dataesr/scientific-paragraphs-categorization huggingface dataset:
We can therefore use the total radio power estimated with ATCA at 1. 4 GHz in order to infer the associated jet power. We note that the data used in  Cavagnolo et al. (2010)  to calibrate the scaling relation between AGN jet power and radio power at 1. 4 GHz are highly scattered around the mean relation. For example, P cav varies between 10 43 and 10 46 erg s -1 for 1. 4 GHz radio powers measured between 10 40 and 10 41 erg s -1. We take the intrinsic scatter of this relation into account in our estimates of the cavity power uncertainties to obtain a reliable estimate of the mean redshift evolution of the P cav /L cool ratio in the following. Furthermore, several studies have shown that the radio power measured at the center of local cool-core clusters can vary significantly over decades (e.
g. ,  Rose et al. 2022). Such variability partly explains the intrinsic scatter of the  Cavagnolo et al. (2010)  scaling relation and is therefore included in the uncertainties of the P cav estimates in the following. We model the ATCA radio data using a sum of 2D Gaussian functions with a position angle and minor and major axes lengths fixed to the ones of the point-spread function model in each frequency band. If an AGN is detected within <5″ of the BCG location in the considered ATCA map, we perform a Markov Chain Monte Carlo analysis in order to estimate the best-fit values of the amplitude and sky position of each Gaussian function considered in our model. We iterate this analysis with an increasing number of Gaussian functions until the best-fit χ 2 value does not decrease significantly given the rms noise in the data.
The AGN flux density is obtained by integrating the signal in the best-fit model and the corresponding uncertainty is estimated by sampling the posterior distribution of all model parameters. If the AGN detection is not associated with the BCG or no radio AGN is detected in the cluster region, we estimate an upper limit on the AGN flux density based on the ATCA rms noise measured in a region empty of a radio signal within a 5′ radius from the BCG location. We show the results obtained with the ATCA data of SPT-CLJ0307-6225 in Figure  2. We use it as a representative example of a cluster with a significant AGN detection. The signal in most maps at 2 GHz is well modeled with a single 2D Gaussian function as shown in the left column of the left panel. Indeed, the ATCA angular resolution at this frequency is often too small to resolve the AGN signal.
Our model is flexible enough to subtract all the significant signal observed in the data as shown in the residuals shown in the bottom row. Among the 48 clusters considered in this work, 11 have been observed at 2, 5, and 9 GHz with ATCA. We use the flux densities estimated in each band in order to fit the spectral energy distribution (SED) of the AGN at the core of these systems. The SED is modeled as a power law with a free amplitude at 1 GHz and a spectral index. The choice for this model is motivated by previous studies of AGN radio emission in clusters such as  Kokotanekov et al. (2017)  who find a very good agreement between this model and the data obtained in the [0. 1-10] GHz range. We show the best-fit SED model of the AGN detected in SPT-CLJ0307-6225 in the right panel of Figure  2  along with its associated 1σ and 2σ confidence regions.
X-Ray Luminosity within the Cooling Radius The X-ray data reduction is made using the Chandra Interactive Analysis of Observations (CIAO) software v4. 13 along with the calibration database (CALDB) v4. 9. 5. 20  After reprocessing the level 1 event files and removing flares from the light curves  (Markevitch 2001) , we identify point sources with wavelet filters  (Vikhlinin et al. 1998 ) and mask them to produce a cleaned event file. We further extract the X-ray surface brightness profile of each cluster in the 0. 7-2. 0 keV band using the same binning definition considered in  McDonald et al. (2017)  and  Ruppin et al. (2021). The center that we consider to extract the X-ray surface brightness profile is the BCG location found with the available optical-IR data. The surface brightness profiles are vignetting corrected using the exposure map estimated in the same energy band.
The analysis procedure used to estimate the ICM thermodynamic profiles of each cluster depends on the quality of the Chandra observations. The 36 clusters observed in the context of the XVP program can be fully processed with an X-ray only analysis as we have enough X-ray counts to constrain the ICM temperature from an analysis of their X-ray spectra. For these clusters, we extract spectra at different angular distances from the deprojection center requiring at least 500 counts in the 0. 7-7. 0 keV band. We subtract the particle background using stowed background files scaled to the number of counts observed in the 9-12 keV band. We repeat the same procedure in regions of the ACIS-I chips free from cluster emission in order to estimate the astrophysical background spectrum. We jointly fit the cluster and background spectra using a singletemperature plasma (APEC;  Smith et al.
Evidence for Constant Feedback to Cooling Ratio with Redshift We use our estimates of the AGN cavity power P cav (Section 3) and X-ray luminosity inside the cooling radius L cool (Section 4) to study the redshift evolution of the P cav /L cool ratio in our sample of cool-core clusters. The results are presented as red points in Figure  3. Among the 27 cool-core clusters in our sample, 15 do not display any significant radio signal at the BCG location in the ATCA data and are presented as upper limits. All but two clusters in our cool-core sample verify P cav /L cool < 10, in agreement with the results obtained by  Rafferty et al. (2006 ) and Hlavacek-Larrondo et al. (2012 , 2015)  at low redshift (blue symbols). The cluster SPT-CLJ0528-5300 at z = 0. 77 has already been studied in detail by  Calzadilla et al.
(2015)  data are included in the power-law fits. We highlight SPT-CLJ2245-6206 with a black background hexagon. discussed in Section 6. 2 along with the results obtained for noncool-core clusters (purple points) in Section 6. 3. We use the Bayesian linear regression package LinMix (Kelly 2007) in order to fit the redshift evolution of the P cav /L cool ratio, taking upper limits into account. We model the redshift evolution of [ ] P L log cav cool as ( ) a b s + z, 2  , where α and β are, respectively, the intercept and slope of the power law defining the mean of the Gaussian distribution  with intrinsic scatter σ. The clusters from  Rafferty et al. (2006)  and Hlavacek-Larrondo et al. (2012) that satisfy our progenitor selection (filled symbols in Figure  3 ) and our constraints on cool-core clusters are jointly fit to obtain the dark line in Figure  3.
2. We note however that the interpretation of the P cav /L cool ratio estimated for single clusters is highly affected by the intrinsic scatter in the  Cavagnolo et al. (2010)  scaling relation. The high P cav /L cool values obtained for some noncool-core clusters could be explained by large variations from the mean relation that links P 1. 4 GHz to P cav. In particular, beaming or radio AGN variability could induce high measured values of P 1. 4 GHz that would in turn translate into high estimated values of P cav even in systems with a low cooling luminosity. Deeper X-ray data combined with dedicated simulations would thus be necessary to understand these results better.
S. Department of Energy, Office of High Energy Physics , under contract  DE-AC02-06CH11357. B. B. is supported by the  Fermi Research Alliance LLC  under contract no. De-AC02-07CH11359  with the  U. S. Department of Energy. G. M. acknowledges funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. MARA-CHAS-DLV-896778. The Australia Telescope Compact Array is part of the  Australia Telescope National Facility  (https://ror. org/05qajvd42), which is funded by the  Australian Government  for operation as a National Facility managed by  CSIRO. We acknowledge the Gomeroi people as the traditional owners of the Observatory site. C. R. acknowledges support from the  Australian Research Council 's  Discovery Projects scheme  ( DP200101068 ).
Nutrients acquisition by rust fungi during their biotrophic growth has been assigned to a few transporters expressed in haustorial infection structures. We performed a comparative genomic analysis of all transporter genes (hereafter termed transportome) classified according to the Transporter Classification Database (TCDB) focusing specifically on rust fungi (order Pucciniales) versus other species in the Dikarya. We also surveyed expression of transporter genes in the poplar rust fungus for which transcriptomics data are available across the whole life cycle. Despite a significant increase in gene number, rust fungi presented a reduced transportome compared to a vast majority of fungi in the Dikarya. However, a few transporter families in the subclass Porters showed significant expansions.
2016; Saier 1999). The transporter classification database (TCDB) categorizes the variety of conserved or unique transport systems in prokaryotes and eukaryotes  (Saier et al. 2006 ). The TCDB is regularly updated and provides a solid support for the survey of transporters in genomic studies  (Saier et al. 2021). The organization of transporters into classes and families allows a better understanding of their evolution and specialization in various organisms. How transporters localize and function at the interface between organisms has been a central theme in plant-microbe interactions and it was particularly well studied in model, mycorrhizal or pathogenic fungi  (Brohée et al. 2010; Garcia et al. 2016; Perlin et al. 2014). Only a handful of transporters have been studied in rust fungi, most of which were specifically detected or preferentially expressed in haustoria, and most often were localized at the haustorial membrane facing the extra-haustorial matrix and the host plasmalemma  (Lorrain et al.
Expert annotation of OPT genes Predicted OPT genes in the order Pucciniales and selected species in the phyla Ascomycota and Basidiomycota were reviewed on the JGI MycoCosm portal using homology and expression information displayed on corresponding tracks. Predicted OPT genes belonging to the different classes of the rust fungi M. larici-populina and Melampsora allii-populina genomes were manually curated based on support from Sanger-and Illumina-based Expressed Sequence Tags (ESTs) shown on the MycoCosm genome browser, and/or homology to reference OPT genes. Curated OPT were validated by a CD-Search against the Conserved Domain Database (CDD) at the National Center for Biotechnology Information (NCBI, https://www. ncbi. nlm. nih. gov/Structure/cdd/cdd. shtm l), and transmembrane domains organization was verified with the TMHMM prediction tool (https://services.
2020). A maximum likelihood inference for our phylogenetic dataset was achieved with RAxML-NG (v0. 9. 0,  Kozlov et al. 2019 ) using the VT+I+G4 model and 1000 bootstraps replicates. The tree was visualized with FigTree v1. 4. 4 (http://tree. bio. ed. ac. uk/software/figtree/).
Transcriptomic resources Transcriptomic profiling of M. larici-populina was established along the life cycle from previously published studies transposed onto the version 2 of the genome  (Persoons et al. 2022). Briefly, gene expression data were retrieved for dormant and germinated urediniospores, time course infection of poplar leaves and telia by oligoarrays  (Duplessis et al. 2011b; Hacquard et al. 2013 ) and for basidia, spermogonia and aecia on larch needles by RNAseq  (Lorrain et al. 2018a). For this study, we extracted expression profiling of M. larici-populina transporter genes from an open transcriptomic dataset  (Guerillot et al. 2021 ; Supplementary Table  S3 ). The Morpheus tool from the Broad Institute website (https://software. broadinstitute. org/morpheus/) was used to present normalized transcript expression levels using the k-means partitioning method.
Annotation survey of OPTs confirms their expansion in the order Pucciniales. In order to better define the evolution of rust OPTs, we first performed a manual curation of OPT genes in the poplar rust fungus M. larici-populina, a model rust species for which extensive transcript resources are available for expert annotation. A total of 23 OPT genes were predicted in the genome of M. larici-populina, of which 17 needed curation (Table  1 ). With one exception (fusion of two alternative gene models), an alternative best gene model could systematically be retrieved from the JGI MycoCosm at the loci of OPT gene which needed curation (Table  1 ). OPT genes exhibited a complex structure with an average of 26 exons (9-36 exons; Table  1 ) and most of the curations were exon additions (12) or exon corrections (4).
Discussion Rust fungi are obligate biotrophic fungal plant pathogens that strictly depend on their hosts to feed, grow and reproduce  (Voegele and Mendgen 2011). Early molecular studies of rust infection structures such as the haustorium pointed out the specific expression and localization of a suite of transporters contributing to fungal nutrition during the biotrophic growth  (Lorrain et al. 2019; Voegele et al. 2009 ). However, our knowledge about transport systems in rust fungi remains limited. At the time of the publication of the first rust fungal genomes M. larici-populina and P. graminis f. sp. tritici, a survey of transporter genes was carried out with limited possibilities in term of comparison with only a handful of fungal genomes available  (Duplessis et al. 2011a ). Here, we performed an annotation survey of the transportome of rust fungi and a comparison within the Dikarya by harvesting genomic data from more than a thousand fungi.
Biological stages showing high expression levels (i. e. , above average values) in each k-mean are depicted on the right side of the profile and the predominant stage, at which expression peaks, is highlighted in bold. URE and UREg, dormant and germinating poplar rust urediniospores, respectively; T24 to T168, hours post-inoculation in a time course infection series on poplar leaves inoculated with urediniospores; TEL, telia; BSD, basidia; SPG and AEC, spermogonia and aecia formed on larch needles, respectively. blue to red represents normalized expression levels from 0 (deep blue) to maximum value (deep red) at each stage. The Morpheus program parameters used for kmeans were as follows: one minus Pearson's correlation, cluster by rows, number of clusters 10 (1000 iterations). For each gene, the id shown on the right side of the profile indicates the Joint Genome Institute MycoCosm protein-ID number with the associated TCDB identifier and description.
Acknowledgements CL and SD are supported by the  Labex Arbre  ( Programme Investissement d'Avenir ,  ANR-11-LABX-0002-01 ). CL thesis is funded by the  Region Grand Est  and the  French National Research Agency  ( ANR-18-CE32-0001 ,  Clonix2D  project). SD and PF acknowledge the  JGI Community Sequencing Program 2014 for the project 1416 Further Studies in Poplar Rust Fungal Genomics. The work (proposal:  10. 46936/10. 25585/60000832 )conducted by the  U. S. Department of Energy Joint Genome Institute  (https://ror. org/04xm1d337),a  DOE Office of Science User Facility , is supported by the  Office of Science of the U. S. Department of Energy  operated under Contract No. DE-AC02-05CH11231.
sp. fusiforme c Croqu Pucciniales 18 (17) Melampsora allii-populina c Melap Pucciniales 28 (26) Melampsora larici-populina c Mellp Pucciniales 23 (23) Melampsora lini c Melli Pucciniales 19 (18) Puccinia graminis f. sp. tritici c Pucgr Pucciniales 26 (22) Puccinia striiformis f. sp. tritici c Pucst Pucciniales 21 (20) Puccinia triticina c Puctr Pucciniales 23 (22) Sporisorium reilianum c Spore Ustilaginomycotina 8 (8) Ustilago maydis c Ustma Ustilaginomycotina 8 (8) a ID, MycoCosm species abbreviations (used in the phylogenetic dataset); b.
Altogether, we collected data from 1795 plants. For each plant, the leaf rank of the youngest visible leaf was recorded at various time points. The appearance of the last two leaves was not modeled and corresponding data were not included in the analysis. Note that the actual time of appearance of each leaf, which characterizes the phyllochron, was not observed; data here pertain to the time interval in which each leaf appears. Because of measurement errors, some plants displayed a decreasing number of leaves between two successive time points; these plants were discarded from the final data set. In 2014, complete phyllochron data were collected from 318 plants over  8-17 [mean 12. 5].
Statistical model of phyllochron The phyllochron of a plant is characterized by the time between the appearance of successive leaves, or equivalently the time when a new leaf appears. When measurements are made at set times, these values cannot be observed (Fig. 1a ). Thus, we defined a statistical phyllochron model that, combined with an inference algorithm, enabled us to estimate the mean phyllochron at the genotype level. We denote by subscripts lsg the gth genotype from selection population s (Early or Late) derived from ancestral line l (F252 or MBS). This phyllochron model was implemented separately for each year of the experiment. All notations are summarized in Table  3.
µ C(f min ) y, lsg = f min f ′ =1 µ y, lsg, f ′ , σ C(f min ) y, lsg = f min f ′ =1 σ 2 y, lsg, f ′ Subscript C(f min ) indicates the highest leaf rank used to calculate the cumulated phyllochron. This value is the result of the plant development since sowing. Note that the distinction between cumulated and instant phyllochron results from f min which depends on the experimental conditions: if recording starts later in development, the cumulated phyllochron will be the resultant of more developmental stages.
Permutation-like test The MSE may over-estimate the predictive ability of the model since the same climate data were used in the training and validation sets. To correct this bias, MSE calculations were repeated using "false" climates whose seasonal variations are not associated with phyllochron variation. False climate were generated using climate records from years 2009 to 2017 on three periods of time, leading to 252 false climates. Then, the proportion of false climates where the MSE was smaller than the one obtained with the "true" climate represents a p-value of the impact of climate on phyllochron, where the null hypothesis assumes no effect of climate on the phyllochron: γ y, l, c, w = 0 for all (y, l, c, w). Indeed, a low proportion indicates that the climate partly explains the phyllochron variations.
Weights Lasso regression was implemented without and with weights accounting for the precision in the estimation of each µ y, lsg, f. More precisely, for each genotype lsg at year y, µ y, lsg, f was weighted by the number of plants for which at least one observations was made before and after appearance of leaf f. Given the design of our dataset, the number of plants was smaller for higher leaf ranks as a portion of the plants have been dissected. Besides, as the number of plants depended on the year (smaller in 2014 and higher in 2016), the weights amounted to give unequal importance to the association between climate and phyllochron on the three years.
Generally, a price to pay for flexibility is having a larger number of parameters, which requires more data to be correctly estimated. This limitation is particularly true in the context of interval censoring, where information is restricted to time intervals during which leaves appear. Nevertheless, our results indicate that approximately 50 plants per genotype with complete phyllochron observations (in 2015) were sufficient to highlight significant differences between genotypic groups resulting from a recent divergence. The impact of climate on the phyllochron variations through season was assessed using a two-step procedure:  first, the parameters of the phyllochron model were inferred without taking climate into account, then the cumulative climate variables before the appearance of each leaf were regressed on the genotype level phyllochron.
Conclusion We developed a hypothesis-testing model of phyllochron dynamics based on a successive time-to-event stochastic process, combining a flexible and accurate modeling with an unbiased statistical testing procedure. The model detected fine-scale differences between related genotypes with a moderate experimental effort (10-20 measurements throughout the season on 30-50 plants per genotype). The comparison of our model with the classic regression models of phyllochron in terms of statistical testing would quantitatively assess the benefit of this more accurate modelling. On the DSE dataset, we showed that the major sources of differences in phyllochron were not the selection population (Early or Late), but rather the ancestral line (F252 or MBS), the year of experimentation, and the leaf rank.
Analysis of an antibiotic resistance dataset As test data, we retrieved 603 genomes of Streptococcus pneumoniae  (Chewapreecha et al. , 2014; Croucher et al. , 2013 Croucher et al. , , 2015;; Lees et al. , 2018). In this data set, the resistance to penicillin is known and encoded as a binary variable (R for resistant strains, S for susceptible strains). Also, 198,248 single-nucleotide polymorphisms (SNPs) have been called in the genomes. For the post-hoc MCMC analysis, we retrieved a significant association from evoscope and ran epocs_mcmc with a chain length of 10^7 steps and sampling every 50 steps on this pair.
Availability The source code of evo-scope, epics and epocs are available on github under the GNU General Public License v3: https://github. com/Maxime5G/EvoScope, and pastml is available either on bioconda  (Grüning et al. , 2018)  or on github: https://github. com/evolbioinfo/pastml. We also provide a galaxy access to all tools and a "push-button" workflow at the Pasteur institute server: https://galaxy. pasteur. fr/u/maximeg/w/evoscope.
Discussion Inferring correlations between evolutionary events has multiple useful applications. However, fast and easily interpretable tools are still lacking. Here, we provide a self-contained and fully automated pipeline to detect correlated evolution, with minimal input required from the user. Evo-scope takes as input any number of discrete traits and a rooted phylogenetic tree and outputs a model of correlated evolution best explaining the repartition of the events on the tree. The flexibility of evo-scope allows to perform different types of analyses, such as GWAS as exemplified in this paper. Here, we applied evo-scope on a well-described dataset of antibiotic resistance and SNPs in S. pneumoniae to 1) test the accuracy of the pipeline and 2) to add on the literature possible inductions either from the mutations to the resistance or in the other way round.
Methods and results We generated conditional KO of Chd7 in CPM and analysed cardiac progenitor cells using transcriptomic and epigenomic analyses, in vivo expression analysis, and bioinformatic comparisons with existing datasets. We show CHD7 is required for correct expression of several genes established as major players in cardiac development, especially within the second heart field (SHF). We identified CHD7 binding sites in cardiac progenitor cells and found strong association with histone marks suggestive of dynamically regulated enhancers during the mesodermal to cardiac progenitor transition of mESC differentiation. Moreover, CHD7 shares a subset of its target sites with ISL1, a pioneer transcription factor in the cardiogenic gene regulatory network, including one enhancer modulating Fgf10 expression in SHF progenitor cells vs.