dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ) and summed the data to create white-light curves per spectral order (Extended Data Fig. 4 ). The spectrophotometric and white-light curves are largely free of instrumental systematics except for a constant-rate linear trend in time and an exponential ramp effect within the first 15 min of the time series. The fitted transit depths were binned into 80 spectral wavelength changes in order 1 and 20 in order 2 to create transmission spectra at R ≈ 300. We present the spectra from the nirHiss, supreme-SPOON and transitspectroscopy reduction pipelines in Fig. 2. We find consistent results between the pipelines, with the derived spectra also being in agreement with previous Hubble Space Telescope (HST) observations (see also Extended Data Fig. 5 ). We investigated the atmospheric properties of WASP-39b by comparing our measured transmission spectrum from the nirHiss pipeline to grids of one-dimensional, radiative-convective-thermochemical equilibrium models.
These models explore the impact of atmospheric We quote the ratio of the predicted photon noise for each bin in brackets. The reductions are from the nirHiss and chromatic_fitting routines described in Methods. We define our errors as the 1σ uncertainties extracted from the stellar spectra. (https://github. com/afeinstein20/wasp39b_niriss_paper/blob/ main/scripts/figure1. py). Article metallicity (M/H), carbon-to-oxygen ratio (C/O), potassium-to-oxygen ratio (K/O), heat redistribution (f ) and cloud coverage on the transmission spectrum of the planet. We explored several cloud models ranging from parametric treatments  13, 14  to a droplet sedimentation model  15  that calculates the vertical distributions of cloud mass mixing ratio and mean particle size from the balance between gravitational sedimentation and eddy diffusion of cloud particles.
Using a Bayesian inference framework (see Methods), we compared these grids of models to the observations and inferred the ranges of M/H, C/O ratio, K/O ratio and f that best explain the data while marginalizing over different cloud treatments. WASP-39, the host star, has a metallicity equal to that of the Sun within measurement precision  [16] [17] [18] [19]  , so we reference the atmospheric abundances of the planet to the solar pattern of elemental abundances  20. We compared the grid spectra computed by various models (PICASO, ATMO, PHOENIX and ScCHIMERA) with an observational spectrum obtained from each data-reduction pipeline and obtained broadly consistent results on the inferred atmospheric properties. We report the results from the comparison between the nirHiss spectrum and ScCHIMERA grid that allows the most comprehensive treatments of cloud properties.
We note that we only consider wavelengths <0. 85 μm for order 2, as order 1 has much higher fidelity in the overlapping 0. 85-1. 0-μm range. We define our errors as the 1σ uncertainties extracted from the 16th and 84th percentiles of the transit depths fit from each pipeline. The JWST and HST data agree across the three broad H 2 O features that they have in common. We find evidence of a K absorption feature at 0. 76 μm in the new JWST data, which was proposed in the previous HST data  18. (https://github. com/afeinstein20/wasp39b_niriss_paper/blob/main/scripts/figure2. py). highlights some gases that were not identified in these data but may be present in future observations of other exoplanets. We define our errors as the 1σ uncertainties extracted from the 16th and 84th percentiles of the transit depths fit from each pipeline.
(https://github. com/afeinstein20/wasp39b_niriss_paper/ blob/main/scripts/figure3. py). We find that the observations are best explained by an atmospheric metallicity of 10-30 times solar. Metallicity inferences over the wavelength range of these observations are largely driven by the size and shape of the water-vapour features, with some minor contributions because of CO and/or CO 2 at longer wavelengths (>2 μm; see Figs. 3  and 4b ). The preferred range of metallicities provides the best fit to the shape and size of the muted water-vapour features shortward of 2 μm in combination with the larger water and CO/CO 2 feature longward of 2 μm, regardless of the assumed cloud treatment in our models. Owing to the simultaneous detection of potassium and water vapour, we are able to place constraints on the K/O ratio, which is a refractory-to-volatile elemental ratio, being a solar-to-super-solar value.
28 ) and the general quietness of WASP-39 (see ref. 21  ). It is also in line with larger population studies of hot giant planets that broadly found solar-to-super-solar refractory abundances and solar-to-sub-solar H 2 O abundances  27, 29. The shape and strength of the potassium doublet are best explained by K/O ratios of 0. 1-0. 5, equivalent to 1-3 times solar (see Extended Data Fig. 8 ), whereas the suggested K/O ratio might be a lower limit owing to the photoionization of K at upper atmospheres  30. The NIRISS/SOSS observations enable the detection of clouds in the atmosphere of WASP-39b. Clear atmosphere models cannot explain the amplitudes of all of the water-vapour features simultaneously, which strongly indicates the presence of clouds (see Methods and Extended Data Fig. 6 ). The atmospheric models explored here indicate the presence of non-grey and non-homogeneous clouds, with model preferences of 8σ and greater for models with both non-grey and non-homogeneous clouds over models with grey homogeneous clouds only.
b, The same as for panel a but instead we vary the metallicity parameter. The metallicity constraint is driven by the λ > 2 μm data; the high-metallicity models (M/H > 2) expect larger transit depths than that seen in the data. The same reference model is plotted as a thick black line in both panels. We define our errors as the 1σ uncertainties extracted from the 16th and 84th percentiles of the transit depths fit from each pipeline. (https://github. com/afeinstein20/ wasp39b_niriss_paper/blob/main/scripts/figure4. py). pathways of this planet requires statistical constraints on the complete chemical inventory of the planet and the relative abundances of the carbon-bearing, oxygen-bearing and alkali-bearing species. Such efforts will be possible when applying retrieval techniques to the complete transmission spectrum of WASP-39b from 0.
Methods Given the newness of the data, we applied six independent datareduction and light-curve-fitting routines to the data: nirHiss, supreme-SPOON, transitspectroscopy, NAMELESS, iraclis and FIREFLy. Each pipeline extracts the stellar spectra from orders 1 and 2 (λ = 0. 6-2. 8 μm) with the exception of FIREFLy, which only extracts data from order 1. There is an extra order 3 that has a spectral range of λ = 0. 6-0. 95 μm (ref. 50 ). However, the signal of order 3 is generally weak and, because it provides no new wavelength information beyond what is covered in orders 1 and 2, is not used by any of the presented pipelines. Below, we first describe the important reduction steps taken by each, followed by their light-curve-fitting methodologies. We note here that, in each pipeline, the position of the SOSS trace was found to match almost perfectly with that measured during commissioning (Fig.
1 ). Furthermore, each pipeline trimmed the first 10-15 integrations to remove the effects of the exponential ramp in the fitting routines. We present a summary of all pipelines in Extended Data Table  1. The nirHiss pipeline nirHiss is a Python open-source package that uses the stage 2 outputs from the Eureka! pipeline and performs further background and cosmic-ray removal, as well as extraction of the stellar spectra. For this analysis, we took the uncalibrated images and ran our own stages 1 and 2 calibration using Eureka!  51 , an open-source package that performs spectral extraction and light-curve fitting for several JWST instruments. We use the default steps presented in Eureka!, which includes detector-level corrections, production of count-rate images, application of physical corrections and calibrations to individual exposures.
ATOCA is at present built into the Extract1dStep of the jwst pipeline, although it is not the default option and must be toggled to on by means of the 'soss_atoca' parameter. To improve the performance of ATOCA, we do not use the default specprofile reference file included in the jwst pipeline but instead construct estimates of the underlying spatial profiles of the first and second orders, on which ATOCA relies, using the APPLESOSS (A Producer of ProfiLEs for SOSS) algorithm  54. We determine the centroid positions for each order on a median stack using the 'edgetrigger' algorithm  54  , and these positions are found to match to within a pixel with the default centroids contained in the jwst_niriss_spectrace_0023. fits reference file; the spectrace file is available on the JWST Calibration Reference Data System (CRDS).
FIREFLy Although FIREFLy (Fast InfraRed Exoplanet Fitting for Light curves)  57  was written and optimized for reducing NIRSpec-PRISM and G395H time-series observations, it worked well on the NIRISS/SOSS dataset, in which it selected and processed the spectrophotometry from order 1 only with minimal tuning or intervention. FIREFLy is not written in such a way to extract data from order 2 (λ < 0. 9 μm). In our reduction, we perform standard calibrations on the raw data using the jwst pipeline for stages 1 and 2 reduction. On the jwst stage 2 outputs, we perform bad-pixel and cosmic-ray cleaning on each integration. We perform 1/f destriping and background subtraction using a pixel mask generated from the temporally medianed image that selects regions of the data image below a specified count threshold.
This left the planet-to-star radius ratio R p /R * , the limb-darkening coefficients and parameters for any further systematics models to vary. These four routines also fit spectroscopic light curves at the native instrument resolution. However, two routines, iraclis and FIREFLy, instead fixed the orbital parameters in their spectroscopic fits to values obtained through their white-light curve fits. iraclis also fits directly for the orbital inclination, i, as opposed to b and a/R * like the other routines. iraclis fits for their spectrophotometric light curves at the pixel resolution, whereas FIREFLy binned the spectroscopic light curves first and fits for the transit parameters. We present all of the best-fit white-light-curve parameters for order 1 in Extended Data Table  2. Furthermore, for the spectroscopic light-curve fits, we only considered the region of order 2 with wavelength <0.
chromatic_fitting chromatic_fitting is an open-source Python tool for modelling multiwavelength photometric light curves. This tool is built on the framework of chromatic, a package for importing, visualizing and comparing spectroscopic datasets from a variety of sources, including Eureka! and the jwst pipeline. In this paper, we applied chromatic_fitting to the nirHiss reduction. chromatic_fitting uses the PyMC3 (NUTS) sampler  58  to fit the exoplanet transit model to the light curves. First, we fit the white-light curves for order 1. The white-light curve was generated using an inverse variance-weighted average of the unbinned data. We fixed the orbital period to 4. 05528 days (ref. 1  ) and assumed a circular orbit. We fit for the mid-transit epoch t 0 , the stellar mass M * and radius R * , the impact parameter b, the planet-to-star radius ratio R p /R * , quadratic limb-darkening coefficients (u 1 , u 2 ) and out-of-transit flux F 0.
iraclis We analysed all the light curves using the open-source Python package PyLightcurve  76. For every light curve, PyLightcurve: (1) calculates the limb-darkening coefficients using the ExoTETHyS package  77, 78  , the wavelength range of the bin, the response curves for each of the NIRISS orders ( jwst_niriss_spectrace_0023. fits file, provided by the JWST CRDS) and the stellar parameters (T eff = 5,540 K, logg = 4. 42 cm s -2 , Fe/H = 0. 14 dex (ref. 79 )); (2) finds the maximum-likelihood model for the data (an exposure-integrated transit model together with a quadratic trend model using the Nelder-Mead minimization algorithm included in the SciPy package 80 ; (3) removes outliers that deviate from the maximum-likelihood model by more than three times the standard deviation of the normalized residuals; (4) scales the uncertainties by the RMS of the normalized residuals, to take into account any extra scatter; (5) and, finally, performs an MCMC optimization process using the emcee package  75.
PHOENIX The atmospheric PT profile is computed using the 1D radiative-convective equilibrium model PHOENIX  [90] [91] [92]. We considered atmospheric metallicities of M/H = -1. 0, +0. 0, +1. 0 and +2. 0, C/O ratio ranging from 0. 3 to 1. 0 divided into 136 grid points, planetary intrinsic temperatures T int = 200 and 400 K and day-night energy redistribution factors of 0. 172, 0. 25 and 0. 351, in which full heat redistribution corresponds to 0. 85  ), other molecular lines from HITRAN 2008 (ref. 93 ) and atomic lines from the database of Kurucz and Bell  94. For cloudy models, the small non-grey cloud particle opacity is treated as a sum of Rayleigh scattering opacity of all gas species enhanced by a factor of either 0 (clear atmosphere) or 10; large grey particle opacity is treated as grey cloud deck pressure levels of 0.
5 dex between -1 and 0 and 0. 1 dex between 0 and 1, overall spanning a range from -1 to 1 or 0. 1-10 times solar. In these calculations, the atmospheric metallicity scales the sum of K, C and O. This sum determines the final elemental abundances after scaling metallicity, C/O ratio and K/O ratio. That is, the total oxygen elemental abundance is O′ = Extended Data Fig. 5  | Transmission spectra for WASP-39b for all reduction techniques. Our best-fit reference model to the nirHiss spectrum (red) is plotted as a solid black line in all panels and the spectra are separated into three panels for ease of reading. Wavelengths that overlap with zeroth-order contaminants are masked. (https://github. com/afeinstein20/wasp39b_niriss_ paper/blob/main/scripts/edfigure5. py).
+ C/O + 1 , the total carbon elemental abundance is C′ = O′ × C/O and the total Extended Data Fig. 4 | White-light curves and residuals between the light curve and best-fit exoplanet transit model for each reduction pipeline. a, White-light curves for order 1 (top) and order 2 (bottom) with the out-of-transit scatter noted in the figure text. b, The residuals, in ppm, between the plotted light curves and best-fit exoplanet transit model. The start of transit ingress and end of transit egress are marked with dashed vertical lines; the transit midpoint is marked with a solid vertical line. (https://github. com/afeinstein20/ wasp39b_niriss_paper/blob/main/scripts/edfigure4. py).
Acknowledgements This work is based on observations made with the  NASA/ESA/CSA JWST. The data were obtained from the  Mikulski Archive for Space Telescopes at the Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127  for the JWST. These observations are associated with programme no. 1366. Support for this programme was provided by  NASA  through a grant from the  Space Telescope Science Institute. A. D. F. acknowledges support by the  National Science Foundation Graduate Research Fellowships Program  under grant no. DGE-1746045. M. R. acknowledges support from the  Natural Sciences and Engineering Research Council of Canada , the  Fonds de Recherche du Québec , as well as the  Institut de Recherche sur les Exoplanètes.
Code availability The following are open-source pipelines written in Python available either through the Python Package Index (PyPI) or GitHub that were used throughout this work: Eureka! (https://github. com/ kevin218/Eureka); nirHiss (https://github. com/afeinstein20/nirhiss); supreme-SPOON (https://github. com/radicamc/supreme-spoon); transitspectroscopy (https://github. com/nespinoza/transitspectroscopy/ tree/dev); iraclis (https://github. com/ucl-exoplanets/Iraclis); juliet (https://github. com/nespinoza/juliet); chromatic (https://github. com/ zkbt/chromatic); chromatic_fitting (https://github. com/catrionamurray/chromatic_fitting); ExoTiC-LD  13, 57  (https://github. com/Exo-TiC/ ExoTiC-LD); ExoTETHyS  125  (https://github. com/ucl-exoplanets/Exo-TETHyS); PICASO  92, 93  (https://github.
com/natashabatalha/picaso); Virga  98, 99  (https://github. com/natashabatalha/virga); CHIMERA (https://github. com/mrline/CHIMERA); PyMultiNest (https://github. com/JohannesBuchner/PyMultiNest); MultiNest (https://github. com/ JohannesBuchner/MultiNest).
Here we investigated in Arabidopsis thaliana guard cells, from a quantitative point of view, the link between ion transport reactions and the rearrangement of subcellular compartments. We therefore developed a strategy based on a genetically encoded biosensor and 3D imaging to follow, in living guard cells, pH cyt and [Cl -] cyt during stomatal opening in parallel with the volumetric changes of the subcellular compartments. We quantified, in individual guard cells, the volume and the morphological modifications of the vacuolar and cytosolic compartments together with the pH cyt and [Cl -] cyt dynamics. Surprisingly, our data show that the cytosolic volume is constant and that the vacuole is responsible of the guard cell volume changes. Consequently, our data show that the morphological and volume modifications of the vacuole are the processes driving stomatal opening.
S3 ). The z-Stacks were then manually segmented using 3D Slicer software (https:// www. slicer. org)  27  to reconstruct the vacuole in each guard cell (see "Material and methods"). In closed stomata the vacuole was fragmented in guard cells expressing both ClopHensor (n = 24 guard cells) and VAMP711-YFP (n = 12 guard cells) (Fig. 2a, b ). The vacuolar compartment was divided in multiple vesicles of different sizes (Fig. 2a, b ). Interestingly, in all cases we observed the presence of two major vacuolar vesicles systematically localized at the two poles of each guard cell (Fig. 2a, b ). The nuclear region was in the central area of the guard cell separating the two polar vacuolar vesicles (Fig. 2a, b ). In the guard cells expressing VAMP711-YFP, we observed fluorescence in the area surrounding the nucleus suggesting the presence of tonoplastic structures (Fig.
S4 ). These data uncover an intriguing characteristics of stomatal opening: the whole guard cell volume increase relies on the vacuolar expansion. Indeed, in contrast with the suggestion of the 2D confocal images (Fig. 1b ) the volume occupied by nucleo-cytosolic compartment remains constant during stomatal opening. We further analysed the kinetics of the subcellular changes by measuring the guard cell volumes in closed, fully open stomata, and in stomata in an intermediate state (Fig. 3 ; Table  S1 ). ClopHensor fluorescence was simultaneously imaged to measure pH cyt , [Cl -] cyt , and for obtaining 3D reconstructions (Figs. 1, 3 ). We acquired z-Stacks of the stomata at only three time points to limit the effects of phototoxicity. We reconstructed 3D models 30 min after fusicoccin application, when the stomatal aperture was approximatively halfway from its maximum (Fig.
4 NA). Fluorescence was detected using a GaAsP Hybrid photon detector in photon counting mode. For 3D reconstruction z-Stacks were composed by 8 bit images with an image size of 512 × 512 pixels acquired with a 1 airy units pinhole. The optical section was 0. 849 μm, the z-step size was 0. 29 μm and the voxel size was 0. 12 × 0. 12 × 0. 285 μm. Thus, datasets of 30-50 frames/stomata were obtained. For 3D reconstruction, the fluorescence emission of the E 2 GFP moiety of ClopHensor was detected between 500 and 550 nm after excitation at 488 nm by using an Argon laser. Chloroplasts were detected by exciting the chlorophyll at 488 nm, and the emitted fluorescence was collected in the 650-675 nm range. Transmitted light images were obtained with a PMT (Photomultiplier tube) detector. For cytosolic pH and Cl -quantifications, the same CLSM setup was used with a sequential excitation mode (Excitation at 561, 488, and 458 nm).
Upon excitation at 458 nm, 488 nm (Argon laser) and 561 nm (Diode Pump Solid State laser, DPSS), the emission was detected at 500-550 nm and 600-625 nm, respectively. Images were 12 bit with an image size of 256 × 256, pinhole 3 Airy units. The CLSM was driven by Las X software (Leica).
Geometrical measurements. The volume of the subcellular compartments was calculated by using the Segment Statistic tool from 3D slicer. The stomatal pore width was manually measured with Fiji from the middle image of the stomata. The geometric centre of 3D nuclear models was obtained importing the '. stl files of models in the software MeshLab (https:// www. meshl ab. net/). Nuclear displacement was visualized by using the 3D calculator tool from the software GeoGebra (https:// www. geoge bra. org/). Software. All software used (Fiji, 3D Slicer, MeshLab and GeoGebra) are free and open source.
Acknowledgements We acknowledge the imaging facility MRI, member of the  France-BioImaging national infrastructure  supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future»). We acknowledge  C. Alcon  ( IPSiM, Montpellier ) for her assistance,  J. Jaslan  (IPSiM, Montpellier),  R. Doireau  ( IPSiM, Montpellier ),  J-M Frachisse  ( I2BC ,  Gif-sur-Yvette ),  A. Costa  ( University of Milano, Italy ) for critical discussions and  K. Bertaux  ( IPSiM, Montpellier ) for technical assistance. A. D. A. discloses support for the research of this work from  CNRS  [ ATIP-AVENIR 2018 ] and  ANR  [ NetFlux  project].
Our model is also dynamic: modelled processes influence the evolution of the territory through scenarios 2. 4. 1. Overview of dynamic modelling approach on Ocelet Our modelling approach aims to simulate potential future evolutions and support stakeholder's decision-making similarly to other studies  (Schubert et al. , 2015; Zhang et al. , 2018). Our model was built using the Ocelet dynamic and spatially explicit modelling software  (Degenne and Lo Seen, 2016). In this modelling approach, the studied system was composed of interaction graphs. On the nodes of the graphs were spatial entities (e. g. , plots of land, urban areas), interconnected with edges holding interaction functions potentially activated during a scenario to change the state of the system (e. g. land-use type). From an initial land-use map (see Fig.
As recommended in  Bourgeois et al. (2017) , we focused on "internal drivers of change", which are under potential local stakeholder's influence. Secondly, we conducted the description of relationships based on literature and interview material, using the Micmac software  (Arcade et al. , 2009) , dedicated to structural analysis. Such a description of relationships between drivers of change takes the form of a matrix of direct influences (MDI): a cross-impact matrix where each element (a ij ) equals 1 if an influence exists from the driver in row (i) over the driver in column (j) (Appendix C). By summing all values in row (i) or in a column (j), we calculated the influence and dependence scores for each driver. We also calculated a matrix of indirect influences (MII) from MDI to encompass mid and long-term propagation of indirect influences between drivers of change  (Delgado-Serrano et al.