dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The authors are grateful for the work of  Miranda Jarnot  and  Tammy Powell  at the  National Library of Medicine  for leading the work to import the HPO into the UMLS. The views expressed in this publication are those of the authors and not necessarily those of the funding agencies involved. FUNDING  National Institutes of Health (NIH) Monarch Initiative  [ NIH OD #5R24OD011883 ];  E-RARE 2015 program ,  Hipbi-RD  (harmonizing phenomics information for a better interoperability in the RD field); Director,  Office of Science, Office of Basic Energy Sciences , of the  U. S. Department of Energy  under [ DE-AC02-05CH11231 ];  Bundesministerium f ür Bildung und Forschung (BMBF)  [0313911];  Raine Clinician Research Fellowship  (to G. B. );  Stanley Institute for Cognitive Genomics at Cold Spring Harbor Laboratory (CSHL  to G.
In Sect. 3, PIC is applied to SPHERE-IFS data for two typical kinds of target: extended objects (Ganymede and Io, Sect. 3. 1) and coronagraphic data (dedicated to the exoplanets and disc detection and characterisation around HR 8799 and RY lup; Sect. 3. 2). Comparisons with the reductions performed with the SPHERE pipeline as well as angular differential imaging (ADI) postprocessing on the reduced datacubes (Sect. 3. 5) highlight the benefits of the proposed approach. Appendices A-D focus on the tuning of the method with the available SPHERE calibration files associated to the reduced science data. Special care is taken on the detector characterisation (Appendix A) and on the chromatic model of the spectral dispersion (Appendix B).
Photo Credit: Lucasfilm Ltd. Like any other inverse approach, the proposed method relies on precise modelling of the recorded raw signal y raw to trustfully retrieve all the information contained in the data. It is therefore mandatory to have good knowledge of the gain g, the bias b, and the noise parameters η k and v 0 k for each pixel k. These parameters are estimated from calibration exposures in different calibration steps described in Appendix A for the SPHERE instrument. After the calibration step, to perform the data reduction, the raw data are preprocessed, leading to the calibrated data y considered in Eq. (1): y k = g -1 k y raw k -b k. (5).
Reconstruction algorithm After the calibration of the detector (to get a preliminary list of defective pixels and determine η k , g k , and b k irrespective of k) and of the instrument (to code M = P I C), the reduction algorithm consists in recovering x by minimising the objective function defined in Eq. (  1 ) and updating the factors δ k. This is implemented by the iterative Algorithm 1. Algorithm 1 Data-reduction algorithm 1: x ← 0 Initialisation of the object 2: ∀k, y k ← Eq. (5) g -1 k y raw k -b k Preprocessing 3: y m ← medfilter (y) Initialisation of the model 4: for i from 1 to n it do n it optimisations 5: ∀k, δ k ← δ cal k Initialisation of δ in w 6: if i ≥ 3 and w ρ (r k ) < Eq. (  10 ) w th then Equivalent weight 7: δ k ← 0 δ update in w 8: ∀k, w k ← Eq. (  7 ) δ k η k y m k + g -1 k b k + v 0 k -1 w update 9: x ← Eq.
SPHERE The BIGRE SPHERE-IFS provides low-resolution spectroscopy (R ∼ 50) in the near-infrared (0. 95-1. 7 microns), an ideal spectral range for the detection of exoplanets and the characterisation of their features. Its IFU is composed of 145 × 145 lenslets and covers a field of view of about 1. 8 × 1. 8 square arcseconds  (Claudi et al. 2008; Dohlen et al. 2016). The spatial dimension of the raw data taken on the Hawaii-2RG sensor is equal to m 1 × m 2 = 2048 × 2048 pixels. This section presents the results of the proposed PIC method on representative datasets of the SPHERE-IFS instrument compared to the reduction obtained with the SPHERE reduction pipeline  (Delorme et al. 2017 ) used to date. Two situations are considered: the direct imaging of extended objects, and the coronagraphic mode of SPHERE dedicated to exoplanet detection and characterisation.
The output cubes of the SPHERE reduction pipeline are composed of n 1 × n 2 × n λ = 291 × 291 × 39 voxels. The proposed reduction pipeline provides cubes of n 1 × n 2 × n λ = 300 × 300 × n λ voxels with n λ = 44 for the YH-mode and n λ = 47 for the YJ-mode, which represents a ratio of approximately eight between the pitch of the angular position θ of the lenslets in the reduced cube and the pitch of their position k on the sensor in terms of pixel. An important component of our method is the calibration of the parameters of the forward model. We describe the methodology to estimate those parameters in Appendices A and B. The code is implemented in Matlab TM and uses the opensource GlobalBioIm framework  (Soubies et al. 2019). The optimisation problem (1) is solved by the VMLM-B algorithm  (Thiébaut 2002) , a limited-memory quasi-Newton method with BFGS updates  (Nocedal 1980 ) that handles bound constraints.
Extended-object imaging Even if SPHERE-IFS is mainly used in its coronagraphic mode and the exoplanet search, it is also suitable for the observation of extended objects (see e. g. ,  Hanus et al. 2017; King et al. 2019). To highlight the pertinence of the proposed method for the imaging of such targets, we reduced two Jovian moons with the SPHERE reduction pipeline  (Delorme et al. 2017 ) and the proposed PIC method: Ganymede (60. A-9372 -1 Feb. 2015, 298th out of 300 acquisitions) and Io (60. A-9357 -6 Dec. 2014, 2nd out of 8 acquisitions), both acquired in YH mode. The results are given in Figs. 2  and 3. For each reduced cube, spectra are extracted along three positions θ of interest. For comparison, the transmission t atm of the Earth's atmosphere, computed via the software "HITRAN on the Web" proposed by  Rothman et al.
Coronagraphic imaging SPHERE-IFS is mainly used for high-contrast observation with a coronagraph. To directly image an extrasolar planet, the light from its host star must be reduced by several orders of magnitude. Associated with dedicated detection algorithms, SPHERE as well as other instruments of its generation such as GPI and CHARIS, allow the spectral characterisation of the imaged exoplanets at low resolution (R ∼ 30-50) and achieve typically planet-to-star contrast ratios down to 10 -6 at 0. 2 angular separation (see e. g. ,  Currie et al. 2015; Macintosh et al. 2015; Ruffio et al. 2018; Mesa et al. 2019). To illustrate the present method when SPHERE-IFS operates in its coronagraphic mode, we considered two datasets: one on the HR 8799 star that hosts a cortege of exoplanets  (Marois et al.
Statistics of the residuals The reconstructed model compared to the raw data is further detailed in Fig. 7  for the two datasets. The zooms in Figs. 7d, h  Despite the numerous discarded pixels in the raw data flagged in Figs. 7d, f , the algorithm predicts a model, as shown in Figs. 7h, j , where no structure is visible in the residuals displayed in Figs. 7l, n , except for the defective pixels and the pixels masked by a dust particle. The zooms in Figs. 7c,g,k,e,i,m focus on regions closer to the coronagraph mask where the flux is the highest. No structure is visible in the residuals of Fig. 7m. On the contrary, on the limit of the coronagraph mask in Fig. 7k , vertical blue and red structures can still be discerned. This is because of a slight lateral shift of the spectra in the raw data compared to the positions predicted by the calibrated forward model.
4 voxels ( 2. 6 mas). The results are shown in Fig. 8d  for the two datasets and for two different acquisitions in the same night. Most of the centres are within 0. 25 voxels ( 1. 7 mas) of the estimated centre. No noticeable difference can be seen in the estimated parameters according to the dataset or the acquisition date. Being a diffraction-driven phenomenon, these square patterns should scale with the wavelength. This can be seen by the four radial rainbow-coloured features on the coloured projection x λ in Fig. 8a. To study this scaling law, the square best-fitting the four spots in the reconstructed cube is estimated at each wavelength. The slope ξ of the linear law that best fits the diagonal s according to the wavelength s ≈ ξ λ is given in Fig. 8c  for the two datasets (blue and red) for each acquisition date in the observation night (squares and circles).
The differences in the chromatic slope are due to the choice of smaller pixels in the reconstructions in the proposed method compared to the SPHERE reduction pipeline. We note here that this is not a measure of the spatial resolution of the reduced datacubes that would be different with the SPHERE reduction pipeline or PIC. Indeed, the SPHERE-IFS instrument resolution is fixed and is given by the pitch of the lenslets in the array. With the chosen reduction parameters, there are roughly two voxels per lenslet. The small standard deviations on the estimated chromatic slope (≈0. 02 voxel µm -1 for all datasets) reflect the good spectral calibration of both PIC and the SPHERE pipeline. The residuals in Fig. 8c  were used to estimate the relative spectral calibration errors given in Table  1  that are well under 1%.
A. 4. Defective pixels identification The different calibration files can be used to form a first estimate of the map of defective pixels, noted δ cal in Algorithm 1. 4 ). Outside of this area the pixels are discarded. In addition we discard also the first four and the last four columns and rows of the sensor. This active area corresponds to the grey square region in the figure. The defective pixels are then identified by analysing the statistics of the different calibration datasets y c ∈ R m 1 ×m 2 ×n a ×n t and their model y c k,a,t ≈ p c k u c t (∀a) with a pixel-wise map p c ∈ R m 1 ×m 2 and exposure-wise factors u c ∈ R n t respectively given by: (y c , p c , u c ) ∈ (y bg/o , f bg/o , τ bg/o ), (y bg/c , f bg/c , τ bg/c ), (y f , g, p f ) , A90, page 14 of 21 where we recall that p f = M k ( ỹf a ).
Here, η p c is consequently the variance associated with the Poisson noise induced by the median flux. Weights   Accounting for the median absolute deviation σ of the empirical variance improves the estimation: the estimated line (in black) is less affected by values with large uncertainty than an unweighted least square fit (grey line). w c = [A k (V a (y c ))] -2 ∈ R n t , q c -η p c + v 0 1 2 w c , (A. 7) η = c, It appears that the smaller errors on the low values help to counterbalance the lever arm effect on the large fluxes that are noisier. In the case of the RY lup dataset, the weighted fit reduces the influence of an outlier point around a flux of 2750 adu whose variance exceeds 4000 adu 2. This can be seen in the estimation of ṽ0 presented in Table  A. 1: as expected the weighted least squares gives more similar coefficients for the two datasets than the standard least square estimation.
C. 2e for each dataset. Maxima match well the wavelength of the calibration lasers, represented by vertical dashed lines. The effective resolution is estimated from the full width at half maximum of each laser. The average resolution of the YJ-mode and YH-mode is R = 57 and R = 31 respectively. These values are compared to the standard reduction method based on a direct mapping  (Vigan et al. 2015)  in the YH-mode from which the estimated average spectral resolution is R = 20. The better spectral resolution using PIC is close to the SPHERE-IFS theoretical resolution R = 30 of  Beuzit et al. (2019)  and shows the impact of the spectral deconvolution performed by the inverse problems approach (this deconvolution is possible because the forward model accounts for the spectral mixing).
8), applied on the region of interest, assuming that the shift δ k is known; -the shift δ k is fitted by combining all the regions of interest by adapting Eq. (B. 6), for fixed spectra Λ a r. The histograms of the residuals before and after the alignment are compared in Fig. C. 4e. After the alignment they are less dispersed and more symmetrical. The estimated shifts for the two datasets are given in Table  C. 1. The shifts are small, of the order of a quarter of a pixel for the strongest correction in the case of the RY lup dataset. For the HR 8799 dataset, the correction is just a few percent of a pixel. Nonetheless, this shift corresponds to a measurable signal as seen in  Hyperparameter of the spectral regularisation defined in Eq. (  13 ) µ 0 2D 2. 5 × 10 -2 Hyperparameter of the spatial regularisation defined in Eq.
(  13 ) 10 -16 Spatial regularisation threshold defined in Eq. (  11 ) Algorithm 2: Remnant signal estimation n rs 100 Number of iterations of the algorithm for the HR 8799 and RY lup datasets (the algorithm is not run for Ganymede and Io, the background obtained with the shutter opened is used instead). Hyperparameter of the spectral regularisation defined in Eq. (B. 8) Notes. The parameters that are unnamed in the text are noted "-".
Numerical values of the parameters and hyperparameters used in the different algorithms. equivalent weights defined in Eq. (10) to identify outlier pixels ∀k, s k = s 0 5 for RY lup/25 for others Scaling factor of the robust penalisation in Eq. (9) (∝ the standard deviation of the residuals as seen on Figs. 7o,p for the HR 8799 and RY lup datasets) ∀k, η k , v 0 k Eq. (A. 7), η, ṽ0Noise model used in Eq. (4) (Readout noise and Poisson noise)n 1 n 2 n λ m 1 m 2Normalisation factor of the hyperparameters to balance the unknowns and the measurements µ 0 λ 5 × 10 -6.
Acknowledgements. This work was performed within the framework of the  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). It has been supported by the (project  DIAGHOLO ). This work has made use of the SPHERE Data Centre, jointly operated by  OSUG/IPAG (Grenoble), PYTHEAS/LAM/CeSAM (Marseille) ,  OCA/Lagrange (Nice) ,  Observatoire de Paris/LESIA (Paris) , and  Observatoire de Lyon/CRAL , and supported by a grant from  Labex   OSUG@2020 , within the program " Investissements d'Avenir"  ( ANR-10-LABX-56 ). All the reductions are based on public data provided by the  ESO Science Archive Facility  and acquired for different programs:  60. A-9372  (Fletcher L. , 1 Feb.
mangle and A. germinans or A. germinans and Laguncularia racemosa towards the southern end of the lagoon  49, 51. Within this mangrove forest, 30 x 30 m permanent plots established in 2010 are arranged along the salinity gradient. They are all oriented to true north and located equidistant from the main water body. The seven plots selected for this study are located at increasing distances from the lagoon's inlet to the GoM (between 500 and 3,000 m) to capture the salinity gradient along the lagoon. A new plot within the highest salinity range was established in 2017 to include a site with a stand density similar to other sites but with contrasting salinity. For the pre-established plots, existing tree parameters were recovered from a publicly available database  52  , including a unique ID, species, x-and y-axis positions in the plot, stem diameter at 130 cm from the soil surface ( +,-) and height ().
Root graft data collection A non-destructive method was used to detect the potential location of root grafts using a portable Doppler ultrasound probe (DU; SonoTrax Basic; Edan Instruments GmbH, Hessen, Germany) and a set of steel rods. The mangrove roots were gently located with steel rods with the DU probe placed on the tree stem. Following an adapted method originally developed to measure the woody root extensions of A. germinans  37  , the probe was then gradually moved from the stem to the consecutive rods in contact with the target root. Each tree was examined following the consecutive order of the tree tag numbers within the plots by assessing their grafting to all immediate neighbours. Placing the DU on a tree stem collar ring, a steel rod was used to probe the soil to shallow depths, and an amplitude monitor indicated when a root belonging to the stem was touched.
Data analysis Both the density of the target species A. germinans and the total stand density (including Avicennia germinans, Rhizophora mangle and Laguncularia racemosa) were calculated as the number of trees per hectare. The replicate pore water salinity values for each sampling point were averaged, and the resulting five salinity values were used to estimate a mean plot salinity, including the standard error. The proportion of grafted trees at each plot was calculated as the number of A. germinans grafted trees divided by the total number of grafted A. germinans trees in the stand. The top-height trees at each stand (the 20% biggest) were selected as per stem diameter because it was measured in the field and is considered more accurate than tree height, which is estimated through stem diameter measurements  53.
The best model explaining tree height was selected using a minimal Akaike information criterion value following a stepwise removal of non-significant response variables (N = 138 single-stem A. germinans trees with a computed asymmetric neighbourhood). In the existing database of tree parameters  52  , multiple-stem trees are recorded following the traditional convention of summing the diameters of each stem but by measuring only the height of the tallest stem  35  , leading to inaccurate diameter-height allometry. To avoid biased results when relating stem diameter to stem height and the probability of root grafting, multiple-stemmed trees (52 trees) were not included in the logistic regression, nor GAMM, (which also excluded trees that did not have their full neighbourhoods inside de plots), resulting in a final number of trees of 324 and 138 included in the logistic regression and GAMM, respectively.
e. log-normal, exponential and Poisson). Scale-free networks do not occur randomly because a relative change in one node results in a proportional, relative change in another node. Scale-free power-law distributions indicate the continuous expansion of networks and preferential attachment, where new nodes are constantly added and previously well-connected nodes are more likely to acquire new connections  11, 56. We then related network node degree, group size and frequency (number of trees grafted within groups and frequency of groups per hectare, respectively) to stand density and site salinity using linear regressions. All 376 A. germinans trees, including multi-stemmed trees, were used for this analysis. All the statistical analyses were conducted using R programming language  57. Specifically, we used the lme4  58  , DHARMa 59 and gamm4 60 packages for the logistic regression and the GAMM construction and diagnosis.
Acknowledgements This paper is dedicated to the memory of  Adán Vez Lira , a man of wit and faith, who drew the path of conservation and sustainability on the coast of  Veracruz  and died on the 8th of April 2020 defending nature and the well-being of his community, and our extended family of  La Mancha en Movimiento. Our  work would not be possible without your conservation efforts, drive and smiles. This research was financed by the  Volkswagen Foundation  ( Volkswagen Stiftung , Project No. 94 844 ) and partially financed by the  MOMENTS  project ( NERC ,  UK  grant No. NE/P014127/1 ). Support by the  Association of Friends and Sponsors of TU-Dresden  e. V. and INECOL was provided to F. S. (GFF, grant No. 61/2017  and  INECOL , project No. 00016 , respectively). Editing support was provided by the  Research and Innovation services of the University of Glasgow.
These distributions were derived from histograms computed with 13 bins, i. e. , square root of the number of data points. Each bin width was chosen according to the difference between hardness minimum and maximum values. It is worth noting that some indents may be located near grain boundaries, resulting in an artificially higher hardness than in the grain core. In such a case, the self-similarity of the indentation test is not respected. These indents were identified a posteriori and were not considered in the further statistical nanoindentation analysis  [15]. Microstructural analyses were performed using electron back-scattering diffraction (EBSD) in a scanning electron microscope (JEOL FEG 6500F). EBSD data were processed using the MATLAB toolbox MTEX  [26]. A grain was considered as a region surrounded by high angle boundaries greater than 15 •.
5 × 10 6 C. vulgaris cells per Daphnia per day). Temperature was controlled at 20 °C, photoperiod was maintained at 16-8 h light-dark, and the test solution (with food) was renewed every 2 days. The assessment endpoints were survival, number of cumulative molts, days to first brood, number of broods, number of neonates per surviving adult, and body length (measured from the top of the head to the base of the tail spine using the image analysis software Motic Image Plus 2. 0 (Motic China Group Ltd, Xiamen, China). To minimize DM adsorption, each exposure vessel was previously saturated for 24 h with the corresponding DM concentration before each test. Seven combinations of experimental conditions were studied (Table  1 ): a control (LCV medium only, MP0-DM0) and a solvent control (MP0-DM0ac; medium + acetone at 8 x 10 -7 %), three MP concentrations (MP: 0, 1, and 10 mg/L) mixed with two DM concentrations (DM: 0 and 40 ng/L).
Survival Time-to-event data analysis  (Hosmer et al. , 2008)  was used to investigate the effects of MP and DM concentrations on survival. This approach considers events occurring during the whole study period (not only at the final time point) and takes into account right-censored values (i. e. , organisms for which death was not observed within the study period). Kaplan-Meier curves were used for time-to-event data representation. Log-rank tests were performed to compare survival curves between pairwise treatments  (Peto & Peto, 1972). A Bonferroni correction was applied for multiple comparisons (n = 15 comparisons). Interaction significance on survival was assessed using the two-way Scheirer-Ray-Hare test on D. magna longevity response (time alive). Because this test requires a perfectly balanced design, data were randomly selected to obtain the same number of replicates per treatment based on the lowest number of replicates among treatments (minimum of 13 replicates for longevity data).
Stress on stress Acute potassium dichromate LC50s (with 95% confidence interval) were calculated for neonates born during the chronic experiment (see Section 2. 6) and further used in the acute toxicity test. For each time point (24 and 48 h) and for each pre-exposure treatment (MP0-DM0, MP1-DM0, MP10-DM0, MP0-DM40), two-parameter log-logistic models were fitted to survival data assuming a binomial error model.
MULTIPHASE APPROACH The volume of fluid (VoF) method was used to simulate three-phase interactions (the interface of air/water and water/mud). Using this approach, both interfaces can be captured in a fixed grid by solving the continuity equation of the volume fraction (Eq. 5), as follows: ∂α ) ∂t + ∇α ) = 0 (p = 1, 2) (5) α ) denotes the volume fraction of the p th fluid, and:. α ) / )0 = 1 (n = 2 or 3) (6) Equations presented thus far (Eq. 1-6) are solved using the commercial code Ansys-Fluent 13. 0 based on the finite volume method. The pressure-velocity coupling was ensured by using a steady pressure-based coupled algorithm, and the interpolation method selected to compute the cell-face pressure was the PREssure STaggering Option (PRESTO). The second order was set for the VoF's special discretisation.
Analyses were carried out using helium as carrier gas at a flow rate of 1. 0 mL min -1 in a split ratio 1:10 and the following temperature program: Linear retention indices were calculated against C6-C30 n-alkanes mix (Sigma-Aldrich, Saint Louis, MO, USA). Quantification was based on the peak area normalization. For data processing, ACD/Spectrus Processor 2017. 2. 1 software (Advanced Chemistry Development, Inc. Toronto, Ontario M5C 1B5, Canada) was used.