dataesr/scientific-paragraphs-categorization huggingface dataset:
As a consequence ORCHIDEE simulates too much small-scale damage at wind speeds below the critical value, while the maximum damage rate in ORCHIDEE is too low. Furthermore, ORCHIDEE could only partially represent the effects of forest stand edges on the propagation of wind disturbance. Indeed, damage due to windstorm Klaus was particularly amplified due to the amount of damage arising at vulnerable forest stand edges and then propagating through the uniform Pinus radiata stands  (Hart et al. , 2019; Kamimura et al. , 2015). These results show that evaluating the capacity of land surface models to project storm damage hinges on our ability to precisely define the storm events recorded in the databases and our ability to use this information to estimate key model parameters such as the relaxation factor and the maximum damage rate.
Indirect effects of wind damage on slope instability FORWIND may also be employed to improve the predictive performances of slope stability models that rely on watersoil interactions and soil mechanics. Vegetation affects terrain properties in a variety of way, including the modification of hydraulic conductivity, the regulation of evapotranspiration and the increase in soil strength by apparent root cohesion  (Amundson et al. , 2015; De Baets et al. , 2008). This, in turn, may strongly condition terrain response to external forcing, such as intense rainfall and seismic shaking, leading to mass wasting in the form of shallow landslides and soil erosion  (Moos et al. , 2016; Ruiz-Colmenero et al. , 2013). We have tested the capability of FORWIND to provide data for assimilation in shallow landslide hazard models and for model validation by selecting the dataset relative to windstorm Vaia in October 2018 in the Dolomites in northern Italy and using it to model indirect effects of wind disturbance on slope stability.
A multivariate machine learning model for shallow landslide susceptibility has been trained and applied on pre-storm terrain attributes to reveal relative probability of occurrence and then applied again to post-storm conditions to measure the effects of forest disturbance on the hazard. The terrain attributes considered in the analysis include elevation, slope angle, slope curvature variability, local rainfall patterns, geo-mechanical classes, potential soil saturation, contributing area and pre-and post-storm Normalized Difference Vegetation Index (NDVI) maps from Landsat 8 level-2 imagery. The dataset was trained by a Random Undersampling (RUS) Boosted Random Forest regressor  (Catani et al. , 2013)  on a validated shallow-landslide dataset derived from the Italian National catalogue IFFI  (Trigila et al.
, 2013). The training process highlights that NDVI, typically considered a good proxy of biomass density, is ranked second in terms of explained variance and seems to strongly condition landslide susceptibility in the Dolomites. The FOR-WIND database collects dated and graded information on wind damage to forests that directly correlates to marked changes in NDVI values, as can be observed in Fig. 8a. The effects of the damage recorded in the FORWIND dataset are measurable by comparing the levels of susceptibility before and after the occurrence of windstorm Vaia (Fig. 8b ). As can be appreciated in the map, the red areas, which reveal a marked increase in the probability of landslides, match the FORWIND polygons very well and clearly indicate the usefulness of the wind damage geographical databases in slope hazard prediction and modelling.
In Fig. 8b , we also note some omission and commission errors. They, however, can be easily explained by noting that vegetation stripping (or vegetation scantiness) is only one of the factors contributing to landslides. Therefore, wherever Vaia has damaged forests but slopes are very gentle, no shallow landslides can be generated. On the other hand, outside FORWIND polygons landslides may still develop, due to the prevailing action of other factors, such as unfavourable geological conditions or strong concentrated rainfall. The use of FORWIND data in landslide modelling is not limited to the cross-validation of biomass volume changes but can also be extended to the usage of the dataset as an additional predictor in multi-variate statistics. We noted that the overlapping of FORWIND polygons and NDVI stress (brown) areas shows few exceptions.
Conclusions Modern and forthcoming Earth observation systems  (Mc-Dowell et al. , 2015) , new generation of land surface models  (Bonan and Doney, 2018) , recent developments of cloud computing platforms  (Gorelick et al. , 2017)  and machine learning approaches  (Reichstein et al. , 2019)  are offering unprecedented opportunities to explore and predict ecosystem dynamics at an increasing spatio-temporal resolution and sophistication level. In light of such progress, it is of paramount importance to implement robust calibration and validation procedures based on reliable ground observations. In order to capture the variability of ecosystem response across wide environmental gradients, reference ground truth needs to be collected over large spatial scales. In this context, FORWIND represents an essential dataset to improve our capacity to detect, understand and predict wind disturbances and quantify their impact on forest ecosystems and the land-atmosphere system.
The FORWIND database is the first Pan-European collection of spatially delineated forest areas affected by wind disturbances and includes all major events that occurred over the 2000-2018 period. Future research should aim to further populate FORWIND with missing damaging wind events. Author contributions. GF designed the study. MP performed the data harmonization. MG assisted in data integration tasks. MM, CNik, MR, JT, DS, CNis, DJ, BG, FG, RC, AW, FP, FM, SI, WLS, KS, KZK, PSJ, MM, FS, LK, IH, MN, PW,and GC collected forest disturbance data. FP ran the classification models. FC ran the slope instability model. YYC and SL ran the ORCHIDEE model. GF analysed the data and wrote the manuscript with contributions from all co-authors. Competing interests. The authors declare that they have no conflict of interest.
Images were stacked and ciliated cells were counted using ImageJ software. For analysis of cell specific RFX2 or RFX3 deletion: brains were dissected and fixed in 4% paraformaldehyde overnight at 4 • C. Tissues were embedded in sucrose. 12 m cryostat sections were saturated in 0. 1% triton, 10% normal goat serum, 1% BSA in PBS for at least 1h at RT, before O/N incubation at 4 • C with RFX3 (Sigma HPA035689) and RFX2 (Sigma HPA048969) antibodies and against mouse acetyl-tubulin antibody (1/150, Sigma T6793) in 0. 1% triton, 10% normal goat serum, 1% BSA in PBS. Samples were then washed several times in PBS before incubation with secondary goat antimouse Alexa 488 antibody and Alexa 555 anti-rabbit antibody (Thermo Fisher, France) and for 5 min in Hoechst. Brains or trachea were mounted in Vectashield and visualized with a Confocal spectral Leica SP5.
RNA-seq experiments Four to eight replicates (see above) were processed for each condition. cDNA libraries were constructed by the Genomics platform of the University of Geneva using the Illumina TruSeq RNA Sample Preparation Kit according to the manufacturer's protocol. Libraries were sequenced using single-end 100nt-long reads on Illumina HiSeq2000. FastQ reads were mapped to the ENSEMBL reference genome (GRCm38. 89) using STAR version 2. 4. 0j (52)  with standard settings, except that any reads mapping to more than one location in the genome (ambiguous reads) were discarded (m = 1). Sequence data has been submitted to the GEO database under the accession number GSE145324. A unique gene model was used to quantify reads per gene. Briefly, the model considers all annotated exons of all annotated protein coding isoforms of a gene to create a unique gene where the genomic region of all exons are considered coming from the same RNA molecule and merged together.
All reads overlapping with the exons of each unique gene model were reported using featureCounts version 1. 4. 6-p1 (53). Gene expression was reported as raw counts and normalized as reads per kb per million (RPKM) to filter out genes with low expression values (<1 RPKM) before identifying differentially expressed genes. Library size normalization and differential gene expression calculations were performed using the package edgeR (54) designed for the R software. Only genes having a significant fold-change (Benjamini-Hochberg corrected P-value < 0. 001) were considered for the rest of the RNAseq analysis.
ChIP-seq experiments Chromatin was purified from ex vivo derived ependymal cells from WT mice (OF1 strain). Chromatin immunoprecipitation was performed as previously described  (46). Immunoprecipitated DNA was sequenced by the Genomics platform of the University of Geneva using the Illumina TruSeq ChIP Library Prep Kit according to the manufacturer's protocol. Libraries were sequenced using single-end 50nt-long reads on Illumina HiSeq2000. FastQ reads were mapped to the ENSEMBL reference genome (GRCm38. 89) using bowtie version 0. 12. 7  (55)  with standard settings, except that any reads mapping to more than one location in the genome (ambiguous reads) were discarded (m = 1). Duplicate reads were removed using Samtools version 0. 1. 18 (56). Sequence data has been submitted to the GEO database under the accession number n • GSE145324.
Fragment length was estimated using cross-correlation  (57). The Phantompeakqualtools R package (https: //www. encodeproject. org/software/ phantompeakqualtools/) was used to measure the quality of the ChIPseq data, as assessed by the normalized ratio between the fragment-length cross-correlation and the background cross-correlation (normalized strand coefficient, NSC), the ratio between the fragment-length peak and the read-length peak (relative strand correlation, RSC) and the Qtag code. Peak calling was done with MACS2 version 2. 0. 10. 20130520 (58)  with no-model setting and shiftsize parameter set to half of the estimated fragment length. Peak calling stringency was decreased by using P = 0. 05 as threshold and applying the '-to-large' setting. Reproducible peaks were obtained by assessing the IDR for all pairs of replicates.
De novo motif discovery and motif enrichment analysis For analysis of differentially expressed genes, promoter sequences (-1000 bp to +500 bp relative to the TSS) were extracted, oriented according to gene orientation and used as input. For analysis of ChIP-seq data, peak sequences were used as input. De novo motif discovery was done using the cosmo package (65) designed for R  (53). The probabilistic model used for the motif discovery was the zero-orone-occurrence-per-sequence (ZOOPS), considering only the orientation of each promoter and motif lengths between 10nt and 20nt. Pscan  (66)  was used to identify overrepresented TF-binding-sites (TFBSs) using the JASPAR database (67) as matrices. TFBSs with P-values lower than 0. 001 were considered to be significantly overrepresented. The motifs defined by the de novo analysis of ChIP-seq peaks (Figure  4C ) was averaged and used to scan both strands of gene promoters (-1000/+500) to determine the presence of a X-box motifs.
Gene ontology GO term enrichment was performed using Panther classification tool  (68). Rfx1 -/-, Rfx2 -/- and Rfx3 -/ - mice We focused our study on three mouse RFX factors--RFX1, RFX2 and RFX3--exhibiting different patterns of expression in vivo. Expression of the Rfx1 gene is ubiquitous and does not exhibit selective enrichment in specific tissues  (59, 69). In contrast, expression data available in public databases reveal that the mouse Rfx2 and Rfx3 genes are both expressed preferentially in ciliated tissues: whereas Rfx2 expression is largely restricted to tissues harboring motile cilia, expression of Rfx3 is more widespread but enriched in tissues presenting motile cilia.
Genes regulated by RFX2 and RFX3 share indistinguishable X-Box motifs Two complementary strategies were used to identify potential DNA-binding motifs for RFX factors in the promoterproximal regions (defined here as -1000 to +500 bp relative to the transcription start site, TSS) of genes that are downregulated in Rfx2 -/-and/or Rfx3 -/-ependymal cells. As a first approach, Pscan software was used to quantify the enrichment of known transcription factor binding site (TFBS) motifs described in the JASPAR database. TFBS motifs were clustered on the basis of their enrichment scores in different downregulated gene sets, and results are presented as a heatmap (Figure  3A , Supplementary Figure  S5 ). This analysis revealed that X-box motifs (TFBS motifs defined for RFXs are presented in Figure  3B ) are highly enriched in the promoters of most subsets of genes downregulated in Rfx2 -/-and Rfx3 -/-cells, with the exception of genes regulated exclusively by RFX2 (Figure  3A , Supplementary Figure  S5 ).
As a second unbiased approach, a de novo motif discovery method (cosmo package for R) was used to identify motifs that are enriched in the promoters of different sets of genes downregulated in Rfx2 -/-and/or Rfx3 -/- cells (Figure  3C ). Although applying this de novo search to -1000 to +500 promoter regions did not give significant results, using a more restricted promoter definition (-250 to +50) identified very similar motifs, all exhibiting nearly perfect matches to the previously defined X-box, in most subsets, with the exception of genes regulated by RFX2 alone. This motif discovery strategy revealed no differences between the X-box signatures for different downregulated gene subsets, as confirmed by TOMTOM (73) analysis of The volcano plots represent statistical significance (Y-axis, -log 10 P-value) and fold change (X-axis, log 2 fold change) for alterations in gene expression observed in Rfx1 -/-, Rfx2 -/-or Rfx3 -/- cells relative to WT cells.
A B C Figure  3. Identification of TFBS motifs in the promoters of genes that are downregulated in Rfx2 and/or Rfx3 KO cells. The promoters (-1000 to +500 pb relative to the TSS) of different sets of downregulated genes were examined by two unbiased approaches for the presence of specific TFBS motifs. (A) Selected region of a heatmap focusing on the most enriched motifs, determined using Pscan for the complete set of TFBS motifs from the JASPAR database, in the promoters of the indicated sets of downregulated genes. The full heatmap is presented in Supplementary Figure  S5. Colour key: blue, under-represented motifs; yellow, over-represented motifs. In most gene sets examined, the most strongly enriched TFBS motifs were the X-box motifs previously defined for RFX factors (the corresponding sequence logos are provided in (B)).
Only promoters of genes that are downregulated exclusively in Rfx2 -/-cells did not exhibit an enrichment in these X-box motifs. (B) Sequence logos of RFX motifs from JASPAR database enriched in RFX-regulated genes. (C) A de novo motif discovery approach (cosmo package) was applied to the promoters (-250 to +50 pb relative to the TSS) of the same sets of downregulated genes. Typical X-box motifs were again revealed in the promoters of most sets of downregulated genes. As in (A) no motif resembling the X-box was revealed in the promoters of genes downregulated only in Rfx2 -/-cells. In the latter gene set, a G-rich motif was instead identified, which could be related to the Egr1 motif (UP00007 1). Downloaded from https://academic. oup. com/nar/article/48/16/9019/5877810 by Washington University, Law School Library user on 24 October 2020 the identified motifs, which demonstrated that all show the best ranking value with the RFX1 MA0509.
RFX1-3 share numerous binding sites in mouse ependymal cells To identify genes that are regulated directly by RFX1, RFX2 and/or RFX3 in ependymal cells, we performed chromatin immunoprecipitation sequencing (ChIP-seq) experiments for each transcription factor using ex-vivo cultured ependymal cells from WT mice. Total numbers of RFX occupied sites (ChIP-seq peaks) in the genome were 7440 for RFX1, 1250 for RFX2 and 9318 for RFX3. Sites occupied by the three factors exhibited similar distributions in promoter regions (defined here as -1000 to + 500 bp relative to the TSS), intergenic regions, introns and exons (Figure  4A ). The peak distribution for each factor was centered just upstream of the TSS (Figure  4B ). TFBS motif analysis was performed as described above. The de novo motif discovery approach (cosmo package for R) was performed for the 100 most robust peaks (based on the number of sequence reads) for each RFX factor.
e. <500 bp spacing between peaks. This strategy revealed nearly identical X-box motifs for RFX1 and RFX3 (Supplementary Figure  S6C ), indicating that no RFX1 and RFX3 motif preference could be discriminated. This analysis was not possible for RFX2, as only 10 of its most robust peaks did not overlap with any RFX1 or RFX3 peaks, a number that is too low to perform a reliable de novo motif analysis. A Pscan analysis was next performed on all peaks using the newly identified X-box motifs and all TFBS motifs described in the JASPAR database. For each RFX factor the ten most enriched motifs included the newly identified RFX binding sites and TFBS motifs previously defined for RFX2, RFX1 and RFX5 (Figure  4D , Supplementary Table  S5 ), again indicating that the three RFX factors bind very similar motifs.
All of these genes have at least one X-box motif in their promoter (-1000 to +500 bp), as highlighted by the blue font. Downloaded from https://academic. oup. com/nar/article/48/16/9019/5877810 by Washington University, Law School Library user on 24 October 2020 be controlled by distal RFX-regulated enhancers that are difficult to correctly assign to a single gene. Among downregulated genes with RFX occupied sites in their promoters or putative distal enhancers, most were bound by at least two RFX factors--either all three RFX factors or RFX1 plus RFX3--whereas only a minority were bound by RFX3 or RFX1 alone, and none were bound by RFX2 alone or RFX2 plus RFX3 (Figure  6A , Supplementary Figure  S8 ). We also performed a GSEA analysis of sets of genes containing RFX1, RFX2 or RFX3 occupied sites in their promoters in the expression data obtained for Rfx2 -/-and Rfx3 -/-cells (Figure  6B ).
Motif discovery analyses of our ChIP-seq and RNA-seq data sets did not reveal any significant differences between the X-box motifs for RFX1, RFX2 and RFX3. Yet these transcription factors occupy distinct, albeit overlapping, sets of genomic loci. This may reflect inherent limitations in the motif discovery algorithms that were used, which could be insufficiently sensitive to reveal subtle differences. Alternatively, RFX1, RFX2 and RFX3 target site specificity could rely on associated promoter or enhancer binding sites for other transcription factors. Our de novo motif discovery approach did not reveal specific sequence motifs linked to the X-box in RFX1-3 target genes, indicating that potential associated motifs are more diverse or less well defined than the X-box motif. However, motif discovery analyses using Pscan did allow us to identify a number of transcription factor motifs enriched selectively in promoters occupied by RFX1-3 proteins.
As each of the three RFX factors can form homodimers and heterodimers, it is likely that RFX1 homodimers as well as heterodimers with RFX2 and/or RFX3 can occupy regulatory regions containing X box motifs. However, our data suggests that RFX1 homodimers and heterodimers could be less efficient at activating transcription than RFX2 and RFX3 homodimers or heterodimers. Hence, removing RFX1 alone would not be sufficient to observe a major consequence on gene expression. These considerations further raise the question of whether or not RFX1 actually has any function in ciliogenesis. We have observed that the deletion of Rfx1 in conjunction with Rfx3 is highly deleterious during embryogenesis, leading to early embryonic lethality between E9 and E10 (data not shown). We have been unable to relate this lethality to evident defects in ciliogenesis in the early embryonic tissues that we have analyzed (primary cilia of the neural tube or mesenchymal cells, data not shown).
We did also not observe clear defects in ciliogenesis in ependymal cells by a combined conditional deletion of RFX1 and RFX3 using the FoxJ1-cre deleter strain (data not shown). Thus, although we cannot fully exclude that RFX1 could have cilia-related functions during development, our data suggests that embryonic lethality induced by the combined deletion of Rfx1 and Rfx3 could well be due to the regulation of other pathways. In this respect, it should be mentioned that in yeast or fungi, RFX factors have been implicated in regulation of the cell cycle and genome repair  (83) (84) (85) (86). Defects in such pathways could also account for embryonic lethality observed in the double mutants. We identified >1400 genes which can potentially be regulated directly by RFX1 and RFX3 (Figure  2 ) and GO classification of these genes indicate that, in addition to ciliary genes, several could be essential for development, such as genes involved in assembly of the mitochondrial respiratory chain complex, in the assembly of ribonucleoprotein complexes, or in cell cycle progression.
Only the 5 top ranking RFX motifs and non-RFX motifs are represented. The scan was done with all TFBS motifs from the JASPAR database. The de novo motifs (red) identified in (C) were included in the scan and are among the five best motifs. P-values are indicated in parentheses: values <1. 0E-300 were rounded off to 0. The full list of motifs and P-values for their enrichment are provided in Supplementary TableS5. (E) The Venn diagrams show the overlap between sets of DNA motifs, distinct from the X-box, identified in regions bound by RFX1, RFX2 or RFX3 (see Supplementary TableS5). Certain motifs appear to be specifically enriched in RFX1 (17 motifs) or RFX3 (47 motifs) occupied regions. A significant number of other motifs are found in regions bound by both RFX1 and RFX3 (73 motifs) or by all three RFX factors (310 motifs).
ACKNOWLEDGEMENTS We would like to thank  Sophie Mohamed Sidi  from the mouse facility and  Aurélie Mongope  and  Jean-Michel Vicat  from the  ALEC SPF  platform for help with mouse husbandry. We would like to thank  I. Kimura ,  E. Cortier ,  S. Ben Larbi  for help with mouse genotyping. Electron Microscopy observations were performed at the CTMu from the  University of Lyon -1. We are grateful to the iGE3 genomics platform of the  University of Geneva  for performing the high throughput sequencing.
FUNDING Fondation pour la Recherche Médicale (FRM)  [ DEQ20131029168  to B. D. ];  ANR  (Ciliopath-X); F. S. was supported by a doctoral fellowship from the  Région Rh ône-Alpes ; J. J. was supported by a doctoral fellowship from the  Fondation pour la Recherche Médicale  ( Prix Line Pomaret-Delalande ); Work in the team of W. Reith was supported by multiple grants from the  Swiss National Science Foundation. Funding for open access charge: Research grants.
Randomisation and blinding Patients with informed consent for participation who fulfil the inclusion criteria are randomised. Randomisation is performed by the site investigator or by the research coordinator through a dedicated password protected, SSL-encrypted website (Medsharing, Fontenay-sous-Bois, France) to allow concealed computer-generated random allocation. Protocol allocation is stratified by participating site and age (<50 years and ≥50 years), and patients are assigned to ICP management (ICP group) or ICP and PbtO 2 management (ICP + PbtO 2 group) in a 1:1 ratio. This trial is an open-label, non-blinded trial for the patient and the in-charge physician due to the nature of the intervention (presence or not of PbtO 2 monitoring). Blinded assessment of the primary outcome will be performed at the coordinating centre and ensured by the entering of no identifying data or allocated group into the database.