studio 2. 5 software from Accelrys (San Diego, CA). The crystal structure of the ligand-free TNFR1 (Protein Data Bank (PDB) ID code 1NCF) was used as template to build a model of dimeric CD40. The receptor monomer corresponds to the crystal structure of the complex CD40-CD40L (PDB ID code 3QD6).