dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Statistical calculations were carried out using Prism 10 statistical software (Graph Pad Inc. , La Jolla, CA, USA). Non-parametric Kruskal-Wallis tests were used for statistical comparisons of groups of data. Then, individual differences between treatments were identified using Dunn's test as a post hoc test. Prism 10 was also used for determination of the IC 50 and CC 50 values.
Acknowledgments: We thank  Volker Thiel  for providing HCoV-229E-Luc. We are also grateful to  Robin Prath  and  Nicolas Vandenabeele  for their technical help in the BSL3 facility. We thank  Solenn Ferron  ( CorInt team ) for the preparation of the samples and  Robert Lücking  and  Harrie Sipman  ( Botanical Garden and Botanical Museum in Berlin ) for giving us access to samples from  Siegfried Huneck ' chemical library.
We then use this informative prior to obtain the distribution of parameters that best describes the observations t cat in each strain. As a result, we find that with the exception of mcp1D, mal3D, and tip1D mutant strains (all regulators of MT growth dynamics), all other strains studied have very similar parameters associated with the t cat distribution (Figure  7A ). In addition, a truncated distribution obtained by restricting the full distribution at experimentally observed minimum and maximum values of t cat suffices to explain the observed empirical distribution. We now proceed to estimate the remaining MT parameters, the stall force f s , and the flexural rigidity k (the growth and shrinkage velocities have been measured directly from experiments). We do this by fitting the simulated distributions of d and s x from the model Equations 1 and 2 (see STAR Methods) to the distribution obtained from the WT data (see Figure  S10 ).
Following this, we constructed a color-combined image stack using the GFP and mCherry channels. The MTs that emanate from the SPB were manually segmented using the ImageJ segmented line tool (see Figure  S7 ) and analyzed using custom script written in MATLAB. The number of MT bundles, total MT mass, and orientation were measured using static z-stacks (using the microscopy procedure described above). The number of MT bundles was counted manually. The quantification of the total MT mass and orientation was done using the following steps. First, we segmented the MT cytoskeleton using the ''Trainable Weka Segmentation'' plugin in ImageJ. 95 A manually curated dataset was used to train the classifier to distinguish between the MT cytoskeleton and cytosol. The segmented probability histogram was bimodal, and we used the value corresponding to the minima to segment the MT cytoskeleton and create a segmented mask.
This is achieved by the Bayesian inference method. 48 Bayes rule assigns the posterior probability on a set of distribution parameters (q) given the observations (O) by: pðqjOÞ = pðOjqÞ pðqÞ pðOÞ ; (Equation  15 ) where pðqÞ is the prior distribution of the model parameters, pðOjqÞ is the likelihood function of the observations O given the parameters q and pðOÞ = R pðOjqÞpðqÞ dq is the marginal likelihood distribution of the observations. Apart from the dataset of observations O, we also require the following to evaluate the above expression: 1. a probability distribution function (e. g. exponential, gamma, etc. ) which is parametrized by the set q and, 2. a distribution of prior probabilities of pðqÞ. We use a gamma distribution, defined by a step parameter (N) and a time-scale parameter T, to describes the MT catastrophe time (t cat )  78,79,104 :  Pðt cat jN; TÞ = t N À 1 cat expðÀ t cat =TÞ T N GðNÞ ; where G(N) is the gamma function.
Statistical analysis Statistical analyses were performed as summarized in each figure. Wilcoxon rank-sum test was performed using MATLAB (Statistics and Machine Learning Toolbox) to determine the significance as indicated in figure legends. 92 Results of statistical tests are indicated as follow: **** for p % 10 À5 , *** for p % 10 À4 , ** for p % 0. 005, * for p < 0. 05, ns is not significant.
ACKNOWLEDGMENTS We thank members of  Tran lab at Institut Curie  and members of the  Simons Center, NCBS-TIFR  for fruitful discussions. I. J. thanks  Anjana Badrinarayanan 's lab at  NCBS  for providing access to their lab space during the revision of the manuscript. We thank  Mukund Thattai ,  Matthieu Piel ,  Vaishnavi Ananthanarayanan ,  Alkesh Yadav , and  Krishnan Iyer  for comments and insights on the manuscript. I. J. and M. R. acknowledge support from the  Department of Atomic Energy, Government of India , under project no. RTI4006 , and the  Simons Foundation  (Grant No. 287975 ). M. R. acknowledges a J. C. Bose Fellowship from  DST-SERB (India). I. J. acknowledges a  Curie-NCBS-InStem postdoctoral fellowship. P. T. T. acknowledges grant support from  INCa ,  Fondation ARC , and  La Ligue National Contre le Cancer -Ile de France.
91 https://imagej. nih. gov/ij/ MATLAB Version R2016a, The https://www. mathworks. com/ MathWorks Inc. 92 products/matlab. html Simulation code This manuscript https://github. com/IJ-NCBS/MT_Sim_Code d RESOURCE AVAILABILITY B Lead contact B Materials availability B Data and code availability d EXPERIMENTAL MODEL AND SUBJECT DETAILS B Genetics, cell culture, and strains d METHOD DETAILS B Microscopy and image analysis B Stochastic model for MT-dependent nucleus centering d QUANTIFICATION AND STATISTICAL ANALYSIS B Segmentation and quantification of microscopy data B Determining MT-catastrophe time distribution using bayesian inference B Statistical analysis.
ACKNOWLEDGMENTS This study is funded by the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant  ANR-10-LABX-62-IBEID ), and by  ANR JCJC MosMi  funding to M. G. (grant  ANR-18-CE15-0007 ). We thank  Claudia Wyer  for English proofreading of the manuscript.
For each strain and population, all the AB lines and parents were evaluated within a single disease test comprising four randomized complete blocks with five plants/block, as described by  Moussart et al. (2001). Disease severity was scored on each plant using the same 0 (healthy plant) to 5 (dead plant) scoring scale as used for the field scorings  (Hamon et al. , 2011). Phenotyping datasets produced on the RIL populations by  Pilet-Nayel et al. (2002; 2005) ;  Hamon et al. (2011; 2013)  and Lavaud et al. (submitted 1 ), and datasets produced on the pea-Aphanomyces collection by  Desgroux et al. (2016) , were used in this study.
Statistical analysis of phenotypic data Phenotypic datasets for plant morphology and resistance to A. euteiches, obtained from the Eden x E11 and Eden x LISA populations, were analyzed using the R 4. 0. 2 software (R Core Team, 2020). Global statistical analyses were conducted to assess: (i) genotype x environment interactions in field experiments, employing a global linear model [R function lm], and (ii) genotype x strain interactions in controlled conditions tests, utilizing a cumulative link model (R function clm of package ordinal;  Christensen, 2019). In addition, individual statistical analyses of phenotypic data obtained from each field and controlled conditions experiment were computed using (i) a linear mixed model (R function lmer of package lme4;  Bates et al. , 2019)  for field variables, including G (genotype) as fixed factor and replicates with assessor (R A ) and gradient (R Gr ) blocks, as random factors, and (ii) a cumulative link mixed model (R function clmm of package ordinal;  Christensen, 2019)  for controlled conditions variables, including G as fixed factor, and B (block) and GxB interaction as random factors.
Broad-sense heritability (H 2 ) was assessed for each variable from variance estimates in a global linear model, including only G as fixed factor, and using the formula: H² = s G 2 /[s G 2 + (s E 2 /r)], where s G 2 is the genetic variance, s E 2 the residual variance and r the number of replicates per genotype. Significance of factor effects in each model was tested (R function Anova of package car and RVAideMemoire;  Fox and Weisberg, 2020; Herve, 2020)  and estimated marginal means (EMMs) were computed for all individual variables on each genotype and from each model (R function and package emmeans;  Lenth, 2020). Pearson correlation analysis was carried out between EMMs (R function rcorr of package Hmisc;  Harrell and Dupont, 2020 ) and a correlation matrix was drawn using the Pearson coefficient (r; R function rcorr of package corrplot;  Wei and Simko, 2017).
The Eden x E11 and Eden x LISA genotyping datasets were then reduced to 993 and 478 SNPs respectively, based on marker quality (heterozygosity ratio < 15%) and quality of linkage mapping (see next section) in each LG. A total of 160 and 178 Eden x E11 and Eden x LISA pea lines were selected, respectively, based on marker quality (missing data < 15% and heterozygosity ratio < 15%), quality of linkage mapping, and pea line homozygosity for flower color, foliar type, and plant morphology, observed in controlled conditions and greenhouse in Le Rheu, Ille-et-Vilaine, as well as in infested field nurseries in Riecsur-Belon and Dijon-Epoisses. Genotyping datasets produced on RIL populations by  Pilet-Nayel et al. (2002; 2005)  and  Hamon et al. (2011; 2013) , and genotyping dataset of 12,067 SNPs previously selected on the pea-Aphanomyces collection by  Desgroux et al.
(2016) , were supplemented with the 1,850 markers which were used as bridges for comparative AB-, RIL-QTL mapping and GWAS. Genotyping matrices consisting  of 1,866, 1,082, 950  and 669 SNPs  and 176, 178,  111  and 121  RILs were established for Baccara x PI180693, Baccara x 552, DSP x 90-2131 and Puget x 90-2079 populations, respectively, based on quality of linkage mapping in each LG. A genotyping matrix consisting of 10,824 SNPs and 172 pea genotypes from the pea-Aphanomyces collection was retained, based on marker quality (markers with MAF > 5% and missing data < 10%; individuals with heterozygosity ratio < 15% and missing data < 10%). The filtered genotyping matrix, containing 0. 94% missing values, was imputed using Beagle 5. 1 software  (Browning et al. , 2018). Imputation parameters were established using a sliding window, length of overlap between adjacent sliding windows, and number of iterations of 15 cM, 5 cM, and 15, respectively.
Linkage mapping and consensus marker map construction Genetic maps for AB and RIL populations were established using the "sem" and "annealing 100 100 0. 1 0. 9" commands of CarthaGene software  (de Givry et al. , 2005) , as presented in  Boutet et al. (2016). These commands allowed the computation and optimization of the maximum likelihood for the order and position of markers (in cM Haldane) on each LG. For each SNP marker from each AB or RIL genetic map, a c 2 test (p-value < 0. 001) was used to analyze adjustments of allelic segregation to the expected Mendelian ratios (1:1 in RIL populations, 1:7 in BC 2 F 7 populations). A consensus marker map, named "DORA" as the project name of this study, including 16,647 markers, was obtained by projecting the positions of the 1,850 markers used as supplemental marker set in this study onto the pea reference consensus genetic map described by  Tayeh et al.
AB and RIL populations QTL analysis Composite interval mapping models were performed using R/ qtl package  (Broman et al. , 2003)  to identify QTL for resistance to A. euteiches, flowering, and morphological traits from the RIL and AB populations. For each variable, a forward-backward stepwise selection of QTL covariate (window size = 5cM) was performed using the Haley-Knott regression method. To limit the number of covariables and potential impacts of overparameterization, new LOD score thresholds were estimated for each additional covariable introduced in the composite interval mapping models. These thresholds were computed by conducting 1,000 permutations to determine, with a genome-wide a error risk of 5%, the significance of putative QTL. For each QTL, the percentage of phenotypic (R 2 ) and genotypic variation, additive and epistatic interaction effects, and one LOD drop-off confidence intervals were computed.
Population structure, individual relatedness, and genome-wide association study The structure of the collection was investigated using the ADMIXTURE 1. 3 software  (Alexander et al. , 2009)  with the whole SNP dataset. Ten groups were determined after 15 crossvalidations according to the procedure presented in ADMIXTURE. The structure matrix was represented using pophelper  (Francis, 2017). IBD relatedness between individuals was computed using the  Astle and Balding (2009)  algorithm. Ward's clustering of pea lines was computed according to their estimating IBD relatedness values and was represented with the kinship matrix using GAPIT R packages  (Lipka et al. , 2012). GWAS was performed using a modified version of the multilocus mixed model (MLMM) R package  (Segura et al. , 2012) , as described by  Desgroux et al.
Comparative mapping Genetic maps, QTL identified in this study from linkage analysis of AB and RIL populations, and QTL recently detected for resistance to the Ae109 and RB84 strains in the Puget x 90-2079 RIL population  (Lavaud et al. , submitted 1 ) were projected onto the consensus marker map DORA using Biomercator 4. 2 software. QTL and marker-trait associations identified by linkage analysis and GWAS, respectively, were visualized on the consensus marker map DORA using MapChart 2. 1 software  (Voorrips, 2002). Consistent genetic regions controlling partial resistance to A. euteiches were defined according to the colocalization of at least four partial resistance QTL and/or resistance-associated SNPs identified by linkage analysis and/or GWAS, respectively, based on their genetic positions on the consensus genetic map.
Haplotype analysis Local LD analysis was performed to define LD block intervals around significant markers detected by GWAS in consistent genetic regions of partial resistance, using PLINK 1. 9 software. A LD block was determined as the interval including all markers in LD (r 2 > 0. 7) with the targeted marker. For each variable associated with a significant marker in a given LD block, EMMs of pea line groups carrying haplotypes that were not considered as rare, i. e. comprising more than 8 individuals (> 5% of the total number of accessions), were computed and compared using the Tukey-HSD test (a = 5%; R functions emmeans and cld of package emmeans). For each LD block, haplotypes significantly associated with higher or lower mean phenotypic scores compared to the other haplotypes, were defined as favorable (named a) or unfavorable (named b or c), respectively.
At each LD block containing most significant markers, the phenotypic mean and range (adjusted EMMs) of the group of pea lines carrying different rare haplotypes (cumulated frequency > 5%; n ≥ 2 rare haplotypes) was compared to that of the group of pea lines carrying the favorable or unfavorable haplotype, using the Tukey-HSD test (a = 5%). Results of mean comparison tests were shown using box plots (R function ggplot of package ggplot2;  Wickham, 2016).
Phenotypic data analysis In both Eden x E11 and Eden x LISA AB populations, global statistical analyses of disease scores obtained in field and controlled conditions experiments revealed significant genotype x environment (p-value < 0. 05) and highly significant genotype x strain (p-value < 0. 001) interaction effects, respectively, except for the first ADI ratings evaluated in Riec-sur-Belon and Dijon-Epoisses in the Eden x E11 population. The analysis of phenotypic and QTL data was therefore carried out for each environment and strain. Individual statistical analyses of all A. euteiches resistance and flowering traits evaluated in both AB populations displayed significant G effects (p-value < 0. 01), except for the EL_RI21_RRI variable (p-value = 0. 07). They showed significant R A or R Gr effect for field variables (p-value < 0.
95). Frequency distributions of EMMs values for each variable tended to fit normal curves except for flowering traits which showed bimodal distribution. Transgressive segregation was observed for all the traits (Supplementary Figure  1 ; Supplementary Table  1 ). In both AB populations, all ADI scores assessed in Riec-sur-Belon and Dijon-Epoisses were highly significantly and positively (r > 0. 41, p-value < 0. 001) correlated within each location, and most of the ADI variables showed significant and positive correlations between locations. RRI scores assessed in Riec-sur-Belon were poorly correlated with other field data except for ADI scores evaluated in the Eden x LISA population in the same environment (r > 0. 21, pvalue < 0. 01). In the Eden x E11 population, RB84 and Ae109 strain data were significantly correlated between each other (r = 0.
Four additive-effect QTL associated with early flowering were identified, including QTL co-localizing with resistance genetic regions detected on LGII in the Ae-Ps2. 2 region and LGIII close to the Hr or Le locus, in both populations. In the co-localizing region on LGII, the QTL detected for flowering showed a major additive-effect (46. 8% < R² < 70. 6%, depending on the AB population) and an epistatic effect (R 2 = 3. 2%) with another QTL detected for flowering in the Eden x E11 population. In this region, resistance-increasing alleles from E11 or LISA contributed to late flowering. A total of 50, 31, 37, and 13 resistance additive-effect QTL previously detected in  Pilet-Nayel et al. (2002; 2005)  and  Hamon et al. (2011; 2013) , were re-identified in the Baccara x PI180693, Baccara x 552, DSP x 90-2131, and Puget x 90-2079 RIL populations, respectively, using supplemented genotyping datasets and updated linkage analysis studies.
Genome-wide association mapping LD, structure and kinship data on the pea-Aphanomyces collection were updated using the genotyping dataset of 10,824 SNPs on 172 pea genotypes, presented in  Desgroux et al. (2016). The LD decay value averaged 0. 14 cM (Supplementary Figure  2A ), which was close to the value (0. 12 cM) presented in  Desgroux et al. (2016). Ten genetic sub-populations were identified in the collection, according to the optimal likelihood value of K computed by ADMIXTURE (Supplementary Figure  2B ). These results completed the PCA analysis conducted in  Desgroux et al. (2016) , which integrated into GWAS the structure captured by the three first PCA axis. Kinship analysis revealed IBD relatedness coefficients ranging from -0. 34 to 1. 12 among individuals with an estimated average of -0.
In QTL mapping from the four RIL populations, higher minimum LOD score thresholds (2. 8 < LOD < 4. 8) were estimated in updated composite interval mapping analyses compared to the ones (2. 8 < LOD < 2. 9) computed in previous studies. This resulted in the detection of more consistent QTL and fewer putative false positives, confirming the seven main Ae-Ps meta-QTL and invalidating 15 to 42 minor-effect resistance QTL (R 2 < 27. 2%) previously detected, depending on the population. In addition, in the Baccara x PI180693 population, updated analysis identified mismatches between phenotyping and genotyping datasets from  Hamon et al. (2011) , showing three spurious resistance QTL associated with resistance to the Ae109 and/or RB84 strains that were previously detected on LGII, LGVI, and LGVII (8.
Conclusion This study provides an overview of the diversity of QTL and haplotypes that significantly contribute to Aphanomyces root rot resistance in pea, by integrating AB-, RIL-linkage mapping and GWAS data using 1,850 common SNP markers. Most of the previously identified resistance QTL were confirmed and mapped onto a consensus marker map. No new consistent resistance QTL were identified in both Eden x E11 and Eden x LISA AB populations. However, ten consistent genetic regions comprising resistance QTL with closely linked new SNPs, as well as favorable haplotypes in these regions, were identified and appear to be good choices for future resistance allele pyramiding in marker-assisted selection strategies. New relevant rare haplotypes identified in new sources of resistance and negative associations between resistance and undesirable alleles in targeted regions will remain to be explored in future pea breeding programs.
Another major challenge will consist in identifying and validating candidate genes underlying Aphanomyces resistance QTL in pea. variables evaluated in the Eden x E11 and Eden x LISA AB populations, respectively. 7 th sheet: description of resistance, flowering, and morphological variables presented in these phenotyping datasets. N. B. The adjusted means of phenotypic data in the pea-Aphanomyces collection can be found in the Additional file 16 of  Desgroux et al. (2016).
SUPPLEMENTARY TABLE 2 Consensus marker map and genotyping data used in this study. 1 st sheet: consensus marker map DORA including 16,647 markers used to genotype one or several of the seven populations used in this study, synonymous marker names from  Duarte et al. (2014)  and  Tayeh et al. (2015)  are indicated in the second column; Genotyping matrices from: (2 nd sheet) the pea-Aphanomyces collection (10,824 filtered markers), (3 rd to 6 th sheets) RIL populations derived from the crosses Baccara x PI180693, Baccara x 552, DSP x 90-2131, and Puget x 90-2079, respectively (1,866, 1,082, 950, and 669 filtered markers, respectively), and (7 th to 8 th sheets) AB populations derived from the crosses Eden x E11 and Eden x LISA, respectively (993 and 478 filtered markers, respectively). 9 th sheet: coding information of reference parental alleles presented in previous genotyping datasets.
Acknowledgments We greatly thank the  Greenhouse-Experimental Facilities platform of IGEPP  for its contribution to plant material production and experimentation in controlled conditions, as well as the  Bioinformatics GO-GEPP  platform for having provided computing and data storage space on servers. We greatly thank the  INRAE experimental units of Dijon-Epoisses (U2E)  and  Le Rheu  ( UE La Motte) , as well as  UNILET  ( Union Nationale Interprofessionnelle des Leǵumes Transformeś, Quimperle, France)  for their contribution to the field experiments. We acknowledge  LGC Genomics service lab, UK , and  Limagrain Europe , FR, for the genotyping of plant material. We acknowledge GSP ( Groupement des Sélectionneurs de Proteágineux, France ) for having provided improved breeding lines for Aphanomyces resistance.
Funding This study was supported by the  pre-doctoral fellowship  of TL, provided by the  SPECIFICS project and Brittany region (France). It was funded by the  French DORA ,  PEAMAS  and  PEAPOL  projects, which were supported by the  FASO (Fonds d'Action Strateǵique des Oleóproteágineux)  managed by  SOFIPROTEOL , as well as by the  SPECIFICS and PeaMUST  projects. SPECIFICS  ( ANR-20-PCPA-0008 ) was funded by the " Growing and Protecting crops Differently" French Priority Research Program (PPR-CPA ), part of the  3 rd Program for Future Investments (France 2030 ) operated by the  French National Research Agency (ANR). PeaMUST  ( ANR-11-BTBR-0002 ) was funded by the  2 nd Program for Future Investments  operated by  ANR.
The Regional Estimates of VEgetation Abundance from Large Sites (REVEALS) model corrects for biases caused by these factors and provides the regional vegetation composition and land cover within a 1°× 1°area  (27). The REVEALS model has been extensively validated using both modern and historical analogs (see Materials and Methods)  (30) (31) (32). Pollen-based REVEALS reconstructions of vegetation openness over the Holocene were produced for 1°× 1°g rid cells across Europe  (7). However, REVEALS has only been used to reconstruct vegetation of the Last Interglacial period at single sites  (33)  and not at the continental scale. In this study, we applied REVEALS to a large dataset of Last Interglacial pollen records across Europe. We assessed vegetation openness in the European temperate forest biome, as well as adjoining biomes, before the arrival of H.