dataesr/scientific-paragraphs-categorization huggingface dataset:
Bos mutus isolate yakQH1 genome, https://www. ncbi. nlm. nih. gov/nuccore/AGSK01000000; water buffalo reference genome UMD_CASPUR_ WB_2. 0, https://www. ncbi. nlm. nih. gov/assembly/GCA_000471725. 1/#/st; LASTZ, http://www. bx. psu. edu/miller_lab/dist/README. lastz-1. 02. 00/README. lastz-1. 02. 00a. html; QualityTrim, https://bitbucket. org/arobinson/qualitytrim; hapFLK software, https://forge-dga. jouy. inra. fr/projects/hapflk; human GWAS catalog, https://www. ebi. ac. uk/gwas/. Corresponding author(s): Ben Hayes nature research | life sciences reporting summary Association, http://www. galloway. asn. au/ miniaturegalloways. html; Belted Galloway Society, http://www. beltie. org/breed- surveys-data. php; Miniature Herefords, http://www. miniatureherefords. org. au/; Bos_taurus_UMD_3. 1/bosTau6 assembly of the cow genome, http://hgdownload.
Under a more general genome model, that allows for changes in copy numbers of genes (e. g. insertions, deletions, and duplications), the spine of our results still holds due to the convenient representation of missing genes as ghost adjacencies in an Eulerian 2-edge-colored multi-graph  [15]  (see Fig. 1 ). All of our results hold for pairs of genomes with non-duplicated genes, but unequal gene content. Indeed, a breakpoint graph (i. e. graph with limited degree for most nodes) can still represent the pair of genomes in this case. Caprara proved that MAECD is NP-Hard for Eulerian 2-edge-colored multi-graphs where each vertex is incident to at most two gray and two black edges (which is the case when there are two copies of each gene)  [22]. We present a simple integer linear program (or ILP) that solves ϕ-MCPS for these types of graphs, given a method to solve ϕ-MCPS on a circle.
450 of them could be implemented in our modeling approach. Our results confirm that natural sediment yields are clearly above the world average, with the greatest values for Morocco (506 t km -2 year -1 ), followed by Algeria (328 t km -2 year -1 ) and by Tunisia (250 t km -2 year -1 ). Including dams in the downstream routing of suspended sediments to the sea reduces the natural sediment flux in the entire MMB to the sea from 96 to 36 Mt km -1 year -1 , which corresponds to an average sediment retention of 62%. Trapping rates are highest in the Tunisian basin part, with about 72%, followed by the Algerian (63%) and the Moroccan basin parts (55%). Small reservoirs and hillside reservoirs are quantitatively important in the interception of these sediments compared to large reservoirs. If we only considered the dams included in the widely used Global Reservoir and Dam (GRanD) database which comprises mainly large reservoirs sediment retention behind dams would account for 36% of the natural suspended particulate matter (SPM) flux to the Mediterranean Sea.
The impact of reservoir construction on riverine sediment fluxes to the Mediterranean Sea was examined in a recent study  [7]. By combining spatially explicit modeling of natural sediment and water fluxes with the precise locations and properties of dams as they are documented in the GRanD database (Global Reservoir and Dam,  [8] ), they calculated that about 35% of the natural sediment discharges should nowadays be retained in reservoirs. This underlines their significant impact on the basin-wide budgets, although the percentage is comparable to what has been estimated at the global scale  [9, 10] , which questions the idea of the Mediterranean being a hot spot area for anthropogenic perturbations. Nevertheless, Mediterranean rivers are often small in size  [11]  and GRanD is far from being complete in this area.
We therefore attempted to go a step further and to examine the role of the "missing dams". We selected the Maghreb region of Northern Africa as a regional focus and intended to assemble a much more complete database from various sources, also including small-scale and hillside reservoirs. This allowed us to produce for the first time a detailed regional assessment on dams in the area, which is not only useful for general budget considerations, but which may also serve as a management tool for the evaluation of potential environmental risks in terms of coastal erosion and/ or reservoir silting. Our method may further be applied to other regions of the Mediterranean drainage basin in order to put together a more complete picture of human perturbations in this key environment for global change studies.
Large dams serve in general to store drinking water and for industrial and electrical energy production. In addition, many small dams and hillside reservoirs exist and serve to mitigate floods, contribute to water table recharge and improve water storage for irrigation and livestock watering and domestic uses. A detailed census of the different reservoir categories is nevertheless difficult. The only existing database is the GRanD database  [8] , which includes 53 dams in the Maghreb basin with a total storage capacity of about 6. 5 km 3  [7]. Anthropogenic degradation in MMB is very high, increasing erosion and enhancing early silting of reservoirs in Morocco  [20, 26] , in Algeria  [24, 27]  and in Tunisia  [28]. According to  [25] , who monitored and evaluated silting in the 230 dams of North Africa, the average capacity loss in these dams can be estimated to about 0.
Quantification of specific riverine suspended matter (SPM) and specific water (Q) fluxes was obtained through empirical modeling relating these parameters at the grid point scale to a series of environmental factors that are considered to represent their major controls. The corresponding multi-regression models could be established on the basis of the average long-term basin characteristics from a large data set of 126 (Q) and 80 (SPM) Mediterranean rivers  [7]. In short, specific water discharge (Q) was produced as a combination of annual precipitation, potential evapotranspiration, seasonal variability of precipitation (all climatic parameters were taken from  [31] ) and grid point slope  [30] , whereas specific SPM fluxes could be linked to specific water discharge, grid point slope, the percentage of sedimentary rocks in altitudes above 600 m  [32]  and the percentage of erodible land use classes (agriculture, grasslands and bare soils-  [33] ).
Inventory of Dams We started our inventory with the geo-referenced dams included in the GRanD database of  [8]. It contains 53 dams in the Maghreb basin (6 for Morocco, 28 for Algeria and 19 for Tunisia). To this, 48 dams could be added from repertoires from various national agencies in the Maghreb, including 36 dams from the ANBT (National Agency for Dams and Transfers,  [36] ) in Algeria, 7 dams from the ABHL (Loukkos Hydraulic Basin Agency,  [37] ) in Morocco and 5 dams from the DGBGTH (General Directorate for Dams and Major Hydraulic Works,  [38] ) in Tunisia. Generally, these agencies provide information on storage capacity, water discharge and the year of reservoir construction, but no digitized files in a GIS format for the location of the dams. Manual location of these dams from Google satellite maps was necessary (consulted in June 2016).
A total of 101 dams were consequently assembled (Table  1 ). The majority of these dams were constructed in recent decades. More than 30% were built after 2000, and 60% between 1950 and 2000. Less than 10% were erected over the period from 1860 to 1950. In a second step, we enlarged our database through manual digitization of additional reservoirs from Google satellite maps. In this approach, we mainly focused on the inclusion of small and hillside reservoirs, but also a few large reservoirs previously not identified could be added that way. To facilitate our work, we first imported our river network map and the previously collected dams (GRanD and Maghreb agencies) in order to avoid the re-digitization of existing reservoirs. The maps were imported in kml-format at a resolution of 5 arc-minutes and consisted of polygons, vectors and points.
Reservoirs are generally characterized by their longitudinal shapes and the existence of a dike downstream the flow direction, whereas natural lakes are usually characterized by a round shape and the absence of the dike. Hillside reservoirs are not implemented on major river courses and typically found in agricultural fields. For the manually-digitized reservoirs we had no information on the storage capacity. In order to produce an estimate for this parameter too, we calculated a linear regression between the storage capacity (CAP) and the surface (S) of the reservoir. In fact, two regression models were established, one for the GRanD database reservoirs (Figure  2A  Linear relationships between storage capacity and reservoir surface (A) using the Mediterranean reservoirs of the GRanD database  [8]  and (B) using the hillside reservoir database of  [39].
Both regressions were forced to pass through the origin (i. e. ; no intercept). Equation (  1 ) is based on information for all Mediterranean reservoirs in the GRanD database, and Equation (2) on the compilation of hillside reservoirs in the Tizi-Ouzou region (Algeria) produced by  [39] , which is the only available study in the area (Table  2 ). It should be noted that the database produced by  [39]  was only used to determine Equation (  2 ). Since the database does not include the geographic coordinates of the reservoirs, it is difficult to differentiate them from other newly digitalized hillside reservoirs. To avoid confusion and duplications, we kept this database only to determine Equation  (2). The storage capacity of the reservoirs can be strongly influenced by their morphology, and notably by the slope of the reservoir surface.
The slope of hillside reservoirs is generally very low compared to that of the dams, in particular large dams. Two reservoirs that have the same surface but a different slope can have a very different storage capacity; this is the reason why we kept two different regression models (Figure  2A, B ). Overall, we were able to digitize the surface of 569 additional reservoirs, mostly consisting of small dams and hillside reservoirs. This expanded our database to 670 reservoirs with areas larger than one hectare. On the basis of Equations (  1 ) and (2), we then calculated the storage capacity of these additional reservoirs (i. e. , when the newly digitized reservoirs were identified as hillside reservoirs, we applied Equation (2); elsewhere, we used Equation  (1). All the reservoirs were then classified according to their storage capacities into seven classes: C 1 (CAP > 200 Mm 3 ), C 2 (100 < CAP < 200 Mm 3 ), C 3 (50 < CAP < 100 Mm 3 ), C 4 (20 < CAP < 50 Mm 3 ), C 5 (5 < CAP < 20 Mm 3 ), C 6 (1 < CAP <5 Mm 3 ), and C 7 (CAP < 1 Mm 3 ).
Interestingly, when only considering the dams included in the GRanD database (as was the case in the study by  [7] ), sediment retention behind the dams accounts for only 36% of the natural SPM flux to the Mediterranean Sea. The present results therefore demonstrate that small reservoirs are quantitatively crucial for the interception of sediments in river basins compared to large reservoirs. The majority of the MMB reservoirs (~85%) in the GRanD database have a storage capacity greater than or equal to 20 Mm 3. If we consider this value as an appropriate threshold between small and large reservoirs, we can make the following observations: small reservoirs have very limited capacities (cumulative capacity of 1. 1 versus 12. 8 km 3 for large dams), intercept annually less water discharge (cumulative interception of 8.
It varies from less than 10 years to 143 × 10 3 years, with a median of 150 years and lower and upper quartiles of 50 and 550 years. About 35% of these reservoirs have a LS of less than 50 years in relation to reduced storage capacities (90% have a storage capacity of less than 2. 5 Mm 3 ). The majority is in the Algerian basin part (62%), followed by the Tunisian (28%) and the Moroccan basin parts (10%). On the other side, 25% of all reservoirs have LS between 50 and 200 years, and 23% between 200 and 1000 years. Only for 17% of the reservoirs LS exceeds 1000 years. For the 101 dams in our database which were collected from the GRanD database and the national agencies of the Maghreb countries (see Section 2. 3), the precise construction dates were known (Table  1 ) and therefore we could predict their filling.
Nevertheless, the values we produced fit very well with other literature estimates, both in terms of the general sediment budgets and with regard to the measured reservoir silting rates, which makes us confident on their reliability. Our results clearly demonstrate that, because of damming, nowadays only slightly more than one third of the natural river sediment fluxes reach the coastal Mediterranean waters of the Maghreb. Contrary to other hot spot areas of river damming, such as the East, Southern and Southeast Asian region where most of the sediment retention is related to the existence of mega dams  [46] , a considerable part of the sediment retention is here related to the existence of small dams and hillside reservoirs. Consequently, when only considering the large dams as those documented in the GRanD database, sediment retention is strongly underestimated.
Using the latter database alone results in an overall sediment retention of about 36%, while the supplementary dams and hillside reservoirs we collected increase this value to 62%. Large and small reservoirs in regulated basins trap 30% and 23% of the global riverine sediment fluxes according to  [9]. Our retention estimates are therefore substantially higher than those given at the global scale, but the differences are not as striking as one may expect. In the  [9]  estimate, large reservoirs correspond to CAP > 500 Mm 3 , which are rare in the MMB, and the 62% retention we calculated should rather be compared to the 23% retention that  [9]  found for small dams. The major difference is that small dams have shorter life spans than large dams, and the economic exploitation of these structures is limited in time.
7 9 ANBT 100 Zardezaz Algeria 1973 31 1. 3 24 ANBT 101 Zit Emba Algeria 2002 117 7. 7 15 ANBT Note: NB: number of dams; CAP: reservoir storage capacity; S: reservoir surface; H: height of dike; ABHL: Loukkos Hydraulic Basin Agency, Morocco; ANBT: National Agency for Dams and Transfers, Algeria; DGBGTH: General Directorate for Dams and Major Hydraulic Works, Tunisia; GRanD: Global Reservoir and Dam database.
Discussion We report an unprecedented case of convergent evolution, with five parallel recombination suppression events independently linking PR and HD mating-type loci in anther-smut fungi and generating megabase-long supergenes beneficial under selfing mating systems. We also reveal the convergent evolution of young evolutionary strata in multiple closely related species. Furthermore, our unique dataset suggests a progression of genomic decay in non-recombining mating-type chromosomes, with older regions of suppressed recombination displaying higher levels of gene loss and TE accumulation. The convergent evolution of megabase-long supergenes in Microbotryum mating-type chromosomes, through distinct genomic rearrangements, has repeatedly led to the beneficial cosegregation of different mating-type functions (pheromones and pheromone receptors controlling pre-mating compatibility encoded by the PR locus, and the homeodomain proteins controlling post-mating compatibility encoded by the HD locus).
Assembly and annotation. Genome assemblies were generated with the wgs-8. 2 version of the PBcR assembler  49  with the following parameters: genomeSize = 30000000, assembleCoverage = 50. Assemblies were polished with quiver algorithm of smrtanalysis suite 2. 3. 0 (https://github. com/PacificBiosciences/ GenomicConsensus). A summary of raw data and assembly statistics for matingtype chromosomes is reported in Supplementary Table  1. The protein-coding gene models were predicted with EuGene 50 , trained for Microbotryum. Similarities to the fungal subset of the uniprot database plus the M. lychnidis-dioicae Lamole proteome  19  were integrated into EuGene for the prediction of gene models. Mating-type chromosomes were identified by: (1) identifying the contigs carrying the PR and HD mating-type genes, (2) blasting the a 1 against the a 2 haploid genomes and visualizing the output using Circos 51 to identify contigs lacking collinearity, (3) blasting the haploid genomes against the completely assembled mating-type chromosomes of M.
For d S plots, we identified alleles using orthologous groups with a single sequence in each haploid genome for a given species. We used MUSCLE  53  embedded in TranslatorX  54  to align the two alleles per gene per species. Synonymous divergence and its standard error were estimated with the yn00 program of the PAML package  57. Figures and statistical tests. Supplementary Figs. 4-8 were prepared using Circos  51. We analyzed gene order after removing TEs to identify larger blocks of synteny. We identified syntenic blocks by searching all one-to-one gene correspondences between pairs of haploid genomes based on the orthologous groups reconstruction (see above). Statistical tests (Student's t-test, analysis of variance, and Wilcoxon rank tests) were performed using JMP v7 (SAS Institute). Date estimates for recombination suppression and genealogies.
For the second and following rounds, PRICE considers the extended contigs as the reference to restart the process of picking, mapping reads, assembling the unmapped reads, and extending the reference contigs. We analyzed the presence of tandem repeats in each of the 10 assembly cycle outputs using the following parameters in a TRF wrapper perl script  62  : match = 1, mismatch = 1, indel = 2, probability of match = 80, probability of indel = 5, min score = 200, max period = 2000. We performed these steps 15 times, picking randomly 500,000 input reads and 1,000,000 reads for the seed file. The repeats detected in the Illumina genomes were blasted against the corresponding high-quality genomes. We identified the putative centromeres in M. lagerheimii as the most gene-poor and repeat-rich regions and with the most abundant tandem repeats.
We computed the number of gene losses across the PARs and the evolutionary strata that were not too rearranged to be delimited. Polymorphism data and analyses. To rule out high levels of polymorphism as the cause for the observed high d S values in the youngest strata of the Microbotryum mating-type chromosomes, we assessed the level of polymorphism and a 1 -a 2 allelic segregation in gene genealogies. When genes are linked to mating-type loci, alleles associated with the a 1 versus a 2 alleles accumulate differences until completely segregating according to mating-type allele rather than according to strain in gene genealogies. To test for this pattern, we re-sequenced multiple strains of M. v. paradoxa and of M. v. caroliniana (4 strains for M. v. paradoxa and 11 strains for M. v. caroliniana, Supplementary Table  3 , strains collected before 2014 and thus not falling under the Nagoya protocol) from a 1 and a 2 haploid sporidia isolated from single tetrads using micromanipulation.
For M. v. caroliniana, we also used spores collected from S. virginica, as this plant species is parasitized by the same anther-smut species as the one parasitizing S. caroliniana. Haploid sporidia were cultured on potato dextro agar and DNA was extracted using the Nucleospin Soil Kit (Macherey-Nagel, Germany). Haploid genomes of identified mating type were sequenced (Illumina paired-end sequencing with 46× mean coverage). After trimming and filtering for quality (length >50; quality base >10) using cutadapt  66  , reads were mapped against the high-quality PacBio reference genome of the same mating type and species. We used bowtie2  67  in the "very-sensitivelocal" mode with the default parameters. Mapped reads were filtered for PCR duplicates using picard-tools (http://broadinstitute. github.
io/picard) and realigned on the PacBio reference genome using GATK  68. Single-nucleotide polymorphisms (SNPs) were called with GATK HaplotypeCaller, which provide a gVCF per strain. For each strain, we filtered on a quality above 100 and other parameters (QD, FS, MQ, MQRankSum and ReadPosRankSum) for which the threshold was the fifth percentile (95th for FS parameter). The subsequent SNPs having >90% of missing data among strains were excluded from the dataset. We generated pseudo-sequences per species and mating type by substituting reference nucleotides by their variants in the reference sequence, using the predicted CDS of the PacBio reference genome and the VCF file produced by GATK GenotypeVCF that combine gVCF into one file. We then computed the θπ statistic of diversity with EggLib version 2  69  for each species and mating type.
We generated alignments of a 1 and a 2 alleles per species and per predicted coding sequence and with a 1 and a 2 alleles from the predicted orthologous genes of the M. lagerheimii reference genome. Codon-based alignments were performed for each single-copy gene in the mating-type chromosome, for the various evolutionary strata, and the pseudo-autosomal regions, as well as for a wellassembled autosome, using TranslatorX  54. Trees were computed with the RAxML rapid bootstrap mode (-f a -m GTRGAMMA -# 100) and plotted and rooted on the M. lagerheimii a 1 strain using the R ape package  70. Clustering of mating-type-associated alleles in genealogies. For genes linked to the mating type loci, a 1 -and a 2 -associated alleles will differentiate to the point of forming two distinct clades in gene genealogies.
Index values close to 1 mean a 1 and a 2 alleles were completely separated in the tree (i. e. , permutations always brought a 1and a 2 -associated alleles closer than they actually were). Conversely, an index value close to 0 meant that the a 1 -and a 2 -associated alleles were all forming pairs ("cherries") in the tree (i. e. , random permutations always increased their distances). Nodes with low bootstrap support were collapsed prior to the analysis. A custom script for the computation of this index was written in R using the ape package for tree manipulations  70  and is available in Supplementary Note 1.
Acknowledgements This work was supported by the  ERC  starting grant GenomeFun  309403  to T. G. , the Louis D. foundation award to T. G. , the  NSF   DEB-1115765  and  NIH R15GM119092  grants to M. E. H. , the  Marie Curie European  grant  701646  to S. B. , a postdoctoral fellowship ( SFRH/BPD/79198/2011 ) from  Fundação para a Ciência e a Tecnologia, Portugal  to M. A. C, and  DAAD , the  Marie Curie European  grant  PRESTIGE-2016-4-0013  to F. H. and  Campus France  ( PPP 57211753 ) to T. G. and D. B. , and the  Montana State University Agricultural Experiment Station. F. H. received the  Young Biological Researcher Prize  from the  Fondation des Treilles , created by  Anne Gruner Schlumberger , which supports research in Science and Art (http://www. les-treilles. com). We thank  Cécile Fairhead  for help with DNA extraction.
Data availability. The assemblies are available from EMBL or GenBank: PRJEB12080 GCA_900015485 Microbotryum violaceum s. l. from Silene paradoxa (1252) a 2 PRJEB12080 GCA_900015495 Microbotryum violaceum s. l. from Silene paradoxa (1252) a 1 PRJEB12080 GCA_900015415 Microbotryum scabiosae from Knautia arvensis (1118) a 2 PRJEB12080 GCA_900008855 Microbotryum scabiosae from Knautia arvensis (1118) a 1 PRJEB12080 GCA_900014955 Microbotryum violaceum s. l. from Silene caroliniana (1250) a 2 PRJEB12080 GCA_900014965 Microbotryum violaceum s. l. from Silene caroliniana (1250) a 1 PRJEB16741 ERZ348353 Microbotryum silenes-acaulis from Silene acaulis (1248) a 1 PRJEB16741 ERZ348354 Microbotryum silenes-acaulis from Silene acaulis (1248) a 2 PRJEB16741 ERP018599 Illumina genomes of M. violaceum s.