dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, in R, the function 'snpgdsPCA' was used with the setting 'autosome. only = FALSE' to perform the PCA. Eigenvectors one and two were plotted on the x and y axes, respectively. Population structure was further assessed using ADMIXTURE version 1. 3. 0 [52]. For this analysis, the PLINK file that had been filtered for SNPs showing strong linkage disequilibrium was used. ADMIXTURE was run for K = 1. 10 with the option '-cv = 10' for cross-validation. To calculate linkage disequilibrium decay, a random 30% of the SNPs for each group were selected using the GATK module 'SelectVariants' with the settings '-fraction 0. 3', '-exclude-Filtered', and '-nonDeterministicRandomSeed'. Pairwise linkage disequilibrium was calculated between all SNPs between 1,000 and 999,999 bp apart using PLINK with the settings 'allow-extra-chr', '-r2', '-ld-window-r2 0', '-ld-window-kb 1000', '-ld-window 999,999'.
Determination of polymorphism effects on coding sequences The software package SNPEff version 4. 3i  [53]  was used with default settings to determine what coding sequences were affected by SNPs in the filtered VCF from the previous section 'Variant calling and quality filtering'. The GFF3 file containing reference coding sequences considered in this analysis was downloaded from NCBI (Bioproject number: PRJNA348385). The SNP classifications obtained from this analysis were 'LOW', 'MODERATE' and 'HIGH'. These correspond to synonymous mutations with a theoretically lower effect on coding sequences, non-synonymous mutations that may change amino acid sequence without causing major disruption of protein function and highly disruptive non-synonymous mutations that cause alterations such as frame shifts or premature stop codons.
Estimation of recombination rate in the Sclerotinia sclerotiorum populations Recombination rate was estimated for both populations using LDhat  [55]. The two VCF files for the populations were converted to LDhat unphased genotype files. A finite sites estimate of Watterson's theta per site was calculated for each chromosome. Lookup tables were then created using the LDhat module 'complete' with the settings '-rhomax 100 -n_pts 101'. The LDhat module 'interval' was then used with the settings '-its 5000000 -bpen 10 -samp 5000'. The LDhat module 'stat' was then run with the setting '-burn 100'. This produced recombination rates for each chromosome. The recombination rate used in the MS simulations was the average of the two average chromosome recombination rates for the two populations.
Modelling of demographic history of Sclerotinia sclerotiorum samples Dadi version 1. 7. 0  [56]  was used to model demographic history of two S. sclerotiorum samples representing two genotypic groups referred to as 'population 1' and 'population 2' henceforth. These groups were decided on based on the ADMIXTURE, PCA and neighbor-net trees described in a previous section. Population 1 consisted of 11 isolates from Europe and North America and population 2 consisted of nine isolates from Australia and one isolate from Morocco. Before conducting the analysis, the VCF file containing genotype data for both populations was filtered to remove SNPs with effects on coding sequences (identified using SNPEff) using negative regex expression searches with 'grep'. The Python script 'easySFS. py' (available at the time of writing at https://github.
To find genes associated with these regions, Awk was used to generate a BED formatted file containing coordinates 1 / alpha either side of each putative selective sweep. The calculation 1 / alpha is used to obtain the extent of a selective sweep in  Nielsen et al. (2005) , where alpha is the strength of positive selection. The Bedtools module 'intersect' was then used to determine which genes were within these regions. For each population, Tajima's D was calculated across a 5 Kb sliding window using the R package 'PopGenome' version 2. 2. 4. Fixation index (F ST ) between populations was also calculated across a 5 Kb sliding window using this package.
https://doi. org/10. 1371/journal. pone. 0214201. g001 This single selective sweep was immediately downstream of a Major Facilitator Superfamily (MFS), sugar transporter-like domain (PF00083) (sscle_10g_079770, start: 1,734,268, stop: 1,736,172 (antisense strand)). Intriguingly, this selective sweep exhibited a potential transposable element insertion at 1,732,785 bp (1,483 bp downstream of sscle_10g_079770). We further investigated this insertion by visually inspecting read mappings surrounding the site and looking for discordant read pairs or gapped alignments. However, we found that the insertion was present in individuals in both populations. Therefore, we cannot make any strong conclusions about the nature of the selective sweep at this stage.
Acknowledgments MCD, MD-G and LGK are funded through a bilateral agreement between the  Grains Research and Development Corporation of Australia and the Centre for Crop and Disease Management in Curtin University  on grant number  CUR00023. Part of this work was carried out using the facilities of the  Pawsey Supercomputing Centre in Kensington, WA, Australia. MCD would like to personally thank  Ellen Elizabeth Derbyshire  of  Edinburgh University Laboratory for Foundations of Computer Science  for guidance and support in data analysis using the diffusion equation employed in Dadi. SR is supported by a starting grant of the  European Research Council  ( ERC StG 336808 ) and the  French Laboratory of Excellence  project  TULIP  ( ANR 10 LABX 41 ;  ANR 11 IDEX 0002 02 ). This project was initiated by  Professor Richard Oliver  of the  Centre for Crop and Disease Management.
More precisely, it develops a method for stochastic simulation of rain type time series conditional to the state of the atmosphere, i. e. conditional to meteorological variables such as pressure, temperature, humidity or wind. The advantage of such an approach is twofold: firstly, using a stochastic simulation to generate rain types allows to properly reproduce the natural variability of rain type occurrence, and thereby to indirectly model the non-stationarity of rain statistics observed in historical datasets. Secondly, the conditioning of the stochastic rain type model to the state of the atmosphere preserves the relationships between rain type occurrence and the value of meteorological covariates, which ensures the climatological coherence of the stochastic weather generator. Once realistic rain type time series have been simulated, high-resolution rain fields can be simulated conditional to rain types using any high-resolution stochastic rainfall generator  (Leblois and Creutin, 2013; Paschalis et al.
, 2013; Nerini et al. , 2017; Benoit et al. , 2018a). The remainder of the paper is structured as follows. First, Sect. 2 presents an example of sub-daily rain type time series, and Sect. 3 proposes two different stochastic models to capture the main statistical features of this dataset. Next, Sect. 4 compares the performance of these two models through a cross-validation procedure, and applies the most reliable model to the downscaling of EURO-CORDEX RCM precipitation future projections as an illustration of the method. Finally, Sect. 5 provides some conclusions about stochastic rain type modelling.
Example dataset of rain type time series Before proposing stochastic models able to mimic the rain type occurrence process (Sect. 3), the present section explains how rain type time series are derived from weather radar observations, and investigates the main features of rain type occurrence in a mid-latitude climate. We focus hereafter on a 100 km x 100 km squared area centered on the city of Jena in the Land of Thüringen, Germany (Fig. 1a ). This area has been chosen because its flat topography and its location far from coastlines or major topographic barriers ensure spatially homogeneous rain fields, allowing to focus on the temporal component of rainfall non-stationarity. Over this area, data used for rain typing consists of radar images extracted from the RADOLAN (RAdar-OnLine-ANeichung) dataset  (Winterrath et al.
, 2012; Kaspar et al. , 2013)  The RADOLAN dataset is used as baseline information for rain typing, following the approach proposed by  Benoit et al. (2018b). This rain typing method consists of classifying radar images according to their space-time statistical signature. To this end, rainy time steps are first defined as periods with more than 10% radar pixels measuring rain. The other time steps are classified as dry, and are not considered for rain typing. Next, 10 statistical metrics are computed for each rainy radar image in order to assess the space-time-intensity behavior of the rain field observed in the image. Among these metrics, 3 relate to the statistical distribution of rain intensity observed in the radar image, 3 characterize the spatial arrangement of rain patterns within the image, and 4 evaluate the temporal evolution of the rain field due to rain advection and diffusion between consecutive periods.
Then, the 10 metrics are used as a basis for classification using a Gaussian Mixture Model (GMM). All details about the 10 metrics and the clustering approach with GMM can be found in  Benoit et al. (2018b). The resulting clusters correspond to rain fields with similar space-time behaviors. The number of rain types is selected as a compromise between goodness of fit to rain field statistics and model parsimony. In the present case, the parsimony is favored in order to allow for a physical interpretation of the resulting rain types. As a result, 6 rain types are identified in the example dataset (Fig. 1b ). Among them, rain types 1 and 4 correspond to rather stratiform and spread rain events, rain type 3 corresponds to frontal rain storms, and rain types 5 and 6 can be associated with rather convective rains.
Building a Markov-chain-based model of rain type occurrence The first approach we consider to model rain type occurrence builds on Markov-chain models, which were originally developed to model dry/wet sequences in daily resolution applications  (Richardson, 1981; Wilby, 1994; Wilks and Wilby, 1999). The existing models are substantially amended to match with the features of sub-daily resolution rain type time series, which significantly differ from dry/wet sequences at daily resolution. To model sub-daily resolution rain type time series, we adopt a non-homogeneous semi-Markov model (i. e. with non-stationary transition probabilities and time-varying step lengths) with N+2 states (Fig. 4 ). Among these N+2 states, N states model rain types (in the example dataset above N=6), and 2 states model dry periods that are split into 'short dry' (duration <24h) and 'long dry' (duration >24h) states.
dry states) occurring in the calibration dataset, and by normalizing the result by the total number of transitions. Then, the parameters required to make the transition matrix non-homogeneous (i. e. µ ij and Σ, cf Eq. (  1 )) are estimated using a synchronous record of covariate observations. More precisely, Σ is obtained by computing the empirical covariance matrix of the covariates for the whole calibration dataset, while the µ ij coefficients are estimated by computing the mean value of the covariates for the time steps where the transition from i to j occurs. Finally, the PDFs of rain type duration are assumed to be gamma distributions whose parameters are inferred by likelihood maximization using the observed rain type durations. After inference of model parameters, the calibrated model can be used to generate synthetic rain type time series conditioned to meteorological covariates.
Non-parametric resampling using Multiple-Point Simulations Because sub-daily rain type time series and their dependence to atmospheric conditions are complex, one can suspect that some part of their behavior cannot be fully captured by a parametric model  (Oriani et al. , 2018)  as the one described above, even if the model is relatively sophisticated. One option to account for higher-order properties when generating synthetic rain type time series is to adopt a non-parametric approach based on the resampling of historical datasets. To do so, we adopt the framework of multiple-point simulations (MPS). MPS consist of using a training dataset (here a past rain type record) to estimate empirically the probability distribution of the variable of interest (here rain type occurrence at a given time step) conditionally to the values already simulated in its temporal neighborhood (Fig.
5a ). In the specific MPS algorithm we use  (Gravey and Mariethoz, 2018, Under Review) , the conditional pdf is indirectly assessed by making a random sampling of the training dataset that aims at finding a pattern that is similar to the local conditioning neighborhood (Fig. 5b ). In practice, we use a 100 h neighborhood for the present application. Once a match is found (i. e. a pattern in the training image that minimizes the Hamming distance with the target pattern), the corresponding value is imported in the simulation grid (Fig. 5c ), and the procedure is iterated until the full simulation grid is filled. In the MPS framework, the dependence of rain type occurrence to meteorological covariates can be handled by multivariate-MPS simulation  (Mariethoz et al. , 2010)   dataset, simulations are obtained by resampling the training dataset using the MPS algorithm described above.
Assessment of the stochastic rain type models by cross-validation The performance of both stochastic rain type models is assessed by cross-validation, using the dataset introduced in Sect. 2. In practice, we adopt a leave-one-year-out procedure. For a given simulation year, the rain type model is first calibrated using data from the 2001-2017 period, excluding the year to simulate. Next, 50 realizations of rain type time series are generated for the year of interest using the previously calibrated stochastic model, conditioned to observations of the meteorological covariates derived from the ERA5 reanalysis as described in Sect. 2. Finally, the same procedure is iterated for each year of the test period For model evaluation, the 50 realizations of each model are compared to the reference rain type time series (Fig.
Outlook In this paper, two different methods have been proposed and thoroughly tested for the simulation of rain types time series conditional to meteorological covariates: a parametric approach based on a non-homogeneous semi-Markov model and a nonparametric approach based on the resampling of historical records using multiple-point statistics. Both methods have been assessed by cross-validation using a 17-year long rain type dataset in a mid-latitude climate (central Germany). Evaluation results favor the non-parametric approach because it better reproduces the complex rain type -climate relationships existing in our test dataset. After validation, stochastic rain type simulation is applied to the downscaling of RCM projections over the 21 st century. Rain type simulations conditioned to meteorological covariates simulated by a Regional Climate Model under an RCP8.
dwd. de/climate_environment/CDC/grids_germany/5_minutes/radolan. -Rain type data: https://github. com/LionelBenoit/Stochastic_Raintype_Generator/Raintype_data. -Rain typing software: https://github. com/LionelBenoit/Rain_typing. -Stochastic rain type models: https://github. com/LionelBenoit/Stochastic_Raintype_Generator/codes. -MPS simulation software: https://github. com/GAIA-UNIL/G2S.
Results Strain diversity in wild and domestic sheep and goats. We found a remarkably high number of M. ovipneumoniae strains (each defined as a group of sequence variants that differed by no more than 4 base pairs in pairwise comparisons) in domestic sheep flocks, with a total of 184 strains in 207 sheep sampled in the U. S (Dataset 1). The vast majority (159 out of 207, 77%) of individual domestic sheep possessed unique strains, and a single strain was never detected in more than three sheep. Only three strains (DS-7, DS-22, DS-23) were detected in more than a single operation, each in two domestic sheep operations located in different states. Otherwise herd strain composition was 100% divergent between any two operations. Although fewer samples were obtained from domestic goats in the U.
S. , a relatively high number of strains were observed, with a total of 16 strains in 26 individual goats. A moderate proportion of goats also had unique strains (9 out of 26, 35%); one strain (DG-3) was shared among three operations in WA, and another (DG-6) between three operations located in two different states (WA, NV). In contrast, 88 strains were identified in 349 bighorn sheep (Dataset 1). Only 9% of individual bighorn sheep had unique strains, and 82% of bighorn sheep possessed a strain that was shared with at least two other bighorn sheep in our dataset. Of the approximately 134 bighorn sites sampled, we observed more than one strain at any given site only 35 times (26% of sites). Two strains detected in bighorn sheep were shared with domestic species: one strain from domestic sheep (BHS-55/DS-96) and one from domestic goats (BHS-50/DG-6).
Hence, the sequencing of additional loci may enable the discovery of more strains within our dataset or better topological resolution of the phylogenetic relationships among strains. One study on the poultry pathogen, M. synoviae, found that while a MLST scheme (based on 7 housekeeping genes) identified the same number of strain types as a conventional single locus assay, it did provide better phylogenetic resolution to aid in the identification of epidemiologically-linked infections  65. Mycoplasma ovipneumoniae diagnostics based on culture-independent PCR assays were also used in our study, which limited our ability to identify cases of individuals being infected by multiple strains simultaneously, as has been reported to occur in domestic sheep  67. Also, using this approach, we assumed the amplified sequences for each of the four loci originated from the same strain.
Materials and Methods Sampling and detection of Mycoplasma ovipneumoniae. We obtained 594 samples from M. ovipneumoniae-infected bighorn sheep (n = 349), mountain goats (n = 12), domestic sheep (n = 207), and domestic goats (n = 26) that were submitted between 1984 and 2017 to the Washington Animal Disease and Diagnostic Laboratory (WADDL) for diagnostic testing or for research purposes (Table  S1 ; Dataset 1). Sample types submitted varied widely: from live animals, they were predominantly submitted as nasal swabs, but from sick animals or necropsy cases included pneumonic lung tissue and/or swabs of the bronchi, trachea, sinus linings or middle ears. Domestic sheep were sampled by the USDA-APHIS-VS National Animal Health Monitoring System (NAHMS) during a survey conducted in 2011, as detailed in Manlove et al.
The majority of the domestic sheep sequence data used were obtained from GenBank (Accession Nos. : MH042304-MH042516, MH045511-MH045514, MH087248-MH087420, MH107389-MH107763)  20  and only regional localizations (East, Central, or West) within the United States were available. Additional reference strains were included from Australia (n = 1, Y98, domestic sheep, 1976, NCBI BioProject PRJNA253514) and China (n = 8, domestic goat, 2010, NCBI BioProjects PRJNA253501-4, PRJNA253506, PRJNA253509, PRJNA253511, PRJNA63641). DnA extraction and strain typing. The majority of DNA extracts were obtained through the laboratory that performed the original diagnostic testing, predominantly from WADDL, but also included approximately 50 bighorn sheep extracts prepared by the Wyoming Game and Fish Department's Wildlife Health Laboratory (Laramie, WY).
Saturation occurs at a rate determined by the total number of strains, S max , as well as a rate parameter, B, so that S(n) = (S max n)/(B + n). We used the data to estimate parameters for the Michaelis-Menten-like hyperbolic fit  75, 77. Parameters were estimated using non-linear least squares (R function nls() in package stats), and confidence intervals were based on profile likelihoods. Recombination and phylogenetic analyses. Recombination within each locus was assessed using the RDP  78  , GENECONV  79  , BOOTSCAN  80  , MAXCHI  81  , CHIMAERA  82  , SISSCAN  83  , and 3SEQ 84 programs in RDP4 v4. 83  85. Evidence for a specific recombination event was based on significant support by at least three out of the seven methods  78. Individual locus alignments were trimmed and concatenated, resulting in a total alignment length of 1,778 bp, which included variant indels within the IGS locus.
The cold chain is the primary sampled chain, which accepts incremental steps that increase the likelihood of the tree state; whereas, heated chains explore parameter space more freely and can swap with the cold chain upon sampling a state of higher likelihood. Posterior distributions were sampled every 100 generations and model convergence evaluated by ensuring the standard deviations of the split frequencies approached 0 (<0. 05) and the potential scale reduction factor was 1 for all parameters. The consensus tree was estimated using combined posterior trees from the two runs, after discarding the first 25% of trees as "burn-in". The phylogenetic analyses described above were run with all data (n = 603), and then limited to unique strains (n = 363), after removing duplicate strains, to reduce the over-representation of bighorn sheep outbreaks and of repeated sampling within intensively studied post-outbreak bighorn sheep populations.
Acknowledgements We thank bighorn sheep managers and wildlife health staff at the state wildlife agencies and the  Western Association of Fish and Wildlife (WAFWA) Wild Sheep Working Group  for their data contributions to this project. We also thank  Katherine Baker  for genotyping M. ovipneumoniae isolates in the laboratory. This project was supported by the  USDA National Institute of Food and Agriculture ,  Hatch  project # ME0-21908  through the  Maine Agricultural & Forest Experiment Station  (Publication Number 3686),  U. S. Geological Survey ,  USDA Forest Service ,  Federal Aid to Wildlife Restoration ,  Wild Sheep Foundation Grant -in Aid Program , and the  Washington State University Rocky Crate endowment. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U.
for complete list of strains and clade assignments. Spatial structure of M. ovipneumoniae strains in wildlife hosts. The Taxon labels are color-coded by host species (red = bighorn sheep, BHS; purple = mountain goat, MTG; blue = domestic sheep, DS; light green = domestic goat, DG). The red star represents a M. ovipneumoniae isolate derived from a bighorn sheep outside of its native range, in a Wisconsin zoo. Bolded text indicates identification numbers for strains found in multiple host species or referred to in the text. See Dataset 1 M. ovipneumoniae phylogeny revealed high phylogenetic diversity within geographic regions. Related strains in bighorn sheep tend to cluster by geography, with identical strains often (~94% of strains) observed only within the same population or neighbor- ing populations (Figs 5 and 6).
Most of the reanalyses which assimilate satellite data exhibit large increasing trends in wave variance ( ∼ 15 %-50 % increase in 30 years at 100-10 hPa), whereas one reanalysis (Japanese 55-year Reanalysis assimilating conventional observations only; JRA-55C) produced without satellite data does not. Several discontinuities are found around 1998 in the time series of the Kelvin and MRG wave variances, which manifest in different ways depending on the reanalysis, and are indicative of impacts of the transition of satellite measurements during that year. The equatorial wave forcing of the QBO, estimated by the Eliassen-Palm (EP) flux divergence, occurs in similar phase-speed ranges in the lower stratosphere among the reanalyses. However, the EP flux and its divergence are found to be dependent on the zonal-mean winds represented in reanalyses, exhibiting different magnitudes, altitudes, and phase-speed ranges of the Kelvin wave forcing between the reanalyses, especially at 20-10 hPa.