dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of predictions Pre-miRNA predictions were compared to pre-miRNAs annotated in the A. thaliana genome assembly using the "intersect" function from the Bedtools suite (version 2. 27. 1)  [28]. Predicted pre-miRNAs were considered as annotated, i. e. corresponding to a known pre-miRNA in the A. thaliana genome, when the overlap was larger than 30 bp. Pre-miRNAs predicted by ShortStack were then compared to the whole set of Viridiplantae mature miRNAs deposited into miRBase using Exonerate (version 2. 4. 7) [29] , allowing at most 3 mismatches/indels and no consecutive indels. It was not necessary to perform this analysis for pre-miRNAs predicted by miRkwood since it is already included into the pipeline. A miRNA prediction was considered as associated with AGO1 or AGO4 when the miRNA sequence was observed at least once in the dataset (without mismatch nor additional nucleotide).
When the mean number of reads in the two AGO1-IP datasets was greater than that observed in the AGO4-IP dataset the miRNA were considered as preferentially associated to AGO1, and vice versa. For this calculation, the number of reads was normalized by the number of unique reads in each of the datasets (reads per million). The colocalization between pre-miRNA predictions and TE annotations in the A. thaliana genome was evaluated using the "intersect" function from the Bedtools suite (with an overlap of at least 1 bp) and the recent deep annotation of TEs in A. thaliana  [30]. Predictions obtained by miRkwood and ShortStack were considered as identical when pre-miRNAs presented an overlap of at least 30 bp (also defined using the "intersect" function from the Bedtools suite).
Results and discussion We evaluated the performance of miRkwood using a sRNA-seq dataset from A. thaliana previously published (see methods)  [23]. Following the cleaning step, we conserved 4. 8 million reads (i. e. 77% of the raw reads), representing 2. 6 million non-redundant reads. MiRkwood predicted a total of 2049 pre-miRNAs/miRNAs, including 87, 41, 42 and 70 with a quality score equal to 6, 5, 4 and 3, respectively. MiRkwood allowed to retrieve 173 miRNA precursors from A. thaliana. Since we performed miRNA predictions from only one sRNA-seq experiment representing a single tissue (inflorescence) at a single time point, we did not expect to find all the 326 miRNAs annotated in the A. thaliana genome (miRBase, V22). Predictions below a quality score of 3 will not be further considered, since they do not systematically exhibit a clearly identifiable miRNA sequence.
To validate the predictions, the association between the predicted miRNAs and AGO1 was evaluated using deep-sequencing data of AGO1 associated small RNAs (AGO1-IP), AGO1 being the main argonaute protein used in the miRNA pathway loading canonical 21 nt miRNAs. We also evaluated the association of the predicted miRNAs with AGO4 that binds to lmiRNAs of 24 nt. The performance of miRkwood was compared to that of ShortStack  [10] , one of the most popular and effective tools for the prediction of miRNAs in plants, particularly because of its ability to avoid false positive predictions, compared to other tools  [9, 14].
3d ). In contrast, only 28 of the 41 predictions with a quality score of 5 were annotated in the reference genome. However, among the 13 unannotated predictions, 8 presented homology with plant miR-NAs deposited in miRBase and the 5 remaining miRNAs were all validated using the AGO1-IP data (including one 24 nt prediction preferentially associated with AGO4) suggesting that they are indeed bona fide miRNAs. From the same dataset, ShortStack provided 90 predictions (Fig. 4 ), i. e. 29. 7% less predictions than miRkwood set up with a quality score ≥ 5. All of them were annotated in the reference genome and/or aligned with known miRNAs from miRBase and/or associated with AGO proteins and could be consequently considered as bona fide miRNAs. Two-thirds (60) of the predictions from miRkwood set up with a quality score threshold equal to 6 were obtained by both tools and one-third of the predictions were specific to each one (27 and 30 for miRkwood and ShortStack, respectively, Fig.
Concerning the comparison of the results with Short-Stack, even if decreasing the threshold score to 4 or 3 did not deeply reduce the number of specific predictions to ShortStack, it increased the number of those specific to miRkwood (95 and 165, respectively, data not shown). In summary, decreasing the threshold score to 4 or 3 may increase the proportion of false positives, but potentially offers in return the detection of novel canonical miRNAs and lmiRNAs.
Availability and requirements Project name: miRkwood Project home page: http://bioinfo. cristal. univ-lille. fr/mirkwood, https://github. com/miRkwood-RNA/miRkwood Contact: mirkwood@univ-lille. fr Operating system(s): Unix or web server Programming languages: Perl, C and C++ Other requirements for the Unix version: bedtools (v2. 14. 2 or higher), Vienna package (v2. 1. 6-1), Blast+ (2. 2. 25+ or higher), miRdup (1. 2 or higher), VARNA (v3-91 or higher, optional). License: GNU Affero GPL Other requirements for the web version: none. The version of miRBAse will be regularly updated. New plant genomes can be added upon request. Any restrictions to use by non-academics: none Availability of data and materials Not applicable.
Material and Methods An international, prospective cohort study recruited patients in 26 hospitals in eight countries between April 2014 and March 2017. Target recruitment was 5,300 patients. Eligible patients had breast, lung or prostate cancer and planned potentially-curable radiotherapy. Radiotherapy was prescribed according to local regimens, but centres used standardised data collection forms including patient reported outcome measures available in multiple languages. Pre-treatment blood samples were collected. Patients were followed for a minimum of 12 (lung) or 24 (breast/prostate) months and summary descriptive statistics generated.
Conclusion The comprehensive centralised database and linked biobank is a valuable resource for the radiotherapy community for validating models and biomarkers that predict for risk of the late effects that most effect longterm quality-of-life. Most cancer patients gave consent to share their data and samples with external researchers, and a formal process for requesting data access for specific research questions is in operation (32 projects approved). A data discovery platform to search on numbers of patients with various attributes collected by the consortium is available at www. requite. eu.
2a ). This result suggested that these five candidates differ functionally from all of the primary metabolite genes used as queries in A. capillaris. A subsequent Reads per kilobase of exon model per million mapped reads (RPKM)-based search for the five candidates showed that the expression of unigene 9515 was higher than that of the other genes (Supplementary Fig. 2b ). The full coding sequence (CDS) of unigene 9515 was therefore isolated from a total RNA pool extracted from A. capillaris leaves by reverse transcriptionpolymerase chain reaction (RT-PCR)-based cloning. The resulting sequence, tentatively named A. capillaris prenyltransferase1 (AcPT1), has been submitted to GenBank. Using ChloroP and TMHMM software tools, AcPT1 was found to encode a polypeptide of 401 amino acids with a predicted Nterminal transit peptide (TP) of 38 amino acids and multiple transmembrane regions, respectively (Supplementary Fig.
Similarly, the content of artepillin C did not vary widely among the organs tested (Fig. 2b ). p-Coumaric acid and drupanin, precursors of artepillin C were not detected in the specimens used in this study (Fig. 2b ). To investigate the subcellular localization of this protein in planta, the N-terminal 50 amino acids of AcPT1 were fused to synthetic green fluorescence protein (AcPT1TP-sGFP). i. e. , orange, green, and blue for neighbors of tocopherol, plastoquinone, and ubiquinone clades, respectively. Gray circles represent Asteraceae PT polypeptides used as queries during in silico screening of AcPT1. The plant origin and accession number of each PT in the tree are described in Supplementary Tables  2  and 3  Microscopic analysis indicated that the sGFP fusion protein was distributed specifically to dotted structures in onion cells and almost completely matched the localization of the transit peptide region of waxy gene fused to Discosoma sp.
Synthetic dextrose (SD) minimal media for yeast culture were prepared essentially according to the protocol provided by Sigma-Aldrich (Catalog ID: Y2001). Construction of RNA-seq data from A. capillaris leaves. Leaves of A. capillaris were harvested in August 2013 and frozen immediately in liquid nitrogen. Total RNA from frozen plant tissues was extracted using PureLink Plant RNA Reagent (Thermo Fisher Scientific), treated with recombinant DNase I (RNase-free) (TAKARA BIO), and purified using RNeasy Plant Mini Kits (Qiagen), according to the RNA clean-up protocol. A 10-μg aliquot of total RNA was used to construct a cDNA library using an Illumina TruSeq Prep Kit v2 according to the manufacturer's protocol (Illumina). The resulting cDNA library was sequenced using HiSeq 2000 (Illumina) with 100 bp paired-end reads in high output mode.
Sequence reads were assembled de novo and their RPKM values calculated using CLC Genomics Workbench software ver. 5. 5. 2 (CLC Bio). A minimum contig length of 300 bp and the 'perform scaffolding' function provided assembled contigs after the removal of adaptor sequences and low-quality reads. After removing redundant sequences using the TIGR Gene Indices clustering tools (TGICL), 39,615 unigenes were assembled  56. In silico analysis. Unigenes belonging to the UbiA superfamily were screened by tblastn using Bioedit software (http://www. mbio. ncsu. edu/BioEdit/bioedit. html), and their amino acid identities in comparison to queries were calculated. Plant UbiA proteins were multiply aligned by ClustalW, and a phylogenetic analysis was performed using a maximum likelihood method with 1000 bootstrap tests in MEGA6 software (http://www.
megasoftware. net/). Transmembrane domains and the transit peptide sequence of AcPT1 were predicted with TMHMM Server v. 2. 0 (http://www. cbs. dtu. dk/services/TMHMM/) and ChloroP (http://www. cbs. dtu. dk/ services/ChloroP/), respectively. Quantification of phenylpropanes in A. capillaris organs. Leaves, stems, and flowers of A. capillaris were frozen in liquid nitrogen and crushed to fine powder with mortars and pestles. Each sample was added to a tenfold volume of methanol, vigorously shaken at 2500 r. p. m. for 15 min and centrifuged at 10,000 × g for 5 min. Each supernatant was filtered with Minisart® RC4 (0. 2-mm pore, Sartorius Stedim Biotech) and subjected to HPLC analysis. Phenolic compounds were quantified using a D-2000 Elite HPLC System equipped with a LiChrosphere RP-18 column (4 × 250 mm, Merck) and a L-2455 Photodiode array detector (Hitachi).
5 min. PDA cells were kept at 40 °C, and products were scanned at 190-450 nm. HPLC/MS analysis was performed using an Ultimate 3000 system coupled to a PDA array detector and Linear Ion Trap Mass Spectrometer LTQ-XL (Thermo Electron Corporation). LTQ was equipped with an electrospray (ESI) interface operating in positive or negative ion mode. Xcalibur 2. 1 software was used for computer control and data process. Compounds were separated on a Phenomenex Kinetex XB-C18 column (150 × 2. 1 mm, 2. 6 µm) heated at 25 °C; with mobile phases consisting of (A) water + 0. 1% (v/v) formic acid and (B) acetonitrile + 0. 1% (v/v) formic acid at a flow rate of 0. 3 ml min -1. The gradient program consisted of 10% (B) for 1 min, 10-70% (B) over 11 min, 70-100% (B) over 2 min, 100% (B) for 2. 5 min, and a return to the starting conditions in 0.
Acknowledgements We thank  H. Yamamoto  ( Toyo University ) for providing DMAPP,  Dr. T. Kuzuyama  ( The University of Tokyo ) and  Dr. T. Kawasaki  ( Kyoto University ) for GPP,  Dr. S. Takahashi  ( Tohoku University ) for FPP, and  Api. Co. , Ltd. for standard specimens of drupanin and artepillin C. We are grateful to  Dr. T. Nakagawa  ( Shimane University ) for providing the pGWB vectors,  Dr. T. Mitsui  ( Niigata University ) for the pWxTP-DsRed plasmid,  Dr. H. Kouchi  ( International Christian University ) for the pHKN29 plasmid, and  Dr. D. Baulcombe  ( University of Cambridge ) for the pBIN61-P19 plasmid. The COUM11 strain and related plasmids (pESC-PAL-C4H and pESC-Trp-CPR) were kindly provided by  Dr. F. Ververidis  ( Technological Educational Institute of Crete ). Dr. M. J. C. Fischer  ( INRA, Colmar ) and  Dr.
D. Werck-Reichhart  ( CNRS, University of Strasbourg ) kindly provided the DD104 strain. Dr. T. Miyakawa  ( Hiroshima University ) and  Dr. A. Goffeau  ( Université Catholique de Louvain ) also kindly provided Δpdr5 and Δsnq2 strains, and AD12345678 strain, respectively. We also thank  Mr. T. Hosouchi  and  Ms. S. Shinpo  ( Kazusa DNA Research Institute ) for technical support with the Illumina sequencing. LC-IT-TOF/MS analyses were carried out with the  Development and Assessment of Sustainable Humanosphere system of the Research Institute for Sustainable Humanosphere (RISH) ,  Kyoto University. This work was supported in part by  MEXT KAKENHI  Grant No. 16H03282  (to K. Yazaki), by  JSPS KAKENHI  Grant No. 17H05441  (to K. Yazaki), by  JSPS Overseas Research Fellowships  (to R. M. ), by " Bio-prolor2 " project ( Région Grand-Est ), by the " Impact Biomolecules" project of the "Lorraine Université d'Excellence" (Investissements d'avenir -ANR ), by the  New Energy and Industrial Technology Development Organization (NEDO , No.
Results We tested 35 individuals, aged 17 to 87, for 16 frequencies (125 Hz to 30 kHz) and for five sound pressure levels (55 to 80 dB). All measures were taken on both sides. Due to missing data, the sample size was 453 (Cf. above Material and Methods). This sample constitutes an unbalanced panel data sample estimated by ordinary least squares with the introduction of a so-called "fixed effect" dependent on the individual and the side (left or right). For the raw data, Cf. 10. 6084/m9. figshare. 5671807. The empirical evidence of a significant effect of frequency and sound pressure on the difference in potential was verified by statistical analysis using the "R" software. The explained variable is the microvoltage V ijks measured for individual i, frequency j, sound pressure level k and side s (left or right).
Si 14A Description of the data We did 221 experimental series of microvoltage measurements in function of the sound frequencies (Hz) and the sound level (dB). These experimental series were measured on 62 individuals (A01-A10, B11-B20, C21-C30, D31-D35, E43-E44, E48-E50, F1-F12). Of these recordings, 110 correspond to a male and 111 to a female. The ages of individuals were < 93 years S 61. From these 221 series of measurements, 79 were obtained at the level of an eardrum. To check the properties of collagen fibers different from those of the eardrum, we also made these measurements on the forearm, the Achilles tendon, the nasal cartilage and especially, in a very systematic way, the ligaments patellar right and left: 123 series of measurements were obtained at the level of a knee. For eardrums 40 were the left one, and 39 the right one.
Acknowledgments We are very thankful to the volunteers which gave their time for participating to the experimental measurements and to the complementary tests. We thank for scientific assistance : INP of CNRS;  Andrews S. , PhD ,  LSU Medical School ;  Auriol J. B. , PhD ,  BD (New Jersey, US) ;  Azaïs C. , PhD , ex-LAMI,  Toulouse University ;  Banquet J. P. , MD, PhD ,  CNRS UMR , Paris; Bibé B. , Emeritus Research Director,  INRA , genetics and wording; Calvas F. , MD,  CIC ,  Inserm ,  CHU-Purpan Toulouse ;  Chernomordik L. V. , Ph. D. ,  Senior Biomedical Researcher, NICHD, NIH, Bethesda, USA ; Csillag P. , ENSEEIHT -Toulouse France; Demmou H, Senior Researcher,  LAAS -CNRS, Toulouse, France; Faurie-Grepon A. , MD, CIC, Inserm, CHU-Purpan Toulouse, France ;  Gaye-Palettes J. F. , MD, EHPAD Isatis ,  Quint-Fonsegrives ;  Harnagea C.
AME, Total HP, and NE AME was determined by the total collection method previously described by  Bourdillon et al. (1990)  with modifications. Values were corrected to zero nitrogen retention (AMEn) using 8. 22 kcal/g of N (or 34. 4 kJ/g) as the correction factor where AMEn was used  (Hill and Anderson 1958). Total HP was calculated for 3 d by measuring O 2 consumption and CO 2 production in sealed chambers as described. Each day during the collection period, the system was suspended for about 2 h for replenishing feed, water, KOH, and silicone gel and for excreta collection. Heat production values were obtained by applying chamber CO 2 and O 2 data to the modified Brouwer equation  (Brouwer 1965; McLean 1972 ) by removing methane and N in expired gas from the equation as: Total heat (kcal) = 3.
Statistical Analyses The performance and nitrogen balance data were expressed per bird per day, with FI and FCR reported on a DM basis. Energy balance data were expressed per bird per kg BW 0. 70 per day due to the linear correlation between FHP and BW 0. 70 of growing broiler chickens  (Noblet et al. , 2015). Energy values diets were expressed per kg DM, and energy utilization data were expressed as percent. All data analyses were performed using SAS software, version 9 (SAS Institute Inc. , Cary, NC). PROC GLM was used to determine the effects of: (a) replicate runs in experiment 1; (b) replicate runs and diets in experiment 2; and (c) diets in experiment 3. The model was: Y ij = μ + a i + b j + ε ij where Y ij is the response expected independent variables, μ is the mean, a i , is the effect of diet, b j is the effect of replicate run, and ε ij is the random error.
Particular emphasis was placed on practical relevance of the experimental design and respective equations to predict energy components from dietary chemical components in ingredients were then generated and validated in closed-circuit calorimetry chambers. Based on the prediction equations, a database of NE values can be easily established according to existing or analyzed ingredient AME, EE, CP, and CF values. In addition, computed NE and NE/AMEn values appear to be appropriate with balanced diets according to the consensus from scattered values estimated or measured by other studies  (Fraps and Carlyle 1942; Carre et al. , 2014; Liu et al. , 2016). Further validation studies are warranted to assess potential economic benefits of using NE in place of AMEn in commercial feed formulation. These studies will be needed to assess whether the NE system is beneficial in regard to the utilization efficiency of the feed, feed costs for production, broiler growth, uniformity, and carcass quality.
Ehrlichiosis are severe, feverish tick-borne illnesses caused by specific species within the genus Ehrlichia (Anaplasmataceae family). Recent data suggest that ruminants in Corsica area reservoir for several Anaplasmataceae species. The purpose of our study was to determine whether Ehrlichia species could be detected in ticks collected in Corsican ruminants by using molecular methods. Ticks were collected in northern Corsica: (i) in May 2016 from sheep bred in one farm located in a 5000-inhabitants village and (ii) from cattle in June and July 2016 in a slaughterhouse. There sheep and cattle whole skin was inspected and ticks were collected manually. A total of 647 ticks was collected in northern Corsica during this study: 556 (86%) belonged to the Rhipicephalus bursa species and 91 (14%) to Hyalomma marginatum.
PCR conditions and primer sequences are described in Table  1. The reactions were carried out using a Gen-eAmp PCR Systems 9700 Applied Biosystems (Courtaboeuf, France). The PCR products were UV light-visualized in 2% agarose gel in Tris-Acetate-EDTA (TAE Buffer) after staining with ethidium bromide. Sequences obtained in this study were deposited in the GenBank using the National Center for Biotechnology Information (NCBI) BankIt 3. 0 submission tool (https://www. ncbi. nlm. nih. gov/Web Sub/) (accession number for H. marginatum MH663977-83 and for R. bursa MH663984-90). Sequences of Ehrlichia 16S rRNA and groEL genes correspond to acc. nos MH657222 and MH675614 respectively. All sequences were assembled and compared with selected homologous sequenced retrieved from the GenBank nucleotide database using BLASTn  (Altschul et al.
2004; Grech-Angelini et al. 2016). At the outset of this   All sequences were assembled and compared with homologous sequenced retrieved from the GenBank nucleotide database using BLASTn  (Altschul et al. 1997). The Hasegawa-Kishino-Yano and the Tamura 3-parameter models were identified as the best-fit models under the Akaike Information Criterion, for 16Sr RNA and groEL sequences respectively. Each model was inferred using the Maximum Likelihood method implemented in Mega X  (Kumar et al. 2018). The bootstrap consensus tree was conducted with 1000 replicates  (Felsenstein 1985). © studies addressing the potential impact of vectorborne diseases of human and veterinary impact through large-scale research and surveillance programmes in Corsica.
Phenotypic analyses The analyses were conducted using Restricted Maximum Likelihood/Best Linear Unbiased Predictor (REML/BLUP) mixed models, by ASReml R package  [44] , considering the following model: y ¼ X E β E þ X B β B þ X C β C þ X I β I þ X EI β EI þ Z G u G þ Z GE u GE þ Z GI u GI þ Z GEI u GEI þ ε where y is the vector of phenotypic observations of the traits evaluated on maize hybrids; β E is the vector of fixed effects of year; β B is the vector of fixed effects of block within year; β C is the vector of fixed effects of countertop within block and year; β I is the vector of fixed effects of inoculation; β EI is the vector of fixed effects of inoculation × year interaction; u G is the vector of random effects of genotype, where u G e N 0; Is À �. X E , X B , X C , X I , X EI , Z G , Z GE , Z GI , and Z GEI are the respective incidence matrices related to each vector.
The significance of fixed effects was tested using the Wald test implemented in the ASReml R package, while the significance of random effects was assessed by Likelihood Ratio Test (LTR) from the asremlPlus R package  [45]. The variance components by treatment were estimated through reduced models disregarding the inoculation effect and its interaction with genotype. Broad-sense heritabilities were estimated as H 2 ¼ s 2 G =ðs 2 G þ s 2 GE =j þ s 2 ε =rjÞ, where the s 2 G is the genetic variance; s 2 GE is the genotype-byyear variance; s 2 ε is the error variance; j and r are the number of years and replications in each experiment, respectively.
Genotypic data The Affymetrix 1 Axiom 1 Maize Genotyping Array  [46]  of 616,201 SNPs markers was used to genotype the parental inbred lines. Markers with call rate <95% and heterozygous loci on at least one individual were removed. Remaining missing data were imputed through the algorithms of Beagle 4. 0 using the codeGeno function from the Synbreed R package  [47]. The hybrid genotypes were obtained in silico from the genotypes of the corresponding parental inbred lines. After that, one more filter was applied to the matrix, eliminating SNPs with Minor Allele Frequency (MAF) � 0. 05. A final SNP set of 59,215 was obtained and used for the subsequent analyses.