dataesr/scientific-paragraphs-categorization huggingface dataset:
For this we created a candidate set of models with all possible linear combinations of explanatory variables and we extracted the corrected Akaike's information criterion (AICc) of each model using the R package glmulti  73. We retained only plausible models with δAICc ≤ 2 of the best model (i. e. the one(s) with the lowest AICc 72 ) and computed the relative importance of each predictor variable as the sum of the Akaike's weights of all the selected models in which that variable was included. We computed the model-averaged coefficients (±95% C. I. ) for each predictor variable in each selected model, weighted by the Akaike's weights (Supplementary Table  2 ). To evaluate the effect of the interaction among climatic and local stressors, we added to the selected models the interactions between site naturalness and temperature trends, and the interaction between site naturalness and precipitation trends.
Acknowledgements We are grateful to the  ILTER network  and the eLTER  PLUS  project (Grand Agreement No. 871128 ) for financial support. We acknowledge the E-OBS dataset from the  EU-FP6  project  ENSEMBLES  (http://ensembles-eu. metoffice. com) and the data providers in the  ECA&D  project (http://www. ecad. eu). The evaluation of forest plant diversity was based on data collected by partners of the official  UNECE ICP Forests Network  (http://icp-forests. net/contributors); part of the data were co-financed by the  European Commission , project  LIFE 07 ENV/D/000218  " Further Development and Implementation  of an  EU-level Forest monitoring Systeme (FutMon) ". Data on wintering water birds in Bulgaria were provided by the  national Executive Environment Agency  with the  Ministry of Environment  and Waters.
f. 8 8 8 p 0. 002 <0. 001 0. 01 Terrestrial z -2. 95 invertebrates p 0. 003 Birds z 4. 11 4. 78 p <0. 001 <0. 001 Aquatic z 2. 42 2. 26 invertebrates p 0. 015 0. 024 Benthic algae z -5. 33 p <0. 001 Plants z 3. 7 p <0. 001 Biodiversity trends for the whole dataset (overall trends) and within the different biogeoregions, realms and taxonomic groups, as resulting from meta-analysis mixed models. Note that only significant results (p ≤ 0. 05) are reported for the biogeoregion, realm and taxonomic group- specific analysis.
Tomographic imaging While including information from different disciplines, the focus of this work lies in comparing and interpreting tomographic images derived from different studies. However, the comparability of models obtained with different methods and different datasets is not straightforward. We show several models based on body waves  (Piromallo and Morelli 2003; Lippitsch et al. 2003; Koulakov et al. 2009; Dando et al. 2011; Mitterbauer et al. 2011; Zhao et al. 2016a; Hua et al. 2017 ), a recent one based on surface waves  (Kästle et al. 2018)  and two more based on full-waveform inversion, i. e. , using both body and surface waves, in the supplementary material  (Zhu et al. 2015; Fichtner et al. 2018). The imbalance between the number of surface-and body-wave models in this comparison is explained by the fact that it has only been possible after the recent densification of seismic arrays to use surface waves to image the relatively 'smallscale' slab geometries underneath the Alps.
The difficulty in comparing models lies, however, not only in using different wave types but also in differences in datasets (number and geometry of station networks), data corrections (e. g. , correcting for crustal structure or elevation), data preparation (e. g. , filtering, picking of P-/S-phases), data quality (strict or loose rejection of noisy data) as well as technical choices of the researchers such as model regularization (damping and smoothing), parameterization (grid spacing/ cell size) or the underlying physical model (e. g. , wave propagation as straight rays, bent rays or sensitivity kernels). All of these will finally influence the resolution of a model and consequently it is not possible to absolutely quantify the uncertainties for each of the models. For example, the imaged amplitudes of the velocity variations in the subsurface vary significantly between different models although the underlying dataset may be quite similar (e.
This means, at the bottom of the model domain at 200 km a slab anomaly can easily be misplaced by 150 km. Smearing and weakening of the imaged anomalies are not only caused by the large wavelengths but also by the reduction in data coverage at long periods in the dataset  (Kästle et al. 2018). Complexities in the propagation path of surface waves can also affect the uncertainty in the deeper part of the model where it is largely constrained by earthquake data  (Kolínskỳ et al. 2019). Given that we use a pre-AlpArray  (Hetényi et al. 2018a ) dataset, there is a considerable variability in station coverage which can additionally cause lateral variations in anomaly strength. Areas with sparse station coverage tend to show reduced anomaly strengths and vice versa. This may enhance the anomaly, for example, in Switzerland (Fig.
2 ), where the station coverage is denser. To ensure that these uncertainties do not affect our interpretation, we evaluated and tested two independent datasets (from ambient noise and from earthquake data) before merging them into the model shown in Figs. 2 and 3  (Kästle et al. 2016 (Kästle et al. , 2018)). Resolution tests that show how the station coverage affects the recovered anomalies are shown in  Kästle et al. (2018). Table  1  Comparison of the key parameters for the models discussed in the text and shown in Figs. 2, 3 , S1-S14. The abbreviations t and r stand for teleseismic and regional Note that the parameter values are not a measure for the resolution or quality of the models. Resolution depends additionally on the data quality, geometry of the data coverage and quality of the corrections.
inspiring discussions on the Alpine tectonics. We are also grateful for the comments and suggestions received from S. Schmid, C. Piromallo, M. Malusà, B. Romanowicz and H. Pedersen. We acknowledge all the network operators providing data to the EIDA archive. Graphics were created with Python Matplotlib, Blender and Inkscape. EK has received funding from the French GRNE graduate school (ED-398) and the German Science Foundation (SPP-2017, Project Ha 2403/21-1). LB has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 641943. All data used in the surface-wave study is stored in the EIDA archive (http://www. orfeu s-eu. org/eida). The surface-wave model of  Kästle et al. (2018)  is freely accessible, including Python routines for plotting (http://hesti a.
Acknowledgements Open Access funding provided by Projekt DEAL. We are thankful to  A. Paul ,  B. Dando ,  Y. Hua ,  I. Koulakov ,  U. Mitterbauer ,  R. Lippitsch ,  C. Piromallo ,  H. Zhu  and  A. Fichtner  for providing the tomographic models. We want to thank  M. Handy  for the.
The 3'UTR of human USP18 is targeted by at least four miRNAs Using the bioinformatic prediction programs miRWalk and RNAhybrid, we identified 27 miRNAs predicted to target with high score the 580 nucleotide (nt)-long 3'UTR of the human USP18 mRNA (Fig. 1A ). USP18 is basally detected in most human tissues, albeit at variable levels (Fig. S1 ). We therefore analyzed the expression of the 27 miRNA candidates in the 90 cell types of the FANTOM5 dataset  (17). Fourteen miRNAs were found to be expressed at > 50 CPM (counts per million mapped reads) in at least one cell type (Fig. 1A ). We next tested the functional impact of these 14 miRNAs on USP18. For this, we expressed in HeLa S3 cells each miRNA as mimic and measured the level of USP18 mRNA by qPCR. As shown in Fig. 1B , seven miRNAs led to a reduction of endogenous USP18, four of them exhibiting a stronger effect.
These miRNAs appeared to contribute to the distribution of the cell populations analyzed (Fig. 2B ). In particular, miR-191-5p, miR-24-3p and miR-532-3p were found to be enriched in monocytes, which clustered apart from all lymphoid cells (Fig. S2B ). To reinforce these data, we surveyed an independent dataset (Cohort Roche, GSE28492)  (18) , which confirmed a higher level of the three miRNAs in monocytes with respect to NK, T and B cells (Fig. 2C ). Of note, miR-191-5p and miR-24-3p were more abundant than miR-532-3p in monocytes. The enrichment of miR-191-5p, miR-24-3p and miR-532-3p in monocytes prompted us to search for a correlation with USP18. For this, CD14+ monocytes and monocyte-depleted PBMCs (here PBL for peripheral blood lymphocytes) were freshly isolated from blood of four donors and levels of the three miRNAs and of USP18 were measured by qRT-PCR.
4C, D ). Next we measured the expression of these transcripts by qPCR using a commercial panel of cDNAs from polyA+ RNAs of 20 human tissues. Importantly, we designed specific primers across the unique exon-exon junction of the linc-UR-A transcripts (A1, A3 and A4) and across the unique exon-exon junction of linc-UR-B1 (Fig. 4C, D ) (primers in Table  S2 ). Linc-UR-A transcripts were lowly expressed in most tissues and showed the highest expression in fetal and adult liver, kidney, and thymus (Fig. 4E ). On the other hand, linc-UR-B1 was uniquely and abundantly expressed in testis (Fig. 4F ). To confirm the testis-restricted expression of linc-UR-B1, we interrogated RNA-seq datasets covering a panel of 32 tissues (E-MTAB-2836). To distinguish linc-UR-B1 from all other USP18-containing transcripts, uniquely mapped reads were selected and aligned again to the human genome (hg38).
This analysis revealed that linc-UR-B1 is expressed only in testis as a single isoform corresponding to the TCONS_00029754 annotation (Fig. S5B ). This was further confirmed by sequencing the only product amplified by qPCR on testis cDNA (Fig. S5C ). High inter-individual variability in the level of linc-UR-B1 was noticed in the RNA-seq dataset (eight donors) (Fig. S5B ). Our qPCR analyses on testis fragments from six donors displaying normal spermatogenesis and one donor with impaired spermatogenesis (as observed by transillumination of seminiferous tubules) confirmed this variability and showed the lowest linc-UR-B1 expression in the latter donor (Fig. S5D ). As opposed to protein-coding genes, the annotations of lincRNAs are far from being complete  (21). To validate the existing annotations, we performed 3' RACE on RNAs from liver and testis.
Linc-UR-B1 is uniquely expressed in male germ cells and positively correlates with USP18 Given the high and specific expression of linc-UR-B1 in the testis, we sought to identify in which testicular cell type(s) this RNA is expressed. We first performed qPCR analysis on cDNAs from freshly isolated testicular germ cell populations (spermatocytes and spermatids) and from primary somatic peritubular, Leydig and Sertoli cells and detected linc-UR-B1 only in spermatocytes and spermatids (Fig. 6A ). To obtain further information on linc-UR-B1 expression, we surveyed a single-cell RNA-seq dataset of testicular cells from three individuals  (22). The expression profile of linc-UR-B1 was analyzed by tracking the sequence of the unique exon-exon junction between the 3'UTR and the upstream exon. As shown in Fig.
6B , linc-UR-B1 was primarily detected in specific germ cell populations (clusters 4-6). Interestingly, the induction of linc-UR-B1 expression occured in the transition between meiotic early primary and late primary spermatocytes. This burst was followed by a gradual decrease in postmeiotic round and elongated differentiating spermatids. No or low expression was detected in somatic cell populations (clusters 9-13). To identify more precisely the meiotic phase in which linc-UR-B1 induction occurs, we analyzed the dataset of early and late primary spermatocytes reclustered for the five different stages of meiotic prophase I. Linc-UR-B1 was first detectable in the late pachytene stage and was maximal during the diplotene stage (Fig. 6C ). Importantly, we observed that the expression of USP18 paralleled that of linc-UR-B1 in germ cell populations and during prophase I (Fig.
All qPCR products were verified by sequencing. S3B. PolyA+ RNA-seq track from testis provided by the ENCODE project (GEO accession: GSM2453457-GSM2453458) and shown in Fig. S4  were visualized using autoscale mode in IGV browser. The data of the single cell RNA-seq on testis used in Fig. 6  were retrieved from  (22). Reads were aligned on the hg38 assembly of the human genome and only unique reads were filtered.
UTR (580 nt) million mapped reads, CPM > 50) in 90 human cell types (FANTOM5 dataset). The selected candidates (14)  are listed. B) The 14 selected miRNAs and a miR-control (miR-ctrl) were expressed as mimics in HeLa S3 cells and USP18 mRNA was measured by qPCR (n = 4). Results (± SEM) shown as expression (2 -ΔCt ) relative to 18S, used as housekeeping gene. Raw-matched one-way ANOVA with Geisser-Greenhouse correction was performed (Dunnett's multiple comparison -compared to miR-ctrl), adjusted p-values shown as stars * < 0. 05, ** < 0. 01, *** < 0. 001, **** < 0. 0001. C) The miR-ctrl and the indicated miRNAs were expressed as mimics in HeLa S3 cells and 48 hr later endogenous USP18 was measured by western blot. Actin B (ACTB) as loading control. The data are representative of three experiments.
ACKNOWLEDGMENTS We wish to thank  P. Miesen  for help in initial prediction analyses of miRNAs targeting USP18 3'UTR;  J. Guo  for assisting us in retrieving scRNA-seq dataset on the Human Testis Cell Atlas; the  Flow Cytometry Platform of Institut Pasteur  and  S. Meunier  for technical assistance for cytometric analyses; the members of the  Unit of Cytokine Signaling ,  M. Livingstone ,  G. Uzé ,  V. Libri  and  N. Jouvenet  for advice, discussions and support;  M. C. Gauzzi  for critical reviewing of the manuscript.
FUNDING Research in the Unit of  Cytokine Signaling  is funded by the  Institut Pasteur , the  Fondation pour la Recherche Médicale  (Equipe FRM  DEQ20170336741 ) and the  Institut National de la Santé et de la Recherche Médicale (INSERM). ER was supported by  Sorbonne Université  and by  FRM. MC was supported by the  FRM  grant above. GG and ÖV were supported by the  Erasmus plus programme  of the  EU commission. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
USP18 3 ' 3 UTR (580 nt) million mapped reads, CPM > 50) in 90 human cell types (FANTOM5 dataset). The selected candidates (14)  are listed. B) The 14 selected miRNAs and a miR-control (miR-ctrl) were expressed as mimics in HeLa S3 cells and USP18 mRNA was measured by qPCR (n = 4). Results (± SEM) shown as expression (2 -ΔCt ) relative to 18S, used as housekeeping gene. Raw-matched one-way ANOVA with Geisser-Greenhouse correction was performed (Dunnett's multiple comparison -compared to miR-ctrl), adjusted p-values shown as stars * < 0. 05, ** < 0. 01, *** < 0. 001, **** < 0. 0001. C) The miR-ctrl and the indicated miRNAs were expressed as mimics in HeLa S3 cells and 48 hr later endogenous USP18 was measured by western blot. Actin B (ACTB) as loading control. The data are representative of three experiments.
Regardless of the RNA input amount, we can recapitulate single-nucleotide resolution TSSs for all stages (Extended Data Figure  1a ). Tight clusters of CAGE-identified TSSs (CTSSs) at a nucleotide resolution originate from the same transcription preinitiation complexes, and thereby represent transcription initiation from the same core promoter (Figure  1b , Extended Data Figure  1a ). The CTSS with the highest expression level within the same cluster (referred to as tag cluster) is the dominant CTSS 26 (Figure  1b ). We constructed tag clusters by joining neighbouring CTSSs separated by 20 or fewer base pairs (bp)  26. The genomic locations of identified tag clusters and the distributions of their interquantile widths (IQ-width; a stable measure of promoter width, Figure  1b , see Methods) indicated high quality datasets (Extended Data Figure  2a-d; 5  ).
Late GGCs (E16. 5F/M) clearly segregated from the earlier stages. The early 2-cell embryo grouped closely with the oocytes, indicating that the majority of transcripts are still of maternal origin (deposited in the oocyte), while the 4-cell embryo moved halfway to mESC cells along the PC1 component, in line with the timing of the major ZGA in the mouse and the time needed to replace the maternal with the zygotic transcriptome  27, 28. Taken together, these results support the high quality of the obtained SLIC-CAGE datasets.
These shifts indicate that the TSS selection in the oocyte is guided by the rules distinct from the rules utilised in the early embryo, i. e. maternal vs somatic TSS usage "grammar". We note that the TSS shifts identified using SLIC-CAGE are TSS-redistributions within the same promoter region and do not correspond to the alternative maternal promoters reported to be used by the oocyte  29. On the contrary, SLIC-CAGE-identified oocyte tag clusters are on average closer to the UCSC-based mm10 annotation than the alternative promoter locations inferred from RNA-seq in Veselovska et al. , many of which lack CAGE evidence (Extended Data Figure  3 ). Our CAGE-identified promoters display a consistency in sequence properties within both somatic and maternal promoters, and systematic differences between the two (see below), providing the most accurate single-nucleotide resolution, TSS-based annotation of oocyte transcripts.
Maternal TSS grammar is established during germline development in the primordial follicle (P6) oocytes While the shift from the maternal to somatic TSS usage occurs following the ZGA, it is unknown when the maternal TSS usage is established in the context of germline development. We thus analysed the TSS usage across our datasets (Figure  1a ) for their association with Wbox elements: the rationale was that the bulk transition to motif-dependent transcription initiation should indicate the stage in which the maternal grammar takes over from somatic. We constructed heatmaps to visualise WW or SS (C,G/C,G) dinucleotide density in 500 or 1000 bp sequence windows centred on the dominant TSSs (Figure  3c ) and ordered by the IQ-width of the corresponding tag cluster. PGCs (E9. 5) and all gonadal germ cells (E10.
5F or mESC dominant TSSs), there was a match peak aligned with the dominant TSS (Extended Data Figure  14c, d ), suggesting that the extra GC-rich sequence seen in Figure  4d   We sought to verify our sequence-based model predictions using experimental data reflecting nucleosome positions. We used the H3K4me3 ChIP-seq data from E13. 5F and E17. 5M GGC stages because 1) of publicly available datasets, only these were of sufficient quality to allow the visualisation of +1 nucleosome; 2) SLIC-CAGE monitors accumulated RNA, so changes in the TSS selection may reflect a change in the nucleosome configuration from an earlier stage; 3) The onset of the promoter shifts in gonadal germ cells can be observed in earlier stages (Figure  4a , Extended Data Figure  12e ), and the classification of the dominant CTSSs showed that some gonadal germ cell-specific dominant TSSs start being expressed in the 13.
What exactly increases the accessibility of these regions remains unknown, but SLIC-CAGE provides a nucleosome-resolution monitoring method for future mechanistic studies. Additional features that may influence nucleosome reconfigurations are histone composition of the +1 nucleosomes and alternative general transcription factors. Non-canonical histone variant H3. 3 is encoded by H3f3a and H3f3b in the mouse genome and it is incorporated into chromatin in transcriptionally active areas that are undergoing nucleosome displacement (reviewed in (Szenker et al. 2011)). As expected for non-proliferative cells, we see a significant increase in the expression levels of both gene variants in the male and female late gonadal germ cells (Extended Data Figure  16b ). Interestingly, the E16. 5F vs mESC shifting genes show a significant decrease in expression levels upon H3.
Collection of Oocytes, in vitro Fertilization (IVF) and Embryo Culture to obtain 2-cell and 4-cell embryos In this study, all preimplantation embryos were prepared by IVF. Collection of P6 and MII oocytes, and fertilized embryos was performed based on previous reports  [37] [38] [39]. P6 oocytes were collected from C57BL/6J strain female mice at P6. The ovaries were dissected well with a microscissors before being incubated in 0. 05% collagenase (gibco) dissolved in Dulbecco's modified Eagle's medium-F12 (DMEM/F12; Invitrogen) supplemented with 4 mg/ml bovine serum albumin (BSA; SIGMA), 80 µg/ml Kanamycin (SIGMA), with frequent pipetting. After 30 min, the oocytes were picked up using mouth pipette and transferred to PBS solution for washing. To select the oocytes at primordial follicle stage, the size of the oocytes was measured by an imaging software system (Octax EyeWare, Octax) and the 20-30 µm of oocytes were collected for further experiments.
Processing of SLIC-CAGE tags Sequenced libraries were demultiplexed using CASAVA allowing zero mismatches for barcode identification. Demultiplexed CAGE tags (47 bp) were mapped to a reference mm10 genome using Bowtie2 40 with default parameters that allow zero mismatches per seed sequence (default 22 nucleotides). The mapped reads were first sorted using samtools  41  and only uniquely mapped reads were kept for downstream analysis in R graphical and statistical computing environment (http://www. R-project. org/). The mapped and sorted unique reads were imported into R as bam files using the standard workflow within the CAGEr package v 1. 20  26. The 5'G that is commonly added through template free activity of reverse transcriptase was removed using CAGEr G-correction workflow: 1) if the first nucleotide in a read is a G and flagged as a mismatch, it is removed from the read; 2) if the first nucleotide is G and not a mismatch, it is removed or retained according to the percentage of identified mismatched G's.
All unique 5'ends of reads are CAGE-supported transcription start sites (CTSSs) and the number of each CTSS (number of tags) reflects expression levels. The highest expressed CTSS is termed the 'dominant CTSS'. Raw tags were normalised using a referent power-law distribution and expressed as normalized tags per million (tpm)  42. Biological replicates were highly correlated and were therefore merged prior to downstream analyses using standard Bioconductor packages (http://www. bioconductor. org/) and custom scripts. mESC CAGE data used in the manuscript is from E-MTAB-6519 (ArrayExpress,  5  ).
Heatmap visualisations Heatmaps Bioconductor package (Perry M (18). heatmaps: Flexible Heatmaps for Functional Genomics and Sequence Features. R package version 1. 2. 0) was used to visualize motif locations or dinucleotide patterns across sequences. Sequences were in most cases centred on the SLIC-CAGE-identified dominant TSSs and ordered by the interquantile width of the corresponding tag cluster, placing the narrowest on top and the broadest tag cluster on the bottom of the heatmap. In the case of shifting promoters, the ordering was based on the distances between the dominant TSSs. Raw data with the dinucleotide matches was smoothed prior to plotting using the smoothing within the heatmaps package (Extended Data Figure  5a ). Coverage plots (nucleosome positioning H3K4me3 ChIP-seq signal, CTSS or tag cluster coverage), were created using the CoverageHeatmap function from the Heatmaps package.
The signal was weighted using the corresponding score (either nucleosome positioning score or CTSS/tag cluster log10(tpm + 1) value, as stated in the results and/or figure legends). H3K4me3 ChIP-seq data that was used for gonadal germ cells E17. 5M is under GEO Accession number GSM3426393 (SRR8029752 and SRR8029753)  44. Biological replicates were merged prior to visualisation. H3K4me3 ChIP-seq data for MII oocyte is under GEO Accession number GSM2082662 (SRR3208745, SRR3208746, SRR3208747, SRR3208748)  45. Biological replicates were merged prior to visualisation.