dataesr/scientific-paragraphs-categorization huggingface dataset:
petraea and Q. robur. The second assay targeted 240 SNPs in six multiplexes of 40 SNPs each. This assay was developed for this study from available genomic resources  (Lesur et al. , 2015)  and included 104 SNPs located in 51 genes differentially expressed in quiescent buds (QB) as well as 116 SNPs located in 58 genes differentially expressed in swelling buds (SB). This assay also included 20 unrelated control SNPs located in 15 housekeeping genes. Genotyping was performed on a MassARRAY System (Agena Bioscience, USA) using the iPLEX Gold chemistry following  Gabriel et al. (2009). Data analysis was performed with Typer Analyzer 4. 0. 26. 75 (Agena Bioscience). We filtered out all monomorphic SNPs, as well as loci with a weak or ambiguous signal (i. e. , displaying more than three clusters of genotypes or unclear cluster delimitation).
Linkage disequilibrium Pairwise linkage disequilibrium (LD) estimates were obtained within each of the genetic clusters identified by genetic clustering analyses (see below) using the LD() function in the R package genetics  (Warnes, Gorjanc, Leisch, & Man, 2021). The p. adjust() function was used to correct p-values for multiple testing with the Bonferroni method, and association between allele frequencies was deemed as statistically significant at a nominal significance threshold equal to 1✕10 -3. An ad hoc algorithm was devised to iteratively identify and remove the loci most frequently involved in pairwise significant tests, leading to cluster-specific lists of SNPs showing statistical evidence of linkage. The markers shared across all analyses were removed to obtain an LD-pruned version of the SNP dataset to be used in the analyses assuming linkage equilibrium among loci (i.
Each run consisted of 5×10 4 burn-in iterations and 1×10 5 data collection iterations. Ten independent runs were performed for each value of K. The average likelihood and ΔK statistics described in  Evanno et al. (2005)  were calculated for each K and used to identify the most-likely K-value. For informative values of K, distinct runs were averaged using CLUMPAK  (Kopelman, Mayzel, Jakobsson, Rosenberg, & Mayrose, 2015)  to obtain the final estimates of the membership coefficients (q-values) at individual and population levels. To comply with model assumptions, STRUCTURE was run first with the complete SNPs dataset, and then with the LD-pruned version of the dataset (once LD estimated within each cluster). DAPC is based on a discriminant analysis (DA) of genetic data preceded by a few analytical steps to meet its requirements, all implemented in the R package adegenet  (Jombart, 2008).
Since DA requires a priori definition of clusters, K-means clustering of principal components (PC) on individual allele frequencies was first used to identify both group priors and the most likely number of genetic clusters. K-means was run on 150 PCs with K ranging from one to 40, and the Bayesian Information Criterion (BIC) was used to assess the best supported K-value. Then, as DA requires the variables to be uncorrelated and fewer than the number of observations, we used a principal component analysis (PCA) and the randomization approach implemented in the a. score() function in adegenet to select the number of PCs optimizing the trade-off between power of discrimination and over-fitting. Finally, the DA was run on 23 PCs extracted from the original dataset.
Basic diversity and differentiation statistics We computed allelic richness (Ar) and mean number of alleles per locus (Na) using the R package diveRsity  (Keenan, McGinnity, Cross, Crozier, & Prodöhl, 2013) ; percentage of polymorphic loci using GenAlEx 6. 5  (Peakall & Smouse, 2012) ; observed (HO) and expected (HE) heterozygosity, Wright's inbreeding coefficient (FIS), and βWT (a plot-specific index of genetic differentiation relative to the entire pool; Weir and Goudet 2017) using the R package hierfstat  (Goudet, 2005). Parameters of genetic fixation (GST;  Nei, 1977)  and differentiation  (Jost's D; Jost, 2008)  among populations/clusters were also calculated with GenAlEx and their statistical significance assessed with 999 permutations.
Isolation by distance and barriers to gene flow We estimated spatial genetic structure (SGS) among populations and tested whether geographic distance significantly shaped the patterns of genetic differentiation (as estimated by FST/1-FST) using the software SpaGeDi 1. 4c  (Hardy and Vekemans 2002). To test for isolation by distance (IBD), the (FST/1-FST) values were regressed on ln(dij), where dij is the spatial distance between populations i and j. Then, we tested the regression slope (null hypothesis: blogFST = 0) using 5,000 permutations of genotypes over populations. These analyses were run both on the 64 populations and within each cluster identified with DAPC. For within-cluster analyses, we retained all the individuals successfully assigned to a given cluster (i. e. , those having a q-value above the nominal threshold of 0.
We ran the runeems_snps executable with 500,000 burn-in MCMC steps and 2✕10 6 subsequent iterations. To reduce the potential influence of grid size, we averaged the results over nine independent runs with different numbers of demes (nDemes=800, 1200 and 1600 with three repetitions each) and combined the results across the three independent analyses. A higher value for nDemes produces a denser grid, which increases the flexibility of the EEMS model but also the computational cost; so we chose nDemes values for which estimations remained tractable. We assessed convergence of runs, plotted geographic distance and genetic dissimilarity across demes, and generated effective diversity (q) and effective migration rates (m) surfaces using the R package reemsplots  (Petkova et al. , 2016).
Bioclimatic data Each sampling site was characterized by a set of 19 bioclimatic variables extracted from the WorldClim database v. 1. 4 (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005)  with a grid cell resolution of 30-arc second (ca 1×1 km) using DIVA-GIS v. 7. 5. These bioclimatic variables represent annual trends (e. g. , mean annual temperature, annual precipitation), seasonality (e. g. , annual range in temperature and precipitation) and extreme or limiting climatic factors (e. g. , temperature of the coldest and warmest month, and precipitation of the wettest and driest quarter). To calculate predictors in the following analyses, we used the mean values of these 19 bioclimatic variables (Table  S1 ) over the period from 1950 to 2000. To reduce the multidimensional bioclimatic data set to a few uncorrelated factors, we performed two PCAs using the R package FactoMineR  (Lê, Josse, & Husson, 2008) , one focusing on the temperature-related variables (BIO1 to 11), and the other focusing on the precipitation-related predictors (BIO12 to 19).
Isolation by distance and environment To evaluate the respective importance of IBD versus isolation by environment (IBE), we used a variance partitioning approach  (Legendre, Fortin, & Borcard, 2015). We partitioned the explanatory power (as expressed by the adjusted R²) of the climatic and spatial structures on the genetic structure. Genetic structure was obtained through a Principal Coordinate Analysis (PCA) on the matrix of pairwise population genetic distances as returned by GenAlEx  (Peakall & Smouse, 2012). Principal coordinates explaining up to 80% of the total variance were included in the response data table. The spatial structure was modelled by distance-based Moran's eigenvector maps (dbMEM;  Dray et al. , 2006) , as suggested by  Legendre et al. (2015) , and estimated by the mem() function in the R package adespatial  (Dray et al.
, 2018). We retained only the statistically significant eigenvectors modelling positive spatial autocorrelation and, therefore, describing global patterns sensu  Jombart et al. (2008). The climatic structure was summarized by temperature-or precipitation-related synthetic climatic variables (see "Bioclimatic data" section above). We assessed the relative contribution of climatic and spatial structure in explaining the genetic structure of populations using the function varpart() of the R package vegan  (Oksanen et al. , 2019). Significance of the variance components was calculated through an ANOVA-like permutation test for redundancy analysis (RDA) and partial ReDundancy Analysis (pRDA) based on 10,000 permutations  (Legendre & Legendre, 2012). All statistical analyses were conducted using R 3.
SNP dataset Of the 405 SNPs from the two multiplex arrays, 135 were discarded from the analyses: 9 failed to amplify in all samples; 50 were monomorphic; 75 were of poor quality (based on visual inspection of genotyping outputs); and one had a call rate <85%. Sixteen individuals were discarded due to a low call rate. The resulting dataset comprised 430 individuals and 270 SNPs (Supplementary Online Appendix A1), with 3-10 individuals per population (6. 7 on average). The 270 SNPs included 150 SNPs in 93 phenology genes, 109 SNPs in 38 stressrelated genes, and 11 SNPs in 8 housekeeping genes. After removing linked markers, the LDpruned version of the dataset comprised 212 SNPs.
Climate data analysis The first three principal components of the temperature-focussed PCA (Temp1, Temp2, Temp3) were retained, and accounted for 90. 9% of the total variance of the dataset (Online Appendix A3). Temp1 is an axis of mean temperatures, opposing hot (southern) to cold (northern) climates. Temp2 can be interpreted as an axis of climate continentality, opposing climates with strong versus weak variation of temperatures among years and seasons (e. g. continental vs. oceanic climates). Temp3 can be interpreted as an axis of climate xericity, opposing climates with a high diurnal range and the wettest season corresponding to the coldest months (i. e. , Mediterranean climates) to climates with a low diurnal range and the wettest season corresponding to the warmest months (i. e. temperate mesic climates).
The first three principal components of the precipitation-focussed PCA (Precip1, Precip2, Precip3) were also retained, and accounted for 98. 6% of the total variance of the dataset. Precip1 is a precipitation abundance axis, opposing wet (Great-Britain, Northern Italy) to dry climates (Greece, Spain). Precip2 is a precipitation variability axis, opposing climates with strong (Greece, Italy) versus weak (France) variation of precipitation. Precip3 captures the coupling between precipitation and seasonal temperatures, opposing climate where high precipitation occurs during the vegetation period (Poland, Romania) to those where high precipitation occurs in winter (Greece, Italy). populations in the blue areas are connected by higher migration rates than expected under isolation by distance (IBD) while the ones in the orange areas have lower migration rates than expected and are interpreted as migration barriers.
Acknowledgements We thank  Dalibor Ballian ,  Peter Zhelev  and  Aristotelis C. Papageorgiou  for providing beech samples from  Bosnia and Herzegovina, Bulgaria and Greece , as well as  Ilaria Spanu ,  Mariaceleste Labriola ,  Catia Boggi  and  Adline Delcamp  for technical assistance during lab work. SOM and AH were supported by the  French National Research Agency Biodiversa  project  TipTree  ( ANR-12-EBID-0003 ); AP, FB and GGV by the  Italian Ministry of University and Research  ( FOE-2019 ) under the projects " Climate Change " ( CNR DTA. AD003. 474 )and " Green & Circular Economy " ( CNR DTA. AD003. 139 );and AH by the  French National Research Agency Cluster of Excellence  project  COTE  ( ANR-10-LABX-45 , CLIMBEECH). The MassARRAY genotyping was performed at the  Genome Transcriptome Facility of Bordeaux  (grants from  EU   FP7 through Trees4Future research infrastructure n° 284181 , from the  Conseil Régional d'Aquitaine n°20030304002FA  and  20040305003FA , from the  European Union   FEDER n°2003227  and from  Investissements d'Avenir   ANR-10-EQPX-16-01 ).
The time and frequency resolution are reached by shifting the wavelet along the time series, then stretching or compressing the wavelet, and repeating the procedure again. This delivers a two-dimensional wavelet power spectrum (WPS), which has a power value for each combination of time and frequency (for more information, see Appendix A). Wavelet analysis has been successfully applied to a number of phenomena in K2 light curves: stellar rotation  (Mathur et al. 2014; García et al. 2014; Bravo et al. 2014) , planetary transits, stellar variability, pulsation, and binaries  (Bravo et al. 2014). The implementation in python runs with the package pywt. Because the time series must be evenly sampled for wavelet analysis and some values in the light curve are flagged out during the data reduction, the light curves are linearly interpolated onto even time steps in a preliminary step.
(2015)  is EPIC 247885481 (Sec. 3. 3). It does not appear in Fig. 11  because the width of the dips cannot be constrained reliably in a standardized way. The amplitude is very small compared to the noise, and the dips do not clearly appear in each phase (see Sec. 3. 3). The star is an M dwarf and is therefore compatible with the group identified in NGC 2264. A correlation between R cor and the dip width in units of phase is presented in Fig. 12. Although weaker (Pearson's r = 0. 63), a behavior similar to that of Fig. 11  is evident. This is expected because the corotation radius as derived in Eq. 4 is a function of the stellar period. In this case, the correlation is evident even without considering the two data points in the lower left corner, which only argue for a logarithmic instead of a linear dependence.
We adopted a spectral type of M0. 4. The inclination of 60 • agrees with the inclination derived here. Guo et al. (2018)  linked the 7 d period that we also found to a spot. In their optical monitoring from 2014 to 2016, the folded light curve is clearly sinusoidal, which is far from the observed behavior in the K2 light curve. Guo et al. (2018)  observed quasiperiodic dips on timescales that are a multiple of the rotation period. The folded K2 light curve is highly irregular and contaminated by GK Tau. The first peak in the periodogram is a periodicity related to GK Tau and not to GI Tau. The 7. 1 d period agrees with previous data  (Artemenko et al. 2012; Percy et al. 2010; Güdel et al. 2007; Rebull et al. 2004). Our observed amplitude of 0. 4 mag is lower than the variability observed by  Guo et al.
Acknowledgements. We wish to thank the anonymous referee for constructive comments which have improved the clarity of this paper, and  Didier Fraix-Burnet  and  Gérard Grégoire  for the helpful discussions. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  742095 ;  SPIDI: Star-Planets-Inner Disk-Interactions ). Some of the data presented in this paper were obtained from the  Mikulski Archive for Space Telescopes (MAST). Support for MAST for non-HST data is provided by the  NASA Office of Space Science via  grant  NNX09AF08G  and by other grants and contracts. This research has made use of data products from the  Two Micron All-Sky Survey  ( 2MASS ), which is a joint project of the  University of Massachusetts  and the Infrared Processing and Analysis Center, funded by the  National Aeronautics and Space Administration  and the  National Science Foundation.
Raw data processing Timing: $12 h on Genotoul Galaxy cluster 48 broadwell nodes (each 64 threads, 256 GB of memory) 33. Quality control of the raw data from sequencing a. Use FastQC (v0. 11. 5) to check the quality of each raw fastq file from each sample: two files per sample, one for read1 and one for read2 of the RNA fragment sequenced paired-end  (Andrews, 2010). Note: This tool provides some quality control checks on raw sequences coming from the sequencing platform in FASTQ format and gives an overview of the data quality with graphs and indicators in a HTML report. The report contains basic statistics, sequence quality summary, sequence content summary, information about read lengths and estimation of duplication levels. It also lists potential overrepresented sequences and verifies the presence of reads' adapters based on a list of known adapters.
b. Use MultiQC (v1. 7) to aggregate all FastQC reports and generate one single HTML report summarizing the output from all the FastQC reports (Figures  3A  and 3B )  (Ewels et al. , 2016). 34. Alignment of the raw data from sequencing on the reference genome Use STAR aligner (v2. 1) to align paired-end files from raw sequencing on the reference mouse genome from Ensembl database: FASTA and GTF file. Note: Based on sequential maximum mappable seed search in uncompressed suffix arrays, STAR is reliable, accurate, aligner designed for RNA-Seq dataset. The output is a single aligned file per sample in sequence alignment map (SAM) format  (Dobin et al. , 2013 ). Note: For example, we perform alignment on the following version of the mouse reference genome: Ensembl Mus Musculus GRCm38. 91 (mm10).
Visualization of the results Timing: $3 h on personal computer with R (v3. 6) 47. Global visualization of the normalized samples a. To get an overview of the samples after normalization, apply Principal Component Analysis and Hierarchical Clustering (Euclidean distance -Ward criterion) on the normalized data set (Figures  5C  and 5D ). b. Control correlation between sample computing Pearson correlation and use the ggplot::heatmaps. 2 R-package (v3. 1. 1) to plot the correlation results in a heatmap plot. (Warnes et al. , 2015)  48. Global visualization of the DESeq2 outputs In order to visualize the result of each DA, generate plots from DESeq2 output: a. MA plot (log (basemean) against log2FC for each gene) b. Volcano plot (log2FC against log10 (adjusted p value)) (Figures  5E-5G ). c. For the top 20% most variable mRNA, based on the |log2FC|, compute euclidean distances between the cell types and plot results using the ggplot::heatmaps.
Potential solution While bioinformatics steps can be done with Galaxy user-friendly interface, the next steps: quality controls of the read count, differential analysis, intersection between differential analysis and figures must be done in R which is both a programming language and free GNU package software designed for statistical computing. R provides precompiled executables that need to be run using the command line. These steps could be a major challenge for a non-bioinformatic user. To overcome this issue free graphical user interfaces such as RStudio, are available to provide an integrated development environment helping the user to address this challenge.
Materials availability This study did not generate new unique materials. Data and code availability RNA-seq data and processed files used during this study are available  (Ohayon et al. , 2021)  and deposited on Gene Expression Omnibus (GEO: GSE158517). An example code for this protocol is on: https://github. com/MarionAguirrebengoa/ CombinedRNAseqAnalysis_AstrocyteSubtypeProject.
ACKNOWLEDGMENTS We especially thank  N. Rouach  and  P. Durbec  for sharing of Aldh1L1-GFP and Olig2-tdTomato mice, respectively, and  E. Nasser  and  P. Viana  for the technical support at the IPBS-FACS platform. We thank the  ABC facility and ANEXPLO  for housing mice. This protocol involves the support of a large number of individuals who contributed the original publication (Ohayon et al. , 2021) whom we acknowledge the help of and we thank. The graphical abstract was generated using https:// biorender. com. This work was supported by grants from  ARC  ( PJA20161204698 ),  ARSEP ,  CNRS , and  University of Toulouse.
It is increasingly evident that the pattern of viral elimination is often bi-exponential, although the second phase, which is close to the limit of accurate viral quantitation, may be unobserved if follow-up is not extended. In the context of a clinical trial viral clearance can be summarised in many different ways. The most commonly employed is time-to-clearance (the time to reach the lower limit of detection for a given assay)  [11]  but this is dependent on both the assay used and the baseline viral load (i. e. the viral load at enrolment in the study, which will depend on how cases are ascertained and the individual peak viral loads which vary substantially across individuals). We used prospectively gathered data from 280 infection episodes from individuals who had frequent qPCR sampling before and after peak viral load to characterise the kinetics of viral clearance and to propose robust summary statistics as pharmacodynamic endpoints for pharmacometric evaluations and phase 2 type clinical trials.
Data We obtained prospective longitudinal SARS-CoV-2 RT-qPCR testing data on 474 infection episodes from individuals in the US National Basketball Association's (NBA) occupational health program, observed between November 28 2020 and January 10, 2022. These infection data have been described previously in detail by  [2, 12, 14]. The data are openly accessible on github at https:// github. com/gradlab/CtTrajectories_AllVariants and https://github. com/gradlab/CtTrajectories_ Omicron. In brief, clinical samples were obtained by combined swabs of the anterior nares and oropharynx for each individual administered by a trained provider. Viral load was measured using the cycle threshold (CT) according to the Roche cobas target 1 assay. Vaccination information was reported and verified by NBA staff and the clinical operational team.
Data pre-processing For each infection episode, we initially defined day 0 as the day with the lowest observed CT value (i. e. highest nasopharyngeal or oropharyngeal viral loads). We selected all infection episodes where there were at least 5 samples with a CT value less than 40 and at least one sample with a CT value less than 30 in the time period -20 to +20 days for each individual (a total of 280 infections). To these infection data, we then fitted a simple Bayesian hierarchical 'up-and-down' phenomenological model to the ∆CT values (40-CT)  [13]. The model was specified as: ∆CT(t) = A 0 + log x + y ye -x(t-tmax) + xe y(t-tmax) where A 0 corresponds to the peak ∆CT occurring at time t = 0 (i. e. the intercept which is proportional to the peak log viral load); x is the growth rate in the initial viral proliferation stage; and y is the decay rate in the viral clearance stage.
Under this model, the intercept at time t = 0 is log e B0 + e A0 (which is approximately equal to A 0 if B 0 << A 0 ); α is the rate constant for the initial 'fast' decay; and β is the rate constant for the second stage slower decay. Model 3: Regularised splines We used penalised linear models from the R package mgcv which implements penalized regression splines with automatic smoothness estimation  [16]. We specified random effect terms for each infection episode. Error model for models 1-2 It was been observed previously that there is considerable variation in viral load estimates from oropharyngeal and nasopharyngeal swabs  [12, 13]. This can result from several factors including variations in local viral densities, in swab size and swabbing technique, number of cells, volume of eluate, and qPCR assay variability.
For each simulated dataset we then estimated the treatment effect by fitting the single exponential model (thus mis-specified by design). The treatment effect γ estimated under the log-linear model was also parameterised on the log scale representing the proportional change in the population clearance rate: ∆CT i (t, Z i ) = (A 0 + θ i,1 ) + αe γ1{Zi=1} e θi,2 t where Z i is the randomised treatment allocation (1 is treated, 0 not treated) and 1 is the indicator function taking value 1 if Z i = 1 and 0 otherwise. We gave γ a Normal(0, 1) prior. If the 95% percentile interval for the posterior distribution over γ did not include 0, this was defined as rejecting the hypothesis that the intervention had no effect. For comparison, we estimated Kaplan-Meier survival curves for the time to clearance in each treatment arm, whereby a clearance event was defined as both swabs on a given day being equal to 40 (∆CT=0).