dataesr/scientific-paragraphs-categorization huggingface dataset:
MarrowQuant 2. 0 for Human Tissue The MarrowQuant workflow, implemented within QuPath software, was first developed on murine bone H&E-stained images and adapted in this study to human BM quantification. In brief, MarrowQuant 2. 0 works by segmenting regions based on color and texture compared with those of background. 38  It predicts 5 mutually exclusive compartments as output areas for each H&E-stained image in the following order: bone (mint blue mask), hematopoietic cells (dark purple mask), interstitial and microvasculature (pink mask), the later detecting red blood cells and other small eosinophilic structures such as microvasculature, and adipocyte ghosts (yellow mask) (Fig. 2  and Supplementary Fig. S1 ). Marrow areas not recognized as any of the abovementioned compartments are categorized as the "other" mask.
Ar as the spaces of the skeleton defined by endosteal surfaces. 22, 23, 44   MarrowQuant Validation: Pathologist Cellularity Assessment For the training and experimental sets, 4 independent hematopathologists (reviewers 1-4) performed a retrospective visual cellularity assessment based on the digitalized H&E-stained images as follows. Pathologists were blinded to the clinical data associated with the patient samples. Electronic booklets were generated for the pathologists to quickly score each BM specimen, which contained an overview of the whole biopsy (4Â) along with two 20Â images (0. 22 mm/pixel) for each trephine biopsy (n ¼ 157þ 36 ¼ 193 H&E-stained images) (Supplementary Fig. S3 ; Fig. 2 ). For intraobserver variability, the same booklet, but with the image order shuffled and relabeled, was sent to the same pathologists to be scored after a washout period of 8 weeks.
The training was performed for 400 epochs on 256-Â 256-pixel patches with a batch size of 4 (100 steps per epoch). The Jupyter notebook is available on GitHub (see Code and Data Availability section). After threshold optimization built in StarDist, the model was evaluated using the validation images. Then, StarDist for adipocytes was integrated within QuPath as described in Supplementary Fig. S5. An additional code was created to classify the size of each adipocyte and report size distribution for each image: very small (300-499 mm 2 ), small (500-899 mm 2 ), medium (900-1999 mm 2 ), large (2000-3499 mm 2 ), and very large (>3500 mm 2 ). The performance of StarDist for adipocytes was assessed at different levels during the training process (Fig. 4G-H  and Supplementary Fig. S4 ). Tissues were fixed for 2 hours at room temperature in 4% paraformaldehyde and paraffin embedded.
Statistical Analysis All values quantified by MarrowQuant 2. 0 are displayed as mean ± standard deviation. GraphPad Prism (version 9. 3. 0; GraphPad La Jolla) was used for Student t test or 2-way ANOVA, linear regression (Pearson correlation), and multiple comparison tests were used for Figures  3 4 5 and Supplementary Figures  S4-S8. Statistical definition of outliers followed the Bonferroni outlier test after verification that the data set is normally distributed using D'Agostino-Pearson normality tests. A significance level of. 05 was set for all statistical tests. RStudio software (version 1. 2. 5001; 2013; R Core Team), with the following libraries: caret, irr, pROC, and HandTill2001 was used for agreement analyses (intraclass correlation [ICC], specificity, sensitivity, area under the receiver operating characteristic [ROC] curve [AUC]).
4A ). For all samples, MarrowQuant 2. 0 calculated both %cellularity (Eq. 1) and %hematopoietic area (Eq. 2). The later equation is reported in this section to describe the dataset because it does not assume reciprocity and allows for the longitudinal assessment of the whole BM space in stacked plots. As expected, we found significant predominance of the hematopoietic compartment at the diagnosis time point, when samples were infiltrated by the hematopoietic malignancy (72. 4% ± 31% for %cellularity and 60. 1% ± 22. 8% for %hematopoietic area) (Fig. 4B-D ). Accordingly, the remaining BM compartments measured by MarrowQuant 2. 0 were significantly smaller in the diagnosis time point samples than those of the age-matched control group: % adiposity (18. 3% ± 23% vs 55. 4% ± 4%), adipocyte density (184 ± 124 adipocytes/mm 2 vs 425 ± 184 adipocytes/mm 2 ), %IMV (6.
2%; %hematopoietic area, 42. 7% ± 25. 7%; %adiposity, 35. 1% ± 17. 3%; adipocyte density, 346 ± 125 adipocytes/mm 2 ; RC2d%cellularity, 48% ± 20. 2%; %hematopoietic area, 35. 9% ± 16. 8%; %adiposity, 38. 9% ± 19. 5%; adipocyte density, 328 ± 127 adipocytes/mm 2 ). Overall, the measured average changes in this longitudinal data set were consistent with hematopoietic progenitor depletion at the peak of aplasia and relative hematopoietic progenitor exhaustion on repeated chemotherapy cycles in patients with AML/high-risk MDS. This extensive remodeling scenario enabled us to validate MarrowQuant 2. 0 on H&E-stained images with a wide cellularity. Next, we measured the agreement of MarrowQuant 2. 0 with the clinical reference of visual cellularity assessment. We first assessed the intraobserver variability among the 4 independent reviewers after scoring the same set of training and validation images at t ¼ 0 and t ¼ 8 weeks (washout period), and we observed an excellent agreement among the 4 independent pathologists, with the ICC ranging from 0.
Quantification of Adipocyte Size Distribution During Bone Marrow Remodeling In addition to quantifying the area of different BM compartments using MarrowQuant 2. 0, we were interested in quantifying BM remodeling by tracking adipocyte size distribution. One limitation of MarrowQuant is that adipocyte segmentation is based in the Fiji/ImageJ analyze particle function,  54, 55  which calculates the total adipocyte mask but cannot accurately detect individual adipocyte size. To accurately segment individual adipocytes, we developed an adaptation of the StarDist extension of QuPath (https://github. com/qupath/qupath-extension-stardist), which segments and measures nuclei size as oval objects fitted within polygons. We applied deep learning to train the model to recognize BM adipocytes instead of nuclei.
Acknowledgments This work could not have been possible without the expertise and investment of the  EPFL Bioimaging  and Optics Platform, especially O. B. and A. S. The authors thank the members of the  Histology Core Facility , notably  Dr Jessica Sordet-Dessimoz ;  Prof Andrew Janowczyk ,  Dr Pierre Moulin ,  Prof Peter Bankhead ,  Prof. Inti Zlobec ,  Prof Gisou van der Goot ,  Prof Sandra Rizk ,  Dr Mohammad Hodroj ,  Marvy Moujabber , and  Wangjie Liu ; and the members of the  Laboratory of Regenerative Hematopoiesis  for critical reading of the manuscript and continuous encouragement to pursue this work.
Data Availability The codes for MarrowQuant2. 0 and StarDist for adipocytes are available on GitHub (https://github. com/Naveiras-Lab/Marrow Quant2. 0), along with a detailed tutorial to install the 2 workflows within QuPath 0. 3. 2. The training set of hematoxylin and eosin images are accessible in the Image Data Resource database (Study idr0096, idr0096-tratwal-marrowquant; https://idr. open microscopy. org/webclient/?show¼project2104, https://idr. open microscopy. org/webclient/?show¼project-2102 and https://idr. openmicroscopy. org/webclient/?show¼project-2103. ).
3. 2: https://github. com/ Naveiras-Lab/MarrowQuant2. 0/blob/main/README. md; https:// zenodo. org/badge/latestdoi/525402383 Training set H&E-stained images (32 images of 36) are accessible in the Image Data Resource database (Study idr0096, https:// idr. openmicroscopy. org/webclient/?show¼project-2104 and https://idr. openmicroscopy. org/webclient/?show¼project-2103 in the experiment B section). Images from the experimental and test data sets are available on request. See Supplementary Methods for sample processing, histology, immunohistochemistry, code parameter tuning, error quantification, and confusion matrix.
More recent works have mainly focused on comparing adaptive-mesh refinement (AMR) grid-based codes against SPH codes. For instance,  Commerçon et al. (2008)  studied 3D isothermal collapse with rotation using RAMSES and DRAGON  (Turner et al. 1995), and Federrath et al. (2010)  investigated stellar cluster formation with sink particles with an isothermal equation of state using FLASH  (Fryxell et al. 2000) , and the SPH code developed by  Bate et al. (1995). These authors showed an encouraging agreement between both types of computational methods, provided that some resolution criteria associated with the Jeans mass are fulfilled. Nevertheless, none of these studies focused on the problem of disk fragmentation in massive star formation nor solved the equations of radiation-hydrodynamics, going beyond the isothermal hypothesis.
The term κ P,⋆ ρcE M1 is the coupling term between the M1 and the FLD modules via the equation of temporal evolution of the internal energy, as follows: C v ∂T ∂t = κ P,⋆ ρcE M1 + κ P,fld ρc E fld -a R T 4. (2) We employ the ideal gas relation for the internal specific energy ρϵ = C v T , where C v is the heat capacity at constant volume. This offers a basic picture of the radiation-hydrodynamical model incorporated in the RAMSES code. As we are interested in comparing RAMSES and PLUTO, we present in the following subsection their differences relevant to the formation of massive multiple stellar systems.
Choosing a setup for a RAMSES-PLUTO comparison: Centrally-condensed versus multiple systems In order to perform the RAMSES-PLUTO comparison, we must make numerical choices regarding the RAMSES setup, in particular with regard the use of sink particles to refine the scope of the present code comparison. A first possibility is to use similar setups as already presented in previous RAMSES projects (e. g. ,  Mignon-Risse et al. 2020). In those setups, no sink particle is initially present and the number of sink particles to form eventually is not limited. The run we present here, using this setup, is dubbed R-NOSINKt0 for the remainder of this section. A second possibility, labeled R-SINKt0, is to mimic some sink properties of the PLUTO runs we aim to compare our results to. In this second case, a single sink particle of initial mass 0.
Conclusions We present self-gravity radiation-hydrodynamical simulations of the collapse of a massive pre-stellar core performed with the Cartesian AMR code RAMSES and we carry out a comparison with the highest resolutions runs of  Oliva & Kuiper (2020) , performed with a modified version of the code PLUTO using a grid in spherical coordinates. A88, page 12 of 17 As a preliminary step, we chose the RAMSES numerical setup for comparison to PLUTO. We compared two RAMSES runs, one with a unique, central, fixed sink particle and the other without any initial sink, but including the possibility to form sinks later on. Those two runs lead to qualitatively distinct systems: the former leads to a centrally-condensed system, while the latter ends up in a multiple stellar system born out of Toomre instability seeded by the Cartesian grid.
g. ,  Beuther et al. 2007; Bontemps et al. 2010; Palau et al. 2013; Girart et al. 2013; Fontani et al. 2016; Nony et al. 2018 ) and may introduce density and velocity perturbations dominating over numerical ones. To perform the code comparison in the context of a centrallycondensed system, we chose the RAMSES run with a unique, central, initial sink particle, as it compares qualitatively with the runs presented in  Oliva & Kuiper (2020). In the early phases of the collapse, gas free-falls towards the central region. The central density increases and switches from isothermality to adiabaticity. Additional infall of rotating gas triggers the formation a rotationally-supported disk whose border is the location of an accretion shock. A good agreement between RAMSES and PLUTO is reached regarding the timeline of these events, as well as on the core and disk radius.
Acknowledgements. R. M. R. thanks the referee for helping improving this manuscript. R. M. R. thanks  Peggy Varniere  and  Ugo Lebreuilly  for fruitful discussions. R. M. R. acknowledges  Patrick Hennebelle  for his insights on numerical angular momentum conservation. This work was supported by the  CNRS "Programme National de Physique Stellaire" (PNPS). This work has received funding from the  French Agence Nationale de la Recherche (ANR)  through the project  COSMHIC  ( ANR-20-CE31-0009 ). The numerical simulations we have presented in this paper were produced on the CEA machine Alfvén and using HPC resources from  GENCI-CINES  (Grant  A0080407247 ). The visualisation of RAMSES data has been done with the OSYRIS python package, for which RMR warmly thanks  Neil Vaytet. G. A. O. -M. acknowledges financial support by the  Deutscher Akademischer Austauschdienst (DAAD) , under the program Research Grants -Doctoral Programmes in Germany, and financial support from the  University of Costa Rica  for the obtention of his doctoral degree.
The agreement is overall good and opens up possibilities for acute characterization of snowfall microphysics on larger datasets. A discussion of the sensitivity and limitations of the method is also conducted. The main contribution of this work is, on the one hand, the theoretical framework itself, which can be applied to other remote-sensing retrieval applications and is thus possibly of interest to a broad audience across atmospheric sciences. On the other hand, the seven retrieved microphysical descriptors provide relevant insights into snowfall processes.
The proposed framework is implemented on data from the ICE GENESIS campaign that took place in the Swiss Jura Mountains in January 2021. The setup included, in particular, X-and W-band Doppler spectral profilers on the ground, complemented with overpasses of a scientific aircraft equipped with microphysical probes. This offers the possibility to validate the retrieval against in situ measurements. A general overview of the retrieval framework and its theoretical foundation is presented in Sect. 2. Section 3 is dedicated to the presentation of the synthetic and real datasets used to train and evaluate the inversion model. In Sect. 4, we detail the technical implementation of the framework. Results are then presented in Sect. 5, with a particular focus on the comparison of the retrieval outputs to in situ aircraft measurements.
g. , for the parameterization of the particle velocity-size relations. Efforts have been devoted to the construction of increasingly accurate forward models, for instance, through computationally costly discrete dipole approximation (DDA) calculations (e. g. ,  Draine and Flatau, 1994; Liu, 2004; Kuo et al. , 2016; Lu et al. , 2016)  and through simulations based on the self-similar Rayleigh-Gans approximation (SSRGA), which are tuned to represent accurately the scattering of various particle types  (Hogan and Westbrook, 2014; Hogan et al. , 2017;  https://doi. org/10. 5194/amt-16-911-2023 Atmos. Meas. Tech. , 16, 911-940, 2023  Ori et al. , 2020). In this work, we use the radiative transfer code PAMTRA  (Mech et al. , 2020)  as a forward model, as it is particularly suited to simulate full Doppler spectra and provides an implementation of several scattering models.
Machine-learning techniques offer the possibility to tackle inverse problems in a different way, with a statistical rather than an analytical approach. Note that, as pointed out by  Geer (2021) , the overarching framework in both cases ultimately remains that of Bayesian probabilities, viewed through different prisms. The typical machinelearning route to solve an inverse problem (e. g. ,  Chase et al. , 2021)  has the following structure: the available forward model is first used to create a large synthetic dataset x s , k , y s , k = f x s , k , k = 1. N , with the "s" subscript denoting synthetic values and N the size of the dataset. Then, a machine-learning model is trained on this dataset to learn a statistical relation between y and x, i. e. , an approximation of the inverse mapping g.
Such tools are relevant for atmospheric sciences and in particular in the context of climate studies, which handle complex, high-dimensional signals  (Behrens et al. , 2022). In our case, the aim is to constrain the dimensions of the latent space to contain microphysical descriptors of snowfall: the originality of the approach presented here is thus that it incorporates physical knowledge by using a physics-informed decoder. -In a first step, a neural network is trained on a synthetic dataset of x s , y s = f (x s ). Instead of learning an inverse mapping, it simply learns to emulate the forward model: taking microphysical and atmospheric (i. e. , latent) variables (x s ) as input, it outputs Doppler spectra (y s ). This model, which we refer to as the decoder and denote as f , is thus a differentiable emulator of PAM-TRA.
When applied not to a single set of microphysical descriptors but to a stack (of multiple range gates) at once, it is denoted with uppercase F. The synthetic dataset should include a wide range of realistic parameters, to not induce bias in the further steps. -In a second step, we shift our attention to a real (i. e. , not synthetic) dataset of full dual-frequency Doppler spectrograms Y r ; the aim is to retrieve the underlying profiles of latent variables X r. The capital letter denotes that, e. g. , Y r is a vertical stack of y r. A second neural network, the encoder G, is trained on this real dataset: it takes as input the spectrograms Y r , and its output X = G(Y r ) has the same dimension as the number of latent features times the number of range gates. X is passed on to the decoder F , which outputs a reconstructed spectrogram Y.
Synthetic dataset As mentioned above (Sect. 2), the first step of the framework consists in training a neural network on a synthetic dataset containing sets of microphysical and atmospheric variables and the corresponding spectra. The focus of this section is the generation of this dataset. https://doi. org/10. 5194/amt-16-911-2023 Atmos. Meas. Tech. , 16, 911-940, 2023.
Forward model assumptions To generate this synthetic dataset, PAMTRA is run by prescribing snowfall microphysics through several parameters. These parameters are the snowfall properties that the algorithm will then learn to retrieve. The definitions of the microphysical parameters are summarized in Table  1. The PSD is assumed to be a negative exponential distribution (N (D) = N 0 exp(-D/D 0 ), e. g. ,  Straka, 2009) , whose size parameter D 0 is prescribed; here and in the following, the size or diameter of a particle is defined as its maximum dimension: D = D max. For an exponential PSD, D 0 is equal to the number-concentrationweighted mean diameter (shortened as "mean diameter"); the effective diameter (ratio of the third to second moment of the PSD), often relevant for radiative transfer models, is D eff = 3D 0.
Forward model inputs When generating this training set, a trade-off has to be defined: if the dataset is too narrow, that is, if it does not cover a large enough range of values and combinations for the microphysical descriptors, this will cause a bias in the retrieval; conversely, if the range of values is much too large, this will hinder the training process, for it will include non-realistic values. It was therefore chosen to parameterize PAMTRA by sampling the microphysical properties using a large observational dataset collected using the Multi-Angle Snowflake Camera (MASC) during 10 field deployments. These data were organized into a database, MASCDB, in  Grazioli et al. (2022). We follow the method presented in this study  (Grazioli et al. , 2022, Sect. "Technical Validation")  to derive from the database the microphysical parameters required in the forward model (Sect.
3. 1, Table  1 ). Four categories of particles are used: aggregates, planar crystals, columnar crystals, and graupel. For each type of particle and for each parameter, a distribution is fitted to the empirical histogram calculated from the database. We refer to Appendix B1 for more detail. When generating the training set, parameters are then randomly sampled from those distributions. It is worth highlighting that all parameters are sampled independently, with the exception of a m and α a. Indeed, as pointed out in Grazioli et al. (  2022 ), a strong correlation exists between a m and b m , and between α a and β a ; thus, empirical fits are used from which a m (respectively α a ) is sampled for a given b m (respectively β a ), with the addition of randomness using the mean squared error of the fit.
The definition of aspect ratio A r is slightly different between the MASC dataset and the SSRGA parameterization: in the former, it is equal to the ratio of minor axis length to major axis length  (Garrett et al. , 2015) , while in the latter -as mentioned above -it is equal to the particle's dimension along the direction of the radar beam, divided by maximum dimension. After some empirical exploration, it was decided to use nonetheless the histograms from MASCDB, given that the distributions were quite broad, indicating that this difference in definition should not bias the retrieval. Ice water content is the only parameter for which MASCDB does not provide estimates; therefore, it was empirically decided, based on the literature  (Noh et al. , 2013)  and preliminary analyses of aircraft in situ measurements during the ICE GENESIS campaign (see Sect.
Generation of the training set Each item of the dataset is generated through the following procedure. 1. A particle type is randomly sampled among the four aforementioned types. Given the large variety that exists within the aggregate category, as observed in MASCDB, it is given more weight in the sampling procedure (aggregates: 40 %; planar crystals: 20 %; graupel: 20 %; columnar crystals: 20 %). 2. Microphysical descriptors are randomly sampled using the MASC-based distributions. 3. PAMTRA is run on these descriptors, under the previously stated assumptions. The corresponding Doppler spectra are computed for 9. 48 and 94 GHz (frequency of the radars used in this study; see Sect. 3. 2), with 512 bins and a Nyquist velocity of 6. 92 m s -1. 4. Then, turbulent broadening and spectrum shift due to radial wind are added, with randomly sampled values, different for the X band and for the W band.
g. , horizontal wind, beam width). It is computed by randomly sampling a value of atmospheric turbulence, represented by the eddy dissipation rate (sampled from a negative exponential distribution with 10 -3 m 2 s -3 mean, consistent with some literature standards, e. g. ,  Sharman et al. , 2014). The resulting broadening is derived following  Shupe et al. (2008, Eq. 4) ; the radar settings (e. g. , beam width) used in these equations are those of the W-and X-band radars used in this study, described in Sect. 3. 2. 5. Finally, for computational reasons, X-and W-band spectra are both reduced to 256 points through bin averaging. Ultimately, each item of the synthetic dataset contains an input vector with 13 dimensions (see Table  1 ) and the corresponding simulated Doppler spectra (X and W band) with 256 bins each.
X-and W-band Doppler spectrograms In this section, we present the experimental dataset used for the implementation of the second part of the pipeline. Measurements were collected during the 2021 ICE GENE-SIS campaign, a joint ground-based and airborne field experiment that was conducted in the Swiss Jura Mountains in January 2021 and is fully described in Billault-Roux et al. (2023). Data from X-and W-band vertically pointing Doppler radars, which were located at Les Éplatures Airport, are used. The X-band radar, in the following referred to as ROXI  (Viltard et al. , 2019, Rain  Observation with an X-band Instrument), is a high-sensitivity 9. 48 GHz Doppler spectral profiler with 1. 8 • , 3 dB beam width. It was deployed next to a dual-polarization W-band 94 GHz Doppler spectral profiler (WProf,  Küchler et al.