dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparing large datasets of effector interactors of a wider and more diverse set of pathogens would allow evaluating where the balance is between specificity and convergence: (a) If the interactor 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 speciesspecific interactors of effectors from Hpa, Psy, and Gor that passed from being 73. 9%, 64. 9%, and 46. 7% in previous works  (Mukhtar et al. , 2011; Weßling et al. , 2014) , to 51. 7%, 58. 9%, and 35. 6%, respectively, in the present study (Figure  S2 ). Nevertheless, a total of five screened species is probably not powerful enough to sustain this claim. (b) If, in contrast, the interactor specificity increased with the number of screened species, it would mean that the different pathogens have evolved unique ways to modulate the interaction with the host.
| 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 interactors (Table  S1 ). We also manually curated several publications to assemble a collection of 287 Ath-effector protein interactions from a wide variety of pathogens (Table  S2 ). All this allowed us to identify 100 novel effector hubs (Table  S3 ). 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 made 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.
| Cloning of Rps and Xcc T3E genes All the cloning of the T3E genes from Rps and Xcc was performed by BP gateway BP or TOPO cloning (Thermo Fisher Scientific, Waltham, MA, USA) to generate pENTRY plasmids, which were later transferred into the appropriate Y2H plasmids  (Mukhtar et al. , 2011)  using the LR gateway reaction (Thermo Fisher Scientific). Table  S5  contains all the PCR primers and final plasmid identities describing the collection of plasmids used in this study. Gene sequence information from Rps strain GMI1000 (GenBank accessions: NC_003295 and NC_003296)  (Salanoubat et al. , 2002)  can be obtained from www. ralst o-T3E. org (Sabbagh et al. , 2019)  and from the published genome of Xcc strain 8,004 (NC_007086)  (Qian, 2005).
| 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  (Dreze et al. , 2011; Smakowska-Luzan et al. , 2018)  and the public databases BioGrid (www. thebi ogrid. org [Stark et al. , 2006] , downloaded in September 2019) and IntAct (www. ebi. ac. uk/intact  [Orchard et al. , 2014] , 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.
| Computational simulations of random targeting of Ath proteins by single pathogen effectors (intraspecific convergence) Significance of the intraspecific convergence was tested, comparing our experimental data with random simulations as previously published  (Weßling et al. , 2014). Briefly, for each effector of Xcc and Rps we assigned randomly the same number of Ath interactors as experimentally observed from the degree-preserved list of 8K proteins. The distribution obtained from 10,000 simulations was plotted and compared to the experimentally obtained data. The p value of the experimental data were calculated as follows: number of simulations where the number of interactors is lower than or equal to experimentally observed is divided by the number of simulations. When the number of simulations with fewer interactors than observed was zero, the p value was set to <.
| Overlap of effector interactors The overlap of effector interactors from the different datasets was calculated without limiting the screening space. For representation of the data, Venn diagrams were generated using the Venn Diagrams tool from VIB-UGent Center for Plant Systems Biology (www. bioin forma tics. psb. ugent. be/webto ols/Venn/). The overlap of effector interactors from the different datasets were calculated not limiting to any limited space. For an area-proportional representation of the data, a Venn diagram was generated using BioVenn  (Hulsen et al. , 2008).
| Network topology analyses The topology parameters of the Ath-effector interactomic network were calculated on Cytoscape 3. 7. 2 (Shannon, 2003). 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: (a) effector degree (i. e. number of interacting effector proteins) and (b) 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 subsets of data and statistical tests were performed in R language (R Core Team, 2019).
| Database construction The databases were built using the software architecture recently described  (Carrère et al. , 2019). 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) built on Cytoscape. js library  (Franz et al. , 2016). The raw data used for the database setup are available in the "Data" section of www. effec tork. org and the source code is available at https:// frama git. org/LIPM-BIOIN FO/KGBB.
sented in this work, we have generated EffectorK (for "Effector Knowledge"), an interactive web-based knowledge database freely available at www. effec tork. org. The latest version (2 October 2019) contains 49,875 interactions for 8,617 proteins coming from 2,035 publications. Of these, 1,300 are Ath-effector protein interactions. Searches can be done based on a wide range of supported identifiers such as different protein names, NCBI or TAIR accession numbers, PubMed identifiers, and InterPro terms. Additionally, users can also query nucleotide or amino acid sequences directly with BLAST or use accession numbers from other model and crop plants to find homologs within the database. All proteins found by query are then listed in tabular format and hyperlinked to the corresponding interactomic data, external resources, and amino acid sequences.
F. was supported by a PhD fellowship from the French Laboratory of Excellence project "TULIP" (ANR-10-LABX-41; ANR-11-(1) p value = number of simulations where the number of interactors ≤ experimentally observed number of interactors number of simulations (2) p value = number of simulations where the number of common interactors ≥ experimentally observed number of common interactors number of simulations O PE N R E S E A RCH BA D G E S This article has earned an Open Data badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at http://www. effec torK. org and https://lipm-brows ers. toulo use. inra. fr/k/Effec torK.
Funding information Grant/ Award Number:  ANR-10-LABX-41  and  ANR-11-IDEX-0002-IDEX-0002-02 ). This work was supported by the  National Science Foundation  ( IOS-1557796 ) to M. S. M. The L. I. P. M. is supported by the  French Laboratory of Excellence  project " TULIP"  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ). The IPS2 is supported by the  French Laboratory of Excellence  project " SPS"  ( ANR-10-LABX-0040-SPS ).
45 mm/year in the Gulf of Taranto. An Interferometric Synthetic Aperture Radar (InSAR) analysis  [63]  did not show evidence of anthropogenic-induced displacement in the Taranto area. This reveals the general tectonic stability in the NE end of the gulf and a very slight subsidence trend in the northern part of the City of Taranto. To assess the current rates of VLM along the coastal zones of the Mediterranean region, a continuous GPS (cGPS) dataset, analyzed in the frame of the SAVEMEDCOASTS Project (www. savemedcoasts. eu [64] ), was used. This study focuses on the uplift velocity estimated at the cGPS station of MMET (Matera METaponto) which falls within the investigated area located in Metaponto (Matera, Southern Italy). This station is managed by the Italian Space Agency (ASI/CGS) and is integrated in the Rete Integrata Nazionale GNSS (RING) network (http://ring.
Tide and Sea-Level Trend The current sea-level trend between Castellaneta and Taranto was obtained using sea-level data recorded by the Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA), tidal station located at the Molo Sant'Eligio, in the Taranto harbor. Data were continuously recorded with a sampling rate of 10 min using an ultrasound transducer SIAP ± MICROS ID0710 (from 1999 to 2010, ISPRA, Taranto, Italy), and a radar sensor SIAP + MICROS TLR (from 2010 to 2020, ISPRA, Taranto, Italy). During the analysis, gaps and outliers were recognized and corrected to avoid artificial signals in the time series. The analysis highlighted a semidiurnal tidal range of 0. 4 ± 0. 1 m and a mean sea-level rise of 1. 04 ± 0. 5 mm/year in the time span 1999-2020 (20 years) (Figure  4 ).
LIDAR and TLS Laser Imaging Detection and Ranging (LIDAR) data were derived from the 2008-2009 surveys performed by the Ministero dell'Ambiente, at a resolution 2 × 2 m. In addition, Terrestrial Laser Scanner (TLS) surveys were performed at different times during the winter of 2018 by means of the Faro Focus X130 (Polo Scientifico Tecnologico Magna Grecia, Taranto, Italy), at a resolution of 2 cm. LIDAR and TLS data and Digital Elevation Models (DEMs) were combined to extract the shoreline configuration and the shore morpho-topography (Figure  6 ). For the considered coastal stretches, DEMs reveal a mean shoreface slope between 2 and 5 degrees which, in response to the tide amplitude, determines a daily horizontal shoreline displacement of 12. 5 ± 3 meters under a low hydrodynamic condition.
LIDAR and TLS Laser Imaging Detection and Ranging (LIDAR) data were derived from the 2008-2009 surveys performed by the Ministero dell'Ambiente, at a resolution 2 × 2 m. In addition, Terrestrial Laser Scanner (TLS) surveys were performed at different times during the winter of 2018 by means of the Faro Focus X130 (Polo Scientifico Tecnologico Magna Grecia, Taranto, Italy), at a resolution of 2 cm. LIDAR and TLS data and Digital Elevation Models (DEMs) were combined to extract the shoreline configuration and the shore morpho-topography (Figure  6 ). For the considered coastal stretches, DEMs reveal a mean shoreface slope between 2 and 5 degrees which, in response to the tide amplitude, determines a daily horizontal shoreline displacement of 12. 5 ± 3 m under a low hydrodynamic condition. A reference value to evaluate the shoreline rate changes was obtained by integrating long-term changes, detected by orthophotos and satellite images, with short-term changes detected by TLS data.
Data Processing An analysis of the spatial and temporal changes in the Ionic littoral zone between Castellaneta and Taranto was performed by digitalizing all the shorelines via aerial photographs, orthophotos, satellite images, and LIDAR data available for that stretch of coast. They were collected for a medium-term analysis and integrated with TLS data acquired at different hours on different days. From all the collected images, the shorelines were digitalized in ArcGIS for each year. Digital Shoreline Analysis System (DSAS) tools were also used  [69]  in order to obtain the shoreline 1947 to 2018 changes. During the processing, uncertainty about the position of the shoreline had to be defined. The following factors were considered: For the entire stretch of the sandy coast, 345 transects (50 m spacing) were extracted perpendicularly to a reference baseline placed 50 meters seaward.
Data Processing An analysis of the spatial and temporal changes in the Ionic littoral zone between Castellaneta and Taranto was performed by digitalizing all the shorelines via aerial photographs, orthophotos, satellite images, and LIDAR data available for that stretch of coast. They were collected for a medium-term analysis and integrated with TLS data acquired at different hours on different days. From all the collected images, the shorelines were digitalized in ArcGIS for each year. Digital Shoreline Analysis System (DSAS) tools were also used  [69]  in order to obtain the shoreline 1947 to 2018 changes. During the processing, uncertainty about the position of the shoreline had to be defined. The following factors were considered: For the entire stretch of the sandy coast, 345 transects (50 m spacing) were extracted perpendicularly to a reference baseline placed 50 m seaward.
The development of a submersion model along a sandy coast requires knowledge of the following parameters: і) sea-level trend; іі) VLM rates, including isostatic adjustments; and ііі) shoreline erosion/accretion. The model was implemented mathematically in a Matlab environment by considering the components conditioning both the vertical and horizontal coastal displacements for the sea level and shoreline changes: ∆z = (v × 10 ) × ∆t ± ∆z (1) ∆x = v × ∆t × cosβ (2) ∆y = v × ∆t × sinβ (3) where: ∆z-relative sea-level rise (m) v -relative sea-level rate (mm/year) ∆t-prediction time span (year).
The development of a submersion model along a sandy coast requires knowledge of the following parameters: (i) sea-level trend; (ii) VLM rates, including isostatic adjustments; and (iii) shoreline erosion/accretion. The model was implemented mathematically in a Matlab environment by considering the components conditioning both the vertical and horizontal coastal displacements for the sea level and shoreline changes: ∆z = (v RSLR × 10 -3 ) × ∆t ± ∆z tide (1) ∆x = v LRR × ∆t × cos β (2) ∆y = v LRR × ∆t × sin β (3) where: ∆z-relative sea-level rise (m) v RSLR -relative sea-level rate (mm/year) ∆t-prediction time span (year) ∆z tide -tide amplitude (m) ∆x-longitude shoreline displacement (m) ∆y-latitude shoreline displacement (m) v LRR -shoreline rate changes (m/year) β-normal shoreline angle (degrees).
Acknowledgments: We thank all those who collaborated with their logistic and technical support throughout every phase of this study. This work was carried out under the umbrella of the  IGCP  Project n. 639 "Sea-level change from minutes to millennia" (Project Leaders:  S. Engelhart ,  G. Hoffmann ,  F. Yu  and  A. Rosentau ) and SAVEMEDCOASTS (www. savemedcoasts. eu),funded by the  European Union (DG-ECHO ) (Project  Coordinator Marco Anzidei ). Our appreciation also goes to  Victoria Sportelli , a native English speaker and lecturer, for the English language revision of the paper. We are also grateful to the reviewers for their revisions that have allowed us to improve the quality of the paper.
, 20 0 0 ) since some instructions accessing sensitive registers are invokable from non-privileged mode. Such an architecture is called partially virtualizable architecture. It is then required to treat these non-virtualizable instructions with other mechanisms than instruction trapping. Three methods are usually considered to address this issue. The binary translation method relies on a live interpretation of all sensitive instructions by VMM routines. Once a VM program has its code loaded into memory and its execution started, the VMM scrutinizes ongoing instructions to dynamically substitute sensitive ones by VMM routine calls before their usage. The paravirtualization approach consists in the modification of the program source code employing sensitive instructions before their instantiations, substituting them by equivalent VMM routine calls (equivalence property) which are referred to hypercalls.
Considering the case of an OS in a virtual machine, the para-virtualization related code modifications are only located in the kernel of the VM, to make it cope with the VMM through hypercalls. The hardwareassisted virtualization consists in a solution based on CPU capabilities. More precisely, when hardware-assisted virtualization is available, the CPU proposes a root operation mode intended to host the VMM, while the non-root operation mode supports the VM code execution. These modes are not related to the privileged and nonprivileged ones: the root mode enables programs to define which instructions, executed in the non-root mode, will trap back.
The host OS kernel is in charge of required virtual resources (i. e. filesystem access, networking) and container isolation enforcement. The container execution environment benefits from kernel features and its configuration is performed by the container engine. This extra layer runs at the host system application level. By these means, OS-level virtualization enables almost-baremetal  ( Felter et al. , 2015 ; Soltesz et al. , 2007 )  performances, outrunning the regular system virtualization ones, at the cost of coupling containers with a specific operating system. LXC  ( Linux containers, 2018 ), Docker ( docker, 2018 )  and gVisor ( gVisorRuntime Sandbox, 2018 ) are three thriving examples of container engines supporting the Linux OS.
Type-I hypervisor and unikernelrelated hypervisor may completely manage themselves hardware resources, or delegate this management to privileged VM (e. g. storage or network device handling). • The hypervisor level , mentioned in the figure in the next row, refers to hypervisors and container engine softwares. We assume it handles VMM capabilities (VM instruction trapping and sensitive instructions handling), VM memory management, VM networking (virtual network adapter and inter-VM networks) and VM I/O interfaces. The hypervisor is configured through a management console, enabling a system administrator or a service to manage the hypervisor and VM configurations. • The virtual machine level refers to VMs, containers and unikernels, and corresponds to the top row of the figure. This level includes applications, their configurations, their runtime environment and the libraries they depend on.
The OS kernel carries its own design principles, which may lead to the inapplicability of security mechanisms from the OS kernel to protect applications. For instance in Linux, process address space isolation is a non-sense in kernel space, as the notion of process is not defined there  ( Bovet and Cesati, 2005 ). Unikernel is one known exception, as its design is based on a common framework for both the development of hardware resource management and applications  ( Madhavapeddy et al. , 2013 ). Therefore, this vulnerability affects most significantly the type-I and type-II hypervisor virtualization models, while neither the OS-level nor the unikernel virtualization models are concerned. The access to user space is another source of vulnerabilities. For instance, the Linux kernel contains routines to read or write memory allocated to applications without restriction, ( copy_from_user() and copy_to_user() ), enabling it to intervene with no control  ( Bovet and Cesati, 2005 ).
This leaves them more vulnerable to these isolation issues. In the area of networking infrastructures , the hypervisor may provide a networking feature to support communications amongst hosted virtual environments and external resources. It can either rely on a virtualized network sustained only by the hypervisor or by an external program (e. g OpenVSwitch Open vSwitch, 2018 ). The networking configuration has to enforce the isolation amongst resources to only authorized communications. A misconfiguration leads to potential vulnerabilities enabling to bypass the isolation. Hypervisors may also feature other resource sharing , whose misconfiguration enables communications amongst VMs. The most obvious one is persistent storage sharing amongst several VMs ( volume ). Several VMs may be an instance of a common image issued from a registry  ( Tak et al.
Vulnerabilities may also be due to the implementation of virtualization methods. From a software engineering perspective, hypervisor routines can be flawed, and carry software vulnerabilities  ( MITRE, 2012 ). It may also affect their implementations  ( Quist et al. , 2006 ). System virtualization may exploit hardware mechanisms that can mitigate the effects of software flaws. This contributes to make them a bit more secured. Finally, the hypervisor controls the execution of VMs, and has a holistic view over the usage of virtualized resources. The oversight of hypervisors can be seen as a potential vulnerability, as they can access and modify the exposed virtual hardware resources  ( Nance et al. , 2008 ). Models related to system virtualization are less prone to these vulnerabilities. OS-level virtualization exposes the whole system call interface of the host OS kernel to containers, jeopardizing its isolation.
Type-I hypervisor and unikernel virtualization models are less affected, as the OSes can be managed and combined in the hypervisor. Hardware As nested virtualization is considered as out-of-scope of this analysis, we consider cases where the hypervisor (and eventually the host OS) are running on a bare-metal hardware. The hardware layer is affected by vulnerabilities, independently from the considered virtualization model. The hypervisor is a software component requiring a hardware infrastructure to operate. This hardware has an oversight above the running hypervisor, the running VMs and the current communications of the virtual interfaces exploiting the physical one. The persistent storage of the hardware infrastructures also affects the stopped VMs, and their own storage. This makes hardware an attractive target to compromise VMs.
This can typically consist in a distributed denial of service attack. Software flaws are also sources of software failures (e. g. MITRE, 2014 related to a given appliance) and can be exploited to perform denial of service attacks. VM appliances may be attacked using their intrinsic flaws, or the ones of their dependencies. These attacks exploit mostly the memory management and concurrency vulnerabilities, but might also rely on software interface vulnerabilities. The OS kernel of the VM may also be used for that respect ( kernel criticality vulnerabilities). According to  Gu et al. (2003)  , linux kernel faults are mainly due to unsecure software development. We then focus on attacks exploiting software flawed design. Software applications may carry flaws in their design, but not specifically related to their code.