dataesr/scientific-paragraphs-categorization huggingface dataset:
To test if IAA application on induced PMEI3oe naked apices could trigger changes in tissue mechanics even though no organs were formed, we measured mechanical properties of such apices ,18 hours after application. No changes in rigidity were observed around the application site in either IAA treated or mock treated apices (Fig. 2C-E ; Fig. S6 ). Together, these data point to a required downstream role for HG de-methyl-esterification in auxin induced tissue softening and organ emergence. Local HG de-methyl-esterification is sufficient for local tissue outgrowth, but not whole organ development, in the absence of functional auxin transport Next we tested if HG de-methyl-esterification alone could induce organ formation in the absence of auxin transport. We achieved local HG de-methyl-esterification on naked pin1 meristems by applying PECTIN METHYLESTERASE (PME)loaded beads; the PME enzyme acts to de-methyl-esterifiy HG.
This posits that a localized change in an isotropic material could yield anisotropic outgrowth via feed forward signaling. If we assume that not everything we measure is pectins, but also that a contribution for the important xyloglucans is detected, this framework still holds as it is unlikely that hemicelluloses display anisotropy independent of cellulose microfibrils. Lastly, what happens when a curved surface is probed with a nano-indenter? For the most part, it is assumed during data interpretation that the indentation occurs normal to the material surface. This is obviously an over simplification. At any given position, the indenter tip will be at an angle to the sample, and the degree of the angle will be determined by the curvature of the sample-when the degree is large enough some of the energy in the system is lost leading to bias in the data due simply to geometry.
Confocal Microscopy A. thaliana pin1-7/DR5:GFP meristems were treated with the appropriate chemical and imaged after ,18 h (time of rigidity response but before IAA-triggered bulging). For imaging, meristems were dissected from plants and stained in 0. 05% propidium iodide for 10 minutes (for visualization of cell walls). Confocal stacks were taken for x meristems per treatment (+PME n = 9,+IAA n = 8, pin1 Controls n = 4, WT sibling Controls n = 5), using a 636 long-distance water immersion lens attached to a Leica DMR XE7 as described in  [67]. Samples were imaged in water, and 0. 5 mm deep optical sections were taken to cover the depth of the meristem and a significant portion of the flank. Images were collected in two channels: GFP and propidium iodide. Resulting image stacks were processed using Leica LAS AF software (v.
AFM measurements The AFM data were collected following the same protocol as previously described  [34] , except that the AFM machine, a standalone NanoWizard AFM, was now equipped with a CellHesion module allowing greater z-movement (JPK Instruments AG, Germany). Meristems were dissected from soil grown plants and immobilized on glass slides and surrounded by stiff agarose. Measurement of wall properties alone were ensured by suppres-sion of turgor pressure by immersion of all meristems in a hypertonic solution a minimum of 30 minutes before measurement (0. 55 M mannitol). We have previously demonstrated that this causes plasmolysis in mersitems  [34]. The following numbers of meristems were analyzed: pin1 (+ IAA n = 13, + inactive IAA n = 7, + PME n = 6, + denatured PME n = 3), 24-48 h induced PMEI3oe (+ IAA n = 9, + inactive IAA n = 11).
Apparent Young's modulus calculations The Young's modulus is a parameter that relates applied force to indentation; in stricter terms it is the ratio of uniaxial strain and uniaxial stress, within a liner elastic behavior. In order to ensure that indentations are performed within a linear elastic range the following technical controls are confirmed in all tissues used: 1) Indentation depth is ,10% of total cell height, 2) approach and retraction curves from the experiments are examined for hysteresis  [32] , and 3) indentation times are 0. 2 s total to avoid viscous deformation. In contrast to our previous work  [34]  the Apparent Young's modulus in this study was calculated using the JPK Data Processing software (ver. spm-4. 0. 23, JPK Instruments AG, Germany), which allows for a more standardized analysis (although possibly less accurate in certain situations).
Supporting Information Figure  S1  Rigidity of pin1 meristems after IAA application as measured with 5 mm and 1 mm tips. Changes in rigidity for pin1 meristems treated with inactive-or active-IAA loaded beads as measured with a 5 mm shperical tip (A) or a 1 mm spherical tip (B). Black bars are data from meristem, white bars are data from application site. Each set of black/white bars represents an average of 50-100 data points from a single meristem. At the begining of each graph, mean values for all points of all meristem/ application site values are displayed. Asterisk idicates when meristem is significantly more rigid than the application site (P, 0. 01). Note that 3/7 meristems show significant softening with the 5 mm tip after innactive IAA application, although this does not affect the average data.
Acknowledgments All raw AFM-based data is available upon request. While representative images and averaged data are shown in figures, all analyzed data for all samples can be found as Supplementary Material. Statistical summaries for Supplemental Material can be found in Figure S2. A special thanks to  Yves Couder  and  Herman Hofte  for many helpful discussions, and to  Cris Kuhlemeier  for use of microscopy equipment and lab space. Thank-you to  Cris Kuhlemeier ,  Arezki Boudaoud ,  Olivier Hamant ,  Naomi Nakayama ,  Sarah Robinson , and  Therese Mandel  for critical reading of the manuscript.
AP was funded by the  Human Frontiers Science Program ,  Agence Nationale de la Recherche (ANR)   Blanc 2009 ANR-09-BLAN-0007-02  ' GrowPec ',  ANR-10-BLAN-1516 2010  'Mechastem'. SB is funded by a  United States National Science Foundation   International Research Fellowship  (Award No. OISE-0853105 ) and the  Swiss National Science Foundation. No additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
LM were also fitted to test spatial differences in the isotopic values of A. gibbosa workers in burned and unburned zones (site identity was included as random variable). The fire effect was nested within the pairs of sites. Temporal variation in samples collected 1 month and 1 year after the fire in burned and unburned zones was tested using LM (site identity was included as random variable). The SIBER (Stable Isotope Bayesian Ellipses in R; Jackson et al. 2011) procedure from the SIAR package (version 4. 1. 3) was employed to compare ecosystem (all plants and invertebrates) and ant assemblage (all ant species) isotopic niche widths between the burned and unburned plots. Our sample size was too small to conduct this analysis at the ant species level. The SIBER procedure generates standard ellipse areas (SEA B ), which are bivariate equivalents of standard deviation in univariate analysis.
Acknowledgments We are grateful to  Ana Carvajal  for her assistance in the laboratory and  Ana Carvajal ,  Helena Barril ,  Alba L azaro ,  Sergio Osorio ,  Maria Alba Recasens ,  Anna Revolt  os, and  Anna Torn  e for their help in the field. Loic Michel  and  Gilles Lepoint  provided important technical support during the mass spectrometer analyses. This work was funded by  MICINN  (project  CONSOLIDER-MONTES CSD2008-00040 ),  MICINN  and  FEDER  (projects  CGL2009-12472  to R. B. and  CGL2009-09690  to X. C. ), the  Consejo Superior de Investigaciones Cientıficas  (JAE postdoctoral contract to S. C. ), and the  National Science Foundation  ( International Research Fellowship  to J. M. C. P. -D. ). All experiments comply with current Spanish regulations.
Funding Information This work was funded by  MICINN  (project  CONSOLIDER-MONTES CSD2008-00040 ),  MICINN  and  FEDER  (projects  CGL2009-12472  to RB and  CGL2009-09690  to XC), the  Consejo Superior de Investigaciones Cientıficas  (JAE postdoctoral contract to SC), and the  National Science Foundation  ( International Research Fellowship  to J. M. C P-D).
(2011). Quantitatively, NH 3 is currently believed to account for approximately half of all global biospheric, anthropogenic and natural atmospheric N r emissions, with N r defined and inventoried as the sum of NH 3 -N and oxidized nitrogen NO x -N. Global estimates of NH 3 and NO x emissions provided by the Emissions Database for Global Atmospheric Research (EDGAR, 2011) were 40. 6 and 37. 2 Tg N yr -1 for the year 2008, respectively. Agricultural NH 3 emissions dominate and are of the order of 27-38 Tg NH 3 -N yr -1  (Beusen et al. , 2008). Uncertainties in global NH 3 emissions are large, possibly up to 30-40 %, as shown by the variability in other published global figures (e. g. calculated estimates of  75 (50-128) , by  Schlesinger and Hartley, 1992; 45  Tg NH 3 -N yr -1 by  Dentener and Crutzen, 1994; 54  Tg NH 3 -N yr -1 by  Bouwman et al.
Ammonia measurement and modelling approaches The development, parameterization and validation of models over the years has been, to a large extent, underpinned by the ever-increasing availability of NH 3 concentration and/or flux datasets across all scales. At sub-landscape scales (cuvette, chamber, plot, field), this has stemmed from technological advances in NH 3 flux measurement instrumentation, capable of adequate lower detection limits, continuous online analysis for extended periods of time, selective quantification of gaseous NH 3 from aerosol NH + 4 , together with tolerable troubleshooting and maintenance workloads. In particular, at the field scale, wet denuder systems with automated online detection  (Wyers et al. , 1993; Blatter et al. , 1994; Erisman et al. , 2001; Thomas et al.
, 2009)  have helped produce many exchange flux datasets by aerodynamic gradient methods (AGM) or Bowen ratio techniques, both at remote background locations with low (sub-ppb) concentration levels  (Flechard and Fowler, 1998b; Milford et al. , 2001a) , and over polluted semi-natural ecosystems and fertilized agricultural systems  (Wyers and Erisman, 1998; Ne-mitz et al. , 2000a, b; Neirynck and Ceulemans, 2008; Sutton et al. , 2009b; Flechard et al. , 2010; Wolff et al. , 2010a; Loubet et al. , 2012; Walker et al. , 2013). Relaxed eddy accumulation systems have allowed NH 3 flux measurements at one single height  (Nemitz et al. , 2001b; Meyers et al. , 2006; Hensen et al. , 2009a). In parallel, a range of new generation, fast-response optical and mass spectrometry instruments have emerged over the last 15 yr (see von  Bobrutzki et al.
This is only partly due to computational limits (CPU time), as the very C. R. Flechard et al. : Biosphere-atmosphere ammonia exchange detailed processes operating at very short timescales might become prohibitive when run over regional and multi-annual scales. More likely, however, it is often a consequence of the lack of fine resolution, detailed input data required to run the schemes, compounded by the difficulty of turning largely heterogeneous measurement (flux) datasets into a generalised, unified and self-consistent modelling theory.
, 2006). Those authors pondered whether their results were representative of soybean within their study area, but it should be stressed that such measurements are extremely valuable to characterize NH 3 exchange in dry con-ditions and regions of the world, since a large majority of existing field NH 3 flux datasets are representative of reasonably well-watered conditions in temperate climates. Further, research over the past 20-30 yr has shown the impact of rising CO 2  (Ainsworth and Rogers, 2007)  and O 3  (Wittig et al. , 2007)  concentrations on stomatal conductance, with expected reductions of G s of the order of -20 % to -30 % for elevated CO 2 and -10 % to -20 % for elevated O 3. Within the context of global change, such impacts on G s should be accounted for when considering present and future scenarios of NH 3 exchange.
g. Søgaard et al. , 2002, and  the Ammonia Loss from Field-applied Animal Manure (ALFAM) database), there remain substantial uncertainties in field-scale NH 3 fluxes and the associated emission factors (EF). Sintermann et al. (2012)  compiled and reviewed over 350 EF measurements published between 1991 and 2011 and raised questions about the representativeness, and possible overestimation, of NH 3 fluxes measured in mediumsized (20 m diameter) plots by mass balance methods such as the integrated horizontal flux (IHF) approach. The authors point out that emerging NH 3 volatilisation flux measurements at the field (> 1 ha) scale over the last 5-10 yr generally indicate much lower (∼ factor 2) NH 3 losses, typically below 30 % of slurry NH x content, than did many mediumsized plot measurements carried out in the early 1990s (typically 50-80 % losses), with serious implications for local and regional scale NH 3 budgets.
A recent re-assessment by  Neftel et al. (2013)  of EF measurements made in Switzerland in the early 1990s, using the z inst (simplified IHF) method  (Wilson et al. , 1982) , hinted that these early EF values may have been significantly overestimated due to a combination of at least three factors, all leading to a systematic overestimation: over-speeding of the cup anemometers near the ground, cross-interference of plots located at distances of 70 m, and inadequate values of the z inst scaling factor. Such careful reanalyses of historical EF datasets from other countries might provide clues for the apparent discrepancies, or inconsistencies, reported in  Sintermann et al. (2012).
(1998)  simple empirical models, for Swiss datasets (e. g. Spirig et al. , 2010; Sintermann et al. , 2011) , with estimates TAN losses in the range 5-30 % by measurements vs. 20-70 % by these two models; and (ii) that EF estimates by measurements depended on the spatial scale at which they were carried out (chamber, small or medium plot, field), suggesting strong potential methodological biases. This provides a very clear indication that the current level of validation for models of NH 3 volatilisation from field applied manures is rather poor. The authors concluded that new series of measurements are urgently needed in order to (i) provide systematic comparisons of measurements from medium-scale plots and field-scale measurements under identical conditions, and using a range of different measurement techniques, and (ii) pursue the characterisation of NH 3 EFs in terms of the influence of slurry composition and application method, soil properties and meteorology.
The  Sutton et al. (1998)  C d /R d simple dynamic approach was subsequently adopted by  Neirynck and Ceulemans (2008)  for Scots Pine forest; here, however, water film thickness was calculated as a function of the normalized output of a leaf wetness sensor (LW), while parameterizations of both K r and surface pH were obtained by optimizing the model results to minimize bias and maximize the R 2 between observed and modelled fluxes. A significant development of the capacitance model was provided by  Flechard et al. (1999) , here termed DEWS (Dynamic pollutant Exchange with Water films on vegetation Surfaces), originally developed in moorland vegetation. This model has since been applied for managed grassland to the Braunschweig flux dataset  (Burkhardt et al. , 2009). By contrast to the  Sutton et al.
The advances in GPIC/flux interaction modelling over the last 15 yr have therefore been very substantial, but models have not yet been applied on a routine basis at spatial scales larger than the field. Also, despite the increasing availability of multiple gas and aerosol species concentrations and fluxes over a range of ecosystems (e. g. Douglas fir forest,  van Oss et al. , 1998; oilseed rape, Nemitz et al. 2000b; heathland, Nemitz et al. , 2004, Nemitz and Sutton, 2004; tropical pasture, Trebs et al. , 2004; grassland, Nemitz, 1998 , Nemitz et al. , 2009b , Wolff et al. , 2010a , Thomas et al. , 2009; spruce forest, Wolff et al. , 2010a, b) , model results have only rarely been compared with measurements. Significant future model improvements could be anticipated from a systematic processing of all existing datasets and from conducting model sensitivity analyses of the minimum complexity required to reproduce measurements adequately.
g. Fig. 1d ), presumably due to additional contributions by litter and soil emissions to the latter estimates. Zhang et al. (2010)  proposed parameterizations for their 2layer χ s /χ g /R w model within AURAMS based on an extensive literature review. Their approach was to compile a large database of published χ s and χ g values, and to create a model look-up table (cf Table  5  in  Zhang et al. , 2010)  for both parameters. For each of their 26 land-use classes (LUC), they derived representative model input values based on statistics of literature data. For LUC classes with fertilized vegetation, a much larger value was used (typically factor 10 to 100) for both χ s and χ g than for semi-natural ecosystems. For the former (fertilized), one single value was used throughout, while for the latter (semi-natural), both s and g can take either one of two default values, either "high" or "low", depending on the background atmospheric N input by wet and dry deposition.
, 2009a). One may thus also expect much higher bulk tissue N or [NH + 4 ] and higher s close to the farm buildings, as well as higher NH x concentrations in soil ( g ) and especially on leaf surfaces ( d ), together with higher pH, which theoretically lead to less efficient NH 3 removal by vegetation (per unit ambient NH 3 concentration)  (Jones et al. , 2007). Such feedbacks of cuticular saturation and apoplastic NH + 4 enrichment on NH 3 deposition rates  (Walker et al. , 2008)  can potentially affect spatial NH 3 deposition budgets very significantly at the scale of the landscape, but uncertainties are very large, datasets are few, and parameterizations to account for N en-richment feedbacks for landscape-scale models have yet to emerge. These processes and their coupled emission/ dispersion/deposition modelling have recently been thoroughly reviewed by  Loubet et al.
Improving the performance of high-resolution localscale models requires high quality emission inventories with sufficiently high spatial resolution  (Skjøth et al. , 2011). In addition, a high temporal resolution for emissions is also crucial for the performance of CTMs, and dynamic calculations of NH 3 emissions are needed for a better prediction of high particulate matter episodes  (Menut and Bessagnet, 2010; Henze et al. , 2009). This is especially relevant as NH 3 emissions in winter will lead to a higher contribution to particulate matter than NH 3 emissions in summer. Data requirements for such models are access to detailed information about activity data and the spatial distribution in emissions on annual basis. Such requirements are met in very few countries, e. g. in Denmark and the Netherlands, where the ammonia emission inventory relies on highly detailed national agricultural registers, containing the exact location of farm houses, storages, and associated fields, as well as data on type and number of livestock, and information about applied production methods  (Skjøth et al.
, 2004). In many other countries agricultural activity and NH 3 emission data are either very crude, based on e. g. default emission factors, and/or confidential at resolutions finer than typically 10 × 10 km 2. To address both spatial and temporal issues, the Danish Ammonia Modelling System (DAMOS) has been established as a coupled system consisting of the Danish 3-D Eulerian Hemispheric Model (DEHM) CTM covering the Northern Hemisphere (6 × 6 km 2 resolution) and of the local-scale (up to ca. 20 km) Gaussian plume dispersion and deposition model OML-DEP (400 × 400 m 2 resolution)  (Geels et al. , 2012). The model may be coupled to a code  (Skjøth et al. , 2011)  for calculating ammonia emission on the European scale, accounting for local climate and local management, in which a modular approach is applied for deriving data as input to the temporally varying ammonia emission model.
, 2012) , by the model grid square size. Laguel-Hamaoui (2012) coupled the 1-D Volt'Air model  (Génermont and Cellier, 1997) , originally developed for field-applied slurry and adapted here for mineral fertilizers, to the CHIMERE CTM  (Vautard et al. , 2001; LMD, 2011) , in order to assess the impact of fertilizer NH 3 emissions on PM10 and NH 4 NO 3 aerosol at the national scale. Ammonia emissions were computed from mineral fertilizer spread over agricultural soils, using datasets of crop management practices, soil properties and meteorology. Considerable effort went first into collecting management practices data at the national level, together with data processing to derive their spatial distribution. Three sets of CHIMERE runs were made, using as NH 3 emission inputs to the CTM either (i) the official EMEP data under the CLRTAP convention, (ii) the French national emissions inventory (INS) data, or (iii) a combination of the coupled Volt'Air emissions for mineral fertilizers and INS data for other sources.
This reflects both the improvement in process understanding and the increasing availability of flux datasets, which are needed to parameterize models. Yet there remain substantial challenges at all spatial scales (leaf to globe). The predictive capability of existing models at the field scale is often poor when tested against new flux measurement or at new sites, and a local re-parameterization is often necessary to describe observations satisfactorily (even accounting for potentially large errors in flux measurements, as shown by intercomparison exercises). Semi-empirical parameterization schemes that are developed on the basis of a literature review and many flux datasets  (Massad et al. , 2010b; Zhang et al. , 2010; Søgaard et al. , 2002)  should in principle, statistically, reproduce large-scale features of NH 3 exchange, as least within the multi-dimensional climate/vegetation/soil/management matrix, from which they derive.
Further needs for flux measurements, model input data, and validation data For regional and global representativeness, model development and parameterization rely heavily on new field-scale flux measurement datasets becoming available, but it is also clear that the availability of model input data and of spatially distributed validation data can be limiting factors for CTMs at regional and global scales. The most pressing data needs are summarised below. -Flux measurements for under-represented ecosystems in temperate regions. The NH 3 flux literature is heavily dominated by grasslands, cereal crops, heathlands/moorlands and coniferous forests. There are few measurements over root crops, leguminous crops and legume-rich grasslands, deciduous forests, dry scrubland. -Flux measurements in the tropics: data are needed for all ecosystem types including rain forests, savannah, tropical crops.
These experimental estimates should be collected with a view to both i) underpinning the development of empirical parameterizations for bi-directional models and ii) validating process-based ecosystem model predictions. Longterm (e. g. annual, growing season) flux and datasets are needed to better represent background conditions, as campaign-based measurements over fertilised systems have traditionally tended to focus on emission events. Wherever possible, the determination of s values should be attempted using different techniques (micrometeorological surface concentration extrapolation; controlled gas exchange chamber experiments; apoplastic extraction), as they tend to yield different results and the discrepancies between techniques are as yet poorly understood, given the current paucity of parallel measurements.