dataesr/scientific-paragraphs-categorization huggingface dataset:
To assess correlations between evolved differences in dispersal ability and intrinsic growth rates, we used data from the dispersal and growth rate assays from descendants of the edge populations of structured landscapes. Using landscapes with both a dispersal and growth rate assay, we plotted the proportion of beetles that dispersed away from the origin patch under low-density conditions (from the dispersal assays) against the population growth rate averaged across assay densities (from the growth rate assays). We then calculated Pearson's r to assess the correlation between the two. All statistical analyses were performed using R 49 (version 3. 2. 3). Linear mixed effects models and generalized linear mixed effects models were fitted using the lmer and glmer functions in the package lme4 (ref.
Acknowledgements Funding for this work was provided by  National Science Foundation  Grants  DEB 0949595  to B. A. M. and  DEB 0949619  to  R. A. H. During  this work C. W. -L. was supported by a  Graduate Research Fellowship  from the  National Science Foundation  (Grant No. 1144083 ) and R. A. H. received support from the  USDA  via the  Colorado Agricultural Experiment Station. We thank  J. Vidreo ,  T. Myers ,  S. Race ,  N. Richter ,  F. Janz ,  M. Gibson ,  J. Depompolo ,  K. Ghale ,  A. Solon ,  N. Ochwat ,  T. Turk ,  M. Duc Le  and  R. Ghale  for assistance with data collection. We thank  B. Ochocki ,  T. Miller ,  S. Endriss  and  M. Vahsen  for comments on the manuscript and analyses. Publication of this article was partially funded by the  University of Colorado Boulder Libraries Open Access Fund.
Acknowledgments This work has been partially supported by the  Spanish Ministerio de Economía y Competitividad  through Grant No. TRA2013-40853-R. Additionally , the Ph. D. student  E. J. Sanchis  has been funded by a grant from  Universitat Politècnica de València  with reference  FPI-2016-S2-1355. These supports are gratefully acknowledged by the authors. Finally, the authors wish also to acknowledge the suggestions of the referees, which have resulted in a considerable improvement of the text.
INTRODUCTION Pellegrino et al. in the interpretative strategies for lung function tests stated that special attention must be paid when FEV 1 and FVC are concomitantly decreased and the FEV 1 /FVC ratio is normal or almost normal. Apart incomplete inhalation or exhalation, these authors stated that a "possible cause of this pattern is patchy collapse of small airways early in exhalation"  [1]. This small airways obstructive pattern (SAOP) is not infrequent, represents ~ 7% to 10% of all pulmonary function tests in two large databases and is not specific of any disease in adults  [2, 3]. It can also be encountered in asthmatic children  [4]. Since this pattern is deemed to be the consequence of "patchy collapse of small airways early in exhalation" one may wonder whether bronchodilator response should be assessed on flow (increase in FEV 1 ), volume (FVC and/or inspiratory capacity [IC]  [5] ) or both flow and volume.
Statistical Analyses Results are expressed as median [25 th -75 th percentiles]. Univariate correlations were estimated using non parametric Spearman coefficients. A P value <0. 05 was deemed significant. All statistical analyses were performed with Statview 5. 0 software (SAS institute, Cary, North Carolina, USA). Sample size calculation: due to its descriptive design our objective was to give observed percentages with a 95%CI range < 30% that can be obtained with a sample of 50 patients.
Background: The role of endoscopic ultrasound (EUS) in the management of pancreatobiliary and digestive diseases is well established in adults, but it remains limited in children. The aim of this study was to evaluate the feasibility, safety, and clinical impact of EUS use in children. Methods: This is a retrospective analysis of a prospectively acquired database of consecutive pediatric (< 18 years) patients presenting an indication for EUS for pancreatobiliary and gastrointestinal disorders. Results: Between January 2010 and January 2016, 47 procedures were performed in 40 children (mean age of 15. 1 ± 4. 7 years; range 3-18). The majority of EUS (n = 32; 68. 1%) were performed for pancreatobiliary and upper gastrointestinal pathologies, including suspected common bile duct stones (CBDs), acute biliary pancreatitis, recurrent/chronic pancreatitis, cystic pancreatic mass, recurrent hypoglycemia, duodenal polyp, gastric submucosal lesion, and perigastric abscess.
Study population The present study is a retrospective analysis of a prospectively acquired database of consecutive pediatric (< 18 years) patients presenting an indication for EUS or EUS-FNA. All procedures were performed between January 2010 and January 2016 at the Endoscopy Unit of the University of Parma. EUS and EUS-FNA were performed by a senior gastroenterologist (GLdeA) with expertise in both adult and pediatric endoscopy. Written consent was obtained from both parents or legal guardians, and it included consent for the therapeutic procedures. All data were collected in compliance with the ethical principles stated in the Declaration of Helsinki, and according to the Good Clinical Practice protocols and Privacy Protection Law of the institution.
EEG Recordings and Preprocessing EEG Data were acquired using a g. USBamp (g. tec Medical Engineering GmbH, Austria) from 28 electrodes (AF3, AF4, F1, F2, F3, F4, F5, F6, FC1, FC2, FC3, FC4, C1, C2, C3, C4, CP1, CP2, CP3, CP4, CP5, CP6, P1, P2, P3, P4, P5, P6), according to the 10-20 system, with a sampling frequency of 512 Hz. Three electrodes were used for recording the electrooculogram (EOG); two electrodes were placed above and below the left eye to record the vertical EOG, and another electrode was positioned at the outer canthus of the left eye to record the horizontal EOG. A band pass 0. 5-60 Hz filter was applied to remove low and high frequency artifacts from the EEG signals. EEG data were later re-referenced to the algebraic mean of the left and right mastoids. Artifacts (e. g. , eye-blink eye-movement, and FIGURE 2 | Experimental procedure.
Participates were asked to listen to pieces and detect timbre deviants while watching the silent movie (March of Penguins, Warner, ASIN B000BI5KV0). 265 stimuli were presented randomly in two parts with a 15 min rest interval in between. Baseline recording was performed at the beginning of each part using 30 s of brown noise. Black arrows represent the times at which a short beep sound was played. muscle activity) were then removed by employing Independent Component Analysis (ICA) using EEGLAB toolbox  (Delorme and Makeig, 2004). Next, data were filtered with a 25-Hz low pass filter (FIR, 550 points) to remove the remaining high frequency noise. The preprocessed data were later epoched. Finally, epochs were excluded if within a moving window of 600 ms the standard deviation of amplitude exceeded 25 µV, or any sampling point exceeded ±75 µV at any electrode location.
ACKNOWLEDGMENTS The work described in this paper was supported by a grant from the research office of  Ferdowsi University of Mashhad, Mashhad, Iran. We would like to thank  Kaveh Kasaie  for composing the Iranian excerpt and  Keyvan Alaie  for his assistance in stimuli preparation. We would also like to thank  Amin KhoshSabk  and  Dr. Alireza Shademan  for valuable discussion.
The spatial and temporal coverage of the Landsat satellite imagery make it an ideal resource for studies on the long term evolution of lake surface temperature and for geographical studies of temperature patterns. The Lake Skin Surface Temperature (LakeSST) data set contains skin surface temperature data for 442 French water bodies (natural lakes, reservoirs, ponds, gravel pit lakes and quarry lakes) for the period 1999-2016 obtained from the thermal band of Landsat 5 and Landsat 7 archive images. The skin temperature measured by satellites differs slightly from water temperature in the first meters of the water column because of cool skin and warm layer effects. Nevertheless surface temperature parameterizations originally developed for the sea can be used to adjust LakeSST to commonly used lake water temperature, e.
In addition to in situ data, meteorological data were gathered for the five French water bodies from the SAFRAN reanalysis data  (Quintana-Seguí et al. , 2008; Vidal et al. , 2010) , available for the study period at the nearest grid point at a daily resolution. The following variables were extracted: air temperature, specific humidity, wind speed, liquid precipitation, solid precipitation, downwelling longwave radiation and incoming solar radiation. An adiabatic correction of -0. 0065 ºC/m was applied to air temperature data to account for the difference of altitudes between the measurement point and the reservoir of Bimont. Additionally, we calculated solar elevation and extraterrestrial solar radiation following  Lenoble (1993). We calculated the daily clearness index as 𝑘 𝑡 = 𝑅 𝑠 𝑅 𝑒 ⁄ (1) where R s is the daily solar radiation at the Earth surface and R e is the extraterrestrial daily solar radiation through a plane parallel to the surface.
Deriving skin temperature from Landsat imagery The Thermal InfraRed band both of the Landsat 5 TM (Thematic Mapper) and Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) instruments were used for deriving skin temperature. The TIR band (band 6) on both sensors measures emitted radiation at wavelengths of 10. 40-12. 50 µm. Both satellites have similar orbits, with a delay of 8 days, and they fly over France at 10:00-10:30 UTC. Two consecutive scenes overlap at 5 % of their surface and are taken with a delay of 23. 92 s  (NASA, 2011). The TM instrument acquired Band 6 data at a 120-metre resolution and the ETM+ instrument acquired Band 6 data at a 60-metre resolution. TIR Landsat data were extracted from Landsat Climate Data Records (CDR) data. Landsat CDR consist of surface reflectance products from the Landsat archive  (Masek et al.
During the preprocessing phase, we extracted the valid pixels for each water body and image. For each image the preprocessing included the following steps: 1. Extraction of the TIR band, masks and metadata from the original hdf files. 2. Selection of water pixels using land-water mask and vector file containing the water body outline. 3. Suppression of unexploitable pixels by application of mask products associated to the image. For the image processing, brightness temperature in the remaining pixels was converted to surface temperature following the single-channel algorithm proposed by  Jiménez-Muñoz and Sobrino (2003) , adapted to Landsat images  (Jiménez-Muñoz et al. , 2009). For further details on the algorithm or its calibration for Landsat thermal bands, please refer to these articles.
Warm layer estimations based on field measurements We used data from the 0. 55 m chain installed at the reservoir of Bimont to assess the relation between surface temperature and temperature at 0. 55 m. The instrumentation used was not adapted to measure the cool skin effect, since the surface thermistor measured temperature at a depth of about 1 cm, below the cool skin. However, the Bimont data could be used to measure the warm layer effect. We calculated the warm layer effect at 0. 55 m as: ∆  (0. 55) =  0 - 0. 55. (3).
The FORTRAN code and test data were obtained from ftp://ftp1. esrl. noaa. gov/psd3/cruises/NTAS_2009/RHB/Scientific_analysis/programs/VOCALS2008_programs_leg1/coare/b ulkalg/cor3_0/. In the original parameterization, the physical properties of water (viscosity, density, thermal and salinity expansivities and thermal conductivity) are constant and adapted to the seawater case. We modified the algorithm to calculate the physical properties of water as a function of temperature and salinity using expressions provided by  Sharqawy et al. (2010). An R file (Coare_3_0. R) with the implementation of the model is supplied as supplementary information. The simulation of ΔT w for the test data was almost equal for both algorithms. However, the algorithm with physical properties of water depending on temperature and salinity simulated a less intense cool skin effect (with an average difference of 0.
Assessment of the precision and accuracy of satellite-based surface water temperature measurements Consecutive satellite scenes overlap by 5%. As a consequence, at these overlapping zones repeated temperature measurements are available with just a few seconds of difference. We used the data from 45 water bodies located in the overlapping zones between consecutive scenes to estimate the precision of satellite-based surface water temperatures. The assessment of the accuracy of satellite measurements was made by comparing them to in situ SML temperature and temperature at 0. 5 m for 5 lakes with continuous monitoring (Table  1 ). We calculated the bias for the five water bodies with continuous measurements as the difference between field measurements and satellite temperature measurements at the nearest pixel (T sat ) as 𝑏 0.
50𝑚 = 𝑇 𝑠𝑎𝑡 -𝑇 0. 50𝑚 (8) 𝑏 𝑠𝑚𝑙 = 𝑇 𝑠𝑎𝑡 -𝑇 𝑠𝑚𝑙 (9) where b 0. 50m is the bias in relation to temperature at 0. 50 m and b sml is bias in relation to the SML temperature. The bias depended on the distance of the measurement location to the nearest pixel, because of spatial variability of the surface temperature (Figure  3 ). Absolute bias increased for distances greater than 500 m (the slope of the linear model of bias as a function of distance was not statistically significant at the 0. 05 level, but we found p-value = 0. 10). Most of the satellite-field data pairs separated by more than 500 m correspond to SCR04, the largest water body. Satellite temperatures measured near the field measurement point at this reservoir (near the dam) were warmer than in the open waters. To exclude this distance effect, we kept for further analyses the data pairs separated by less than 400 m.
Software used We analysed the data using Python 2. 7 and R 3. 2. 0 (R Core Team, 2015). We used the Python packages NumPy (van der  Walt et al. , 2011) , Matplotlib  (Hunter, 2007)  and Pandas  (McKinney, 2010)  and the R package MASS  (Venables and Ripley, 2002)  and rLakeAnalyzer  (Winslow et al. , 2016).
45 and the root mean square error was 0. 22 ºC, with the model overestimating the surface thermal gradient. The highest correlation between estimated and measured warm layer effect was found for z r = 2. 50 m, with a correlation coefficient of 0. 78 and RMSE = 0. 32 ºC. The discrepancies between the simulation and the measurements can be attributed to several factors: a) The model proposed by  Fairall et al. (1996a)  does not allow for negative warm layers (or cool layers). Part of the effects of convective cooling are included in the definition of the cool skin, but they are constrained to the upper millimetres of the water column. b) The meteorological data used to force the COARE algorithm was measured at about 10 km from the study site. Although air temperature data was corrected for altitude effects, local micrometeorological conditions at the reservoir of Bimont are probably different to those measured at the meteorological station of Aix-en-Provence  (Prats et al.
Conclusion The LakeSST data set contains skin surface water temperature data for 442 French water bodies for the period 1999-2016 obtained from archives of Landsat 5 and Landsat 7 thermal infrared images. The overall accuracy of the satellite-derived temperature measurements is about 1. 5 ºC, similar to other applications of satellite images to estimate freshwater surface temperatures. The spatial and temporal coverage of the database make it an ideal resource for studies on the long term evolution of lake surface temperature and for geographical studies of temperature patterns. Since cool skin and warm layer algorithms have been developed mainly for the sea and have not been extensively tested in a freshwater environment -except in extreme cases such as cooling ponds  (Wesely, 1979; Adams et al.
Acknowledgements. This study was funded by the  Onema (French National Office for Water and Aquatic Environments ) action 61B, 102 and CK. The authors thank  R. Simon  for his contribution in preparing the script to process satellite images. The authors thank  Météo-France  for the meteorological data at the station of Aix-en-Provence and the data from the SAFRAN analysis, provided free of charge to Irstea for this study and for the project " Temperature and oxygen indicators for lakes ". The authors are grateful to  ECMWF  and  USGS  for providing the data used in producing the surface temperature data set. Special thanks to  Julien Dublon ,  Thierry Point ,  Jean-Michel Foissy ,  Michael Cagnant ,  Jean-Claude Raymond ,  William Sremski ,  Gaël Olivier ,  Sylvain Richard ,  Laurent Tachot  and the agents of the Departmental Services of the  French Agency for Biodiversity  for the Departments 04, 13, 38 and 63.
Acknowledgements We want to thank to our Algerian colleagues, in particular to  F. Azira-Atroune ,  S. Hadjadj-Aoul ,  L. Kadik  and  M. Laribi , as well as  Z. Lososová  and  R. Gavilán  kindly provided some less accessible literature. Jean-Paul Theurillat kindly advised with some nomenclatural issues. R. Gavilán kindly translated our abstract and keywords into Spanish. L. M. acknowledges the logistic support of the  Iluka Chair at The University of Western Australia.
CP 1 over CP 2 Let us now examine in detail the construction of an SU (3) structure on the CP 1 bundle over CP 2. This is the simplest example in the class of 3d SCTV of the form CP 1 bundle over M , where M is a 2d SCTV, but it already captures the main idea of the construction. We will treat the general case in section 4. The toric data in this case are: k = 5 (the complex dimension of the parent space), s = 2 (the number of charges), d = 3 (the complex dimension of the toric variety). Explicitly the charges are given by, Q = 1 1 1 -n 0 0 0 0 1 1 , (3. 1) where n ∈ N. The corresponding moment map equations read, using the notation introduced in section 2. 4, |z 1 | 2 + |z 2 | 2 + |z 3 | 2 = ξ 1 + n|u| 2 |u| 2 + |v| 2 = ξ. (3. 2) This is a CP 1 bundle over CP 2 , with twisting parameterized by n.
Activation was routinely initiated by manual trigger immediately during image acquisition. Frame rates were 0. 33 Hz for the time-lapse recordings shown in Figure  5  and 0. 75 Hz in all other experiments. Red/green dual-channel imaging was performed in the experiment in Figure  5  (red channel not shown in Figure  5B ) and PA-GFP-actin imaging alone in all other experiments. Expression of mCherry-tagged VASP full-length or mDia1-FH1-FH2 was confirmed by individual images before and after time-lapse recordings for each cell. Images were acquired with a 100×/1. 4 NA Plan apochromatic oil immersion objective, except for images in Figure  5 , which were done with a 63×/1. 4 NA Plan-Apochromat objective. To analyze the reduction of cytosolically photoactivated actin within photoactivated regions over time, a region encompassing the photoactivated cytosolic region was manually drawn on Meta-Morph software, and fluorescence intensities in this region were recorded for each frame.
Protein amounts were adjusted to 30 µg for each sample before addition of 8× SDS loading buffer in a 1:1 volume ratio. Samples were syringed several times through a experiments, an inverted microscope (Axiovert 100 TV; Carl Zeiss) was used, equipped with an HXP 120 lamp for epifluorescence illumination, a halogen lamp for phase-contrast imaging, and a Cool-Snap-HQ2 camera, as well as electronic shutters driven by Meta-Morph software (Molecular Devices, Sunnyvale, CA) for image acquisition. Verification of protein expression of EGFP or mCherrytagged overexpression constructs was done using standard epifluorescence imaging.
Determination of rates of lamellipodium protrusion Lamellipodium protrusion at 2 d posttransfection was determined based on kymographs generated from time-lapse movies as follows: lamellipodia of B16-F1 cells were recorded over at least 5 min, acquiring images every 15 s. Kymographs were generated using MetaMorph software by manually drawing lines from inside the cell and across the lamellipodium. Corresponding regions from each time point of a time-lapse series were pasted next to each other along the x-axis. Protrusion rates were determined by measuring advance of lamellipodia tips (y-axis) over time (x-axis). Time-lapse images for generation of kymographs for protrusion assays were obtained with 63×/1. 4 NA Apochromat or 63×/1. 25 NA Neofluor Plan oil objectives. Average values of lamellipodial protrusion rates, as shown in Figure  1C , are represented as percentage of the average protrusion rate of control EGFP-C1 blank vector-overexpressing cells, which were measured in parallel and used as a reference for each experiment.
Photoactivation of fluorescently tagged actin For photoactivation of actin, B16-F1 cells were cotransfected with human β-actin tagged to PA-GFP  (Patterson and Lippincott-Schwartz, 2002)  and either mRuby-Lifeact (for Figure  5 ; not shown in Figure  5B ) or mCherry-tagged versions of either VASP full-length or mDia1-FH1-FH2 (for Figure  6  and Supplemental Figures  S3  and S4 ). FRAP experiments were carried out essentially as described  (Steffen et al. , 2014)  on an inverted Axio Observer equipped with DG4 light source (Sutter Instrument) for epifluorescence illumination, a CoolSnap-HQ2 camera driven by VisiView software (2. 1. 0), and a photomanipulation device (Visitron Systems). PA-GFP-actin was photoactivated in selected regions with the 2D-VisiFRAP Realtime Scanner, using ∼3-to 15-mW output power of an acousto-optical tunable filter-controlled 405-nm diode laser (settings: 10-pixel laser 27-gauge needle and boiled for 10 min at 95°C.
ACKNOWLEDGMENTS We thank  Roland Wedlich-Söldner  for DNA constructs,  T. E. Stradal  ( Helmholtz Centre for Infection Research, Braunschweig, Germany ) and  J. Faix  ( Hannover Medical School, Hannover, Germany ) for discussion, and  Gerd Landsberg  and  Brigitte Denker  for excellent technical assistance. This work was supported in part by  Deutsche Forschungsgemeinschaft  Grants  RO2414/3-2  and  RO2414/5-1  to K. R. and Seventh  Framework Programme Grant  FORCEFULACTIN of the  European Research Council  to M. F. C. and K. R.
Data processing and statistical analyses Brightness and contrast levels were adjusted using MetaMorph software v7. 7. 8. 0. Images were further processed for figure preparation using Adobe Photoshop CS4 (Adobe Systems, San Jose, CA). Line scans in Supplemental Figure  S1  were generated using the line scan function in MetaMorph software v7. 7. 8. 0. Final figures were assembled with Photoshop CS4 or Adobe Illustrator CS4 (Adobe Systems). Data analyses were carried out in ImageJ and MetaMorph, Microsoft Excel 2010, and Sigma plot 12. 0 (Systat Software, Erkrath, Germany). Data sets were compared using Mann-Whitney rank sum test or Spearman rank order correlation test (Sigma plot 12. 0). Probability of error of 5% or less (*p < 0. 05) was considered statistically significant.
Histopathological analysis Kidney tissues fixed in 4% buffered formalin were paraffin-embedded and 3 μm thick sections were stained with Masson-Trichrome for qualitative and Sirius Red for quantitative analysis of tissue fibrosis, and with periodic acid-Schiff (PAS) to determine the degree of sclerosis within the glomerular tuft. Samples stained with Sirius Red were scanned using Aperio Spectrum software (Aperio Technologies). Images were captured using Aperio ImageScope software at total magnification of 300. The percentage of sirius red-stained tubulointerstitial area was measured in 30 randomly sampled fields per kidney using Image-Pro Plus software (Media Cybernetics, Silver Spring, MD) as described previously  [48]. The degree of sclerosis within the glomerular tuft as a parameter of renal disease progression was determined in a blinded manner on PAS-stained paraffin sections using the semiquantitative scoring system  [49].