dataesr/scientific-paragraphs-categorization huggingface dataset:
Glycosyltransferases catalyze the transfer of sugar moieties from activated donor molecules to specific acceptor molecules such as sugars, lipids, proteins, or small molecules including phenylpropanoids. Information on GHs and GTs, as well as polysaccharide lyases and carbohydrate esterases, can be found in the CarbohydrateActive enZymes (CAZy) database  (Davies et al. , 2005)  and a specific PlantCAZyme database is now also available  (Ekstrom et al. , 2014). CAZy takes into account similarities in amino acid sequences, 3D-structures, sugar donors, transferred sugars, acceptors, catalytic mechanisms (inverting or retaining mechanisms) and more recently the combination of modules which can be catalytic or not  (Lombard et al. , 2014). Among the 98 GT families present in the CAZy database in may 2016, the GTs catalyzing secondary metabolite conjugation all belong to the GT1 family and are commonly named UDP-glycosyltransferases (UGTs) as UDPsugars are used as the sugar donor.
, 2001; Osmani et al. , 2008; Yonekura-Sakakibara et al. , 2008). The conjugation of acceptor molecules such as hormones, xenobiotics or secondary metabolites by UGTs allows the plant cell to modulate their biochemical proprieties and thus has a strong influence on their biological activity and compartment storage. Although, the primary sequences of the UGT proteins show only low similarities  (Vogt and Jones, 2000) , a highly conserved 44-amino acid sequence named plant secondary product glycosyltransferase (PSPG)-box believed to be involved in binding to the UDP moiety, is present in the C-terminal region  (Mackenzie et al. , 1997). This sequence is therefore commonly used as a query to identify the UGT genes in plant genomes by using the Blastp program in addition to position-specific weight matrix (PSWM) guided search and a hidden Markov model (HMMER) program (Figure  2 ).
The sequences can then be confirmed by using the MEME suite or the Pfam (http://pfam. sanger. ac. uk/search), SMART (http://smart. embl-heidelberg. de/) and Interpro (https://www. ebi. ac. uk/interpro/) databases. The very large spectrum of molecules potentially glycosylated by UGTs is in accordance with the high number of sequences identified at a genome-wide scale in plants as compared to other kingdoms including animals. However, there does not seem to be any relationship between the number of predicted UGT models and the taxonomic families such as liliopsida and eudicotyledons. Among the numerous phenylpropanoids, flavonoids are probably among the best-characterized molecules in terms of glycosylation due to their numerous medical and commercial benefits. The basic structure of flavonoids consists of two phenyl groups joined by three carbons cyclized with oxygen (Figure  1 ).
, 2009). This coexpression database (v8. 0; March 2016) contains 58 experiments, 1388 GeneChips collected by AtGenExpress and about 20,000 publicly available files to provide gene-to-gene mutual ranks. We focused on a specific comparison between the known phenylpropanoid synthesis genes and the UGT genes found to be expressed in Arabidopsis. The complete results are presented as a heat map in Supplementary Figure  1  and a focus on five zones determined by hierarchical clustering containing a high number of coregulated genes is presented in Figure  3. The UGT genes known to be involved in the glycosylation of the major phenylpropanoids are included in groups 1, 2, and 5. When the flavonoid biosynthesis pathway is considered (Figure  3 , group 1), UGT78D1, UGT78D2, and UGT89C1 are all highly co-regulated with the four successive genes involved in the transformation of p-coumaroyl CoA to kaempferol/quercetin i.
Design, Setting and Participants All French patients aged 40 years or older, having undergone primary THR from January 1, 2009, through December 31, 2012, for arthritis according to French national health insurance databases were included in the cohort. Outcome of interest was THR revision, including any surgical procedure in which the implant or any component was changed or removed. Follow-up started the day the primary THR was performed. Observations were right-censored on December 31, 2014, if neither revision nor death had yet occurred. Exposure of interest was the cumulative defined daily doses per day (cDDD/day) of benzodiazepines and Z-drugs dispensed within 6 months before or after inclusion. We defined four exposure groups; cDDD/d = 0: unexposed; <0. 08: low exposure;] 0. 08-0. 38]: medium exposure; >0.
Although the association between BZDs exposure and increased risk of hip fracture leading to THR is well-documented  [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] , few researches studied the association with THR revision. The aim of this study was to examine the association between BZDs and Z-drugs exposure and THR short-term survivorship in a population-based cohort, using French health insurance system linked to hospital discharge databases  [24] [25] [26] [27] [28].
Data Sources We used the French Health Insurance Information System (SNIIRAM),  [24, [27] [28] [29] , covering the entire French population, with different employment-based schemes. We only used the data for general scheme beneficiaries (approximately 77% of the population), for whom SNIIRAM records, with dates, outpatient drugs (Anatomical Therapeutic Classification codes), medical devices, services and procedures reimbursed. The database does not state medical indications per reimbursement but contains patients' demographic, administrative, and medical details (chronic conditions such as diabetes mellitus, cancer or cardiovascular disease) and date of death. An anonymous, unique patient identifier links SNIIRAM information to national hospital discharge databases (Programme de Médicalisation des Systèmes d'Information, PMSI), providing reasons for admission (using International Statistical Classification of Diseases, 10 th Revision, ICD-10).
Inclusion and Exclusion Criteria The eligible population was patients aged 40 years or older, having undergone unilateral primary THR for osteoarthritis from January 1, 2009, through December 31, 2012 (48 months). Patients having primary THR for trauma or bone cancer, prosthetic revision before the inclusion period, simultaneous bilateral THR, not being reimbursed 6 months after THR or with non-matching procedure in PMSI were excluded. The entire study cohort population of 249,597 patients was described. THR characteristics were missing for 2,657, excluded from subsequent analyses, leaving 246,940 (Fig  1  Approval was obtained from the French data protection agency (Commission Nationale de l'Informatique et des Libertés). Informed consent was not required as information was collected anonymously.
Covariates We collected a series of patient, implantation centre and THR characteristics known to be or suspected of being associated with a risk of post-arthroplasty complications. Patients' age, sex and date of death, came from the SNIIRAM database. At baseline, treatments were identified by prescriptions (Anatomical Therapeutic Classification codes) reimbursed at least once within 180 days before or after inclusion, antidepressants, antihypertensives, oral corticosteroids, osteoporosis treatments, psychostimulants, antiepileptics, anxiolytic or hypnotic non-benzodiazepines (non-BZD), antipsychotics. Diabetes mellitus, obesity, Parkinson's disease, immunodeficiency and chronic kidney disease were defined (ICD-10 categories) on the basis of hospital discharge reports or chronic condition recorded the year before inclusion with relevant prescriptions.
HRs for revision following anxiolytic-BZDs alone, hypnotic-BZDs alone and both anxiolytic-and hypnotic-BZDs were assessed using univariate and multivariate Cox proportional hazards regression models. Diazepam, oxazepam, clorazepate, chlordiazepoxide, clonazepam, bromazepam, clobazam, lorazepam, alprazolam, loflazepate and clotiazepam as anxiolytic-BZDs and nitrazepam, flunitrazepam, estazolam, triazolam, lormetazepam, temazepam, midazolam, loprazolam, Zopiclone and Zolpidem as hypnotic-BZDs. Statistical tests were 2-tailed with 5% alpha-risk and analyses were performed using SAS Base 9. 4 and SAS Enterprise Guide 4. 3 software (SAS Institute Inc).
2 to 1. 4  [12, 36, 37]. Consequently, it seems reasonable to assume that in exposed patients, BZDs may increase the risk of falls, possibly leading to THR fracture and revision; it is our hypothesis. Nonetheless, we were unable to identify direct causes for revision; studies covering both BZDs exposure and indications for revision would be necessary to investigate a possible underlying mechanism. In our study, we measured baseline BZDs exposure in terms of cDDD/d. The DDD was first constructed as a technical measure to estimate specific drug consumption per capita, based on aggregate sales data records. It is nonetheless an internationally recognized measure which enables dosage comparisons between drug categories, making it the first choice for comparing prevalence of use and doses, over time and between areas  [15, 18, 38].
Like all studies using healthcare databases, exposure to treatment is derived from drug dispensing data instead of effective drug intake. However, the possible classification bias is non-differential and given that BZDs dispensations are regularly renewed overtime, it is reasonable to assume that the drug is ingested. Due to missing THR characteristics, we excluded 2,657 individuals (1. 1%) from the analyses. In this group, revision rate was 3. 8% and BZDs exposure 51. 6% which is comparable to those in the analyzed individuals. We do not think excluding these patients substantially affected the observed associations. Implant survivorship may not be the only relevant outcome after THR. Although prosthetic revision is an unambiguous outcome, it is not the only sign of implant failure; considering revision as the only outcome in implant survivorship has been debated in orthopaedic literature.
Other markers, including quality of life and/or functional improvement, could be used as secondary outcome to identify non-revised implant failure, but the SNIIRAM database does not provide such information. Although direct costs related to BZDs are not an economic priority  [39] , their misuse and/or overuse result in significant indirect costs which is not our topic, but could be assessed by dedicated medico economic studies. Lastly, despite the large number of covariates taken for adjustment in the multivariate Cox model, residual confounding due to factors not collected in our study (for being absent from the data base, such as smoking history, alcohol ingestion, socioeconomic factors or premorbid nutrition. ) cannot be ruled out.
Quantifying the microtubule response to mechanical perturbations As shown above, responses appeared somewhat variable, depending on genotype, marker line or even individual sample. To go beyond the qualitative assessment of the microtubule response to compression, we analyzed the variations of anisotropy and orientation of the microtubule arrays and quantified the variations in the degree of apparent bundling between time 6 h 45 min (i. e. 15 min after the release of compression) and time zero (before compression). The approach was partially automated with the prospect to ease statistical analysis of bigger data sets. Here, we analyzed thirteen GFP-MBD meristems (four indented and nine controls), six GFP-TUA6 meristems (three indented and three controls), seven spr2-2 GFP-MBD meristems (four indented and three controls) and six bot1-7 GFP-MBD meristems (three indented and three controls).
On each meristem, on the snapshots taken at time zero and at time 6 h 45 min, 80 cells were segmented manually using the Fiji Polygon tool  (Schindelin et al. , 2012). The Fiji plugin FibrilTool  (Boudaoud et al. , 2014)  was used to compute microtubule array anisotropy and mean orientation per cell. Polygon segmentation was then imported in the R software with the R ImageJ Region of Interest (ROI) package (R Core Team, 2015;  Sterratt and Vihtakari, 2015)  to quantify the degree of apparent bundling. Compression from the top is known to induce a better alignment of microtubule arrays per cell in cotyledons and leaves, i. e. an increase in the anisotropy of microtubule arrays  (Jacques et al. , 2013; Sampathkumar et al. , 2014). Lateral compression of the shoot apical meristem is known to induce the formation of supracellular microtubule alignments in the direction of maximal tensile stress  (Hamant et al.
DISCUSSION 'Complex self-organizing phenomena can only be fully understood with quantitative mathematical frameworks that allow specific hypotheses to be formulated and tested'  (Oates et al. , 2009). The microtubule response to mechanical perturbations is a perfect illustration of a complex selforganization phenomena and, in the present article, we provided several tools to obtain quantitative data on microtubule behaviour in response to quantified loads. We used a microindenter to apply a quantified and constant mechanical compression on growing shoot apical meristems, we performed correlative microscopy, with confocal imaging, before and after compression, and we analyzed the orientation, anisotropy and apparent bundling of microtubules in the confocal images. The proposed indenting protocol and its coupling with confocal live-imaging offers interesting perspectives to test quantitatively the response of cytoskeleton and gene (a) Example of analyses performed on a GFP-MBD compressed cell (white asterisk).
Several data sets were extracted from the green channel of confocal images ('raw image') before and after indentation: distribution of fluorescence intensity in each cell (delineated with the red polygons) and its characteristic parameters (coefficient of variation i. e. standard deviation over mean, skewness, a measure of the symmetry of the distribution, and kurtosis, a measure of the presence of outliers). Spatial autocorrelation between fluorescent pixels was computed with the Geary index. The percentage of microtubule fibers per cell ('P. fibers') and width of bundles ('fiber width') were computed on the binarized image. A threshold was applied on the distribution of fluorescence intensity to binarize the image. Pixels above 50 were considered as belonging to microtubule fibers, whereas pixels under 50 were considered as belonging to the background.
Image analysis All confocal stacks were projected in 2D with the MerryProj software (Barbier de  Reuille et al. , 2005). On each stack, starting from the top of the stack, 80 cells were manually delineated as ROI with ImageJ Polygon tool  (Schindelin et al. , 2012). The total area of these 80 cells had a diameter between 53. 3 and 75. 0 lm, smaller than the diameter of the indenter tip (96. 96 lm). FibrilTool macro  (Boudaoud et al. , 2014)  was used to compute direction and quality of orientation of microtubules on 2D projection within each ROI. Reorientation of microtubules during the two snapshots, prior to and 15 min after indentation, was computed as the difference of angle with the x-axis, which was checked to not rotate between snapshots with the ImageJ plugin Align3_TP  (Parker, 2010).
Polygon segmentation was then imported in the R software thanks to the RImageJROI package  (R Core Team, 2015; Sterratt and Vihtakari, 2015). Although a great attention was taken to ensure similar fluorescence exposure conditions among snapshots, differences could remain, possibly intensified by the 2D projection. Two different ways of normalization were thus tested, allowing comparison of results with the 'null' normalization, which is simply the raw image. Both methods assumed that fluorescence intensity near anticlinal walls should be maximal, following the idea that contrast should normally have been fixed so that all anticlinal walls should be overexposed in each slice. Hence, for each pixel, if no nearby cell wall reaches maximum intensity (green = 255), then it is probably under-exposed and its green intensity should be increased: (i).
As for 'focal,' the predictions of the model for each pixel were rescaled from 0 to 255 to estimate the smoothed local maximum for each pixel of the meristem. If the maximum (  255 ) is not reached in the prediction, the pixel intensity is multiplied by 255 over the prediction of the model. The GAM was fitted with library 'mgcv' in the R software  (Wood, 2011; R Core Team, 2015). The distribution of fluorescent intensity of pixels and the microtubule network found in each cell of a meristem were described with different indices computed with the R software  (R Core Team, 2015). Skewness and kurtosis of distributions were computed with the R package moments  (Komsta and Novomestky, 2015). The Geary spatial autocorrelation index and the width index (i. e. distance to the nearest non-microtubule pixel) were computed with the R package raster  (Hijmans, 2015).
ACKNOWLEDGEMENTS We are grateful to  Nelly Dubrulle  for additional tests and help while preparing this manuscript. We also thank  Platim  ( UMS 3444 Biosciences Gerland-Lyon Sud ) for providing support on imaging. This work was supported by a PhD grant from  R egion Rhône-Alpes  and by the  European Research Council  ( ERC grant   307387  « PhyMorph » and  ERC  grant  615739  ' MechanoDevo ').
1 to 3 kV•m -1 was used for heating. A take-away sampling system allowed sampling of three volumes of 4 mL each per batch. The extracted samples were also rapidly cooled in an ice bath. The bulk temperature was continuously monitored with a type K (NiCr/NiAl) thermocouple (diameter 1. 5 mm, precision ±1. 5 °C) positioned at the bottom of the cell. The calibration of the thermocouple was checked with boiling water. The temperature signal was compared with and without application of the electrical field and no deviation was found regarding the sensor. This sensor controlled the monitoring equipment (Simulator Control And Data Acquisition -SCADA) which, via a LabVIEW ® program, acted on the voltage applied between the electrodes in order to regulate the holding temperature at a targeted value (proportional-integral controller).
, USA): Confidence intervals of the simulated concentrations (eq. 4) were obtained via the of bootstrapping simulation method. The principle is the generation of a high number (500) of resamples of the kinetic dataset added with a Gaussian perturbation within the experimental standard deviations (Fig. 2 ). These virtual data were generated via Matlab® software (The Mathworks, Inc. , USA). ] [ )) ) ( 1 70 1 ( exp( ] [ 70 C t T R Ea k dt C d C        eq. 4 Ea,.
Regarding the activation energies, the lowest were obtained for carotene and the highest for zeaxanthin and β-cryptoxanthin. A similar activation energy of 156 kJ. mol -1 was obtained for this latter molecule in  Dhuique-Mayer, et al. , (2007). To represent the prediction error, 95% confidence intervals were calculated from the kinetic parameter uncertainty (Fig. 2 ). Confidence intervals were obtained by 500 initializations of kinetic data within their experimental error intervals. The results are presented in Fig. 5  for conventional and ohmic heating for a pasteurization value of 150 min. Logically, the 95% confidence intervals were broader for conventional heating as the degradation was greater. The final reduced concentrations (C/C t=0 ) ranged from 0. 47 to 0. 57 and from 0. 59 to 0.
The most frequently found B31-B39 base pair (boxed in black) is C = G, especially in tRNAs of the 4-codon family boxes (Figure  6A , upper part of the wheel). In contrast, a A-pair is predominantly present in tRNAs of the split 2-codon boxes (lower part of the wheel). Concerning B32/B38, the combination Y32/A38 is the most frequently found one, with both U32 and C32 occasionally modified to or Um and C32 to Cm or s 2 C, respectively (Figure  6B ). These modifications of the base, especially the pseudouridine and the 2'O-methylation of the ribose, are known to limit significantly the conformational flexibility of the nucleotide and thereby locally rigidify the corresponding portion of the nucleic acid  (75). 14 ) that currently contains all sequences available by 2009 in the tRNA database as in ref.
(76). Analysis was performed using the software tool tRNAmodviz (http://genesilico. pl/trnamodviz). Distributions of nucleotide residues at each position of the hairpin (positions 27 to 43) are visualized as a pea chart, of which the color code is indicated at the top right corner of the figure. Universal numbering system is used  (144). Acronyms of modified nucleotides present in certain isoacceptor tRNA are indicated outside the anticodon hairpin ('b' means bacteria, 'e', eukaryote and 'a', archaea). Those present in the proximal 'extended anticodon' (in green square box, see also text) are in grey background. Modified nucleotides present at positions 37 and 34 are listed in Figure  5A  and B, respectively. The red arrow pointing from B34 of anticodon to B3 of the codon and the plateau of B35/B2 and the red arrow from B37 to the plateau of B36/B1 symbolize the stabilizing effects of B34/B37 on codon-anticodon pairings.
ACKNOWLEDGEMENTS Drawings of the genetic code as a wheel in various formats are available at http://www-ibmc. u-strasbg. fr/spiparn/rubrique138?lang=fr or upon requests to the authors. The authors would like to dedicate this article to two friends and colleagues recently deceased,  Knut Nierhaus  (Berlin) and  Mathias Sprinzl  (Bayreuth), who both contributed significantly to our understanding of ribosomal translation. The authors wish to thank numerous colleagues for constructive discussions on decoding during the preparation of this article, for help with the drawings, and for critical reading of the MS ( J. Chihade ,  P. Farabaugh ,  A. Krol ,  D. Moras ,  M. Ryckelynck ,  M. Sissler ,  G. Yusupova ,  M. Yusupov ).
95, and the recorded temperature was automatically input into a computer database, one datum per second. The behaviour of individuals was reported as they were warming up, especially the basking behaviour, and the position of the wings. The take-off temperature was taken as the last temperature recorded before the butterfl y took off. A 4-th degree curve was fi tted to the raw data, and its slope was used to estimate the for fl ight can have a signifi cant impact on species fi tness. For example, the ability to fl y at lower temperatures may lead to more time available for dispersal and searching for mates and food  (Kingsolver, 1983). Conversely, the more thorax temperature increases above ambient air temperature, the greater the risk of overheating, potentially leading to reduced survival and fi tness.
Allometry between wing loading p w (in mm²) and body mass w (in mg) was studied directly using the R function nls, a nonlinear regression modelling function, by fi tting the power function p w = b w a directly rather than through its logarithmic transformation  (Zar, 1968). A total of 126 Pieris rapae specimens had been subjected to the same experimental design in France  (Nève, 2010) , and direct comparisons with that data set were performed where appropriate, using Wilcoxon Signed Rank tests in R. Voucher specimens have been deposited either in the private collection of Roger Kitching (Griffi th University) for Queensland species, or at the South Australian Museum, Adelaide, for South Australian species and Eurema species (South Australian Museum Accession numbers 31-015517 to 31-015538).
ACKNOWLEDGEMENTS. We wish to thank  A. Thiéry ,  E. Meglécz  and  D. Mackay  for help during the fi eld work and for numerous discussions. R. Kitching  introduced the two authors to each other, and he and  B. Kitching  allowed the fi eld and laboratory work in Queensland to take place. Field work in South Australia was carried out with permit number  A26006-1. No such permit was necessary in Queensland, as research there was not undertaken on protected grounds.