dataesr/scientific-paragraphs-categorization huggingface dataset:
Data extraction and critical appraisal The findings from the initial searches were used to decide the clinical outcome of the present review. The primary outcomes were to assess the prevalence of oral sex in the United States and the prevalence of HPV 16 infection in oropharyngeal cancer in the United States. Data was extracted to analyze each article for: prevalence of oral sex in the United States from the 90's when the first study was conducted until 2015, prevalence of HPV 16 in the cancer biopsy specimen from the 90's when it was first reported in the United States until 2015. The technique of HPV 16 detection in the study was also reported as in situ hybridization (ISH) may be less sensitive than polymerase chain reaction (PCR) to identify the virus in the biopsy specimen. Under-reporting the prevalence of HPV 16 virus in oropharyngeal cancer was a possible bias associated with the method of detection.
Thermal variables We installed a temperature data logger (iButton® hygrochron, Maxim Integrated™) in each quadrat at about 10 cm above ground and sheltered it from direct sunlight with a square and opaque Plexiglas plate. The data loggers recorded air temperature at the nearest half-degree every three hours. Data were recovered once per year during the annual census. Measures from quadrats of the same sites were highly correlated (R² ≈ 0. 90). Some data loggers were lost and for some others the data was unreadable due to battery exhaustion or other unknown causes, especially in the last years of the survey. These data loggers were replaced with new ones; however, this resulted in loss of data. To replace missing data, we duplicated the data from the quadrats of the same site. When data were missing for all the quadrats of the site for a short period of time (23 occurrences), we reconstructed the missing data from nearby weather stations of the "Centre de Recherches sur les Ecosystèmes d'Altitude" (CREA -Mont-Blanc, creamontblanc.
org): number 207 and 209 for the Vercors sites, and 218 and 29-217 for the Lautaret-Galibier sites. These stations were on average 12 km away from the closest study site (Table  S1 ) and record temperature every 15 minutes. We fit a simple linear model with the temperature of the study site as response variable and the temperatures at ground level, 30 cm and 2 m above ground level of the two weather stations, as well as hour of the day, as explanatory variables. The models had a very good fit (average R 2 = 0. 82) and provided a reliable way to estimate the missing observations. To account for uncertainty in the estimates of the coefficients of the linear model, 10 replicated datasets were generated using values sampled from normal distributions defined by mean and standard error of the estimates of the coefficients From the raw three-hourly measures, we calculated daily mean, minimal and maximal temperatures (T mean , T min and T max ).
These data were used to define thermal variables for the vegetative season; we disregarded the non-vegetative season (autumn and winter) because plants are dormant and less sensitive to environmental conditions. The limits of the vegetative season were derived from temperatures through the curve of the growing degree days, defined as dd = (T max + T min )/2 -T base. We set T base = 0, because artico-alpine plants can grow at very low temperatures. For each quadrat, we determined graphically when the cumulative curve of dd started to increase and when it started to stabilize and used these two dates as the limits of the vegetative seasons (Figure  S1  in Supplementary information). The vegetative season was further divided into two consecutive periods separated by the census date, to define thermal variables that had the same temporal structure as the census data.
Statistical analysis We analysed the thermal variables separately for the early and late season. Correlations between variables were measured by Pearson's linear correlation coefficient. We reduced the number of thermal variables used in the subsequent analyses by retaining only the ones showing a pairwise Pearson's correlation coefficient below 0. 7 (Table  2 ). Vital rates (growth, reproductive effort and survival) were analyzed using generalized linear mixed models including plant size and the uncorrelated thermal variables as potential explanatory variables. Two distinct sets of analyses were performed, a site-specific analysis and a global analysis. In the site-specific analysis, we analyzed vital rates and environmental data separately for BRU, VIL and GAL (the other sites had too few observations to provide meaningful results).
Quadrat and year were included as random effects. In the global analysis, we averaged vital rates, plant size and thermal variables over the years of the same site. Thus, we modeled the relationship between average thermal conditions and average vital rates. In the global analysis, we weighted the data points by the number of individuals used to compute the means to account for small sample sizes in some populations (e. g. PIC) and site was included as a random effect. In both the site-specific and the global analysis, the explanatory variables were standardized to facilitate the comparison of their effects. Growth was modeled as a function of plant size in year t -1, the environmental variables of the late season of year t -1 and the environmental variables of the early season of year t. This choice of variables respect the time frame of the demographic process of growth, which takes place during the time from the census of year t -1 until the census of year t.
Ground stability was included as a categorical variable (Table  1 ) and the effect of sheep trampling in LAU in 2010 as a binary variable. In the site-specific analyses, the error structure was binomial. In the global analyses, the error structure was Gaussian. Because of the uncertainty in the estimates of missing temperature measures by weather station measures, we used the 10 replicated datasets in a multiple imputation framework: we fit a model on each of the 10 dataset, averaged the estimates over models and computed the degrees of freedom according to  Barnard and Rubin (1999). The efficiency of the multiple imputation framework was always above 0. 99, indicating that 10 replicated datasets were largely sufficient to recover missing information. Because the multiple imputation framework relies heavily on t-tests, we selected the best models using a sequential t-test approach: the full model was fitted and variables with nonsignificant estimates were discarded; a new reference model was then fitted, and non-significant variables were again discarded until all remaining variables were significant.
Effect of thermal variables on vital rates Explaining variation in plant growth, survival and reproduction on the basis of topographical and geographical site characteristics is challenging because plant life is the result of many interacting factors such as elevation, aspect and microtopography  (Körner 2007). This is why we decided to use local environmental data collected with data loggers located at vegetative height (a few cm above ground). This avoids the confusion of using environmental data from weather stations located many km away, which may not be representative of the environmental conditions experienced by the plants. Nonetheless, when looking at short periods of time, temperatures measured in nearby weather stations were correlated to temperatures measured in situ and could be used to reconstruct missing data within the vegetative season.
We used thermal variables estimated on periods that are relevant for the vegetative cycle of the plants rather than relying on calendar-based measures. Studies on alpine plants often use calendarbased variables such as monthly mean temperatures or annual minimal temperature to infer biogeographic patterns of species occurrence  (Austin and Van Niel 2011)  or local adaptation  (Manel et al. 2012). While these data may lead to significant correlations at large spatial scale, it is difficult to interpret them at the scale of individual sites because the same calendar month corresponds to different phenological phases in different sites. Our choice of defining the vegetative phase on the basis of in situ temperature instead of calendar dates proved more realistic. Using these data, we studied the relationships between vital rates (growth, reproductive effort and survival) and thermal variables.
Acknowledgements We wish to thank  Serge Aubert ,  Rolland Douzet  and  Bénédicte Poncet  for helping us find the populations,  Philippe Choler  for interesting suggestions on the thermal determination of the vegetative season,  Anne Delestrade  for providing us with the weather station data and all the people helping with the field work, in particular  Stefan Willhoit ,  Blaise Tymen ,  Elsa Jullien ,  Jérémy Camazzola ,  Anne-Lise Bartalucci ,  Lucas Hemery ,  Julie Chauvin ,  Florian Alberto ,  Loïc Chalmandrier ,  Aymeric Pilleux ,  Perrine Augrit  and  Justine Bisson. MA and PdV were funded by a PhD scholarship by the  French Ministry of Research. This research was partially conducted at the Station Alpine  J. Fourier , a member of the  AnaEE network (Infrastructure for Analysis and Experimentation on Ecosystems ).
This study used empirical data on weeds, herbicide practices and winter wheat yields from 150 fields belonging to 30 farmers, to determine whether the use of herbicides improved yields and/or decreased weed abundance. As no clear relationships between herbicide use and weeds nor between yields and weeds were detected using standard statistical models, we modelled these relationships taking into account implicitly the effects of farmers' behaviour and of environmental conditions on the effectiveness of weed management. Although farmers' behaviour is usually taken into account in decision support systems  31, 33  and in mental models  30, 32, 36  using data obtained from surveys of farmers, for this study a hierarchical Bayesian framework was developed  37  which modelled farmers' behaviour (sensu lato) as a parameter influencing the latent variable, λ, of the expected number of weeds per unit area.
g. meteorological conditions are known to affect the effectiveness of herbicides  38  ). Herbicide management options varied for 19 farmers out of the 30 (63. 3%) in our dataset suggesting that farmers changed their management practices to some extent depending on the field. Only one third of the 30 farmers applied exactly the same herbicide treatment to each of their five fields and so the differences between fields is likely to have been due to the effectiveness of the treatment rather than the herbicide used. Furthermore, there was only weak and non-significant correlation between η R Ff (η A Ff ) and the farmers' weeding strategies, described by the diversity and date of introduction of products applied as well as the number of tank-mixed commercial herbicides (SM, Fig. S5  and Table  S5 ).
Discussion The main purpose of this study was to determine whether decreasing the amount of herbicide used would significantly reduce yield owing to an increase in weed richness and/or abundance, as has frequently been suggested  20  ; see review in ref. 21. However, using a dataset of 150 fields, there was no correlation between weed richness or frequency and winter wheat yields. Furthermore, no correlation was found to indicate that the herbicide application rate had an effect on weeds or on yield. Taking account of the possible role of farmers and environmental conditions in the effectiveness of treatment, the results suggested that many treatments were ineffective (Fig. 3c ), probably accounting for the lack of effects. Even where treatment was effective, however, there was no correlation between the effectiveness of treatment and yield (Fig.
20,000 iterations were run with three independent chains in the MCMC procedure. For each chain, the first 10,000 iterations were discarded. After this "burn-in" period, inferences were derived from a sample of 20,000 iterations. Modelling was performed using Winbugs  63  under R with the BRugs package  64. Both the convergence of MCMC chains using Gelman-Rubin convergence statistic  65  and the performance of the estimate (ESM Fig. S5A, B ) were assessed  66.
Acknowledgements The data set analysed came from the  Agripopes ESF research program  (led by  Jan Bengtsson  and  Pablo Inchausti ) which was funded by  European Science Foundation  and  CNRS. P. Inchausti  selected and sought authorization from the farmers and carried out the survey of the farmers. Botanical surveys were performed by  D. Charbonnier. We are very grateful to  C. Gauvrit  for giving us useful insights on herbicides, and the two reviewers for constructive comments on an earlier version of the manuscript. We should like to thank the  30  farmers who participated in this study. This research project was supported by  INRA (SG, JC) ,  CNRS  (VB) and  Avignon University  (EG, FB) and partly funded by  ANR AGROBIOSPHERE AGROBIOSE  ( AGRO-2013-001 ).
No symmetry operations were enforced when calculating point defects and all simulations were spin-polarized. Since these systems are metallic, density mixing and cold smearing of bands (broadening width = 0. 1 eV) were used. Electronic densities of states were analyzed with the aid of the Optados program  [31]  with the adaptive broadening scheme  [32]. 2  (a). The micrograph taken in backscattered electron (BSE) mode shows three different phases:.
First the elemental powders are mixed by ball-milling (Nanjing University Instrument Plant) according to the ratio Ti+Cr/Al/C = 3/1. 1/1. Then the powder mixes are poured in graphite crucibles and placed in a hot-press (FCT Systeme HP W/25/1, Rauenstein, Germany) used as a regular furnace (pressureless mode). The furnace is heated up to 1450°C at a ~20°C. min -  rate and held at the target temperature for 1 hour. The furnace is allowed to cool to room temperature before opening it to ambient atmosphere. Sintering of the reacted powder was then performed by spark plasma sintering (FCT Systeme HP D/25/1 equipment) at 1220 o C and 35 MPa. X-ray diffraction (XRD) characterization was performed with a Bruker D2 Phaser SSD160 (Karlsruhe, Germany) and the treatment of results was done with Fullprof Suite program and Xpert High Score Plus software (PANalytical, Almelo, the Netherlands) using the implemented ICDD (International Center for Diffraction Data) database.
7. Acknowledgments P. A. B. would like to thank  J. N. Hart  for the fruitful discussions and  ANSTO  and the  Tyree foundation  for financial support. Computational resources were provided by the  Imperial College HPC service , the  Australian National Computational Infrastructure  supported by  Intersect Australia Ltd (Raijin and Orange) , and the  Multimodal Australian ScienceS Imaging and Visualisation Environment (MASSIVE). D. H. is grateful to  S. Grasso  and  B. Milsom  of  Nanoforce lab, Queen Mary University, London  for their help with powder preparation and SPS. This work was partially funded by XMat and  CAFFE research projects  ( EPSRC grants   EP/K008749/1  and  EP/M018563/1 , respectively).
Microtubules were labeled either with Rhodamin-labeled tubulin at 100 nM final (Cytoskeleton Inc. ), Fluorescein-labeled tubulin at 100 nM final (Cytoskeleton Inc. ), or EB1-GFP at 150 nM. Actin structures were assembled using metaphase Xenopus laevis egg extracts, containing an ATP regenerating system (1 mM ATP, 10 mM creatine phosphate, 100 μ g/μ L creatine phosphokinase, final concentrations). The actin flow was disturbed using blebbistatin at 130 nM final and cytochalasin D at 2 μ g/mL final or latrunculin at 25 μ M final. Actin was labeled with GFP-utrophin at 110 nM final or Alexa-Fluor 568 Phalloidin at 50 nM final. Imaging and Data Analysis. Fluorescence imaging of microtubule asters and actin network was performed using an IX81 (Olympus) and X60 (PlanApo, NA 1,42) oil objective, equipped with an EM-CCD camera (electron multiplying CCD, C9100-02, Hamamatsu, Corporation), and a LED system of illumination (Spectra X, Lumencor).
Microscope settings and functions were controlled using Simple PCI software (Hamamatsu). Image analysis was performed using ImageJ Software, Matlab, and Simple PCI software. Confocal microscopy was performed with a Zeiss LSM 710 META laser scanning confocal microscope using X63 (PlanApochromatic, NA 1,4) objective. Image analysis was performed using LSM Software Zen 2009 and ImageJ. To quantify the concentration enhancement of organelles within the F-actin ring-like structures, contractile cell extracts were supplemented with lysotracker (Lysotracker Green DND-26 final concentration 2 μ M, Fig. 4a ). We first monitored F-actin droplets and computed the intensity profile to quantify the lysotracker spatial distribution along the droplet axis for both contractile and non-contractile F-actin states.
After normalization and averaging, we obtained fluorescent profiles that were deconvoluted to correct the volume contribution. Then the concentration enhancement was estimated by computing the ratio between the maximum and the minimum of the intensity profile. The same procedure was applied for computing the concentration enhancement of labeled F-actin at late F-actin contraction (Fig. 5b ). To estimate viscosity of cell extracts in different conditions, we monitored the Brownian motion of latex beads (328 nm diameter, Estapor). The trajectories of the beads were then recovered thanks to IcY software  69. Mean Square Displacement (MSD) analysis was computed using Matlab and the diffusion coefficient of the beads was obtained from the 25 first percent of the MSD slope. For the experiments performed after ring formation, the motions of immobile particles within the F-actin ring have not been considered for the estimation of the viscosity.
We estimated the meshsize of the network from confocal images. We plotted intensity profiles and then measured the distance between two fibers. We only took the values that were above the diffraction limit of our optical system (500 nm). Student's t-tests were performed with Matlab. For the interpretation of the p-values: NS means there is no significant difference between the two distributions. One star means pvalue < 0,05, two stars means pvalue < 0,01, three stars means pvalue < 0,001. To quantitatively assess the degree of colocalization between the actin and microtubule structures we used the ImageJ plugin "Coloc 2" to plot the 2d-histograms and get the Pearson's coefficients for the two representative images in Supplementary Figure  10b. Visualization of Ran using mCherry reporters. In order to see how RanQ69L is distributed within the droplet, we generated a dimer between Ran and mCherry using FRB and FKBP dimerizing system.
The present work reports a numerical analysis of a supersonic ejector in single-phase conditions using R134a as the working fluid. A numerical benchmark of some thermodynamic and two-equation turbulence models has been carried out to highlight the numerical model offering the best compromise between accuracy and calculation cost. The validation is achieved by comparing the predicted entrainment ratio with the experimental data of Garcia del Valle et al. (  2014 ). The k-ω SST model together with the REFPROP 7. 0 database equation appears to be the best combination to predict accurately the ejector performance and capture the shock wave structure. The influence of the outlet temperature, the discussion about the validity of some assumptions made by one-dimensional (1D) models and the exergy analysis within the ejector for the present operating conditions will later be discussed in Part 2 (Croquer   et al.
Their work was extended by  Ruangtrakoon et al. (2013)  to quantify the effect of primary nozzle geometries over the local flow features inside the ejector. All these research groups did not reach a real consensus about the CFD (computational fluid dynamics) model, which could be confidently used to investigate the fluid flow and heat transfer in various types of supersonic single-phase ejectors. Moreover, no particular attention was paid to the influence of the solver or the numerical schemes on the predicted results. The purpose of this work is then to carefully build a CFD model offering the best compromise between accuracy and computational efforts to investigate in details the local flow features of a supersonic single-phase ejector working with R134a. The experimental database established by García del Valle et al.
Flow solver The governing equations (conservations of mass, momentum and total energy) for a compressible fluid are solved using the commercial software ANSYS Fluent v. 15, based on the finite volume method. The flow is assumed as being 2D axisymmetric. To our knowledge, there is indeed no evidence reported in the literature of three-dimensional effects in supersonic ejectors  (Pianthong et al. , 2007). Unsteady calculations have been first performed but all leading to steady-state solutions. The flow has been then considered in a steady-state regime. A second-order upwind scheme is used to discretize the advective terms of each equation, except for the pressure equation. For this equation, the PRESTO! scheme designed for flows involving steep pressure gradients has been chosen. The diffusive terms are discretized using second-order central differenced schemes.
α (2) b RT P c c = 0 08664. (3) α T f f T T c ( ) = + + - ( )-⎛ ⎝ ⎜ ⎞ ⎠ ⎟ ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ ⎡ ⎣ ⎢ ⎤ ⎦ ⎥ 1 0 480 1 574 0 176 1 2 1 2 2. (4) • The REFPROP 7. 0 equation database. The REFPROP model  (Lemmon et al. , 2013)  for refrigerant R134a is based on the formulation of Tillner-Roth and Baehr  (1994). Fluid thermodynamic and transport properties (viscosity, specific heat, and thermal conductivity) are computed using the Helmholtz free energy equation of state. This equation depends on 21 parameters, obtained from statistical analysis and least-squares fitting of the most accurate R134a measurements available at that time  (Tillner-Roth and Baehr, 1994). This model accurately represents real gas behavior in the temperature range [170-455] K and for pressures up to 70 MPa. For the perfect gas model, all thermo-physical properties (viscosity, specific heat, and thermal conductivity) are considered as constant and fixed to the average between the inlet and outlet values.
For example, for OP3, the numerical predictions using the standard k-ε model and the REFPROP database give ω = 0. 232 for a roughness height equal to 2 μm and 0. 028 for 20 μm, which is to be compared to the experimental value ω exp = 0 339. All turbulence models provide similar results and confirm the results of  Mazzelli and Milazzo (2015) , who reported the same steepness and extension of the offdesign region for similar condenser temperatures. The inner surface of the ejector designed by García del Valle et al. (  2014 ) may be considered as a mirror-like surface and as a smooth wall in the CFD model. A hybrid initialization based on boundary interpolation methods is used to start the calculations. A Laplace equation is solved to determine the velocity and pressure fields from the imposed boundary conditions.
All other variables, such as temperature or turbulence quantities, are automatically patched based on domain averaged values. A mesh sensitivity analysis was carried out for both lowand high-Reynolds number approaches to ensure the independence of the solution from the spatial discretization: • For all turbulence models based on a high-Reynolds number formulation, the mesh grid was built using ANSYS Meshing software v. 15 with standard wall functions for the nearwall treatment. An unstructured type mesh is used with 5 prismatic layers in the near-wall regions. A mesh grid with 51,000 cells has proven to be sufficient to get grid independent solutions  (Croquer et al. , 2015a). The maximum value for the wall coordinate remains then in the range  (2010; 2013; Camporese et al. , 1985; Croquer et al.