dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion started: 12 June 2019 c Author(s) 2019. CC BY 4. 0 License. integration and providing a user-friendly interface  (Dufour-Kowalski et al. , 2012). HETEROFOR was therefore progressively elaborated through the integration of various modules (light interception, phenology, water cycling, photosynthesis and respiration, carbon allocation, mineral nutrition and nutrient cycling) within CAPSIS. The advantage of such a platform is to use common development environment, model execution system, user-interface and visualization tools and to share data structures, objects, methods and libraries. 5 To simulate the response of forests to management and changing environmental conditions, integrate and structure the existing knowledge into process-based models is essential but not sufficient. These models must also be documented and evaluated in order to know exactly their strengths and limits when analysing their outputs.
This compartment receives the nutrients coming from atmospheric deposition, organic matter mineralization and primary mineral weathering, and is depleted by root uptake and immobilization in microorganisms. The chemical equilibrium within the soil solution, with the exchange complex or the minerals is updated yearly https://doi. org/10. 5194/gmd-2019-101 Preprint. Discussion started: 12 June 2019 c Author(s) 2019. CC BY 4. 0 License. with the PHREEQC geochemical model  (Charlton and Parkhurst, 2011)  coupled to HETEROFOR through a dynamic link library. In this paper, we present a detailed description of the processes regulating the carbon fluxes (Fig. 1 ) while the coupling with the radiation transfer library (SAMSARALIGHT), the phenology module, the water balance module and the nutrient cycling and tree nutrition module will be described in details in other papers.
Taking advantage of the massive oak regeneration in the 1880s, the forest developed progressively into a high forest and was then invaded by beech. In 2001, the area was covered by even-aged oak trees and heterogeneously sized beech trees. In addition, an understory of hornbeam (Carpinus betulus L. ) occurred in oak dominated areas. A 1 ha plot was installed in an intimate mixture of oak and beech, in which all trees with a circumference higher than 15 cm were mapped (coordinates) and measured (stem circumference at a 5 height of 1. 3 m, total tree height, height of largest crown extension, height to crown base, crown diameters in two directions) at the end of the years 2001 and 2011. Meteorological data were monitored with an automatic meteorological station located in an open field 300 m away from the forest site.
Discussion Very few tree-level, process-based and spatially explicit models have been developed and these often contain only some of the modules necessary to estimate resource availability (solar radiation, water and nutrients). While a description of these models is generally available in the literature, their evaluation by comparison with tree growth measurements is not always accessible or was carried out based on stand-level variables. We have therefore very few information to compare the performances of 5 HETEROFOR at the tree level with those of similar models. Simioni et al. (2016)  faced the same problem with the NOTG 3D model. HETEROFOR first estimates the key phenological dates, the radiation interception by trees and the hourly water balance (de  Wergifosse et al. , in prep). Then, based on the absorbed PAR radiation, individual gpp is calculated with a PUE approach or with the photosynthesis routine of CASTANEA  (Dufrêne et al.
More sophisticated relationships considering age and dominant height can be used for even-aged stands (Le Moguédec and Dhôte, 2012) but are hardly 10 applicable in uneven-aged stands for which tree age is unknown. On the other hand, the functional-structural models based on resource availability at organ level and using a short time step can only be applied to a limited number of trees given the long computing times  (Letort et al. , 2008). Our individual height growth model was fitted with height data measured ten years apart (Appendix E). A large uncertainty was however associated to these data. First, height measurements were obtained to the nearest meter given the difficulty to 15 clearly identify the top of the trees in closed canopy forests. Second, two errors were added since the height increment was obtained by the difference between two height measurements.
Here, only the core of HETEROFOR was described. The water balance and phenology modules are presented and evaluated in a companion paper  (de Wergifosse et al. , in prep)  while the radiation transfer and nutrient modules will be described later. 10 For the next steps, we plan to couple HETEROFOR with existing libraries such as regeneration, genetics and economics. As HETEROFOR was developed within the CAPSIS platform, it is continually improving thanks to the collaborative dynamics among modellers. A broader assessment of the model performances will be carried out based on forest monitoring plots distributed all over Europe. Indeed, HETEROFOR was designed to be particularly suitable for the level II plots of ICP Forests. The processes 15 were described at a scale that facilitates the comparison between model predictions and observations.
Code availability The source code of CAPSIS and HETEROFOR is accessible to all the members of the CAPSIS co-development community. Those who want to join this community are welcome but must contact François de Coligny (coligny@cirad. fr) or Nicolas Beudez (nicolas. beudez@inra. fr) and sign the CAPSIS charter (http://capsis. cirad. fr/capsis/charter). This charter grants access on all the models to the modellers of the CAPSIS community but only to them. The modellers may distribute the  CAPSIS 5  platform with their own model but not with the models of the others without their agreement. CAPSIS4 is a free software (LGPL licence) which includes the kernel, the generic pilots, the extensions and the libraries. HETEROFOR is basically not free and belongs to its authors who decided to distribute it through an installer containing the CAPSIS4 kernel and the latest version (or any previous one) of HETEROFOR upon request from Mathieu Jonard (mathieu.
jonard@uclouvain. be). The version 1. 0 used for this paper is temporarily available at http://amap-dev. cirad. fr/projects/capsis/files. The end-users can 10 install CAPSIS from an installer containing only the HETEROFOR model while the modellers who signed the CAPSIS charter can access to the complete version of CAPSIS with all the models. Depending on your status (end-user vs modeller or developer), the instructions to install CAPSIS are given on the CAPSIS website (http://capsis. cirad. fr/capsis/documentation). The source code for the modules published in Geoscientific Model Development  (Jonard et al. , submitted; de Wergifosse et al. , in prep. ) The temperature of the mineral soil (T in °C) is calculated by soil depth increment (∆z in m) using a simplification of the soil heat transfer equation assuming a constant thermal diffusivity (D in m² s -1 ) across the soil profile.
This network contains our experimental data and protein-protein interactions from 2,035 peer-reviewed publications (48,200 Arabidopsis-Arabidopsis and 1,300 Arabidopsis-effector protein interactions). Our results show that effectors from different species interact with both common and specific Arabidopsis targets suggesting dual roles as modulators of generic and adaptive host processes. Network analyses revealed that effector targets, particularly effector hubs and bacterial core effector targets, occupy important positions for network organization as shown by their larger number of protein interactions and centrality. These interactomic data were incorporated in EffectorK, a new graph-oriented knowledge database that allows users to navigate the network, search for homology or find possible paths between host and/or effector proteins.
Combining all these data allowed us to identify 100 new 'effector hubs' (i. e. , Ath proteins interacting with two or more effectors). Together with Ath-Ath protein interactions retrieved from public databases, we generated a comprehensive Atheffector protein network that captures the wide diversity of Ath pathogens. This network allowed us to detect general trends of effector interference with the host proteome. We have created a publicly available interactive knowledge database called EffectorK (for Effector Knowledge) which allows users to access and augment this network. used to test interactions with effector proteins from Hpa, Psy (8K space) and Gor (12K space)  [11, 12]. The subset of interactions of effectors from Rps, Xcc and Gor in the 8K space was used to compare with previously published Hpa and Psy data (Fig  1 ).
EffectorK, an online interactive knowledge database to explore the Arabidopsis-effector interactomic data In order to facilitate the access and exploration of all the data presented in this work, we for a given protein can be then explored and downloaded in graphical or tabular format. The visual interface for the graphical representation of the interactomic data allows users to expand or recenter a local subnetwork based on a given protein, get information and access to external resources linked to either a protein (node) or an interaction (edge) or modify the layout and the position of the elements for optimal visualization. Additionally, EffectorK also allows users to find the shortest paths between two queried proteins in the network. In order to update, expand and further improve EffectorK, we encourage users to submit their own interactomic data by filing in and sending the dedicated template.
Discussion In this study we identified systematically Ath targets of effectors from the vascular bacterial pathogens Rps and Xcc. We combined this information with other Ath targets identified in similar experimental setups. Additionally, we conducted an extensive literature review to gather published Ath targets of effectors from a wide variety of pathogens including other bacterial species and also oomycete, fungal and animal pathogens. Studying this combined interactomic dataset allowed us to identify new trends of how effectors interfere with the plant proteome and evaluate whether previously described network principles were still supported on a wider scale. We showed that there are no substantial differences in terms of connectivity among the effectomes of five different pathogen species screened systematically (Fig  1 ).
This issue can only be addressed by increasing the number of pathogen effectors screened thoroughly and at a large-scale. Comparing large datasets of effector targets of a wider and more diverse set of pathogens would allow evaluating in which sense this balance between specificity and convergence tilts: 1) If the target specificity decreased, it would mean that the effectomes from the different pathogens tend to interact similarly with the host proteome. This was the case when we compared the percentage of species-specific targets of effectors from Hpa, Psy and Gor that passed from being 73. 9%, 64. 9% and 46. 7% in previous works  [11, 12] , to 51. 7%, 58. 9% and 35. 6%, respectively in the present study (S2 Fig). Nevertheless, a total of five screened species is probably not powerful enough to sustain this claim.
However, 346 out the 564 described Arabidopsis effector targets have been identified exclusively through large-scale Y2H screenings against partial libraries of Ath cDNAs. As with any other large-scale screening, the technical limitations together with the incompleteness of the library might have probably led to an underestimation of the plant-effector interactome of the five screened species  [51]. The relatively small overlap between the large-scale Y2H screenings and manually curated literature datasets might be a consequence of this limitation (Fig  3 ). This small overlap illustrates the current knowledge gap in the characterization of the full plant interactome of pathogen effectors. Extensive work will be required to characterize further effector-host protein interactions in other pathosystems.
EffectorK, an entry point to explore and make sense of plant-effector interactomics To conclude, our work also provides valuable resources for the plant-pathogen interaction community. We described 540 new Ath-Rps and Ath-Xcc effector protein interactions that allowed us to identify 166 new effector targets (S1 Table ). We also manually curated several publications to assemble a collection of 287 Ath-effector protein interactions from a wide variety of pathogens (S2 Table ). All this, allowed us to identify 100 novel effector hubs (S3 Table ). The contribution to plant immunity of these effector hubs has been described for 19 of them, but remains untested for the majority (Table  1 ). This constitutes a list of promising candidates for further functional characterization. All these data were integrated in EffectorK, a knowledge database where users can have easy access to the Ath-effector protein interactions and explore the resulting interactomic network visually and interactively.
While major efforts were done to capture the maximal diversity on the pathogen side, we limited our work to the Arabidopsis plant model. Thanks to the built-in homology search tools available, users can also use their own data as query regardless of the species studied. It is therefore feasible to use EffectorK as a starting point to build on and extend to crop plant-effector protein interactomics. In the long term, these data could be exploited to better understand how pathogens interact with these crops with the prospect of selecting breeding candidates for improved tolerance or resistance against pathogens.
Database content and manual curation Binary interactions between Ath proteins with each other and with pathogen effector proteins were compiled on tabular form keeping track of the protein names and accessions, species and ecotypes/strains of origin, techniques used to detect the interactions and the reference. Ath-Ath protein interactions were compiled from the Arabidopsis Interactome  [41, 42]  and the public databases BioGrid (www. thebiogrid. org [39] ; downloaded in September 2019) and IntAct (www. ebi. ac. uk/intact [40]; downloaded in September 2019). We only kept the direct interactions with the evidence codes 'co-crystal structure', 'FRET' (fluorescence resonance energy transfer), 'PCA' (protein-fragment complementation assay), 'reconstituted complex' or 'two-hybrid' on BioGrid and 'physical association' on IntAct.
At-effector protein interactions were gathered from our experimental Y2H data together with the similarly produced data on Hpa, Psy and Gor effectors  [11, 12]. In addition, an extensive keyword search on effector-Arabidopsis literature was done to retrieve interactions from 80 published articles. A confidence level was assigned to each interaction depending on the number of independent techniques used in a publication for validation: "1" if the interaction was detected by only one technique and "2" if the interaction was validated by at least a second technique. Some interactions lacked important information but, in order to maximize the extent of our network, several assumptions were taken instead of discarding useful data. First, gene models for Ath proteins were rarely mentioned on publications so we assumed the first gene model available on the latest version of the Arabidopsis genome (Araport11  [55] ).
For representation of the data, Venn diagrams were generated using the Venn Diagrams tool from VIB-UGent Center for Plant Systems Biology (www. bioinformatics. psb. ugent. be/webtools/Venn/). The overlap of effector targets from the different datasets was calculated not limiting to any limited space. For an area-proportional representation of the data, a Venn diagram was generated using BioVenn  [56]. Network topology analyses. The topology parameters of the Ath-effector interactomic network were calculated on Cytoscape 3. 7. 2 [57]. Our analyses focused on two key node parameters: degree and betweenness centrality. The degree of a protein is a measure of its connectivity and denotes the number of proteins interacting with it. Throughout this work, we have differentiated two kinds of degrees: 1) effector degree (i.
e. , number of interacting effector proteins) and 2) Ath degree (i. e. , number of interacting Ath proteins). The betweenness centrality measures the proportion of shortest pathways between two proteins that passes through a given node. These parameters were compared against different subset of data and statistical tests were performed in R language  [58]. The cumulative distribution of these parameters among different subset of data was plotted and the area under the curve was estimated using Simpson's rule with the 'Bolstad2' package  [59].
Database construction The database was built using the software architecture recently described  [60]. The files submitted by the curator team were automatically checked for typographic mistakes using ad-hoc Perl scripts and loaded into a Neo4J database and indexed in an ElasticSearch search engine. Each release was rebuilt from scratch. Data were made accessible through a web interface (see Results and discussion section) built upon Cytoscape. js library  [61]. Area-proportional Venn diagram showing the overlap among effector targets identified in the large-scale Y2H screenings performed in this study, in similar large-scale Y2H already published  [11, 12]  and in the manual curation of the literature. The total number of effector targets coming from each dataset is indicated in brackets.
The raw data used for the database setup are available in the 'Data' section of www. effectork. org and the source code is available at https://framagit. org/LIPM-BIOINFO/KGBB. 29. Lim CW, Luan S, Lee SC. A prominent role for RCAR3-mediated ABA signaling in response to Pseudomonas syringae pv. tomato DC3000 infection in Arabidopsis. Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, et al. The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014;42: D358-63. doi:10. 1093/nar/gkt1115 Encodes a Novel Class of Ralstonia solanacearum Type III Effectors Displaying Virulence and Avirulence Activities. Mol Plant-Microbe Interact. 2012;25: 941-953. doi:10. 1094/MPMI-12-11-0321 63. Lonjon F, Lohou D, Cazalé A-C, Büttner D, Ribeiro BG, Péanne C, et al.
AT3G02540 RAD23 family 2 rad23BCD mutant (and not rad23BD) did not show [36] protein C Candidatus Phytoplasma-induced flower virescence (RAD23C) and phyllody. AT5G38470 RAD23 family 2 rad23D mutant did not show flower virescence and [36] protein D phyllody upon transgenic expression of C. (RAD23D) Phytoplasma SAP54 effector. AT2G37630 Asymmetric leaves [37] 1 (AS1) 205 a Ranked in decreasing order. 206 b 2 as1 mutant showed reduced lesion size caused by B. cinerea and Alternaria brassicicola and enhanced Pseudomonas fluorescens and P. syringae growth. Effector targets tend to occupy key positions for the network organization We constructed an Ath-effector protein interaction network compiling the previously described experimental and literature-compiled data with Ath-Ath protein interactions from public databases and the literature [39-42].
Li X, editor. PLoS Pathog. 2014;10: e1004243. 38. Kersey PJ, Allen JE, Allot A, Barba M, Boddu S, Bolt BJ, et al. Ensembl Genomes 2018: doi:10. 1371/journal. ppat. 1004243 an integrated omics infrastructure for non-vertebrate species. Nucleic Acids Res. 2018;46: 33. Simon C, Langlois-Meurinne M, Bellvert F, Garmier M, Didierlaurent L, Massoud K, et al. D802-D808. doi:10. 1093/nar/gkx1011 The differential spatial distribution of secondary metabolites in Arabidopsis leaves reacting 39. Stark C, Breitkreutz B-J, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general hypersensitively to Pseudomonas syringae pv. tomato is dependent on the oxidative burst. repository for interaction datasets. Nucleic Acids Res. 2006;34: D535-9. J Exp Bot. 2010;61: 3355-70. doi:10. 1093/jxb/erq157 doi:10.
ATPase assay ClpB ATPase activities were determined in Reaction buffer (50 mM Tris pH 7. 5, 25 mM KCl, 20 mM MgCl 2 , 2 mM DTT) in the absence or presence of substrate (10 mM casein) using a NADH-coupled colorimetric assay (Sigma) by measuring the decrease of NADH absorption at 340 nm in a BMG Labtech FLUOstar Omega plate reader. ClpB wild-type and variants were typically used at 0. 5 mM except for K476C (0. 15 mM in presence of casein) and E279A or K476C/E279A (0. 25 mM in presence of casein). ATPase activities were derived from the linear decrease of NADH absorbance and K 0. 5 , v max and n Hill values were determined by fitting the curves using nonlinear regression applying an allosteric sigmoidal model (Origin, Prism software).
Model building and refinement Because we obtained several maps with a variety of resolutions from 3. 2 to 4. 1 A ˚resolution, we first built a model for the highest resolution map and used it as a starting model for building into the other maps. The crystal structure of ClpB (PDB: 4CIU), used as an initial model, was segmented into small and large sub-domains of AAA1 and AAA2 and middle domain. These segments were rigid body fitted into the density of class 10 protomer D using the Fit in map tool of Chimera  (Pettersen et al. , 2004). Missing loops and connecting regions were built in COOT  (Emsley et al. , 2010)  and the model was further refined using COOT and Phenix  (Adams et al. , 2010). This subunit was copied and rigid body fitted into the densities for protomers B, C and E, which adopt a similar conformation to protomer D, with little difference between the 4 states.
QUANTIFICATION AND STATISTICAL ANALYSIS ATPase activities were determined by calculating the linear decrease of NADH absorbance using Excel. The corresponding standard deviations were calculated by Excel. Hill coefficients of ATPase activities and their respective standard deviations were determined by Origin software. Binding affinities of ClpB to FITC-casein and their respective standard deviations were determined by Prism software. MDH disaggregation activities were determined by calculating the linear increase in MDH activity using Excel. Corresponding standard deviations were calculated by Excel. Quantification, statistical analysis and validation related to cryo-EM image processing are implemented in the software described in the image processing section of the methods details. The global resolution estimates of refined cryo-EM maps are based on the 0.
ACKNOWLEDGMENTS This work was funded by grants from  Deutsche Forschungsgemeinschaft  ( BB617/17-2 ,  MO970/4-2 , and  MO 970/4-3 ) to B. B. and A. M. , the  AmPro program  of the Helmholtz Society to B. B. , and  Wellcome Trust  grants  106252 ,  101488 , and  079605  to H. R. S. K. F. was supported by the  Hartmut Hoffmann-Berling International Graduate School of Molecular and Cellular Biology (HBIGS). We acknowledge  Diamond Light Source  for access to and support of the cryo-EM facilities at the  UK national Electron Bio-Imaging Centre (eBIC)  (proposal  EM14704 ), funded by the  Wellcome Trust , the  Medical Research Council (MRC) , and the  Biotechnology and Biological Sciences Research Council (BBSRC) , and we thank  Y. Chaban ,  D. Clare ,  K. Dent , and  A. Siebert  for support;  N.
CONTACT FOR REAGENT AND RESOURCE SHARING Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Helen Saibil (h. saibil@mail. cryst. bbk. ac. uk). Continued REAGENT or RESOURCE SOURCE IDENTIFIER MotionCor2 Zheng et al. , 2017 http://msg. ucsf. edu/em/software/ motioncor2. html cryoSPARC Punjani et al. , 2017 https://www. nature. com/articles/ nmeth. 4169 Coot v0. 8. 8 Emsley et al. , 2010 http://scripts. iucr. org/cgi-bin/ paper?S0907444910007493 PHENIX v1. 13 Adams et al. , 2010 http://www. phenix-online. org/ UCSF Chimera UCSF Resource for Biocomputing, Visualization, and Informatics https://www. cgl. ucsf. edu/chimera/.