dataesr/scientific-paragraphs-categorization huggingface dataset:
Automatic line detection Complementing the extraction method based on HST continuum levels, we search the MUSE datacube for emission lines using the dedicated software MUSELET 4. This analysis tool produces a large number of pseudo-narrow band images over the entire wavelength range of the MUSE cube, summing the flux over 5 wavelength bins (6. 25 Å) and subtracting the corresponding median-filtered continuum estimated over two cube slices of 25 Å width each. SExtractor is then used on each of these narrow-band images to detect the flux excess due to emission lines. All SExtractor catalogs are then matched and merged to produce a list of line emissions which may or may not be associated with strong continuum flux. When multiple emission lines are identified for a single source, the redshift is automatically provided, otherwise the remaining lines are visually inspected to identify [O ii] λ λ 3727, 3729 , Ly α or another line.
Catalog construction Redshift assessment was performed independently by six authors (GM, JR, BC, DL, VP, and JM), using several methods. We systematically reviewed all HST-based extracted sources down to a signal-to-noise in the continuum where no secure redshift relying on continuum or absorption features were able to be assessed. This empirically corresponds to an HST magnitude of m F814W = 24. 4. Each of these spectra was at least reviewed by one of the authors. The redshift catalog was completed with information from the emission line finder MUSELET where reviewers also checked every line suggested by the software. Multiply-imaged systems already recorded throughout the literature  (Jauzac et al. 2015 ,Zitrin et al. 2015 , Kawamata et al. 2015 , Johnson et al. 2014 , Lam et al. 2014 and Richard et al.
2014)  were carefully vetted by the same six authors in order to increase confidence in the redshift assessment. We assigned each measured redshift a confidence level based on the strength of spectral features according to the following rules: • Confidence 3 : secure redshift, with several strong spectral features. • Confidence 2 : probable redshift, relying on 1 spectral feature or several faint absorption features. • Confidence 1 : tentative redshift Examples of spectra assigned confidence 1, 2, and 3 are shown in Fig. 4. We next construct a master redshift catalog, including only spectra with a confidence level of 2 or 3. The only exceptions are made for multiply imaged systems ranked as very secure photometric candidates by HFF lens modelers (see Sect. 4 for more details). The master redshift catalog was compared to entries in the NASA/IPAC Extragalactic Database (NED, https://ned.
ipac. caltech. edu), the publicly available redshift catalog from the GLASS collaboration 5  and the redshifts presented by  Wang et al. (2015) , and corrected as needed. The details of this comparison is presented in Table  B1  of Appendix B The final catalog contains 514 redshifts, including 10 with confidence 1 and 133 with confidence 2 and 371 with confidence 3. The spectral and spatial distributions of this catalog can be seen in Fig. 5. Table  1  presents the very first entries of the catalog and the full version is available in the online version 6. We compared the MUSE redshift catalog presented here to the NED database, checking in particular the redshifts presented by the GLASS team  (Wang et al. 2015). In Appendix B we list corrections made to redshifts published in the literature based on the MUSE data.
5 , middle panel). To get a more robust estimate of their relative velocities we refine these redshifts using the Auto-Z  (Baldry et al. 2014 ) software, cross-correlating each spectrum with template spectra consisting of both passive and star-forming galaxies. In Fig. 9  we present a 2D map of the LOS velocities relative to the cluster redshift z = 0. 3064, defined as the mean redshift in  Owers et al. (2011). The colour scheme used in the figure reflects the relative velocity of each cluster member, while the symbol size is scaled according to the brightness in the ACS/F814W band from our photometric catalog. Figure  9  reveals a clear dichotomy in the distribution of velocities, with two groups of cluster members at low and high velocities centered around the NW and SE regions, respectively.
CONCLUSIONS @ARTICLEJauzac2016, author = Jauzac, M. and  Eckert, D. and Schwinn, J. and Harvey, D. and Baugh, C. M. and Robertson, A. and Bose, S. and Massey, R. and Owers, M. and Ebeling, H. and Shan, H. Y. and Jullo, E. and Kneib, J. -P. and Richard, J. and Atek, H. and Clément, B. and Egami, E. and Israel, H. and Knowles, K. and Limousin, M. and Natarajan, P. and Rexroth, M. and Taylor, P. and Tchernin, C. , title = "The Extraordinary Amount of Substructure in the Hubble Frontier Fields Cluster Abell 2744", journal = MNRAS, archivePrefix = "arXiv", eprint = 1606. 04527, keywords = Gravitational Lensing, Galaxy Clusters, Individual (Abell 2744), year = 2016, month = sep, doi = 10. 1093/mnras/stw2251, adsurl = http://adsabs. harvard. edu/abs/2016MNRAS. tmp. 1357J, adsnote = Provided by the SAO/NASA Astrophysics Data System In this paper, we use ultra-deep imaging data from the HFF program in combination with spectroscopic data from the VLT/MUSE to build a new strong lensing mass model for the HFF cluster Abell 2744.
by the generous financial support of the W. M. Keck Foundation. The authors wish to recognise and acknowledge the very significant cultural role and reverence that the summit of Mauna Kea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. This works makes use of MPDAF, the MUSE Python Data Analysis Framework, an open-source (BSD licensed) Python package developed and maintained by CRAL and partially funded by the ERC advanced grant 339659-MUSICOS. Table  A1. Multiply imaged systems considered in this work. In the column z ref , the letter refers to previous studies reporting spectroscopic redshifts in agreement with our detection: J for  (Johnson et al. 2014 ), R for  (Richard et al. 2014) , and W for  (Wang et al.
ACKNOWLEDGMENTS GM, JR, DL, BC, VP, JM acknowledge support from the  ERC  starting grant  336736-CALENDS. RB, HI, FL acknowledge support from the  ERC  advanced grant 339659-MUSICOS. RJB gratefully acknowledges support from  TOP  grant  TOP1. 16. 057  from the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek. LW acknowledges support by the  Competitive Fund of the Leibniz Association  through grant  SAW-2015-AIP-2. We acknowledge fruitful discussions with  Mathilde Jauzac ,  Keren Sharon ,  Jean-Paul Kneib ,  Eric Jullo  and  Marceau Limousin. This work utilises gravitational lensing models produced by  Bradač ,  Natarajan & Kneib  (CATS),  Merten & Zitrin ,  Sharon , and  Williams , and the  GLAFIC and Diego groups. This lens modeling was partially funded by the  HST Frontier Fields program  conducted by  STScI.
In upper secondary school, their projects grew increasingly sophisticated, both in terms of the disciplinary requirements and academic presentation. Extended writing constituted the basic standard of papers, and personal experience and topicality were no longer relevant criteria when selecting topics. Interview data reveal that in grade 9, Jens spent most of the week "nerding" with the video on COP 15 submitted by his group. He was enthusiastic about the self-regulated organization of the work that made this possible. In upper secondary school, the challenge of the history assignment was how to get a grip on the text sources. This assignment did not mean much to Jens on a personal level; still, he appreciated the concentrated work period. In the study specialization project his challenge was to understand the mathematical theory, but nevertheless he found this part of the project the most exciting.
Long proteins encoded by the genome of generalist fungi likely increase natural selection on codon optimization The efficiency and accuracy of protein translation constrains cell growth rate and depends on sequence properties of expressed proteins  (Ingolia et al. , 2009; Kafri et al. , 2016). To evaluate the impact of translation efficiency on cell growth during host colonization, we calculated theoretical maximal growth rates for cells of generalist and specialist fungal parasites. For this, we designed an in silico model of the cell translation machinery describing the accumulation over time of ribosomal proteins, other intracellular proteins and secreted proteins from which the maximal cell growth rate can be deduced (see 'Materials and methods', Figure  1 -source data 1). To calculate the typical protein synthesis rate, the model uses the median length of ribosomal, intracellular and secreted proteins as input.
Codon optimization and host range co-evolved multiple times across fungal phylogeny To document the evolution of codon optimization in fungi, we reconstructed the ancestral state of codon optimization in fungal phylogeny. To this end, we built a phylogeny of our 36 fungal parasites and 9 non-parasitic fungi distributed among all major clades covered in our study  (Figure 3A, Figure   3 -source data 1 and 2). We determined the degree of codon optimization in non-parasitic fungal genomes and found that codon optimization was generally lower in non-parasitic fungi than in their generalist relatives (average values for non parasitic fungi were S = 0. 157, S CAI = 0. 315 and S scnRCA = 0. 354). This does not exclude that some lineages of non-parasitic fungi could have evolved high codon optimization, but supports the view that codon optimization increased after the divergence of generalist parasites.
62. Both measures indicated that evolution of codon optimization is not correlated with the phylogeny along the tree at p<0. 01. Then, we used phylosignal  (Keck et al. , 2016)  to build a correlogram between phylogenetic distance and codon optimization (Figure  3B ). Correlations were insignificant (in the range À0. 05 to 0. 1) along the whole tree. These results support the view that codon optimization is associated with host range in fungal parasites. The phylogenetic tree was generated using the TimeTree database  (Hedges et al. , 2015)  and PATHd8  (Britton et al. , 2007). Codon optimization shown as the size of terminal nodes corresponds to the degree S of coadaptation of all genes to the genomic tRNA pool  (dos Reis et al. , 2004). Terminal nodes are sized according to genome-scale codon optimization and colored according to host range (grey was used for non pathogen species).
tai function in R (dos  Reis et al. , 2004)  with the 'sking' parameter set to '0' (Eukaryote) for the get. ws function. The degree of codon optimization in each genome was determined using the get. s function in R (dos  Reis et al. , 2004). Confidence intervals were determined using the CI function from the package 'psychometric' in R. For each genome, the probability of selection acting on codon optimization was determined using the ts. test function in R (dos  Reis et al. , 2004)  with sample size = 500 and n = 1000 and p values were adjusted using Bonferroni correction. The repertoire of ribosomal proteins was identified based on interproscan annotation or de novo gene annotation performed with Blast2GO version 3. 3 (RRID:SCR_005828)  (Conesa et al. , 2005). For each genome, the CDS of full length ribosomal proteins were used to compute a codon usage table with the CUSP function in EMBOSS (RRID:SCR_008493)  (Rice et al.
, 2000)  to calculate codon adaptation indices (CAI) for every gene in the corresponding genome, using the CAI function in EMBOSS. The degree of codon optimization at the whole genome scale was determined using the get. s function in R with gene CAI or scnRCA instead of tAI. Host range was determined according to sources listed in Figure 2-source data 2. For most plant pathogens, the Systematic Mycology and Microbiology Laboratory Fungus-Host Database of the United States Department of Agriculture was used  (Farr and Rossman, 2016). For Cryptococcus neoformans, Aspergillus fumigatus, Beauveria bassiana, and Metarhizium acridum, a list of infected Orders were retrieved. We collected the list of Genera recorded in these Orders through various sources (Figure  2 -source data 2). The likelihood that a parasite can infect two host species decreases continuously with phylogenetic distance between the hosts  (Gilbert and Webb, 2007).
In their study, Gilbert and Webb reported that the proportion of hosts colonized was about ~20% at a 300 Mya phylogenetic distance, decreasing logarithmically. We considered that 8% of Genera in a given Order were likely to be host for the above-mentioned pathogens to reach a conservative estimate. Core orthologous genes were identified through a BlastP search against a database of 36 complete fungal proteomes, using the CEGMA set downloaded from http://korflab. ucdavis. edu as a query. Only orthologous groups containing a single hit in all fungal species were selected for further analysis. tRNA adaptation indices for each genome were median-normalized before analyzing their distribution in COGs. We used the TimeTree database  (Hedges et al. , 2015)  to obtain ages for a maximum of nodes of the tree (calibration points).
Then, we ultrametricized the tree using PATHd8  (Britton et al. , 2007). We tested for a potential presence of a phylogenetic signal estimated by Blomberg's K, Pagel's l and autocorrelogram with the phylosignal R-package  (Keck et al. , 2016). We calculated phylogenetic independent contrasts  (Felsenstein, 1985)  and verified the significance of trait correlations using the pic function from the ape package in R.
Sequencing of S. sclerotiorum isolates Isolates C014, P163, P314 and C104 were provided by Bruno Grezes-Besset (Biogemma, Mondonville, France) and were collected from infected rapeseed fields near Blois (France) in 2010. Isolate FrB5, collected from clover seed lot in Dijon (France) in 2010, was obtained from  Vleugels et al. (2013). Isolate 1980 (ATCC18683) was collected in prior 1970 on bean pods near New York  (Maxwell and Lumsden, 1970)  and was provided by Martin Dickman (Texas A and M University, USA). For IGS phylogeny, Intergenic sequences (IGS) were retrieved on sequenced genomes with the Blast tool of CLC Genomics software (RRID:SCR_011853). The sequences were aligned with MAFFT v7 (RRID:SCR_011811)  (Katoh and Standley, 2013) , and the phylogenetic tree was build with PhyML 3  (Guindon et al.
, 2010)  with SH-like approximate likelihood ratio test (aLRT) as support statistics. The tree was mid-point rooted using MEGA6 software (RRID:SCR_000667)  (Tamura et al. , 2013). Sclerotinia sclerotiorum isolates were grown in liquid potato dextrose medium (P6685 -Sigma) for 4 days at 24 ˚C under shaking at 180 rpm. Cultures were filtered using vacuum and a fritted glass column protected with a doubled miracloth membrane and ground in liquid nitrogen. One gram of mycelium powder was used for DNA extraction using a DNA Maxi Kit (Quiagen) and following manufacturer's instructions. DNA sequencing was outsourced to Fasteris SA (Switzerland) to produce Illumina paired-end reads (2 Â 100 bp) using a HiSeq 2500 instrument. Paired-end reads for each S. sclerotiorum isolate were mapped to version 2 of the S.
Analysis of codon polymorphisms in plant pathogen populations SNPs in gene coding sequences were extracted and mapped to the corresponding codons using R scripts to calculate the percentage of polymorphic codons of each type. The number of tRNAs able to decode each codon type is given as a percentage of the genomic tRNA pool that was determined based on tRNA gene predictions obtained through tRNAscan-SE. Whenever isoacceptor codons had no cognate tRNA predicted, the number of tRNA copies was divided evenly among isoacceptor codons according to general codon-anticodon wobble rules as described in dos  Reis et al. (2004). We considered that the 11 codons that did not have isodecoder tRNAs in Zymoseptoria tritici were decoded evenly by the four tRNA genes that had no accepting codons identified by tRNAscan-SE.
s function in R (dos  Reis et al. , 2004). The average normalized tAI for all genes annotated with a given Gene Ontology was computed for generalist and specialist genomes. GO mapping was retrieved from the repositories given in Figure  2 -source data 1 or performed de novo with Blast2GO software. Only GOs associated with 200 genes or more were analyzed. supported by the Australian Grains Research and Development Corporation and Curtin University. Sequences are deposited in GenBank under the BioProject ID PRJNA341340.
Acknowledgements We thank  Marielle Barascud ,  Aline Lacaze  and  Remy Vincent  for technical assistance,  Bruno Grezes-Besset  and  Tim Vleugels  for providing S. sclerotiorum isolates. This work was supported by a starting grant of the  European Research Council  ( ERC-StG 336808  project  VariWhim ) to SR and the  French Laboratory of Excellence  project  TULIP  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ). MD and RPO are.
Funding Funder Grant reference number  Author European Research Council   ERC-StG 336808 Thomas Badet Remi Peyraud Malick Mbengue Olivier Navaud Sylvain Raffaele Labex   TULIP ANR-11-IDEX-0002-02   Thomas Badet Remi Peyraud Malick Mbengue Olivier Navaud Adelin Barbacci Sylvain Raffaele Australian grains research and development corporation Mark Derbyshire Richard P Oliver Curtin University of Technology Mark Derbyshire Richard P Oliver Labex   TULIP ANR-11-IDEX-0002-02   Thomas Badet Remi Peyraud Malick Mbengue Olivier Navaud Adelin Barbacci Sylvain Raffaele The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
The relationships at the root of the animal tree have proven difficult to resolve, with the current debate focusing on whether sponges (phylum Porifera) or comb jellies (phylum Ctenophora) are the sister group of all other animals  [1] [2] [3] [4] [5]. The choice of evolutionary models seems to be at the core of the problem because Porifera tends to emerge as the sister group of all other animals (''Porifera-sister'') when site-specific amino acid differences are modeled (e. g. ,  [6, 7] ), whereas Ctenophora emerges as the sister group of all other animals (''Ctenophora-sister'') when they are ignored (e. g. ,  [8] [9] [10] [11] ). We show that two key phylogenomic datasets that previously supported Ctenophora-sister  [10, 12]  display strong heterogeneity in amino acid composition across sites and taxa and that no routinely used evolutionary model can adequately describe both forms of heterogeneity.
We show that data-recoding methods  [13] [14] [15]  reduce compositional heterogeneity in these datasets and that models accommodating site-specific amino acid preferences can better describe the recoded datasets. Increased model adequacy is associated with significant topological changes in support of Porifera-sister. Because adequate modeling of the evolutionary process that generated the data is fundamental to recovering an accurate phylogeny  [16] [17] [18] [19] [20] , our results strongly support sponges as the sister group of all other animals and provide further evidence that Ctenophora-sister represents a tree reconstruction artifact.
RESULTS AND DISCUSSION Data Recoding Reduces Compositional Heterogeneity, and Models That Accommodate Site-Specific Compositional Heterogeneity Most Adequately Describe Recoded Datasets An adequate modeling of the evolutionary process that generated the data is fundamental to the recovery of accurate phylogenies  [16] [17] [18] [19] [20]. We used posterior predictive analyses (PPAs)  [17]  to perform model adequacy tests on two key datasets used in arguments to support Ctenophora as the sister group of all other animals (Ctenophora-sister). These are dataset D20 from Whelan et al. [10] , hereafter ''WhelanD20,'' and the dataset of Chang et al. [12] , hereafter ''Chang'' (see STAR Methods). We performed multiple PPA tests and used Z scores to measure the deviation of the test statistics from the null expectation.
We showed that for both datasets, models that have previously been used to study the animal phylogeny fell short, to various degrees, of adequately describing among-site amino acid preferences (i. e. , site-specific replacement-pattern heterogeneity) and among-lineage compositional heterogeneity. These models are WAG+G  [21] , LG+G  [22] , GTR+G  [23] , dataset-specific Par-titionFinder-defined models  [24]  (hereafter PF-schemes), and CAT-GTR+G  [25]  (see STAR Methods for a detailed comparison of these models). To test whether available models could adequately describe among-site amino acid preferences, we used three alternative statistics. The first is site-specific amino acid diversity (hereafter PPA-DIV)  [26]. PPA-DIV is a well-established test (e. g. ,  [27] ) measuring whether models can adequately estimate a simple but fundamental property of amino acid alignments, the mean number of distinct amino acids observed at each site (STAR Methods for details and  [26]  for theoretical justifications).
To further check our results, we developed two new tests. The first (hereafter PPA-CONV) measures whether models can approximate the site-specific propensity to undergo convergent evolution toward the same amino acid in distantly related taxa. The second (hereafter PPA-VAR) measures the variance in empirical amino acid frequencies across sites (STAR Methods for details). A comparison of the spread of the Z scores obtained, across all models and from all of our PPAs (Tables  1  and S1 ), indicates that PPA-DIV is more discriminatory: it has a broader distribution of Z scores across models. Nevertheless, for both datasets, absolute Z scores larger than 5 (i. e. , jZj > 5) were obtained for all three PPAs (Tables  1  and S1 ). Absolute Z scores of this magnitude indicate a strong rejection of the null hypothesis that the model adequately describes the data (see STAR Methods for guidelines on interpreting jZj scores), showing that all models fell short of adequately describing site-specific amino acid preferences.
However, PPAs also agreed that models varied greatly in their ability to describe replacement-pattern heterogeneity and indicated that CAT-GTR+G, the model of  [6] , describes site-specific amino acid preferences much better than any other model. This result was not unexpected  [26]  given that CAT-GTR+G was the only considered model that can explicitly accommodate this form of heterogeneity (see STAR Methods). The relative ranking of the remaining models changed depending on the statistic implemented in the PPA tests (Tables  1  and S1 ). However, for both datasets, PPA-DIV  [26]  identifies GTR+G (the model of  [8] ) as a distant second best (Table  1 ), followed by either the PF-scheme  [10]  or LG+G  [11]  and WAG+G  [9]. PPAs indicate that PF-schemes (strongly advocated by  [10, 28, 29] ) do not model site-specific amino acid preferences much better than LG+G, with CAT-GTR+G invariably describing the data better.
All recoded datasets obtained lower Z scores than the original amino acid datasets for both PPA-MAX and PPA-MEAN (Tables  1  and S1 ), indicating that recoding is effective at reducing lineage-specific compositional heterogeneity. In the case of site-specific heterogeneity, Z scores lower than those   S3  for the definition of the PF-schemes. obtained for the amino acid datasets are invariably observed only for GTR+G (Table  1  and S1 ). Under CAT-GTR+G, only PPA-DIV invariably recovered reduced Z scores from the recoded datasets (Table  1 ), with Z scores from PPA-CONV and PPA-VAR being similar in analyses performed with and without recoding (Table  S1 ). When considered together, these results indicate that only the combined use of data recoding and CAT-GTR+G minimizes inadequacy in the modeling of compositional heterogeneity across both sites and lineages.