dataesr/scientific-paragraphs-categorization huggingface dataset:
The degree of decoding for each time point was then quantified by subtracting the condition-matched MD from the condition-mismatched MD. If the multivariate neural patterns of suppression in the arm dataset were in fact predictive of the patterns of suppression in the tool dataset, per our hypothesis, the condition-matched MD should yield smaller values than the condition-mismatched MD. We assessed this using the nonparametric cluster-based permutation test described above. Prior to this analysis, the trial-averaged MD values for each participant were smoothed using a Gaussian-kernel with a standard deviation of 8 ms. This approach increases the sensitivity of the analysis  [34]  and did not have an effect on our pattern of results. We restricted the present analysis (and therefore the multivariate patterns) to nine of the sensorimotor channels from the significant cluster in Experiment 1 (CP3, CP1, CPz, C3, C1, Cz, FC3, FC1, FCz) and seven ventral electrodes where no significant classification was expected (P7, TP9, TP7, T7, FT9, FT7, F7).
Hierarchical clustering The full dataset for each participant contains four distinct trial types that are derived from the combinations of trial condition and the surface touched. We used hierarchical clustering to assess their representational similarity. The Euclidean distance between the trialaverage neural patterns for each trial type was calculated for each participant. We then averaged the matrix of Euclidean distances across all participants and clustered the data using the hclust function in R version 3. 2. 3 [51]. This was done for two time points: (i) the earliest time point (52 ms) of significant cross-surface decoding described above; (ii) a later time point (80 ms) corresponding to the peak of the N80 (Figures  S1  and S3 ). We restricted the present analysis to nine of the sensorimotor channels from the significant cluster in Experiment 1 (CP3, CP1, CPz, C3, C1, Cz, FC3, FC1, FCz).
Support vector classification For each participant, we attempted to classify the four distinct trial types in their full dataset using a support vector machine (SVM)  [56]  with a radial basis kernel. Our classification scheme used 5-fold cross validation. Thus, we trained the classifier on four subsamples of the data (i. e. , 80% of the trials) and tested classifier performance on the leftover subsample (i. e. , the remaining 20% of the trials). Each fold had an equal number of items per trial type. The hyperparameters of the SVM, C and g, were tuned using grid search; tuning occurred separately for each of the five classification iterations. The features for classification were the neural patterns of suppression for each of the two time points mentioned above (i. e. , 52 and 80 ms). Classification was performed using the e1071 package  [57]  and its interface with LIBSVM  [58].
05). Given prior studies on the sources between 40 and 100 ms after stimulation  [22, 23, 26, 27] , we constrained our analyses to a region-of-interest covering contralateral MI, SI, and the PPC (1300 vertices). Our analysis focused on two time points in the observed windows of suppression discussed above: 52 and 80 ms. Qualitatively similar results were found when using a cluster-based approach to source reconstruction (data not shown). We further compared the sources for each surface, tool and arm, in the ten participants who completed both experiments. We first investigated the interaction between condition ('same', 'different') and surface (tool, arm) by using the above methods to compare the sources of suppression (i. e. , the arithmetic difference between both conditions) for both surfaces.
, the remaining 20% of the trials). Each fold had an equal number of items per impact location. The hyperparameters of the SVM, C and g, were tuned using grid search; tuning occurred separately for each of the five classification iterations. We specifically sought to characterize when a location-specific pattern emerged in the PC population's spiking pattern. The features for classification were therefore subsets of this population spiking across multiple temporal window sizes (5 to 50 ms, in steps of 1 ms). Classification was performed using the e1071 package  [57]  and its interface with LIBSVM  [58]. This was implemented with R version 3. 2. 3 [51]. Chance classification (i. e. , random guessing) was $50%.
DATA AND CODE AVAILABILITY Data for the EEG experiments and skin-neuron modeling is available here (https://osf. io/c4qmr/). For privacy reasons, this does not include the data from participant DC. Additional requests should be sent to the Lead Contact, Luke E. Miller (L. Miller@donders. ru. nl). e5 Current Biology 29, 4276-4283. e1-e5, December 16, 2019.
ACKNOWLEDGMENTS We thank  Fr ed eric Volland  for his help constructing the experimental setup. This work was supported by an  Fondation pour la Recherche Medicale Postdoctoral Fellowship   SPF20160936329  to L. E. M. , from  ANR-16-CE28-0015  Developmental Tool Mastery to A. F. and V. H. , from  ANR   BLIND_TOUCH 2019CE37  to A. F. and L. E. M. , by a  Leverhulme Trust   Visiting Professorship Grant  to V. H. , and from  IHU  CeSaMe  ANR-10-IBHU-0003 ,  Defi Auton Sublima , and the  James S. McDonnell Scholar Award  to A. F. All work was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Universit e de Lyon.
These values were selected as the 10% and 90% quantile of the empirical site depth distribution. Visual inspection of the depth distribution (Fig. S1 ), confirmed that these cutoffs were sufficient to remove the majority of sequencing depth outlier sites. To reduce the impacts of biased mapping between somatic and germline libraries, which could produce strongly skewed ancestry ratios, we mapped only the first read in each pair and subsampled the read length distributions of reads in each library to be exactly identical between somatic and germline samples after trimming adapter sequences using Trimmomatic v0. 32  (Bolger et al. 2014). Scripts to perform these trimming functions are provided in the GitHub repository associated with this project (https://github. com/russcd/MAP_SD). Finally, we discarded ancestry informative sites that are within 100 bp of one another to preclude the possibility that single reads would be counted twice.
We then evaluate the likelihood of the ratio K s in our germline sample and optimize the germlinederived read data to obtain K g. The relative likelihoods of the two ratios given the germline allele count data, R g , L(k = K s |R g ) and L(k = K g |R g ), then provides a straightforward means of evaluating the significance of the skew in ancestry at candidate distorting position i. Additionally, this approach can be used to identify the maximum likelihood estimate of the true distorting position and effect size by maximizing the likelihood ratio obtained across all possible positions along a chromosome. Software to perform this procedure is available from https://github. com/russcd/MAP_SD.
These k values were selected to approximate rates we discovered in our sequence data. For each replicate set of simulations, we recorded the width and positions of the confidence interval as well as the maximum likelihood position estimate of the distorting element. For each value of k, we ran 100 replicates and performed 200 bootstraps for each replicate simulation. Scripts to generate simulated distorting read counts are provided in the GitHub repository. Expression territories for A. thaliana orthologs as designated in Phytozome (https://phytozome. jgi. doe. gov/pz/portal. html) were determined from the Plant Ontology database (https://www. arabidopsis. org/tools/bulk/po/index. jsp). We used Gene Ontology functional annotation to determine whether genes in the identified intervals are involved in meiotic processes (GO:0051321), reproduction (GO:0000003), reproductive processes (GO:0022414), or any their 871 descendant GO terms.
Signatures of selection To test for selective sweeps in the intervals identified, we used genomic resequencing data  (Hämälä et al. 2018 ) from a population of A. lyrata that we chose from the same A. lyrata subspecies (A. lyrata petraea) as our A. lyrata parents. We aligned all short read data to the A. lyrata reference and recovered genotypes as described in  (Corbett-Detig et al. 2015a ). We used SWEEPFINDER version 2 (DeGiorgio et al. 2016) under default conditions to scan for signatures of recent positive selection and we retained outliers for the composite likelihood ratio (CLR) statistic that were greater than all of the nearest CLR peaks within 50 Kb. We then retained only those CLR peaks within the predicted confidence interval for each of the putative segregation distortion loci that showed an excess of A.
We note that this idea bears some similarity to commonly applied bootstrapping approaches for mapping confidence intervals in quantitative trait loci  (Visscher et al. 1996). In applying this bootstrapping procedure to simulated datasets, we found that the 95% mapping confidence interval performs approximately as expected, except for very weak effect distorters (k < 0. 505, Table  S3 ). That is, the 95% confidence interval contains the true distorter position in approximately 95% of replicate simulations. Furthermore, the width of the confidence intervals is inversely related to k. Nonetheless, at larger k values, the confidence interval may be slightly conservative and our program tends to overestimate the width of the confidence interval in simulations. These results therefore suggest that a bootstrap resampling approach can be used to construct map confidence intervals around distorting loci.
ACKNOWLEDGMENTS We thank  Chloé Ponitzki  for plant care and pollen collection and  Rasmus Nielsen  for help conceiving the likelihood-based mapping method. We thank  Tuomas Hämälä  for sharing the A. lyrata recombination map. Discussions with  Pierre Gerard ,  Adrienne Ressayre ,  Pierre Saumitou-Laprade ,  Juliette De Meaux ,  Gregor Schmitz ,  Claudia Köhler ,  Sylvain Legrand , and  Xavier Vekemans  are gratefully acknowledged. The project was initiated when VC was a  Fulbright Scholar  at  UC Berkeley in the Nielsen lab. The authors thank the  French Ministère de l'Enseignement Supérieur et de la Recherche , the  Hauts de France Region  and the  European Funds for Regional Economical Development  for their financial support to this project.
Thus, the daily REW was estimated for each combination of individual tree within each site and year for which ring data were available. The water balance model is described in Supplementary 2. To summarize a year of fluctuation of REW, we selected the lowest decile of the whole series of the year's daily REW values (D1rew). This index was considered because of its easy interpretation (the REW below which lay the 35 driest days of the year), because of its likelihood to carry the information of drought events and therefore to affect the growth, and finally because it ensures a proper coverage of the environmental gradient (Supplementary 1, Fig. S2 ), unlike e. g. , indexes based on a drought threshold that may not be reached some humid years. The environmental gradient coverage guarantees the stability of the regression parameters and the quality of the subsequent analyses.
Data availability Our datasets are available at the INRA repository GnpIS https://data. inra. fr/dataset. xhtml?persistentId=doi:10. 15454/REMJWZ&version=DRAFT (https://doi. org/10. 15454/REMJWZ). The Metagene simulator is available on the NOVELTREE project page https://data. inra. fr/dataset. xhtml?persistentId=doi:10. 15454/ ELCSZI (https://doi. org/10. 15454/ELCSZI). Supplemental material available at Figshare: https://doi. org/10. 25387/g3. 7211597.
Finally, we also fitted random regression using only a single observation per tree from the empirical dataset. As show in Supplementary 4, Fig. S10 , the average taxon-scale reaction norms thus estimated were close to the ones estimated using the whole dataset (Figure  4 ). The uncertainty was much higher using a single observation per tree, n Table  1  Estimation of the average slope for each taxon (1 st order parameter of the fixed component of the order 3 model) for ring width along the water availability gradient (D1rew), in each site (SA and SS). Point estimate: maximum a posteriori and in brackets: 95% CI.
ACKNOWLEDGMENTS The authors sincerely acknowledge the technical staff of  INRA experimental units (UE GBFOR and UE BIOGECO)  who have established, maintained and assessed the field trials as well as collected the increments cores. The authors thank Genobois platform technical staff who have prepared and X-rayed wood samples and provided microdensitometry profiles. We thank  Philippe Rozenberg  for his implication all along the paper. We thank the  think-tank 'PlasPhen'  for providing opportunities to move forward in our thinking on this paper, and in particular  Patricia Gibert  and  Vincent Debat  who organized it. We thank  Isabelle Cousin  and  Ghislain Girot  for their help with the interpretation of the soil data. We thank  Vincent Ducrocq  for his advices with the random regression model.
We thank  Météo-France  and the platform  INRA CLIMATIK  for the availability of the climatic data. F. Muñoz was partially funded by research grant  MTM2016-77501-P  from the  Spanish Ministry of Economy and Competitiveness. F. Muñoz  and  L. Sánchez  received funding from the  European Union 's  Seventh Framework Program  for research, technological development, and demonstration under grant agreement no. 284181  (' Trees4Future ', coord. L. E. Pâques).
Metabolomics Analysis A targeted, quantitative approach was used based on the AbsolutIDQ™ p180 kit (Biocrates, Innsbruck, Austria) using a Flow Injection Analysis and High-Performance Liquid Chromatography (HPLC-) Mass Spectrometry (MS/MS) assay. This assay kit enables the quantification of 188 metabolites  [7]. Cell lysates were obtained from 3 × 10 6 cells resuspended in ice-cold ethanol/0. 01M PBS subjected to freeze/thaw cycles. Samples were loaded onto a filter paper and dried in a stream of nitrogen for derivation with a solution of phenyl-isothiocyanate 5%. Subsequently, dried residues were extracted with methanol containing 5 mM ammonium acetate. The MetIDQ ® software (Biocrates) is used to calculate the concentrations of individual metabolites. Quality controls (QC) were analyzed regularly on the plate (every 8 samples) to ensure the stability of the mass system over time.
thaliana roots are ubiquitous in distantly related plant species. The conserved taxa that consistently associate with roots of A. thaliana might represent a widespread microbial multi-kingdom community that also associate with distantly related plant species. To test this hypothesis, we harvested co-occurring grasses at each of the 17 sites across Europe and compared microbial community composition between roots of A. thaliana and grasses. The factor host species weakly, but significantly explained bacterial, fungal, and oomycetal community composition in root endosphere samples when considering the whole dataset (1. 3%, 0. 89% 1. 8% of the variance, respectively, PERMANOVA with Bray-Curtis distances, p < 0. 01, Supplementary Table  5 ). The overall effect of host species is likely underestimated because we could not sample the same grass species at all sites and we pooled plant individuals for efficient RP and root fractionation (see Methods).
01) communities (Fig. 3a, b , and Supplementary Fig. 4b  and Supplementary Table  5 ). Consistent with earlier reports, a significant proportion of the variance remained unexplained in bacterial, fungal, and oomycetal communities in both soil and root compartments (> 40%, data not shown), likely arising from unmeasured environmental variables, stochastic processes, or species interactions such as microbe-microbe interactions. To identify signatures of microbial interactions, we quantified correlations between bacterial and fungal alpha diversity across sites. The correlation between bacterial and fungal diversity was positive in soil samples (Spearman´s rank correlation, observed OTUs: rs = 0. 30, p < 0. 001, Shannon index: rs = 0. 21, p = 0. 002), but negative in root endosphere samples (Observed OTUs: rs = -0.
16S rRNA gene and ITS read processing. All paired rRNA amplicon sequencing reads have been analysed with a pipeline described recently  3. The main parts include scripts from QIIME  56  and usearch  57. Differential abundance of taxonomic groups was tested using the Wilcoxon rank sum test (wlicox. test in R, FDR < 0. 05). For calculating site prevalence of OTUs, OTU tables were restricted to samples having > 1,000 reads. Count tables were transformed to relative abundances by division of total column (sample) sums. An OTU was marked as present in a given sample if its RA was > 0. 1%. OTUs present in < 5 samples were not considered further. Site prevalence reflects in how many sites (out of all European sites sampled in a given year) an OTU was present on average across years (here only years where an OTU was detected were taken into account).
To compare fungal OTUs, the representative sequences from geographically widespread OTUs were blasted against full ITS sequences from the UNITE database  62. The best hits received were then used to find similar OTUs in the L. japonicus data (98% sequence identity). Enrichment patterns of root associated OTUs in the transplant experiment (see heatmap in Supplementary Fig. 8 ) were examined as follows. For bacterial and fungal datasets, samples with less than 1,000 reads were removed, whereas for the oomycetal dataset, samples with less than 250 reads were removed. OTUs having a mean relative abundance > 0. 01% across all root samples were kept for the analysis. Relative abundance entries of zero were replaced by 0. 001%. All relative abundance values were log2 transformed and these data were used as input for generating a heatmap (heatmap.
2 function in R, gplots library). Enrichment in one of the six tested conditions (Swedish location, Italian location, Swedish soil, Italian soil, Italian location and soil, Swedish location and soil) was estimated by comparing the mean relative abundance of each OTU across all samples with the mean relative abundance in respective sample combinations. E. g. to be enriched at the Italian location, an OTU must be more abundant at the Italian location compared to the Swedish location, irrespective of the soil (see panel in Supplementary Fig. 8 ). OTUs that are not enriched in any of the conditions but are prevalent across samples (mean relative abundance above 0. 1%) are marked in grey in the heatmap. OTUs that were not prevalent and not enriched in any condition are not shown. Geographically widespread OTUs were identified by comparing representative sequences from the transect data with those of the reciprocal transplant experiment (Blast at 98% sequence identity).
reads. Individual data points within each box correspond to samples from the 17 natural sites (Kruskal-Wallis test, p < 0. 05). c, Comparison of taxa relative abundance (RA) between Swedish soils (SW1-4, blue) and the other European soils (grey) for bacteria (left), fungi (middle), and oomycetes (right). RA is aggregated at the class (bacteria and fungi) and order (oomycetes) levels and significant differences are marked with an asterisk (Wilcoxon rank sum test, FDR < 0. 05). d, Comparison of taxa RA between soil (dark red) and root (dark green) samples for bacteria (left), fungi (middle), and oomycetes (right). RA measured in soil and root samples across the 17 A. thaliana populations were aggregated at the class (bacteria and fungi) and order (oomycetes) levels. Significant differences are marked with an asterisk (Wilcoxon rank sum test, FDR < 0.
05). compartments and three successive years. Microbial communities are presented for the whole dataset for bacteria (upper panel), fungi (middle panel), and oomycetes (lower panel) and color-coded either according to the root pooling strategy (root from a single individual compared to pooled roots from four individuals), the harvesting year (2015, 2016, 2017), or the compartment. Additional soil, RP, RS, and Root samples (bacteria n = 238, fungi n = 241, oomycetes n = 236) from neighboring grasses were included in the PCoA plot where samples are color coded according to the host species. OTUs with relative abundance < 0. 1% were excluded from the datasets. samples (left panel) and root samples (right panel) (Spearman´s rank correlation, p < 0. 01). e, Correlation between properties and RA of oomycetal taxa (aggregated at order level, if available) in soil samples (left panel) and root samples (right panel) (Spearman´s rank correlation p < 0.
Acknowledgements We thank  Neysan Donnelly  for scientific English editing. This work was supported by funds from the  Max Planck Society  to S. H. and P. S-L. , a  European Research Council  starting grant (MICRORULES) to S. H. , a  European Research Council  advanced grant (ROOTMICROBIOTA), and the " Cluster of Excellence on Plant Sciences" program  funded by the  Deutsche Forschungsgemeinschaft  to P. S. -L. , and grants from the  Swedish Research Council  to JÅ. C. A. -B. lab was funded by grant  BIO2016-75754-P (AEI/FEDER ).
The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). DMI and IT were recorded with load cells. A total of 37 individual dynamic patterns of methane production were analysed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin's concordance correlation coefficient (CCC) of 0. 78 on average. When predicting the daily methane production, the CCC was 0. 99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. Since IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.
After a sensitivity analysis (not shown) the step time was fixed to ∆ = 1. 0 min. The model was implemented in the open source software Scilab (https://www. scilab. org). Model calibration was performed by minimizing the sum of squared errors beween experimental data and predicted output for each of the 37 dynamic methane patterns. The minimization was performed using the Nelder-Mead algorithm implemented in the fminsearch function of Scilab. Our grey box model has the simplest structure to represent the dynamics of methane emissions from time series data of DMI or IT. To assess if increasing model complexity could lead to gains in goodness of fitting, we tested the performance of different linear models (described by Laplace transfer functions) with higher number of parameters than our model using the Matlab® System Identification Toolbox  (Ljung, 1997).
Acknowledgements We thank  Dr. John A. Rooke  for helpful explanations on the respiration data from the study conducted at  SRUC. We thank  Dr. Glenn Marion  ( BioSS ) for his valuable comments for improving the clarity of the manuscript. John Fredy Ramírez Agudelo  is supported by a PhD grant of Colciencias (Colombia). BioSS and SRUC are funded by the  Scottish Government  through the  Strategic Research programme  of the  Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS). Helen Kettle  was also funded under the  Scottish Government's Strategic Partnership in Animal Science Excellence.
ACKNOWLEDGMENTS M. L-R is a fellow of DivIDE (https://divideeunetwork. org). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 675737. F. J. N is supported by the Gatsby foundation. We thank S. Diez, M. Braun and T. Surrey for discussion and all the authors of  [4]  which inspired this work. We thank S. Dmitrieff for critical reading of the manuscript.