dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene Cloning and Vector Construction EgMYB88 coding sequence was obtained from Eucalyptus gunnii (Eg) xylem cDNA using the primers CACCCATAT GGAGAAATCATCAGCTGCAA and GAGCTCTCCTGATCT CTCATCACA with the Phusion taq (Finnzymes). EgMYB88 sequence from E. gunnii (accession number KX470407) was highly similar to EgrMYB88 (Egr indicates E. grandis) sequence (Eucgr. F04423. 1), with just one nucleotide change at the R3 MYB domain which do not modify the amino acid sequence. Amplicon was then inserted in the pENTR D-TOPO vector (Invitrogen) and subsequently transferred into several destination vectors using LR clonase II (Invitrogen) following manufacturer's instructions. Destination vectors were pGBD-GTW (kindly provide by Dr Laurent Deslandes, LIPM, INRA, France) for yeast auto activation assays, pFAST-G02  (Shimada et al.
1 mm) column (Phenomenex). The mobile phase consisted in 0. 1% formic acid in ultra-pure water (solvent A) and 0. 1% formic acid in methanol (solvent B). The molecules were eluted through a gradient elution from 1 to 99% B for 13 min with a flow rate of 400 µL/min and then 3 min in 99% B. The column was then re-equilibrated to 1% B prior to the next run. Mass spectrometry analysis was carried out in ESI negative mode. Quantification was performed by measuring the area under each peak at 280, 320, or 350 nm, depending on the lambda max of each molecule. Total metabolic signal was performed at 280 nm. Due to co-eluting signal at 280 nm, catechin quantification was realized by measuring the area under peak at m/z 289, corresponding to the [M-H] -of the molecule. Experimental exact masses and MS fragments were compared to metabolomics data banks (Respect: http://spectra.
tremula was obtained from Popgenie database  (Sjödin et al. , 2009)  based on the RNAseq survey performed by  Sundell et al. (2015). Similarly to EgMYB88, PtMYB11, and PtMYB171 were highly expressed in wood, but exhibited distinct expression profiles in other non-woody tissues (Figure  S4 ). Because of the importance of the vascular cambium and differentiating xylem in the secondary growth characteristic of woody plants, we decided to investigate the function of EgrMYB88 by cloning its coding sequence from E. gunnii growing in Southwest France (EgMYB88). Nucleotide coding sequence is nearly identical between EgrMYB88 and EgMYB88, with just a base change at the R3 MYB domain that do not modify the amino acid sequence.
SP compounds are present in lignin from poplar as well as other plants  (Morreel et al. , 2004). The fact that EgMYB88-EAR overexpressing poplars contain a significant reduction of oligolignols deriving from benzoic acid, whereas EgMYB88 overexpressing plants show an important accumulation of salicinoid phenolic glycosides also deriving from benzoic acid suggests an unexpected link between these Short name, full name and accession number (P. trichocarpa genome v3. 0) are indicated for each gene. Transcript abundance (mean ± standard deviation) was calculated from nine Pro35S:EgMYB88 poplar (i. e. , three plants for each of the three selected independent transgenic lines specified in Figure  S2A ) and nine control plants. Ratios of transcript abundance in transgenic poplars relative to controls are also indicated.
Statistics were calculated with Student's t-test, ***P-value < 0. 001, **P-value < 0. 01, *P-value < 0. 05. Significant differences relative to the control values are highlighted in bold. Primer sequences are indicated in Table  S1. Short name, full name and accession number (P. trichocarpa genome v3. 0) are indicated for each gene. Transcript abundance (mean ± standard deviation) was calculated from eight Pro35S:EgMYB88-EAR poplar (i. e. , two-three plants for each of the three selected independent transgenic lines specified in Figure  S2B ) and seven control plants. Ratios of transcript abundance in transgenic poplars relative to controls are also indicated. Statistics were calculated with Student's t-test, *P-value < 0. 05. Significant differences relative to the control values are highlighted in bold.
ACKNOWLEDGMENTS The authors are grateful to  A. Moreno  and  I. Allona  ( UPM/INIA, Madrid, Spain ) for their help with poplar transformation, to  H. Yu ,  A. -L Fonseca ,  A. Desplat , and  M. Liu  ( LRSV, Castanet-Tolosan, France ) for her help with sampling and transformant analysis, to  Y. Martinez  ( FR3450, Castanet-Tolosan, France ) for assistance with imaging, to  A. Fauvet  ( INRA/UL, Vandoeuvre, France ) for her precious help in the preparation of poplar methanolic extractions, to  J. J. Molina-Rueda  ( UMA, Málaga, Spain ) for his precious advice, and to  J. Takahashi-Schmidt  ( UPSC, Umea, Sweden ) for her guidance in Py-GC/MS.
FUNDING This work was supported by the  Centre National pour la Recherche Scientifique (CNRS) , the  University Paul Sabatier Toulouse III (UPS) , the  French Laboratory of Excellence  project " TULIP"  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ), and the  TREEFORJOULES  project ( ANR-2010-KBBE-007-01 ). MS was supported by the postdoctoral fellowship " Beatriu de Pinós " ( 2009 BP-A 00185 ) from the  Departament d'Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya  and by grants from  TREEFORJOULES  and TULIP. AP by a PhD grant from the  Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche. JL by a postdoctoral fellowship from the  Sao Paulo Research Foundation  ( FAPESP ,  2013/17846-0 ). EC by a postdoctoral fellowship from  CNPQ-Brazil  ( 202228/2015-0 ).
Study design and randomisation The study design has been published previously  [14]. The ETIC was a cluster randomised controlled clinical trial with general practices as the unit of randomisation (Fig. 1 ). The trial was carried out in the four areas of the Auvergne region of France, with stratification on each. The trial is reported according to the extended CON-SORT statement for cluster randomised trials. The ETIC was registered with ClinicalTrials. gov (Registration Number: NCT01065142)  [15].
Primary outcomes Quality of life scores are presented in Table  5. The ICC associated with the MLHFQ primary outcome at 19 months was 0. 11. Changes from the baseline were analysed in the Intervention and Control Groups only for patients whose data were available at the end of the trial. The regression coefficient, adjusted for the baseline results, of the MLHFQ score was b = 1. 19 (95 % CI: -5. 94-8. 32, P = 0. 74). The differences in MLHFQ score in the Intervention and Control Groups at the 19-month follow-up were 3 (-4-13) and 1 (-5-13), respectively. There was no difference in MLHFQ score during followup at 7 and 13 months, respectively: 33. 9 (± 29. 2) in the Intervention Group versus 28. 3 (± 22. 5) in the Control Group at 7 months, P = 0. 14; 33. 4 (± 22. 1) in the Intervention Group versus 29.
Many published studies include only patients with HFrEF  [12] , younger patients  [7, 9] , or predominantly male patients  [8, 11]. Consequently, most data published to date relate to patients with HFrEF  [27]. The ETIC chose a pragmatic design and included a broad range of patients with CHF, most of whom had HFpEF, because we deliberately chose not focus on just one segment of the CHF population  [27]. In the IM-PROVEMENT study, only 51 % of patients with an echocardiogram exhibited left-ventricular systolic dysfunction (poorly contracting left ventricle, enlarged left ventricle or ejection fraction under 40 %)  [26]. The quality of life of the patients was measured using two questionnaires, the MLHFQ and the SF-36, because the first is specific and the second is generic. The sample size was estimated by taking into account an anticipated ICC of between 0.
Acknowledgements We wish to thank all participating patients and general practitioners. This study relies on the investigating general practitioners who agreed to participate. We would like to thank the  Clinical Research and Innovation Office in Clermont-Ferrand  for their help with the administrative management of this project and for their independent data analyses. We also thank  Dr. Sylvie Pruilhère-Vaquier  (nutritionist and patient education trainer),  Dr. Béatrice Roche  (endocrinologist),.
Figures 1 and 2 about here Detailed analyses of wood formation need repeated sampling (generally at a weekly time step) of the developing tree ring during the growing season  (Rossi et al. , 2006). One of the main problem of this method is that growth is not homogeneous along and around tree stem, so that the number of wood cells produced at a given time can considerably vary according to the position of the sampling  (Wodzicki and Zajaczkowski, 1970). Therefore, it is difficult to know whether the wood cell number variations observed between the successive samples are related to growth or within-tree growth variability, making the characterization of the wood cell number variation in the successive phases of tracheid differentiation during the season difficult from a simple description of the raw cell count dataset.
ACCEPTED MANUSCRIPT additive) models or generalized (partial linear) models discussed in  Pardinas and Sperlich (2010). First, we propose to focus on Generalized Linear and Additive Models (GLMs and GAMs, respectively) for parametric and semiparametric regressions, respectively, of function m, useful in the situation of a regression model for which the error term is not normally distributed, as for binomial response variables or count data  (Hastie and Tibshirani, 1990; McCullagh and Nelder, 1989). More precisely, because the observations are repeated on the same trees during the year, we are concerned with mixed effects versions of GLMs and GAMs, denoted GLMM and GAMM respectively. Mixed models are recommended to reduce the bias of the estimations in the case of repeated measurements since random effects allow to take into account the correlation between observations  (Zuur et al.
ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT over-smoothed, while those provided by GAMM and semiparametric binomial regression reflected more data variations. Moreover, the modal value was globally under-estimated and moved from a few days by GLMM; and, this parametric model over-estimated the null values observed at left boundary points. Finally, concerning GAMM and semiparametric regression, they allowed to highlight similar tendencies, except for tree i = 5. Table  1  and Figure  4  about here For thickening phase, the conclusions are similar as previously. In particular, let us present the following remarks. In Figure  5 , for individual tree i = 2, the semiparametric binomial regression clearly pointed out some estimations with a bimodal tendency in contrast to GLMM and GAMM. Moreover, GLMM largely over-estimated the values observed at right boundary points.
The GL(M)M method, which allows for a high degree of smoothing, may be sufficient when the will is to describe the wood formation general pattern, because it highlights the general shape of cell number variations during the season. But its lack of fitting makes it unsuitable when the will is to assess in detail the dynamics of the wood formation process. For example, one of the central aim of wood formation studies is to understand how climate influences wood formation, and this needs to relate climatic and wood formation data at a fine temporal resolution  (Gricar et al. , 2011). In this case, the GA(M)M, GAMLSS and semiparametric kernel procedures could offer great perspectives because of their higher sensibility to the high frequency variations in the dataset. The incorporation of mixed effects in GA(M)M is also a good point because it allows to accurately account for the non-independence of weekly cell count data An h-value denoted h cv can be selected by the well-known cross-validation procedure adapted to the estimator m n in  (3).
Aortic morphology Thickness and internal diameter determinations were performed according to two methods. First, aortic rings were immersed in PSS, and optical images of the aortic cross-section were recorded to allow determination of basal internal diameter and thickness. Second, light microscope images were taken after hematoxylin-eosin staining realized on paraffin-embedded tissue sections. The mean internal aortic diameter and media thickness were determined from three measures realized on at least four arterial segments per animal by using ImageJ (National Institutes of Health, Bethesda, MD). Media cross-sectional area was calculated from these measures. Both methods gave similar results, and data represent means of all values obtained.
Data collection The EHNBPCCG is described in detail elsewhere  (1, 4). Studies were eligible for the current collaborative individual participant meta-analysis if they had data on circulating levels of IGF-I, IGF-II, IGFBP-1, IGFBP-2 or IGFBP-3 and subsequent prostate cancer risk. Studies were identified through searches using the search terms "insulin-like growth factor", and "prostate cancer" on computerized bibliographic systems, including PubMed, Web of Science, Cochrane Library, and CancerLit, through the reference lists of publications identified in this search, and through correspondence with study investigators. Further details of data collection and processing are provided in Supplementary Methods. Individual participant data were available from 19 studies by the dataset closure for these analyses on November 8 th , 2012; Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC)  (5) ; Baltimore Longitudinal Study of Aging (BLSA)  (6) , British United Provident Association Study (BUPA)  (7) , the Cardiovascular Health Study (CHS)  (8) , the CLUE 1 Study  (9) , European Prospective Investigation into Cancer and Nutrition (EPIC)  (10) , European Randomized Study of Screening for Prostate Cancer (ERSPC)  (11) , Health Professionals Follow-up Study (HPFS)  (12) , Japan Collaborative Cohort Study (JACC)  (13) , Kaiser Permanente Medical Care Programme (KPMCP)  (14) , Melbourne Collaborative Cohort Study (MCCS)  (15) , Multiethnic Cohort (MEC)  (16) , Northern Sweden Health and Disease Cohort (NSHDC)  (17) , Prostate Cancer Prevention Trial (PCPT) (3), Physicians' Health Study (PHS)  (18) , Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO)  (19) , the Prostate Testing for Cancer and Treatment (ProtecT) feasibility study  (20)  and main study  (2) , and the SUpplémentation en VItamines et Minéraux AntioXydants (SU.
Tests for heterogeneity for case-defined factors were obtained by fitting separate models for each subgroup and assuming independence of the ORs using a method analogous to a meta-analysis. Tests for heterogeneity for non-case defined factors were assessed with a χ 2 -test of interaction between subgroup and the continuous trend test variable. A χ 2 -test of interaction was also used to determine whether risks by study-specific thirds of one analyte varied according to the study-specific third of another analyte. All tests of statistical significance were 2-sided, and statistical significance was set at the 5% level. All statistical tests were carried out with Stata Statistical Software, Release 12 (StataCorp, LP, College Station, Texas).
We found that Msgn1  179, 180  is the most reliable and specific marker of paraxial mesoderm progenitors. While, unfortunately, no validated Msgn1 antibody is currently available, Msgn1 expression can be monitored by RT-qPCR or using reporter cell lines (data not shown). Like Tbx6, Msgn1 expression is expected to peak around day 4-5 of differentiation with this protocol, accounting for 70-90% of the cells. Myogenesis per se should be monitored starting on week 2 by following MyoD and Myogenin expression  181- 183  , while myotubes formation and fiber maturation can be assessed using skeletal muscle proteins or isoforms, such as Myosin Heavy chains  184  (Figure  2 , Table  2 ). The presence of satellite-like cells in long term culture can be evaluated with the expression of Pax7  185, 186  (Figure  2b , 4a,b).
Acknowledgements We thank all co-authors of the original article describing this technology for their initial support and contribution. We thank  Getzabel Guevara  for lab assistance. We thank  Charlotte Fugier  and  Fanny Bousson  for comments and feedbacks. We are grateful to  Laurence Daheron  and members of the  Pourquié laboratory  for comments. We thank  Amélie Freismuth  and  Marion Humbert  from the IGBMC cell culture service for hiPSC culture assistance. This work was supported by an advanced grant from the  European Research Council  to O. P. , by the  FP7 EU grant Plurimes  (agreement no. 602423 ), by a strategic grant from the  French Muscular Dystrophy Association (AFM)  to O. P. , and by the  INGESTEM  project (ANR).
Data Acquisition and Treatment Confining pressure, load, and axial piston displacement are recorded in parallel with an analogue chart recorder (sampling frequency of 50 Hz) and a digital data logging system (LabView 7. 1) sampling at 1 or 2 Hz (corresponding to 0. 001, 0. 01, 0. 1, or 0. 5 μm of piston displacement depending on the applied displacement rate). The data are evaluated with a MATLAB program ("rig," http://sites. google. com/site/ rigprogram). The mechanical data are then corrected for decreasing forcing block overlap with increasing slip (Figure  2b ), for apparatus compliance, for changes in confining pressure relative to hit-point (±10 MPa or less, i. e. , ∼ 4% at 500 MPa confining pressure), and "friction" associated with driving the tungsten carbide piston into the sample assembly (1.
Different assemblies were compared and validated using MAUVE software  (26). The entire genome was annotated in MaGe  (27)  based on a combination of bioinformatics tools, including homology-, structure-, and synteny-based approaches. Other AOA genomes are included in MaGe, which relies on the PkGDB relational database with data from both public data banks and manually curated genomes. Manual curation of the "Ca. Nitrosotalea devanaterra" genome was performed in MaGe, with emphasis on genes absent in other AOA, genes located at synteny breaks, genes characterized in other AOA, and functions involved in major metabolic pathways. Functional categorization into clusters of orthologous genes (COGs) and protein families (Pfams) was performed with COGnitor and HMMPfam software implemented in MaGe, respectively.
tRNA predictions were made using tRNAscan-SE  (28)  and ARAGORN  (29). Transporters were annotated using TransAAP implemented in TransportDB  (30). Average nucleotide identity (ANI) and tetranucleotide frequency analyses were carried out in JSpecies  (31). z scores of the nucleotide word frequency analysis were used to build the matrix for the principal-component analysis in PAST software  (32)  and SigmaPlot v12 (Systat Software Inc. , London, United Kingdom). To construct Venn diagrams, the presence of open reading frames (ORFs) in archaeal genomes was identified using reciprocal BLASTP searches (selected cutoffs were an E value of 10 Ϫ3 and 30% amino acid identity) implemented in the stand-alone BLAST-2. 2. 25ϩ suite  (33). The cutoff for absence and presence of coding sequences (CDS) in comparison with other archaeal and bacterial genomes was selected as 30% DNA identity with 80% CDS coverage, as recommended in the MaGe guidelines.
Synteny analysis was performed in MaGe  (27). Identification of genes potentially linked to acidophily. To identify candidate genes enabling acidophily, the "Ca. Nitrosotalea devanaterra" genome was compared to those of previously characterized archaeal and bacterial acidophiles to detect gene homologues conserved between "Ca. Nitrosotalea devanaterra" and acidophiles and absent in other AOA (cutoff, 30% DNA identity with 80% CDS coverage). However, as "Ca. Nitrosotalea devanaterra" represents an uncharacterized genus, many ORFs had no database hits in RefSeq, Swiss-Prot, and TrEMBL. Many ORFs also had homology only to uncharacterized proteins in databases. In addition, some (novel) acid tolerance mechanisms may be a result of convergent evolution and not share genetic homology. Therefore, a literature search was performed on pH adaptation strategies of acidophilic microorganisms, as well as on biochemical limitations of ammonia oxidizers with regard to pH, and potential functional analogues were manually identified from the curated "Ca.
For AMO, all AOA amoB and amoC sequences deposited in GenBank (July 2014) were used in alignments, and the active site of AMO was modeled against PmoB and PmoC of Methylococcus capsulatus Bath and other previously characterized model organisms  (36) (37) (38) (39). This resulted in an alignment of 115 and 147 sequences for amoB and amoC, respectively. Transmembrane helices and protein topology were predicted initially using TmHMM  (40). However, TmHMM failed to detect some of the helices that experimentally have been indicated to be present in AOA AmoB sequences  (39) , and TMPred  (41)  and TopPred (42) were subsequently used. Signal peptides were initially predicted by SignalP  (43) , which also failed to detect signal peptides of AmoB of AOA  (39) , and prediction was implemented in Phobius  (44).
Phobius was used additionally for validation of the transmembrane helix predictions. Phobius uses both signal peptides and transmembrane helices in its topology prediction, which requires that amino acid residues immediately downstream from the signal peptide cleavage site must be extracellular. For Amt/Rh, the database was compiled from a selected set of sequences (total, 89) from GenBank, with the emphasis on sequences that had been previously characterized electrophysiologically and structurally. Phylogenetic analysis of derived Amt/Rh protein sequences used 228 unambiguously aligned positions. Maximum-likelihood analysis was performed with PhyML (45) with invariable sites and eight variable gamma rates modeled. Bootstrap support was also calculated using parsimony and distance analyses (MEGA6  [46] ) with 1,000 replicates.
The quality of qPCR products was verified by melt curve analysis and agarose gel electrophoresis. amoA qPCR was performed as previously described  (6). Statistical analysis was performed in SigmaPlot v12 (Systat Software Inc. , London, United Kingdom). To determine whether there was a significant increase or decrease in transcript abundance over time, linear regression was performed using individual (rather than averaged) values of transcript/gene data as the dependent variable and time as the independent variable. This analysis was additionally performed for nitrite accumulation, assessed with Griess reagent as previously described  (7). Nucleotide sequence accession number. The closed genome sequence with full annotations has been deposited in ENA with accession number LN890280.
Nitrosotalea devanaterra" is achieved by cytoplasmic pH regulation mechanisms not shared by neutrophilic AOA. The current study (as with all genome studies) is limited by   S9  in the supplemental material). existing sequence databases but proposes hypothetical mechanisms for growth of acidophilic archaea that can provide the basis for future experimental testing. Although the presence and activity of previously uncharacterized genes were associated with the acidophilic growth of "Ca. Nitrosotalea devanaterra," one caveat of this approach is that "Ca. Nitrosotalea devanaterra" cannot grow at neutral pH. It is therefore impossible to unequivocally confirm that these genes are a prerequisite for acidophily. This question could be further explored by heterologous expression of candidate acidophily genes in a neutrophilic organism (preferably a neutrophilic AOA) to examine whether they confer an acidophilic phenotype on the host.
FUNDING INFORMATION This work, including the efforts of Luis Sayavedra-Soto, was funded by  National Science Foundation (NSF)  ( 0541797 ). This work, including the efforts of  Laura E. Lehtovirta-Morley ,  James I. Prosser , and  Graeme W. Nicol , was funded by  Natural Environment Research Council (NERC)  ( NE/I027835/1 ). Graeme W. Nicol  is also supported by the  AXA Research Fund.