dataesr/scientific-paragraphs-categorization huggingface dataset:
As such using a version of the redox sensitive eroGFP that is attached to the ER membrane with the eroGFP facing the luminal side, we found that the redox state of eroGFP remained oxidized even during ER stress  (Appendix Fig S2E). Those data indicate that during ER protein reflux, the ER membrane is not significantly damaged neither after digitonin treatment nor following differential centrifugation protocols used in our study. Next, we sought to systematically characterize the spectrum of proteins from the secretory pathway refluxed from the ER. To this end, we enriched N-glycosylated proteins from the digitonin fraction extracted from HEK293T cells treated with Tg (Appendix Fig S2F ). The purified material was subjected to mass spectrometry analysis. We focused on soluble glycoproteins that were enriched in the cytosolic fraction after Tg treatment compared with control.
ER protein reflux was also observed in other cancer cell lines such as GL261, U87, and A549 (lung adenocarcinoma)  using the two aforementioned subcellular protein fractionation protocols. Next, analysis of immunogold-labeled electron microscopy images unveiled that PDIA3 distributed to non-ER locations in cells treated with ER stressors compared with DMSO treated cells (Fig 4A  -C ). It is worth to note that in cancer cells the amount of ER proteins refluxed to the cytosol was higher than in non-cancer cells  (Appendix Fig S3G). These data indicate that upon ER stress, ER luminal proteins (and ER-targeted sfGFP/mEOS3. 2) are refluxed to the cytosol. Fluorescence microscopy with ER-sfGFP and ER-mEOS3. 2 in HEK293T cells  (Fig 2A, Appendix Fig S2A and Fig 4)  confirmed the results obtained using cell fractionation and serve as alternative detergentfree methods to monitor reflux from the ER.
Acknowledgements We thank  Giannino Del Sal  ( LNCIB, Trieste, Italy ) for the antibodiy. We also thank  David Y Thomas  ( McGill University, Canada ) and  Gisou van der Goot  ( EPFL, Switzerland ) for critically reading the manuscript. This work has benefited from the  Microscopy Rennes Imaging Center (Mric) facility  for both electron and light microscopy. This work was funded by grants from  Institut National de la Sant e et de la Recherche M edicale  ( INSERM-U1242 ),  Institut National du Cancer (INCa ,  PLBIO18 ,  PLBIO19 ,  PLBIO20 ),  Fondation pour la Recherche M edicale (FRM , equipe labellis ee 2018,  DEQ20180339169 ),  Agence National de la Recherche  ( ANR, ERANET ERAAT ),  MSCA   RISE-734749  (INSPIRED),  Canc eropôle Grand Ouest (Gliotreat)  grants to EC; the  BBSRC UK  (EM;  BB/C511599/1 ; and  BB/J00751X/1 ) and  UKM Fellowship and Ministry of Higher Education (MOHE) of Malaysia KPT(BS) 870809015063  (MAM).
After washing with PBS supplemented with 10% fetal calf serum and 0. 1% Triton X-100, sections were incubated 3 h with their corresponding secondary antibodies AlexaFluo ® 488 or AlexaFluo ® 594 or AlexaFluo ® 647 (Table  1 ). All antibodies were diluted in PBS supplemented with 10% fetal calf serum and 0. 1% Triton X-100. Sections were counterstained with 4 , 6-diamino-2-phenylindol (DAPI). Images were observed and captured with a confocal microscope (LSM 710 software, ZEISS, Oberkochen, Germany).
RNA-Sequencing Data Analysis RNA samples from either the neural retina or the RPE/choroid complex were sent for sequencing at the iGenSeq transcriptomic platform of the Brain and Spine Institute (ICM, Paris, France). RNA quality was checked by capillary electrophoresis (Agilent 2100 Bioanalyzer system) and RNA with integrity numbers (RIN) ranging from 7. 8 to 8. 2 was accepted for library generation. Quality of raw data has been evaluated with FastQC. Poor quality sequences have been trimmed and adaptors removed with Fastp software to retain only good quality paired reads. Star v2. 5. 3a  [21]  has been used to align reads on reference genome rn6 using standard options. Between 30 and 38 million reads were mapped. Quantification of gene and isoform abundances has been done with rsem 1. 2. 28  [22] , prior to normalization on library size with edgeR  [23]  bioconductor package.
Low-expressed genes have been filtered-out. Reproducibility of replicates has been controlled with PCA representations. Finally, differential analysis has been conducted with the GLM framework likelihood ratio test from edgeR. Multiple hypothesis adjusted p-values were calculated with the Benjamini-Hochberg procedure to control FDR. Pheatmap Bioconductor package has been used to represent as a heatmap for the differentially expressed genes. GSEA function from clusterProfiler package has been used on HALLMARK geneset; on Gene Ontology; on Reactome gene sets; and on KEGG pathways, to get deregulated pathways from analysis, with a representation in dot plot. A genes/pathways network has been created with cytoscape, node color representing log fold change.
The array was probed with a recombinant PSMAD3-SMAD4 trimer, generated by co-expressing SMAD3 and SMAD4 with the TGFBR1 kinase domain in insect cells. The PSMAD3-SMAD4 complex was then detected using a fluorescently-labeled SMAD2/3 antibody. Eight residues are intolerant to almost any amino acid substitution (Thr20, Leu21, Phe24, Ser28, Ser31, Leu32, Gly34, and Pro35). Strikingly, six of these residues are the amino acids known to be mutated in SGS patients, and the array results readily explain why these residues are mutated to a number of different amino acids in SGS (Figure  4E ; for quantification, see Figure  4 -figure supplement 1C and Figure  4 -source data 2). In addition, Thr20 and Phe24 are also crucial residues for binding the PSMAD3-SMAD4 complex, but have not yet been reported as disease mutations.
Size exclusion chromatography with multiangle laser light scattering The trimeric arrangement of the phosphorylated SMAD2 MH2 domain was confirmed by SEC-MALLS. In brief, an S200 10/300 Increase column was attached to an AKTA Micro FPLC system (GE Healthcare), which was connected to a Heleos Dawn 8+ followed by an Optilab TRex (Wyatt). For data collection, 100 ml of a 2 mg/ml stock of phosphorylated SMAD2 MH2 domain was injected and data collected at 0. 5 ml/min for 60 min. The data were analyzed by ASTRA6. 1 software.
Biolayer interferometry Biolayer interferometry was carried out using an Octet RED96 instrument (ForteBio). Biotinylated N-SKI peptide (residues 11-45) was immobilized on streptavidin-coated biosensors (ForteBio) at a concentration of 1 mg/ml in buffer containing 50 mM HEPES pH 7. 5, 200 mM NaCl, 1 mg/ml bovine serum albumin, and 0. 1% Tween-20 for 100 s. The immobilization typically reached a response level of 2 nm. Association and dissociation curves were obtained through addition of a dilution series of trimeric phosphorylated SMAD2 MH2 domain complex (15. 6-1. 95 mM) for 100 s followed by dissociation in buffer for 350 s using the Octet acquisition software. The binding data were fitted using the Octet analysis software.
Crystallization of the N-SKI-SMAD2 MH2 domain complex The SKI peptide (amino acids 11-45) was synthesized by the Peptide Chemistry Group and added in a 2:1 ratio to the phosphorylated SMAD2 MH2 domain trimer. The complex was concentrated to 6 mg/ml and subject to crystallization trials. Initial screening gave rise to fine needles, in several conditions, and these were used as seedstock for rescreening. This gave rise to 25 mm crystals, with a cubic morphology, in the Crystal Screen Cryo screen (Hampton Research); the condition being 1. 5 M ammonium sulphate, 0. 15 M K Na tartrate, 0. 08 M Na 3 citrate, and 25% (vol/vol) glycerol. The crystals were flash-frozen in liquid nitrogen, and data were collected on the I24 beamline at Diamond Light Source (DLS). Data was processed automatically using the DLS Xia2/XDS pipeline  (Winter, 2010; Kabsch, 2010).
Structure determination Molecular replacement was undertaken with the CCP4 program Phaser  (McCoy et al. , 2007) , utilising pdbfile 1khx (with the C-terminal tail removed), as the search model  (Winn et al. , 2011). Initial structure refinement was undertaken with Refmac  (Murshudov et al. , 2011) , with manual model building in Coot  (Emsley et al. , 2010) , before switching to Phenix. Refine to finalize the model  (Liebschner et al. , 2019; Afonine et al. , 2012). Coordinates and data are available from the Protein Data Bank, with accession code: 6ZVQ. RNA extraction, qRT-PCR, and RNA-sequencing Total RNA was extracted using Trizol (Thermo Fisher Scientific) according to the manufacturer's instructions. cDNA synthesis and qPCRs were performed as described  (Gro ¨nroos et al. , 2012). Primer sequences are listed in Key Resources Table.
All qPCRs were performed with the PowerUp SYBR Green Master Mix (Thermo Fisher Scientific) with 300 nM of each primer and 2 ml of diluted cDNA. Fluorescence acquisition was performed on either a 7500 FAST machine or QuantStudio 12 Flex (Thermo Fisher Scientific). Calculations were performed using the DDCt method, and levels of mRNA are expressed as fold change relative to untreated or SB-431542-treated control cells. Means ± SEM from at least three independent experiments are shown. Results were analyzed using Graph-Pad Prism 8 software and statistics were performed on these data using one-way or two-way analysis of variance (ANOVA) as stated in the figure legends. For the RNA-sequencing experiment four biological replicates were used. Total RNA was extracted, and the quality of the RNA was assessed using a bioanalyzer (Agilent).
RNA sequencing analysis Raw reads were quality and adapter trimmed using cutadapt-1. 9. 1  (Martin, 2011)  prior to alignment. Reads were then aligned and quantified using RSEM-1. 3. 0/STAR-2. 5. 2  (Dobin et al. , 2013; Li and Dewey, 2011)  against the human genome GRCh38 and annotation release 89, both from Ensembl. TPM (transcripts per kilobase million) values were also generated using RSEM/STAR. Differential gene expression analysis was performed in R-3. 6. 1 (R Development Core Team, 2009) using the DESeq2 package (version 1. 24. 0) (Love et al. , 2014). Differential genes were selected using a 0. 05 false discovery rate threshold using the pairwise comparisons between time points within each cell line individually. Normalization and variance-stabilizing transformation was applied on raw counts before performing principal component analysis and Euclidean distance-based clustering.
For the heatmaps, we selected those genes that were significantly differentially expressed in the control cell line with an absolute log2 (fold change) of at least 0. 5, not significantly differentially expressed in the SKI-mutated cell lines, and detected with at least a TPM value of 2 in the control cell lines in any condition. We then visualized the log2 (fold change) for each time point against the control condition. Heatmaps were generated in R-3. 6. 1 using the ComplexHeatmap  (Gu et al. , 2016)  package (version 2. 0. 0). The raw data files have been uploaded to the European Genomephenome Archive (EGA), accession number EGAS00001004908.
Quantifications Quantification of Western blots was performed by densitometry measurements of each lane using ImageJ software. The measurements were normalized to the loading control in the same blot. In each case, quantifications were normalized to the SB-431542-treated samples. Quantification for the flow cytometry was performed by measurement of the fluorescence emitted by Alexa 647 in EGFPexpressing single cells as a measure of SKIL protein, and levels were normalized to the SB-431542treated sample in the SMAD4 knockout cells. The program FlowJo was used to analyze the results. For the peptide arrays, two independent experiments were quantified. The staining intensity of each array was quantified by systematically moving a circular selection (diameter 20 pixels) across the array and measuring the 8-bit greyscale intensity for each spot.
Acknowledgements We would like to thank  David Abraham  for the normal dermal fibroblasts. We are very grateful to the  Francis Crick Institute Advanced Sequencing Facility , the  Light Microscopy Facility , the  Flow Cytometry Facility, Cell Services , the  Genomics Equipment Park  and to the  Peptide Chemistry Facility. We thank  Andrew Economou  for quantification of the peptide arrays. We thank all the members of the  Hill lab  and  Davide Coda ,  Daniel Miller , and  Anassuya Ramachandran  for helpful discussions and very useful comments on the manuscript. This work was supported by the  Francis Crick Institute  which receives its core funding from  Cancer Research UK  ( FC001095 ), the  UK Medical Research Council  ( FC001095 ), and the  Wellcome Trust  ( FC001095 ).
Data availability Sequencing data have been uploaded to the European Genome-phenome Archive (EGA), accession number EGAS00001004908. Diffraction data have been deposited in PDB under the accession code 6ZVQ. All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1,  2, 4, 5, 6, 7,   The following datasets were generated:.
In other words, managers could use a shorter list of TreMs as a surrogate for the full list that indicate the presence of further TreMs with a high probability. However, TreM co-occurrence patterns are poorly known. Preliminary studies revealed co-occurrence patterns at the tree scale for European beech (Fagus sylvatica L. ), pubescent oak (Quercus pubescens), holm oak (Quercus ilex), silver fir (Abies alba Mill. ) and Douglas fir (Pseudotsuga menziesii Franco)  (Larrieu and Cabanettes, 2012; Regnery et al. , 2013a; Winter et al. , 2015; Puverel et al. , 2019). However, these studies used databases with a narrow geographical range and a limited number of observed trees. Larrieu and Cabanettes (2012)  highlighted, for example, that bark loss and rot-holes co-occur in both beech and fir while other co-occurrences are tree-species specific: rot-holes and saproxylic fungi co-occur only on beech, and dendrotelms and bark loss only on fir.
Data We collected data from a large range of temperate and boreal forests from Northern Iran to Western Europe (Fig. 1 ; Table  1SM  in Supplementary Material). These forests cover a wide range of degrees of naturalness, from regularly harvested stands to primeval forests (see e. g. Commarmot et al. , 2013 ; REMOTE project https://www. remoteforests. org). The datasets from the managed stands cover various forest types and silvicultural regimes and do not focus on TreM-rich stands only. Each dataset provided was standardized according to the TreM typology by  Larrieu et al. (2018). However, since the typologies used by the field agents recording the TreMs differed slightly, we were not able to follow exactly the same typology as  Larrieu et al. (2018). In order to optimize the available data, we designated eleven TreM subgroups (Table  1 ), very close to the 15 TreM groups described by  Larrieu et al.
Presence/absence of non-random TreM co-occurrences The data used was an absence/presence matrix for the eleven TreM subgroups, with one row for each tree observed. To quantify the nature of a co-occurrence, we counted for the corresponding pair of TreMs (e. g. crack and polypore) the number of (1,1) in the data matrix for the columns associated to this pair (in this example, the first column for crack and the second column for polypore) meaning that both TreMs are present on the same tree; there is no co-occurrence if both TreMs are absent or if only one is present. If this count is low when a TreM is present (e. g. crack) but the other (e. g. polypore) is often absent, the cooccurrence can be qualified as negative; if the count is high since the two TreMs are often both present on the same tree, the co-occurrence can be qualified as positive.
To decide if the co-occurrence is significantly positive/negative or random, we compared this count with a similar one calculated on a sample where pairs of 0 and 1 are obtained by resampling on the 0/1 vectors observed for each TreM of the considered pairs independently; so we did a resampling test for each TreM pair. This resampling procedure (the R-script is provided in the supplementary material) gave min, mean and max counts and when an observed count fell inside the min-max interval, the corresponding pair was considered to be random. Otherwise, the co-occurrence was considered non-random (negative or positive). As we observed considerable heterogeneity of presences and co-occurrences at the plot level, we did the resampling at each plot level to tackle specific plot characteristics (number of presences for each TreM and number of trees involved).
We ran 10,000 iterations of this resampling. We then calculated, for each pair of TreMs, the difference between the observed co-occurrence frequency (i. e. the count of trees in the database that bore the TreM pair) and the mean frequency obtained by the 10,000 iterations of the resampling; we called this difference "the magnitude of the co-occurrence". The results for broadleaves and conifers were treated separately. Graphical representations (Figs. 2  and 3 ) were inspired by those provided in the "co-occur" package  (Griffith et al, 2016).
As explanatory variables, we used: (i) tree dbh, (ii) time since the last harvest on the plot (five classes: 1-<15 years, 2-from 15 to 30y, 3 -from 30 to 50y, 4 -from 50 to 100y and 5 -unharvested for at least 100y), (iii) tree-dynamic status (two categories: long-lived and shade-tolerant species, and pioneer/postpioneer together in order to balance tree numbers between categories since post-pioneers were underrepresented in the dataset) according to  Rameau et al. (1993) , and (iv) compartmentalization capacity according to  Shigo's, 1984 concept (two classes: weak and high; Gilman, 2011; Oven and Torelli, 1999; Schneuwly-Bollschweiler and Schneuwly, 2012; Dujesiefken and Liese, 2015 ; Table  5SM ). We used the plot identity as a random-effect variable (i. e. (1|SitePlot) since several plots were nested in the same site).
We considered this criterion highly important since our main aim was to reduce the number of TreMs to observe. The third criterion was the number of occurrences in the database, with a weight of 3. This criterion focused on the most frequent TreMs to increase the probability of observing at least one TreM on the short list whatever the stand; this criterion is also important in terms of data collection and training practitioners in TreM observation. The fourth criterion was TreM life-span (i. e. permanent versus temporary) with a weight of 2. This criterion was deemed somewhat less important even though TreM longevity makes year-round observation possible. The fifth criterion was observer effect (according to  Paillet et al. , 2015)  with a weight of 1. We included this criterion because the presence of an observer effect in certain field records could lead to stand mischaracterization.
Discussion Based on a large-scale database combining 11 TreM groups, we showed significant high-magnitude co-occurrences between TreMs at the tree scale. We also showed that these co-occurrences are more frequent on broadleaves than on conifers, and that dbh had a consistent effect on the co-occurrence, while life traits of trees (i. e. category in dynamic succession and compartmentalization capacity) and forest management had a lesser effect.
We found mutually-exclusive co-occurrences for conifers only. This is in accordance with the results of  Winter et al. (2015)  who only found a slightly exclusive co-occurrence between bark pockets and broken tree parts on Douglas fir (Pseudotsuga menziesii Mirb. Franco) while studying TreM co-occurrence patterns in European Beech (Fagus sylvatica L. ) and Douglas fir forests. Although our TreM group exposed heartwood is quite similar to the group broken tree parts used by  Winter et al. (2015) , we could not consolidate the two results since we were not able to analyze bark pockets through our database. At the same level of significance (p < 0. 0001), our results were in accordance with co-occurrences highlighted by  Larrieu and Cabanettes (2012)  for (i) European beech, between rot hole and root concavity, and (ii) Silver fir, between bark loss and crack, and sap run and rot hole.
Tree diameter mainly drives TreM co-occurrence patterns The effect of tree dbh on TreM co-occurrence probability had not been evaluated before the present study. For our dataset, dbh was the most relevant variable explaining co-occurrence patterns, both for broadleaves and conifers. Generally, the larger the tree, the greater variety of TreMs it bears (e. g. Larrieu and Cabanettes, 2012; Paillet et al. , 2019). Thus, a larger dbh favors TreM co-occurrence both "by sampling" (larger trees have more chances to have several types of microhabitats) and by ontogeny (the same processes apply for different TreMs). Dbh is used as a proxy of tree-age since it is easier to record in the field than age. However, several TreMs might be linked with age rather than with dbh since they are more likely to occur over a long period, e.
Therefore, obtaining diachronic data would require both permanent plots dedicated to this topic and long-term funding for periodic monitoring hard to imagine given the area and time required to obtain enough trees in a dbh range equivalent to the one in our synchronic data. In this context, TreM cooccurrence patterns might help to identify certain TreM dynamic relationships (e. g. shift of dominance between two TreMs when tree-dbh increases), or at least to identify local conditions that lead to the formation of different TreMs on a given tree. These patterns could guide specific studies, as improved knowledge of TreM dynamics may lead to better management of a continuous TreM supply, both at the stand and forest levels. Despite the large number of trees in our database, it was not possible to perform analyses at the TreM-type level since some TreMs were rarely recorded.
Furthermore, due to the heterogeneity of the TreM definitions in the available datasets, we were also unable to analyze all the TreM groups sensu  Larrieu et al. (2018). Further research should analyze cooccurrence patterns on standing dead trees since they significantly bear TreMs  (Larrieu and Cabanettes, 2012; Vuidot et al. , 2011; Regnery et al. , 2013a; Paillet et al. , 2017). However, thanks to the size of our database and the conservative approach we used (significance with a pvalue < 0. 0001), our results can benefit forest managers during routine practices (tree-marking, inspection visits or plot assessments) or can provide input for management planning based on sound and robust scientific data.
Acknowledgments Part of this research was funded by the  French Ministry in Charge of Ecology (Convention Cemagref-DEB (MEEDDAT) ,  Action  GNB; the program " Biodiversite, Gestion Forestière et Politiques Publiques " (BGF), convention GNB  10-MBGD-BGF-1-CVS-092 , no CHORUS  2100 214651. The Uholka dataset was collected in a joint project of the  Swiss Federal Institute for Forest, Snow and Landscape Research WSL , the  Carpathian Biosphere Reserve  and the  Ukrainian National Forestry University , with financial support from the  State Secretariat for Education, Research and Innovation SERI, Switzerland. The REMOTE dataset was collected within the  MSMT projects  ( CZ. 02. 1. 01/0. 0/0. 0/16_019/0000803  and  LTT20016 ). We thank  Sergey Zudin  ( EFI ) for data management and validation of the I + TreM Dataset as well as the  German Federal Ministry of Nutrition and Agriculture (BMEL)  for funding the projects Integrate + and Informar, which allowed  EFI  to build this extensive I + TreM Dataset.