dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-seq based genotyping The BYxRM yeast cross has been widely used for QTL mapping. Microarray-based genotype information of the segregants is available  19. However, deep sequencing enables a more accurate genotyping of recombinant lines. To this end we have exploited published resequencing data of the parental strains  20  together with the RNA-seq data generated here to infer the genotype of the segregants of the BYxRM cross using a method that we previously developed  18.
Genotype inferring from RNA-seq data RNA-seq data were mapped to the S. cerevisiae reference genome (SaCer3) using TopHat 2 (Ref. 38 ) with the following options: --min-intron-length 10 --min-segmentintron 10 --b2-very-sensitive --max-multihits 1 --library-type fr-secondstrand. Read group information was added, and BAM files were sorted using Picard utilities (http://broadinstitute. github. io/picard/). RNA-seq data were further processed using the GATK pipeline (version 3. 4-46-gbc02625) following the best practice guide  39. First reads containing exon-exon junction were split using the following options: -T SplitNCigarReads -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW_N_CIGAR_READS, then the variants were called using the UnifiedGenotyper at the 42,769 polymorphic sites identified in the resequencing data of the parental strains.
LC-MS data acquisition The peptide concentration in all samples was measured on a NanoDrop at OD280 and normalized to allow injection ~1 µg of material into the mass spectrometer. The samples were randomized and then either injected individually for SWATH-MS acquisition or pooled and injected in technical duplicates for shotgun acquisition. The LC-MS acquisitions were performed on an AB Sciex 5600 TripleTOF coupled to a NanoLC2Dplus HPLC system. The liquid chromatographic separation and mass spectrometric acquisition parameters were essentially as described earlier  43. The peptide separation was performed on a 75 µm diameter PicoTip/PicoFrit emitter packed with 20 cm of Magic C18 AQ 3 resin using a 2-35% buffer B at 300 nl/min (buffer A: 2% acetonitrile, 0. 1% formic acid; buffer B: 98% acetonitrile, 0.
LC-MS database searching The shotgun data was searched with Sorcerer-Sequest (TurboSequest v4. 0. 3rev11 running on a Sage-N Sorcerer v4. 0. 4) and Mascot (version 2. 3. 0) against the SGD database (release 03 Feb. 2011, containing 6,750 yeast protein entries, concatenated with 6,750 corresponding "tryptic peptide pseudo-reverse" decoy protein sequences). For the search, we allowed for semi-tryptic peptides and up to two missed cleavages per peptide. For the non-enriched samples, we used carbamidomethylation as a fixed modification on cysteine residues and oxidation as variable modification on methionine residues. , including the specific splitting of the consensus spectra when MS/MS scans identifying the same peptide sequence were recorded more than 2 minutes apart, also described earlier  44.
1. 10)  46  and pyProphet  47. The precursors were then realigned across runs using TRIC  48. The two resulting SWATH identification result files contained a total of 18,273 identified peptides (thereof 16,922 proteotypic peptides matching a total of 2,940 proteins) for the non-enriched datasets; in the phospho-enriched datasets there were 13,748 identified peptides (thereof 12,412 phosphopeptides) or 13,218 proteotypic peptides (thereof 12,139 proteotypic phosphopeptides matching a total of 2,247 unique phosphoproteins). After alignment, we used a set of in-house scripts to compare the chromatographic elution profiles of the various isobaric phosphopeptide isoforms matching a same delocalized peptide form (peptide sequence + number of phosphorylations) within each single run and to eventually group those co-eluting phosphopeptide assays into the proper corresponding number of phospho-peak clusters (labeled _cluster0 to _clusterN, respectively).
QTL hotspot detection To formally identify hotspots in our dataset, the genome was divided into 40-kb bins (293 bins, bins at chromosome extremities could be bigger). As proposed before  15  , if the linkages were randomly distributed across the genome, the number of linkages in each bin would be expected to follow a Poisson distribution with the mean of Ν Ν ⁄. We use this distribution to estimate the highest number of linkages that a bin could contain at a probability lower than 0. 01. These numbers were 30 for eQTL, 13 for pQTL, 9 for ptQTL, 11 for phQTL, and 4 for phResQTL. Bins with a sufficient number of QTLs at a given molecular layer were considered as QTL-hotspots. Consecutive bins on the same chromosome with enough QTLs were combined to single hotspots.
Broad-sense heritability estimation Broad-sense heritability estimates were computed based on replicate measurements of some strains, as described elsewhere  20. We used six different segregants with three replicates each, as well as the parents with six (BY) and eight (RM) replicates each. In short, the 'lmer' function from the lme4 R package was used to create a linear mixedeffects model with the phenotype as the response and the segregant labels as random effects  57. The variance components σ²G (the variance due to genetic effects, i. e. , different segregants), and σ 2 E, (the error variance), were extracted, and broad-sense heritability was calculated as H² = σ²G / (σ²G+σ²E). Standard errors were calculated using the deleteone jackknife procedure, as proposed previously  20. Random distributions of heritability estimates for each molecular layer were generated by permuting the strain labels and compared to the real distributions using a Mann-Whitney test.
All the MS data files were visually inspected and curated at this stage for low total ion chromatogram intensities, and the corresponding samples re-injected, if possible. This resulted in a final set of 179 SWATH data files for non-enriched and 179 SWATH data files for phospho-enriched samples (Supplementary Data S1) that were used for data extraction. Similarly, 40 DDA files for non-enriched and 30 DDA files for phosphoenriched samples were selected for database searching and library generation.
Acknowledgements JG and CS received funding from the  German Science Foundation  ( DFG CRC 680 & CRC 1310 ). MCZ and CS received funding from the  German Federal Ministry for Science and Education (BMBF)  as part of the e: Med program  (grant no. 01ZX1406 ). OTS received funding from the  German Academic Exchange Service (DAAD) , the  Swiss National Science Foundation , and the  Human Frontiers Science Program. IB was supported by the  Swiss National Science Foundation  (grant no. 31003A_166435 ). We thank  Dr. Rachel Brem  for providing the strains of the BY/RM cross.
A number of LMICs are in the process of setting up national dialysis registries; the ISN is supporting these efforts through the SharE-RR project (https://www. theisn. org/advocacy/share-rr), which will develop a resource that supports establishment of a renal registry to support advocacy, quality assurance, and research. This project will facilitate sharing of a data dictionary, registry policies, procedures, governance structures, databases, data sets, technology platforms, files, and consent processes. Surveys of existing registries will inform the establishment of a minimum data set to permit benchmarking between registries and to monitor the quality of care and outcomes. A minimum data set will (i) help health care professionals and people with ESKD make better-informed treatment decisions; and (ii) capture serious health-related suffering in people with ESKD for inclusion in global health reports.
0] [31] [32] [33] [34]  For instance, the huge gap in access to KRT between the private and public sectors demonstrated by the South Africa Renal Registry  35  is being used to advocate for increased access to KRT in the public sector in South Africa. These experiences should be drawn upon to help establish successful registries in other resource-limited countries. Another possible strategy is incorporating data from resource-limited countries into existing registries, as has happened on occasion with North African countries contributing to the European Renal Association -European Dialysis and Transplant Association (ERA-EDTA) Registry  30, 33  and the United States Renal Data System (USRDS) registry. 12 The increasing number of countries included in the Latin American Registry of Dialysis and Transplantation (currently collecting information on 20 countries of the region) is another example of the incorporation of resource-limited countries into existing registries.
34 Pacific Island countries (e. g. , Fiji) could leverage the infrastructure and collaborative expertise of the Australian and New Zealand Dialysis and Transplant (ANZDATA) Registry. Recently, agreement was reached between the African Association of Nephrology and the African Paediatric Nephrology Association to establish the African Renal Registry, which will use the shared web-based technology platform and common data dictionary of the South African Renal Registry. 30 egistry data could be linked with geographic information systems to identify ESKD "hot spots" and areas where there are major mismatches between KRT supply and demand. 36 egistry data should be further enhanced by linkages with country population data, death registries, and other mortality data sets (e. g. Global Burden of Disease data), where possible.
The scope of data that should be collected and collated is more than can be captured routinely by registries alone. Multiple sources of data, some of which can be linked to registry data, can be obtained during care of individual patients and from health systems. Examples include routinely collected clinical data and data obtained from dialysis machines and other equipment. Such data can be used to evaluate ESKD care at multiple levels, including that of the individual patient, kidney unit, and geographic region. Countries are responding to the challenge of kidney disease and/or failure in different ways,  39  with variable provision of the components of a kidney care strategy (prevention, supportive care, conservative care, kidney transplantation, and an appropriate mix of dialysis modalities).
To be maximally useful to decision makers in LMICs, new evidence summaries (rapid reviews, systematic reviews, and economic analyses) are required that will provide updated and accessible information to support decision-making on kidney care programs. In addition, data that facilitate application of general principles in real-world settings would be beneficial. For example, identifying the threshold cost for PD fluids at which PD becomes more expensive than HD would help to identify settings in which principle 4 (Table  5 ) may not apply. Similarly, local information on the incremental cost of PD compared with HD would help decision makers to understand the net financial costs of a "free choice" strategy to dialysis modality selection. Therefore, there is an urgent need to produce and disseminate these summaries in partnership with end users.
This information should be supplemented by a comprehensive review of published and gray literature. Data pertaining to ESKD care financing in LMICs can be extracted from the ISN Kidney Collection (https://www. theisn. org/news/item/3282-presenting-the-kidney-collection-survey). Compare impact of different models of provision and funding: public-private partnerships. Governments may enter into agreements with the private sector to finance, build, and operate dialysis units for an agreed-upon return on investment. The rationale for using public-private partnerships (PPPs) emerges from the gaps in dialysis delivery and private investment in areas where there are limited public providers. Many LMICs that are developing dialysis as part of their UHC agenda are doing so through PPPs. PPP offers innovation opportunities, such as implementation of uniform protocols across a large number of units, reduced costs through strategic purchasing, better outcomes and service quality tracking through information technology services, and tying service delivery with requirements for workforce capacity-building and ancillary programs.
ACKNOWLEDGMENTS This manuscript emerged as a product of the Global Kidney Health Summit held in Sharjah, UAE in March 2018. In addition to the  International Society of Nephrology , support of the Summit was provided through unrestricted grants from  Baxter Healthcare and B. Braun Avitum. We thank  Drs. John Knight  and  Dieter Frei  for their review and comments during development of the manuscript. We also thank  Mohamed Osman  and  Feng Ye  for their assistance with figures and analyses.
Measurements and data collection Measurements were carried out in March and April 2017 for all sites. To resolve the problem of replications both in space and in time and given the importance of the area of the study sites both within the rested sites and the open grazing control, a total of 28 experimental A case study from Southern Tunisia plots within the different representative plant communities were established for collecting data on plant cover attributes and biomass production. Within each experimental plot, three random transects of 50 m long each were established in the different representative plant communities of the target rangeland site and used to determine plant cover parameters according to the points-quadrats method described by  Daget and Poissonet (1971). This design permitted an appreciation of the potential of regeneration and the persistence of plant species by monitoring the evolution of several descriptors (global plant cover, specific frequencies, flora richness and plant density).
Plant density The data presented in Table  III  demonstrates the great variability in plant density according to the applied restoration and management mode and the original condition of the site before applying the resting technique. The highest value (4. 43 plants/m 2 ) was recorded at the site subjected to two years of controlled grazing. This confirms the common belief that adequate grazing can have a beneficial effect over the duration of the vegetative period of certain species, making the alternation of short periods of grazing with periods of vegetative rest generally more favorable than strict or long term protection  (Ouled Belgacem et al. , 2008). The next highest value was in the site subjected to only one year of rest (2. 84 plants/m 2 ). The perennial species and their variability as shown in Table  III  can be summarized as follows: species such as Gymnocarpos decander, Salsola vermiculata, Stipa lagascae and Artemisia herba-alba recorded higher densities in the protected plots than in the grazed plot.
Flagship Core 2 project ( GA-785219 ), the  Marie Sklodowska-Curie  projects  ITN  project  iSwitch  ( GA-642196 ), the  Marie-Curie IEF   MULTI2DSWITCH  ( GA-700802 ) and the  Marie-Curie IEF   STELLAR  ( GA-795615 ), the  M-ERA. NET  project  MODIGLIANI , the  Agence Nationale de la Recherche  through the  Labex projects CSC  ( ANR-10-LABX-0026 CSC ) and  NIE  ( ANR-11-LABX-0058 NIE ) within the  Investissement d'Avenir program  ( ANR-10-120 IDEX-0002-02 ), and the  International Center for Frontier Research in Chemistry (icFRC).
Assembly completeness was estimated in two ways. First, gene completeness was determined by running BUSCO v. 3. 0. 2  (Simão et al. , 2015)  using the embryophyta_odb9 database comprising 1440 single-copy plant orthologous groups (BUSCOs). Second, a pairwise comparison of k-mers present in both input reads and the assembly was performed using KAT  (Mapleson et al. , 2017)  using all whole-genome sequencing PE Illumina reads and a kmer length of 27 (Figure  S12 ). Comparison of the P. dulcis anchored assembly to the linkage map and peach v. 2. 0 a1 genome sequence The almond assembly was compared with the T9E linkage map that contains 1833 SNP markers  (Donoso et al. , 2015). Markers were mapped onto the almond pseudomolecule-based assembly using BLAST and coordinate data of both almond and peach were used as input in MapChart software  (Voorrips, 2017)  for representing graphically the comparison between the two species.
The genetic and physical distances of SNP markers from the T9E population were used for calculating the recombination rate across the pseudomolecules of the almond assembly. The peach genome sequence and annotation data were downloaded from the GDR (ftp://ftp. bioinfo. wsu. edu/species/Prunus_per sica/Prunus_persica-genome. v2. 0. a1/). Synteny between almond with peach genomes was assessed using SyMap software v. 4. 2  (Soderlund et al. , 2007)  with default parameters, except that the 'min dot' parameter was set to 25.
Annotation The P. dulcis genome assembly was annotated by combining transcript alignments, protein alignments and ab initio gene predictions. A flowchart of the annotation process is shown in Fig-  ure S13. Scripts are available at https://github. com/jesgomez/ annotation_pipeline. First, almond RNA-seq reads were downloaded from NCBI with the accession number SRR1251980 and aligned to the genome with STAR (v. 2. 5. 3a)  (Dobin et al. , 2013). Transcript models were subsequently generated using Stringtie (v. 1. 0. 4)  (Pertea et al. , 2015)  and, along with the P. persica transcriptome (annotation Pp2. 0a) and 4509 almond expressed sequence tags downloaded from NCBI on July 2015, were assembled into a non-redundant set by PASA (v. 2. 3. 3) (Haas et al. , 2008). The TransDecoder program, which is part of the PASA package, was run on the PASA assemblies to detect coding regions in the transcripts.
Second, the complete Rosaceae proteome was downloaded from Uniprot on July 2015 and aligned to the genome using Exonerate (v. 2. 4. 7)  (Slater and Birney, 2005). Third, ab initio gene predictions were performed on the repeat masked pdulcis26 assembly with three different programs: GeneID v. 1. 4 (Alioto et al. , 2018 ), Augustus v. 3. 2. 3 (Stanke et al. , 2015)  and GeneMark-ES v. 2. 3e  (Lomsadze et al. , 2014)  with and without incorporating evidence from the RNA-seq data. Finally, all the data were combined into consensus coding sequence models using EvidenceModeler-1. 1. 1 (EVM)  (Haas et al. , 2008). Additionally, untranslated regions and alternative splicing forms were annotated through two rounds of PASA annotation updates. Non-coding RNAs were annotated as follows: first, the program cmsearch v.
1. 1  (Cui et al. , 2016)  from the INFERNAL package  (Nawrocki and Eddy, 2013)  was run against the RFAM  (Nawrocki et al. , 2015)  database of RNA families (v. 12. 0). Also, tRNAscan-SE v. 1. 23 (Lowe, 1997)  was run to detect the transfer RNA genes present in the genome assembly. To annotate long non-coding RNAs (lncRNAs) we first selected PASA assemblies that had not been included in the annotation of protein-coding genes. Those longer than 200 bp and whose length was not covered to at least 80% by a small ncRNA were incorporated into the ncRNA annotation as lncRNAs. The resulting transcripts were clustered into genes using shared splice sites or significant sequence overlap as criteria for designation as the same gene. Functional annotation. Functional annotation was performed by integrating several data sources to infer protein function based on sequence similarity to annotated sequences or/and the presence of particular domains and sequence motifs.
We used the InterPro  (Hunter et al. , 2012) , KEGG  (Kanehisa et al. , 2012) , signalP  (Petersen et al. , 2011) and NCBI CDsearch (Marchler-Bauer et al. , 2011)  databases. InterProScan v. 5. 19-58  (Zdobnov and Apweiler, 2012)  was used to scan though all available InterPro databases, including PANTHER, Pfam, TIGRFAM, HAMAP and SUPERFAMILY. Initial sequence similarity search was determined using BLASTP v. 2. 6. 0 + against the NCBI non-redundant (NR) collection of protein sequences (release 2018-08). KEGG orthology (KO) groups were assigned by the KEGG Automatic Annotation Server (KAAS)  (Moriya et al. , 2007)  using the bi-directional best hit (BBH) method against a representative gene set from 27 different species, which includes a core set of species for gene annotation and additional plant species from the Rosaceae family.
The KO identifiers were then used to retrieve the relevant KEGG functional annotation using the KEGG REST-based API service, KEGG release v. 87. 1. To predict plant disease resistance genes, each protein was searched against a manually curated list of 'reference' R-genes with the DRAGO pipeline  (Sanseverino et al. , 2013). For each hit, classes were assigned based on combination of specific domains, such as TIR, nucleotide-binding site (NBS), leucine-rich region (LRR) or coiled-coil domain. Putative transcription factor genes were predicted using the Plant Transcriptional Factor database  (Jin et al. , 2017) v. 4. 0  Annotation and analysis of transposable elements in genome assemblies. The Ilumina PE reads corresponding to the re-sequencing of the almond and peach cultivars described in section "DNA extraction, sequencing and K-mer analysis" were trimmed with SKEWER (v.
0. 2. 2, -mean-quality 25, -min 35) and aligned to their respective reference genome with BWA-aln/ sampe 1 (v. 0. 7. 5, parameters: -t 6, -n 5, -o 1, -e 3)  (Li and Durbin, 2009)  and  SAMTOOLS (v. 0. 1. 18) (Li, 2011). Bam files were later submitted to the package PINDEL (v. 0. 2. 5, parameters: -T 4, -x 5, -r false, -t false, -A 35)  (Ye et al. , 2018)  to identify deletions in samples. The TEs were annotated in P. dulcis and P. persica assemblies using TEdenovo and TEannot pipelines of the REPET package  (Quesneville et al. , 2005; Flutre et al. , 2011)  installed in the PiRATE virtual machine  (Berthelier et al. , 2018). Classification of TEdenovo consensus sequences at the order level was done with PASTEC  (Hoede et al. , 2014). Annotation of TEs for masking purposes was done using RepeatMasker (http://www.
repeatmasker. org/) with a reduced TE representative library. The TE representatives obtained using the TEdenovo pipeline library were screened for coding domains with hmmscan (HMMER 3. 1b1, http://hmmer. org/) against the PFAM database  (Finn et al. , 2016). The TE representatives containing regions potentially coding for known domains of non-TE proteins usually found in multigene families (kinases, NB-ARC, LRR, TIR) or with an N content higher than 30% or with length shorter than 200 nucleotides were discarded. Moreover, all TE representatives not © 2019 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd, The Plant Journal, (2020), 101, 455-472 categorized in one of the classical TE superfamilies (defined as 'noCat' by TEdenovo) were also removed.
A total of 661 representatives were removed from the library. The final library contained 6898 TE representatives. MITE-hunter  (Han and Wessler, 2010)  was run to detect potential MITE families. In order to complete the annotation, the potential almond MITE families were combined with the P. persica family annotation available in the PMITE database (families carrying target site duplication)  (Chen et al. , 2014). These sequences were grouped in clusters of 90% identity with cd-hit  (Fu et al. , 2012)  to remove redundancy and produce a final library of family representatives. RepeatMasker (http://www. repeatmasker. org/) was run to annotate all regions having significant similarity to MITE families, and the results were filtered to retain only fulllength elements (consensus length AE 20%).