dataesr/scientific-paragraphs-categorization huggingface dataset:
(f ) Statistical analysis All measures calculated from the fish trajectories were analysed as response variables in Mixed Models. All models included the treatment ( pile-driving or ambient-sound playback) as a between-subjects term, and the half of the trial (1st or 2nd) as a within-subjects term. The interaction between these two fixed terms was included in the initial models, but was removed where it was non-significant and models were re-run with main effects only. All models included fish identity nested within trial (which is equivalent to the group the individuals belonged to) as the random term. The average difference between fish perpendicular or parallel to their direction of travel, the bearing of the nearest neighbour in-front or behind, and the difference in heading between nearest neighbours were analysed for each fish using negative binomial Generalized Linear Mixed Models (GLMMs), as the data were typically right skewed.
MTT assay Cell viability was determined using an MTT assay based on the mitochondrial activity of viable cells. X 0 -CGD iPSC-derived macrophages (15×10 3 cells) were seeded in a 96-well plate and treated with NOX2/p22 phox (20 pmol cytochrome b 558 ) and negative liposomes for 8, 12 and 24 h at 37°C in IMDM. MTT was then added, and the cells were incubated for a further 3-5 h at 37°C. The absorbance was measured at 560 nm using a multiwall scanning Varioskan™ Flash spectrophotometer and SkanIt software (version 2. 4. 1; Thermo Fisher Scientific, Courtaboeuf, France). The percentage of cell viability was calculated as follows: The viability of untreated cells was arbitrarily set at 100%. All assays were performed in triplicate. Data were expressed as mean ± standard error. A nonparametric Mann-Whitney U-test was used for statistical analysis using StatView 5.
Flow cytometry X 0 -CGD macrophages (1. 5×10 5 ) were incubated for 8 h at 37°C with NOX2/p22 phox (5 pmol cytochrome b 558 ) or negative liposomes in IMDM and then stained with fluorescein isothiocyanate (FITC)-coupled anti-NOX2 (7D5) and anti-p22 phox (clone 44. 1) antibodies as previously described. 23 ontrol staining with appropriate isotype-matched control was included to establish thresholds for positive staining. WT iPSC-derived macrophages were used as positive control. Cell fluorescence was quantified using a FACSCanto™ II flow cytometer (BD Biosciences, Le Pont de Claix, France). Data were collected and analyzed with the FACS DIVA software (BD Biosciences) and FlowJo software (Tree Star, Ashland, OR, USA).
confocal microscopy A total of 1×10 5 X 0 -CGD macrophages were incubated at 37°C in IMDM with NOX2/p22 phox (5 pmol cytochrome b 558 ) or negative liposomes and were treated as previously described. 31 Cells were stained with the 7D5 antibody (1:20) for 1 h at 4°C followed by incubation with an Alexa Fluor ® 488 goat-F(ab′) 2 fragment anti-mouse IgG1 (H+L) for 1 h at room temperature. Nuclei were stained with 1 µg/mL Hoechst 33258. Slides were scanned using a Zeiss LSM710 confocal laser-scanning microscope (Carl Zeiss, Jena, Germany) equipped with 488 nm argon and 405 nm diode lasers at the microscopy facility of the Institut Albert Bonniot (Grenoble, France). Image acquisition was performed by Plan-Apochromat 63x/1. 4 Oil objective using ZEN software (Carl Zeiss). Images were processed using ImageJ software (National Institutes of Health, Bethesda, MD, USA).
Acknowledgments The authors thank the  University Grenoble Alpes  (AGIR program 2014) and the  French-Swiss Interregional program  ( Interreg -Feder ,  2013-2015 ) for funding this work. They also thank  Sylvain Beaumel ,  Cécile Martel  and  Michèle Mollin  for their helpful and valuable work at the  Centre Diagnostic et Recherche sur la CGD , and  Pascal Gabard  for his advice on the miniaturized recombinant cell-free system. They acknowledge the platforms of the Grenoble Instruct Center (ISBG;  UMS 3518 CNRS-CEA-UGA-EMBL ) supported by the  French Infrastructure for Integrated Structural Biology Initiative FRISBI  ( ANR-10-INSB-05-02 ) and by the  Grenoble Alliance for Integrated Structural Cell Biology GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
g. the Mediterranean Sea. In this work, a longline mussel farm located in the north western Adriatic Sea was regarded as a local source of perturbation 20 of natural organic matter downward fluxes. Average yearly increase in POC flux induced by the farm was first quantified by applying a biogeochemical model of POC production and deposition (mussel faeces and pseudofaeces), coupled to sediment traps data, and subsequently used to force an early-diagenesis model simulation, which was calibrated by collecting new data in sediment porewaters. The objectives of this study are a better understanding of seasonal and inter-annual variability in POC deposition fluxes from the mussel farm, and the quantification of benthic recycling of organic matter under contrasted 25 forcings linked to mussel farm, ecological mechanisms associated (bioirrigation), and relative importance of oxic versus anoxic processes.
35 , and Dissolved Inorganic Carbon (DIC); 4) two early diagenesis models (EDM) were set-up and constrained by the observed field data in the sampled cores at the two stations: bioirrigation parameters and ratio among degradation pathways were estimated on the basis of model application. Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-206 Manuscript under review for journal Biogeosciences Discussion started: 7 June 2017 c Author(s) 2017. CC BY 3. 0 License.
, 1987). The Lagrangian equation for the deposition model was solved following  Jusup et al. (2007). The set of partial differential equations in the EDM was numerically solved by means of an implicit method; details on operator splitting techniquesequential non-iterative approach, and definition of the function residuals and the Jacobian matrix are provided in  Aguilera et al. (2005)  and  Regnier et al. (2002). Model runs were performed on SCSCF (www. dais. unive. it/scscf), a multiprocessor 25 cluster system owned by Ca' Foscari University of Venice running under GNU/Linux.
Time series of monthly Sea Surface Temperature (SST) and concentration of chlorophyll-a were extracted from the EMIS (http://emis. jrc. ec. europa. eu/) data base from July 2002 to December 2012,  (longmin 12. 5; longmax 12. 6; latmin 45. 4; latmax 45. 5 ) by means of the R package EMISR v0. 1 (R version 3. 0. 3). Chlorophyll-a and SST data were derived from the sensor Modis (Moderate Resolution Imaging Spectroradiometer) Aqua and Terra respectively, with a spatial resolution of 4km. These data were used as inputs for individual-based population dynamics model, following a procedure previously 40 applied -see e. g. Filgueira et al. (2013). Due to the lack of long term time series of data, an average POC concentration had to be imposed, 0. 1 mg L -1 , on the basis of a time series of monthly data collected at a nearby farm between 2006 and 2007 Biogeosciences Discuss.
, https://doi. org/10. 5194/bg-2017-206 Manuscript under review for journal Biogeosciences Discussion started: 7 June 2017 c Author(s) 2017. CC BY 3. 0 License. (Brigolin et al. , 2009). The POM/TSM ratio was fixed on the basis of the time series collected within the same work  (Brigolin et al. , 2009; Rampazzo et al. , 2013) , providing an average AE of 0. 6. The median daily value of faeces and pseudofaeces fluxes from the 10 simulations was used as an input for the deposition model. Modelling deposition requires an input time series of water velocity at an hourly time step. These data were provided on the basis of a current meter deployment carried out between March and September 2010 at a station located approximately 500 5 m from the NE edge of the farm (Boldrin A. pers. comm). Current meter data were first processed by means of a classical harmonic analysis  (Pawlowicz et al.
5 Rate laws  DB was supported by an individual mobility grant from the University of Venice Ca' Foscari (IRIDE-DHAMACO project). R 1 = [ ] acc k k OM o k OM k f ⋅ ⋅ ⋅ 2 ,with k=1,2,3 R 2 = [ ] acc k k OM NO k OM k f ⋅ ⋅ ⋅ - 3 ,with k=1,2,3 R 3 = [ ] k k OM SO OM k f ⋅ ⋅ 4 ,with k=1,2,3 R 4 = [ ] [ ] 2 4 4 O NH k ⋅ ⋅ + R 5 = [ ] [ ] Model runs were performed on SCSCF (www. dais. unive. it/scscf), a multiprocessor cluster system owned by Ca' Foscari University of Venice running under GNU/Linux. We gratefully acknowledge G. Monvoisin from GEOPS France for the assistance in the analyses, A. Boldrin from CNR for giving access to the current meter data, and Nautica Dal Vì for providing space for setting up lab facilities. 10 Figure Captions.
Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-206 Manuscript under review for journal Biogeosciences Discussion started: 7 June 2017 c Author(s) 2017. CC BY 3. 0 License. respect to IN1 (1. 4 mm on average). DIC, NH 4 + and SO 4 2-data are shown in Figures 5c-e. DIC concentration profiles at the two stations are comparable, although at depths >10 cm they stabilize at values around 3. 6 mM at EST1 and 2. 8 mM at IN1. A similar pattern is visible for ammonium, which average concentration below 10 cm depth reaches values of 50 µM at IN1 and 125 µM at EST1. The effect of bioirrigation is visible within the upper 10 cm, and, is particularly marked at station IN1, where DIC shows a decrease in concentration starting from 4 cm depth. SO 4 2-concentrations are similar between the two 5 stations, and do not present marked variations going down-core.
InDedieu et al. (2007)  this was accompanied by a remarkable enhancement in NH 4 + concentrations, which was not visible in the field data reported in the present study. This can certainly be related to the difference in the rate of biodeposit accumulation, although other factors 40 can also play a role (changes of local macrobenthic activity, and of coupled nitrification-denitrification rates). Mean OC fluxes estimated by Dedieu et al. (2007) by calibrating the diagenesis model at the mussel farm site, were 38. 4, 108. 0, and Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-206 Manuscript under review for journal Biogeosciences Discussion started: 7 June 2017 c Author(s) 2017. CC BY 3. 0 License. 108. 0 mmol C m -2 d -1 , respectively in winter, spring and summer, with increases by 26.
Acknowledgments We thank  Finn Cilius Nielsen ,  Rehannah Borup  and  Susanne Smed  ( RH Microarray Center, Rigshospitalet, Copenhagen ) for array hybridizations,  Anouchka L. Skoudy  for ES cells,  Mark Kalisz ,  Jose Luis Gomez-Skarmeta  and  Jacques Mallet  for insightful comments and funding from  Ministerio de Economı ´a y Competitividad  ( SAF2011-27086 ), ( BFU2014-54284-R ) and a  Wellcome Trust   Senior Investigator Award  ( 101033 ) to J. F. P. R. received funding for the  Association de Langue Francaise pour l'Etude du Diabète et des Maladies Metaboliques (ALFE-DIAM)  and a  joint JDRF / INSERM grant.
They include genes that encode carbohydrate active enzymes (which can be found in the CAZy database [http://www. cazy. org]) (3)  and include multigene families of chitin and glucan synthases as well as remodeling enzymes such as the glycohydrolases (glucanases, chitinases) and transglycosidases. Many of the building blocks of the cell wall are conserved in different fungal species  (4) , while other components of the wall are species-specific, and there is perhaps no part of the cell that exhibits more phenotypic diversity and plasticity than the cell wall. Walls are built to be both malleable and mechanically robust. The high total solute concentration inside fungal cells results in the osmotic uptake of water and the pressing of the cell membrane onto the wall. The resulting turgor pressure has been estimated to be between 0.
ACKNOWLEDGMENTS N. G. is funded by the  Wellcome Trust  via a senior investigator award and a strategic award and by the  MRC Centre for Medical Mycology. C. M. acknowledges the support of the  Wellcome Trust  and the  MRC. N. G. and C. M. are part of the  MRC Centre for Medical Mycology. J. P. L. acknowledges support from  ANR, Aviesan , and  FRM. ASMscience. org/MicrobiolSpectrumGow et al. Downloaded from www. asmscience. orgby IP:  139. 133. 148. 14On: Tue, 11 Jul 2017 11:57:44 ASMscience. org/MicrobiolSpectrumGow et al. Downloaded from www. asmscience. orgby IP:  139. 133. 148. 14On: Tue, 11 Jul 2017 11:57:44 ASMscience. org/MicrobiolSpectrumGow et al. Downloaded from www. asmscience. orgby IP:  139. 133. 148. 14On: Tue, 11 Jul 2017 11:57:44.
As in previous studies (e. g. [5] ), we defined foraging trips from the time when the birds departed from the colony to the sea until returning to the colony. Bathymetry data were obtained from the global sea floor topography from satellite altimetry and ship depth soundings (Global Topography; Additional file 1: Figure  S1 )  [52]  available at  [53]. Positional data obtained from GPS-TDloggers were used to plot and analyse the trips performed by the birds in ArcGIS 9. 3 (ESRI, Redlands, USA). Trip length was calculated as the total cumulative linear distance between all positional fixes along the foraging trip, outside of the colony. For each trip, the maximum distance from the colony was calculated as the linear grand circle distance between the furthest point of the plotted trip and the geographical coordinates of the departure colony, determined by GPS.
Trip duration was determined as the time elapsed between departure and return from the colony. Foraging dives were identified using purpose-written software in Matlab (The Mathworks Inc. , Nattick, USA) and purpose-written script for IGOR Pro 6. 3. 7. 2 (WaveMetrics, Lake Oswego, USA). Following  Mattern et al. (2007)  dive events could only be accepted when depths >3 m were reached. The bottom phase was defined as a period of the dive with little vertical undulation following a steady descent and before a steady ascent back to the surface  [50, 54]. The maximum depth (in m) reached during a dive event (hereafter event maximum depth), and the number of dive events during a particular foraging trip were also calculated (Table  2 ). For each dive, we calculated a geographical position either by using the half way point between GPS fixes recorded immediately before and after the dive, or by calculating the relative position along a linear interpolated line between the last fix obtained and before the first fix after the dive occurred based on the time the dive occurred relative to these fixes.
As the inspection of histograms showed that the data for pelagic dives was left shifted, the median dive depth per colony per year was used for further calculations involving pelagic dives (Table  2 ; see Additional file 1: Figure  S2 ; see also 'Calculation of energy'). The geographical location of benthic and pelagic dives was checked in order to detect any potential bias in the distribution of the data. Benthic and pelagic dives were distributed evenly in the same depth areas of the ocean around New I. (see Additional file 1: Figures  S3, S4 ). We also calculated the mean number of dives performed during the foraging trips (Table  1 ). In all calculations, only the first foraging trip of each individual was included in order to avoid individuals with more than one trip having more weight in the data.
Calculation of energy Using a purpose-written script for IGOR Pro 6. 3. 7. 2 (WaveMetrics, Lake Oswego, USA) and tri-axial acceleration data from Axy accelerometers, we calculated the Overall Dynamic Body Acceleration (ODBA) for all first foraging trips and individuals. ODBA is a linear proxy for metabolic energy that can be further converted into energy expenditure (e. g. [51, [59] [60] [61] [62] [63]  but see  [64] ). ODBA (expressed as gravitational force g) was calculated as described in Wilson et al. [21]. We used the sum of the absolute values of dynamic acceleration from each of the three spatial axes (i. e. surge, sway, and heave) after subtracting the static acceleration (= smoothed acceleration) from the raw acceleration values  [21] : ODBA ¼ Ax j j þ Ay j j þ Az j j ð1Þ A x , A y and A z are the derived dynamic accelerations at any point in time corresponding to the three orthogonal axes.
The sum of ODBA during dives was related to the maximum dive depth (see Additional file 1: Figures  S5-S10 ). However, a general additive model (GAM; see Additional file 1: Table  S1 ) revealed that this relationship differed between studied years, colonies, and between benthic and pelagic dives. Thus, the regressions with the best fit were determined for the different combination of years, colonies and dive types in SigmaPlot 10. 0 (Systat Software, San Jose, USA; see Additional file 1: Table  S2 ; Figures  S5-S10 ). We used the regressions between the sum of ODBA during the dive of the deployed penguins and the maximum dive depth (see Additional file 1: Table  S2 ), together with the bathymetric data points from the Global Topography  [52]  to calculate benthic ODBAs for a grid of the marine area around New I.
Acknowledgments We are grateful to the  New Island Conservation Trust  for permission to work on the island and for providing logistic support. Martin Wikelski  ( Max Planck Institute for Ornithology, Radolfzell, Germany ) provided the GPS data loggers. We would like to thank  Ian, Maria  and  Georgina Strange ,  Melanie Marx ,  Cristoph Kaula ,  Jessica Winter ,  Richard Phillips  and  Pauline Sackett  ( British Antarctic Survey, UK ),  Sylvia Kuhn  ( Max Planck Institute for Ornithology ),  Paul Brickle  ( South Atlantic Environmental Research Institute, Stanley, Falkland Islands ),  Mikako Saito ,  Leiv Poncet , and  Tatiana de Mendonça Pinto Alves , for their contributions to the fieldwork, lab work, and logistics.
The data were collected in the framework of the APRES3 project (Antarctic Precipitation, Remote Sensing from Surface and Space, see http://apres3. osug. fr). We provide statistics of precipitation quantity and occurrence, and we compare them with model reanalyses and with the visual observations collected by the French meteorological office (Météo France) all year long. The main scientific objectives of this work are to contribute to a better quantification of precipitation in Antarctica (also by evaluating the products of numerical weather models) and to underline the innovative and promising aspects of the data collected until now, that may serve as an example for long-term monitoring of precipitation in other Antarctic regions. The paper is structured as follows: Sec. 2 describes the precipitation measurements, Sec.
Methods We present here data collected in a coastal location of Antarctica: the station Dumont d'Urville (DDU). The base is situated in Terre Adélie, -66. 6628S, 140. 0014E, (41 m above sea level), on a coastal location highlighted in Fig. 1. This region is located at the transition between the Antarctic continent and the Southern Ocean, where the terrain, which slopes downward from the inner continent to the coast, meets the ocean.
APRES3 Instruments Several instruments were deployed at DDU, starting from November 2015. The instruments were deployed as illustrated in Fig. 2 , and they are listed in Table  1. A Micro Rain Radar (MRR) was installed within an existing radome and has collected uninterrupted measurements since the 22 November 2015. This radar system is used to vertically profile precipitation with a resolution of 100 m at height levels ranging from 341 m to 3141 m above sea level 2. The processed data were collected with a temporal resolution of one minute. The potential of the MRR to monitor polar regions has already been highlighted by the works of  Maahn et al. (2014)  and  Gorodetskaya et al. (2015). The simplicity of its deployment and operation makes it an attractive tool for long-term measurements in places with complex logistics and with limited possibility of support, in the case of instrumental failures.
The raw K-band reflectivity measurements collected by the MRR were first processed with the method proposed by  Maahn and Kollias (2012) , then converted to X-band reflectivities and in a third step to snowfall 5 intensities. Additional information about the processing of the MRR data is provided in Sec. 2. 2. 1. A second radar, named MXPol (Mobile X-band dual-Polarization) collected measurements in the months of December 2015 and January 2016. This system, described in  Schneebeli et al. (2013)  and in  Scipion et al. (2013) , is a scanning dualpolarization Doppler radar. During its operation period at DDU, it was mainly collecting data at 75 m radial resolution and a maximum radial distance of 30 km, mostly conducting different types of scans within a repeating scanning sequence of 5 10 minutes: (i) Plan Position Indicator (PPI) scans, i.
Supercooled liquid water appears in the lidar data as a layer of enhanced signal and low depolarization ratio (e. g. Del  Guasta et al. , 1993; Hogan et al. , 2003) , often when no MRR signal is visible. On the contrary, when precipitation occurs, (around 04 UTC, and from 14 to 24 UTC) the lidar signal gets fully attenuated in the lowest 500 m while the MRR is still able to sample the vertical precipitation column. A weighing precipitation gauge (Pluvio 2 , manufactured by OTT) was deployed from November 2015 to January 2016. This instrument provides the liquid water equivalent of snowfall falling within its measurement area at a time resolution of one minute. To avoid excessive contamination of precipitation signals by blowing snow, the Pluvio 2 was installed at a height of about 3 m above ground and its inlet was protected with a standard wind fence designed by the same manufacturer as the instrument.
For example, the six relations proposed by  Matrosov et al. (2009) , and listed in Table  2  can be used. These relations were obtained by combining two different snowflake size distribution datasets, and three different mass-to-size relations. The error component of Eq. 1, and the large variability of the Z-S relations, lead to very uncertain retrieval of snowfall rate. For this reason, we optimized a local power law, by fitting its two parameters in the Z-S space given by the MRR measurements at the lowest available height and the Pluvio 2 measurements collected close to the ground, during the Summer period 2015/2016. The parameters (intercept and exponent) of the power law are obtained by means of nonlinear least square estimation. The local relation, also listed in Table  2 , takes the form of Z = 76 S 0.
The riming degree is a textural information obtained by supervised classification originating from a manually-labelled training set including almost 3400 images, as detailed in  Praz et al. (2017). The choice of the available classes is based on the widely used scheme of  Magono and Lee (1966). Because the instruments are deployed at a height lower than 3 m above ground level, both precipitation and blowing snow particles are recorded and classified. A second classification method is obtained from the polarimetric data of MXPol, that can be converted into hydrometeor measurements with an hydrometeor classification algorithm  (Grazioli et al. , 2015). This algorithm was developed by partitioning a large number of radar observations into spatially coherent clusters by means of data mining techniques, and then to assign to each cluster a dominant hydrometeor type by means of scattering simulations, interpretation of polarimetric signatures, and comparison with in-situ data.
One year of MRR precipitation data The MRR instrument collected precipitation data uninterruptedly, covering the evolution of precipitation over the entire year. It therefore offers an interesting ground-based (but remotely sensed) set of data to compare with model-based data and with available human observations. Figure  9  shows the estimates coming from the MRR and other available sources of information over a year of measurements. As expected, the agreement of the local MRR relation with the Pluvio 2 is good over the summer period (Dec-January), during which the relation was obtained. In this period also the estimate of ERA-Interim provides a total cumulated precipitation within the envelope of values of the optimized Z-S relation, even though the curves shows some differences in precipitation occurrence.