dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational details. To fit the models described above we took advantage of the routines already built in BEAST (https://github. com/beast-dev/beast-mcmc) but in a non-phylogenetic setting. Once again, posterior distributions for the parameters were explored using Markov chain Monte Carlo (MCMC). We ran each chain for 50 million iterations and discarded at least 10% of the samples as burn-in. Convergence was checked by visual inspection of the chains and checking that all parameters had effective sample sizes (ESS) greater than 200. We ran multiple chains to ensure results were consistent. To make predictions, we used 50,000 Monte Carlo samples from the posterior distribution of coefficients and the overdispersion parameter (r) to simulate case counts for all locations with zero recorded EVD cases.
Acknowledgements The authors acknowledge support from:  European Union   Seventh Framework 278433-PREDEMICS  (P. L. , A. R. ) and  ERC 260864  (P. L. , A. R. , M. A. S. ) European Union Horizon 2020 643476-COMPARE (M. P. G. K. , A. R. ), 634650-VIROGENESIS (P. L. , M. P. G. K. ),  666100-EVIDENT  and  European Commission   IFS/2011/272-372 , EMLab (S. G. ),  National Institutes of Health   R01 AI107034 ,  R01 AI117011  and  R01 HG006139  and  National Science Foundation   IIS 1251151  and  DMS 1264153  (M. A. S. ),  NIH   AI081982 ,  AI082119 ,  AI082805 AI088843 ,  AI104216 ,  AI104621 ,  AI115754 ,  HSN272200900049C ,  HHSN272201400048C  (R. F. G. ),  National Health & Medical Research Council (Australia)  (E. C. H. ). The  Research Foundation -Flanders   G0D5117N  (G. B. , P. L. ), Work in Liberia was funded by the  Defense Threat Reduction Agency , the Global Emerging Infections System, and the Targeted Acquisition of Reference Materials Augmenting Capabilities (TARMAC) Initiative agencies from the  U.
The clinical and biochemical (post-operative serum Prostate-Specific Antigen (PSA)) outcomes of the patients were updated in March 2015. CCR3 staining was then performed in this second TMA and scored by two pathologists into low-, intermediate-and high-expressing tumours. Stained areas of all tumours were digitalized and protein expression was quantified using ImageJ software plugins to obtain more accurate measures than the traditional evaluation relying on manual scoring  35, 36. We verified that the results obtained were in accordance with the three groups defined by the manual scoring (see Methods section). As shown in Table  1  and Fig  (c) In vitro migration of PC-3 cells towards mu-VAT-CM obtained from lean or obese animals (three animals per group) treated or not with CCR3 inhibitor (UCB35625 200 nM), blocking m/pAbs directed against CCR3 or CCL7, or control isotype (all used at 10 mg ml À 1 ).
Data were analysed by the 2 À DDCt method and presented as fold change relative to a control sample after normalization against the expression of housekeeping genes. Here Ct corresponds to the number of cycles needed to generate a fluorescent signal above a predefined threshold. All primers used in this study have been validated for PCR efficiency and are presented in Table  2. Analysis of the adipocyte secretome by mass spectrometry. Ad-CM (serum-and phenol red-free) was collected on ice, centrifuged and filtered to remove cell debris and supplemented with complete protease inhibitor cocktail (Sigma-Aldrich). A total of 5 ml of Ad-CM was concentrated with StrataClean resin (Stevens, CA, USA) according to the manufacturer's instructions. After reduction and alkylation of cysteine, the sample was separated by 12% acrylamide SDS-PAGE.
Proteins were visualized by Coomassie Blue staining and each lane was cut into 13 homogenous slices and subjected to in-gel tryptic digestion. The tryptic digest was analysed by nano liquid chromatography coupled with tandemmass spectrometry with an Ultimate3000 system (Dionex, Amsterdam, The Netherlands) coupled to an LTQ-Orbitrap mass spectrometer (Thermo Fisher Scientific, Bremen, Germany)  67  , except that peptides were eluted with a 5-50% gradient of solvent B for 60 min at a 300 nl min À 1 flow rate. Mascot was used to automatically extract peak lists from raw files. MS/MS data were searched against all entries in the Mus musculus database and identified peptides were validated with in-house software  67  , except that 1% false discovery rate was used for validation. Surgical orthotopic implantation of TRAMP-C1P3 cells.
0RS and analysed with the device software provided by the manufacturer. The intensity of CCR3 staining has been quantified by computer through the use of ImageJ software, with tumour staining separated using deconvolution plug-in  35, 36. As a first step, we used a color deconvolution technique to separate the pure DAB and hematoxylin stained areas leaving a complimentary image. The pixel intensities of separated DAB or hematoxylin images range from 0 to 255. Value 0 represents the lightest shade of the color while 255 represent the darkest shade of the color in the image. To assign an automated score by judging the pure DAB staining pattern, a histogram profile of every image, Murine 5 0 -AAGATCCCCAAGAGGAATCTCAA-3 0 5 0 -CTTCCCAGGGACACCGACTA-3 0 900 nM CCL7 Human 5 0 -AAACCTCCAATTCTCATGTGGAA-3 0 5 0 -CAGAAGTGCTGCAGAGGCTTT-3 0 900 nM Leptin Murine 5 0 -GGGCTTCACCCCATTCTGA-3 0 5 0 -TGGCTATCTGCAGCACATTTTG-3 0 900 nM Leptin Human 5 0 -CCTTCCAGAAACGTGATCCAA-3 0 5 0 -GGCCAGCACGTGAAGAAGAT-3 0 900 nM Adiponectin Murine 5 0 -TGGAATGACAGGAGCTGAAGG-3 0 5 0 -TATAAGCGGCTTCTCCAGGCT-3 0 300 nM Adiponectin Human 5 0 -GCAGAGATGGCACCCCTG 5 0 -GGTTTCACCGATGTCTCCCTTA-3 0 900 nM TNF-a Murine 5 0 -TGGGACAGTGACCTGGACTGT-3 0 5 0 -TTCGGAAAGCCCATTTGAGT-3 0 300 nM TNF-a Human 5 0 -CCGAGTCTGGGCAGGTCTAC-3 0 5 0 -TGGGAAGGTTGGATGTTCGT-3 0 300 nM GAPDH Murine and Human 5 0 -TGCACCACCAACTGCTTAGC-3 0 5 0 -GGCATGGACTGTGGTCATGAG-3 0 500 nM HPRT Murine and Human 5 0 -TGGCCATCTGCCTAGTAAAGC-3 0 5 0 -GGACGCAGCAACTGACATTTC-3 0 500 nM RT-qPCR, quantitative reverse transcription PCR.
We then examined the clinical and biological characteristics of patients to identify patients with aggressive and extended tumours. For the correlation between CCR3 expression and quantitative data (for example, prostate weight) or variables with more than two ordered classes regarding disease severity (for example, Gleason score or tumour stage), we used a Spearman's rank correlation test assuming a monotonic relation between considered variables. We used the Student's t-test to correlate CCR3 expression to non-ordered variables represented by a category (for example, presence or absence of lymphatic emboli). Non-parametric tests are more conservative than parametric tests. In some cases, we also expected a lack of statistical power given the limited size of some sub-populations of variables.
Acknowledgements This study was supported by the  French National Cancer Institute  (INCA PL 2010-2014 to P. V. and C. M. ), the  'Ligue Regionale Midi-Pyrenees contre le Cancer'  (to C. M. ), the  'Association de Recherche sur les Tumeurs prostatiques'  (to C. M. ). K. C. , B. M. and O. S. were supported by grants from the  'Agence Nationale de la Recherche' (ANR Plates -formes technologiques du vivant), ' Fondation pour la Recherche Medicale  ( Programme Grands Equipements)', 'Region Midi-Pyrenees' , and ' Infrastructures en Biologie Santeet Agronomie (IBISA) '. Victor Laurent received a PhD fellowship from the  'Association de Recherche contre le Cancer'. We would like to acknowledge the  'Anexplo Phenotypage ' platform ( INSERM US006 ) and the staff of animal facilities for their expertise.
For the double mutant N69CE258CMreB, the fluorescence anisotropy values are lower due to the homo-FRET between the two fluorophores. This evolution and the occurrence of homo-FRET is confirmed for all the different complexes under investigation (Fig. 4c, d ). We compute the distance R between the N69C and E258C positions in MreB using the measured FRET efficiency deduced from the fluorescence anisotropy according to Eqs (4) and (5). The calculated distances between the fluorophores in the positions 69 and 258 under the different experimental conditions are presented in Fig. 4e, f. Theoretical distance between the 69 and 258 positions in MreB is ~20 Å (calculated by using the Yasara software, considering 1JCE pdb structure). However, the experimental distance reported is in a range of 41-45 Å for the final native-like MreB molecule.
Conclusion In this study we investigated the refolding modulations of MreB, the prokaryotic homologue of actin upon interaction with type I and type II chaperonins. Both fluorescence correlation spectroscopy (FCS) and time-resolved The conformational rearrangements imposed on the MreB molecule during chaperonin binding illustrate that the captured states are globular and not extended random coils. All structure models were generated by PyMol. Pdb codes used to make the figure: GroES and GroEL/ES/ATP (1AON)  36  , TRiC/ADP (4A13)  44  , MreB (1JCE). The extended random coil MreB was generated from 1JCE using the NMR server http://spin. niddk. nih. gov/ bax/nmrserver. fluorescence polarization anisotropy reported the binding of MreB to the chaperonin components GroEL, GroES and eukaryotic tail-less complex polypeptide 1 ring complex (TRiC) chaperonin.
Fluorescence Correlation Spectroscopy (FCS). All FCS experiments were carried out at 22 °C on a custom-built confocal fluorescence microscope with a Zeiss C-Apochromat 40× 1. 2NA water-immersion objective using 100 mM PBS, 10 mM KCl, 10 mM MgCl 2 as a refolding buffer at pH 7. 5. To diminish the surface interactions with the glass coverslip we added 0. 1% Tween-20 (Sigma) to all the samples. The fluorescence intensity temporal fluctuations were analyzed with a hardware correlator (Flex02-12D/C correlator. com, Bridgewater NJ with 12. 5 ns minimum channel width). All the experimental data were fitted using Eq. 1 by considering a single species and free Brownian 3D diffusion in the case of a Gaussian molecular detection efficiency: τ τ = +      -     .       +.       - τ             .
IsbpProbeDesign The IsbpProbeDesign program is written in Perl. It uses data generated by IsbpFinder  (Paux et al. , 2010)  and retrieves only the ISBP markers that correspond to a junction between a TE and a low-copy sequence. It then uses Tallymer  (Kurtz et al. , 2008)  to mask k-mers that are repeated x times or more at the whole genome level. Finally, it extracts subsequences of unmasked nucleotides in the low-copy part of the ISBP marker flanking the TE sequence. Parameters such as k-mer length, number of k-mer occurrences in the Tallymer index, and subsequence length are customizable. In this study, the Tallymer index was computed on a 1× genome coverage sampled in the reads produced on the cultivar Chinese Spring  (Brenchley et al. , 2012). K-mer length was set up to 20, the number of occurrences to 10, and the subsequence length to 120 nt.
Sequence Alignment and SNP Discovery Read mapping was performed using the Mosaik assembler  (Strömberg, 2009). MosaikBuild was used with FASTQ mate files for each accession. A multi-FASTA file containing 52,265 ISBP sequences was used as a reference. Reads were aligned with MosaikAligner using the following criteria: maximum number of mismatches, 3; alignment candidate threshold, 60; minimum percentage of the read length that should be aligned, 0. 6; use of aligned read length when counting errors. MosaikSort, MosaikMerge, and MosaikAssembler were then used to generate a single assembly file. This file was then submitted to the GigaBayes package  (Hillier et al. , 2008; Marth et al. , 1999)  for SNP discovery using the following parameters: ploidy, diploid; sample, multiple; SNP lower probability threshold for reporting polymorphism candidates, 1; output detail level for candidate polymorphism report, 4; minimum total read coverage for position to be considered, 20.
GigaBayes results were then processed with a Perl script to classify SNPs into four different classes. These classes were based on the homozygous or heterozygous nature of the SNP in the selected panel. A SNP was considered as heterozygous if more than 10% of the reads from a given accession showed a variation in its sequence. Four different classes were defined: (i) the locus showed only homozygous AA and BB alleles among all 10 lines, (ii) the locus showed homozygous AA and BB and heterozygous AB alleles, (iii) the locus showed only homozygous AA and heterozygous AB alleles, and (iv) the locus was heterozygous AB in all 10 lines. To be conservative, SNPs were considered only if the position was covered by at least 20 reads (from 10 lines).
Variability Estimation and Diversity Analysis Variability for each locus was measured using the polymorphism index content (PIC)  (Anderson et al. , 1993) : 2 1 n i i PIC p = -å. Genotyping data were used to describe diversity within both the elite and diversity panels. For each sample set, a dissimilarity matrix was built using simple matching coefficient between each pair of accessions. For the elite panel, a Neighbor-Joining tree was created using this dissimilarity matrix and 1000 bootstrapped trees were calculated to assess the uncertainty of the tree structure. For the INRA core collection, dissimilarities were calculated between individuals and the geographical structure of the wheat germplasm diversity was analyzed by a Ward dendrogram between accessions. Data analyses were conducted using DARwin software  (Perrier et al.
, 2003; Perrier and Jacquemoud-Collet, 2006). Mixograph parameters were estimated from the height and width of the mixograph curve as described by  Martinant et al. (1998). The average height of the curve measured at peak time, peak time plus 2 min, and peak time plus 8 min were used as a measure of the dough consistency (mixing consistency). Association between markers and mixing consistency was tested for the mean of the 2 yr with the TASSEL version 5. 0 software  (Bradbury et al. , 2007)  using the mixed linear model (MLM-Q-K) with controls for population structure (Q-matrix) and kinship between accessions (K-matrix). The kinship matrix was estimated with 527 of the 554 DArT markers described in  Bordes et al. (2011)  located on all chromosomes except chromosome 3B, according to the method proposed by  Rincent et al.
Out of 410,124 ISBPs, 184,452 corresponded to a junction between a TE and a low-copy sequence (58,676 high-confidence and 125,776 medium-confidence). A de novo repeat detection approach based on k-mer frequency was further applied to identify and mask any potential repeated stretches of DNA in the low-copy sequence. The purpose was to ensure that the template used for bait design was free from repeated DNA (to prevent cross-hybridization). We finally designed 120-nt baits in the unrepeated DNA flanking a TE for target enrichment. This process was fully automatized in a program called IsbpProbeDesign (see the Material and Methods section). Eventually, 57,705 120-nt baits were identified in the 184,452 TE low-copy ISBPs. These baits were filtered according to their guanine-cytosine (GC) content to avoid the presence of too GC-poor or GC-rich sequences in the capture kit.
34, which is slightly higher than the average PIC value observed for transcript-specific SNPs in a set of 23 wheat varieties representing a broad cross-section of wheat germplasm  (Allen et al. , 2011). In addition, SNPs located on the same ISBP can be used to define haplotypes  (Rafalski, 2002). To assess the number of haplotypes in our dataset, we focused on a subset of 25,832 Class 1 SNPs without missing data. The number of SNPs per ISBP ranged from 1 to 11. Among the 6830 multi-SNP ISBPs, the number of haplotypes ranged from two to seven and was highly correlated with the SNP number (R = 0. 977, p = 10 -7 ). When haplotypes were considered, PIC values ranged from 0. 18 to 0. 82, with an average of 0. 42 ± 0. 15.
, 2014). To assess the accuracy of KASPar results, the genotyping data were compared with the sequencing data of 7 out of the 10 lines used for SNP discovery. In total, 94. 8% of the two sets were consistent. Most of the discrepancies (3. 3% out of 5. 2%) were caused by missing data either in the sequencing or in the genotyping results. An additional 1. 7% of discrepancies was caused by genotypes that were called heterozygous by one technique and homozygous by the other one. The remaining 0. 2% corresponded to genotypes with two different homozygous alleles with the two techniques. Interestingly, out of the 519 SureSelect missing data, almost one half (243) were confirmed by KASPar data, reinforcing the hypothesis that these missing data might be true presence or absence variations. Over the past few years, KASPar technology has become one of the most widely used technologies in plant breeding because of its flexibility, relatively low cost and high throughput  (Michael, 2014).
Insertion Site-based Polymorphism SNPs Can be Used Efficiently for Wheat Breeding To assess the usefulness of ISBP-SNPs to discriminate between wheat lines, genotyping data from 2842 KASPar assays were used to describe diversity within the elite panel and the INRA core collection. Figure  2  shows the Neighbor-Joining tree among the 93 elite lines of the elite panel. As indicated by the high bootstrap values, each accession was clearly differentiated from the genetically nearest one. It is worth noting that a group of nine varieties (on the top of the tree) differentiates from the others. These correspond to the European material carrying an Asian-related pericentromere, as discussed previously. Figure  S1  illustrates the Ward dendrogram of the 367 accessions of the INRA core collection, which was originally designed to capture more than 98% of the total allelic diversity observed at 38 polymorphic SSR loci in a sample of 4000 bread wheat accessions.
Consistent with this is the bottom branch of the tree that contains accessions harboring the minor Asian pericentromeric haplotype mentioned previously. KASPar genotyping data from 2745 ISBP-SNPs with a minor allele frequency greater than 5% were also used to conduct a genome-wide association study for dough rheology on chromosome 3B. Phenotypic data derived from the INRA core collection were retrieved from  Bordes et al. (2011). A strong association (LOD score >3) was found with 34 SNPs. These SNPs defined a region of ~3 Mb located on the long arm of chromosome 3B (Fig. 3 ). With 34 SNPs, one might theoretically expect more than 17 billion possible haplotypes. Instead, only 11 haplotypes were observed, the two most common ones representing 52. 3 and 37. 8%, respectively. The average mixograph consistency values of these two haplotypes were 32.
Acknowledgments This work was supported by two grants from the  French National Research Agency  ( ANR-09-GENM-025 3BSEQ  and  ANR-09-GENM-027 GAIN-SPEED ), grants from  France Agrimer , and the private-public consortium DIGITAL (INRA, AgriObtentions,  ARVALIS -Institut du Végétal, Bayer CropScience ,  Florimond-Desprez ,  KWS LOCHOW GMBH, RAGT, and Syngenta ). Sequence capture experiments were conducted on the genotyping platform GENTYANE at  INRA Clermont-Ferrand  (gentyane. clermont. inra. fr,accessed 2 Dec. 2015). Seeds from the  INRA  core collection were provided by the  Biological Resources Centre on small-grain cereals at INRA Clermont-Ferrand  (www6. clermont. inra. fr/umr1095_eng/ Teams/Experimental-Infrastructure/Biological-Resources-Centre , accessed 2 Dec. 2015).
Data extraction Data were extracted from the 2014 prostate cancer WCRF CUP project  [4] , which includes systematic reviews and meta-analyses separately by single foods, nutrients, indices of physical activity and somatometric factors. The literature search in the CUP was performed in MEDLINE and included randomised controlled trials and cohort studies published up to 30th April 2013. Whenever possible, meta-analyses were reported for each exposure using both binary (i. e. high versus low category of intake) and continuous (i. e. per unit increment) comparisons in relation to risk of total prostate cancer development and/or total prostate cancer mortality or prostate cancer development by stage and grade. In the current umbrella review, the primary analysis focussed only in associations with continuous exposure assessment and sensitivity analysis was performed for studies with categorical assessment of exposures, because the categories for several exposures were unstandardised between studies.
Acknowledgements The authors would like to further acknowledge the following collaborators from  Imperial College, London  (i. e. Ana Rita Vieira ,  Doris Chan ,  Dagfinn Aune ,  Leila Abar ,  Deborah Navarro-Rosenblatt  and  Snieguole Vingeliene ), who did the systematic literature review and computed the study doseeresponse estimates for the WCRF CUP, and the  CUP database manager ,  Christophe Stevens , who helped with identifying the additional data needed for the bias assessment in the current umbrella review. Appendix A. Supplementary data.
Life cycle of Molossus molossus Captures We collected data during 81 capture events of 14 social groups and a total of 490 individuals (adults and juveniles). We recaptured only a subset of the bats we marked as post-dispersal adults (121 females and 31 males) and none of them were observed switching roosts over the entire study period (maximum of 4. 3 years). We caught pregnant females between March and August and observed a pregnancy peak in April. Anecdotal data indicate a second minor birth peak at the end of the year, but we cannot confirm this. Based on capture data, we assume that females disperse from the social group to settle permanently in a social group. It also appears that they can start reproducing directly after natal dispersal. We cannot infer the complete life cycle of males based on the capture data but we suspect a bachelor phase between natal dispersal and tenure of harems as well as after the end of tenure  [49, 50].
BaTLis Data from the BaTLis (automated transponder readers custom-made by the workshop of the University of Konstanz) are consistent with captures. In addition, activity of unmarked individuals on the BaTLis suddenly increased around June, and this activity peak corresponded with juvenile fledging. We monitored a subset of 24 juvenile females and 19 juvenile males with the BaT-Lis. They were marked between July and September and were last detected by the transponder readers between July of the same year and February of the following year indicating their death or natal dispersal. The life cycle of female Molossus molossus is illustrated in Fig. 1.
Predictors of female lifespan and monthly survival Cox proportional hazard model using capture data We filtered out 44 % of the captures when including only adult females that had immigrated since last capture. This resulted in 70 adult females with 114 recapture events. Corrected lifespan estimates (from potential birth to last capture) ranged here from 132 days (0. 4 years) to 1210 days (3. 3 years) with a median lifespan of 436 (1. 2 years). The selected variables (adult or total group size) complied with the test of proportionality and had no statistical influence on survival estimates for both survival datasets (Table  1 ). 24 females survived less than a year (34. 3 %), 30 between 1 and 2 years (42. 9 %), 8 between 2 and 3 years (11. 4 %) and 8 other between 3 and 4 years (11. 4 %).
Multistate mark recapture models using BaTLi data Four groups were randomly selected based on whether it was possible to install a BaTLi at the entrance. We used the transponder reader data to investigate the influence of a set of variables on adult female survival (n = 63). As we monitored these groups only for 15 months, the estimates here represent monthly survival rather than complete lifespans. Size of the four focal groups changed over time, ranging from three to 13 adult males and females but with very similar mean and SD for group size. We divided the range of group sizes into three categories to obtain higher confidence in our monthly survival estimates ("small": 3-6, "medium": 7-9, "large": 10-13). Multistate models were found to adequately fit the data for the four groups (Group 1: χ 2 = 4.