dataesr/scientific-paragraphs-categorization huggingface dataset:
Sharpsnout seabream assembled transcriptome Illumina HiSeq2500 sequencing yielded 429,988,050 paired reads (214,994,025 read pairs) (Table  1 ). The filtering process resulted in 229,843,938 paired and 74,027,237 single or orphan high quality reads used for the transcriptome assembly. The initial assembly process produced 374,149 putative transcripts (N50: 1,712 nucleotides, mean length: 871 nucleotides). Following further editing and transcript quality assessment (see Methods), we limited our dataset to 118,258 transcripts belonging to 82,331 loci (N50: 2,092; mean length: 1,352 nucleotides) (Figure  1 ). The unfiltered transcripts dataset had significant similarity to 18,527 O. niloticus genes, while the filtered assembly had significant similarity to 17,346 out of 21,462 genes reported in Ensembl.
Given that the filtering resulted in elimination of 255,891 possibly spurious transcripts, while the hits to unique genes of O. niloticus were only slightly reduced, the restricted dataset constituted the final transcriptome. Out of the 82,331 loci, 31,895 had significant BLASTx similarity hits with publicly available protein sequences, 22,574 of the loci were assigned GO terms and 40,823 had an InterProScan protein domain match. The great majority of the top-hits of the assembled transcripts had significant similarity with sequences of Maylandia zebra, Oreochromis niloticus, Takifugu rubripes, Oryzias latipes and Dicentrarchus labrax. Further, 14,041 of the genes without any significant similarity to known proteins had InterProScan protein domains assigned.
Genetic marker discovery through RNA-sequencing The possibility to reconstruct the expressed genic sequences at a global scale through RNA-Seq, gives access to a plethora of genetic markers widely distributed in the genome. We identified a considerable dataset of STR and SNP markers that can be used as raw material in future population genetics, broodstock management, QTL mapping and Marker Assisted Selection (MAS) in sharpsnout seabream and other phylogenetically close sparid species. Discovering SNP markers can be a relatively easy task in a dataset like the one produced here, as pooling individuals allows identifying robustly (although this can be proven only through further targeted experiments). However, further filtering of the SNPs for individual genotyping (see Methods) reduced dramatically the available SNPs.
Acknowledgements Financial support for this study has been provided by the  Ministry of Education and Religious Affairs , under the Call "ARISTEIA I" of the National Strategic Reference Framework 2007-2013 (SPARCOMP, # 36 ), co-funded by the  EU  and the  Hellenic Republic  through the  European Social Fund. We would like to thank  V. Terzoglou  and  E. Kaitetzidou  for help in RNA extractions,  Irini Sigelaki  for help in sampling and  Dr. Hooman Moghadam ,  Dr. Gianpaolo Zampicinini ,  Dr. Jon B. Kristoffersen  and  Ji Hyoun Kang  for valuable discussions. Finally, we would like to thank three anonymous reviewers for their valuable comments on the manuscript.
Assessment of causes of death Vital status and information on cause and date of death were ascertained using record linkage with cancer registries, boards of health and death registries (Denmark, Italy, the Netherlands, Spain, the UK) or by active follow-up (France Germany, Greece). Data were coded using the 10th revision of the International Statistical Classification of Diseases, Injuries and Causes of Death (ICD-10) where the underlying cause is the official cause of death. In this work, six different causes of deaths were selected: cardiovascular disease (CVD) (I00-I99 excluding I20-I25) and coronary heart disease (CHD) (I20-I25), alcohol-related cancer (ARC), including colorectal cancer (C18-C20), female breast cancer (C50), upper aerodigestive cancers (UADT, including cancer of the mouth (C01-C10 without C08=salivary gland), larynx (C21), pharynx (C11-C14), oesophagus (C15)), violent deaths and injuries (injury, poisoning and certain other consequences of external causes (S00-T98); deaths due to respiratory diseases (J00-J99); a group for all other causes (including external causes of morbidity and mortality (V01-Y98), unknown causes (R96-R99)).
Using the derived L36AL structure, we modeled the interactions between this protein and tRNA. Fig. (1B)  shows the overlaid 3-D structures of tRNAPhe (PDB ID 1JGQ) and the modeled human RPL36AL, suggesting that the protein interacts with tRNA at the inner side of the elbow. This mode of interaction is reminiscent of the general model of tRNA-aminoacyl-tRNA synthetase interaction which shows, as described by Chu and Horowitz  [14] , that "synthetases bind their cognate tRNAs along and around the inside of Lshaped structure, recognizing the acceptor stem and, depending on the particular enzyme and tRNA, possibly also the D-stem and the anticodon". The high-resolution crystal structure of the E. coli glutaminyl-tRNA synthetase-tRNAGln-ATP complex  [15]  is representative of this model, which is the source of the specificity and fidelity of the aminoacylation reaction.
2A  and 2B ). Experimental data from individual kinetic binding experiments were analyzed and fitted using BIAevaluation software with 1:1 binding model, in accordance to the 1:1 stoichiometries of binding of tRNA to the aminoacyl-tRNA synthetases polypeptide chains  [9, 16]. The resulting kinetic constants given in Table  1  revealed that the interactions between full length or truncated tRNA molecules and purified recombinant human RPL36AL take place with association (ka or kon in the range 0. 76-1. 55 X 10+6 M-1. s-1) and dissociation rates (kd or koff in the range 1. 65-3. 17 X 10-3 s-1), defining strong binding affinities (KD values in the range 1. 23-3. 02 X 10-9 M). The KD values in the nanomolar range reflect high tRNA-binding affinities with full length and truncated tRNA species.
Finally, the crosslinking The 49GG50 dipeptide of the GGQ motif is highlighted in green. A zinc ion represented by a cadmium colored yellow is also shown. Fragment 86-94 corresponding to the nucleotide binding motif 2 (NBD2) common to all eukaryotic RPL7  [29]  and RPL36A/RPL44 is shown in wheat. (B), overlaid structures of tRNAPhe (PDB ID 1JGQ) colored grey and of human RPL36AL (blue). The GGQ motif is shown in red, the 3' terminal CCA trinucleotide of tRNA in pink, and the side chain of Lys-53 in green. (C), NBD2  [29]   Prim. cons. KKxxHFxxGGD yield with tRNA76ox is weaker than with the shortened tRNAox species (Fig. 3 ), in agreement with previously reported crosslinking data with the endogenous ribosomal protein L36AL in situ  [7].
Our crosslinking data agree well with photocrosslinking data using a tRNAAsp analogue substituted with 4-thiouridine in position 76 (tRNAAspp-s4U76), which could be cross-linked to C4335 located very close to Lys-53 of RPL36AL in the E-site region of human 80S ribosome  [18]. Crosslinking of the CCA-arm of a P-tRNA with both RPL36AL and eRF1 implies that the P-tRNA is present in the P/P state, while the L36AL protein due to the flexibility of its loop extension is capable of reaching the P-site, where it could interact with the CCAarm and the acceptor stem of tRNA. Accordingly, we propose the model in Fig. (11) , where the CCA end of the P-tRNA is sandwiched between the methylated GGQ motifs of RPL36AL and the A-site bound eRF1. In this model, the GGQ loop of eRF1 faces nucleotide A76 of P-tRNA, since at the end of the translation process, this nucleotide which bears the ester carbone of the peptidyl-tRNA is the natural target of the GGQ motif of eRF1 for the nucleophilic attack (via a water molecule) leading to the release of the polypeptide chain.
Modeling of the Human RPL36AL three-dimensional Structure and of Its Interaction with tRNA Comparative modeling of the RPL36AL structure was achieved using as template the structure of the Haloarcula Fig. (11). Model for the interactions between the ribosomal protein L36AL, the CCA-arm of P-site bound tRNA and the translation termination factor eRF1 bound to an A-site stop codon. marismortui large ribosomal subunit chain 3 at 2. 2 Å (PDB: P32411). This template was identified by hhsearch  [34] , the resulting alignment has 21 % sequence identity. 3-D model generation was performed using modeler  [35]  to generate 50 models with only slight variations. Not surprisingly, the models are close to the corresponding structure obtained in a previous study by Chandramouli and co-workers  [13] , who modeled the structure of the mammalian 80S ribosome from cry-electron microscopy data.
005% surfactant P20) and binding buffer (10 mM HEPES pH 7. 4, 150 mM NaCl, 2 mM MgCl2) were used. Between two binding assays at two different concentrations, the surface was regenerated by injection of 50 mM NaOH at a flow rate of 10 µl/min and 30 s contact time, followed by a 10 min dissociation by washing at a continuous flow of the running buffer. The experimental data from individual kinetic binding experiments were overlaid and analyzed using BIAevaluation 4. 1. F46 Bindi software (GE Healthcare) with 1:1 binding model.
1% in 50% CH3CN) and subjected to MALDI TOF MS. Samples (1 L) were mixed with MALDI matrix material (1 L; saturated solution of 3,5-dimethoxy-4-hydroxycinnamic acid in acetonitrile/TFA (0. 1%) 1:2, v/v). Mass spectra were obtained on an Autoflex speed series MALDI-TOF mass spectrometer (Bruker Daltonics) equipped with a pulsed N2 laser (337 nm) in a positive reflectron mode. Ions formed by the laser beam were accelerated to 23 keV. The final spectra were obtained by accumulating of ~1500 single laser shots. External calibration in positive mode was done by using Peptide Calibration Standard II (#222570, Bruker Daltonics). Mass accuracy of ~15 ppm was usually achieved. Mass spectra were processed with flexAnalysis 2. 4 software (Bruker Daltonics). Peptide mass calculations were performed by using the protein utilities at http://www.
ACKNOWLEDGEMENTS We are grateful to  Professors Jean Cognet  and  Yannick Andéol  for insightful advises, and to  Drs. Arsène P. Der Garabedian ,  Joël Pothier  and  Gérard Keith  for fruitful discussions. We gratefully thank  Mathilde Sayegh  and  Jilali Dalouz  for technical assistance,  Jean-Pierre Le Caër  for the mass spectrometric facilities, and  Drs. Cheikhe Hadjibou Soumare ,  Guy-Amédée Ajanohoun ,  Mahamane Hamidine  and  Safiatou Ouattara  for their constant interest for this work.
Randomization Patients will be randomized in blocks, with a distribution of 1:1 for the control group and experimental group. The block size will be random and will be informed in the report of the study. Randomization will be stratified by investigator centre, type of hepatectomy and presence of an underlying hepatopathy. The randomization list will be established using the software NQuery Advisor®v6. 01, a validated system using a generator of pseudo-random numbers, so that the sequence of treatments is both repeatable and nonpredictable. The physician-investigator will enter data for inclusion in computerized case report forms (eCRF), implemented using the software "Cleanweb" (Telemedicine technologies). The physician-investigator will be able then to access the randomization module of the software that will award the group in which the patient is randomized.
A unique identification alphanumeric number will be assigned to each patient: "number of the center (3 characters) -Number of inclusion in the center (3 characters) -Initials of first and family names (1 and 1 characters) -randomization group". Patients who left the study keep their number included if it has already been given. New patients will always receive a new issue of inclusion. The randomization list will not be known in advance by the investigators. The statistical analysis and preparation of tables and graphs for the report of the study by the statistician of the study will be blinded to the extent possible. The unblinding may take place only after all data has been entered into the database of the study, all requests have been closed and the database has been frozen by the Data Manager of the study.
Statistical analyses The statistical analysis will be based on the intention-totreat principle with one-sided test for the primary and secondary endpoints. However, attempts will be made to analyse "per protocol", "completer", and "intent-to-treat" populations separately, when statistically appropriate. A p < 0. 05 will be considered as significant. First of all, an establishment of patient flow chart according with CONSORT 2010 statement  [27]  will be realized. This one will describe precisely the progress through the different phases of the randomized trial (enrolment, intervention, allocation, follow-up and data analysis). Secondary, a description of demographic and clinic features of patients in the 2 groups will be calculated by using valid number, frequency count and percentage for categorical data and by using mean, standard deviation, 95%-confidence interval of the mean, minimum lower quartile, median and upper quartile for continuous data.
gov website under the identification number NCT02041871. The institutional promoter is the Paul Brousse University Hospital, Villejuif, France. This study received a grant from the French National Cancer Institute (Institut National Cancer -INCa) in 2012 and the study protocol has undergone peer-review by the funding body. The study products (ORAL IMPACT and IMPACT CONTROL) were donated by NESTLE Clinical Nutrition, France. Informed consent will be obtained from each patient in a written form before enrolment and randomization.
Statistical analysis Results are expressed by their mean and standard deviation (SD) shown in brackets for the 25 subjects. Student t tests on the paired samples allow us to analyse the effects of Cs on: travelling velocity (Vc and Vi), HR, VO 2 and subjective strains. Distribution normality was checked and achieved by variable transform if needed. Three-factor variance analyses (ANOVA) with correction for co-variable effects qualify the effects of Cs, velocity and injury level on the cardiac, energetic and subjective strains. Simple and multiple regression models based on the least square method were used to establish the relationships between the different variables. A 5% significance threshold was retained (p<0. 05). Statistical analyses were conducted using Statgraphics ® Centurion XVI software.
Using only the later to model strains can involve large and dangerous errors. MWC literature focuses, above all, on the technical and biomechanical data for improving exertion efficiency (Van der  Woude et al. , 2001)  and on the maximum capacities of sportsmen in MWCs  (Huonker et al. , 1998; Paré et al. , 1993; Van der Woude et al. , 2001; Veeger et al. , 1991). The main aim of MWC studies conducted between 1975 and 1995 was to improve wheelchairs by taking advantage of the wheelchair sports boom  (Cooper, 1990). During this period, Boston (USA) and Oita (Japan) marathon winning times dropped from over 2. 5 hours to less than 1. 5 hours. This progress was made possible by improving the technical and technological characteristics of wheelchairs (weight, stabilities, wheels, propulsion, etc.
Flow cytometry analysis Cells were grown in WIT medium (mammary culture medium developed by Tan Ince  [2] ), detached using 0. 5% trypsin-ethylenediaminetetraacetic acid (Invitrogen, Carlsbad, CA, USA), pelleted, washed and counted. A total of 10 5 cells were incubated for 20 minutes with the following antibodies: allophycocyanin (APC)-conjugated anti-epithelial cell adhesion molecule (EPCAM), phycoerythrin (PE)-conjugated anti-CD49f and PE-conjugated anti-CD10 (BD Biosciences, San Jose, CA, USA). Nonspecific PE-IgG2a and APC-IgG1 (BD Biosciences) were used as control antibodies. The gates marking the quadrants in the figures were set to exclude cells labeled by the control antibodies. Cells were washed three times, then analyzed on a FACSCalibur flow cytometer (BD Biosciences) using CellQuest Pro software.
Bioinformatics To identify the normal cell types expressing EGFR family members in human reduction mammoplasty tissue, the most variable probes for each EGFR family member were selected from the Lim et al. Gene Expression Omnibus [GEO:GSE16997] series matrix file  [14]. The error bars in Figure  2A  are standard errors of the mean. The P-values shown are derived from t-tests performed to compare the mature luminal with the indicated populations. The tumor analysis in Figure  2B  and 2C was performed on [GEO:GSE6861] Affymetrix GeneChip Human X3P Array (Affymetrix, Santa Clara, CA, USA) gene expression data derived from large operable or locally advanced breast cancer  [15, 16]. The. CEL files were normalized with robust multiarray average (RMA) in R  [17] , filtered to remove probe sets spanning a region <56 base pairs, and the most variable probe sets for EGFR family members were selected based on the standard deviation across all the tumors.
Viral particles were produced by calcium phosphate transfection of 293 T cells  [20]. Infections were performed at a multiplicity of 10 infectious units per cell based on the MCF7 titer. The final 4G-shp53-PI3K cells were uniformly positive for CFP and GFP, which are carried by the BMI1 and shp53 vectors, respectively, but showed heterogeneous staining for tdTomato because the tdTomato vector was added last, not selected with an antibiotic and conferred no growth advantage on the cells. For proliferation assays, cells were treated with 2 nM 17β-estradiol or 1 μM fulvestrant and counted at each time point with a Coulter counter (Beckman Coulter, Brea, CA, USA). To test for significant differences between the growth curves in Figure  4B  and Figure  10C , the slopes were compared after linear regression on logged data with the lm (linear model) function in R, with a correction in Figure  4B  for performing three tests.
Tumor size after subcutaneous injection was measured with a PhotonIMAGER (Biospace, Paris, France) and scaled to the size at the first time point in each tumor group. To identify significant differences, t tests were performed in R on logged data with a correction for performing five tests in Figure  6A  and ten tests in Additional file 2: Figure  S2E  and S2F. To measure tumor size after intraductal injection, the glands were removed at the end of the experiment and scanned as a single group with the PhotonIMAGER.
Materials and Methods This study was approved by the Joint Institute Review Board of Taipei Medical University and patients' written informed consent was waived. Patient information was anonymized and deidentified prior to analysis. The study was conducted in three tertiary-care hospitals located in Northern, Eastern and Southern Taiwan at which the investigators work. A list of all TB patients treated at the three hospitals notified to health authorities from 2005-2010 was obtained from the national TB registry at Taiwan Center for Disease Control. Notification data and patients' medical records maintained at hospitals were reviewed to identify all culturepositive pulmonary TB patients. Age, sex and type of TB were recorded from the notification record. Type of TB was defined as new or previously treated, according to what was recorded.
In order to determine the association of chest radiographic abnormalities with DM, a culture-positive pulmonary TB patient without DM who was notified to the health authority immediately prior to each culture-positive pulmonary TB patient with DM was selected for comparison. These patients were those who had never been documented to have 1) HbA1C. 6. 5%, or 2) fasting plasma glucose. 126 mg/dl, or 2) post-prandial plasma glucose. 200 mg/dl, or 3) random plasma glucose. 200 mg/dl. Medical charts were reviewed for data collection by using a structured questionnaire. Glycemic control was assessed by glycated haemoglobin A1C (HbA1C) measured within 3 months of the initiation of TB treatment; diabetes patients were categorized into 3 groups: HbA1C,7%, HbA1C 7-9%, HbA1C. 9%. A pre-treatment postero-anterior chest radiograph (taken within 30 days of initiation of TB treatment) of each patient was collected by research assistants and read by two qualified pulmonologists (readers) at each hospital.
After reading a set of 100 films, discordant films were discussed among the three readers of each hospital aiming at achieving consensus on reading. After discussion, the second set of 100 films was read independently by 2 readers and discordant films were again read by a third senior reader followed by discussion. This exercise was continued till all films of enrolled patients at each hospital were read. Reading of chest radiographs was recorded on an electronic standard reading form using EpiData Entry 3. 1 (The EpiData Association, Odense, Denmark). STATA Version 12 (StataCorp LP, College Station, Texas, USA) was used for statistical analysis. The presence of abnormal parenchymal opacity, location of opacity, extent of disease, presence of cavity, location of cavity, number of cavities and size of the largest cavity in were analyzed in relation to diabetes and glycemic control by Pearson Chi-square test.
Sociodemographic (maternal age, geographic origin which includes ethnicity), lifestyle (single or couple) and obstetrical characteristics were collected at inclusion from medical records. Stored frozen serum from 826 of the women was available. Some of the data collected were of interest for targeting pregnant women susceptible to CMV: sociodemographic characteristics, socioeconomic status (based on French National Institute for Statistics and Economic Studies (INSEE) nomenclatures for socioprofessional categories (http://www. insee. fr/nomenclatures), obstetrical characteristics (gravidity including the present pregnancy; parity excluding the present pregnancy) and factors associated with a higher risk of viral exposure and disease spreading (number of children,18 years old at home, work in contact with children and healthcare workers).
Discussion Data from the COFLUPREG prospective cohort allow us to study numerous determinants associated with CMV seropositivity and their relative importance. The study was multicentric involving three major university maternity hospitals in Paris in 2009 and a large number of blood samples recently and randomly collected was available (n = 826). This study design and the quality of the data reinforce the reliability of the results. To our knowledge, in France, or in other West European countries, no other recent results on the characterization of women susceptible to CMV infection that highlight, like in this study, the discrepancy of seroprevalence between women of African origin and those of French origin. Paris Metropolitan area, even if not representative of all French population, is an area with a high proportion of immigrants from sub-Saharan Africa and their children.
Acknowledgments Inserm COFLUPREG Study Group members:  O. Launay ,  P. Loulergue ,  V. Truster ,  C. Villeret ,  M. Cervantes-Gonzales  ( Centre d'Investigation Clinique de vaccinologie Cochin Pasteur, Hopital Cochin ),  F. Goffinet ,  V. Tsatsaris ,  C. Le Ray ,  D. Cabrol  ( MaternitePort-Royal, Hopital Cochin ),  C. Charlier ,  M. Lecuit ,  O. Lortholary  ( Service de maladies infectieuses, Hopital Necker Enfants Malades ),  Y. Ville ,  S. Parat  ( MaterniteNecker-Brune, Hopital Necker-Enfants Malades ),  J. Lepercq ,  C. Francoual  ( Maternite, Hopital Saint Vincent de Paul ),  PH. Jarreau  ( service de neonatalogie, Hopital Cochin ),  F. Rozenberg ,  A Krivine  ( service de virologie, Hopital Cochin ),  M. Leruez-Ville  ( service de virologie, Hopital Cochin ),  S. van der Werf  ( CNR grippe, Institut Pasteur), JM Treluyer (service de pharmacologie, Hopital Cochin ),  F Batteux  ( service d'immunologie biologique, Hopital Cochin ),  ML Gougeon (Unite« Immunitevirale , biotherapie et vaccins »,  Institut Pasteur).