dataesr/scientific-paragraphs-categorization huggingface dataset:
LPS docking onto the LptC dimer or the LptC-LptA m complex was also performed with HADDOCK. The chemical structure of the LPS molecule, containing the lipid A and core sugars, was extracted from the TLR4/ MD2 complex (PDB 3FXI), and topology and parameter files were generated from the PRODRG software  48. All of the atoms of the LPS molecule were considered as passive. LptC residues L58, L62, V67, I136, V143, L167, A172 and S59, Y60, Y70, F78, T79, L83, T93 with CSP superior to twice the standard deviation of shifts were selected from [ 1 H,  13  C]-methyl and [ 1 H,  15  N]-correlation experiments, respectively, and were considered as active residues. HADDOCK rigid body first iteration was performed with the inter_rigid parameter set to zero to allow LPS to reach the inside of the LptC cavity.
Acknowledgements This work was supported by the  Train2Target  project granted from the  European Union 's  Horizon 2020 framework program  for research and innovation (Project # 721484 ). This work used the  SEC-MALLS, NMR  and isotope labeling platforms of the Grenoble Instruct center (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ),  GRAL  ( ANR-10-LABX-49-01 ) and a financial support from the  TGIR-RMN-THC   FR3050. Access to the  ESRF SAXS beamline BM29  and technical support for data collection.
, 2017)  in both COPD and healthy subjects. Briefly, subjects were seated on a chair with a headrest and armrests. A structured light grid pattern was projected onto the chest and abdominal wall, and two overhead digital stereoscopic cameras recorded the distortions of this pattern induced by breathing. This information was used to reconstruct breathing pattern from a dedicated algorithm  (de Boer et al. , 2010). In our study, participants were asked to change into a close fitting white t-shirt that followed the contours of the body or, if they preferred, SLP data acquisition could be performed on the bare chest. Participants were asked to sit upright in a high-backed chair with their neck in a neutral position and their back as straight as possible. They were also asked not to move. The projector was lined up to project the grid of light over the participant's chest and upper abdomen (Figure  1 ).
Data was collected during 5 min of tidal breathing while subjects were, in random order, (1) breathing normally (SLP condition), and (2) breathing normally through a mouthpiece and low-resistance PNT (M. E. C. PFT Systems Pocket-Spiro, Medical Electronic Construction, Brussels, Belgium) with a nose-clip on (SLP+PNT condition); at this time, data for analysis was generated simultaneously from SLP and from PNT (respectively SLP+PNT condition and PNT condition). The choice of 5 min recording was dictated by technical reasons, such as: (1) the difficulty of having the subjects not to move for 5 min while sitting on a chair with their neck in a neutral position and their back as straight as possible and (2) the need of avoiding artifacts related to saliva due to mouthpiece. Measurements obtained from PNT and SLP included: respiratory frequency (Rf ), inspiratory and expiratory time (TI and TE) and total breath time/duration (TT); specific measurements obtained exclusively from SLP included: time to peak tidal expiratory flow over expiratory time (TPTEF/TE, a measure of airway obstruction and flowlimitation during tidal breathing;  Morris et al.
To evaluate the disruption of breathing pattern and breathing variability in healthy and COPD subjects, comparisons were made only between SLP and SLP+PNT conditions by using Wilcoxon's signed rank test. In all cases, p < 0. 05 was considered statistically significant. The within subject variability of the different ventilatory variables was assessed through their coefficients of variation (CV). Statistical analyses were performed using Medcalc (MedCalc Software, Ostend, Belgium) and SigmaPlot (Systat Software, San Jose, CA).
Effects on Respiratory Mechanics and Thoraco-Abdominal Coupling Our data demonstrate that the use of PNT-mouthpiece-nose clip combination modifies respiratory mechanics and thoracoabdominal coupling at rest in COPD patients to a greater extent than in healthy subjects (Tables  3, 4 ). Of interest, the observed modifications are different in nature and profile, some of them being only captured by SLP. In the healthy subjects, TPTEF/TE was the only variable significantly different between the two conditions (slower expiration in the presence of the PNT). This expiratory index should have been determined by the sole elastic recoil of the lungs (no neural drive, and therefore no possibility for adjustment): finding it altered by an expiratory resistance, even small, is not surprising. In contrast, many variables significantly differed between conditions in the COPD patients.
ACKNOWLEDGMENTS We would like to thank  Mark Harwood ,  Rachel Wilson , and  Shayan Motamedi Fakhr  from  PneumaCare Limited (Ely, Cambridgeshire, United Kingdom ) for their technical and methodological assistance in carrying on the study as well as the technicians of our pulmonary function testing laboratory at  "Service d'Explorations Fonctionnelles de la Respiration, de l'Exercice et de la Dyspnée " for their practical and technical assistance.
After three PBS washes, the cells were resuspended in PBS plus rabbit serum diluted to 1:20; preimmune and 5 weeks post vaccination serum were tested. After 1 h at R. T. incubation, cells were washed in PBS three times, and the cells were resuspended in PBS plus 1:40 FITC-conjugated goat antirabbit IgG (F0382, SIGMA) secondary antibody. After 30 min of incubation in the dark at 4°C, the samples were PBS washed three times, resuspended in PBS and visualized on a plastic chamber slide with the fluorescence microscope Axiovert S100 (Zeiss). Images were achieved using a digital CCD camera, and images were processed using the AxioVision 40-V4. 6. 3. 0 (Carl Zeiss, Imaging Solution) software program.
Acknowledgments The authors are grateful to  Stéphane Mandard  ( INSERM U866 ,  University of Bourgogne, France ) and  Promega Corporation  for providing the PPRE-pGL3 and pNL1. 3 plasmids, respectively. The authors also thank the  Cochin Institute Imaging facility. This work was supported by  ANSES  ( Phtha-latPreG  project).
Ciliogenesis experiments hTERT RPE-1cells were reverse transfected with Dharmacon ON-TARGETplus siRNA deconvolved pool on 6-well plates in complete medium. Twenty-four hours after transfection, the cells were starved in serum-free medium for 48 h after which they were fixed in ice-cold methanol and immunestained with antibodies directed against the protein-of-interest and GT335/ARL13B as cilia markers. Images were acquired on DeltaVision microscopes. Three-dimensional datasets were acquired for up to 300 cells per condition. Levels of ciliogenesis were manually calculated using GT335/ARL13B channel for cilia. Ciliation in cells transfected with non-targeting siRNA was used as control.
Super-resolution microscopy Super-resolution imaging was performed as described previously  [20]. Briefly, cells were imaged on a 3D-SIM (OMX Blaze v4, GE Biosciences PA) equipped with 405, 445, 488, 514, 568 and 642 nm diode lasers, four high-speed sCMOS cameras (scientific CMOS, 2560 Â 2560 pixels 2 , manufactured by PCO), and a Â60/1. 42 NA planApochromat oil-immersion objective (Olympus). Multi-channel 3D-SIM image Z stacks (25 sections, 0. 125 mm apart) were reconstructed, threedimensional aligned using calibrations based on a GE reference slide and 100 nm diameter TetraSpeck Microspheres and maximum intensity projected using the SOFTWORX 6. 0 software package (GE). The 3D-SIM modality of our Optical Microscopy eXperiment (OMX) provides an axial resolution of 340-380 nm depending on the imaging wavelength, thus sampling axially every 125 nm satisfied the Nyquist criterion for oversampling (GE Biosciences PA).
Image analysis: line profiles For line profile analysis, we used the same method as in  [18]. Briefly, 3D-SIM, three-channel, z-stack images of protein-ofinterests were taken in 488, 568 and 642 nm channels, and then reconstructed and aligned in softWoRx. The 3D-SIM modality of our OMX provides an axial resolution of 340-380 nm depending on the imaging wavelength, thus sampling axially every 125 nm satisfied the Nyquist criterion rsob. royalsocietypublishing. org Open Biol. 7: 170114 for oversampling (GE). For each imaging channel, all 16-bit z-sections were manually chained in a montage strip in IMAGEJ. For each triplet of images, line profiles across montages were drawn at the same position and the intensity profiles were recorded (in IMAGEJ). The profiles were normalized to their respective maximum intensity, and the peaks brighter than 30% of the maximum intensity were fit with a one-dimensional Gaussian function (in MATLAB).
Image analysis: diameter measurements Measurements of diameters of protein-of-interests in interphase were derived following the same procedure described in  [18]. Briefly, we used our in-house developed MATLAB routine to interactively select the boundary pixels of centrosome XY rings in 16-bit SOFTWORX reconstructed, aligned and projected 3D-SIM images in one of the 488, 568 and 642 nm channels. The diameter of the fitted circle to the boundary pixels provided the protein size in xy dimensions. Figure  2d  shows average measurements for protein-of-interests.
Image analysis: protein recruitment and quantification at centrioles or centrosomes To quantify centriole or centrosome recruitment of various antigens, six z-planes of 1. 5 mm and greater than 300 cells per sample were acquired at 60Â/1. 4NA (2Â binning) on a Deltavision Elite DV with a 2048 Â 2048 sCMOS camera (GE Life Sciences, PA). Image analysis employing adaptive thresholding was carried out on deconvolved, z-projected stacks with MATLAB to identify centrioles or centrosomes using specific markers as specified in the text (see also  [13] ). The resulting centriole mask was dilated by 3 pixels radially and then applied to dark-noise subtracted original images to obtain the mean and integrated pixel intensity for each channel, for every cell.
MASCOT (v. 2. 3) was used for database searching against the human and adenovirus complement of the NCBI RefSeq V53 database. Oxidized methionine and deamidated asparagine and glutamine were considered as variable modifications, and charges þ2 and þ3 were allowed. Mass tolerance was set at 3 Da for the precursor and 0. 6 Da for the fragment ions. Proteins with a minimum ion score of 35 were parsed to the relational module of the ProHits LIMS  [39] , spectral count matrices were exported for interaction scoring.
Acknowledgements. We are grateful to  Suzanna Prosser  and  Joa ˜o Gonc  ¸alves for critical comments on the manuscript,  Joa ˜o Gonc  ¸alves for the GFP-RAB8A cell line,  D. Holmyard  and  Suzanna Prosser  for EM assistance and  Zhen-Yuan Lin  for help with the Flag AP-MS data. We thank  Peter Jackson  for discussing unpublished work and note that their work was published which this manuscript was in press [42].
The total DNA of BerOc1 was extracted using an UltraClean microbial DNA isolation kit (Mo Bio). The genome was sequenced with 454 Roche Technology, using 454 GS FLEX Titanium version. Sequences were assembled using Newbler 2. 6 (454 Life Sciences). We obtained 36 contigs, with a mean depth of coverage of 45ϫ. Contigs were reordered using Promer  (7)  and the Desulfovibrio desulfuricans ND132 genome (RefSeq GenBank accession no. NC_016803) as the template. They were further extended by PCR. The draft genome includes five contigs, with the longest and shortest sequences being 2,803,249 and 2,705 bp, respectively. The total size of the genome is 4,081,579 bp, with a GϩC content of 63. 79%. The final assembly was annotated using Prokka version 1. 10 (8) and identified 3,686 coding regions, three rRNAs, 59 tRNAs, and one transfer-messenger RNA (tmRNA).
While no plasmid amplicon was detected, five putative genomic islands (29,653 bp, 27,947 bp, 11,484 bp, 19,691 bp, and 7,691 bp) were predicted using IslandViewer (9). There were three transposases annotated and four integrases; the presence of a bacteriophage is suspected by the presence of prophage-derived endonuclease YokF precursor and some proteins involved in tail constitution. No clustered regularly interspaced short palindromic repeat (CRISPR) could be detected using the CRISPR Recognition Tool (CRT) version 1. 0 software  (10). The products of the hgcA and hgcB genes have been described to be involved in mercury methylation  (11). The HgcA protein, belonging to Pterin-binding superfamily, shared 69% identity with HgcA of D. desulfuricans ND132 and Desulfovibrio aespoeensis (GenBank accession no.
NC_014844). Notably, the motif NVWCAAGKG, known to be necessary for mercury methylation, was identical  (12). HgcB shared 77% and 63% identity with HgcB of D. desulfuricans ND132 and Desulfovibrio aespoeensis, respectively. The operon mer, involved in methylmercury demethylation, could not be detected in the BerOc1 genome, suggesting that this process is performed through another unknown metabolic pathway. Mostly all of the Desulfovibrio strains tested are able to demethylate mercury, but few strains are able to methylate it  (13). The comparison of the genomes of Desulfovibrio strains sharing the capacity to produce this highly toxic mercury species with those unable to drive this process will give new tracks in the understanding of methylmercury production by bacteria. Accession number(s).
Statistics analysis Primary endpoint was the proportion of patients developing an immunological response toward at least 70% of serotypes at one month in ELISA. These Patients, developing an immunological response toward at least 70% of serotypes at one month, were defined as responders. Patient characteristics were compared using Mann-Whitney's Ranksum test for quantitative variable and Fisher's exact test for categorical variable. The percentages of responders are provided together with their 95% confidence interval (95%CI). Quantitative variables were compared using Student's t-test and categorical variables were compared using the chi-square test. All tests were 2-sided at the level of 0. 05. All analyses were performed using Graph Pad Prism 5. 0 software (GraphPad, San Diego, CA).
Population data Twenty patients with SMM were recruited for the study. The median age of the study population was 62 years (range 50-74) with 15 females (75%) and 5 males (25%). Fourteen patients had IgG MM, five had IgA MM, and one had Bence Jones MM. No one received a treatment for their SMM. We compared the age, gender, type and quantity of monoclonal immunoglobulin, quantity of IgG -IgA -IgM and percentage of plasma cells in bone marrow (Table  1 ). We also did a phenotypic analysis in our patients (CD3, CD4, CD8, CD19, CD20, CD5, CD38, CD27, CD16-CD56)(data not shown). We did not found any significant differences between responders and non-responders group.
Area measurements were taken with a scanning image analyzer in 2014 (Agvision Monochrome System ® ; Decagon Devices Inc. , Pullman Wash. ) and a leaf area meter in 2015 (LI-COR LI-3000A Portable Leaf Area Meter ® , LI-COR, Inc. , Lincoln, Nebr. ). We had to switch devices because the former became unavailable. However, the data produced by the two devices were comparable. Reproductive node success rate was calculated by dividing the number of nodes recovered from each plot across all collections by the initial number of nodes in each plot at the outset of the study following any removal carried out. For this trait, we coded nodes from the bloom and midseason collections as "successful" although some F I G U R E 1 Treatment assignment schematic for study plots used to address objective 1 (n = 33 clones; see Introduction).
| Objective 2 For our second set of analyses to determine whether a more ordinal or a more continuous measure of reproductive effort would produce a better fit to the trade-off data, we performed two linear mixed-effect regressions for each trait  (Bates et al. , 2015). One model had the proportion of reproductive nodes remaining per plot after any node removal as a measure of reproductive effort, whereas the other had the average number of nodes remaining per stem as the reproductive effort measure instead. We predicted that the latter would produce a superior fit for each trade-off. Here and in our third set of analyses, we included only data from 2015, both from the group of 20 clones subjected to multiple removal levels (20 clones, 80 plots) and from the full-control and 0. 7 removal-only plots (11 clones, 44 plots) from all other clones from that year.
| Objective 3 In the third set of analyses to determine whether the reproductive trade-offs under study could be nonlinear, we fit a pair of regressions for each trade-off using the nonlinear regression function nlsLM in the package minpack. lm  (Elzhov, Mullen, Spiess, & Bolker, 2015). In each pair of regressions, the average number of nodes remaining per stem was the sole fixed factor. Otherwise, the models took contrasting forms: In Equations 1 and 2, Y represents the data for the other trait involved in the trade-off, X represents the reproductive effort data, and β 0 , β 1 , and β 2 are regression coefficients. Equation 1 is the traditional linear function, whereas Equation 2 is an exponential-family function. We chose this particular form for Equation 2 because it can fit both forms of concavity that trade-offs are thought to exhibitconcave up and concave down  (Sletvold & Ågren, 2015).
When β 1 < 0 and β 2 > 0, the curve will be concave down, and when β 1 > 0 and β 2 < 0, the curve will be concave up. Additionally, in contrast to the quadratic-family functions (e. g. , Y = β 0 + β 1 X + β 2 X 2 ) used in the past to model trade-off nonlinearity (e. g. ,  Maust, Williamson, & Darnell, 1999 , 2000; Sletvold & Ågren, 2015) , this exponential function is assured to be monotonic (i. e. , continuously decreasing) as reproductive effort increases. We discovered, in initial diagnoses, that within-clone data were highly auto-correlated. Because the nlsLM function does not allow for inclusion of grouping factors, we first performed a linear mixedeffect regression for each trait with no fixed factors and clone as a grouping factor  (Bates et al. , 2015). We extracted the residuals from these models and used them as our dependent trait data in the models described above.
This sequential modeling approach satisfactorily eliminated the auto-correlative effect. We derived 95% confidence intervals (CIs) for each model's regression coefficients using the bootstrapping function nlsBoot in the package nlstools (n = 999 iterations;  Baty et al. , 2015). The nlsLM function  (Elzhov et al. , 2015)  frequently did not converge during bootstrapping for the exponential models, so we modified it to permit up to 1000 iterations per bootstrap, which allowed it to converge 100% of the time. This increased our conservativeness by allowing data distributions very unlike our real data to converge, widening the average width of the CIs. We deemed coefficients significant if 0 was not within their CI. We compared the fit of the linear and exponential models via Akaike's information criterion values corrected for small sample sizes (AICc) retrieved with the function AICc (package: AICcmodavg;  Mazerolle, 2016).
METHOD Setting Data were extracted from a large prospective controlled study of children attending Danish public schools that was part of a natural experiment on additional physical education in school (the Childhood Health, Activity, and Motor Performance School Study Denmark, CHAMPS-study DK). The purpose of the CHAMPS project was to evaluate the effects of increased physical education on childhood health and well-being. It has been described in more detail elsewhere,  13  and a number of studies have been published. 10 11 14 he 19 schools in the municipality of Svendborg were invited to participate in the CHAMPS-study DK. Of these, six became sport schools and had six physical education lessons per week, while four schools (the control group) continued their normal activities with two physical education lessons per week.
Participants and data collection For the purposes of this study, we used the CHAMPS-study DK data collected between October 2008 and July 2011, meaning that we had data for boys and girls in school grades 0-4 in the first year of the study and grades 2-6 in the last year of the study. The data covered 2. 5 years, minus the first 5 weeks of the study that were considered to be a pilot period for all participants. Data collection occurred weekly but was suspended during Christmas holidays and the 6 weeks of summer holidays. Since the CHAMPS-study DK was a natural experiment, entry to the study was kept open with the possibility for new children to enter and for others to exit if the family moved in or out of the school district.
Musculoskeletal pain and classification of injury Information on musculoskeletal pain was also collected weekly using SMS-track. Each Sunday, parents answered a text message asking about any musculoskeletal pain during the previous week: "Has (name of child) during the last week had any pain in: 1-neck, back, or low back; 2-shoulder, arm, or hand; 3-hip, leg, or foot; 4-no, my child has not had any pain". The returned answers were automatically recorded and inserted into a database. A reminder was sent if no response had been received within 72 hours and, if necessary, again 120 hours after the initial text message was sent. The SMS-track data were monitored and cleaned during data collection, and any inappropriate answers were checked through direct telephone contact with parents. If pain was reported by parents, a telephone consultation was carried out on the following Monday.
Statistical Analysis. Results were expressed as a measure of central tendency and dispersion for continuous variables and as absolute numbers and relative frequencies for categorical variables. Comparisons in the clinical variables were performed using a Student's -test or one-way ANOVA for continuous variables (normality assumption was previously confirmed) and Fisher exact test for categorical variables. Statistical significance was set at  < 0. 05. The statistical analysis was performed using SAS v. 9. 1. 3 (SAS Institute Inc. , USA). The minimum sample size ( = 150) was representative from the Spanish neurologists and was calculated to obtain a sample error of 0. 0535, considering a maximum variability of  =  = 50% and a confidence interval of 90%. The minimum sample size of the third-round ( = 125) was calculated to obtain a sample error of 0.
This is all the more important in the light of emerging evidence that suggests that industry-funded postmarketing studies, which are often conducted to detect rare adverse events, are not improving drug safety surveillance  (Spelsberg et al. , 2017). The international TREatment of ATopic eczema (TREAT) Registry Taskforce (www. cometinitiative. org/studies/details/825?result=true) seeks to find consensus on core domains and domain items for AE research registries and to harmonise data collection on patients receiving systemic immuno-modulatory therapies, following recently published best-practice guidelines of the European Commission funded PAtient REgistries iNiTiative joint action (PARENT JA) (http://patientregistrieseu/guidelines)  (Zaletel and Kralj, 2015). PARENT was a collaborating partner of the TREAT Registry Taskforce.