dataesr/scientific-paragraphs-categorization huggingface dataset:
Aims. We study the coevolution between the black hole accretion rate (BHAR) and the star formation rate (SFR) in different phases of galaxy life: main-sequence star-forming galaxies, quiescent galaxies, and starburst galaxies at different cosmic epochs. Methods. We exploited the unique combination of depth and area in the COSMOS field and took advantage of the X-ray data from the Chandra COSMOS-Legacy survey and the extensive multiwavelength ancillary data presented in the COSMOS2015 catalog, including in particular the UVista Ultra-deep observations. These large datasets allowed us to perform an X-ray stacking analysis and combine it with detected sources in a broad redshift interval (0. 1 < z < 3. 5) with unprecedented statistics for normal star-forming, quiescent, and starburst galaxies.
Therefore we selected our samples of star-forming and quiescent galaxies from the catalog by  Laigle et al. (2016) , which is NIR selected, while we used the FIR-selected catalog by  Gruppioni et al. (2013)  to identify starburst galaxies. For the BHAR we used the catalog by  Civano et al. (2016) , obtained from the Chandra COSMOS-Legacy program, and complemented it by stacking on their X-ray images. We estimated the SFR for starforming and quiescent galaxies by combining FIR stacking on Herschel images and detections with UV luminosity. We present in this section the catalogs we used for the sample selection and the data we used to estimate the average BHAR and SFR.
IR data We selected the starburst galaxies sample for this work from a FIR-selected catalog. In particular, we used the  Gruppioni et al. (2013)  catalog, which is selected from PACS/Herschel PEP observations in the COSMOS field. This catalog was also matched with the deep 24 µm imaging of Le  Floc'h et al. (2009) , with the HerMES extragalactic survey  (Oliver et al. 2012)  observed with SPIRE at 250, 350, and 500 µm in the same fields that were covered by PEP and with the IRAC-based catalog of  Ilbert et al. (2010) , including optical and NIR photometry and photometric redshifts. Gruppioni et al. (2013) ) used as reference the blind catalogs at 100 and 160 µm from  Berta et al. (2010 Berta et al. ( , 2011)) , selected down to the 3σ level, which in COSMOS contain 5355 and 5105 sources at 100 and 160 µm, respectively.
The redshift for each object was fixed to the spectroscopic redshift when available or else to the photometric redshift; then the SED was fit to a best-fit model that we selected from a library built by combining different SFH, metallicities, and dust contents. Each SFH is the combination of an exponentially declining SFR model, to which random bursts of star formation are superimposed (see da  Cunha et al. 2008 Cunha et al. , 2010)). The emission of a possible AGN component was taken into account using a modified version of the MAGPHYS code (SED3FIT,  Berta et al. 2013) , which adds a torus component to the modeled SED emission by combining the da  Cunha et al. (2008)  original code with the  Fritz, Franceschini, & Hatziminaoglou (2006)  AGN torus library (see also  Feltre et al. 2012). In order to estimate the SFR of their sample,  Gruppioni et al.
X-ray data We used X-ray data from the Chandra COSMOS Legacy Survey (COSMOS-Legacy,  Civano et al. 2016 ), a 4. 6 Ms Chandra program that combines new observations obtained during Chandra Cycle 14 with the previous C-COSMOS Survey, allowing the X-ray data to uniformly cover the whole 2. 2 deg 2 of the COSMOS field. The limiting fluxes are 2. 2 × 10 -16 , 1. 5 × 10 -15 , and 8. 9 × 10 -16 erg cm -2 s -1 in the 0. 5-2, 2-10, and 0. 5-10 keV bands, respectively. The optical counterparts to the X-ray COSMOS-Legacy sources are available in  Marchesi et al. (2016). This was obtained by using the maximum likelihood ratio technique (e. g. ,  Sutherland & Saunders 1992; Brusa et al. 2005; Civano et al. 2012 ) by matching X-ray sources to three separate bands: i-band data from  Ilbert et al. (2009) , K s -band data using UltraVISTA DR2, and IRAC 3.
X-ray stacking analysis In order to estimate the average X-ray luminosity for each mass and redshift subsample, we followed the same method as in  Rodighiero et al. (2015). We used individual X-ray detections from the COSMOS-Legacy catalog  (Civano et al. 2016 ) when possible, taking advantage of the match between optical and Xray sources performed by  Marchesi et al. (2016)  (see section 2. 3). For the non-X-ray detected sources we performed stacking on the same images using the CSTACK tool v4. 32 1   (Miyaji et al. 2008). We propagated the probability distributions of count rates with Monte Carlo simulations. We focused on the 2-7 keV band A&A proofs: manuscript no. main and took advantage of the CSTACK bootstrap output. We simulated it 10 6 times by interpolating the inverted cumulative distribution function of the bootstrap probability distribution.
4. 3), and from the old stellar populations, that is, low-mass X-ray binaries, whose emission is proportional to the stellar mass of the galaxy. This is particularly important in quiescent galaxies. We confirmed the effect of this correction by comparing our results with the corrections from  Fornasini et al. (2018)  and  Aird et al. (2017). The correction from  Aird et al. (2017)  gives no apparent difference to our results, while the correction from Fornasini et al. , which is to be considered an upper limit because it includes some residual AGN emission, has some effect at the lowest redshift that causes some data points to become compatible with zero and a lower normalization by ∼ 0. 2 dex in star-forming and quiescent galaxies. The overall results are not affected by it, however. In order to correct for the average internal absorption from the galaxy (N H ), we estimated the hardness ratios (HR) of our sample.
Comparisons with the literature In Figure  4  we compare our results for the star-forming sample with results from the literature. We show our data points from Figure  3  in the left column and compare them with results from  Yang et al. (2018)  as solid lines, with results from  Aird et al. (2019)  as a dashed beige line, and with results from  Delvecchio et al. (2019)  as a dot-dashed violet line. Our data points qualitatively agree with those from  Yang et al. (2018)  in the three panels. Yang et al. (2018)  selected their star-forming sample from GOODS-N, GOODS-S, and COS-MOS UltraVISTA DR1 based on the SFR from SED fitting with a SFR threshold at 1. 3 dex below the main-sequence. SFRs are from  Santini et al. (2015)  and  Barro et al. (2019) ). Their average SFRs are slightly higher than ours.
Downsizing has previously been observed for stellar mass  (Cowie et al. 1996; Cimatti et al. 2006 ) and black hole luminosity (e. g. ,  Marconi et al. 2004  but also  Shankar et al. 2004 Shankar et al. , 2009;; Aversa et al. 2015 , who reached similar conclusions from continuity equation arguments). In our plots, downsizing can be observed in the steepening of the slope γ at higher masses in star-forming and starburst galaxies and in the two specific accretions, which indicate a faster decrease for higher masses. Downsizing can also be seen in high-mass galaxies (darker data points), which show lower sSFR(sBHAR) on average than lower mass galaxies. This means that at even at higher redshifts, the most massive galaxies have already accreted most of their stellar and black hole mass, even though within the error bars, sSFR (sBHAR) data points are often compatible with a unique value for all stellar masses at a given redshift.
5. sBHAR and sSFR follow very similar decreasing trends in time. We see signs of downsizing in all types of galaxies, a faster accretion (of M BH and M * ) in starbursts followed by star-forming galaxies, and finally, by quiescent galaxies with mass-doubling timescales of about a Hubble time. 6. The resulting M BH -M * relation from our data agrees well with independent determinations of the relation that were retrieved from AGN samples and Monte Carlo simulations. All of these results confirm the coevolution of host galaxy and black hole follows the pattern of downsizing at all redshifts and in different galaxy evolutionary phases. In this picture, the bulk of the black hole and stellar masses is accreted in galaxies during the main-sequence phase through secular processes, where more massive galaxies are more efficient at accreting the black hole.
Acknowledgements. The authors want to thank the anonymous referee for his/her comments that clearly improved the consistency and overall quality of this paper. We acknowledge helpful conversations with  Patricia Arévalo ,  Francesca Civano ,  Francesca M. Fornasini ,  Mauro Giavalisco ,  Alister Graham  and  Claudio Ricci. RC  acknowledges financial support from  CONICYT Doctorado Nacional N • 21161487  and  CONICYT PIA ACT172033 ; RC and PC acknowledge support from the  CONICYT/FONDECYT  program N • 1150216; GR and CM acknowledge support from an  INAF   PRIN-SKA 2017  grant  1. 05. 01. 88. 04  and from the  STARS@UniPD  grant; PC acknowledges support from the  BIRD  2018 research grant from the  Università degli Studi di Padova ; ID is supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement N • 78867; FS acknowledges partial support from a  Leverhulme Trust Research Fellowship.
Variables First of all, the dependent variable Mapdisq i,t is, for nation i in year t, a corrected number of medal wins, which may not be equal to the actual number of medals won and publicized right after ending the Games. Mapdisq i,t is a national medals total after deducting all ex post medals lost due to (often doping) disqualifications of nation i's athletes. 1 Data are from https://en. wikipedia. org/wiki/Summer_Olympic_Games , the Summer Olympics Wikipedia English site that links to web pages of different Games where tables are found regarding medal totals, medallists' disqualifications, and medals' reallocations; references to IOC official data are reported so that double-checking can be done. It is worth noting that a better assessment and accounting of the doping impact on Olympic performances would 1 Now, the World Anti-Doping Agency (WADA) and its national agencies can make anti-doping tests during 10 years after the Games.
Data are collected from the World Bank https://data. world bank. org/indicator/SP. POP. TOTL , and the variable logarithm is used in estimating our model variants. (2) (Y/N) i,t-4 stands for gross domestic product (GDP) per inhabitant in nation i four years earlier than year t Olympics and is assumed to capture the nation's level of economic development, differentiating rich/developed and poor/developing countries. These first two variables are taken four years earlier under the assumption that nation i needs to mobilize economic and demographic resources four years in advance to prepare its Olympic team and have it ready for the year t Olympics. In the background, the rationale is that human and economic resources need to be available from the starting point of the national Olympic team's preparation for the next Games, which we assume to start up right after the end of previous Games, that is, four years earlier.
Data are constant purchasing power parity GDP, in 2011 million U. S. international dollars, and data are collected from the CEPII database called CHELEM open on the DBnomics site https://db. nomics. world/CEPII/CHELEM-GDP , except for Puerto Rico (absent in the database). For the latter country, constant PPP GDP has been found in the World Bank database. (3) Host i,t is a dummy variable supposed to capture a host country effect on medal wins and is equal to 1 for host countries and equal to 0 for other participating nations. (4) Political Regime p,i is a dummy that differentiates among participating nations between former socialist centrally planned economies, that is, Central Eastern European countries (CEEC), which have joined the European Union, then all other (post)communist economies (POSTCOM), and capitalist market economies (CAPME), which all other countries in the world are assumed to be.
Cranial sutures play critical roles in facilitating postnatal skull development and function. The diversity of function is reflected in the highly variable suture morphology and complexity. Suture complexity has seldom been studied, resulting in little consensus on the most appropriate approach for comparative, quantitative analyses. Here, we provide the first comprehensive comparison of current approaches for quantifying suture morphology, using a wide range of two-dimensional suture outlines across extinct and extant mammals (n = 79). Five complexity metrics (sinuosity index (SI), suture complexity index (SCI), fractal dimension (FD) box counting, FD madogram and a windowed short-time Fourier transform with power spectrum density (PSD) calculation) were compared with each other and with the shape variation in the dataset.
In addition, the direct comparison of the approaches will offer the potential to integrate the results of studies implementing different complexity metrics. Here, we compare five methods for quantifying suture complexity: sinuosity index (SI), suture complexity index (SCI), fractal dimension (FD) box counting approach, FD madogram approach and windowed short-time Fourier transform (STFT) with a power spectrum density (PSD) calculation, using a dataset sampled across a diverse range of mammal suture morphologies. Mammal crania exhibit an incredible variety of shape and suture morphology, likely related to their complex mastication, and dietary and ecological diversity  [46]. To accompany this cranial diversity, a wide range of suture morphologies are observable across mammals. Moreover, they are abundant and easily available in museum collections, thus providing the ideal sample dataset for comparing the metrics for quantifying suture morphology and complexity.
Data collection A sample of 79 extinct (n = 34) and extant (n = 45) mammals was generated to assess and test approaches available for quantifying suture morphological complexity (electronic supplementary material, table  S1 ). Multiple sutures were sampled across this sample dataset rather than a single homologous suture, as the goal was to determine the most appropriate metric for a sample with a broad range of morphologies and not to assess the morphological variation within the sample dataset (figure  1 ). Spirit preserved specimens were scanned using the X-Tek HMX ST 225 µCT scanner (Nikon, Tokyo, Japan) and Go!Scan 20 (Creaform), Go!Scan 50 (Creaform) or EDGE ScanArm HD (FARO) scanners were used for osteological specimens. Scans were reconstructed in Avizo v. 9. 3 (FEI, Hillsboro, OR, USA), Geomagic Wrap (3D Systems), and VXElements v6.
0 (Creaform) and cleaned in Geomagic Wrap leaving only the skull elements in the three-dimensional isosurface. Two-dimensional images (n = 79) of the multiple different sutures across the sample dataset were captured using uniform positioning to minimize the impact of parallax in the rgl R package  [47]. Two-dimensional semi-landmarks were manually positioned on the suture images using the StereoMorph R package  [48]  and resampled at 500 semi-landmarks per suture to capture the open suture outline. Studies should first determine the number of manual landmarks required to capture the entire suture curve profile and resample to a number of landmarks that does not risk losing morphological complexity. This is particularly important as fractal-like sutures could be more sensitive to the number of landmarks positioned.
estim. boxcount' and 'fd. estim. madogram' functions, respectively. STFT was computed using the 'stft' function in the e1071 R package  [55]. The PSD of each suture was calculated using the STFT results, by averaging the squared STFT coefficients Dasypus navemictus extant placental Rattus norvegicus extant placental Cricetomys gambianus extant placental Homotherium sp.
Comparative analysis of the complexity metrics The appropriateness and effectivity of each metric was assessed using multiple comparative approaches. Comparisons between suture shape data and complexity scores were made to determine whether the metrics captured functionally relevant suture morphologies. Functionally relevant aspects of suture morphology include, but are not limited to, the multiple features of interdigitations (number and amplitude) thought to be shaped by biomechanical stresses as a result of behaviour and diet. Correspondence between complexity score and suture shape PC score were quantified with Pearson's correlation coefficient (r) and visualized with a heatmap using the 'corrplot' function from the corrplot R package  [56]. The significance ( p < 0. 05) of the correlations were tested using the 'cor.
mtest' function  [54]. A heatmap reflecting complexity scores for each specimen was mapped onto the PCA of Procrustes superimposed two-dimensional semi-landmarks to further determine whether the complexity metrics captured the functionally relevant aspects of morphological variation. Similarly, correlations between the five complexity metrics were quantified and visualized using the 'corrplot' function in the corrplot R package, significance was tested using the 'cor. mtest' function  [56]. Finally, we conducted a further PCA on the specimen complexity scores, rather than the shape data, using the factoextra R package  [57]. The contribution of each variable (n = 5; complexity metrics) on the PC axes (i. e. the scaled PC loadings) were plotted on a correlation circle. The PC loadings were superimposed onto the PCA, of the first two axes, thus representing both variables (n = 5) and individuals (n = 79) using the 'fviz_pca_biplot' function, in order to assess how the approaches, relate to each other and the complexity across the sample dataset.
Complexity analysis Complexity analysis captures variation in the complexity scores across the dataset, which are estimated from the suture shapes, but not the shapes themselves. The five methods produced different ranges of complexity across the sample dataset, with the largest value indicative of the most complex suture: 1. 024 to 4. 861 for SI; 0. 205 to 87. 963 for SCI; 1. 055 to 1. 120 for FD (box counting method); 1. 452 to 1. 657 for FD (madogram method); 1. 449 to 1. 928 for PSD, extreme morphologies for each metric are presented in electronic supplementary material, figure  S3. Full results for each suture are presented in electronic supplementary material, table  S3 , with a subset of sutures representative of the range of suture complexity detailed further in figure  4. Sutures recognized as 'simple' (figure  4a, b ) following SI, SCI, FD (box counting), FD (madogram) and PSD analysis presented with a largely straight morphology and very few interdigitations.
Discussion Sutures play a critical role in the development and functioning of the skull throughout an organism's life  [23] [24] [25] [26] [59] [60] [61] , and thus suture morphology is important for understanding skull morphology and function. Complexity, in particular, has been discussed as a critical variable for skull function  [30] , but approaches that quantify complexity and their relationship to suture shape have not been directly compared. We tested and compared five complexity metrics (SI, SCI, FD box counting, FD madogram and PSD) based on different mathematical approaches for quantifying suture complexity across a range of mammalian cranial suture morphologies. While there was some correspondence across methods, they each captured different aspects of suture morphological complexity, with PSD showing a particularly strong correlation with PC2, which captured diverse aspects of complexity represented in this sample dataset.
Indeed, the complexity metrics are only extracting part of the suture shape information, but these metrics are most highly correlated with the features of suture shape that have    (d-h ) PSD, FD madogram, FD box counting, SI and SCI morphological complexity extremes, respectively. Specimen ID and their species associations are detailed in electronic supplementary material, table  S1. the highest functional relevance to the suture. This suggests that using the raw shape data alone to understand sutural complexity and morphological variation could be misleading and instead supports the use of a complexity score. The use of a complexity score which provides a single value is a useful tool to make direct comparisons across diverse datasets. Attempts to use complexity scores to make comparisons of suture complexity across different species from various published studies has previously been attempted  [30, 37].
However, these studies have been performed on various taxa using various methods, thus with little overlap, meaning comparative data are sparse despite the consensus to evaluate complexity based on interdigitations. Therefore, determining a single complexity metric, as our study aimed to do, will prove incredibly useful to make such comparisons. Moreover, the comparisons made by our study of the previously implemented complexity metrics, identify which aspects suture shape each approach captures. This will further prove useful for future studies, to aid in the determination of the most appropriate metric dependent on the biological questions and empirical dataset proposed by the study.
As FD madogram has been previously identified to be the most robust of the FD methods, this supports our findings of a stronger correlation between FD madogram and PSD, than the FD box counting approach and PSD. However, it was surprising that the stronger correlation was observed between the FD madogram and PSD metrics than between the two FD metrics which are based upon very similar fractal methods. The FD box counting metric mapped well to both the features reflected by PC2 and PC3 of shape variation. Of the approaches, this metric appears least sensitive to the extreme forms sampled within the dataset thus producing well-behaved results. However, it has been described as less robust and efficient than the FD madogram metric  [64]. There is, however, a critical issue in the use of fractal analysis for patterns in nature, such as suture morphology, because natural forms are well documented to be intricate and often do not follow the self-similarity assumptions of fractal analysis  [29].
It is possible that these previously disregarded additional aspects of complexity are biologically important or entirely irrelevant to the suture functioning, but this is currently unknown. Each metric has strengths and weaknesses relative to different aspects of suture complexity. Therefore, in future studies, the appropriate metric should be determined based on the questions posed. For the sample dataset of mammalian cranial sutures, spanning a range of morphologies, used in our study, we prefer PSD, due to its ability to discriminate between disparate morphologies based on multiple aspects of complexity. However, a combination of metrics could also prove useful, such as PSD and FD box counting, to ensure the range of suture morphologies across the major axes of complexity variation (PC2 and PC3) are captured.
Understanding the features of suture complexity captured by each of the methods will assist in making direct comparisons across the literature where various approaches have previously been employed. While we found PSD to be a promising metric for comparative quantitative analysis of suture morphological complexity, we only compared mammalian taxa, and thus other methods may prove more appropriate for other systems. Nevertheless, we have no reason to believe that the PSD method would be unsuccessful for other taxa, in particular since it has been previously identified as a useful method for ammonoid sutures  [29]. Furthermore, this study compared sutures covering a broad range of morphologies and thus may not be as powerful for detecting minor differences in sutures, such as those that may be encountered in an intraspecific dataset for a single suture, depending on the attributes of interest.