dataesr/scientific-paragraphs-categorization huggingface dataset:
Left: Beagle duo/trio switch error rate (0. 269% average SE) against switch error rate for SHAPEIT2 when all individuals are treated as unrelated (0. 104% average SE). Centre: After applying the duoHMM haplotype corrections to SHAPEIT2 (0. 065% average SE). Right: Switch error after masking genotypes flagged as erroneous by the SHAPEIT2+duoHMM, Beagle duo/trio phasing is reduced to 0. 231% and SHAPEIT2+duoHMM to 0. 034%. (TIFF) Figure  S25  Genome-wide number of recombinations found per individual for SHAPEIT versus Merlin for all informative meioses in real data sets. The number of recombinations found by SHAPEIT2 against Merlin for each of 661 meioses (all informative duos from all cohorts). Maternal are in green and paternal purple. Triangles represent a truncated value. While there is correlation between the two methods, Merlin more frequently finds an implausible number of recombinations.
GPC: We acknowledge  Johanna Riha  for her help with data curation of the GPC census and survey, and  Cristina Pomilla  for sample management for genotyping. Wrote the paper: JM JO. Provided access and support for the ORCADES, VIS, Split and Korcula datasets: JEH RMF HC CH AFW RM VV PN JH IR OP JFW. Provided access and support for the GPC dataset: DG GA KE MSS. Provided access and support for the Val Borbera, FVG and CARL cohorts:  NP SU MC MT JH PG DT NS.
The term ∆ ǫj with j = 1, 2 represents the normalization variations of the two reference time profiles with respect to the computed ones, and σ j the 20 % normalization error. The χ 2 is computed using the MINUIT minimization embedded in the ROOT package  [17]. To discard from the analysis events for which the fit did not converge properly, an upper limit of 2 was applied on the reduced χ 2. For the same reason, an additional constraint was applied on the ratio between the integral of the fitted function and the number of pulses, in the first and second peak region separately. Events with an integral ratio larger than 2 in either the first or the second peak region (i. e. the interval between 3 ns before the peak and 10 ns after it) are discarded. The applied cuts removed 0. 2 % of the signal and resulted in no bias in the event selection.
Results on the ortho-positronium properties To unambiguously demonstrate the capability of Double Chooz to observe the o-Ps formation, the algorithm is applied to a pure 60 Co sample and compared to the results obtained for the neutrino candidates. The 60 Co reference time profile obtained locating the radioactive source at the center of the detector was used for this analysis. Systematic uncertainty related to the choice of reference time profile will be evaluated later on in this section. Since an increase of the error of the reconstructed vertex is known to rapidly degrade the time profile, a maximal distance of 20 cm between the position where the source was deployed and the reconstructed vertex was required for a clean sample selection. Such a selection could only be applied on the source calibration data used to build the reference time profile.
Acknowledgments We thank the  French electricity company EDF ; the  European fund FEDER ; the  Région de Champagne Ardenne ; the  Département des Ardennes ; and the  Communauté des Communes Ardennes Rives de Meuse. We acknowledge the support of the  ANR   NuToPs  project (grant  2011-JS04-009-01 ), the  CEA ,  CNRS/IN2P3 , the computer center  CCIN2P3 , and the  UnivEarthS LabEx program of Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ) in France; the  Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT)  and the  Japan Society for the Promotion of Science (JSPS) ; the  Department of Energy  and the  National Science Foundation  of the United States; the  Ministerio de Ciencia e Innovación (MICINN) of Spain ; the  Max Planck Gesellschaft , and the  Deutsche Forschungsgemeinschaft DFG  ( SBH WI 2152 ), the  Transregional Collaborative Research Center   TR27 , the excellence cluster " Origin and Structure of the Universe ", and the  Maier-Leibnitz-Laboratorium Garching in Germany ; the  Russian Academy of Science , the  Kurchatov Institute  and  RFBR  (the  Russian Foundation for Basic Research) ; the  Brazilian Ministry of Science, Technology and Innovation (MCTI) , the  Financiadora de Estudos e Projetos (FINEP) , the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) , the  São Paulo Research Foundation (FAPESP) , and the  Brazilian Network for High Energy Physics (RENAFAE) in Brazil.
The time to fall off the rod was recorded. The animals performed 2 trials with a maximum time fixed at 60 s. Finally, the total recording time was calculated by adding the time of the two trials. Ladder-climbing test. The ladder-climbing test is used to evaluate the sensorimotor capacities to correctly grip the rung while rats climbed up a inclined ladder (Figure  1C )  [38]. In our study, the ladder was inclined to a 45u angle (length: 100 cm; width scale: 13 cm). The animals walked across the inclined ladder with rungs of equal spacing (1 cm). The rats performed 4-5 trials per testing session. The video analysis of the left paw grip was recorded with a 100-Hz acquisition frequency using SimiMotion software (Unterschleissheim, Germany) associated with a numerical camcorder (MV 830i; Canon, Courbevoie, France).
Statistical analysis Statistical analysis was performed using SigmaStat software program (Statistical software, San Jose, CA, USA). All data are presented as Mean6SD and where compared by ANOVA tests. Post-hoc comparisons were performed with Student-Newman-Keuls multiple post-test comparisons. Results were considered statistically significant, highly significant or very highly significant if the p-value fell below 0. 05, 0. 01 and 0. 001, respectively.
ChIP-seq Ovaries of 1-5 day old transheterozygous piwi Nt /piwi 2 flies and their heterozygous siblings (piwi/+) were used. Two replicates of each ChIP-seq experiment were done. Shromatin IP assay was performed as described previously  (7) , using polyclonal rabbit anti-HP1 (PRB-291C Covance innovative) and polyclonal rabbit antibodies (Upstate): Anti-H3K4me2 (#07-030) and Anti-H3K9me3 (#07-523). The quality of DNA precipitates was checked by real-time quantitative PCR before sequencing. For this we amplified intergenic 60D region, rp49 gene and HeT-A transposon using primers indicated previously  (10). Samples containing 10-30 ng of precipitated DNA were used for sequencing. Input IP samples for each genotype were prepared and sequenced in parallel and the obtained data were used further for the normalization and calculation of enrichment levels.
The parameters of the assembler were: mismatch cost = 2, insertion cost = 3, deletion cost = 3, limit = 8. Ambiguity nucleotides were replaced by FASTA codes. Final genome coverage (in both genotypes is approximately the same) was HP1a sample--5×, H3K4me2--7. 4×, H3K9me3--5. 7×, input--3. 5×. Coverage data for different regions were exported from CLC Genomics workbench into CSV files (chromosome position coverage) and used for subsequent analysis. Consensus sequences of transposable elements were obtained from Flybase (http://flybase. org/static pages/downloads/FB2013 06/ transposons/transposon sequence set. embl. txt. gz). piRNA cluster coordinates were taken from the piRNA bank http://pirnabank. ibab. ac. in and converted to Release 5 of D. melanogaster genome for subsequent analysis. For the analysis of dual-strand clusters we took the clusters, producing nearly equal amounts of piRNAs homologous to two complementary strands.
Microarray analysis Total RNA samples were extracted from ovaries of piwi Nt /piwi 2 and piwi/+ 2-3 day old females. Total RNA (30-40 g) was isolated from 30 to 50 pairs of ovaries using Qiagen RNAeasy Mini Kit according to the manufacturer's instructions. RNA was immediately used for indirect labeling reaction. cDNA samples were prepared by reverse transcription, IVT-amplified and labeled with Cy3 or Cy5. Mix of differently labeled RNAs was hybridized to Oligo14Kv2 microarray slides (CDMC), washed, scanned and treated in GenePix 6 (Molecular Devices) and subsequently in FlexArray 1. 6. 2 (McGill University and Génome Québec Innovation Centre). Three biological replicas (one sample dye-swapped) were produced and analyzed. The microarray data were submitted to GEO (Series GSE40768). Detailed procedure of microarray treatment is described in GEO submission.
Chromatin state of dual-strand piRNA clusters is resistant to nuclear Piwi loss piRNA-producing clusters composed of defective transposon copies encode piRNAs either on both genomic strands (dual-strand clusters) or predominantly on one strand (unistrand clusters--e. g. flamenco)  (15). Transcripts of the dualstrand clusters might be potentially recognized by Piwi complexes guided by piRNAs derived from the opposite RNA strand of the cluster. It has been shown that piRNA clusters require the H3K9me3 modification produced by the methyltransferase SetDB1 (Egg) for their transcription  (46). To check whether nuclear Piwi is involved in the regulation of chromatin state of dual-strand piRNA clusters, we mapped unique ChIP-seq reads to the clusters, which produce practically equal amounts of piRNAs related to complementary strands (according to the databases).
This effect was confirmed by real-time PCR and northern blotting (Figure  4D  and E ). ChIP-seq data show no alterations in the levels of HP1a/H3K9me3 and H3K4me2 marks in the chromatin of snRNA genes, indicating nuclear Piwi regulation at the level of RNA stability. We suggest that Piwi may be involved in the post-transcriptional control of snRNA abundance in the nucleus that is realized by a mechanism distinct from piRNA-mediated repression, because no prominent fraction of small RNAs antisense to the snR-NAs was found in the existing databases. It has been previously shown by IP experiments that Piwi may interact with numerous proteins participating in splicing  (13). We cannot completely exclude the possibility that snRNA accumulation upon nuclear Piwi loss is an indirect effect, for example a stress response caused by transposon activation.
ACKNOWLEDGEMENT We are grateful to  Eileen Furlong  and  Jacob Degner  for discussions. We thank the  EMBL Gene Core Facility  for deep sequencing. We thank members of  Gvozdev and Pillai laboratories  for helpful discussions on the manuscript. FUNDING Russian Foundation for Basic Researches  [ 12-04-92421  to V. G. ,  13-04-02156  to M. K. ];  Programs of Russian Academeof Sciences: Molecular and Cell Biology and Basic Science for Medicine  [to V. G]. Work in the Pillai lab was supported by a grant from the  EU [ERC, pisilence , no. 260534 ] to R. S. P. Funding for open access charge: grant no. 260534  to R. S. P.
PCR products were genotyped on a ABI 3100 automated sequencer (Applied Biosystems). The size of the different alleles was determined using the Peak Scanner version 1. 0 analysis software (Applied Biosystems) and used to construct a multilocus genotype for each individual. Control for genotyping errors due to null alleles and allele drop-outs was performed with Micro-Checker  (Van Oosterhout et al. , 2004). Possible linkage disequilibrium were tested with GENEPOP ON THE WEB  (Rousset, 2008). Genetic analyses were performed on a sample of 168 workers (X ±SE = 11. 2 ± 1. 6, N = 14 nests) from Rishon Leziyyon, and 168 workers (12 ± 0, N = 15 nests) from Palmahim. The number of alleles, allele frequencies, observed (H O ) and expected (H E ) heterozygosities were calculated using FSTAT 2. 9. 3.
The correlation coefficients between genetic differentiation [Fst/(1-Fst)], ln of geographical distances and chemical distances were assessed with a Mantel test as implemented in GENEPOP ON THE WEB  (Rousset, 2008). Geneticdistance based PCoA was performed with GENALEX v. 6. 41. Relatedness coefficients among workers were estimated using the program FSTAT 2. 9. 3. 2  (Goudet, 1995). The mean relatedness was estimated between workers of the same colony relative to the collection site.
BEHAVIORAL BIOASSAYS Encounters were conducted in dyads using randomly selected workers from two randomly selected physical nests either within a site or between sites. Different workers were used for each trial, and the order of encounter types was random. Before each test, each of the workers was gently placed in an open glass tube (3 cm in diameter and 8 cm in height) within the neutral arena (10 cm in diameter and 8. 5 cm in height) for 90 s for acclimatization. The test started by lifting the enclosing glass tubes and lasted for 3 min. The behavior of the ants was recorded in real time using the program JWatcher (version 0. 9; http://www. jwatcher. ucla. edu), as well as videotaped, as backup, using a Logitech HD webcam pro C910 video camera. Encounters between individuals from the same nests were considered as nestmate control assays; encounters between individuals belonging to different physical nests, whether from the same or different sites, were considered as non-nestmate experimental assays.
, 1995)  and was based on their fragmentation patterns, as compared to authentic standards. Sample quantification was done by gas chromatography, using a VF-5ms capillary column that was temperature-programmed from 150 • C (with 1 min initial hold) to 300 • C at a rate of 5 • C per min, with a final hold of 15 min. Peak integration was performed using the program Galaxie Varian 1. 9. Compounds that constituted less than 1% across all samples were omitted from the analyses, because their quantification is unreliable. For assessing hydrocarbon profilespecificity we used multivariate statistics, i. e. , discriminant analysis using the stepwise forward mode. This module of the program works initially as a principle component analysis for reducing the number of variables to fit the number of cases, and then perform the discriminant function.
BEHAVIORAL ASSAYS Effect of the workers' origin (nest, colony origin, site) on aggression levels was tested using dyadic encounters. The initial selection of nests for the dyadic encounters was done randomly, without knowledge of their genetic compositions. However, since we hypothesize that nests that are genetically indistinguishable show also high degree of tolerance, the analyses of the aggression tests proceeded according to colony classification using the molecular data as follows: nestmates (i. e. , workers from the same physical nest); workers of different nests but within the same genetic colony; workers from nests belonging to different genetic colonies within a site; and workers from nests of the two sites. Figure  2A  presents the results according to colony and Figure  2B  present the pooled results as well as the result of the encounters between colonies of the different populations.
ACKNOWLEDGEMENTS We thank  Tovit Simon  ( TAU ) and  L. Grumiau  ( ULB ) for technical assistance. This work was supported by grants from  Tel Aviv University  internal grant (# 2191 ) ( Abraham Hefetz ), the  Belgian FRS-FNRS  ( Laurianne Leniaud  and  Serge Aron ) and  Action de Recherché Concertée  Grant N • 2010-2015# 5  ( Serge Aron ).
Acknowledgments We thank the  Cancer Research Center of Toulouse, and M. Celeste Simon's laboratory  for helpful discussions and comments. We sincerely thank  Anne-Laure Iscache , and  Pauline Jeanjean  for technical support. This work was funded by  INSERM , the  Fondation ARC pour la Recherche sur le Cancer  (Grant  SF20121205349 ) and the  Cancéropôle Grand Sud-Ouest  (Grant  RPS13005BBA ).
All solid-state 13 C CPMAS NMR spectra were obtained on a Bruker Avance-400 MHz spectrometer (Bruker, Bremen, Germany) operating at a 13 C resonance frequency of 100. 7 MHz. Samples were placed in a 7 mm zirconium rotor and spun at the magic-angle at 6 kHz. All measurements were made at room temperature. The 13 C chemical shifts were referenced to tetramethylsilane and calibrated with the glycine carbonyl signal, set at 176. 5 ppm. Deconvolution of the NMR spectra was performed using the DmFit software  (Massiot et al. 2002). This software adjusts the spectra to obtain the line-width and the peak positions (in ppm), allowing each peak to be integrated to get the percentage of each contribution. NMR resonances were assigned to chemical structures according to previous NMR studies on compost and SOM  (Inbar et al.
Statistical analysis The relationships between major classes of organic constituents given by RE pyrolysis and 13 C CPMAS NMR were calculated using Pearson coefficients of determination (r 2 ). Significant coefficients were retained for p value \ 0. 05. Principal component analysis (PCA) was performed to get dimension reduction in RE pyrolysis and 13 C NMR dataset. The key idea of PCA is to represent the variation of a data matrix in a reduced number of dimensions to obtain the general structure of the variability. A circle of correlation expresses the influence of chemical compounds and RE pyrolysis main parameters on the PCA sample ordination. All statistical analyses were performed using XLSTAT 2012.
Elementary components obtained with RE pyrolysis RE pyrolysis results of compost samples are given in Table  1. S2 curves were deconvoluted into a combination of the elementary components A1-A5. Trends displayed by the four elementary components A1-A4 are particularly interesting. Two major and opposite trends are easily identified (they are partly co-linear as A1-A5 components constitute a closed dataset--their sum is equal to 1). The first group (biopolymers), which comprises A1 and A2 components, steadily decreases from 32 to 33 % (after 4 days) to a final value of 23 and 26 % after 128 days of composting, for A1 and A2 respectively. The other group (medium to high mature OM) comprises A3 and A4 components, and increases from 20 to 26 % and 14 to 23 %, for A3 and A4, respectively. TpS2 increases from 312.
1 ). PCA was performed on RE pyrolysis and 13 C NMR measured parameters to confirm correlation between thermal resistance and OM transformation. PCA made it possible to ordinate RE pyrolysis data according to the stage of composting on both first and second principal components PC1 and PC2 (Fig. 2 ). PC1 and PC2 explained 37. 2 and 27. 4 % of total variance in RE pyrolysis data, respectively. Three clusters are identified in the factorial projection. Young composts (4-57 days) are clearly separated from all others on PC1 and form a first cluster (left hand side of the diagram). Fifty-seven and 101 day-old composts form a second cluster (middle/right hand side of the diagram). The third cluster is composed by oldest composts of 114 and 128 days at the right hand side of the plot. From this specific ordination of samples, a chronological distribution of composts on PC1 is clearly emphasized.
2 ). Time of composting (i. e. humification of OM) is shown by a black dashed arrow in Fig. 2. This chronological distribution of composts is negatively linked with the I-index (left hand side of the diagram) but positively linked with R-index related to the oldest compost (right hand side of the diagram). Among all PCA parameters, I-and R-indices accounted for 10. 6 and 12. 4 %, respectively, of the variance explained on PC1 and 23. 4 and 19. 9 % on PC2. These large percentages of variance explained on both axes reinforce the above-mentioned trend. This representation of data highlights links between the I-index and easily degradable fractions of OM, which both decrease during composting (negatively correlated), and R-index and complex OM, which both increase (positively correlated). In order to follow OM evolution using RE pyrolysis and during the composting process, study results have been projected on a I versus R plot built using a soil database (in grey, Fig.
Microarray data analysis All fluorescent images from the microarrays were analyzed using the Genepix 4000B scanner and the Genepix Pro software v6. 0 (Axon Instruments, Molecular Devices Co. , Sunnyvale, California, USA). Raw data were corrected using MADSCAN software  (Le Meur et al. , 2004). After subtraction of the background fluorescence, a rank-invariant method and a spatial normalization were performed before scaling of the variance within each slide and between all the slides at the same rank. The corrected values of fluorescence were used to perform statistical analysis using the GeneANOVA software  (Didier et al. , 2002)  considering the three following factors: gene (fluorescence level), treatment (insecticide or water), dye (Cy3 or Cy5) and technical replicates (T1; T2 and C1; C2).
Gene ontology analysis In order to know which biological processes were overrepresented among regulated transcripts, a gene ontology (GO) analysis was performed using the online available software Blast2GO  (Conesa et al. , 2005). After the identification of the putative roles for each predicted protein, the regulated transcripts were grouped in the three domains of molecular biology: Biological Processes, Molecular Functions and Cellular Components. Quantitative RT-PCR Total RNAs were isolated from three aphids with the RNeasy Plant Mini Kit, resulting in samples containing between 100 and 400 ng µL -1 according to Nanodrop quantifications (Nanodrop Technologies, USA). A reverse transcription using AffinityScript QPCR cDNA Synthesis kit (Agilent) was conducted using 1. 5 µg of total RNA previously treated with DNAse (DNA-free™ Kit, Ambion, Canada).
The cDNA was then diluted 10 times, using 2 µL for PCR in 15 µL reaction volume containing: 1 µL (10 µmol) of each primer, 12. 25 µL of SYBR Green PCR Master Mix (Applied Biosystems), 0. 375 µL of Rox Dye (1:500) and 4. 374 µL of water. All qPCRs assays were performed in triplicate on a Mx3000P QPCR System (Stratagene) under the following conditions: 10 min at 95 °C, followed by 40 cycles of 15 s at 95 °C, 15 s at 57 °C, 20 s a 72 °C. To confirm the absence of nonspecific amplicons, a melting curve was performed immediately after the PCR containing a ramp of 65 to 95 °C. The level of expression for six genes was tested. Specific primers were designed for these genes using the available online softwares FastPCR v5. 4. 30 and AmplifX v1. 3. 7. An annealing temperature of 60 °C, an amplicon size of 80-120 bp, and 45%-55% GC content were selected as parameters.
Microarray and gene ontology analyses Following the normalization by MADSCAN software and the statistical analysis by GeneANOVA, 38 transcripts appeared to be significantly regulated (p < 0. 05) in response to insecticide treatment: 13 transcripts were found to be up-regulated (including two unknown transcripts) and 25 transcripts were shown to be down-regulated (including eight unknown transcripts) (Table  2 ). These 28 known regulated mRNAs were then functionally classified using a gene ontology analysis (GO). Fifteen GO terms were associated with sixteen the regulated genes (Table  3 ). These GO terms were grouped in three Biological Processes: reproduction (three terms), metabolism (six) and cellular proliferation (six) (Table  3 ). Three up-regulated genes: Acyl-CoA-binding protein (Acyl CoA BP) (Fold-change (FC): 1.
For instance, the UBIQ was down-regulated at 12 h in the microarray, while in qRT-PCR was observed up-regulated. Similarly, the Acyl CoA BP gene, which was shown to be up-regulated in the microarray at 12 h, was noted downregulated in the qPCR. Cell proliferation GO:0043461 proton-transporting ATP synthase complex Metabolism assembly GO:0006464 protein modification process Metabolism GO:0042127 regulation of cell proliferation Cell proliferation Table  3. Gene ontology (GO) annotation of differentially expressed transcripts. Enriched GO terms with their respective identities and function according to GO analysis using Blast2go software are reported.