dataesr/scientific-paragraphs-categorization huggingface dataset:
This indicates that the difference in model variation over space can be reflected in the new uncertainty U e. Each classic metric has its physical meaning: N. s. std represents the uncertainties over space and N. t. std represents the uncertainties across time. The comparison of U e with each of them demonstrates the metric performance on the same physical meaning. It is possible to compare U e with a combination of the two classic metrics, but the combination could be far more complex than a simple sum of the two classic metrics. However, a qualitative comparison is accessible be-Figure  9. Maps of deviations (U , U t , U s , U e ) estimated as the ratio of the square root of the corresponding variances (i. e. V , V t , V s , V e ) to the regional mean (µ) of the ensemble datasets in each of the three different precipitation groups.
These two components are closely related to the two classic metrics N. s. std (Eq. 28) and N. t. std (Eq. 29), respectively. By decomposing Eq. (  30 ), the contributions of the four components to the ensemble variance V e are shown in Fig. 11. For all three precipitation groups, σ 2 e is the dominant component simply because all the information on variations of the original datasets is retained in the uncertainty estimation. The other three components result from estimations after an averaging is performed, either over time, space, or the full spatiotemporal dimensions, which means a loss of information. The contribution of σ 2 e_t and σ 2 e_s approximates 0. 15 for the regions from 1 to 8. But σ 2 e_t increases for regions 9, 10 and 11, indicating that there is significant spatial heterogeneity in these regions.
In contrast, σ 2 e_s decreases because the spatial averaging has collapsed the spatial variations. The very small contribution of σ 2 e_s related to N. t. std is the cause of larger deviations between N. t. std and U e in these subregions (Fig. 10b ). Although any component can be used as a metric for evaluating the variations of multiple datasets, there are limitations for each of the variations. Regarding the variation of the temporal mean σ 2 e_t and spatial mean σ 2 e_s , the collapse of a dimension has ignored part of the information. Moreover, the variation of the grand mean σ 2 e (µ ts ) has ignored both the temporal variability and spatial heterogeneity, which further decreases its applicability to the assessment of uncertainty. The variation σ 2 e is estimated based on the original data without averaging, and thus it represents the most information.
In extreme cases, U e is more than double N. t. std (Fig. 12b ). The results show that the widely applied uncertainty estimates from the two classic metrics have underestimated the uncertainty of the various models or datasets. Such an underestimation may especially occur for the temporal assessment of the uncertainties (N. t. std), which is very commonly seen in scientific reports and articles to illustrate the temporal evolution of the variables of interest. 6 Discussion and conclusions.
Features and applicability of the approach The total variation across the database which consists of multiple datasets is contributed by the spatiotemporal variations as well as the uncertainties of ensemble datasets, while the uncertainty assessment with current approaches (e. g. Eqs. 28 and 29) needs either the temporal variability or the spatial heterogeneity to be averaged, which means a loss of infor-mation. The variance partitioning approach proposed in this study works in three dimensions. It uses all the information over both the temporal and spatial dimensions of the multiple ensemble members. It avoids the collapse of variation along any dimension, and thus the proposed uncertainty estimate U e provides a more accurate estimate of the uncertainty. The estimate U e is especially suitable for an overall assessment of the multiple datasets over a certain period and over a specific space.
Conclusions A new three-dimensional partitioning approach has been proposed in this paper to assess the model uncertainties of multiple ensemble datasets. The new uncertainty metric (U e ) is estimated with an overall consideration of temporal and spatial variations as well as the differences among the ensemble products. The results have shown that U e is generally larger than the classic uncertainty metrics N. s. std and N. t. std, which require a collapse of the variation along either the temporal or spatial dimension. The deviation occurs where the spatial variations are significant but being averaged in the N. t. std estimation. The decomposition of the total variance V e shows the complementary relation between the two classic metrics, and therefore the new uncertainty U e (derived from V e ) is a more comprehensive estimate of the uncertainty of multiple ensemble products.
Thirteen precipitation datasets generated by different methods have been categorized into three groups (namely, gauge-based products, merged products and GCMs) and the model uncertainty in the ensemble products has been analysed with the new approach and with the two classic uncertainty metrics for each precipitation group. Using the classic metrics, in most regions, uncertainty of GCMs has been found to be the largest. But the new estimator U e indicates that the largest model uncertainty occurs in specific regions no matter which precipitation group is considered. The impact of spatial heterogeneity on the model uncertainty has been represented well in the new uncertainty metric (U e ). In addition to the theoretical analysis of the components of U e , the overall model uncertainty U e can be used as a new uncertainty estimate which involves more information and should receive more attention in the field of uncertainty assessment.
Acknowledgements. The work was supported by computing resources of the  IPSL ClimServ cluster at the École Polytechnique, France. Financial support. This research has been supported by the  National Natural Science Foundation of China  (grant nos. 41561134016  and  51879068 ), the  ANR  (grant no. ANR-15-CE01-00L1-0L ), the  National Key Research and Development Program  (grant no. 2018YFC0407900 ), and the  China Scholarship Council  (grant no. 201506710042 ). Review statement. This paper was edited by  Dimitri Solomatine  and reviewed by two anonymous referees.
Data availability. The CMA dataset is available through http://data. cma. cn/data/detail/dataCode/SEVP_CLI_CHN_ PRE_DAY_GRID_0. 50/ (Zhao et al. , 2018). The GPCC is available through https://opendata. dwd. de/climate_ environment/GPCC/html/fulldata_v6_doi_download. html (Schneider et al. , 2011). The CRU is available through https://iridl. ldeo. columbia. edu/SOURCES/. UEA/. CRU/. TS3p21/ (Harris et al. , 2014). The CPC is available through http://ftp. cpc. ncep. noaa. gov/precip/CPC_UNI_PRCP/ (Xie et al. ,  2007). The UDEL is available through http://climate. udel. edu/data (Willmott and Matsuura, 2012). The CMAP is available through https://www. esrl. noaa. gov/psd/data/gridded/data. cmap. html (Xie et al. , 2003). The GPCP is available through https://www. esrl. noaa. gov/psd/data/gridded/data.
gpcp. html (Adler et al. , 2018). The MSWEP is available through https: //platform. princetonclimate. com/PCA_Platform/mswepRetro. html (Beck et al. , 2017). The ERA-I is available through https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era-interim/ (Dee et al. , 2011). The GCMs (including HadCM3, IPSL-CM5A-LR, CMCC-CM and MIROC5) are available through https://esgf-node. llnl. gov/projects/cmip5/ (Taylor et al. , 2012).
Embryonic Exposure to Moderate and High Dose Rates of IR Induces Promoter Hypomethylation To assess whether IR can modify epigenetic marks and thereby modulate transcriptional activity, we analysed DNA methylation at the whole genome scale using WGBS on shield stage embryos. The two highest dose rates of 5 and 50 mGy/h were used as these exposures lead to many DEG. The methylation levels of 18,322,470 and 17,759,785 CpG were quantified, respectively, at 5 and 50 mGy/h, and compared to controls. The global methylation level of 5-methyl cytosine (5mC) in the CpG context was close to 80% in the control group (Figure  3a ), in accordance to previous zebrafish studies on similar embryonic stages  [16]. No change in global methylation levels was detected after exposure to IR (Figure  3a ). As expected, a fraction of known CpG islands (CGIs) was highly methylated in control embryos and confirmed the quality of the WGBS data (Figure  3b ).
It is usually assumed that promoter hypomethylation is associated with an open chromatin state that activates transcription. To check whether IR-induced DNA hypomethylation was associated with an active or inactive transcriptional state, we mapped the 5mC obtained by WGBS against the histone marks H3K27me3 and H3K4me3 from a published ChIP-Seq dataset made at the same developmental stage  [30] , as these marks are associated with promoters repression and activation, respectively, after the MZT  [31]. High levels of 5mC were observed in the vicinity of both histone marks in controlled conditions, as expected (Figure  3h, k ). Furthermore, DNA hypomethylation was observed in the vicinity of both H3K27me3 (Figure  3i ,j) and H3K4me3 (Figure  3l ,m) marks, showing that DNA hypomethylation could be associated with both repressed and active promoters.
Taken together, our results indicate that the functional defects in neurogenesis and muscle development observed at 24 hpf and up to 5 days post fertilisation, might have their root, at least in part, in early developmental perturbations in the morphogenesis of the neuroectoderm and the mesoderm. In a recent study, Hurem et al. [49]  described the effects of IR (at dose rates higher than 0. 54 mGy/h) on zebrafish gastrulation. The authors described a deregulation of RA and Notch signalling, as well as other important developmental genes like vegfab, apoA1b, sox2 and vox. In addition, the authors used the Ingenuity software (QIAGEN, Inc. , California, USA https://targetexplorer. ingenuity. com/) to describe potential upstream regulators (myc, tp53, tnf and hnf4a) and potential disease networks (brain malformation, growth failure, necrosis and hypoplasia of organs).
If the effect on important developmental pathways (in particular RA and Notch signalling) was clear, the authors did not describe how IR can alter the morphogenesis of the three germ layers. In addition, the relationship between upstream regulators and downstream effects did not reveal any clear hypothesis on how the TF expressed during gastrulation were altered and how these effects could translate to detrimental effects at later stages (besides the potential role of myc in the induction of tumorigenesis and tp53 induction of apoptosis). Presumably, the usage of the Ingenuity software precluded such analysis, as its knowledge-base is using annotations from human, rat and mouse  [50]  but not zebrafish. In the present study, we confirmed the deregulation of Notch and RA signalling at 5 and 50 mGy/h.
Sequencing Libraries Preparation and Data Generation for Transcriptomic Analysis Total RNA extraction of biological replicates (3 to 6 per condition) were made from pools of 40 embryos at 6 hpf. Total RNA extraction was performed by TRIzol/chloroform extraction (Life Technologies). RNA integrity (RIN), quality and concentration were assessed using RNA Nano Chips (Bioanalyzer 2100, Agilent). All samples had a RIN > 8. Sequencing libraries were generated from 1 µg of total RNA following the TruSeq mRNA stranded protocol (Illumina, San Diego, CA, USA). After quality check and concentration determination on DNA1000 Chips (Bioanalyzer 2100, Agilent, Les Ulis, France), libraries were run on a NovaSeq 6000 platform to produce 50 bases long paired-end reads (Clinical Research Sequencing Platform, Broad Institute, MIT, Cambridge, USA).
Between 27 and 158 million of good-quality reads (Q > 30) were produced for each sample (Table  S5 ), which corresponds to the minimal coverage recommended by ENCODE (https://www. encodeproject. org/about/experiment-guidelines/). Read quality was assessed with FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/), adapter sequences were removed with TrimGalore! v0. 6. 4 (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore/) and mapped against the GRCz11 zebrafish reference genome using RNA-STAR v020201  [72]  and the known exon-exon junctions from Ensembl release 95  [73]. Normalisation and differential expression analysis were performed with DESeq2 v1. 22. 2  [74]. Biological reproducibility was assessed by hierarchical clustering of the variance stabilised expression data (rlog) obtained from DESeq2 with Pearson's correlation and complete linkage method.
Gene Ontology Analysis Gene ontology (GO) enrichment was performed using the R packages topGO  [76]  and clusterProfiler  [77]  using the zebrafish DEG obtained from DESeq2 (Table  S1 ). For KEGG enrichment, the human orthologous genes were used, as described before  [25]. Enrichments with p-value from Fisher's exact test ≤ 0. 01 were considered significant. MA-plots, heat maps, histograms and Venn diagrams were produced using the R package ggplot2  [78].
TF-Binding Sites Enrichment Significantly (|fold change| ≥ 1. 5 and adjusted p-value < 0. 01) DEG found at 50, 5 and 0. 5 mGy/h were searched for enrichment of TF DNA-binding sites in their promoter sequence. A set of 5000 randomly chosen promoters was used as background. Promoter sequences were defined as DNA genomic sequence located 2 kb upstream and 50 bp downstream the transcriptional start site, and were retrieved with the Perl Applied Program Interface from Ensembl release 95. The resulting promoter sequences were searched for enrichment of TFBS with oPOSSUM v3 [79] using the hidden Markov matrix models from Jaspar core-vertebrate database v2018 [80]. Binding sites with z-score > 6 and Fisher-score > 3 were considered significantly enriched.
Bisulphite Conversion and Production of WGBS Data Precisely, 300 ng of extracted genomic DNA was used for bisulphite conversion using the EZ DNA Methylation-Gold Kit (Zymo research, Irvine, CA, USA) following manufacturer's instructions. All samples were spiked-in with 0. 5 ng of PUC 19 nonmethylated DNA (Zymo research, Irvine, USA) to control bisulphite conversion rate (Table  S6 ). Quality control of converted DNA was assessed on RNA Pico Chip (Bioanalyzer 2011, Agilent, Les Ulis, France) and by measuring recovered ssDNA quantity with a Nanodrop (Thermo Fisher, Villebon-sur-Yvette) spectrophotometer. Sequencing libraries were prepared from 50 ng of converted DNA with TruSeq DNA Methylation Kit and indexes from Illumina following manufacturer's instructions. DNA integrity, quality and concentration were assessed on High Sensitivity DNA Chip (Bioanalyzer 2011, Agilent, Les Ulis, France).
Bioinformatic Analysis of WGBS Data Between 154 and 181 million of good-quality reads were produced for each sample (Table  S7  and Figure  S7 ). Read quality was assessed with FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Adapter sequences and low read quality (Phred score < 30) were trimmed with TrimGalore! v0. 6. 4 (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore/). Trimmed reads were mapped against a fully C-to-T converted version of the zebrafish genome (GRCz11) with Bismark v0. 16. 3  [81]  with the following options: -bowtie2 -X 1000 -N 1 -ambig_bam -nucleotide_coverage -un -ambiguous -phred33-quals. Methylation bias (M-bias) plots were generated with bismark_methylation_extractor with the options -mbias_only -no_overlap. PCR duplicates were removed using the Bismark function deduplicate_bismark and methylation levels obtained with bismark_methylation_extractor using the following options: -bedGraph -cytosine_report -gzip --no_overlap -p -ignore_3prime 6 -ignore 13 -ignore_r2 13 -ignore_3prime_r2 6.
Bisulphite conversion rates were >96% (Table  S6 ) and mapping efficiency against the zebrafish genome were >67% for all samples. Methylation levels in the CpG context were analysed with the R package DSS  [82]. Methylation levels at each CpG site was determined in the biological triplicates from nearby CpGs using DMLtest function (window size of 300 base pairs) which uses an empirical Bayesian procedure to estimate the dispersion among all CpGs within the smoothing window. Methylation difference in pairwise comparison (exposed compared to control) was then assessed by a Wald test and adjusted p-value (False Discovery Rate) computed at each CpG site. Differentially methylated cytosines were considered significant at a threshold FDR < 5% and methylation differences ≥ 10%. Differentially methylated regions (DMR) were analysed with DMRichR  [83] [84] [85]  as a wrapper for dmrseq  [84]  and bsseq  [85].
Association with Genomic Features Heatmap of DEG associated with germ layers information was obtained by querying the expression database from ZFIN for the stage "gastrula" and the expression domains "ectoderm," "endoderm" and "mesoderm" and selecting the DEG with |fold change| ≥ 1. 5 and adjusted p-value < 0. 01 in at least one comparative analysis. Clustering of expression differences (fold change) data was performed using the default of the R package heatmap. 2. For CpG analysis, the genomic coordinates of known TSS were retrieved from all Ensembl transcripts (GRCv11 release 95) via the R package GenomicFeatures and GenomicRanges  [86]. CpG islands, LINE, SINE and transposon genomic coordinates were retrieved from the USCS database (danRer11). Dot plot of cytosine methylation levels mapped to TSS and CpG islands were produced with the R packages EnrichedHeatmap  [87]  and circlize  [88]  using a distance to the feature of interest of 2 kb and a sliding window of 50 bp.
Analysis of H3K3me3 and H3K27me3 ChIP Data ChIP-seq datasets made on shield zebrafish embryos against H3K3me3 (SRR372771) and H3K27me3 (SRR372772) marks were trimmed for quality (Q > 10). Adapter removal was made with TrimGalore! v0. 6. 4. Reads were then mapped against the zebrafish genome (GRCv11) with bowtie v1. 2. 2 using the parameter -m1. Uniquely mapped reads were used for peak detection using MACS2 using an effective genome size of 1. 3. 10 9 bp. Genomic coordinates of the ChIP peaks were manipulated in R with the package GenomicRanges and dot plot of cytosine methylation levels mapped around H3K27me3 and H3K4me3 produced with the R packages EnrichedHeatmap and circlize.
Acknowledgments: We want to thank  Nicolas Dubourg  for the computation with MCNPX and operational dosimetry measures, as well as  Isabelle Cavalié ,  Virginie Camilleri ,  Caroline Arcanjo ,  Olivier Simon  and  Beatrice Gagnaire  for the zebrafish maintenance required for this work. We also want to thank  Philippe Beudaert  and  Adrien Moulin  ( IRSN/DTR/D3NSI ) for their help on the IRSN's Linux cluster CADUX at  IRSN (Cadarache, France ).
Leaf sampling and traits Ten leaves were sampled from the encroaching woody species at each study site (n=10 x 13 sites= 130 leaves). Leaves were kept moist and weighed within 24 hours of sampling on a microbalance to obtain fresh weight (g). Leaves were then placed in paper envelopes and left to air dry until shipping. We measured the following leaf functional traits for each woody plant species: leaf dry matter content LDMC (g/g), specific leaf area SLA (cm 2 /g), leaf N (%), leaf C (%), 13C, and 15N. Leaves were scanned on a flatbed scanner to calculate leaf area (cm 2 ) using ImageJ software (https://imagej. nih. gov/ij/). Leaves were dried (60 C, 72 hours) and then weighed for dry weight (g). LDMC was calculated as the ratio of fresh weight (g) to dry weight (g) and SLA was calculated as leaf area (cm 2 ) to dry weight (g).
We then ran another round of cleanup and quantified PCR products using the Quant-iT PicoGreen dsDNA assay kit (Life Technologies Inc. , Grand Island, NY, USA). As a final step, the samples were pooled in equimolar concentrations and sequenced in a multiplexed 2-x 300-bp paired-end sequencing run on the Illumina MiSeq platform (Illumina Inc. , San Diego, CA, USA) at the Genomics Core Facility, University of California Riverside (USA). Bioinformatics ITS-2 sequences were analyzed using AMPtk: Amplicon Toolkit for NGS data (Palmer, Jusino, Banik, & Lindner, 2018) (https://github. com/nextgenusfs/amptk). Demultiplexed paired-end sequences data were pre-processed by trimming primer sequences, trimming forward and reverse reads to 250 bp (reads length less than 100 bp were dropped), and merging paired-end reads using USEARCH v9.
1. 13 (Edgar, 2010). A total of 8,310,353 reads passed the preprocessing steps and reads were filtered based on quality scores with a cutoff of an expected error less than 0. 9  (Edgar & Flyvbjerg, 2015)  to produce 6,441,443 reads which passed quality filtering. The quality filtered reads were clustered into 19,790 Operational Taxonomic Units (OTUs) using UPARSE  (Edgar, 2013)  at 97% identity threshold. The OTUs were further processed with VSEARCH (v 2. 3. 2)  (Rognes, Flouri, Nichols, Quince, & Mahé, 2016)  to identify and remove 569 chimeras based on comparison to the UNITE database v8. 0  (Nilsson et al. , 2019)  leaving 19,221 OTUs. We assigned taxonomy with the AMPtk "hybrid" approach which uses Global Alignment, SINTAX, and UTAX. Lastly, sequences were rarefied to 10,000 sequences per sample and processed with QIIME Core Diversity pipeline  (Caporaso et al.
Alpha (OTU richness) and Beta diversity measures (Weighted UniFrac distance)  (Lozupone & Knight, 2005)  were estimated using a subsampled feature table containing 10,000 sequences per sample. Taxonomy was assigned to 34,417 unique sequences using a Naïve Bayes classifier trained on the GreenGenes database  (McDonald et al. , 2012)  (version 13_8) using trimmed sequences pre-clustered at 99% similarity. After all sequence processing we retained N=224 unique samples for fungi and N=215 unique samples for bacteria.
Climate data To test the interaction between site specific changes in climate and the influence of woody plant encroachment, we acquired climate data for each site through the WorldClim v 2. 1 database at 30 second resolution  (Fick & Hijmans, 2017). We tested the influence of multiple climate parameters at each site including: Mean Annual Temperature (MAT), Temperature Seasonality (standard deviation x100), Maximum Temperature of Warmest Month, Minimum Temperature of Coldest Month, Mean Annual Precipitation (MAP), and Precipitation Seasonality (Coefficient of Variation). We chose to use the 30-year climate normals (WorldClim) rather than annual climate data because our analyses aimed at understanding climatic control over broad geographic variation in microbial communities. We found substantial climate variability across sites and symbiont types (Fig  S1 ), but found that overall MAT was the best univariate predictor of microbial diversity  (Fig S2).
Statistical methods Leaf traits We used Principal Components Analysis (PCA) to collapse the values of the six measured leaf traits into two PC axes to be used in hierarchical models (below). Prior to the PCA, we infilled missing leaf trait data (LDMC and leaf chemistry) for one site where only SLA could be measured (China) and any NA values using the package mice in R (R Core Team, 2019; van Buuren & Groothuis-oudshoorn, 2011), taking the average of 100 imputed values for each trait estimate. All data were logged prior to PCA. Leaf traits and principal components scores were averaged by (woody) plant species at each site. We also tested for a difference in leaf N between root symbiont types, due to frequently higher N in tissues of N 2 -fixing plants. We used one way ANOVA with leaf N (%) (logged) as the response and symbiont type (N 2 -fix, ECM/ERM, AMF) as the predictor, followed by a Tukey's HSD test.