dataesr/scientific-paragraphs-categorization huggingface dataset:
We then defined coverage criteria to determine whether observations represent forest communities (n = 3324), grassland communities (n = 3738) or none of the above (n = 4110) (Supplementary Tables  3  and 4 ). Estimating productivity and land use intensity. We approximated productivity (for each community observation) and land use intensity (for grassland observations) from irregular NDVI time-series as observed by the Landsat program. To this end, we used generalized additive models (GAMs)  29. The theoretical range of NDVI values is bounded between -1 and 1, but the observed values only covered the central part of that range (-0. 04 to 0. 32). Our data were therefore hardly affected by these boundaries and it was feasible to assume that NDVI values follow a Gaussian error distribution. To approximate productivity, we fitted NDVI as a function of Julian day, using a cyclic cubic regression spline with a flexibility of five degrees of freedom at maximum (k = 5), and year of measurement, using a factor term.
From these fits, we then predicted mean interannual NDVI. For land use intensity, we fitted similar models, but only considering the observations taken during the growing season, where we assumed the majority of land use practices to take place. Within this period, we fitted NDVI as a function of Julian day using ordinary thin plate regression with a flexibility of three degrees of freedom at maximum (k = 3). We assumed that in semi-natural grasslands the model error remaining after correcting for the seasonal signal is mainly driven by land-use practices including mowing, grazing and fertilizing, and used the mean absolute residual of the model fits as a proxy for land use intensity  59. All GAMs were fitted using the R package mgcv  60. Binning plant community observations. Before estimating biodiversity, we split plant community observations into bins defined by distinct combinations of NDVI, ecosystem type, bioclimatic zone, and land use intensity.
Investigating biodiversity within discrete NDVI bins allows determining uncertainty via bootstrapping. The NDVI gradient in the data was split into 18 bins (-0. 04 --0. 02, -0. 02 -0,…, 0. 3 -0. 32) in order to enable comparison of several productivity levels while still having sufficient observations per bin. For ecosystem type, we either pooled all ecosystem types, or we distinguished forest and grassland observations. Furthermore, we distinguished the lower montane, upper montane, and lower alpine bioclimatic zones, as well as three levels of land use intensity in grasslands (separated by tertiles of mean absolute deviations from the seasonal NDVI trend). From the community observations within each bin, biodiversity was estimated for 100 bootstrap samples of 40 observations. When land use intensity levels were compared in grasslands, bin-wise biodiversity was estimated for 100 bootstrap samples of 20 observations, due to the reduced number of observations per bin.
Observations were sampled randomly under the constraint that they had to be at least 5 km apart from each other. Binning with regular bin sizes leads to smaller sample sizes at the boundaries of the productivity range, which may cause undesired edge effects. We tested for such effects by using an alternative binning criterion of 5%-percentiles of the productivity data and found edge effects to have a small impact on our results (Supplementary Fig. 9 ). Calculating biodiversity. We calculated biodiversity by successively defining scale of similarity, type of similarity, and dominance effect. To adjust the scale of similarity, we transformed functional and phylogenetic trees with Pagel's δ transformation  18. This transformation was originally developed in a phylogenetic context, but can also be applied to functional dendrograms  15.
Acknowledgements This work was supported by the  ANR-SNF  bilateral project OriginAlps, with grant numbers  310030L_170059  (P. B. , N. E. Z) and  ANR-16-CE93-004  (W. T. , S. L. , T. M. ). The authors further acknowledge  Swiss National Science Foundation (SNF)  grant numbers  1003A_149508  (N. E. Z. ), and  31003A_173342  (C. H. G. ). W. T. and T. M. acknowledge support from the  'Investissement d'Avenir ' grants managed by the  ANR  ( Trajectories :  ANR-15-IDEX-02 ; Montane: OSUG@2020:  ANR-10-LAB-56 ). Moreover, the authors thank  Isabelle Boulangeat  for providing the stickTips function to calculate random binary trees;  Dirk Karger ,  Julien Renaud , and  Maya Gueguen  for preparing the climate data; and  Achilleas Psomas  for his support in estimating land use intensity. We also thank the  Conservatoire Botanique National Alpin  for its continous effort in collecting high quality vegetation plots.
Data availability NDVI data from the Landsat mission. Climate data from CHELSA. Plant community data, phylogenetic data, and trait data are available from Wilfried Thuiller (wilfried. thuiller@univ-grenoble-alpes. fr) on reasonable request. The source data underlying the main results (Figs. 2a, 3c-e, 4,  and 6 ) are provided as a Source Data file.
Code availability All analyses were conducted in the R environment  63  (version 3. 4. 2). The packages used include ade4 64 (version 1. 7-13), ape 65 (version 5. 2), apTreeshape 57 (version 1. 4-5), geiger 66 (version 2. 0. 6), mgcv 60 (version 1. 8-20), and raster 67 (version 2. 8. 19). Code generated for analyses and illustrations is available from the corresponding author.
11. 5) to obtain per base quality, Guanine-Cytosine (GC) content, and sequence length distribution. Clean reads were obtained by removing the lowquality reads, adapters, and poly-N-containing reads by using Trimmomatic v0. 36 software  (Bolger et al. , 2014). RNAseq reads were aligned to the IRGSP 1. 0 version of the rice genome using HISAT2 v2. 0. 5. 1  (Kim et al. , 2015). The number of reads mapped to each gene locus was counted using HTSEq-count v0. 6. 0  (Anders et al. , 2015).
Differential Gene Expression and Gene Ontology (GO) Term Enrichment Analysis DESeq2 v3. 7  (Love et al. , 2014)  was used to calculate differential gene expression between non-inoculated and inoculated conditions. All genes having an adjusted p-value inferior to 0. 01 were considered as significantly differentially expressed. All functional enrichment analyses were performed using g:Profiler (version e95_eg42_p13_f6e58b9) with g:SCS multiple testing correction method applying significance threshold of 0. 05  (Reimand et al. , 2007; Raudvere et al. , 2019). Quantification of mRNA Levels Using RT-qCPR cDNA was produced from 350 ng of DNase-treated total RNA using the SuperScript III Reverse Transcriptase (Thermo Fisher Scientific). The cDNA reaction was diluted five times before qPCR using MESA BLUE qPCR Master Mix for SYBR ® assay (Eurogentec) on an Mx3005P qPCR system (Agilent Technologies).
The analysis of the specific root transcriptome also revealed processes specifically induced by each strain. Particularly, the response to Pk in roots encompasses the down-regulation of oxidative stress response-related genes and also chitin catabolic process, while the interaction with Bv induced the down-regulation of genes involved in defense, JA signaling, and response to stimuli (Supplementary Figure  5 , corresponding genes in Supplementary Table  8 ). In order to deepen the transcriptome analysis, a functional categorization of the top 200 up-regulated and down-regulated DEGs identified in response to each strain was carried out using all available databases for rice annotation (UniProt, Kyoto Encyclopedia of Genes and Genomes (KEGG), RAP-DB, and Oryzabase). The proportion of genes related to each category is presented in Figure  4  and exemplifies rice-specific responses to the two bacterial strains.
Validation of RNAseq Data by qCPR To validate the RNAseq data, we selected genes implicated in defense, hormone signaling, or development regulated at 7 dpi by one or both strains (Supplementary Table  9 ). We measured the level of expression of the selected genes by qRT-PCR in an independently conducted experiment. According to RNAseq analysis, in leaves, two defense-related genes are specifically regulated by each strain: ALD1 is specifically down-regulated in response to Bv, and WRKY71 is specifically up-regulated in response to Pk. Also, two hormone-related genes were detected as specifically regulated by each strain; namely, bHLH148, a JA signaling component, is down-regulated in roots inoculated with Bv; while RR9, a CK signaling component, is up-regulated in the leaves of Pk-inoculated plants.
Temporal Analysis of Strain-Specific Marker Genes Following the confirmation of strain-specific transcriptional regulations, we wanted to identify genes that are differentially expressed in both conditions but with opposite regulations. Among all DEGs detected in roots and leaves, only three DEGs are potential differential markers: RERJ1 (Os04g0301500), a JA-responsive gene; ATL15 (Os01g0597600), a putative amino acid transporter; and DREB1B (Os09g0522000), a droughtresponsive gene. Noteworthy, these three genes are only detected as DEGs in leaves and follow the same pattern of expression, as they are up-regulated in response to Pk and down-regulated in response to Bv. Among these three genes, two of them, ATL15 and RERJ1, are part of a co-expression network recovered from RiceFREND database  (Sato et al.
FUNDING The authors acknowledge the  CGIAR Research Program on Rice Agri-food Systems (RICE)  and the  MIC-CERES Project  ( FC  Project  ID 2013-1888 ;  AF  Project  ID 1301-003 , jointly supported by  Agropolis Fondation  through the  "Investissements d'avenir " programme with reference number  ANR-10-LABX-0001-01 , and  Fondazione Cariplo)  for funding. EK and AW were supported by a fellowship from the  French Ministry of Higher Education, Research and Innovation.
Library preparation was performed with the TruSeq ChIP Sample Prep Kit (Illumina). Briefly, double-stranded DNA was subjected to 5′ and 3′ protruding end repair and nontemplated adenines were added to the 3′ ends of the blunted DNA fragments to allow ligation of Illumina multiplex adapters. DNA fragments were then size selected (300-500 bp) to remove all nonligated adapters. Eighteen PCR cycles were carried out to amplify the library, which was then quantified by fluorometry with Qubit 2. 0. The integrity of the library was assessed with a 2100 Bioanalyzer (Agilent) before sequencing with the Illumina NextSeq500 sequencer. The BWA software was then used to map sequencing reads to the mouse genome (NCBI Build 37/UCSC hg19; ref. 22). Raw data are available in the Gene Expression Omnibus database.
Read density was computed by counting the reads in nonoverlapping 2-kb windows tiling the whole genome thanks to the "feature-Counts" software  (23). Reads mapping to multiple locations in the reference genome or overlapping two windows were divided among their associated windows. Windows presenting a significantly different 5hmC level between tumor samples and nevi were identified with edgeR software (log fold change > 1. 5 and FDR < 0. 05; ref. 24). For annotation purposes, enhancers were obtained from the Enhancer VISTA database (https://enhancer. lbl. gov/; downloaded on  January 18, 2016; ref. 25)  and lifted to mm10 genome with the liftOver tool from UCSC, gene bodies were defined as regions from the TSS to the TTS of RefSeq genes (downloaded from UCSC on March 6, 2017), while promoters ranged from -2 kb to the TSS, CGI positions were obtained from UCSC (downloaded on March 6, 2017), and shores were defined as regions surrounding CGIs by up to 2 kb.
Duplicate reads mapping to the same location were removed with Picard (http://broadinstitute. github. io/picard). Differential expression analysis was performed with edgeR  (24). A gene was considered differentially expressed when the absolute fold change was above 2 and FDR < 0. 05. A PCA plot was generated on the regularized log 2 transformed (rlog 2 ) read count data for all the samples. rlog 2 was performed with the DESeq2 package (29) of Bioconductor, and the PCA plot was generated with the ggplot2 package.
S1 ). Thus, although a global loss of 5hmC was observed in spontaneous mouse melanoma lesions, several loci exhibited increased hydroxymethylation. This observation is in keeping with recent data showing local elevation of 5hmC in pancreatic cancer samples  (33). Gene ontology analysis revealed an involvement of genes displaying differential 5hydroxymethylation in "Cancer" (P value between 7. 99 × 10 -4 and 5. 15 × 10 - 19 ) and "dermatological diseases and conditions" (P value between 2. 5 × 10 -4 and 7. 82 × 10 -17 ). Some of these genes were also linked to pathways involved in "nervous system development and function" (P between 7. 99 × 10 -4 and 4. 59 × 10 -12 ). To assess whether differential 5hydroxymethylation correlated with specific changes in gene expression, we determined mRNA levels by RNA sequencing (RNA-seq).
Acknowledgments We thank  G. Bervoets  for technical assistance and  F. Rambow  for helpful discussions. E. Bonvin  was supported by the  Belgian Fonds de la Recherche Scientifique (FNRS). M. Bizet was supported by a  Télévie grant. F. Fuks is a  ULB Full Professor  and his lab is funded by grants from the  FNRS and Télévie , the  Interuniversity Attraction Poles  ( P7/03 ) program, the  Action de Recherche Concertée (ARC ;  AUWB-2010-2015 ULB-No 7 ), the  Belgian "Fondation contre le Cancer"  ( FCC 2016-086 FAF-F/2016/872 ), and the  Fonds Gaston Ithier. S. Santagostino  was supported in part by  NIH/NCI Cancer Center Support  Grant  P30 CA008748. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.
Acquired corneal images consisted of 1024 x 1024 pixels over a 780 x 780 μm field of view with 1. 6-μm transverse x 1. 0-μm axial resolutions. Image acquisition time for a typical 500-μm-depth stack in the cornea, acquired every micron in depth, is under five minutes. Throughout image acquisition, the cornea remained immersed in a closed chamber filled with storage medium, decreasing any potential risk of contamination  [19]. FF-OCT images were pre-processed and analyzed offline using custom software developed in Matlab (Mathworks, Inc. , Natick, MA, USA). Pre-processing included 3D-image segmentation and flattening of the corneal surface. Briefly, for each row of the 3D-image stack, that is, for each 2D cross-sectional image, the position of the corneal surface was determined by finding the local maximum (around the epithelium) using a thresholding procedure and smoothing the obtained data by applying a median filter as well as a Savitsky-Golay filter (i.
Bayesian inference of the parameters of a mono-exponential decay model and comparison of the fit residuals to the random noise; in case of significant model inadequacy, 3. Bayesian inference of the parameters of an extended decay model, with variable, z-dependent l s. The Bayesian models are implemented in stan  [21] , using the rstan interface package for R (R Core Team, Vienna, Austria). The main functions are available as an R package at https:// doi. org/10. 5281/zenodo. 1494972, as are graphical and script-based interfaces, along with a detailed vignette of the code implementation, at https://doi. org/10. 5281/zenodo. 1494973. Estimation and modeling of random noise. A cubic smoothing spline function is fitted to the depth-dependent data to estimate its random component. That is, the residuals of the smoothing spline fit are assigned to measurement noise of the stromal-depth profile, from which the signal-to-noise ratio (SNR) is computed (using the respective averages).
The noise distribution is then fitted with a normal distribution of zero mean and a standard deviation following an exponential decay, using Bayesian inference  [22]. This noise model is used in the following steps. Estimation of mono-exponential decay model adequacy. The parameters of the monoexponential decay (Eq 1) are estimated by Bayesian inference, with a Gaussian likelihood based on the sum of squared residuals, weighted by the noise model. The quality of the monoexponential fit is estimated by inspection of the residuals, conforming with the random experimental noise, by means of the Birge ratio (b r , the reduced weighted chi-squared). If b r is below a certain threshold, the mono-exponential decay model is considered an adequate representation of the data, and we proceed with the extraction of the fit parameters from the data, including the scattering mean-free path, l s , as the global parameter or quantitative measure of From the FF-OCT data set, after segmentation and flattening of the corneal surface, we extract the stroma and generate mean amplitude depth profiles by computing the average grey value over the entire field of view for each en face image in depth.
Emission signal was monitored at 600 nm for all fluorophores. The measurement was conducted for 1 h. A relative fluorescent intensity (%) was normalized to the maximum intensity at the starting point. Fluorescence Correlation Spectroscopy (FCS). FCS measurements were performed on a home-built confocal set-up based on a Nikon inverted microscope with a Nikon 60x 1. 2NA water immersion objective. 47 Excitation was provided by a cw laser diode (532 nm, Oxxius) and photons were detected through a bandpass 580/84 nm filter (Semrock) with a fibered Avalanche Photodiode (APD SPCM-AQR-14-FC, Perkin Elmer) connected to an on-line hardware correlator (ALV7000-USB, ALV GmbH, Germany). Typical acquisition time was 5 min (10 × 30 s) with an excitation power of 0. 5 mW at the sample level. The data were analyzed using the PyCorrFit software.
The images were acquired with a Nikon Ti-E inverted epifluorescence microscope, equipped with CFI Plan Apo × 60 oil (NA = 1. 4) objective, and a Hamamatsu Orca Flash 4 sCMOS camera. The acquisition settings were: Hoechst (ex. 395 nm, em. 475±50 nm), DBS-loaded NEs (ex. 470 nm, em. 531±40 nm). The images were recorded using NIS Elements and then processed with Icy software. For TPE imaging, the cells were incubated with NEs in Opti-MEM for 2 hrs at 37C then with Hoechst (5 µg/mL) for 15 min to stain the nuclei and images were taken from home-built two-photon laser scanning microscope (vide supra). Tracking of individual NEs. The movies were acquired at 20 Hz for the total duration of 30 s. The tracking analysis was performed using Imaris 9. 2. 1 software (Bitplane Inc. ) Individual fluorescent foci were identified in each frame and the fluorescent spots in different frames were joined to form trajectories if the centroid positions of the spots were within 500 nm between subsequent frames.
Trajectories that lasted ten frames or longer were kept for subsequent analysis. To calculate the velocity of each spot the displacement between the subsequent frames was divided by the acquisition time (50 ms). For each type of NEs, 3 cells were analyzed with roughly 1000 trajectories and 2-410 4 displacements detected in each cell. The mean fluorescence intensity of the single NEs in the timelapse images was measured by using Image J software. Five images, corresponding to t1 =0s, t2 = 1. 5s, t3=3s, t4 = 4. 5s and t5=6 s were analyzed. The time interval of 1. 5 s between the analyzes was chosen in order to ensure the turnover of the NEs. A threshold was then applied to the images to isolate the fluorescent spots corresponding to NEs from the fluorescent background. Then, a particle analysis function was used to identify their borders.
Dye MCT, castor oil and VEA were detected as interesting candidates for the formulation of fluorescent NE, owing to the outstanding fluorescence properties of DBS when solubilized in these oils. To verify whether high QY values of DBS dyes in oils are related to theirs high viscosity, we studied the dependence of QY on dynamic viscosity  of the organic phase (solvent or oils). Results are reported in Figure  S3 , showing a linear correlation between the value of QY and log(. The viscosity is known to enhance the quantum yield of dyes by restricting intramolecular rotational relaxation, which is com-monly observed for so-called molecular rotors. 55 56 This phenomenon is also known as "Motion-Induced Change in Emission" (MICE). 57 Our data showed that DBS fluorophores exhibit a molecular rotor behavior, which is in line with previously reported push-pull dioxaborine DXB-Red dye.
Genome sequencing, assembly and annotation The assembly of Moumouvirus B australiensis genome sequence was performed on one SMRT cell of pacbio data using the HGAP workflow  (53)   Gene annotation of genomic sequences was done using Augustus (55) trained on already published members of the subfamily gene sets. The tRNAs were searched using tRNAscan-SE  (56)  with default parameters. Functional annotation of predicted protein-coding genes was done using homology-based sequence searches (BlastP against the NR database (57) and search for conserved domains using the Batch CD-Search tool (  58 )).
Phylogenetic analyses The phylogenetic tree of the transpovirons (Fig. 2 ) was computed using a concatenation of the conserved genes. The optimal model "LG+G" was selected using Prottest  (59). Phylogeny of the giant viruses (including Megavirus C vitis, Moumouvirus B australiensis, Moumouvirus B maliensis, Fig. S2 ) was performed on a concatenation of genes shared by all mimiviruses as predicted by CompareM (https://github. com/dparks1134/CompareM). After protein clustering using MCL  (60) , clusters containing exactly one gene per virus were aligned using Mcoffee (61) and concatenated. We next produced a phylogenetic tree using Prottest (59) and the resulting superalignment. The "VT" model was considered the optimal model. Average nucleotide identity between the different strains (Fig. S2 ) was calculated using the OAU tool  (62).
(65)Z. vitis purified from M C. chilensis containing ma B tv. Peptides and proteins were identified and quantified using MaxQuant software (63) (version1. 6. 2. 10). Spectra were searched against the corresponding Megavirus C chilensis, Megavirus C vitis, Moumouvirus B australiensis, Moumouvirus B maliensis, Zamilon vitis, mv C tv, ma B tv and Acanthamoeba castellanii protein sequence databases and the frequently observed contaminants database embedded in MaxQuant. Minimum peptide length was set to 7 aa. Maximum false discovery rates were set to 0. 01 at PSM, peptide and protein levels. Intensity-based absolute quantification (iBAQ)(64)values were calculated from MS intensities of unique+razor peptides. Proteins identified in the reverse and potential contaminants databases as well as proteins only identified by site were discarded form the final list of identified proteins.
Acknowledgments We thank  Laurence De Marchi  and  Daouda Traore  for providing the soil samples and  Olivier Poirot  for helpful discussions. We thank  Dr. Elsa Garcin  for reading and improving the manuscript. We also thank  Dr. A. Kosta  at the IMM imagery platform and  F. Richard  and  A. Aouane  for their expert assistance on the IBDM imaging platform and  Dr. N. Brouilly  for data acquisition that allowed producing the Supplementary movie. We also acknowledge the support of the bottom-up platform and informatics group of EDyP.
(Visual contact was temporarily lost during the distant experiment. ) We attempted to satellite tag other individuals in the group for a maximum of 1 h after the DTAG was attached. Controlled exposure started after 4 h of baseline DTAG data were collected. Each focal whale (with DTAG) was subjected to either a close or a distant exposure treatment. The source distance at the start of the exposure was less than 1 km (close) or 17 km (distant). In both treatments, we transmitted from the drifting sailing vessel a sequence of simulated sonar pulses that was representative of active sonars used by navies; however, the acoustic source and details of navigation protocol (e. g. positioning of the source, source depth) and transmission protocol (e. g. frequency band, signal type, ramp-up and exposure duration) differed between the treatments (table  1 ).