dataesr/scientific-paragraphs-categorization huggingface dataset:
The genotypes for the other animals were then queried at those positions. If the reference allele was detected in one of the other horses, the reference nucleotide at that position was not changed, with the idea that the second allele was either undersampled in the Illumina dataset or that a second allele was identified in the Sanger or PacBio sequence data. Removal of microbial contamination: To build microbial sequence databases, all bacterial, viral, and fungal reference genomes were downloaded from RefSeq. For each of the three databases (bacteria, viruses, and fungi), the sequences were first masked with DustMasker 49. Kraken v1. 0 50 was used to generate k-mers (k = 32) and to search the EquCab3 contigs for exact matches. Contigs with at least one exact 32-mer match were considered microbial contaminants and removed from the reference sequence.
A total of 41 contigs were removed in this way. Removal of small contigs: All scaffolds smaller than 3000 bases in length were removed from the assembly that was submitted for annotation. The contig and scaffold N50s for what was submitted were 4. 73 and 87. 2 Mb, respectively. Phasing with 10× data: The data generated for Twilight on the 10× platform described above was mapped to the reference using the longranger (version 2. 1. 3) wgs module  24. The phased variant file produced was then used to modify individual variant positions to conform to the haplotype whose allele was most common among the FAANG horses, and two other thoroughbreds described above. N50 calculation: The PBJelly (version PBSuite_15. 8. 24) utility summarizeAssembly. py was used to calculate N50 values. The default setting of 25 was used for the minimum gap setting.
This ignored any gaps sized <25 Ns. Universal ortholog analysis: For universal ortholog analysis, we used BUSCO 26 version 3. 0. 2 in protein mode with the lineage dataset mammalia_odb9 version 2016-02-13. For protein set inputs, we used the official NCBI protein sets for EquCab2. 0 (accession GCF_000002305. 2) and EquCab3. 0 (accession GCF_002863925. 1). Comparative annotation: For this analysis, a progressiveCactus 51 alignment of EquCab2 and EquCab3 was performed with pig (susScr3), cattle (bosTau8), white rhinoceros (cerSim1), elephant (loxAfr3), and human (hg38). The guide tree was (((Human:0. 164501,((Pig:0. 12,Cow:0. 16908)1:0. 02,(EquCab3:0. 0001, EquCab2:0. 0001):0. 059397,White_rhinoceros:0. 05)1:0. 060727)1:0. 032898) 1:0. 023664,Elephant:0. 155646), putting EquCab2 and EquCab3 under the same node with a branch length of 0.
0001. CAT  27  was then run using the Ensembl V89 annotation of pig as the source transcript set. No RNA-seq data were provided, so no transcript cleanup steps or comparative gene predictions were performed. Split gene analysis was performed by looking at transcripts that had multiple projections after paralog resolution and that had multiple projections whose start and stop points were within 10 bp of each other in source transcript coordinates. Read filtering and counting: Mapping locations that were not the primary mapping locations of reads were filtered with (getNotPrimaryAlignmentFlag() is false) within the mapped read (getReadUnmappedFlag() is false) count using htsjdk version 2. 12. 01  52. Ancient DNA mapping: We downloaded single-end Illumina reads produced by a previous study  17  (Supplementary Data 2, NCBI Bioproject PRJEB19970).
Acknowledgements This work was supported by  Morris Animal Foundation  Grant  D15EQ-019  and the  NRPS8 Horse Genome Coordinator Fund. E. S. R. is an  ARCS scholar. Support for C. J. F. was provided by the  National Institutes of Health (NIH)  ( 1K01OD015134  and  L40 TR001136 ). The FAANG data (C. J. F, J. L. P. , R. R. B. ) was generated with the support of the  Grayson Jockey Club Foundation. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 681605 -PEGASUS ). Alignment and assembly work for this project were performed on the  University of Louisville Cardinal Research Cluster. The authors are grateful to  Mr. Harrison Simrall  for his assistance in installing and running many of the applications used in this work.
Data availability The sequence read datasets generated during the current study are available in the NCBI SRA repository under accession SRP126689. The final assembly generated during the current study is available in the NCBI Genbank repository under accession GCA_002863925. 1. The mitochondrial sequence has been deposited into GenBank (accession number MH586816). We also provide intermediate assemblies produced during the process, a de novo assembly based solely on the PacBio data, and phased variant calls from the 10× longranger pipeline in a CyVerse Data Commons repository at https://doi. org/10. 7946/P20348  55. Data from Sarkar et al. 12  used in variant calling and genotyping are found in the Sequence Read Archive at BioSample SAMN03838869 and SAMN03838867, experiment accession numbers SRX1097022 and SRX1097495, respectively.
Discussion Mitogenomes to track the migration of malaria parasites. There are clear advantages to using the 6-kb mitogenome to track past migrations of malaria parasites. First, because the mitogenome is uniparentally inherited through the female gamete, lineages do not recombine with each other and their genealogy can be resolved by phylogenetic analysis  30. Moreover, mitochondrial SNPs appear to be evolutionarily neutral and enable an accurate description of the parasite's demographic history  14, 31. Finally, since this genome can be easily sequenced with Sanger-based or next-generation sequencing methods, several hundreds of complete mitogenomes of P. vivax and P. falciparum collected worldwide are currently available in public databases. Some limitations, however, must be considered.
2  (for P. vivax) to represent geographic regions. Maps were built using the open-access R software library rworldmap: mapping global data combined with the ggplot2 library, which are both available at http://www. R-project. org/ (R Core Team, R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2017). Therefore, P. falciparum lineages introduced by slave trade into the Americas originated from a wide variety of African locations and resulted in additional genetic contribution, as suggested by the best-supported migration model of SOA migrants crossing the Indian Ocean to coastal areas and islands (e. g. , Madagascar) of Southeast Africa (Fig. 4A ). The lack of an apparent bottleneck in South American populations of P. falciparum is somewhat surprising.
falciparum and P. vivax amplicon with reads ranging between 450 bp and 530 bp (P. falciparum) and 303 bp and 566 bp (P. vivax). The P. simium mitogenomes were amplified and sequenced with the P. vivax primer set and an additional set of five external and 10 internal primer pairs that were designed to match the only publicly available P. simium mitogenome sequence, which was derived from the Fonseca MRA-353 isolate  55  (accession number, AY722798). All fragments amplified with either primer set were sequenced. Nucleotide ambiguities were removed by further confirming some sequences on two independent PCR reactions on the same DNA template. The sequenced fragments were filtered for quality and assembled into complete mitochondrial sequences (length, 5,884 bp for P. falciparum and 5,990 bp for P.
vivax/P. simium) using the phred/phrap/consed software (http://www. phrap. org/phredphrapconsed. htm) and deposited in the GenBank database under accession numbers KY923289-KY923647. Plasmodium falciparum mitogenomes assembled from short sequence reads. We used publicly available whole-genome paired-end Illumina reads generated from P. falciparum isolates from AFR  60, 61  and SEA  60  with known geographic origin were known (total of 925 short-read data sets) that were retrieved from the European Nucleotide Archive (ENA) of the European Molecular Biology Laboratory (EMBL) (accession numbers, ERS010434-ERS010659 and ERS041968-ERS041991). Sequence reads (average size, 100 bp) that mapped onto the mitochondrial DNA of the 3D7 reference strain (accession number, AY282930) were identified using BLASTN (https://blast.
ncbi. nlm. nih. gov/Blast. cgi) and filtered using the fastq_quality_filter software of the FastX-Toolkit package (http://hannonlab. cshl. edu/fastx_toolkit/index. html); only reads with quality scores ≥15 in every nucleotide were further considered. Mitogenomes were assembled using GS Assembler software (Newbler) version 2. 7 with a minimum level of coverage of 10 × (average coverage of 200×) for each nucleotide. These sequence reads have a potential issue with mixed infections, as they come from clinical isolates that may comprise multiple genetically distinct clones. There is therefore a risk of misassembling several different mitochondrial sequences into a single hybrid mitogenome. To prevent this, we first converted heterozygote calls to the majority allele if ≥75% of the reads in that sample were the majority allele (i.
e. , when the dominant clone could be clearly defined); otherwise, the sample was excluded from further analysis. Next, all P. falciparum mitogenomes retrieved from short sequence reads were analyzed for missassembling that could simulate historical recombination events. For this purpose, we applied five non-parametric recombination detection methods (MAXCHI, CHIMAERA, GENECONV, BOOTSCAN, and SISCAN) with their default parameters; they were implemented in the RPD3 computer program  62. None of the methods revealed statistical evidence of recombination in any assembled mitogenome. Of the 925 short-read data sets retrieved from ENA, 812 (87. 8%) generated mitogenome assemblings that were used in further analyses. Sources of additional mitogenomes. We additionally analyzed 739 complete or nearly complete P.
falciparum mitogenomes deposited in the GenBank database from isolates from AFR, Americas, South Asia, SEA, and MEL (accession numbers, AY283018-AY282924; AB570434-AB570542; AB570544-AB570765, AB570767-AB570951; KJ569502-KJ569459; AJ276845-AJ276847; and KT119847-KT119883), 822 P. vivax complete or nearly complete mitogenomes from Africa, Americas, MCA, South Asia, SEA, EAS, and MEL (accession numbers, AY791517-AY791692, AY598035-AY598140, AB550270-AB550280, JN788737-JN788776, DQ396547-DQ396548, KC330370-KC330678, JQ240429-JQ240331, KF668442-KF668430, and KF668429-KF668361), and a single complete P. simium mitogenome from Brazil (accession number, AY722798). The final data set comprised 1,795 mitogenomes of P. falciparum, 931 of P. vivax, and 10 of P. simium that were aligned using MEGA version 6.
0  63  and edited by hand. Gap stripping left 5,775 sites analyzable in the P. falciparum alignment and 5,812 sites in the P. vivax/P. simium alignment. Supplementary Database 1 gives a list of all sequences analyzed in this study, along with their countries of origin and GenBank accession numbers, and Supplementary Fig. 2  shows the geographic locations of the SAM and CAM sample collection sites.
05  69  ; therefore, Fu's F s test results were considered significant if they were associated with a P value < 0. 02. We also used Arlequin 3. 5 software to estimate the Wright's fixation index F ST , a measure of divergence between populations due to genetic structure  70. Significance was evaluated using one-sided permutation tests with 1,000 simulations. The following regional populations were considered in F ST calculations: AFR, SAM, CAM (including Mexico in P. vivax analyses; CAM), MCA; only for P. vivax, SOA, SEA, EAS (only for P. vivax), and MEL. Phylogeny and historical demography. Bayesian phylogenetic analysis was carried out separately for P. falciparum and P. vivax/P. simium using MrBayes version 3. 2. 1  71  with two runs of four chains each, three heated and one cold, for seven million (P.
vivax) or 10 million generations (P. falciparum). The trees were drawn using Dendroscope version 3. 4 software  72  , and a color code was used to identify the geographic origin of parasites: AFR (red), SAM (dark blue), CAM (light blue), Atlantic Forest from southeast Brazil (ATL [yellow]; only for P. vivax/P. simium); MCA ([brown]; only for P. vivax), SOA (orange), SEA (green), EAS ([dark purple]; only for P. vivax), and MEL (pink). Median-joining phylogenies  73  were generated using Network version 4. 6 (Fluxus Technologies, http://www. fluxu-engeneering. com) with the default parameters and transversions weighted twice as much as transitions. This analysis aimed to reconstruct global haplotype networks of the entire sets of P. falciparum and P. vivax/P. simium mitogenomes, and the same color code described above was used to show the geographic origins of samples.
falciparum; Brazil, Colombia, and Peru for P. vivax). We calculated the sum of square deviations (SDD) and the raggedness index (R) to compare observed mismatch distributions with those expected under a sudden demographic expansion model  74. These analyses were carried out using the pegas package of R software version 3. 3. 0 with 1,000 pseudoreplicates. Next, we used the Markov chain Monte Carlo (MCMC) method implemented in BEAST software version 1. 7. 2 75 to fit Bayesian skyline coalescent models that allowed us to track changes in N e over time and estimate the time TMRCA of mitochondrial lineages. Separate analyses were run for the global population of each species and for the following regional populations: AFR, SAM (comprising ATL), CAM (only for P. vivax), MCA (only for P. vivax), SOA, SEA, EAS (only for P.
8 × 10 -9 substitutions per site per year for mammalian malaria parasites  78. We therefore note that parasite lineages currently found in the Americas may be up to four times older than estimated here, depending on the molecular clock used. BEAST outputs were visualized with Tracer version 1. 5 software  79. We used TreeAnnotator version 2. 1. 2  79 to obtain consensus trees derived from the skyline analyses after discarding the first 20,000 trees as burn-in. TMRCA estimates (with 95% HPD intervals) were obtained for the branches with a posterior probability >0. 70 in the consensus trees. The topology of these consensus trees was remarkably similar to that of the Bayesian phylogenetic trees generated by MrBayes. Parasite migration models. MIGRATE-N version 3. 6. 11 software  29  was used to compare parasite migration models and make inferences regarding the sources of malaria parasites currently found in the Americas.
Acknowledgements Research was supported by research grants from the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq , http://www. cnpq. br/), Brazil  (grant number,  590106/2011-2  to M. U. F. ),  Brazil-Swiss Joint Research Programme  (http://cooperation. epfl. ch/bresil;grant number,  0112-07  to I. F. ),  Fundação de Amparo à Pesquisa do Estado de São Paulo  (FAPESP; http://www. fapesp. br/en/), Brazil  ( 05/56055-1  to R. S. M. and  FAPESP 02/03869-3  to A. M. R. C. D. ), and the  National Institute of Allergy and Infectious Diseases (NIAID) ,  National Institutes of Health (NIH) ,  USA  ( International Centers of Excellence in Malaria Research [ICEMR] program ; https://www. niaid. nih. gov/research/amazonian-icemr,grant  U19 AI089681  to  Joseph M. Vinetz ,  University of California, San Diego, CA ).
9 ± 0. 05, 4. 9 ± 0. 05, 5. 0 ± 0. 04, 5. 0 ± 0. 04}, μk = {0. 045 ± 0. 001, 0. 037 ± 5×10 -4 , 0. 035 ± 0. 004, 0. 038 ± 1×10 -4 }, m = {-0. 012, -0. 008, -0. 008, -0. 009}, R 2 = {0. 33,0. 42,0. 54,0. 59}. Source data are available from https://figshare. com/s/ b14c8e6cb1fc5135dd87. Ln, natural logarithm; spch, speechless. https://doi. org/10. 1371/journal. pbio. 2005952. g004 division properties between these layers (the presence of stomatal lineages in the epidermis) is eliminated. Divisions in the abaxial subepidermis were tracked by digitally removing the overlying epidermal signal (the distalmost subepidermal cells could not be clearly resolved). As with the epidermis, 3D segmentation showed that cell areas were a good proxy for cell size, although average cell thickness was greater (S11 Fig, see also 'Analysis of cell size using 3D segmentation' in Materials and methods).
The most striking difference between subepidermal and epidermal datasets was the smaller size of the distal lamina cells of the subepidermis (compare. For the epidermis, these cells attain areas of about 1,000 μm 2 at later stages, while for the subepidermis they remain below 500 μm 2. This finding was consistent with the subepidermal division competence zone extending more distally (Fig 6B), reaching a distal limit of about 400 μm compared with 300 μm for the epidermis. A more distal limit for the subepidermis has also been observed for cell cycle gene expression in wild type  [10]. Moreover, at early stages, divisions occurred throughout the subepidermis rather than being largely proximal, as observed in the epidermis, further contributing to the smaller size of distal subepidermal cells (S10 Fig).
Tracking growth and divisions Projected confocal images were used to calculate growth rates and cell areas and monitor cell division dynamics in 2D by placing points around the vertices of individual cells using Point-Tracker, as described in  [16]. A toolset (Track 'n' R) was created for ImageJ (https://imagej. nih. gov/ij/) to facilitate access to ImageJ macros and offer improved visualisation of Point-Tracker data using R  [76]. Track 'n' R was used to create leaf outlines, visualise the zone of cell division, and analyse cell lineages and to display cell cycle duration, cell area at division execution, and growth rates (source code and detailed instructions for Track'n'R and PointTracker have been deposited at https://github. com/fpantin/Track-n-R and https://figshare. com/s/ b14c8e6cb1fc5135dd87 respectively).
Graphical outputs from Track 'n' R were reoriented so that the leaf tip pointed upwards. Cellular growth rates over a time interval t1-t2 were calculated according to ln(A t2 -A t1 )/(t2 -t1), where A t1 is cell area at t1 and A t2 is cell area at t2. If a cell divided in this interval, A t2 was the area of the clone it gave rise to at time t2. For each tracking experiment, the first row of nondividing cells was identified in the first time point and coloured orange by hand using Photoshop. These cells were identified in each subsequent image and also coloured orange  (Fig 1A and S3 Fig and S4 Fig). The approximate location of the petiole-lamina boundary was identified based on the shape of the leaf outline in the last image available for each dataset. A cell was identified in the midline of this image, in line with the base of the leaf lamina.
Analysis of cell size using 3D segmentation For 3D segmentation and volume measurements, confocal image stacks were processed using Python scripts, as described  [17] , with additional scripts added to measure the external surfaces of epidermal cells in 3D and the corresponding 2D projections (source code and detailed instructions have been deposited at https://figshare. com/s/b14c8e6cb1fc5135dd87). Fiji macros  [75]  using the 3D Viewer and Point Picker plugins were used to visualise images and select cells during manual quality control. For the epidermis, plotting projected segmentation-based area against vertex-based area gave a good linear fit (R 2 = 0. 87) with a gradient of about 1, showing that vertex-based cell area is a good proxy for projected cell area (S5A Fig). Areas extracted from the cell surface plotted against vertex-based cell area also gave a good linear fit (R 2 = 0.
Modelling leaf growth All models and GFT-box software used for modelling can be downloaded from http:// cmpdartsvr3. cmp. uea. ac. uk/wiki/BanghamLab/index. php/Software. Models are also downloadable from https://figshare. com/s/b14c8e6cb1fc5135dd87. To implement an integrated model of division and growth, we built on a previously published tissue-level model for wild-type leaf growth at early stages of development  [16]. This model has two interconnected networks: the Polarity Regulatory Network specifies tissue polarity and hence specified orientations of growth, and the Growth Regulatory Network (KRN) determines how factors influence specified growth rates. Specified growth orientations are established in relation to a polarity field, determined by the local gradient of a factor determining polarity field (POL) that propagates through the tissue, termed the canvas.
001, 0. 021 ± 0. 001, 0. 026 ± 0. 001}, gradient m = {-0. 008, -0. 004, -0. 002, -0. 006}, and R 2 = {0. 18,-,-,0. 14}. (B) spch epidermis (from dataset shown in Fig 1, time intervals: 14-24 h, 35-47 h, 47-58 h, 58-74 h), μa = {5. 3 ± 0. 08, 5. 3 ± 0. 06, 5. 4 ± 0. 06, 5. 4 ± 0. 06}, μk = {0. 033 ± 0. 001, 0. 036 ± 0. 001, 0. 033 ± 9×10 -4 , 0. 031 ± 8×10 -4 }, m = {-0. 014, -0. 009, -0. 008, -0. 008}, and R 2 = {0. 31,0. 31,0. 27,0. 38}. (C) spch subepidermis (from dataset shown in Fig 6, time intervals: 14-24 h, 35-47 h, 47-58 h, 58-74 h), μa = {5. 0 ± 0. 09, 5. 0 ± 0. 06, 5. 1 ± 0. 05, 5. 1 ± 0. 05}, μk = {0. 033 ± 0. 002, 0. 033 ± 0. 001, 0. 028 ± 0. 001, 0. 026 ± 9×10 -4 }, m = {-0. 003, -0. 010, -0. 002, -0. 008}, and R 2 = {-, 0. 19, -, 0. 15}. (D) spch epidermal model output at stages corresponding to leaf widths in (B), μa = {4.
9 ± 0. 05, 4. 9 ± 0. 05, 5. 0 ± 0. 04, 5. 0 ± 0. 04}, μk = {0. 045 ± 0. 001, 0. 037 ± 5×10 -4 , 0. 035 ± 0. 004, 0. 038 ± 1×10 -4 }, m = {-0. 012, -0. 008, -0. 008, -0. 009}, R 2 = {0. 33,0. 42,0. 54,0. 59}. Source data are available from https://figshare. com/s/ b14c8e6cb1fc5135dd87. Ln, natural logarithm; spch, speechless.
The only change in relation to the late stage spch epidermis model (as used for Fig 9B)was that the physiological ratio (as defined above) was set to 0. 75. S4 Fig. Dynamics of cell division and growth in the spch epidermis, additional dataset. Time-lapse imaging of a spch leaf at approximately 24-h intervals over 5 d (0-118 h, last time point in series not shown). Data shown on first time point (underlined) for each tracking interval. Leaf widths for the first time point (left to right) are 0. 23, 0. 34, 0. 48, 0. 67, and 0. 87 mm. (A) Cells amenable to tracking that were competent to divide (green) and either executed division during the interval (light green) or divided in a later interval (dark green). Cells that did not divide (black, first row in 0-23 h are coloured orange throughout).
Acknowledgments We would like to thank  Grant Calder  for confocal microscopy support,  Przemyslaw Prusinkiewicz  for helpful discussions at early stages of the work, and  Domonique Bergmann  for providing spch and fama mutant seeds. We would also like to thank  Des Bradley ,  Chris Whitewoods ,  Catherine Mansfield ,  Claire Bushell , and  Beatriz Pinto-Goncalves  for helpful discussions and comments on the manuscript. We thank  Cindy Cooper  for laboratory support.
033 ± 0. 001, 0. 021 ± 0. 001, 0. 026 ± 0. 001}, gradient m = {-0. 008, -0. 004, -0. 002, -0. 006}, and R 2 = {0. 18,-,-,0. 14}. (B) spch epidermis (from dataset shown in Fig 1, time intervals: 14-24 h, 35-47 h, 47-58 h, 58-74 h), μa = {5. 3 ± 0. 08, 5. 3 ± 0. 06, 5. 4 ± 0. 06, 5. 4 ± 0. 06}, μk = {0. 033 ± 0. 001, 0. 036 ± 0. 001, 0. 033 ± 9×10 -4 , 0. 031 ± 8×10 -4 }, m = {-0. 014, -0. 009, -0. 008, -0. 008}, and R 2 = {0. 31,0. 31,0. 27,0. 38}. (C) spch subepidermis (from dataset shown in Fig 6, time intervals: 14-24 h, 35-47 h, 47-58 h, 58-74 h), μa = {5. 0 ± 0. 09, 5. 0 ± 0. 06, 5. 1 ± 0. 05, 5. 1 ± 0. 05}, μk = {0. 033 ± 0. 002, 0. 033 ± 0. 001, 0. 028 ± 0. 001, 0. 026 ± 9×10 -4 }, m = {-0. 003, -0. 010, -0. 002, -0. 008}, and R 2 = {-, 0. 19, -, 0. 15}. (D) spch epidermal model output at stages corresponding to leaf widths in (B), μa = {4.