dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This work was supported by the  collaborative laboratory ABIVAX, OSEO-ISI CaReNA  grant and  Fondation pour la Recherche Médicale (FRM)  grant (Equipe  FRM 2011 -n°DEQ20111223745 ). JT is a senior member of the  Institut Universitaire de France. CLH is an  ESR  fellow of the  EU   FP7 Marie Curie ITN RNPnet program  ( 289007 ). ICLM was supported by a graduate fellowship from the  Ministère Délégué à la Recherche et aux Technologies  and  CNRS.
Using a random number generator, schools were randomized according to SEYLE interventions and school size with respect to sociocultural factors, school environment and school system structure in each study site. Data were collected through structured questionnaires administered to adolescents within the school milieu. Representativeness, consent, participation and response rates of the sample are reported in a methodological analysis  [47]. The present study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the local Ethics Committee in each participating country (Project No. HEALTH-F2-2009-223091). Prior to participating in the study, both adolescents and parents provided their informed consent for participation.
Measurements PIU was assessed using Young's Diagnostic Questionnaire (YDQ)  [18]. The YDQ is an 8-item questionnaire assessing patterns of Internet usage that result in psychological or social impairment during the six-month period preceding data collection  [48]. The eight items in the YDQ correspond to the six items in Griffiths' components model and nine items in the diagnostic criteria of IGD in the DSM-5  [49, 50]. Based on the YDQ score, ranging from 0-8, Internet users were categorized into three groups: adaptive Internet users (AIU) (scoring 0-2); maladaptive Internet users (MIU) (scoring 3-4); and pathological Internet users (PIU) (scoring ě 5)  [51]. Moreover, hours online per day were measured using a single-item question in the structured questionnaire. Data on risk-behaviors were obtained by using questions from the Global School-Based Student Health Survey (GSHS)  [52].
Characteristics of the Study Sample Among the initial SEYLE sample of 12,395 adolescents, there were 464 (3. 7%) subjects excluded due to missing data on relevant variables. This yielded a sample size of 11,931 school-based adolescents for the present study. The sample comprised 43. 4% male and 56. 6% female adolescents (M/F: 5179/6752) with a mean age of 14. 89 ˘0. 87 years. The prevalence of MIU was significantly higher among females (14. 3%) compared to males (12. 4%), whereas PIU was significantly higher among males (5. 2%) than females (3. 9%) (χ² (2, 11928) = 19. 92, p < 0. 001).
Griffiths' Components Model Griffiths' components model of addiction  [30]  hypothesizes that behavioral addictions (e. g. , PIU) and substance-related addictions advance via similar biopsychosocial processes and share numerous physiognomies. The addiction criteria of the respective six core components in this model are (1) salience, (2) mood modification, (3) tolerance, (4) withdrawal, (5) conflict and (6) relapse. Kuss et al. [73]  assessed the components model of addiction in two independent samples (n = 3105 and n = 2257). Results showed that the components model of PIU fit the data very well in both samples. In the present study, the YDQ measure was utilized to assess and detect adolescents with maladaptive and pathological risks related to their Internet use and online behaviors. As the YDQ measure comprises all six addiction criteria stipulated in Griffiths' components model, the validity of the outcomes reported in this study is supported by this theoretical framework.
Acknowledgments: The  SEYLE  project was supported through  Coordination Theme 1 (Health) of the European Union Seventh Framework Program  ( FP7) , Grant Agreement No. HEALTH-F2-2009-223091. The authors were independent of the funders in all aspects of the study design, data analysis and writing of this manuscript. The Project Leader and Coordinator of the  SEYLE  project is Professor in Psychiatry and  Suicidology Danuta Wasserman ,  Karolinska Institute (KI) ,  Head of the National Centre for Suicide Research and Prevention of Mental Ill-Health and Suicide (NASP) , at  KI, Stockholm, Sweden. Other members of the  Executive Committee are Senior Lecturer Vladimir Carli, National Centre for Suicide Research and Prevention of Mental Ill-Health (NASP) ,  Karolinska Institute, Stockholm, Sweden ;  Christina W.
Alginate, the main cell-wall polysaccharide of brown algae, is composed of two residues: mannuronic acid (M-residues) and, its C5-epimer, guluronic acid (G-residues). Alginate lyases define a class of enzymes that cleave the glycosidic bond of alginate by β-elimination. They are classified according to their ability to recognize the distribution of M-and Gresidues and are named M-, G-or MG-lyases. In the CAZy database, alginate lyases have been grouped by sequence similarity into seven distinct polysaccharide lyase families. The polysaccharide lyase family PL6 is subdivided into three subfamilies. Subfamily PL6_1 includes three biochemically characterized enzymes (two alginate lyases and one dermatan sulfatase lyase). No characterized enzymes have been described in the two other subfamilies (PL6_2 and PL6_3).
Alginate lyases are classified according to their substrate specificities and refer to the block structures encountered in alginate. For example, poly-β-D-mannuronate lyase (M-lyase, EC 4. 2. 2. 3,) and poly-α-L-guluronate lyase (G-lyase, EC 4. 2. 2. 11) cleave the glycosidic bond between M-M or G-G moieties, respectively. M-G blocks can be cleaved by M-G-lyase or G-M-lyase according to the recognition properties of the enzyme  [2]  Alginate lyases have been also classified by sequence similarity and are found in seven families of polysaccharide lyases (PL) in the carbohydrate-active enzyme database (CAZy, http:// www. cazy. org/; [3] ). To date, five PL families include characterized enzymes that are active on alginate (families PL5, PL7, PL15, PL17 and PL18); however different substrate specificities have been recorded: e.
g. both G-lyases and MG-lyases are included in the PL7 family  [4, 5]. Two other PL families are polyspecific meaning that the alginate lyases are grouped with enzymes inactive on alginate, but active on chondroitin B (PL6;  [6] ) or on β-D-polyglucuronic acid (PL14;  [7] ). In an attempt to improve the prediction of substrate specificity based on sequence data, a subfamily classification of PLs has been proposed following an approach similar to that applied to polyspecific glycoside hydrolase families GH13  [8] , GH5  [9] , GH30  [10]  and GH43  [11]. Based on available biochemical data, most of the proposed GH subfamilies are monospecific and differences within a subfamily generally involve the endo-or exolytic mode of action of the enzymes  [12]. Family PL6 is a polyspecific family that contains 177 proteins listed in the CAZy database (June 2016), all of which are of bacterial origin and among which only three representatives (i.
Bioinformatics of polysaccharide lyases belonging to family PL6 Protein accessions of 137 non-fragmentary family PL6 members were extracted from the CAZy database (December 2015) and used to retrieve the corresponding amino-acid sequences from the NCBI database. After addition of the sequence from Nonlabens ulvanivorans ORF Nonul_2381 (WP_036584323. 1), the amino-acid sequences were trimmed to isolate the PL6 catalytic domain based on the three-dimensional structure of chondroitinase B from Pedobacter heparinus (PDB code 1DBO). The resulting 138 sequences were aligned using MUSCLE  [16]. The alignment was used to compute a distance matrix based on maximum-likelihood distances  [17]  with BLOSUM62 substitution parameters  [18]. The resulting distance matrix was then used to construct a phylogenetic tree that was divided into subfamilies using Secator  [19].
Cloning of alginate lyase genes from family PL6 Fifteen genes were selected to cover the sequence diversity of the PL6 family. The choice of the targets was also determined by the commercial availability of DNA material or by the selection of easily culturable aerobic bacterial strains. Genomic DNA and bacterial strains were obtained from the DSMZ-German Collection of Microorganisms and Cell Cultures. The gene names, their accession number and the template (purified genomic DNA or bacterial clones) used for cloning are listed in Table  1. The presence of signal peptides and their site of cleavage were identified using SignalP  [20]. The primers designed for the amplification of the targeted genes without the signal peptide are listed in Table  2. The primers contained NcoI/XhoI, BamHI/EcoRI, BamHI/XhoI or NheI/Hin-dIII pairs of restriction enzyme sites; all compatible for cloning in the pET28a expression plasmid (Novagen, USA).
Molecular weight determination Samples (100 μL) filtered on 0. 22 μm membrane were injected in Shodex OH pack SB 802. 5 HQ and Shodex OH pack SB 802 HQ columns mounted in series. Elution was performed with 0. 1 M NaNO 3 at a flow rate of 0. 5 mL/min (Shimadzu LC-20AD pump) at 25°C. This HPLC system was coupled to a Wyatt Optilab Rex refractive index detector, used as a mass-sensitive detector, working at 633 nm at 25°C. Laser light scattering measurements were performed at 633 nm with a mini Dawn TREOS system (Wyatt technology). The intensity of scattered light was measured at three different angles, 45°, 90°and 135°. Chromatographic data were collected and processed using the Astra software (Wyatt technology). The calculated dn/dc (refractive index increment) was 0. 115 mL/g. Toluene was to calibrate the detector.
Phylogenetic tree of the PL6 family The phylogenetic tree of the sequences corresponding to the catalytic domain of 138 PL6 family members (Fig 1 ) shows a clear subdivision into three clades that coincide with subfamilies PL6_1, PL6_2 and PL6_3, as indicated in the CAZy database. Interestingly, all three characterized PL6 enzymes belong to subfamily PL6_1. Therefore the activity that prevails in the two other subfamilies cannot be predicted with accuracy  (Lombard et al. 2010 ). We thus used the phylogenetic tree as a guide to select a panel of enzymes for expression and characterization, and focused on the uncharacterized subfamilies PL6_2 and PL6_3 and on regions of subfamily PL6_1 that were distant from the previously characterized enzymes.
In fact, Tan et al  [18]  estimate that 1,500 participants of at least one long-lived parent would be needed to achieve >90% power to detect less common alleles (5% frequency) with effects of 0. 85 (in binary analysis of offspring of 1 long-lived parent vs. controls) with 95% confidence. In the current analysis we aimed to identify common genetic variants (prevalence ≥1%) associated with longer parental lifespan. We focused firstly on a broad range of survival, aiming to identify associations relevant to the interplay of aging and age-related disease (i. e. of relevance to geroscience)  [19]. We then also analyzed associations with extreme parental survival or longevity, which we defined as top 1% of survival. To achieve the sample sizes required, we utilized data from UK Biobank. We first performed genome-wide association studies (GWAS) and then used genetic risk scores (GRS) of known variants to test the hypothesis that offspring of longer-lived parents have lower genetic-risk of common risk traits and diseases.
47%  [33]. It has been reported that with sufficient variation in the phenotypes being studied, results are generalizable to the wider UK population  [34]. We are limited by the coverage of the genotyping microarray utilized; ~800,000 genetic variants were directly measured, allowing imputation of 73 million (9 million of which were common enough and high quality enough for inclusion in this GWAS). Many variants may exist outside of the data available for this study, in particular on the X, Y and mitochondrial chromosomes, for which imputed data are not available. We have studied the normal range of parental ages at death, as well as extreme longevity (top 1% of survival). Some would argue that normal age at death is necessarily influenced by disease and has little relevance to aging. However, age is the major risk factor for most causes of death in later life, and the geroscience view sees aging as a major contributing factor to these diseases and deaths.
Participants with one long-lived and one short-lived parent were excluded from this analysis. Finally, we defined two further binary traits for sensitivity analyses testing the consistency of results using a non-linear definition: offspring of at least one long-lived parent vs. offspring of two intermediate-lived parents, and offspring of at least one long-lived parent vs. offspring of at least one short-lived parent (very low numbers of participants with either "both long" or "both short" meant we used the aforementioned "at least one…" definitions). Supplementary Figure  1  contains a flow-chart showing how the phenotypes are derived and the numbers included in each analysis. UK Biobank genetics data. We used genetic data available (autumn 2015) from 120,286 participants identified as 'white British' through self-report and verified through principal components analysis based on genotypes.
Kinship coefficients were estimated and related individuals (3rd degree or higher) were removed to provide the maximal unrelated set of individuals. The central UK Biobank analysis team performed these analyses. Details of principal component analyses and kinship analyses can be found in the official UK ('axiom' or 'bileve'), assessment center (22 possible categories), and the first 5 genetic principal components. The regression linker functions `logit` or 'Gaussian' were used respectively for logistic and linear outcomes. Forest plots comparing the associations between different GRS' and each outcome were generated using the R package 'rmeta' (v2. 16). For these analyses the GRS was first z-transformed so that the statistics reported could be compared between each GRS on the forest plot. In the rest of the manuscript, we refer to the unstandardized, "per weighted allele" effects.
Funding This work was generously funded by an award to DM, TF, AM, LH and CB by the  Medical Research Council   MR/M023095/1. This research has been conducted using the  UK Biobank Resource , under application 1417. The authors wish to thank the UK Biobank participants and coordinators for this unique dataset. S. E. J. is funded by the  Medical Research Council  (grant:  MR/M005070/1 ). J. T. is funded by a  Diabetes Research and Wellness Foundation  Fellowship. R. B. is funded by the  Wellcome Trust  and  Royal Society  grant:  104150/Z/14/Z. M. A. T. , M. N. W. and A. M. are supported by the  Wellcome Trust   Institutional Strategic Support Award  ( WT097835MF ). R. M. F. is a  Sir Henry Dale Fellow  ( Wellcome Trust  and  Royal Society  grant:  104150/Z/14/Z ). A. R. W. H. Y. , and T. M.
Models for a given developmental stage were built hierarchically to include information about previous stages. Basic initial models included seed mass, radiation level at the collection location, and interaction between these variables, and for models other than germination success and time of the previous developmental stages and the interaction between those and seed mass and radiation level, respectively. For statistical tests a non-linear mixed model procedure was used in the R (version 3. 2. 3) package "lme4" with the glmer function and Laplace approximation (although Adaptive Gauss-Hermite Quadrature, nAGQ = 50, gave similar results)  42. A bimodal distribution with logit link function was applied to analyse germination success, and a Gaussian with log link function (lognormal) was used for all developmental time analyses.
Acknowledgements We gratefully acknowledge the help and support of  Gennadi Milinevsky ,  Laura Martínez-Rodríguez ,  Margarida Isabel Oliveira Barros  and  Ricardo Guerreiro  during field work and greenhouse experiment, and  Nikolaos Evangeliou  for help in calculations of exposure to ground contamination (http://radio. nilu. no). The study was financially supported by the  Academy of Finland  to TM (Grant No. 268670 ). AJMP and ZB were funded by the  Portuguese Foundation for Science and Technology (FCT :  RH/BPD/84822/2012  and  SFRH/BPD/111015/2015 ). CG was supported by the  Portuguese Foundation for Science and Technology (FCT)  through the  Investigador Programme  and two research grants ( FCT-ANR/BIA-BIC/0010/2013  and  PTDC/BIA-BIC/5223/2014 ) co-funded by the  European Program COMPETE  ( FCOMP-01-0124-FEDER-019772 ), and by the Project " Genomics and Evolutionary Biology " cofinanced by North Portugal Regional Operational Programme 2007/2013 (ON.
3 ), 50 ml of 1 mM biotin (dissolved in buffer D) was added to the imaging chamber to saturate all biotin-binding sites (Supplementary Fig. 7b ). To reach an accurate 1:1 ratio between the concentrations of two RNA samples in two-colour measurements, we measured sub-nanomolar concentrations of RNA using single-molecule fluorescence. We adsorbed RNA molecules to a positively charged surface as follows. KOH-etched quartz slides were coated with a layer of positively charged poly-L-lysine. After 5 min of incubation with 20 ml 0. 01% poly-L-lysine (P4707, Sigma), the chamber was washed with 100 ml of buffer T50 (10 mM Tris (pH 8. 0), 50 mM NaCl). After washing, two fluorescently labelled RNA substrates (modified: Cy3; reference: Cy5) were introduced into the microfluidic chamber. After 5 min of incubation, the unbound substrate was washed away with 100 ml of imaging buffer D and data were obtained from 10 fields of view.
For each construct this procedure was repeated with three individual dilutions on three different slides. Single-molecule data acquisition. The fluorescent label Cy3 was imaged using prism-type total internal reflection microscopy at an excitation at 532 nm (Compass 215M-50, Coherent). Cy5 was excited by a 633 nm HeNe laser (CVI Melles Griot 25 LHP 928, 633 nm). When obtaining the time traces, we excited Cy3 and Cy5 molecules with 532 and 633 nm laser light sources, respectively, as weakly as possible to minimize Cy3 and Cy5 photobleaching during our observation. Under this imaging condition, only a minor fraction of the time traces were affected by Cy3 or Cy5 photobleaching or photoblinking during the first few minutes of imaging (Fig. 6e ). Despite this precaution, the long-lived binding event might still be influenced by photobleaching and thus underrepresented in the population analysis.
Fluorescence signals from single molecules were collected with a Â 60 water-immersion objective (UPlanSApo, Olympus) with an inverted microscope (IX71, Olympus). Scattering of the 532 nm laser beam was blocked with a 550 nm long-pass filter (LP03-532RU-25, SemRock). When the 633 nm laser was used, 633 nm laser scattering was blocked with a notch filter (NF03-633E-25, SemRock). Subsequently, the signals from Cy3 and Cy5 were spectrally split with a dichroic mirror (lcutoff ¼ 645 nm, Chroma) and imaged onto two halves of an electron-multiplying charge-coupled device camera (iXon 897, Andor Technology). A series of charge-coupled device images were acquired with in-house software written in Visual C þ þ with a time resolution of 0. 3 s. The CCD images illustrated the binding events over 25 Â 25 mm 2 field of view.
Single-molecule data analysis. Fluorescence images and time traces were extracted with programmes written in IDL (ITT Visual Information Solutions) and analysed with Matlab (MathWorks) and Origin (OriginLab Corporation). To systematically select single-molecule fluorescence signals of Cy3 and Cy5 from the acquired images, we used an algorithm written in IDL that looked for fluorescence spots with a defined Gaussian profile and with signals above a threshold. This algorithm was effective in differentiating non-specific signals from other sources. A dwell-time distribution was fitted by either a single-exponential decay curve ðAe À t=Dt Þ or a double-exponential decay curve ðA 1 e À t=Dtshort þ A 2 e À t=Dtlong Þ. In case of a double-exponential decay, the percentages of Dt short and Dt long populations are determined by A 1 Dt short =ðA 1 Dt short þ A 2 Dt long Þ and A 2 t long =ðA 1 Dt short þ A 2 Dt long Þ, and the average dwell time is determined by ðA 1 Dt 2 short þ A 2 t 2 long Þ= A 1 Dt short þ A 2 Dt long À Á.
Acknowledgements C. J. was funded by  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. (309509). M. F. was partly supported by the  Fondation pour la Recherche Medicale. We thank  Martin Depken ,  Stanley Chandradoss ,  Malwina Szczepaniak ,  Margreet Docter ,  Pawel Tulinski ,  Viktorija Globyte ,  Boseon Kim  and  Yooshik Kim  for their critical reading of this manuscript. We thank  Narry Kim  ( Seoul National University ) for sharing Dicer and TRBP plasmids, and  John Strouboulis (B. S. R. C. Alexander Fleming ) for the BirA plasmid.
Stability assessment The instability of isolated SKE has already been stated; it appeared crucial to test its stability in the S. chamaecyparissus extract incorporated into a cosmetic formulation before any further ingredient development. Creams formulated with 0. 5 (data not shown) and 2% of the S. chamaecyparissus extract together with various cosmetic supports were then subjected to stability tests: these cosmetic products were kept either at room temperature or at 42 C for 13 weeks. The evolution of the SKE concentration in these finished products was followed by HPTLC, using an appropriate mobile phase to properly separate SKE from the other ingredients (retention factor Rf ¼ 0. 42). A stability test was performed over 6 weeks at room temperature; then one week later (day 49), the formulated creams were either stored at room temperature or at 42 C.
Documentation of chromatographic plates (visualization and image capture) was performed at 254 nm, 366 nm and under white light by the TLC Visualizer documentation (Camag) operated with winCATS Version 1. 4. 6 software. Plates were scanned under the following conditions using the TLC scanner 4 controlled by the WinCATS software: scanning mode, reflectance mode at 315 nm, D2 (deuterium) and W (tungsten) lamps, slit dimension 8. 00 mm Â 0. 40 mm, scanning speed 20 mm/s, and data resolution 100 mm/step. SKE presents a good UV absorbance, reaching a maximum at l at 315 nm, without any co-elution. The quantification of SKE content in formulated creams was performed via calibration curves constructed with five relative concentrations of ingredients 1, 2 and 3 diluted in MeOH and applied in triplicate on the HPTLC plate (stock solution prepared at 1 mg/ml; application of respectively, 1, 2, 6, 8 and 12 mL of the corresponding solutions on the HPTLC plate).
Acknowledgements The authors are grateful to  C. Monin  ( ACPHYTAROMA ) who collected and supplied the plants included in the screening presented in this paper. They also wish to thank the botanists  J. -L. Polidori  and  F. Boillot  ( Biophyto ) for the identification of these plants. This work was a part of the  APRF NATUBAVAL project  ( Pole PASS ), which was made possible thanks to the financial support provided by the  FEDER  project number  2009_26268/2010_07953  ( Provence-Alpes Côtes d'Azur region and European Union ) and the  ANRT (French National Association for Research and Technology)  grant  No 748/2010.