dataesr/scientific-paragraphs-categorization huggingface dataset:
github. io/ picard/]. Variants were identified with GATK v3. 8  97  using the emitRefConfidence function of the HaplotypeCaller algorithm to call SNPs and InDels for each accession followed by a joint genotyping step performed by GenotypeGVCFs. To obtain high confidence variants, we excluded SNPs and InDels with the Var-iantFiltration function of GATK with the criteria: QD < 2. 0; FS > 60. 0; MQ < 40. 0; MQRankSum < -12. 5; ReadPosRankSum < -8. 0 and SOR > 4. 0. The complete automated pipeline has been compiled and is available on github [https://github. com/IBEXCluster/IBEX-SNPcaller]. A total of 36. 5 million variants were called. Raw variants were filtered using GATK v3. 8  97, 98  and VCFtools v0. 1. 17  99. Variants located on chromosome unanchored, InDels, 'SNP clusters' defined as three or more SNPs located within 10 bp, missing data >10%, low and high average SNP depth (14 ≤ DP ≥ 42), and accessions having more than 33% of missing data were discarded.
Only biallelic SNPs were retained to perform further analyses representing a final VCF file of 11,046,501 SNPs (Supplementary Table  7 ). These variants were annotated using snpEff v4. 3  100  with the CM05836 gene models. Genetic diversity and population structure. Genetic diversity and population structure analyses were performed using D. exilis and D. longiflora accessions together, or with D. exilis accessions alone. PCA and individual ancestry coefficients estimation were performed using the R package LEA v2. 0  101. The geographical projection of the first PCA axis of the D. exilis samples was done and visualized using the Kriging function in the fields v10. 3 R package [https://cran. rproject. org/web/packages/fields]  102. For ancestry coefficients analyses, the snmf function was used with 10 independent runs for each K from K = 2 to K = 10.
exilis accessions. Bioclimate data and ethnic and linguistic data were extracted for each of the 166 re-sequenced D. exilis accessions from WorldClim v1. 4 [https:// www. worldclim. org/data/v1. 4/worldclim14. html]  105 , passport data (ethnic groups), and Ethnologue v16 (language, [https://www. ethnologue. com/]). The associations between the genetic structure and different factors were evaluated statistically using Pearson's correlation coefficient test (two-sided) for the bioclimatic and geographic data with the R package stats v3. 6. 0 (function cor). For the associations of social factors, two-sided Mantel tests (ecodist R package v2. 0. 5 [https://rdrr. io/cran/ ecodist/]) with 999 permutations were performed between the genetic distance matrix of D. exilis and the dissimilarity matrix of social factors as inputs.
Fisher's exact tests (stats R package v3. 6. 0) were performed with the structure data at K = 6 and accessions having ancestry thresholds of >70% as input. The p-values were computed by Monte Carlo simulation. We used analysis of covariance (ANCOVA) to test for the impact of ethnolinguistic factors on the first PCA coordinates while controlling for other covariates: climatic and geographic variables. Synthetic climate variables were retrieved from the WorldClim v1. 4 database  105. We focused our analysis on two climate variables that were the most pertinent to annual species, i. e. , mean temperature of the wettest quarter (bio8) and mean precipitation of the wettest quarter (bio 16), respectively. A GWAS with these climate variables has been performed  106. Briefly, we first used the impute method from the R package LEA v2.
0 101 to fill the gaps in the genotypic matrix in order to achieve greater power. We then filtered out SNPs with a MAF below 5%. We used three different models of association to perform the GWAS, efficient mixed-model association (EMMA)  107  using the R package emma v1. 1. 2, mixed linear model (MLM) as implemented in the R package gapit v3. 0  108  and latent factor mixed models (LFMM) using LFMM2  109  as implemented in lfmm R package v2. 0. For gapit (MLM), we considered six principal components, which we chose from the previously described structure analysis. The LFMM v2 method implements a Latent Factor Mixed Model that estimates unknown confounding factors (the latent factors) jointly using the genotypic and phenotypic matrices. For this specific model, the estimation of latent factors was performed using both the phenotypic data (i.
Significant associations were identified above the threshold of p value = 1e -05. Manhattan plots were plotted to display the associations using the qqman R package v0. 1. 4  112. GO terms for all fonio protein sequences were assigned using the DeepGOPlus model [https://github. com/bioontology-research-group/deepgoplus]  113. We only considered GO labels that have a confident score >0. 3. Enrichment analysis for GO biological processes, cellular components, and molecular functions of the genes of interests from climate association analyses was performed using the Fisher exact tests implemented in R package topGO v3. 36. 0 114 (algorithm = 'weight01'). We reported GO terms with enrichment p value < 0. 05 for each GO-term category.
Demographic history reconstruction. A Sequentially Markovian Coalescentbased approach  115, 116  was used to infer past evolution of the effective population size of D. exilis and D. longiflora. This analysis was made with the smc++  117  software and additional customized scripts [https://github. com/Africrop/ fonio_smcpp]. We excluded SNPs that were not usable (e. g. repeated regions) according to msmc-tools advices [https://github. com/stschiff/msmc-tools]. Analysis was done with all nine individuals from the genetically closest group of D. longiflora and also with the six groups of D. exilis based on the genetic structure. The smc++ vcf2smc tools was used to generate the input file and smc++ inference was performed for the different sets of genotypes using the smc++ cv command, considering 200 and 25,000 generations lower and upper time points boundaries, respectively.
All other options were set to default. We considered a generation time of one year and a mutation rate of 6. 5 × 10 -9. Different sets of distinguished lineages were considered for the different sets of individuals (all the samples were considered as distinguished). Graphical representation was made using the ggplot2 v3. 3. 2 68 package for R. Genome scanning for selection signals. Detection of selection was performed independently with three different methods: (1) Weir and Cockerham F ST ; (2) nucleotide diversity ratios (π) were calculated between D. exilis and D. longiflora using VCFtools v0. 1. 17  99 with sliding windows of 50 kb every 10 kb; (3) SweeD v3. 3. 1  39  was used on D. exilis to detect signatures of selective sweeps based on the composite likelihood ratio (CLR) test within non-overlapping intervals of 3000 bp along each chromosome.
Acknowledgements We are grateful to  Vinicius M. Lube ,  Samantha Bazan ,  Ablaye Ngom ,  Marie Piquet ,  Hélène Adam , and  Carole Gauron  for their technical assistance in Herbarium sampling and photography. Cirad herbarium samples of Digitaria longiflora were provided by  Samantha Bazan  (ALF [http://publish. plantnet-project. org/]). We thank  Soukeye Conde  for valuable insight, funded by the project  Cultivar , reference  ANR-16-IDEX-0006 , through the  Investissements d'avenir program  ( Labex Agro :  ANR-10-LABX-0001-01 ). This work was supported by the  CIRAD-UMR AGAP  and  IRD UMR DIADE HPC Data Center of the South Green Bioinformatics platform  [http://www. southgreen. fr/]and the  Center for Desert Agriculture of the King Abdullah University of Science and Technology (KAUST). D.
Data availability Data supporting the findings of this work are available within the paper and its Supplementary Information files. A reporting summary for this Article is available as a Supplementary Information file. The datasets generated and analyzed during the current study are available from the corresponding author upon request. The raw sequencing data used for de novo whole-genome assembly, the raw bionano map, the CM05836 genome assembly, the RNA-seq data for the annotation and the 183 re-sequenced accessions of D.
, 2016; Hmiel et al. , 2020). Improved understanding of CH 4 emissions is needed (National Academies of  Sciences and Medicine, 2018). In particular, the estimates of continental US anthropogenic CH 4 emissions disagree. Inventories from the Environment Protection Agency (EPA) and Emissions Database for Global Atmospheric Research (EDGAR) in 2008 reported emission values of 19. 6 and 22. 1 Tg C yr -1 (U. S. EPA, 2013; European Commission Joint Research Centre and Netherlands Environmental Assessment Agency, 2010). However, top-down methodologies using aircraft and inverse modeling frameworks found emission values of 32. 4 ± 4. 5 Tg C yr -1 for 2004 and 33. 4 ± 1. 4 Tg C yr -1 for 2007-2008 respectively  (Kort et al. , 2008; Miller et al. , 2013). Underestimation of natural gas (NG) production and agricultural sources are possible reasons for this disagreement  (Miller et al.
Experimental site This study uses data from a tower-based GHG observational network located in the city and surrounding suburbs of Indianapolis, Indiana, in the Midwestern US. Prior studies have used varying definitions for the region of Indianapolis  (Cambaliza et al. , 2015; Lamb et al. , 2016). In this work, we follow  Gurney et al. (2012)  and define Indianapolis as the area of Marion County. The flat terrain of the region simplifies interpretation of the atmospheric transport. The land-surface heterogeneity inherent in the urban environment (building roughness, spatial variations in the surface energy balance) does have a modest influence on the wind and boundary layer depth within the city compared to nearby rural areas  (Sarmiento et al. , 2017). Figure  2  shows two domains that have been used for the evaluation of Indianapolis CH 4 emissions  (Lamb et al.
Meteorological data Wind speed and direction were measured at the Indianapolis International Airport (KIND), Eagle Creek Airpark (KEYE), and Shelbyville Municipal Airport (KGEZ). The data used are hourly values from the Integrated Surface Dataset (ISD) (https://www. ncdc. noaa. gov/isd, last access: 8 April 2020) and 5 min values directly from the Automated Surface Observing System (ASOS). A complete description of ASOS stations is available at https://www. weather. gov/media/asos/ aum-toc. pdf (last access: 8 April 2020). The accuracy of the wind speed measurements are ±1 m s -1 or 5 % (whichever is greater) and the accuracy of the wind direction is 5 • when the wind speed is ≥ 2. 6 m s -1. The anemometers are located at about 10 m a. g. l. The wind data reported in ISD are given for a single point in time recorded within the last 10 min of an hour and are closest to the value at the top of the hour.
Frequency and bivariate polar plots Frequency and bivariate polar plots are used in this work to gain more knowledge regarding CH 4 background variability based on criteria 1 and 2, and to identify sources located within the city. To generate these polar plots, we use the openair package (from R programming language) created specifically for air quality data analysis  (Carslaw and Ropkins, 2012). Bivariate and frequency polar plots indicate the variability of a pollutant concentration at a receptor (such as an observational tower) as a function of wind speed and wind www. atmos-chem-phys. net/20/4545/2020/ Atmos. Chem. Phys. , 20, 4545-4559, 2020 direction, preferably measured at the location of the receptor or within several kilometers of the receptor. The frequency polar plot is generated by partitioning the CH 4 hourly data into the wind speed and direction bins of 1 m s -1 and 10 • respectively.
Temporal variability and approximate flux estimation Temporal variability in urban CH 4 emissions may play an important role in the corresponding emissions quantification procedures. Lamb et al. (2016)  suggested that such temporal variability might partially explain the differences among CH 4 flux estimates shown in Fig. 1. If temporal variability of CH 4 emissions exists within the city, disagreements in the CH 4 flux between studies could be attributed to differences in their sampling period. Because the INFLUX tower data at Indianapolis contain measurements at all hours of the day over multiple years, we can utilize this dataset to better understand the temporal variability in methane emissions in the city. We apply a simplified atmospheric boundary layer budget, not to estimate precisely the actual city emissions, but rather to evaluate temporal variability of the emissions.
2 ). The CH 4 concentrations are derived from CH 4 mole fractions by approximating average molar density of dry air (in mol m -3 ) within the boundary layer for every hour of the day, where variability of pressure with altitude is calculated using the barometric formula, and it is assumed that temperature decreases with altitude by 6. 5 K per kilometer. The hourly surface data for pressure and temperature are taken from KIND weather station. The difference between concentrations C and C b is instantaneous and not lagged, where C b represents an air parcel entering the city and C represents the same air parcel exiting the city  (Turnbull et al. , 2015). The CH 4 enhancements (C -C b ) are estimated for daytime by averaging observations spanning 12:00-16:00 LST and for nighttime by averaging observations spanning 20:00-05:00 LST.
(2017)  finding of large variability in airborne estimates of Indianapolis CH 4 emissions. Given many samples, the airborne studies converge to an average value of CH 4 flux that is noticeably closer to the inventory estimates for Indianapolis than several of the individual estimates presented in Fig. 1. Measurement and analysis strategies can minimize the impacts of these sources of error. Spatially extensive measurement of upwind CH 4 mole fractions are recommended. For towers or other point-based measurements, multiple upwind measurement locations are clearly beneficial. For the aircraft mass balance approach, we recommend an upwind transect to be measured, lagged in time if possible, to provide a more complete understanding of the urban background conditions. Complex background conditions might suggest that data from certain days or wind directions should not be used for flux calculation.
Acknowledgements. We would like to thank  Bram Maasakkers  for the helpful discussion regarding the EPA 2012 inventory and the relevant error structure. We also thank  Paul Shepson  and  Brian Lamb  for their useful input regarding airborne mass balance flights and the process of compiling an emissions inventory. Most importantly, we would like to acknowledge the significant contributions of both reviewers who rigorously examined our science and noticeably improved clarity of our article. Financial support. This research has been supported by the  National Institute of Standards and Technology  (grant no. 70NANB10H245 ). Review statement. This paper was edited by  Neil Harris  and reviewed by three anonymous referees.
Acknowledgments The authors would like to thank  Charline Smadi  and  Emmanuelle Porcher  for feedbacks on the modeling approach developed here. We also thank  Thomas Aubier  and  Richard Merrill  and the whole Heliconius group for stimulating discussion on mate choice evolution in our favorite butterflies. We are also grateful to  Roger Butlin ,  Charles Mullon ,  Tom Van Dooren  and two anonymous reviewers for their thoughtful review of a previous versions of this manuscript. We also thank  Sylvain Gerber  for his help in improving the English of this article. This work was supported by the  Emergence program  from  Paris City Council  to VL and the  ANR  grant  SU-PERGENE  to VL and MJ. Version 9 of this preprint has been peer-reviewed and recommended by  Peer Community In Evolutionary Biology  (https://doi.
This is especially important for offshore wind developers that face significant financial and operational costs due to uncertainties in wind resource assessment  [4]. Understanding how these uncertainties originate from wind farm design tools is of fundamental importance to mitigate these losses. The OWA Wake Modeling Challenge is an Offshore Wind Accelerator (OWA) project that aims to improve confidence in wake models in the prediction of array efficiency. Model developers and endusers were invited to participate in a benchmarking exercise along a suite of validation datasets that spans a wide range of operational conditions in terms of wind farm topology and wind climate characteristics  [5]. In the absence of free-stream meteorological observations, a meso-wake modeling framework is proposed where the background wind resource conditions are defined from reference mesoscale simulations based on the New European Wind Atlas (NEWA) model configuration of the Weather Research and Forecasting (WRF) model  [6] [7]  [8].
Wake Models Eleven participants registered in the challenge covering a large variety of engineering wake models present in commercial as well as research codes. The results presented here come from six participants that submitted simulations for all the sites to carry out a multi-site assessment (Table  2 ). All participants in this group adopted a time-series approach where each hourly interval is assumed stationary. We use hourly data and not 10-min data to account for the time lag for wind conditions to propagate across the wind farm. In effect, for a 22-km long farm like Anholt, it takes 40 min for air moving at 9 m/s to traverse the wind farm from North to South. Time series of mesoscale wind conditions were provided for each turbine (denoted wt) and for the average of all turbines (ref) to accommodate the needs from the models to use heterogeneous or homogeneous inflow conditions respectively.
eWakLab (TUD) eWakeLab is an in-house engineering wake modelling toolbox (written in MATLAB TM ) which can ingest spatio-temporally varying hub-height wind speeds from a mesoscale model or a reanalysis dataset. It contains a few well-known wake parameterizations (e. g. ,  [22] ,  [24] ) and there is a plan to include several newly proposed ones from the literature (e. g. ,  [29] ). eWakeLab uses a UTM coordinate system-based grid with a user-specified grid-size (typically 100 m) and requires a prescription for the maximum length of wakes (typically assumed to be 50 times the rotor diameter). One of the salient features of eWakeLab is that, for a given time instant and a location, it combines the wakes of all the upstream turbines in an iterative manner. During each iteration, effective wake deficits are updated, and in turn, the thrust coefficients of all the affected turbines are properly adjusted based on the localized wind speeds.
FarmShadow (IFPEN) FarmShadow is an in-house, steady-state code developed at IFP Energies nouvelles. An aerodynamic library based on the IFPEN Blade Element Momentum (BEM) model (integrated in the hydro-aeroservo-elastic solver DeepLinesWind TM  [31] ) is coupled to a set of analytical wake models. When minimal information is available on the turbine, as it was the case here, the BEM model is replaced by an actuator disk method where only C p and C t tables are needed. The disk is discretized using several control points, on which velocities and turbulence intensities are evaluated, including the effect of upstream turbine wakes. Then, these quantities are averaged and used as an input to calculate the wake of a given wind turbine. Here, FarmShadow results are based on three velocity deficit models (Gaussian models of Bastankhah&Porté-Agel  [24] , Ishihara&Qian  [32] , and the super-Gaussian model of Blondel&Cathelain  [33] ) and the added turbulence model of Ishihara&Qian  [32].
Validation Metrics The assessment is based on bin-averages of array efficiency for S ref = 9±1 m/s, where the thrust coefficient is at its maximum generating the strongest wake effects, per 30º-width wind direction sector and stability class. Stability is based on the simulated Obukhov length L ref for: unstable (-0. 2 < z/L ref < -0. 02), neutral (-0. 02 < z/L ref < 0. 02) and stable (0. 02 < z/L ref < 0. 2) conditions, with z = 10 m. Performance is measured in terms of the BIAS and mean absolute error (MAE): (6) | | (7) where the benchmark data come from either the observations or the ensemble (blind test). The metrics are calculated for each turbine and wind farm per bin and then weighted-averaged with the wind climate distribution to calculate integrated quantities. To limit the influence from bins with poor statistical significance, only bins with more than 25 samples are used to compute integrated quantities.
Validation Data Operational data from offshore wind farms provided by OWA partners are filtered and processed in order to generate the validation datasets. To this end, the data is first standardized to a common format and is subject to a quality-check process that will essentially determine if the power from a wind turbine at any given time is working in nominal conditions, i. e. when the power is close to that predicted by the theoretical power curve at the operational nacelle anemometer wind speed. It is under these conditions that wake models are used during the pre-construction phase of a wind farm. The boundaries of nominal conditions are defined by a symmetrical horizontal displacement about the theoretical power curve that end up excluding around 20% of the data points as outliers.
Conclusions As larger and more closely spaced offshore wind farms are deployed the focus in array efficiency prediction is shifting towards producing more realistic inflow conditions that include large-scale phenomena like wind resource gradients that can result in variations of the gross power of more than 20% across the wind farm. Meso-wake modeling approaches can incorporate heterogeneous inflow in array efficiency prediction but the assessment is still limited by the lack of suitable validation datasets. The OWA Challenge shows the benefits of implementing a systematic assessment of wake models based on operational data from industry. Still, it is difficult to assess array efficiency prediction when the background wind resource is not measured at the site and has to be inferred from SCADA data that are affected by stream-wise gradients and turbine performance and interaction effects.
A c c e p t e d a u t h o r m a n u s c r i p t s Distances between chicks according their relatedness were analysed using GLMMr with the mother and the interaction between the relatedness of the 2 individuals of the distance as random factors. The reaction of chicks to the mother's separation were analysed with general linear mixed models (GLMM), relatedness was fixed factor and mother was random factor. The distributions used were Gaussian after logit transformation if there was no-normality of residuals for continuous variables and Poisson for occurrences. To analyse data on behavioural development of chicks, we made a third PCA analysis by varimax rotation with the data of behavioural tests to describe the emotivity and sociality profile of chicks according to relatedness. Factorial scored obtained were analysed by GLMM with relatedness as fixed factor and mothers as random factor.
The threshold of significance was 0. 05 and the tendencies between 0. 1 and 0. 05 were mentioned. PCA were realised with XLStat (www. xlstat. com) and scores were analysed with SPSS Statistics 20. (IBM). GLMMr comparisons revealed a time effect on "Activity" component (F3,272=20. 24, P<0. 001) but not on "Interaction with mother" component (F3,272=1. 737, P=0. 160) (Fig  4 ). There was no significant relatedness effect on "Activity" (F1,274=1. 097, P=0. 296) and a tendency for unrelated chicks to make more neutral and positive interaction with mother (F1,274=2. 767, P=0. 097) (Fig  4 ). There were not significant timerelatedness effects (Activity: F7,268=0. 578, P=0. 630; Interaction with mother: F7,268=0. 572, P=0. 634).