dataesr/scientific-paragraphs-categorization huggingface dataset:
Alpha and beta diversity Faith's phylogenetic diversity (PD)  (Faith, 1992)  was used as measure of alpha diversity, calculated from the phylogenetic tree based on GTDBtk marker genes using the pd() function of the picante package for R  (Kembel et al. , 2010). Genus-level increase of PD from novel SGBs was calculated from the differences of PDs with and without novel SGB annotated as the respective genus. Sample level PDs were calculated from the SGB presence/absence patterns. Beta diversity was assessed as unweighted and weighted UniFrac distances  (Lozupone et al. , 2011)  using the UniFrac() function of the phyloseq package for R  (McMurdie and Holmes, 2013)  and SGB abundances and the phylogenetic tree based on GTDBtk marker genes as input. Aitchison distance  (Aitchison, 1982)  was calculated from the CLR-transformed genuslevel TPM abundances obtained from the clr() function from the compositions package for R  (van den Boogaart and Tolosana-Delgado, 2008)  and adding a pseudocount of 1 to all abundances, setting all CLR-transformed abundances below zero to zero.
Jaccard distances  (Jaccard, 1912)  were also calculated on genus-level presence/absence patterns using the vegdist() function from the vegan package for R  (Oksanen et al. , 2022). Genus-level abundances were chosen for Aitchison and Jaccard distance, as SGBs are highly host-specific, thus would lead to high beta-diversities simply due to host exclusive SGBs, grouping at genuslevel prevents from this and UniFrac distances use phylogenetic relations between SGBs. Beta-diversity on functional abundances were calculated from the Euclidian distances of the log-transformed KO abundances adding a pseudocount of 1 to avoid undefined values. Presence-absence values of KOs were treated in the same way as described above and using Jaccard distance to infer pairwise distances.
, 2013)  and performing SH-aLRT test  (Guindon et al. , 2010). Alignments of GTDBtk marker protein sequences for Prevotella SGBs and Paraprevotella clara were used to reconstruct a genome-level species phylogeny in the same respective way as described above for the cydA sequences (BIC best-fit: LG+I+I+R5). Very low confident branches (< 60% bootstrap support) in the cydA phylogeny were resolved together with the species tree using the OptResolutions supplementary program of the RANGER-DTL 2. 0 software  (Bansal et al. , 2018)  resulting in 495 equally probable trees with optimized duplication-transfer-loss costs using default values (duplication: 2, loss: 1, transfer: 3). A randomly chosen output tree was using in the reconciliation analysis with the species tree in RANGER-DTL 2. 0 using default values and 1,000 random starting seeds in parallel  (Tange, 2011)  to assess robustness.
Co-phylogeny analysis Host phylogenetic trees were obtained from the 10kTrees website (  (Arnold et al. , 2010) ; https://10ktrees. nunn-lab. org/). To assure high quality microbial phylogenies for the cophylogeny analysis, family-level maximum-likelihood trees were reconstructed from the GTDBtk marker gene alignments with the IQTREE2 software  (Minh et al. , 2020 ) and a WAG model including a random SGB outside the respective families as outgroups. Family level trees were rooted and for each SGB traced from tip to root to identify for each SGB the smallest subtree which covered 4, 5, 6, and 7 host groups. Combining information from all SGBs, the overall set of smallest trees to be included in the co-phylogeny analysis were identified, discarding subtrees for which the inclusion criterion was fulfilled already for a smaller tree starting from a different tip.
Correlation of microbial phenotypes with cophylogeny signals SGB representative genome sequences were analyzed using the Traitar tool  (Weimann et al. , 2016)  to infer up to 67 microbial traits. A total of 43 inferred traits present in more than 50 and less than 1017-50 = 967 of the 1,017 SGBs included in the cophylogeny analysis were analyzed for their association with cophylogeny signals. Using the R package lme4qtl  (Ziyatdinov et al. , 2018) , for each of the traits a mixed logistic regression model was fitted across for the 1,017 SGBs, using signal of cophylogeny (binary trait) as dependent variable and the trait as bineray fixed effect explanatory variable, accounting for phylogenetic and taxonomic relatedness between SGBs by including a relatedness matrix and phylum-level categories as random effects in the model and using a binomal function with probit as link.
Acknowledgements We thank the  IKMB Microbiome and NGS laboratories  for excellent technical support. We thank  Sébastien Calvignac-Spencer  and  Grit Schubert  for their valuable contributions to the analysis and the manuscript. This study is an outcome of the  Deutsche Forschungsgemeinschaft  (DFG) Collaborative Research Center 1182 " Origin and Function of Metaorganisms " (https://www. metaorganism-research. com, Project number:  261376515 , Projects A2 and Z2) and received infrastructure support from the  DFG Excellence Cluster 2167  " Precision Medicine in Chronic Inflammation " (PMI; Project number:  390884018 ). JFG was supported by the  DFG  grant " The ecology and evolution of primate phageomes " ( GO 3443/1-1 : Project number:  453352748 ) and the  DFG Research Group "Sociality and Health in Primates " ( FOR2136 ;  CA 1108/3-1 ; Project number:  244372499 ).
Research at Kokolopori was supported by the  Max Planck Society and Harvard University. Research at Ozouga and  Taï National Park  was supported by the  Max Planck Society. Research at Loango was supported by  US Fish and Wildlife Service ,  Tusk Trust ,  Berggorilla Regenwald Direkthilfe , and the  Max Planck Society. Research at Bwindi was supported by the  Max Planck Society. The survey of the Bwindi mountain gorillas was funded by the  International Gorilla Conservation Programme coalition  members with supplemental funding from the  Wildlife Conservation Society. We are extremely grateful to the organizations that facilitated work at these field sites for sample collection: Bwindi -The mountain gorilla survey was conducted by the  Uganda Wildlife Authority, l'Institut Congolais pour la Conservation de la Nature , the  Rwanda Development Board , the  International Gorilla Conservation Programme , the  Max Planck Institute for Evolutionary Anthropology, Conservation Through Public Health , the  Mountain Gorilla Veterinary Project , the  Institute for Tropical Forest Conservation , and The  Dian   Fossey Gorilla Fund ;  Kokolopori -Vie Sauvage , the  Bonobo Conservation Initiative ;  Loango -Agence Nationale des Parcs Nationaux , the  Centre National de la Recherche Scientifique et Technique of Gabon ;  Taï National Park  -the  Centre Suisse de Recherches Scientifiques en Côte d'Ivoire  and the staff members of the  Taï Chimpanzee Project.
8 An impulse dummy named for instance ''I80q4'' takes the value 1 in the last quarter of 1980, and similarly for ''I81q4'' and ''I84q4''. ''I80q4'' and ''I81q4'' capture the sharp rise in the US interest rate and the inflation rate following the oil shock and the start of the Volcker disinflation. ''I84q4'' is related to the start of the 'great moderation' and the fall in the interest rate. The selection of the dummies is based on the following: the descriptive statistics of the series (summarised in Table 1 in the Appendix); the suggestion of the  Lanne et al. (2002)  unit root test (reported in Tables  2  and 5 ) for which the software JMulti offers a search for the break date that is complemented with a residual analysis; and the statistical significance of the corresponding coefficient within the estimated VEC model.
e. 0. 72% quarterly and 2. 9% annually. 15  Kumar (2013)  for the period 1954 to 2008 reports an identical inflation coefficient and a positive, though significantly higher, output gap coefficient; the employed dataset is similar, but the output gap is measured exogenously and the modelling procedure differs, as the series are found to be stationary. For the Greenspan era (1987 to 2006),  Christensen and Nielsen (2009)  find a cointegrating relationship in which the inflation coefficient is either statistically insignificant or, in the case of core inflation, bears the wrong sign and thus cannot be interpreted as a monetary policy rule; the econometric procedure is similar to ours, but the data is monthly and includes unemployment, core inflation or the change in the consumer price index and the market bond yield.
Remarkably, the model also reproduces brain-wide enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.
Integration of AdEx mean-field models in TVB We have used the simulation engines of the Human Brain Project's (HBP's) EBRAINS neuroscience research infrastructure (https://ebrains. eu and The Virtual Brain https://ebrains. eu/service/the-virtual-brain) to make access as wide as possible. Replication of the TVB-AdEx findings can be done here, with a free EBRAINS account, and users can clone the repositories to further test or extend the present capacities. The models can also be downloaded from Github at https://gitlab. ebrains. eu/kancourt/tvbadex-showcase3-git to run locally. Here, the connection of mean-field models was defined by human tractography data (https://zenodo. org/record/4263723, Berlin subjects/QL_20120814) from the Berlin empirical data processing pipeline  [24]  (Fig. 2A ).
The vertical integration across scales is provided by TVB-AdEx-type models, taking advantage of the Big Data in EBRAINS. TVB-AdEx mean-field models constituting each node of the connectome are designed by construction to approximate the mean and covariance of the firing rate in spiking neural networks exhibiting stable dynamics in asynchronous irregular regimes  [20]. This model was extended to two neuronal populations, excitatory neurons with adaptation and inhibitory neurons  [9] , but this extension has limitations. Importantly, the model is imprecise when adaptation varies within a range larger than described here (for adaptation values higher than 100pA  [9] ) and when fast synchronous dynamics like oscillations in the gamma range (between 40 Hz and 80 Hz)  [20] , spindles, or ripples occur.
Acknowledgments This research was funded by the  European Community  ( Human Brain Project ,  H2020-945539 ), the  Centre National de la Recherche Scientifique (CNRS, France) , the  ANR PARADOX , and the  ICODE excellence network. The authors would like to thank  Nuria Tort-Colet ,  Matteo di Volo ,  Mallory Carlu ,  Michele Farisco ,  Thierry Nieus , and  Mavi Sanchez-Vives  for helpful feedback on the work in progress. Many thanks to EBRAINS for help with implementation and to  Kevin Ancourt  for wrangling the Git repository.
This paper extends our previous paper  [41]  in several directions: 1. We extend the DSL (Domain Specific Language) from  [41]  with global permissions to simplify the definition of general policies affecting the bot as a whole and new types of permission constraints for a more finegrained restriction of the granted permissions (e. g. , for temporally-restricted access). 2. We propose a (textual) concrete syntax for the DSL and a modeling editor (with the expected auto-completion and syntax highlighting features, among others) to easily define (and check the well-formedness of) such policies. 3. We discuss a couple of implementation strategies to enforce the rules on top of bot frameworks, including the full architecture and infrastructure for a generative approach that relies on Casbin to integrate the runtime evaluation of policies on top of the Xatkit framework.
This kind of GRANT ALL level permission follows the standard semantics of this type of permission as proposed in the database realm. Besides, for roles that should have almost all permissions, we can add an except for clause listing those bot components to whom the grant all permission does not apply. These global permissions will then be unfolded during the generation process and converted into a set of individual policies over concrete components as the other policies to simplify its treatment and provide a homogeneous environment for the policy enforcement mechanism. Subjects. The metaclass Subject represents the active actors which interact with the CUI. Following an RBAC approach, we define two kinds of subjects: Users and Roles, where users get roles assigned. Role inheritance is supported.
Concrete syntax As mentioned before, domain-specific languages are usually equipped with a concrete syntax that enables users to create instances (i. e. models conforming to the DSL) without dealing directly with the abstract syntax. In this case, we decided to provide our language with a textual syntax instead of a graphical or form-based one, as we believe it results in more compact policy specifications that are easier to create and understand. Nevertheless, we could have several alternative concrete syntaxes for the same metamodel, so it would be equally possible to define a graphical syntax for our language or even a mix of the two. Textual syntaxes are defined via a grammar. As explained in more detail in Sect. 4. 5, our grammar is created with Xtext, 7  a state-of-the-art language workbench.
Modeling editor for RBAC policies To prove the feasibility of our approach, we describe in this section the tool support we provide to facilitate such policy modeling. As mentioned before, we use the Xtext language workbench in order to provide our access-control language with a concrete textual syntax and an associated editing tool. The central artifact of Xtext is the grammar, which provides the syntactic rules valid textual instances must follow. Our grammar, shown in Listing 3, defines a textual language in which RBAC policies are composed by first a number of declarations (lines 1 to 15), and second a number of rules (lines 18 to 42) which refer to these declarations. Note that the definition of the bot itself (i. e. , the state machine expressing the bot behavior with all the intents, transitions,.
) is not part of this grammar. Here we just reference those elements, defined in a separate model with the corresponding CUI modeling language of choice. This way we achieve separation of concerns to facilitate the collaboration between CUI and security experts. Nevertheless, we believe our RBAC language is comprehensible enough to empower CUI designers without a deep security knowledge to still define the key security policies for their bots. As an example, Listing 4 shows an instantiation of the above grammar to represent the access-control policy of our ecommerce bot policy (i. e. , the rules showed in Table  2 ). Xtext generates an Eclipse-based IDE which includes a textual editor of the grammar with auto-completion, syntax highlighting, and error detection with respect to the metamodel and its WFRs.
Note that Xtext uses our metamodel as the abstract syntax for our language and thus, correct textual policies are internally represented as models conforming to it. As an example, Fig. 5  shows a screenshot of our textual editor, where two constraint violations have been introduced. First, the rule Grant All to registered on eCommerceBot exceptFor. contains an intent in the except for list (the intent Get Monthly Goals) that is not part of the ecommerce bot. Second, the rule Grant Match to employee on eCommerceBot. I_BuyProduct is redundant with respect to the rule Grant All to.
Enforcing RBAC policies via an external library The first strategy we propose to enforce RBAC policies is by relying on a third-party library able to evaluate an access request against a policy. Then, based on this decision, the chatbot will need to act accordingly. This enforcement strategy requires then two steps: 1. Translate the modeled policies to the input language used by the external library. 2. Integrate in the bot definition the calls to this external library. Next, we see how we could implement each step. In particular, we will do it using Xatkit  [14]  as chatbot framework and Casbin 10 as a RBAC library. Casbin is an open-source access control library that provides support for enforcing authorization based on various access control models. Although Casbin itself supports many programming languages such as Go, Java, Nodejs, PHP, Python, Microsoft.
Step 1: Generating RBAC policies The policies written with our modeling editor are used at runtime in order to decide upon access requests. This can be done by either developing a brand new runtime component for our language or by re-using some existing authorization framework. In this paper, we opt for this second option as this is a more flexible and simple solution that avoids reinventing the wheel and facilitates the integration of our approach in different technical stacks. As a proof of concept, we show how to policies written in our access-control for CUIs language will be translated to Casbin policies. A similar approach would be followed to translate them to other authorization systems. In order to use Casbin with a specific access-control model, the developer needs to provide two configuration files.
First, a. conf file containing an access-control model definition (e. g. , ABAC, RBAC, etc). As an example, Listing 5 shows a. conf file that corresponds to the default RBAC model as provided by Casbin. And second, the developer needs to provide a. csv file containing the actual access-control policy. As an example, 10 www. casbin. org. 11 https://www. openpolicyagent. org/. 12 https://github. com/ory. Listing 6 shows the policy, in the form of comma-separated values, that corresponds to the aforementioned Casbin model. Each line corresponds to a policy rule that contains: (1) the identifier of a role; (2) the CUI resource on which the permission is going to be granted (an intent -I-, state -S-or transition -T-); and (3) the action on the resource (matching, reaching or navigating). To generate Casbin policies from our policies written in our language (see Listing 4) we employ code generation techniques.
The generation focuses on the. csv Casbin file. Note that the. conf file may be customized (notably to modify the types of elements used in the policy rules, the access requests, and the match process) but this configuration does not depend on the specific access-control policy of a given CUI, and thus, does not need to be (re)generated from it. Again, we use Xtext facilities to implement our code generation. Xtext integrates code generation triggering options in the generated IDE, so policies are automatically (and transparently) translated to the target language (Casbin) as the policy file is saved. As our model and the Casbin model are very close the translation is straightforward. Listing 7 shows the actual code generator written in the Xtend Java dialect. It starts by retrieving the root of the policy model (line 5), creates a.
14 https://github. com/elenaplanas/xatkit-RBACBot/tree/jCasbin. of Xatkit as this is the chatbot engine we created ourselves and therefore we one we are more familiar with. The first step is to extend Xatkit's FluentAPI to enable the definition of security policies as part of the bot definition. Calls to this extended API would just store in the internal bot model of the chatbot engine the security of details of the chatbot execution same as it stores all the info on the intents, states and transitions to be able to execute the bot and evolve it from one state to the other in response to all types of events. Listing 9 shows a small example of how the Update Shop Catalogue intent of our running example would be defined using this extended FluentAPI. Note how now the definition of the security policies is fully integrated with the own chatbot definition, feeling much more natural and easy to use.
General approaches Two general well-known frameworks to model security concerns are UMLsec  [23]  and SecureUML  [31] , which extend UML with RBAC primitives. In particular, UMLsec extends UML so that it supports, among other security concerns, rolebased access control. The extension is provided in the form of a UML profile and a corresponding verification tool. In the following, we show how UMLsec can be used to describe a RBAC policy on the state machine in Fig. 3. The policy will give employees the right to update the catalogue and registered users the right to see full product details and buy products. First, we need to apply the stereotype rbac to the package containing the state machine. This enables three tags: (1) protected actions, which must contain the list of activities to be controlled; (2) role, which may have as its value a list of pairs (actor,role) where actor is an actor in the activity diagram; and (3) right, which value should be a list of pairs (role,right) where role represents any role previously declared in the role tag and right, a protected activity listed in the protected actions tag (meaning the role gets the permission to execute the activity).
In this context, access control is typically called IAM (Identity and Access Management), for instance in DialogFlow,  17  Amazon Lex  18  or IBM Watson Assistant. 19  At most, as part of an IAM policy (see Fig. 9 ), you can define whether users must be authenticated to use the deployed bot without any fine-grained role definition, not individual permissions on what each role could do. Moreover, chatbot definition languages, such as  [14, 38, 40]  do not include modeling primitives to define access control policies.
, 2003)  but also due to the paucity of global observation-based datasets of N cycling: N exists in many forms and is lost from terrestrial ecosystems via numerous pathways (emissions of NH3, N2O, NOx and N2 as well as NO3 -and NH4 + leaching), N processes are generally not measured in situ in networks such as FLUXNET, and remote sensing methodologies for measuring N processes are still in their infancy. Additionally, N processes exhibit extremely high spatial and temporal variabilities and are thus challenging to measure. As such, N cycling has commonly been evaluated by comparing simulated N pools and fluxes to global totals based on a small number of observations that have been scaled up or averaged to yield a value with wide confidence intervals (Davies-  Barnard et al. , 2020). N cycling is implicitly evaluated by comparing terrestrial biosphere models without N cycling to those with coupled C-N cycling in reproducing observations of the C, water, and energy cycles in the absence of N cycle observations.
Simulation Protocol For the 2022 GCP (version 11), the TRENDY ensemble consisted of 16 terrestrial biosphere models, 11 of which represent N cycling (CABLE-POP, CLM5. 0, DLEM, ISAM, JSBACH, JULES-ES, LPJ-GUESS, LPX-Bern, OCNv2, ORCHIDEEv3, and SDGVM). Although SDGVM includes a representation of N cycling, its representation is simplistic and was therefore not included. Additionally, CLASSIC contributed to the 2022 GCP without coupled C-N cycling; the S3 simulation was repeated by CLASSIC with coupled C-N cycling following the 2022 GCP protocol and was used here. Overall, we analysed eleven models with coupled C-N cycling (Table  1 ). (Tian et al. , 2018) , and land use from the LUH2-GCB2022 (Land-Use Harmonization 2) dataset  (Chini et al. , 2021; Hurtt et al. , 2020; Klein Goldewijk et al.