dataesr/scientific-paragraphs-categorization huggingface dataset:
Sugarcane BAC library and BAC selection Two approaches were used to select BACs from the sugarcane hybrid R570  [7] : macro-array hybridization using PCR-amplified and overgo probes, and 3D pool screening by real-time PCR. Overgo probes were designed using the BACMAN database  [48]  and were used as queries in a BLASTn search against the sorghum (v1. 0) [14] and rice (v6. 1)  [49]  genome assemblies. Since the overgo probes are only 40 nts in length, we used a series of cut-off values; for matches from 40 to 38 nt (allowing for 2 mismatches for 40 nt or 1 mismatch for 39 nt and no mismatches for 38 nt alignments). Only those probes that hybridized to 1-10 BACs were used to select BACs for sequencing to reduce false positives and to exclude multigene families. Macro-array hybridizations were performed according the manufacturer's instruction and Bowers et al.
Gene and repeat annotation BACs were first annotated using an automated pipeline for identification of genes and TEs based on de novo prediction and BLASTx. TEs were screened using RepeatMasker  [54]  against repeats from Viridiplantae  [55]  and sugarcane LTR retrotransposons  [10] , with a cut-off score of 250. Both complete and incomplete elements were identified. MITE Hunter  [56]  with default parameters was used to extract all MITEs from the BACs. The alignment files generated were grouped together using clans  [57]  and screened using RepeatMasker  [54]. MITEs were then classified according to type of target site duplication and terminal inverted repeat features  [58]. Genes were annotated using masked BACs, using the software programs Augustus  [59] , Glimmer HMM  [60] , PASA  [61] , Evidence Modeler  [62] , SignalP  [63] , and TMHMM  [64].
Exon-intron boundaries were examined using Artemis  [65] , compared to results based on BLAST alignments against sugarcane ESTs  [8]  and annotated sorghum and rice proteins [14,66,67] and adjusted if necessary. If no hits against sugarcane ESTs, rice and sorghum protein sequences were found, the predicted ORF was not modified. Validation of splice sites were performed by GenBank tools at sequence submission. Lastly, putative intergenic sequences were BLASTx (nr database) screened for additional genes not annotated by the de novo prediction programs. Manual categorization was performed according to the SUCEST project database  [8]. Blast2GO analyses were performed as previously described  [68]  using BLASTp with an e-value cutoff of e -10. Screening for mitochondrial and chloroplast sequence contamination was done by a BLASTn of the sugarcane organellar genomes (GenBank: NC_005878 and NC_008360).
We used the CDSs from the BACs, the 43,141 SASs  [69]  and the KeggMapper tool available at MG-RAST  [70]  for global automatic mapping with cut-offs of an evalue of e -5 , 60% identity, and a minimum alignment length of 15 bp. We used the sugarcane CDSs and the KEGG Automatic Annotation Server  [71] , with the BBH search option, to map sucrose and starch pathways genes. All BAC sequences generated in this study, with protein-coding gene and full-length TE annotation, can be accessed in the GenBank database under accession numbers [GenBank: KF184657 to KF184973].
Colinearity and synteny analyses Two analyses were performed to confirm that the BACs represent a homogeneous sampling of the sugarcane genome, and to evaluate colinearity and synteny with the sorghum genome. First, we estimated the chromosomal location of the BACs in the sorghum genome by a BLASTn analysis, using sugarcane non-TE coding sequences as queries against the sorghum genome (v1. 0)  [25]. We then checked this localization by blasting the predicted protein sequences against the masked sorghum protein database  [14]. The chromosomal location of the BAC in the sorghum genome was directly assigned for predicted protein sequences with single-hits. A high-throughput maximumlikelihood phylogenetic analysis  [23]  was applied for CDSs with multiple-hits using the sorghum predicted proteome  [14].
Analysis of ribosomal and centromeric BACs The BAC SCHRBa_039_D18 was initially identified through the gene annotation as containing ribosomal genes. The region syntenic to the BAC in rice, maize, sorghum and Arabidopsis was identified using CoGE  [73]. The top 2 hits from BLASTn where the description was not an unidentified sequence were downloaded and manually aligned using BioEdit  [74]  with BAC SCHRBa_039_D18 to determine the beginning and end of each ribosomal gene. Three BACs were identified as putatively pericentromeric and/or centromeric by the absence of coding genes during the repeat annotation. The nucleotide sequence of the BACs were further analyzed by appropriate BLASTs against the SCEN repeat  [21]. LTR nucleotide sequence and conceptually translated coding domains from the sugarcane curated LTR retrotransposon database  [10].
sRNA analysis Raw sequences  [10]  were retrieved in a FASTQ formatted file and the adapter sequences were removed using Perl Scripts. Reads in the size 20-25 nucleotides were sorted into two separate files, 20-22 nt and 23-25 nt for subsequent analyses. We used the MAQ software  [78]  to map the collection of sRNA reads against the BACs. We used the low stringent cut-off parameter of 0-2 nt mismatches because the BACs and sRNA reads were derived from different sugarcane cultivars. Graphical representations of sRNA mapping was created using the SeqMonk software  [79].
RNA-seq sequencing and analysis A sugarcane transcriptome was constructed from germinating shoot axillary buds five days after planting. Single budded setts from the sugarcane variety RB92-5345 were placed in trays with buds facing upwards, covered with moist vermiculite, and incubated at 26-30°C under greenhouse conditions. Total RNA was extracted from pooled breaking buds using a lithium chloride protocol  [80]. For the construction of RNA-seq libraries, all procedures were carried out according to Illumina's instructions using the 'TruSeq RNA Sample Prep v2 Low Throughput (LT)' kit. The libraries were paired-end sequenced on the Illumina system (HiScanSQ) (GA3 -ESALQ-USP). Sequencing reads were mapped using the Burrows-Wheeler Aligner BWA  [81]  and the SAM tools  [82]. The RNA-seq library can be accessed in the NCBI high-throughput DNA and RNA sequence read archive under the accession number [SRA: SRX500284].
Acknowledgements We gratefully acknowledge funding from  FAPESP  ( MAVS-2008/52074-0 ;  CBMV-2010/05591-9 ;  RV-2008/58031-0 ;  APS 2008/52197-4 ,  GMS 08/52146-0 ) and  CNPq  ( MV-INCT BIOETHANOL). NS (2009/51632-1 ), CJM ( 2009/09217-7 ), GMQC ( 2008/58243-8 ),  RAC  ( 2009/09116-6 ),  SLI  ( 2011/05317-7 ) and CGL (2008/54201-9) were recipient of  FAPESP  fellowships. CEAO, MYN, VM and RFA were the recipients of CNPq fellowships. PCT and PMS were the recipients of CAPES fellowships. Overgo probe selection was supported by grants to  AHP (International Consortium for Sugarcane Biotechnology  (# 24 ),  Consortium for Plant Biotechnology Research  ( DE-FG36-02GO12026 ), and the  Univ. Georgia Office of the Vice President for Research). We thank CATG and  LNCC labs  for using BAC sequencing facilities, and the  CNRGV team  for the help with the 3D pool development and BAC sequencing.
Availability of supporting data The BAC sequence data set supporting the results of this article is available in the GenBank repository [KF184657 to KF184973 at http://www. ncbi. nlm. nih. gov/genbank], in the CoGe website [Saccharum hybrid cultivar R570 (id23984) in https://genomevolution. org/CoGe/] and in the GaTElab website, as a GBrowser search tool [https://gate. ib. usp. br/ GateWeb/en/gbrowse-pagina]. The RNA-seq library data set is available in the Sequence Read Archive (SRA) repository [SRX500284 in http://www. ncbi. nlm. nih. gov/sra].
Life history traits Body mass data were extracted from the CRC Handbook of Avian Body Masses  [82] , with only data for unambiguously named tip nodes being used. Where multiple entries for a given species were present, the mean value was calculated. We therefore averaged across males and females where these were not already pooled in the dataset. Data on maximum longevity and age of first female sexual maturity were extracted from build 11 of the AnAge database  [83]  for each available species.
Acknowledgments Computational analyses were performed using resources provided by the  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)  of  Uppsala University , supported by the  Swedish National Infrastructure for Computing (SNIC). The study was funded by the  European Research Council  ( AdG 249976 ),  Knut and Alice Wallenberg Foundation  ( Wallenberg Scholar Grant  and the  Swedish Research Council  ( 2010-5650 ; all to HE). JR received funding from  ANR-10-BINF-01-02  ' Ancestrome ' and  European Research Council  ( AdG 232971 ; principal investigator  Nicolas Galtier ). EDJ received funding from the  Howard Hughes Medical Institute. We would like to thank  Laurence Hurst ,  Benoît Nabholz ,  Sankar Subramanian , and  Nicolas Galtier  for helpful discussions.
CDS sequence was amplified by PCR on cDNA extracted from roots using primers DtAux1_cDNA_Fw 5 -ATGTTGGC TCAGAAACAAGCAG-3 , and DtAux1_cDNA_Rv 5 -CTAGT GATGCGGAGGTGCC-3. Quantitative PCR was performed on cDNAs extracted from nodules and non-inoculated roots as described  (Svistoonoff et al. , 2013)  using primers DtAux1F 5 -ACGGCATGACCACCAAAGG and DtAux1R 5 GGTTACTC ACTCTGCTCCATCC-3 to amplify a DtAux1 fragment and DtUbiF 5 -TACCACCACGAAGACGGAGGAC-3 and DtUbiR 5 -GGAAGCAGTTGGAGGATGGAAGG to amplify an ubiquitin fragment used as an internal standard. The sequence of DtAUX1 was deposited at Genbank and was given the accession number KM200713. For the phylogenetic analysis coding sequences of the AUX-LAX family of auxin influx carriers were retrieved by family blast search in the Phytozome v9. 1 database (www.
phytozome. org) using the coding sequence of DtAUX1 as the query. Sequences of AUX-LAX family from D. glomerata were retrieved using BLAST search from D. glomerata nodule transcriptome database  (Demina et al. , 2013 ; https://fido. nsc. liu. se/datisca/nodule/) and the coding sequences of all AUX-LAX family from A. thaliana as the query. The phylogenetic tree was constructed using the coding sequences as described  (Svistoonoff et al. , 2013)  except that RAxML7. 6. 6  (Stamatakis, 2006)  was used to calculate the phylogenetic tree with default parameters including automatic halt of bootstrap.
A maximum likelihood phylogenetic analysis including DtAUX1 and members of the AUX-LAX family from A. thaliana, the legume M. truncatula and the actinorhizal plants C. glauca and Datisca glomerata, showed that DtAUX1 clusters in the same strongly supported group as CgAUX1 and DgAUX1 which belongs to the AUX1/LAX subfamily (Figure  4B ). Using quantitative PCR we monitored DtAUX1 expression and found twice as many transcripts in nodules compared to roots (data not shown). To further investigate the spatio-temporal pattern of expression of DtAUX1, we generated transgenic D. trinervis roots containing a transcriptional fusion between a promoter sequence 1593 bp upstream the ATG and the GUS reporter gene. A strong GUS activity was found in root tips of non-inoculated roots, particularly in the columella cells, the quiescent center and the stem cells (initials), and in lateral root primordia and the vascular tissues (Figure  5A ).
ACKNOWLEDGMENTS We thank  N. Diagne ,  D. Moukouanga  and  V. Vaissayre  for help with histological work and genetic transformation of D. trinervis;  C. Franche ,  D. Bogusz ,  V. Hocher  and  H. Gherbi  for helpful discussions and  N. Diagne  for help with qPCR experiments. We also thank  E. Chaia ,  T. Vernoux  and  C. Rosemberg  for providing D. trinervis seeds, the DR5:Venus and pDISC plasmids. Funding was provided by  University of Quilmes (UNQ) , the  Argentinian National Research Council (CONICET) , the  Institut de Recherche pour le Développement (IRD) , and grants from  UNQ  ( 0395/07 ),  CONICET  ( PIP 2271  and  Bernardo Houssay , 2011),  IRD Department of Capacity-Building for Southern Scientific Communities (IRD-DPF) ,  Agence Nationale de la Recherche  ( ANR-08-JCJC-0070-01 ,  ANR-10 BLAN-1708-01 ,  ANR-12-BSV7-0007-01 ), and  ECOS-SUD  ( A07B02  and  A13B03 ).
At departure and return to the nest albatrosses were weighted (without equipment) to the nearest 50 g using a Salter spring balance (Salter Weightronix Ltd, West Bromwich, UK). Due to several logistic constraints (i. e. , electronic problems, premature loss of logger), prey capture data were available only for 18 foraging trips. Because not all foraging trips recorded simultaneously geographic position and prey capture data, we obtained 5 completely tracked foraging trips, 5 near-complete (tracks stopped recording when birds were heading to the colony), and 8 incomplete. Available tracking data corresponded to a 4-year period and we did not find inter-annual differences in foraging trip characteristics such as mean flying speed (F 3,14 = 0. 38, P = 0. 766), maximum flying speed (F 3,14 = 1.
Estimation of net energy gain Instantaneous net energy gain was estimated at our basic temporal unit (i. e. , 1 min) as the difference between energy gain and energy expenditure based on Eq. 1. Similarly, total trip net energy gain was estimated by cumulative summing instantaneous net energy gain along the foraging trip. Regarding the estimation of instantaneous energy gain, we first estimated instantaneous mass intake (kg min -1 ) based on prey capture data. Then instantaneous mass intake was transformed into energy gain by assuming that the 75% and 25% of prey capture data corresponded to squid and fish (prey identification was not possible), with an energetic content of 5. 61 kJ g -1 and 4. 64 kJ g -1 , respectively  [29, 36]. Regarding estimation of instantaneous energy expenditure (R-based script will be made available on request to the corresponding author), we first identified albatross activities at the instantaneous level along the foraging trip (see workflow in Figure  1 ).
At the trip level, we used the mass gain measured by the birds equipped with stomach temperature pills and assumed that the trip net energy gain is a measure of albatross' body mass difference between arrival and departure for the foraging trip, Eq. 2. Predictions of body mass change were also contrasted against an empirical distribution from the long-term tracking database of wandering albatross  [9, 43]  for 97 independent individual (more details in Additional file 4). Regarding validation at the instantaneous level, we contrasted our predictions of energy expenditure (based on heart rate values) against an empirical distribution of energy expenditure of wandering albatrosses measured by a different method (Doubly Labelled Water, DLW)  [44] [45] [46]. DLW provides energy expenditure estimates averaged over the measurement period which is normally restricted to a few days  [46].
Acknowledgements We wish to thank all participants of the fieldwork implying tracking device deployment, especially  S. A. Shaffer ,  T. Guionnet ,  J. Martin ,  G. Mabille ,  F. Bailleul ,  V. Lecomte ,  A. Jaeger  and  M. Berlincourt. J. González-Solís  provided valuable advice on flying cost function. J. Calabrese ,  Y. Cherel ,  C. Barbraud ,  D. Pinaud ,  J. Xavier  and  K. Delord  provided valuable discussion. J. Garriga  applied the EMBC algorithm and provided the labelled wandering albatross trajectories. The  Ethic Committee of IPEV (Institut Polaire-Paul Emile Victor , programme no. 109 ) approved the field procedure. The oceanographic data were extracted thanks to the  Environmental Research Division, Southwest Fisheries Science Centre  and  US National Marine Fisheries Service.
The study was financed by  IPEV (Institut Polaire-Paul Emile Victor , programme no. 109 ) and the  Prince Albert II de Monaco Foundation. M. L. was funded by different  Marie Curie funding schemes (Individual Fellowship   PIEF-GA-2008-220063  and  Career Integration Grant   PCIG09-GA-2011-293774 ) and  Juan de la Cierva  postdoctoral contract ( JCI-2010 -07639 ,  Ministerio de Ciencia e Innovación). FB acknowledges the  Ramón y Cajal Program (Ref. RyC-2009-04133 ) and  Plan Nacional I + D + I (Ref. BFU2010-22337 ) from the  Ministry of Science and Innovation, Spain. We appreciate the improvements in English usage made by  Peter Lowther  through the  Association of Field Ornithologists ' program of editorial assistance. Finally, we thank three anonymous reviewers and editors whose comments helped to improve the quality of the manuscript.
In order to increase mRNA representation in RNA samples, total RNA were digested with mRNA ONLYTM Procaryotic mRNA isolation kit (Epicenter Biotechnologies, Madison, WI, USA) according to the provided protocol. The microarray cDNA (three independent samples per condition) was synthesized with the Superscript® Double-Stranded cDNA Synthesis Kit (Invitrogen), using a mix (1:1) of random primers (Promega Corporation, Madison, WI, USA) and Oligo-dT (15) primers (Promega). (two probes per gene) of rice Oryza sativa ssp. japonica, based on the TIGR rice genome annotation version 3. 1 genes  (Yuan et al. , 2005)  and 201,691 oligomers corresponding to previously described copies of LTR retrotranposons available on the retrOryza database (www. retroryza. fr; Chaparro et al. , 2007). Probes represent 1000 bp of the LTR-retrotransposon flanking regions at the 3 and 5 side.
The oligonucleotides are therefore regarded as locus specific when they are not matching elsewhere, but having at most three mismatches, which represents 5% of all oligonucleotides. For each condition, three independent cDNA samples were labeled and hybridized by Roche Nimblegen according to their standard protocol. Data analysis was performed using Bioconductor microarray packages for R software (http://www. bioconductor. org/). The robust multi-array average (RMA) method associated with quantile normalization was applied  (Bolstad et al. , 2003; Irizarry et al. , 2003). Analysis of variance with a false discovery rate adjustment method was realized  (Benjamini and Hochberg, 1995). The results of different treatment comparison were obtained in Log2-fold change. The oligonucleotides selected were those which present a two fold increase or decrease in expression, i.
Specific primers were designed using Light Cycler Probe design Software 2. 0 (Roche) with the following criteria: product size ranges 100-400 pb, primer size comprised between 17 and 22 bases, optimal primer Tm 60 • C (Additional file 1: Table  S1 ). Real time PCR conditions were: a denaturation stage of 10 min at 95 • C; an amplification stage of 45 cycles of 15 s at 94 • C, 10 s at 60 • C and 20 s at 72 • C; and a melting curve stage of 5 s at 95 • C and 1 min at 65 • C increased to 97 • C with a ramp rate of 0. 11 • C s -1. All reactions were performed in three technical replicates and carried out in LightCycler 480 Multiwell plate 96 (Roche) with adhesive sealings foils (Roche) in a final volume of 10 µl containing 1 µl of each primer (5 µM), 5 µl of master mix and 3 µl of cDNA diluted 50 times.
Finally, the set of potential plant markers of Azospirillum-rice cooperation also includes a transcription factor (Os01g0952800) and four genes potentially involved in signal transduction (Os07g0537900, Os08g0501500, Os08g0203400, Os09g0358000, this latter being validated by RT-qPCR, Table  1 ). Making use of the Rice Oligonucleotide Array Database  (Cao et al. , 2012) , we identified experiments analyzing gene expression in rice during plant-microbe interactions. Prior to this analysis, RT-qPCR expression profiles observed in rice roots for the 14 genes used to validate our microarray data (Table  1 ) were compared to RT-qPCR expression profiles obtained in rice leaves and none displays differential expression when considering shoot compartment (data not shown). Then, the array database analysis was focused on rice roots and only one experiment analyzing gene expression in the root compartment was evidenced.
ACKNOWLEDGMENTS We are grateful to  C. Louvel  ( Center français du riz, Arles, France ) and  J. B. Morel  ( BGPI, Montpellier, France ) for gift of Cigalon and Nipponbare seeds, respectively. The platforms "Serre" and DTAMB of FR41 (Université Lyon 1) were used to carry out this work. Benoît Drogue  and  Amel Chamam  receive fellowships from, respectively  Région Rhône-Alpes  and  Centre National de la Recherche Scientifique. This work was funded by the  ANR  project  AZORIZ  ( ANR-08-BLAN-0098 ).
After 48-hour incubation, small whitish round shiny domed colonies were observed on both culture media. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) performed as previously described  [2, 3]  yielded P. calida with identification score of 2. 26, 2. 17 and 2. 17 for the three spots deposited on the MALDI-TOF-MS plate. Furthermore partial rpoB gene sequencing performed as previously reported  [4]  confirmed the identification of P. calida with 99. 88% sequence similarity with the reference sequence (GenBank GQ892191). Antimicrobial susceptibility was performed by diffusion method in agar and incubated aerobically for 24 hours at 37°C. The isolate was resistant to ampicillin (minimal inhibitory concentration (MIC) >8mg/L), amoxicillin-clavulanate (MIC, 1mg/L), and cephalothin (MIC, 1mg/L).
Discussion Here, P. calida was isolated from the CSF of a patient with postoperative meningitis  [5]. The isolate was firmly identified by using two different complementary approaches: MALDI-TOF-MS which is an advanced proteomics method  [2, 3]  relying on the analysis of unique peptidic signatures; and partial rpoB gene sequencing which relies on the analysis of unique nucleotidic signatures  [6]. These two technical approaches yielded unambiguous, concordant identification. In particular, MALDI-TOF-MS proved to be efficient to identify this species, despite the fact that no clinical isolate was previously included in the database. The absence of any bacteria other than P. calida on the culture of CSF, as well as the fact that it was the first identification of this microorganism in our laboratory, secure the conclusion that P.
Matching of Controls Human T-lymphotropic virus type 1 seroprevalence varies with age, gender, and place of residence  [1]. Cases were therefore matched to controls 1 : 1 according to age (age of case ±10 years), gender, and primary language, which was determined by 2 Aboriginal Research Officers. Subjects were then matched by another research team member (E. G. ). All were unaware of the HTLV-1 serostatus of enrolled patients. Clinical data were extracted from case notes from date of first admission to date of recruitment using a standardized datacollection form. For cases, data are presented to date of diagnosis. Results for all microbiological tests, full blood examinations, radiology, and other relevant investigations were also extracted. Results were recorded for sputum cultures performed during separate admissions >2 weeks apart.
Pulmonary Injury Scores High-resolution computed tomography chest images for 33 cases were available, and these were reviewed remotely using the Picture Archiving and Communication System with Inteleviewer software by 2 consultant radiologists (V. A. , S. H. ) in Adelaide, 1500 km from ASH. Both radiologists were blinded to HTLV-1 serostatus. A "pulmonary injury score" was calculated using a system modified from Bhalla et al  [18]. Each lobe was scored independently for bronchiectasis severity (0-3), bronchial wall thickening (0-3), mucus plugging/centrilobular nodules (0,1), sacculation (0,1), parenchymal changes of ground-glass density (0,1), and air-trapping/emphysema (0,1). A maximal score of 10 could therefore be assigned to each lobe. A score of 10 was also assigned to lobes with complete collapse/cicatrisation.