dataesr/scientific-paragraphs-categorization huggingface dataset:
12 Mean variance reduction for C2 database (cm 2 ) Tahiti -0. 03 -0. 03 -0. 03 Hawaii -0. 02 -0. 03 -0. 02 SLA Madagascar -0. 03 -0. 04 -0. 05 Luzon -0. 51 -0. 86 -0. 80 global -0. 04 -0. 04 -0. 03 Tahiti -0. 02 -0. 04 -0. 09 Hawaii -0. 20 -0. 12 -0. 09 Crossovers Madagascar -0. 03 -0. 07 -0. 04 Luzon -1. 37 -2. 41 -2. 41 global -0. 10 -0. 12 -0. 08 database allows the investigation of small-amplitude signals over the entire ocean, and the different sampling character- istics of the various missions complement each other well.
Fe-containing protein inventory in Arabidopsis thaliana We established a primary draft of the Fe-dependent proteome in Arabidopsis by compiling data following the analysis of the Gene Ontology annotations available through QuickGO and AmiGO2 (cf. Experimental procedures). In a second step, the list was manually curated by analysing the presence of conserved amino acids or motifs serving as Fe ligands in the protein sequence or potentially involved in Fe transport, as well as by cross-referencing our data through a detailed literature review. With this procedure, we identified 1068 genes encoding Fe-binding proteins in Arabidopsis (Table  S1 ). Among them, 980 genes encode proteins having: (i) a demonstrated Fe-binding motif in their sequence or the ability to transport Fe, and in such cases the referring literature is provided; or (ii) a predicted Gene Ontology annotation related to Fe-S/haem/Fe binding or Fe transport.
Subcellular distribution, associated functions and spatiotemporal expression profiles of Fe-containing proteins during Arabidopsis development To better understand the role of Fe-containing proteins in plants, we investigated their subcellular distribution by using the subcellular localization database for Arabidopsis proteins (SUBA4_v4;  Hooper et al. , 2017). Subcellular targeting prediction tools provide valuable information that is particularly useful in the case of long gene lists. It must be kept in mind that predictions are not always accurate, for at least three reasons: (i) the existence of unconventional targeting sequences (or their apparent absence in some cases); (ii) the limitations associated with each prediction tool; and (iii) the complexity of the biological processes being examined (e.
g. multi-targeted protein, targeting through the interaction with a partner protein, etc. ). Therefore, we manually curated the dataset where possible. Overall, Fe-containing proteins are present in all subcellular compartments, although in varying proportions (Figure  1c ; Table  S1 ). Fe-S proteins. Fe-S proteins are predicted and/or experimentally demonstrated to localize primarily in chloroplasts, mitochondria, cytosol and nuclei. This corroborates with the presence of specific Fe-S cluster assembly machineries (referred to as CIA, ISC and SUF machineries) in these cellular compartments, with the exception of nuclei, for which Fe-S protein maturation depends on the CIA machinery  (Couturier et al. , 2013). Chloroplasts and mitochondria are two organelles in which the demand for Fe-S clusters is high, given that numerous metabolic pathways rely on Fe-S proteins.
Inventory of Fe-containing proteins The primary list of Fe-containing proteins in Arabidopsis was compiled from the analysis of the Gene Ontology 'molecular function' annotations available through QUICKGO (https://www. ebi. ac. uk/ QuickGO) and AMIGO 2. 5 (http://geneontology. org). All proteins annotated as 'iron-sulfur cluster binding' (GO:0051536), '2 iron, 2 sulfur cluster binding' (GO:0051537), '3 iron, 4 sulfur cluster binding' (GO:0051538), '4 iron, 4 sulfur cluster binding' (GO:0051539), 'heme binding' (GO:0020037), 'iron ion binding' (GO:0005506) were initially selected. This list was then manually curated based on a detailed review of the literature. The current atlas (v1. 0 at release date: 1st Feb 2021) encompasses nearly 450 references (Data S3), whereas almost four times more were reviewed during the manual curation but were eliminated from the atlas.
Throughout this curation, specific cases were encountered and dealt with as follows: (i) for a given protein, both databases contained an Fe ion binding annotation, regardless of its formin this case, the protein was included in our table ; (ii) only one database contained an Fe ion binding annotationin such an instance, the protein was classified by default in the 'unclear' category, unless the ability to bind Fe was demonstrated in prokaryotic or eukaryotic orthologues, and the residues required for Fe binding were conserved in Arabidopsis proteins, as determined by sequence alignments. In this latter case, the protein and its corresponding AGI is placed in the appropriate category of our table  (Table S1 ). Finally, on a few occasions a protein was included even though no databases gave an Fe-binding annotation.
Protein subcellular localization Three cases have been encountered: (i) the subcellular localization was experimentally demonstrated, and associated references have been noted; (ii) the subcellular localization has not yet been determined but the prediction performed using the SUBACON 4 algorithm  (Hooper et al. , 2017)  of the subcellular localization database SUBA4 is logical; (iii) the protein is not listed in the SUBA4 database and in this case they have been curated, either manually or by using other prediction tools such as PREDOTAR 1. 04 (https://urgi. versailles. inra. fr/predotar), TAR-GETP 1. 1 (http://www. cbs. dtu. dk/services/TargetP/) and WOLF PSORT (last updated 5 January 2007; https://www. genscript. com/wolfpsort. html?src=leftbar). An asterisk is used to indicate the latter case in Table  S1.
Iron binding residues Residues or putative residues directly involved in Fe binding were primarily compiled from the Uniprot database when the information was available. When Fe-binding residues were not documented in the Uniprot database, they were indicated when the information was available via either a literature survey or the analysis of available tridimensional structures. In cases where Febinding residues were identified for orthologous sequences, corresponding residues have been indicated for Arabidopsis sequences based on sequence alignments (Table  S1 ). For some protein families, if one isoform has been characterized and its Fe-binding residues identified, corresponding residues present in other members of this family were added by extension (e. g. glutaredoxins, glutathione S-transferases, class-III peroxidases, cytochromes P450).
Transcriptomic analysis RNA-Seq experimental data detailing Arabidopsis development are available in Data S2 (log2-transformed transcripts per kilobase million (TPM) intensities for each tissue atlas sample;  Mergner et al. , 2020). The data were then imported into The Institute for Genomic Research Multi Experiment Viewer (MeV) and hierarchically clustered using average linkage, based on Pearson's correlation coefficient. To determine whether there was a statistically significant over-or under-representation of a particular category of Fe-containing proteins (i. e. Fe-S proteins, haem proteins or Fe proteins), a Z-score analysis was carried out as described by  Law et al. (2020) , to compare the two proportions (i. e. subset versus total). A cumulative standard normal table was used to match the resulting Z-score and, based on this, the P values were determined.
ACKNOWLEDGEMENTS Dr Tanya Berardini  from  TAIR/PhoenixBioinformatics  is warmly acknowledged for her guidance and technical support to host this Atlas in Arabidopsis. org(TAIR);  Dr Janneke Balk  is also thanked for her expertise and constructive comments. This work was financially supported by  TC4F , the  Carl Tryggers Stiftelse  ( CTS18-193 ), as well as by the  Kempe Foundations (Gunnar € Oquist Fellowship , to OK). The  UMR1136  is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  'Investissements d'Avenir' programme  ( ANR-11-LABX-0002-01 ,  Laboratory of Excellence, ARBRE).
The validity of the unified active galactic nuclei (AGN) model has been challenged in the last decade, especially when different types of AGNs are considered to only differ in the viewing angle to the torus. We aim to assess the importance of the viewing angle in classifying different types of Seyfert galaxies in spectral energy distribution (SED) modelling. We retrieve photometric data from publicly available astronomical databases: CDS and NED, to model SEDs with X-CIGALE in a sample of 13 173 Seyfert galaxies located at redshift range from  = 0 to  = 3. 5, with a median redshift of  ≈ 0. 2. We assess whether the estimated viewing angle from the SED models reflects different Seyfert classifications. Two AGN models with either a smooth or clumpy torus structure are adopted in this paper. We find that the viewing angle in Type-1 AGNs is better constrained than in Type-2 AGNs.
Limiting the viewing angles representing these two types of AGNs do not affect the physical parameter estimates such as star-formation rate (SFR) or AGN fractional contribution (  AGN ). In addition, the viewing angle is not the most discriminating physical parameter to differentiate Seyfert types. We suggest that the observed and intrinsic AGN disc luminosity can: i) be used in  < 0. 5 studies to distinguish between Type-1 and Type-2 AGNs, and ii) explain the probable evolutionary path between these AGN types. Finally, we propose the use of X-CIGALE for AGN galaxy classification tasks. All data from the 13 173 SED fits are available at Zenodo †.
rug. nl (AFRP) † https://doi. org/10. 5281/zenodo. 5221764 and obscuration hides the BLR (e. g. Antonucci 1993; Kauffmann et al. 2003). However, in the last decade, the "zoo" of AGNs has become more complex and difficult to explain with this simple toroidal structure model  (Padovani et al. 2017 ) and the viewing angle has not been easy to estimate (e. g  Marin 2016). Besides, obscuration is not static and may depend on different physical conditions that may vary (e. g. Hönig & Kishimoto 2017; Hickox & Alexander 2018). In addition, the "changing look" AGNs cannot be explained with the unified model, but rather with the accretion state of the  AGN (LaMassa et al. 2015; Elitzur et al. 2014). Therefore, updates to the unified model of AGNs have been proposed describing new AGN scenarios such as clumpy structures (e.
2015) , which can explain why it is possible to differentiate Type-1 and Type-2 AGNs according to their fractional contribution of the AGN to the IR  (Fritz et al. 2006). Therefore it should also be possible to identify Sy1 and Sy2 galaxies with SED analysis when observing broad-band emissions. In this work, we aim to assess the importance of the estimated viewing angle in classifying AGN galaxies, and highlight its implications in high-redshift studies. Particularly, we gather a sample of Seyfert galaxies with available photometry in astronomical databases to develop a data-driven approach with easily accessible data. We use X-CIGALE  (Yang et al. 2020) , a modified version of CIGALE  (Boquien et al. 2019) , one of the most popular SED tools to obtain physical parameters in host galaxies and AGN itself.
Seyfert Sample Seyfert galaxies are a good starting point to differentiate between Type-1 and Type-2 AGN, as described by the AGN unification model. Thus, we selected a sample of Seyfert galaxies by combining the SIM-BAD astronomical database  (Wenger et al. 2000 , hereafter SMB) 1 and the dedicated catalogue of AGNs by  Véron-Cetty & Véron (2010, hereafter VCV). SMB is widely used for retrieving basic information of galaxies in an homogeneous manner, while VCV is one of the most popular catalogues for AGN studies. From SMB, we picked galaxies whose main type was Seyfert, including: Seyfert 1 (Sy1), Seyfert 2 (Sy2) and, unclassified Seyfert galaxies. From VCV, we selected all Seyfert types galaxies, including all intermediate numerical classifications (e. g. Sy1. 5). We cross-matched SMB and VCV samples using a cross-matching radius of 2.
If the classification source is unknown and the main Seyfert classification in SMB matches VCV, we assume that the classification source is VCV. If the main Seyfert classification source is unknown and the classification in VCV is Seyfert 3 (also known as LINERs) we remove the galaxies from the sample. We re-classified the remaining unknown sources, 49 galaxies, as unclassified Seyfert to study them further. These decisions led us to a sample of 18 921 Seyfert galaxies. For the classifications in SMB, we found: i) Almost half of the classifications (45%) came from the basic data of the galaxy (asigned by the astronomical database); ii)  Toba et al. (2014)  work contributed to 21% of the Sy1 and Sy2 classifications iii)  Zhou et al. (2006) ,  Oh et al. (2015) , and  Rakshit et al. (2017)   where the difference in redshift between the catalogues was below (black line) or above (red line) the threshold at 0.
Parameter grids We modelled the SEDs of the Seyfert galaxies with X-CIGALE  (Yang et al. 2020 ). X-CIGALE is a modified version of CIGALE  (Boquien et al. 2019) , a SED fitting code based on an energy balance principle. The difference between X-CIGALE and CIGALE is the addition of i) an X-ray photometry module and, ii) a polar dust model in AGNs. These two enhancements help to connect the X-ray emission to the UV-to-IR SED, and account for dust extinction in the polar angles, respectively. The X-ray emission is helpful to constrain AGN intrinsic accretion power in the SED  (Lyu & Rieke 2018; Toba et al. 2021) , and the polar dust follow observational results from MIR interferometry  (López-Gonzaga et al. 2016; Ramos Almeida & Ricci 2017 ). We included six modules which account different galactic emission processes to fit the SEDs.
The fourth module takes the dust emission in the SED into account. We modelled the dust emission following  Dale et al. (2014) , implementing a modified blackbody spectrum with a power-law distribution of dust mass at each temperature, 𝑑𝑀 ∝ 𝑈 -𝛼 𝑑𝑈, (1) where  is the local heating intensity. We also included the nebular emission module although we did not change the default parameters. The sixth and most important module for this work is the module that describes the AGN SED. For our experiments setups, we selected the two AGN modules available in X-CIGALE: A simple smooth torus  (Fritz et al. 2006) , and a tho-phase (smooth and clumpy) torus  (Stalevski et al. 2016 , also known as SKIRTOR). For both models, we covered a larger sample of parameters for the viewing angle  and the fraction of AGN contribution to the IR luminosity  AGN  (Ciesla et al.
In summary, we used the parameters and values given in Table  2  to define the grid of X-CIGALE SED models for the sample of Seyfert galaxies. For the remaining parameters not shown in Table  2 , we adopted the X-CIGALE default settings. We decided not to include the X-ray or radio modules due to the lack of homogeneous information for the selected sample of Seyfert galaxies (see Sect. 4. 5). We assumed the redshifts from SMB in the SED fits.
Cleaning X-CIGALE fits We ran another setup of X-CIGALE without the AGN module (hereafter No-AGN) in addition to the X-CIGALE setups with AGN models described in Table  2. The No-AGN setups helped us to identify bad-fittings and ambiguous cases where an AGN is not dominant in the SED, even though the galaxies are classified as Seyfert. X-CIGALE minimises the  2 statistic and produces probability  a We covered viewing angles between 0 • and 90 • in steps of 10 •. We used the values closest to the predefined angle grid in X-CIGALE. For other setups, we used only 30 • and 70 • , taking into account that  = 90 - (with  the angle between equatorial axis and line of sight). distribution functions for the grid parameters by assuming Gaussian measurement errors  (Burgarella et al. 2005; Noll et al.
For the SED setups with AGN, we found that galaxies with  AGN between 0. 2 and 0. 8 have  2 red values close to one. Therefore, for most galaxies outside this  AGN range have poorer fittings. This differences shows the importance of adding the AGN model in the SED fitting and how  2 red changes for non-dominant AGNs (  AGN < 0. 2) and highlydominant AGNs (  AGN > 0. 85). To better compare the No-AGN and AGN setups, we use the Bayesian Inference Criterion (BIC) to see if the AGN module is preferred for the fits, as in other CIGALE works (e. g. Buat et al. 2019). The BIC is defined as BIC =  2 + k × ln(N), with  the number of free parameters and  the number of data points used for the fit  (Ciesla et al. 2018 ) and works as an approximation of the Bayes factor  (Kass & Raftery 1995). Then, the difference between the setups can be calculated as ΔBIC =  2 AGN - 2 No-AGN + ln(N), as we are just fixing the  AGN to zero in the No-AGN setup.
Thus, we include additional information as the databases collect more broadband photometry. The second difference is the assumed grid values for the SED modelling. Although the numerical values in most of the input parameters are not the same, here we mention the three most important differences between the grids. First, the IMF in this work comes from  Chabrier (2003) , while  Vika et al. (2017)  uses the IMF from  Salpeter (1955). The use of the Chabrier IMF will lead to lower Table  3. Summary of the Seyfert samples used in this work. The original samples of Seyfert galaxies are in columns 2 (VCV) and 3 (SMB). Galaxies with photometry from the samples fulfilling our criteria are in column 4. The last four columns show the final counts for well-constrained SEDs in X-CIGALE. We show the counts from VCV and SMB classifications in columns 4-8 for Sy1, Sy2 and unclassified Seyfert rows.
Seyfert Age and  AGN are the only physical parameters that are very different from  Vika et al. (2017). In the case of  AGN , the estimates are constrained by the different grid selection. However, the estimated median error is lower in this work than in  Vika et al. (2017) , as we use more photometric bands and a finer  AGN grid. In the case of the age,  Vika et al. (2017)  noted that age estimates are not well constrained in CIGALE, besides the differences in the grid values. This problem is also obvious from our estimates, which have uncertainties similar to those presented by  Vika et al. (2017). In general, the physical parameters estimated in  Vika et al. (2017)  are similar to those presented in this work, validating our approach of obtaining data directly from astronomical databases.
Feature selection Recent advances in algorithms and machine learning techniques are helping to classify very complicated physical systems  (Carleo et al. 2019; Virtanen et al. 2020). These classification tasks have covered the full range of galactic and extragalactic sources (e. g. Tamayo et al. 2016; Miettinen 2018; Jayasinghe et al. 2018; Bluck et al. 2020; Baqui et al. 2021 ). Nowadays, these methods are helping to classify astrophysical objects not only from reduced fluxes, but also from astronomical imaging surveys, where Type-1 AGN are separated from normal galaxies  (Golob et al. 2021). Therefore, joining X-CIGALE physical parameters with these classification techniques could be useful to solve the AGN "zoo" of galaxies for different AGN scenarios. However, X-CIGALE estimates more than 60 physical parameters depending on the number of modules included in the SED fitting.
2009; Suh et al. 2019). We conclude that the quantity of photometric data in NED and CDS is not enough nor homogeneous to include the X-ray module in the X-CIGALE setups. None the less, X-rays can help to constrain non-physical parameters in SED tools as X-CIGALE  (Yang et al. 2020 ). Recent studies with different SEDs models that include X-ray data show how important the inclusion of these data could be to constrain the AGN models (e. g. Suh et al. 2019; Ogawa et al. 2021; Mountrichas et al. 2021; Masoura et al. 2021 ). In the future, we expect to understand the probable evolutionary paths discussed in this work for different AGN types, with the AGN luminosity,  AGN , and X-ray data. In the next decade, the combination of the Athena space telescope with X-CIGALE will help to unambiguously determine the presence of AGN for this type of works  (Yang et al.
CONCLUSIONS We have used a sample of 13 173 Seyfert galaxies from SMB and VCV to assess the importance of the viewing angle in AGN SED models. We have used a data-driven approach by retrieving photometric data from astronomical databases (CDS and NED) to be used in the SED analysis with X-CIGALE. Two AGN SED toroidal models (Fritz and SKIRTOR setups) were used with different viewing angle configurations to verify the effect of viewing angle selection in the estimated physical parameters. These estimates have been validated by comparing our results with those from  Vika et al. (2017) , showing good agreement except for the AGN fractions, which can be related to the different assumptions of the grids. Our main conclusions are the following: 1. The estimated viewing angle from X-CIGALE seems to be the second best discriminator when assessing AGN type.
ACKNOWLEDGEMENTS K. Małek  has been supported by the  Polish National Science Centre  grant ( UMO-2018/30/E/ST9/00082 ). We would like to thank the  Center for Information Technology of the University of Groningen  for their support and for providing access to the Peregrine high performance computing cluster. We acknowledge the anonymous referee for a careful reading of the manuscript and very helpful suggestions and comments. This research made use of Astropy, 5 a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018). This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This research has made use of the  NASA/IPAC Extragalactic Database (NED) , which is funded by the  National Aeronautics and Space Administration  and operated by the  California Institute of Technology.
DATA AVAILABILITY Most of the data and code underlying this article are available in Zenodo, at https://doi. org/10. 5281/zenodo. 5227294. The X-CIGALE estimated data from the AGN setups are available in a companion repository in Zenodo, at https://doi. org/10. 5281/ zenodo. 5221764, which also includes a script to create similar figures like the one presented in Fig. 2  for all galaxies in this work.
In all the cases, the Scipy extensions (line-search and regularization parameter) are not activated and the relaxation is not applied. The convergences of the residuals are plotted in Fig. 14  for  ''20-40" and ''100-150". The results from ''20-40" seem to confirm the idea that an Mparameter equals to 1 or 2 could be combined with the considered iterations limits, while higher values of M would lead to an unstable algorithm. In respect of ''100-150", a different behaviour is observed: it converges only for M > 1 and the convergence rate improves up to M = 4. In addition, large oscillations of the module of the residual are observed even when the scheme converges. It should be noticed that while the undamped fixed-point with ''20-40" converges, ''100-150" does not. As written in  Anderson (1965) , the convergence of the Anderson iterations is linked to that of the corresponding fixed-point ones, hence, this element should be kept in mind for further analysis.
Forty-five distinct regular expression patterns have been built based on the train set and optimized to improve performance compared to manual annotation. Queries on unstructured content were implemented and performed using the R "stringr" package. At the evaluation step, PreScIOUS was once applied on the testing set. For the machine leaning models a tokenization was performed by converting free text into tabular format using the document term matrix (DTM). DTM values were term frequency-inverse document frequency (TF-IDF) weight a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus  [15]. Bayesian classifier was optimized to improve the optimal value of the estimator of "Laplacian probabilities" compared to manual annotation. Bayesian classifier were performed from the R package naivebayes (with the naive_bayes function)  [16].