dataesr/scientific-paragraphs-categorization huggingface dataset:
For this purpose, we redeploy the equation proposed and employed by refs. 25, 26 respectively, for returning FRP from MODIS satellite data to account for variations in pixel size: 𝑭𝑹𝑷 = 𝑨 𝒔 * 𝜷 * 𝑻 𝒇 𝟖 -𝑻 𝒃 𝟖 (15) Where FRP (MWss) is fire radiative power,  ! is the nominal pixel area evaluated at scan or sample number,  is a coefficient relevant to the MODIS spectral response, while T f and T b are the temperature of the fire pixel and a 'representative' or neighbouring background pixel without fire, respectively. For our purposes, we convert each pixel to km 2 , retain the beta coefficient, and use the calculated flame temperature T F for T f and the input climatological data for T b , respectively. These are discussed in the Supplement.
Acknowledgements S. P. K. B was supported by the  Swiss National Science Foundation (SNSF)  grant number  SNSF 649 200021-178768 , and would like to thank  Chao Yue ,  Jinfeng Chang  and  Ronny Lauerwald  for discussions relating to ORCHIDEE modifications. M. W. J was supported by the  European Commission Horizon 2020  project  VERIFY  (grant no. 776810 ). P. C. was cofunded by the  European Space Agency Climate Change Initiative ESA-CCI   RECCAP2  project  1190  ( ESRIN/ 4000123002/18/I-NB ).
Code Availability: The source code for this version of ORCHIDEE-MICT is available via https://forge. ipsl. jussieu. fr/orchidee/wiki/GroupActivities/CodeAvalaibilityPublication/ORC HIDEE_Biochar (DOI: https://doi. org/10. 14768/054193dc-a5b0-4a51-bd11-3812e8f123079 Bowring, 2021). Please follow the online instructions for accessing the code -we suggest that interested parties contact the corresponding author for latest code versions containing bug fixes, improvements or cleaner code. This software is governed by the CeCILL licence under French law and abiding by the rules of distribution of free software. You can use, modify, and/or redistribute the software under the terms of the CeCILL licence as circulated by CEA, CNRS, and INRIA at the following URL: http://www. cecill. info (last access: 20 November 2021).
Data Availability The data for Figure reconstruction in addition to data for tropical post drought-fire mortality and pyrogenic production and aquatic export are available online as figure source data and supplementary information, respectively, and are also deposited in the Zenodo digital repository [https://www. zenodo. org; DOI: https://doi. org/10. 5281/zenodo. 5789942], which is managed by the European Organization For Nuclear Research (CERN) and OpenAIRE. Due to file size limitations we are unable to deposit primary data (model output) online. These are archived on the Obelix cluster and repository managed by LSCE/IPSL that can be made available upon request by contacting the corresponding author.
An algorithm developed by  Lefèvre et al. (2013)  translates MACC partial aerosol optical depths information into aerosol mixtures designed for the Optical Properties of Aerosols and Clouds (OPAC) software package  (Hess et al. , 1998). These mixtures are associated to aerosol properties: scattering and absorbing coefficients, single scattering albedo, asymmetry parameter and the Angström coefficient. The total AOD at 550 nm is then calculated as the sum of partial AOD at 550 nm provided by CAMS. As libRadtran needs a total AOD input for the simulated wavelength, the OPAC Angström coefficient of the given mixture is used to estimate the AOD at the required wavelength. The reflective properties of land surfaces are described with the RossThick-LiSparse (Ross-Li) model of bidirectional reflectance distribution function  (Roujean et al.
The optical properties of the clouds come from the precalculated Mie tables provided by the libRadtran software package. As no information is provided on the actual cloud vertical structure, ρ ovc are calculated as : ρ ovc = 1 2 (ρ ovc,high + ρ ovc,low ) (8) where ρ ovc,high and ρ ovc,low are respectively derived from the high and low liquid cloud LUTs, interpolated on the viewing and solar geometries of the satellite time series and adapted to the spectral response function of radiometric channels. An ice cloud LUT is also produced, to study the sensitivity of surface irradiance estimates to the assumed cloud phase. The ice cloud characteristics follow the parameterization by  Yang et al. (2013). We use the "aggregate of 8 columns" ice crystal habit and the "severe" degree of roughness, which are notably used for the description of ice clouds in the look-up table of the MODIS collection 6 cloud product  (Amarasinghe et al.
Still, we compared gains coefficients proposed by EUMETSAT g EUM with those provided by  Doelling et al. (2018)  g D2018 for the measurements produced by the Meteosat-9 0. 6 and 0. 8 µm channels in 2011. They show a mean disagreement, calculated as (g EUMg D2018 )/g D2018 , of about -9 % for 0. 6 µm and -8 % for 0. 8 µm during this period (also illustrated on Fig. A1 ). Such errors will affect with the same magnitude the agreement between numerical simulations and measurements of clear-sky TOA reflectances, underlining the importance of absolute calibration for the Heliosat-V method. To compare the results of our method to operational satellite-based products of surface irradiance, we use data from Helio-Clim3 version 5 (HC3v5) and CAMS Radiation (CAMS-RAD) DSSI databases. Both are derived from the imagery of the SE-VIRI sensor and are produced by a Heliosat method: a modified version of Heliosat-2 for HC3v5  (Qu et al.
Conclusion and perspectives A method to compute the cloud index is described in the framework of the development of the future Heliosat-V method for estimating downwelling surface solar irradiance from satellite imagery. The cloud index is built to deal with a single radiometric channel in the spectral range 400-1000 nm. It also does not need archives of data to quantify the cloud effective transmissivity. This approach has advantages. First, the concept of the Heliosat-V cloud index enables the use of imagery from geostationary and non-geostationary platforms, an asset to reach an extended spatial coverage. Moreover, the approach has the potential to deal with long time series of imagery from radiometers characterized by different spectral sensitivities and viewing geometries. Validation results using SEVIRI imagery show that DSSI can be estimated by a cloud index method that does not rely on archives of imagery, with a quality similar to operational satellite-based data products like CAMS Radiation Service and HelioClim3, in terms of RMSD and correlation.
Acknowledgements. We would like to thank operators of the BSRN stations for producing and providing precious validation measurements. We are grateful to the libRadtran team for their open radiative transfer model and interactions. The MODIS MCD43C1 version 6 data product was retrieved from the online  NASA Earthdata Search , courtesy of the  NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) ,  USGS/Earth Resources Observation and Science (EROS) Center ,  Sioux Falls ,  South Dakota , https://search. earthdata. nasa. gov/. We also acknowledge  EUMETSAT  and the  ECMWF  for providing respectively Meteosat-9 and MACC reanalysis aerosol, water vapour and.
2 The magnetic fields between the DNP and the NMR systems in Vienna were measured with a Hirst GM08 Gauss meter. Simulations. All simulations were carried out with the SpinDynamica  35  Software package for Mathematica (time dependence of the magnetization), RedKite 36 (Relaxation matrices), or a home-written script in Matlab (field-dependence of paramagnetic relaxation rates). All codes and scripts can be downloaded (see the Code Availability section). Multiplet intensities have been fitted using the MATLAB-based 'fitnlorentzian. m' function. C spectra of glycerol-d8 and methanol in natural abundance detected in parallel after negative (top) and positive (bottom) DNP. Note that the methanol  13  C signal has an inverted sign with respect to glycerol-d8. b)  13  C spectra of pyruvate-1-13 C and EG detected after negative DNP.
43 ny experiments have been performed with pyruvate without observing the effect reported in this work. This can be ascribed to the fact that such experiments used narrow-band radicals such as trityls to hyperpolarize the carbon-13 spins in the context of clinical or preclinical hyperpolarized MRI. 24, 27   ince these radicals do not hyperpolarize proton spins efficiently, the phenomenon described here is not expected in these experiments. However, as other types of radicals that polarize both proton and carbon spins are more common for applications in physical chemistry,  [44] [45]  we expect more reports of such effect in this context. The NMR signal phase. To model the signal amplitudes and their evolution as a function of time after dissolution, we employed the SpinDynamica  35  software package and let the spin system evolve under the total relaxation superoperator, including CSA, DD, DD-CSA, and random field relaxation.
Acknowledgments The authors acknowledge support by the  NMR core facility of the Faculty of Chemistry, University Vienna. The project leading to this application received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreements  801936  and  339754 ). This project was further supported by an  FWF  stand-alone grant (no. P-33338  N). This work was further supported by the  French CNRS.
Results: In an outbreak of contagious agalactia in a mixed herd of sheep and goats, 80% of the goats were affected displaying swollen udders and loss of milk production but no other symptom such as kerato-conjunctivitis, arthritis or pulmonary distress commonly associated to contagious agalactia. Surprisingly, none of the sheep grazing on a common pasture and belonging to the same farm as the goats were affected. Whole genome sequencing and analysis of M. agalactiae strain GrTh01 isolated from the outbreak, revealed a previously unknown sequence type, ST35, and a particularly small, genome size of 841′635 bp when compared to others available in public databases. Overall, GrTh01 displayed a reduced accessory genome, with repertoires of gene families encoding variable surface proteins involved in host-adhesion and variable antigenicity being scaled down.
Mycoplasma DNA was extracted using the Guanidine thiocyanate extraction method  [28]  with two subsequent phenol extractions and two ethanol precipitations. The genome sequence of M. agalactiae strain GrTh01 was generated using a combination of Pacific Biosciences RSII and Illumina HiSeq sequencing. After sequencing and circularization using Pacific Biosciences (PacBio) RSII (Swiss Institute of Bioinformatics, Lausanne, Switzerland), the sequence was corrected with Illumina HiSeq data for the substitutions due to sequencing errors and for the frameshifts, using the Celera Assembler (CA) PacBio correction module PBcR (version 7. 0)  [29]. Illumina reads were obtained using the Nextera XT DNA protocol for the library preparation and the V3 chemistry to produce 2 × 150 bp paired-end reads that resulted in 9.
1 million read pairs (GATC Biotech, Konstanz, Germany). The resulting full genome was annotated using the Prokka pipeline  [30]  and the NCBI Prokaryotic Genome Annotation Pipeline PGAP https:// www. ncbi. nlm. nih. gov/ genome/ annot ation_ prok/ and deposited at NIH genetic sequence database GenBank. The GenBank/ EMBL accession number of the full genome sequence of M. agalactiae strain GrTh01 is CP039447. Detailed analysis of Vpma loci, potential virulence genes and comparisons of genomes of M. agalactiae strains were performed using the software package Geneious ® software package 9. 1. 8 (Biomatters Ltd. , Auckland 1141, New Zealand), Virulencefinder https:// cge. cbs. dtu. dk/ servi ces/ Virul enceF inder/  [31]  and Artemis  [32]. Multi locus sequence typing (MLST) was performed using the M.
Whole genome sequencing and analysis of strain GrTh01 The whole genome sequence (WGS) of M. agalactiae strain GrTh01was obtained by combining PacBio and Illumina sequencing and revealed a circular chromosome of 841′635 bp (GenBank full Genome accesion nr. CP039447). The Illumina HiSeq coverage was of 1895 x. As for the other M. agalactiae strains so far sequenced, no plasmid was identified in GrTh01. Annotation of the genome using the NCBI Prokaryotic Genome Annotation Pipeline PGAP revealed a total of 767 genes of which 623 encode proteins. Strain GrTh01 contains two full sets of rRNAs (5S, 16S, 23S), 34 tRNAs and 3 non coding RNAs (Table  1 ). The GrTh01 MLST analysis revealed a new sequence type, namely ST35, having the following profile: dnaA: 21; gltX: 1; gyrB: 1, metS: 2; tufA: 1. ST35 stands apart because of its novel dnaA allele, which sequence was verified by additional Sanger sequencing using primers recommended by Mc Auliffe  [33].
The MLST analyses also indicated that the ST35 GrTh01 strain clustered together with several strains isolated from Greece, with dnaA being the only discriminating allele and from the PG2 type strain isolated in Spain, with two discriminating alleles (Table  2 ). The dnaA allele in strain GrTh01 leads to a T/S change at a. a. position 91. While strain GrTh01 is currently the only strain among M. agalactiae and the phylogenetically closely related Mycoplasma bovis, the mutation is in a serine-rich area of DnaA and a serine residue is found at the analogous site of DnaA of Mycoplasma simbae (GenBank accession nr. WP 051630113). Of the currently publicly available M. agalactiae genomes, the GrTh01 genome is the smallest showing ca. 36 and 165 Kbp less than the two representative M. agalactiae strains with circularized genome, namely PG2 and 5632  [7, 34]  (Table  1 ).
HiP-CT requires the sample such as a soft-tissue organ to be embedded in 70% ethanol solution in water and immobilized with agar blocks throughout imaging (see Fig. 1a, b ). The flat-field correction takes reference from a separate container (reference jar) of the same size as the sample jar to enhance the soft tissue contrast (see Fig. 1c ). We provide here the dataset of an intact human left lung imaged by HiP-CT at 25. 08 μm voxel size (full organ, see Fig. 1d ) and at 6. 05-6. 5 μm and 2. 45-2. 5 μm voxel sizes for various local volumes of interest (VOIs) accomplished by optimization of the incident X-ray spectrum, propagation distance, scintillator thickness, and the coupling optics before the detector (see Methods). The X-ray imaging experiments were carried out at the European Synchrotron Radiation Facility (ESRF) BM05 beamline using the recently upgraded fourth-generation extremely brilliant X-ray source (ESRF-EBS)  32, 33.
Eventually, the processed volumes are converted to a 16-bit format and binned further to produce the datasets described in Tables  2 3. The reconstruction and  postprocessing steps are illustrated for the three types of imaged sample volumes, respectively, in Fig. 2. We summarize below the imaging and reconstruction protocols for the human lung at each imaged resolution including the key parameters. • Full-field tomography (the whole organ at 25. 08 μm voxel size, see Fig. 2a, c ): The detected X-ray energy averaged at ~93 keV after filters and sample, the propagation distance was 3475 mm, the scintillator thickness was 2 mm. In total, two sets of 9990 projections were measured by the quarter-acquisition method  26  with an offset of 800 pixels for the half-acquisition. A step size of 2. 2 mm in the vertical (z) direction was used to cover the height of the sample jar with a total of 98 quarter-acquisition subscans.
Data Records We provide the volumetric data after reconstruction and post-processing as greyscale (16-bit) 2D image slices in the JPEG2000 format stored in zipped folders. The compression level of JPEG2000 is carefully chosen to ensure minimal difference from the original TIFF-formatted data when they are used for feature quantification or image segmentation. We list the details of the deposited data in and the 2 × binned version (Binning = 2) are provided, while for the whole lung data  60  , the 4 × binned version (Binning = 4) is also provided. The metadata information in Table  2  is also provided in the corresponding text file contained in each data deposit. The landing page associated with each DOI contains extended information on the parameters of the X-ray beamline for phase-contrast tomography, experimental scanning protocol, and data processing procedures.
acknowledgements We thank  S. Bayat  (INSERM),  P. Masson  ( LADAF ) for extracting the lung from the body donor,  H. Reichert  ( ESRF ) and  R. Torii  ( UCL ) for general support of the project, and  C. Muzelle ,  R. Homs ,  C. Jarnias ,  F. Cianciosi ,  P. Vieux ,  P. Cook ,  L. Capasso  and  A. Mirone  for their help in the X-ray imaging setup developments and improvements. We thank  A. de Maria ,  A. Bocciarelli ,  M. Bodin ,  J. -F. Perrin , and  A. Goetz  at  ESRF  for support on the  Human Organ Atlas database. The authors also thank  C. Werlein ,  R. Engelhardt ,  A. M. Brechlin ,  C. Petzold  and  N. Kroenke. This project has been made possible in part by grants number  2020-225394  from the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation , The ESRF -funding proposal md1252, the  Royal Academy of Engineering  ( PDL -CiET1819/10 ) and the  MRC  ( MR/R025673/1 ).
Other studies investigated the impact of inter-observer delineation on radiomics features  [25, 26]. Both studies, based on a single center dataset, demonstrated that most of the radiomics features are unstable under delineation variations. The results show that for different kinds of tumor (e. g. , H&N squamous cell carcinoma, non-small cell lung cancers, or malignant pleural mesothelioma) it is possible to find a subset of stable features. However, the prognosis power of this subset was not studied. Huang et al. 2017 [27]  observed that both the number of stable features with high prognostic value and their predictive value differed across delineations from three radiologist observers. In this study, we evaluate and compare the Progression-Free Survival (PFS) prognosis performance between radiomics models based on two different VOIs types.
Patient data The dataset used in this work includes the training and test sets of the HEad and NeCK TumOR segmentation in PET/CT images (HECKTOR) 2020 challenge  [28] , organized as a satellite event of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). The dataset was assembled from five centers and includes 239 cases  1. It contains PET/CT images of patients with H&N cancer located in the oropharynx region. The clinical characteristics of the dataset are detailed in Table  2. For each patient, a PET/CT image series and two primary Gross Tumor Volume (GTVt) contours are available. We refer to these two types of delineations as Radiotherapy and Dedicated. The former was made for radiotherapy planning by experts in radiotherapy. Details about these annotations can be found in  [8, 28].
Multivariable analysis The pipeline of the multivariable radiomics analysis used to estimate the influence of using Radiotherapy or Dedicated contours on the PFS prediction performance is depicted in Fig. 2. First (1), we pooled the image data from the five centers and randomly divided into a training/validation (80%) cohort and a testing (20%) cohort using a stratified shuffling method where the stratification criterion is the PFS outcome. This first split was repeated 100 times and we used the same splits of each repetition to statistically compare the results between the two contour types. Second (2), we computed the univariable C-index  [31]  of each feature based on the training dataset and (3) transformed this value (i. e. |C index -0. 5)|) to keep both concordant and anti-concordant features.
(4) We used the resulting Cindex to rank the features based on concordance with the outcome and retained the top 20 concordant features. The number 20 was used to respect a ten to one ratio between the number of features and the number of patients. We then used a grid-search (5) method to determine the feature correlation threshold value: t ∈ {0. 6,0. 65,0. 70,0. 75,0. 80}. We used a stratified 5-folds cross-validation method to divide the subdataset into a train (80%) and a validation (20%) dataset. This step avoids basing the models on highly correlated feature sets. Based on this feature set, we trained a Cox proportional hazards model  [32]  (from scikit-survival  [33]  V0. 14. 1 in Python) on the training set to predict the hazard score and further computed the C-index on the validation set, as the performance measure to estimate the performance of this survival analysis.
Overview of the dataset. The centers include Hôpital Général Juif (HGJ), Montréal, CA; Centre Hospitalier Universitaire de Sherbooke (CHUS), Sherbrooke, CA; Hôpital Maisonneuve-Rosemont (HMR), Montréal, CA; Centre Hospitalier de l'Université de Montréal (CHUM), Montréal; Centre Hospitalier Universitaire Vaudois (CHUV), CH.
The color of the LED strip varied depending on the state of the HAD system (Figure  2 ). The RGB LED strip was 4. 1 m long and was composed of approximately 60 LEDs per meter, at an intensity of four to six lumens. The used RGB colors were white (RGB (255, 255, 255) for manual driving), blue (RGB (0, 0, 255) for available HAD), green (RGB (0, 255, 0) for engaged HAD in normal conditions), orange (RGB (255, 150, 0) when the HAD detected a potential problem, but was able to manage it), and red (RGB (255, 0, 0) for TORs). The SCANeR™ Studio 1. 8 software (AV Simulation, France, https://www. avsimulation. fr, accessed on 6 July 2022), was used to run the driving simulation. The eye-tracking system was composed of four 60 Hz cameras recording data through the Smart-Eye Pro 6. 2 software.
Data Analysis Dependent variables included subjective trust, perceived understanding of the HAD system, and visual strategies. Single trust items and perceived understanding items, both ranging from zero to 100 were used. The 10-item trust scale was scored according to  [14] , and participants were divided into two groups (i. e. , referred as Trustful and Distrustful as compared to the rest of the sample) based on a median-split. Visual strategies data, or time spent looking towards specific Areas of Interest (AOI) were processed in percentages according to ISO 15007 recommendations (International Organization for Standardization, 2020). AOI were defined as "Road", "Rear Mirrors", "Landscape", "Dashboard", "HMI", "Android tablet", "Phone", and "Other" (for all glances directed somewhere else than the previously established AOI).
g. , noninvasive BP and PWVs acquired along alternative arterial paths). Since most clinical evidence on using BP for decision making is based on brachial cuff measurements, it is still valuable to assess BP estimates against noninvasive BPs. Chronological age was also commonly used as a surrogate indicator of vascular age. PPG-derived parameters were compared with reference indicators using statistics indicative of: correlation, agreement, error, and classification ability. Correlation measures are helpful for the development of novel indices, while the limits of agreement technique is helpful for assessing agreement between estimated and reference parameters  (216)  [e. g. , using grand means and standard deviations to weight each subject's data equally  (26) ]. The performance of  Moderate correlations have been observed between reference PWVs (or PTTs) and those derived from PPG signals.