dataesr/scientific-paragraphs-categorization huggingface dataset:
General overview and structure of the code MINOT is a Python-based code available at https://github. com/remi-adam/minot 1. It essentially depends on standard Python libraries, but some functionalities require specific softwares and packages, as discussed below. In this section, we provide a general overview of the working principle of the code, of its structure, and the interactions between the different modules. The list of the code parameters is also discussed, as well as the available functional forms for the radial and spectral models. Figure  1  highlights how the input modeling is used to generate observables via the different plasma processes considered in MINOT, and the general overview of the code is illustrated in Fig. 2.
Code structure In order to model galaxy clusters and predict observables associated with the diffuse thermal and nonthermal components, MINOT is organized in six main parts, each of which is related to specific functions and procedures. We list these parts below. 1. The main class, called Cluster, is written in the file model. py and provides an entry point for the user. It allows defining the model and solve entanglement between parameters. 2. A subclass called Admin allows us to handle administrative tasks, in particular, input and output procedures. 3. The Modpar subclass is dedicated to the model parameterization. It gathers a library of available radial and spectral models. 4. The physical modeling of the cluster is performed in the Physics subclass. It includes many functions for retrieving the desired physical quantities.
Initialization and parameters As illustrated in Fig. 2 , the user can directly define a cluster object by calling the Cluster (main) class of MINOT, cluster = minot. Cluster(optional parameters). Optional parameters, such as cluster name, coordinates, or redshift, can be passed directly to the initialization call. However, any parameter can be modified on the fly, for example, cluster. redshift = 0. 1 The entanglement of parameters is solved in the code. For instance, changing the cluster redshift will automatically change the angular diameter distance of the cluster according to the current cosmological model. Information is provided to the user when the 'silent' parameter is set to 'False'. The list of parameters that describe the cluster object is available in Table  1. We note that whenever possible, the code uses Astropy units for quantities 2.
The CR distributions are normalized according to the ratio of CR and thermal energy enclosed within a given radius. The physical modeling of the radial and spectral properties of the cluster relies on a library of predefined models in the Modpar subclass of MINOT. The list of models that are currently available in the code is given in Tables  2  and 3  for the spectral and spatial component, respectively. Figure  3  illustrates the shape of the different models; they are further discussed in a more physical context in Section 3. We note that the spatial and spectral parts of the modeling are currently decoupled (e. g. , the spectrum of CRp does not change with radius), such that a physical quantity f can be expressed as f (r, E) ∝ f 1 (r) f 2 (E), ( 1 ) where E is the particle energy (only relevant for the CRs), and r is the physical radius in three dimensions.
2016; Eckert et al. 2017; Ghirardini et al. 2019 ). Because we are interested in the nonthermal component of the ICM, we selected clusters from the 12 XCOP for which a diffuse radio halo has been observed, using the GalaxyCluster database 4  , and for which Fermi-LAT constraints have been obtained by  Ackermann et al. (2014). We found three objects: 1) Abell 1795, a relaxed cool-core system; 2) Abell 2142, an elongated, dynamically active cluster with a cool-core; and 3) Abell 2255, a merging cluster with a highly perturbed core (see also the recent work by  Botteon et al. 2020). We thus note that these objects also present the advantage of sampling different dynamical states that are generally observed in clusters. In order to model these three clusters, we extrapolated the precisely measured density profile of the thermal plasma with a high-order polynomial function.
25. We note that we did not account explicitly for the possible reacceleration of seed electrons by ICM turbulences (e. g. ,  Brunetti et al. 2017, and references therein). However, because we did not model the details of the microphysics here, such a population might be included in the CRe 1 , which are modeled independently, as discussed in Section 3. 3. By doing so, we would implicitly assume that the reacceleration process, which would effectively contribute to a loss for our secondary electron population (i. e. , a population transfer) is subdominant with respect to the losses. In this case the physical consistency between the different CR populations will not necessarily be verified. While reacceleration models are beyond the scope of the current work, we leave room for implementing reacceleration options in the MINOT code in the future.
Thermal X-ray emission Because of the gas temperature (a few keV) and the density (typically 10 -3 -10 -5 cm -3 ), the leading emission process at X-ray energies is thermal Bremsstrahlung (see  Sarazin 1986; Böhringer & Werner 2010, for reviews). The X-ray emission is thus a direct probe for the thermal gas density. It presents a characteristic exponential cutoff at high energies, determined by the gas temperature. Heavy elements also induce a large number of spectral lines. The X-ray surface brightness is generally expressed as S X = 1 4π(1 + z) 4 n 2 e Λ(T e , Z)dl, where Λ(T e , Z) is the cooling function, which varies with temperature. In practice, MINOT uses the XSPEC software  (Arnaud 1996)  to directly compute the counts using either the MEKAL or APEC X-ray plasma spectral models. These models require the ICM abundance, the redshift, the temperature, the energy range, and a normalization defined as norm = 10 -14 4π D A 1 cm (1 + z) 2 n e 1 cm 3 n H 1 cm 3 dV.
This package provides different models for the EBL based on  Franceschini et al. (2008) ,  Kneiske & Dole (2010) ,  Finke et al. (2010) ,  Domínguez et al. (2011), and Gilmore et al. (2012). We illustrate the effect of the 6 https://github. com/me-manu/ebltable/ EBL for various models available in Fig. 12  for a cluster redshift z = 0. 02. While it is crucial to account for the EBL, especially at very high energies, the uncertainties associated with the EBL models are expected to be small. The intergalactic magnetic fields probably also affect the predictions for the γ-ray observables (e. g. ,  Neronov & Semikoz 2009). However, because of the current uncertainties on the properties of the magnetic field, its effect remains uncertain. While this effect is not yet implemented, it is being considered for the future improvement of the MINOT code.
Radio synchrotron emission The MINOT code focuses on the diffuse galaxy cluster emission associated with the bulk of the X-ray emitting ICM. In the case of the diffuse synchrotron emission, we therefore focus on the emission associated with radio halos and leave radio shocks (or relics, see van Weeren et al. 2019) aside. Because the orientation of the magnetic field is expected to be chaotic in the bulk ICM regions of a galaxy cluster, we need to average the standard energy distribution of the synchrotron emission over the directions of the field orientation. To do so, we follow the results of  Aharonian et al. (2010) , Appendix D, in which the orientation of the magnetic field is assumed to be randomized. This provides a convenient approximation with an accuracy better than 0. 2% over the entire energy range (see  Aharonian et al.
As in the case of the X-ray luminosity, the differences that we observe are thus likely due to the interpolation and assumption that we have made in defining the clusters. In addition to the fluxes, we also directly compared the X-ray and tSZ images to existing data. We used the publicly available ROSAT  (Truemper 1993 ) X-ray pointed data 7  obtained for our three targets to produce maps in the [0. 1, 2. 4] keV energy band. The maps were subtracted from the background and normalized by the exposure. The ROSAT PSPC response matrices are accounted for in MINOT through the use of XSPEC, as well as the hydrogen column density taken at the location of each cluster, as obtained by the LAB survey  (Kalberla et al. 2005). The model is projected on the same header as the original ROSAT data and accounts for the ROSAT effective area in units of counts per unit of time and solid angle.
We accounted for the point spread function (PSF) by smoothing our model with a mean effective Gaussian function with a full width at half maximum (FWHM) of 30 arcsec, which is the typical number expected for ROSAT pointed observations. While the detailed analysis of the X-ray data including all instrumental effects is beyond the scope of this work, this comparison already provides a useful qualitative comparison to our modeling. The data, model, and residual images are displayed in the left panel of Fig. 17. The data and the model are shown on a log scale, and the residual is shown on a linear scale. While the overall agreement is good for all three clusters, several features are evident. First, many point sources that are not accounted for here affect the residual. Then, the real clusters are not perfectly azimuthally symmetric, which is shown for the residual with a positive and negative butterfly shape in the core of all targets.
Finally, the model of A2255 slightly overpredicts the signal, which agrees with the prediction of the model luminosity, which is too high, as discussed above. We also used the MILCA  (Hurier et al. 2013 ) Compton parameter map obtained from Planck  (Planck Collaboration et al. 2016c)  to compare our tSZ model to real data. We extracted a 1 degree × 1 degree sky patch centered on the individual clusters, and projected the MINOT Compton parameter map on the same grid for comparison. We smoothed the model with a 10 arcmin FWHM Gaussian beam to account for the angular resolution of Planck. In the right panel of Fig. 17  we show the data, the model, and the residual. The data and the model are shown on a log scale, and the residual is shown on a linear scale. The model and the data agree well for all three clusters.
Nevertheless, a low excess in the central part of the A2142 model is evident, which can be explained by the fact that this cluster is slightly elongated, as is also seen in the ROSAT image. In conclusion, we have compared the prediction from MINOT to the X-ray luminosity and tSZ fluxes, finding an overall good consistency between the two. The differences are likely explained by uncertainties in the model extrapolation. Similarly, MINOT is able with dedicated functions to predict the X-ray and tSZ images associated with a cluster model. The comparison to ROSAT and Planck maps has shown an overall good consistency for the targets tested here. In the context of modeling the nonthermal component of galaxy clusters, it is therefore possible to use MINOT together with X-ray or tSZ data to calibrate the thermal part of the model.
Comparison to radio observations In this section, we quantitatively compare the radio predictions of our model to measurements available in the literature. To do so, we used the database at https://galaxyclusters. hs. uni-hamburg. de/, in which available radio data for many clusters are listed. All of our target clusters present diffuse radio emission: a radio mini-halo for A1795  (Giacintucci et al. 2014) , a giant radio halo for A2142  (Giovannini & Feretti 2000; Venturi et al. 2017) , and a giant radio halo plus a relic for A2255  (Kempner & Sarazin 2001; Govoni et al. 2005 ). In the case of A2142, we note that two components were distinguished in  Venturi et al. (2017) , and they likely arise from different physical processes. However, in this qualitative comparison, we only considered the global emission and sum the contribution from the two components.
Conclusions and summary We have provided an exhaustive description of MINOT, a new software dedicated to the modeling the nonthermal components of galaxy clusters and predicting associated observables. While the software was originally developed to describe the γ-ray emission from galaxy clusters, MINOT also accounts for most of the emission associated with the diffuse ICM component: X-rays from thermal bremsstrahlung, tSZ signal in the millimeter band, γ-rays and neutrino emission form hadronic processes, γ-rays from inverse Compton emission, and radio synchrotron emission. Because the γ-ray emission is connected to the same underlying cluster physical properties as these other observables, MNOS provides a useful self-consistent modeling of the signal, and these additional observables can be used, for example, to calibrate a γ-ray model.
However, MINOT can also be used to independently model observables in the different bands. The software is made publicly available at https:// github. com/remi-adam/minot, and this paper aims at providing a reference for any user of the code. To this aim, we have discussed the structure of the code and the interdependencies of the different modules in Section 2, while Sections 3, 4, and 5 provided details about the physical processes considered, how they are accounted for, and the way observables are computed. The different functions were illustrated with the use of a reference cluster model. It allowed us to show the dependence of each wavelength on the physical properties of the cluster. In Section 6 we also compared the predictions from MINOT to data available in the literature in order to show how the code can be used to model real data.
We used Planck and ROSAT data for the thermal component, and Fermi-LAT plus various radio data for the nonthermal component. Finally, we note that the MINOT code is well documented, and many examples are provided in the public repository. The different assumptions made in the code were discussed. In particular, the modeling relies on primary base quantities that are used to derive secondary properties of the cluster and generate observables under the assumption of spherical symmetry. The primary quantities are the thermal electron pressure and density profiles, the CRe 1 and CRp profiles and spectra, and the profile of the magnetic field strength. Regarding the CRe 2 , they are processed assuming no diffusion in a steady-state scenario. However, other electron populations can be accounted for using the CRe 1.
Acknowledgements. We are thankful to the anonymous referee for useful comments that helped improve the quality of the paper. We would like to thank  R. Alves Batista  for useful comments. The work of JPR and MASC was supported by the  Spanish Agencia Estatal de Investigación  through the grants  PGC2018-095161-B-I00  and  IFT Centro de Excelencia Severo Ochoa   SEV-2016-0597 , and the  Atracción de Talento  contract no. 2016-T1/TIC-1542  granted by the  Comunidad de Madrid in Spain. We acknowledge the use of HEALPix (Górski et al. 2005). This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013), in addition to NumPy (van der Walt et al. 2011), SciPy (Jones et al. 2001) and Ipython (Pérez & Granger 2007). Figures were generated using Matplotlib (Hunter 2007).
Beef quality is a complex phenotype that can be evaluated only after animal slaughtering. Previous research has investigated the potential of genetic markers or muscle-derived proteins to assess beef tenderness. Thus, the use of low-invasive biomarkers in living animals is an issue for the beef sector. We hypothesized that publicly available data may help us discovering candidate plasma biomarkers. Thanks to a review of the literature, we built a corpus of articles on beef tenderness. Following data collection, aggregation, and computational reconstruction of the muscle secretome, the putative plasma proteins were searched by comparison with a bovine plasma proteome atlas and submitted to mining of biological information. Of the 44 publications included in the study, 469 unique gene names were extracted for aggregation.
Literature Search and Data Aggregation A total of 459 articles including one GSE were identified using the MEDLINE, GOOGLE and CLAVIRATE analytics as related to meat tenderness (Figure  1 ). Among them, 425 articles were excluded because they did not meet the criteria of inclusion. From the corpus of the 44 remaining publications, 26 articles were identified as eligible for proteomic data  [17,. Eleven articles including the series accession number GSE9256 (PMID: 18443416) were found as eligible for transcriptomic data  [15, 17, 18, 24, 41, [48] [49] [50] [51] [52] [53]. Twelve articles were found as eligible for genetic data  [18, 50, [54] [55] [56] [57] [58] [59] [60] [61] [62] [63]. The computational data aggregation from these 44 publications gave an overview of 1299 ID gene name (GN) related to meat tenderness whatever the muscle, breed, animal type, sex, age at slaughter, geographic area, and methodologies used for tenderness evaluation.
Depending on the type of molecule studied (protein, transcript or gene): 139 unique GN were reported as proteomic data, 249 unique GN as transcriptomic data, and 123 unique GN as genetic data. The compilation of these three lists generated the aggregated dataset comprising 469 non-redundant GN (Table  1 , Figure  2 ). [64]  was limited to the unique ID Gene Names. 13 ) 52  (44)  The table presents the number of ID Gene Names for each dataset. Predictive secreted proteins (conventional pathways, (i): number of ID Gene Names identified as predicted secreted with signal-P sequence using ProteINSIDE predictive analysis (Signal-P > 0. 5; Target-P ≤ 2). Predictive secreted proteins (unconventional pathways (UPS), (ii): number of ID Gene Names identified as predicted secreted without signal-P sequence using ProteINSIDE predictive analysis (Target-P ≤ 3).
Remaining proteins: number of ID Gene Names non-predicted as secreted using ProteINSIDE. Predicted secreted proteins (conventional and alternative) in plasma: Number of ID Gene Names: (i) and (ii) found in the plasma by overlapping with the Bovine Proteome Atlas (BPA). Other proteins hypothetically secreted in plasma: Remaining proteins that were found in plasma by overlapping with the Bovine Proteome Atlas (BPA). In brackets: number of unique ID Gene Names associated with each category of proteins in the aggregated dataset. Unconventional pathways of secretion (UPS).
Computational Prediction Prediction of the secreted proteins. Table  1  illustrates the numbers and characteristics of the proteins associated with the omics datasets. The predictive analysis using ProteINSIDE from the aggregated dataset allowed us to identify 54 proteins (11. 5%) as predicted secreted proteins according to a conventional pathway (with signal-P and/or TM domain) and 36 proteins (7. 7%) as predicted secreted proteins according to UPS pathways (without signal-P). The list of remaining proteins included 379 GN (80. 8%). Prediction of the secreted proteins putatively found in the plasma. The intersection of the datasets and the Bovine Plasma proteome Atlas (BPA) allowed to retrieve proteins that may be secreted by conventional or by UPS pathways and found in the plasma, and the remaining proteins not hallmarked for secretion but found in the plasma respectively (Table  1 ).
The information on the location of the genes encoding proteins of interest within published QTL for tenderness retrieved using the ProteoQTL module of ProteINSIDE. This module interrogates a publicly available QTL library in Animal QTL database that contains cattle QTL and the published data associated. In brackets in the QTL column: chromosome associated with the Tenderness score and/or Shear force QTL. "X" means that the protein was found in the considered HPA and/or Exocarta atlas.
Gene Ontology The full compiled atlas of 469 GN and the repertoires of 13 "predicted secreted proteins in plasma" and of 44 "hypothetically secreted proteins in plasma" were then submitted to Gene ontology (GO) annotation. The biological processes (BP) associated with the different datasets are presented in the Tables 3-5 respectively. The hierarchical "varonoi" visualization of the canonical pathways related to the 13 "predicted secreted proteins in plasma" and the 44 "hypothetically secreted proteins in plasma" are shown in Supplementary Data 1 and 2. A SimRel semantic rapprochement performed on the TOP50 of the GO terms associated with the 469 proteins (p-value adjusted <0. 001, minimum of two proteins annotated in annotation) highlighted 10 BP: "Inflammatory response", "Gluconeogenesis", " Protein stabilization", "chaperone-mediated protein complex assembly", "Carbohydrate metabolism", "Aging", "Muscle contraction and development", "cell adhesion", "protein folding" and "Apoptotic process" (Table  3 ).
Network Analysis and Plasma PPi Identification Examination of the network built from all of the 57 plasma candidates identified in this study (13 "predicted secreted proteins in plasma" and 44 "hypothetically secreted proteins in plasma" combined) revealed 544 interactors of which 75 proteins were present in the BPA (Figure  4 ). Eleven proteins out of the 57 plasma candidates (ATP5B, BPGM, COL11A1, COL13A1, ENO3, FGF12, LRRC16A, PCDH7, PGAM2, PVALB and TG) were not included in the MINT database used to generate the network from Cytoscape. Finally, the investigation of these 75 candidate proteins allowed to identify 14 additional proteins (CASP8AP2, ZBTB21, USP8, NEFL, CAT, GSS, PRKACB, CFL1, MAPK1, CCNB2, ACTN1, YWHAZ, YWHAB and PSMA7) that could be new meat tenderness proteins located in cattle meat Quantitative trait loci (QTL) for Shear force and/or Tenderness score (Table  2 ).
Solid line shows the "primary interaction type". Dotted line shows the interaction through "detection method". Eleven out of the 57 plasma candidates (ATP5B, BPGM, COL11A1, COL13A1, ENO3, FGF12, LRRC16A, PCDH7, PGAM2, PVALB and TG), not included in the MINT database, are not shown in this network.
Relevance of the Aggregated Dataset Over the last two decades, 44 studies meeting our criteria of inclusion have identified genetic markers, and proteins or transcripts of which the abundance was related to tenderness. Some of them were proposed as muscle-derived biomarkers for meat quality  [68]. These studies corresponded to less than 10% of the curated articles on meat tenderness. From this corpus, we aggregated a full compiled Atlas comprising 469 unique Gene Names, which we considered sufficient for further information mining. From this non-exhaustive dataset, we were able to identify 71 plasma candidate biomarkers for beef tenderness. Moreover, by comparison of the full compiled Atlas with the 67 proteins proposed recently in  Picard et al. [68] , four additional proteins (COL4A1, HSPA5, ORM1, PDIA3), both predicted as secreted proteins (with Signal-p and no TM) and found in the BPA, were included in our list of candidate biomarkers for meat beef tenderness.
Thus, these results allowed to enrich, to 75 candidate plasma proteins, the list of candidates proposed in this study. The relevance of the list is supported by the good overview of tenderness mechanisms permitted by the data, as illustrated by GO term enrichment and their semantic analysis. The main pathways involved in meat tenderness (reviewed in  [21, 33] ) were detected with our dataset as illustrated by the top 50 BP terms retrieved by a GO analysis (Table  3 ). Indeed, we report Biological Processes related to muscle structure and contraction (protein stabilization, muscle contraction and development, chaperone-mediated protein complex assembly, cell adhesion), muscle energy metabolism (gluconeogenesis, glycolytic process, oxidation-reduction process, carbohydrate metabolism), "post-mortem proteolysis" (aging, apoptotic process), "oxidative stress and HSP proteins" (cell detoxification, response to hydrogen peroxide, response to oxidative stress), and "metabolism, transport and cell signalling" (protein folding).