Sangeeth Saagar Ponnusamy
email: sangeeth-saagar.ponnusamy@airbus.com

Patrice Thebault
email: patrice.thebault@airbus.com

Vincent Albert
email: valbert@laas.fr

Towards an Ontology-Driven Framework for Simulation Model Development

Keywords: Modeling & Simulation, Ontology, Domain Model, Fidelity, MBSE, Operating Mode

An ontology driven domain model approach for improving the fidelity of the simulation by developing models through the inclusion of simulation objectives for the system Verification & Validation activities (V&V) is presented. The system V&V by simulation ontology used to build this domain model is briefly outlined in the system teleological framework of Structure, Behavior, Function, Interface and Operation. The concept of operating mode is proposed and discussed with an example. The ontology approach is demonstrated with an aircraft nacelle anti-ice system presented in the experimental frame formalism. An example of using the inference and query capabilities of the domain model approach to identify and justify abstractions consistent with the test scenarios is illustrated with a failure mode case study for this application case. The relationship with formal behavioral approach through operating modes is briefly discussed at the end where some theoretical results on the behavioral compatibility of the experimental frame components with interface simulation distances are briefly presented. The paper concludes with a discussion on the benefits of this approach from an industrial perspective along with an overview of the challenges ahead and the future work.

INTRODUCTION

In the development of complex engineering systems, Modeling and Simulation (M&S) is becoming a key capability to perform design and validation studies. However, in developing models to represent the system, often the difficulty is finding and implementing abstractions of the system being simulated, particularly with respect to the context under which it will be used. This not only leads to model validity problems identifiable only at the simulation runtime, but also results in over or under specification and sub optimal development of systems. These challenges in simulation model development necessitate a Model Based Systems Engineering (MBSE) approach which enables a common understanding by making domain assumptions explicit and separate domain knowledge from the operational knowledge. In addition, since modeling can be interpreted as a 'reasoning' problem i.e. inclusion of relevant information about the system being modeled, it is important to identify, relate and organize this information. However, this is often a tedious task which necessitates a domain model approach with reasoning and knowledge exploitation capabilities. Ontologies serve as a good candidate for building such a domain model approach due to their standardization in terms of OWL1 language, scalability, and availability of tools such as Protégé2 with SPARQL3 query capabilities. The flexibility of ontology in expressing different domain knowledge in a succinct and standard form could significantly improve the modeling activities by explicitly incorporating the model context of usage and thereby ensuring better simulation fidelity.

STATE OF THE ART

The interest of ontologies in the M&S domain has been discussed in [Fishwick,2004] and an ontology based dictionary of generic M&S terms has been given in [Oren,2011]. Similarly, an ontology for system V&V has been proposed in [Kezadri,2010] with various formalisms and techniques for the purpose of knowledge sharing between stakeholders. However, a holistic application of ontology in simulation model development for system validation has not been explored adequately to the best of our knowledge. This study envisages such an integrated approach which consolidates knowledge capture via domain model and exploitation techniques to build a modeling abstraction library and automated assembly of model for near seamless deployment. In addition, as remarked in [Wagner,2012], [Jenkins,2012], ontologies could be used in conjunction with industry standard SysML based MBSE and this will help engineers to capitalize on the graphical syntax of SysML and reasoning capabilities of ontology.

The overall ontology based approach to simulation model development is discussed in section 2 and the system V&V by simulation ontology concepts are elaborated in section 3. The classical system teleological notions of Structure, Behavior, Function, Interface (SBFI) are given in [Goel,2009] [Garo,2004]. However, these notions could be restrictive in expressing the test scenarios in the V&V context and are extended with the concept of Operation into SBFIO ontology and implemented in the Protégé tool. The Operating Modes formalism proposed in this approach is similar to mode automata [Maraninchi,1998] but is more flexible and amenable to ascribe functional or system behaviour at higher levels of abstraction. In section 3.2, extending the concept of abstraction hierarchy defined over lattice in formal verification [Cousot,1992] and semantic annotation [Lickly,2011] to V&V domains, a distance notion is ascribed to the elements of lattice since an absolute lattice inclusion relation could be too restrictive. This relative distance approach improves the application of SPARQL query capabilities of the ontology approach to the simulation model assembly [Novk,2011]. This domain model approach is briefly discussed in a process oriented perspective in section 4. An example of using the inference and query capabilities of the domain model approach to identify and justify abstractions consistent with the test scenarios is illustrated with a failure mode case study in section 5.

In addition, the concept of operating modes could serve to bridge the existing gap between the rigorous behavioral abstraction frameworks such as bisimulation [Girard,2005] and less formal system engineering approaches [Retho,2013]. In this context, a brief discussion on using ontology-aided quantitative behavioral interface refinement [Ĉerny,2010] is given in section 5 followed by a brief description of the future work and conclusion.

DESIGNED FIDELITY APPROACH

In the classical simulation model development process, models are usually developed independently of their context of usage and this bottom up approach often results in over or under detailed models which are inadequate to perform V&V activities. Instead of this 'measured fidelity' approach, a 'designed fidelity' approach is proposed where fidelity needs are incorporated apriori in the model development process. This necessitates collection of knowledge about the system to be modeled and scenarios under which it will be operated called System Description (SD) knowledge and Test Description (TD) knowledge respectively. In other words, SD defines the system capabilities whereas TD defines the context of usage and the expected outcomes. The system validation process normally involves interaction between system designers responsible for SD, testers i.e. simulation user responsible for TD and model developers. It is imperative for the model developer to understand and incorporate only the essential elements needed for the test and usually this set of model requirements MR is given by the model specialist. However, owing to the complexity of different domains of knowledge involved which are often implicit and incomplete, it is a tedious task to define this MR manually.

The limitations of the manual approach in its inability to handle the complexity, error prone nature, lack of archival and reuse capabilities necessitates a domain model i.e. a predefined 'template'. Such a template needs to cover the different perspectives of the knowledge description usually expressed in informal natural languages. Since a model can be interpreted as a set of concepts with some relationships between them, ontologies could be used to build such a template i.e. a domain model. This single domain model is instantiated by different actors such as the simulation user and system developer and this ontology serves as a basis for translating text based TD and SD into a standardized model to write MR. An additional advantage of ontologies is its reasoning i.e. ability to infer knowledge which is otherwise hidden or scattered. The existence of plug-in reasoners with Protégé tool such as Fact++, Hermit4 helps to draw inferences and check consistency. Reasoners infer this relationship by reification, a concept in logic where an instance of a relation is made the subject of another relation. The inferred ontology can be queried for specific needs with SPARQL, a query language which is used to retrieve and manipulate data stored as a Resource Description Framework, RDF, a standard for the semantic web. Queries are constructed in triple pattern of subject, predicate and object with conjunctions, disjunctions and optional patterns such as to filter, sort etc. In the next section this ontology is presented followed by some applications of using of queries over them.

SYSTEM V&V BY SIMULATION ONTOLOGY:

In developing a domain model it is important to incorporate different viewpoints in the system teleological perspective such as SBF ontology [Garo,2004]. This can be extended with notation of interface (I) and Operating mode (O) to describe interconnected system with different modes of operation. The SBFIO ontology presented in this paper has different such generic and domain specific concepts with a modest size of about 900 triples and a part of this ontology is illustrated below in figure 1.

Figure 1: SBFIO Domain Model

The key concepts of the SBFIO ontology covering such a perspective are briefly given as follows: Structure: In addition to classical architectural descriptions of how the system is built (eg: composition) [Ponnusamy,2015], spatial information is included in our domain model. Besides ensuring geometric consistency aspects, the spatial information could be related to the corresponding physical phenomenon and the interaction between the systems. Behavior: A system behavior is the temporal evolution of the system when subjected to some scenario and behavioral abstraction will be briefly discussed in section 5. Function: Function describes the system objectives and how they are achieved. A system's function is essentially an energy flow manipulation and ascribing domain specific laws to such flow type the phenomenon can be modeled. For example: an aircraft actuator's function is to move the control surface according to pilot's command which involves electrical to mechanical energy conversion. Based on such abstract information the associated laws can be inferred from the library developed by the domain experts. Interface: Interface refers to how the systems interact among themselves (eg: I/O ports) or with the external user (eg:push button). Interface defines the system boundary and can have different attributes such as range, precision etc. It may also be seen as a manifestation of the observable behavior and is essential in ensuring the consistency at interconnection and composition. Operation: Operation generally refers to the concepts of operating modes and operating condition of the EF. Operating condition implies the conditions of environment of the SUT and is used to ascribe assumptions behind models especially at higher abstraction level. In other words it refers to the assumptions of the EF components and is used in succinctly expressing and identifying operational domains and dependencies. For example, an operating condition of a flight control system at 'takeoff' phase implies associated assumptions for the engine performance model at this phase. In the next section, one particular concept of the domain model, namely, operational modes are explained. In [Ponnusamy,2015] a brief description of other concepts in this ontology in the context of building a model abstraction library and automating extraction of relevant abstraction has been discussed.

Operating Modes

The Operating Modes (OM) proposed in this paper extends the classical notion of mode-charts [Jahanian,1994], and is akin to automata. Modes are essentially partition of a system's state space and a system can have different possible modes (eg: Switch-On/off, Engine-Start/Stop). Then the OM definition is based on a simple causality relation for interconnected systems with interdependent modes (eg: Switch-On THEN Engine-Start). This definition is amenable to ascribe functional behaviour or a semantic behaviour vis à vis the system description. In contrary to the rigorous but less flexible formalisms such as mode automata [Maraninchi,1998], our definition refers to the operational manifestation of a model under a given scenario and eases the TD and SD at different levels of abstraction in a static perspective. In other words, the effects of a component's mode on other components can be observed statically and this helps in better understanding the necessary elements to be modelled whose real dynamic behaviour will be analysed later using established formalisms such as mode automata. Let us denote a system component by 𝐶 𝑖 having modes 𝑀 𝑗 𝑖 ∊ 𝑀 𝑖 where i and j refers to the component id and the corresponding mode respectively. The dependency between modes are given by mode inter-connection 𝐼 𝑖 𝑘 : 𝑀 𝑗 𝑖 → ⋃ 𝑀 𝑗 𝑘 𝑗 i.e. a mode of a component, 𝐶 𝑖 may affect one or more modes of other component, 𝐶 𝑘 such that 𝐼 𝑖 𝑘 ⊆ {𝑀 𝑖 ∪ 𝑀 𝑘 }. The OM then becomes a tuple, 𝑂𝑀 𝑖𝑘 =< 𝐶 𝑖 , 𝐼 𝑖 𝑘 , 𝐶 𝑘 > and the connected modes of 𝐶 𝑖 are called guards i.e. causative and that of 𝐶 𝑘 are called states i.e. resultant. Transitions between modes occur whenever the guard mode changes. For example, consider a system with four components, 𝐶 𝑖=1..4 each having different modes. The dependencies in between them are shown as dotted lines below in figure 2, for example, the mode 𝑀 1 1 affects 𝑀 1 2 which in turn affects 𝑀 1 4 i.e. components 𝐶 4 is dependent on 𝐶 1 .

Figure 2: Mode Dependency Example

Such dependencies could be illustrated using OM in the following figure 3, which could be then reasoned and queried to find implicit information such as modes (un)affected by a particular mode or its attributes (e.g: type of system, associated designer etc). In practice the system designer need only gives the component and its dependent modes and the link between different such pairs are extracted automatically. This is useful since the designer usually knows the causality relation only few components upstream and downstream and it is thus important to relate between all such information to have holistic view before modelling the system. In other words, this helps in capturing each component's operational environment assumptions in terms of modes. In the figure below, the causality relation in mode is denoted by solid arrow line and the transition between modes by dotted arrow lines. In addition, transition can be constrained, for example, once mode 𝑀 3 1 is activated it cannot be switched to other modes of the component and hence the end state will always be 𝑀 1 5 . Thus the transitions can be primary i.e. affects other OM or secondary i.e. does not affect other OM e.g.: OM 5 .

Figure 3: Operational Modes

From such illustration queries can be made on the instantiated domain model for applications such as identification of the transitions between modes and the necessary dependencies to be modelled. For example, reachability notions such as the mode 𝑀 1 4 can be reached from 𝑀 2 1 by changing the mode to 𝑀 1 1 can be queried. Similarly there are two ways of reaching 𝑀 2 2 and associated (or the shortest) path can be queried. This description will also be useful in high level functional failure mode and effect analysis. A failure could be interpreted as the inability of the system mode to transit in response to its associated causality conditional i.e. guards change. Consider a TD stating simulate 𝐶 2 failure and this requirement necessitates inclusion of components associated to 𝐶 2 such that any mode change in upstream component i.e. guards does not have effect on 𝐶 2 since it is already failed and effect of downstream components i.e. states with respect to it. From SDD it is known that 𝐶 2 can fail at 𝑀 1 2 or 𝑀 2 2 and in case of failure at 𝑀 1 2 it can be easily infered that 𝑀 2 1 will not have any effect and it must be included to check the effect. In addition, OM 4 can be abstracted for simulation of OM 5 since it does not have any transition associated. Similarly recovery procedures such as in case of 𝑀 1 2 failure to respond to transition 𝑀 2 1 , 𝑀 2 2 can be reached through OM 5 if there exists a transition i.e. guard change 𝑀 3 1 to 𝑀 2 1 can be seen. It may be reminded that all such inferences are static i.e. from instantiated domain model through queries and this helps in inclusion of necessary abstractions to be implemented for a given test requirement before dynamically simulating.

In the next section, notions of hierarchy between concepts of the domain model to build inheritance relations are discussed. These inheritance relations are then exploited to identify necessary abstractions.

ABSTRACTION HIERARCHY

In general, a system can be modeled at different levels of abstraction which could be related to each other by a binary relation (≼). This hierarchical notion of simulation preorder represented as a lattice has been widely studied in the field of formal verification [Cousot,1992]. An application of such approach to consistency checking of semantic annotation of models has been explored in [Lickly,2011]. Our study extends such property annotation to V&V domains and ascribes a distance notion to the elements of lattice. In other words, elements of lattice which are closer to the desired element than the others have relatively higher fidelity. For example, the lattice for the variable data type concept with elements boolean, int and float are ordered as boolean ≼ int ≼ float where ≼ refers to 'is also' relation i.e. int is also a boolean but the reverse is not true. Assuming the TD demands a variable type float whereas SD offers only boolean or int, intuitively it can be seen that the abstract data type int is closer to concrete data type float than the other abstract data type boolean. Similarly inclusion relations could well be extended to other relevant annotations such as in domain specific laws (e.g. a geopotential model with spherical harmonics is also a flat earth model), model versions etc. These inclusion relations are useful to engineers who do not necessarily share the same domain expertise. Such annotations and inclusion relations are useful in mitigating redundant modeling effort, especially in modeling system derivatives where a combination of legacy and new models will be used in tandem for the V&V activities. A similar approach could be used to document assumptions behind models in a hierarchical manner which in turn could be exploited to find the simplest consistent model meeting the simulation requirements [Ponnusamy, 2015].

Automated Model Assembly

In addition to standardization of knowledge and exchange, query capabilities are exploited to select the model with consistent interfaces based on parameter matching using this distance notion. In addition, it is possible to assign weights to each attribute and the model whose interface having the closest consistency is chosen. In a component based design framework, the assembly of components is an important but often ignored aspect and many integration problems arise due to interface compatibility. In assembling i.e. connecting two models, compatible models are selected from a library of models by matching their input and output parameters of their interfaces. In [START_REF] Novàk | Applications of ontologies for assembling simulation models of industrial systems[END_REF] this task is discussed via queries of ontology but the matching is exact i.e. two models are compatible only if the output of first model is same as the input of second model. This could be true for matching parameters, units etc. but for conditions such as matching data types etc. it could be stringent. Consider an example where a battery model (M 1 ) modeling voltage is connected via an electrical circuit to an antenna model (Ant). The battery output datatype could be 'int' whereas the antenna model input datatype is 'float'. A boolean type checking gives an error despite a float is also an int datatype. In our ontology, when such an instance occurs, the connection is deemed compatible as shown in figure 4(b), since in the datatype lattice described in section 4.2, Float ≼ Int. This is evaluated by simply measuring the length of its relative position in the lattice chain (e.g.: int is located lower than double hence it has higher length and only elements with lower length are chosen for input type compatibility). Let us consider an example where the engine model is connected to the accelerometer (Acc) model to measure the acceleration, a, induced by the thrust, F. The acceleration can be calculated either as function of force or mass or both and from the set of candidate models shown in Fig. 4(d) it is evident that second model cannot be used here. From the two available models the first one is chosen for its higher precision if the output datatype is the same (or better). The associated pseudo-queries for this example are given in the appendix. Similar queries can be written to match or extract other system attributes.

PROCESS OVERVIEW

In [Ponnusamy,2015], the utilisation of such a domain model to build a model abstraction library, and automate the model selection from the library using an algorithm in SysML activity diagram is presented. The overall process of the domain model development, building and exploitation of the model abstraction library is briefly discussed in this section with an illustration of the process [Thebault,2015]. In developing a domain model, an important aspect is to describe its integration or improvement of the existing M&S process in an end user operational context. It can be seen from figure 5 that the proposed approach replaces text with domain model concepts, reasoning over implicit information to make them explicit and evaluate their consistency with respect to each other. This is followed by model selection process and the selected model is instantiated in a classical simulation tool such as Modelica etc. The phases of modeling and simulation along with the respective stakeholders can be seen from the figure below. The meta-model and selection algorithm [Ponnusamy,2015] are denoted in dotted ellipse.

Figure 5: Operational View of M&S domain model

The meta-model instantiation by simulation users and system designers is reasoned by the simulation architect to find implicit data and evaluate its consistency to write the model requirements. In addition, the model developer documents his existing models in a library using the same meta-model, and based on the requirement from architect, a consistent abstraction is selected from this hierarchy of abstractions using the SysML algorithm and then composed with the other models. This assembled model is then deployed on a simulation platform and executed by the simulation user according to the defined V&V plan. An industrial perspective of such approach in the context of simulation fidelity is presented in [Thebault,2015]. This process can be integrated easily in the standard M&S process in industry and it can be seen that this is a non-intrusive method for the engineers since building and exploiting abstraction library is intended to be automated with minimal effort. However, as with any domain model approach in industry, initial effort will be high for tool development, workforce training, process management and deployment. But as several studies demonstrate MBSE is an important enabler in system development especially due to rapid and complex evolution of corpus of engineering knowledge in an organization and the need to capture systematically this engineering knowledge for standardization and exploitation.

APPLICATION CASE: AIRCRAFT NACELLE ANTI-ICE SYSTEM:

A generic description of the aircraft Nacelle Anti-Ice System (NAIS) is presented, followed by instantiation of the domain model built from the ontology defined in section 3. The NAIS is used to prevent ice accretion at the engine nacelle inlet by using hot gases from the engine exhaust. The system is comprised of controllers, valves, solenoids, ducts etc. and is connected to other aircraft systems. In order to perform tests on a component(s) (eg: controller of NAIS), the problem of selecting elements of NAIS and the associated systems An application of the OM concept to the failure mode simulation of NAIS valve is illustrated in the following section similar to the example in section 3.1.

NAIS Failure Simulation

Let us consider a test scenario where TD requires the simulation of valve V 2 failure at closed mode. The test request typically says at which conditions the failure is triggered, where and what are the expected outcomes. On the other hand, SD of NAIS describes all the possible behavior of system, in this case, dependency of valve V 2 modes with the solenoid S 2,3 modes (e.g. : valve is open when solenoid is energized & closed when solenoid de-energized). It then becomes imperative to identify the components and its associated modes causally affected by this failure condition. Inferring the instantiated OM concepts and querying over this knowledge, desired information such as dependent component or the components that can be abstracted can be obtained with ease. It alleviates the burden of the tedious and often error prone task of keeping track of disparately located but hidden information which is related to each other. Following the notation given in section 3, the SD then becomes

𝐶 1 = {𝑉 2 } 𝑀 1 1 = open 𝑀 2 1 = close 𝑀 3 1 = regulating 𝐶 2 = {𝑆 2 } 𝑀 1 2 = de-energised, 𝑀 2 2 = energised 𝐶 3 = {𝑆 3 } 𝑀 1 3 = de-energised, 𝑀 2 3 = energised
The OM is built from the mode data and is illustrated below, for the sake of clarity each OM is shown separately.

Figure 7: Operating Modes of Valve and Solenoid

Consider a test on the controller to validate its failure monitoring and reconfiguration of valves. It can be seen that, in order to simulate the valve failure when closed, it is imperative to simulate the solenoid S 3 in de-energized mode to see it does not have any effect. However this information is not explicitly given in TD as it describes expectations on the system at higher levels of abstraction whereas SD describes all possible behaviors of the system. Thus it becomes important to identify only the necessary functions and associated systems to be modeled to avoid over or under detailing of models.

In addition such an approach will help visualize and identify possible emergent behavior which may not have been modeled otherwise. For example, from the valve which is failed at the closed position, the regulating mode can be reached in two steps by having S 3 de-energised and S 2 energised. Similar extensions are possible and such information is usually not given explicitly either in SD or in TD, and this formalism helps the model specialist in writing a MR with autonomy. This particular example, though done manually, is found to increase the efficiency during test since provisions for failure triggering are explicitly identified and provided along with necessary functionalities to model the failure propagation.

MAPPING TO BEHAVIOURAL FRAMEWORK

The problem of quantitative transition systems and their abstraction has been widely studied by [Alfaro,2004], [Thrane,2009]. However, there exists a gap between the rigorous behavioral abstraction frameworks such as (bi)simulation relations and less formal system engineering approaches [Retho,2013]. The complexity of current engineering systems requires integration of different layers of abstraction and consistency between them. The concept of operating modes could serve as a connection between high level functional description through the domain model approach and low level behavioral description through quantitative transition system. Since OM are high level behavioral descriptions, this would lead to better identification and modeling of transitions to capture the low level behavior, especially during incremental model synthesis. Similarly, the notion of lattice distance leads naturally to behavioral distance quantification based on approximate bisimulation [Girard,2005] and simulation distances [Ĉerny,2010]. In addition, in the context of assume-guarantee contract based design [Benviste A,2012], behavioral refinement of models by extending the concept of interface with interface simulation distances [Ĉerny,2012] is also are being studied. However these studies are still in their infancy and future work includes developing the theory to bridge this behavioral approach with the semi-formal domain model approach to build a unified framework addressing the simulation needs capture from high level to low level model behavioral requirements definition.

CONCLUSION & OUTLOOK

This paper gives a preliminary version of domain model and the SBFIO ontology explained in this paper is being improved with other domain specific concepts in the industry and validated with stakeholders before its integration in the engineering process. The study is currently at the identification and experimentation of solutions phase and preliminary indications are encouraging. This study is also being used to assess and provide feedback to ongoing feasibility studies on using the Cappella tool based on the Arcadia framework [ARCADIA] for aircraft system architecture definition and simulation. Future work includes development of a user-friendly graphical interface for domain model instantiation, queries, and formalization of a centralized ontology management process which are all imperative for the utilization across the enterprise.

The ontology driven domain model approach helps to ensure traceability between different abstraction layers and ensures viewpoint consistency and thus enables seamless integration of models and deployment. It helps the test team to optimize the test scenario through inclusion principles and the modularization of ontologies helps in test independence to reduce redundant test combinations. It alleviates the general difficulty of the lack of synchronization and standardization between system development and testing by incrementally and iteratively improving the systems design and testing knowledge along the program schedule. This not only helps in modeling knowledge archiving and reuse for streamlined development of system variants but also for better coordination and decision making in program development.

However, it is often the case that not all abstractions are or can be documented through such a domain model as it is a time consuming and arduous task especially when multiple stakeholders are involved. This is also compounded by the fact that parsing of documents written in natural language into the domain model concepts described in section 3.2 is a complicated task in itself. Though there are some initial studies such as [Ileiva, 2005], this problem needs to be studied with cognitive techniques such as data analytics and deep mining based on iterative learning techniques for better usage of this domain model.

  (a) Incompatible Assembly (b) Compatible Assembly (c) System Assembly (d) Model Candidates

Figure

  Figure 4: Model Assembly

  (eg: Flight Management System), environment (eg: engine) with respect to this component(s) and the test scenario is outlined in the EF formalism. The following figure illustrates the environment representing the context under which the controller will be tested in the EF formalism. The general system interaction is shown by solid lines and the scenario specific observability of phenomenon (eg: pressure data from sensor) is denoted in dotted lines. Thus the EF helps in a lucid visualization of what is being tested and what is needed for the test in addition to how it is tested (controllability) and what is expected of the test (observability).

Figure 6 :

 6 Figure 6: Experimental Frame of NAIS Controller

http://owl.man.ac.uk/factplusplus/

http://protege.stanford.edu/

www.w3.org/TR/rdf-sparql-query/

http://hermit-reasoner.com/

APPENDIX

PREFIX mm:<http://instantiated model name .owl#> PREFIX nn:<http:// instantiated model name _infered.owl#> #sample code to compare three simulation models input interface with system model input interface. Two of the models have #same parameter (e.g.Force, F) but different datatypes (e.g: double, int)first match the models having same parameters then list #lattice length #the query needs to be customized to suit the respective class, object and data properties respectively SELECT DISTINCT ?iclist ?source ?dest ?system_var_out_name ?system_var_in_name ?sim_var_in_name ?sim_block (COUNT(DISTINCT ?sim_var_class) AS ?sim_var_class_no) WHERE { #List all system Interconnection ?iclist rdf:type mm:Block_InterConnection; mm:connectsFrom ?source; mm:connectsTo ?dest.

#check Source Port and Destination Port have same variable names eg:F for force ?source_port a mm:SourcePort; owl:sameAs ?system_port_out.?system_port_out mm:isAssociatedTo ?system_param_out. ?system_param_out mm:representedBy ?system_var_out. ?system_var_out mm:hasVariableName ?system_var_name. ?system_var_name mm:hasVariableNameString ?system_var_out_name. ?dest_port a mm:DestinationPort; owl:sameAs ?system_port_in. ?system_port_in mm:isAssociatedTo ?system_param_in.?system_param_in mm:representedBy ?system_var_in. ?system_var_in mm:hasVariableName ?system_var_name1. ?system_var_name1 mm:hasVariableNameString ?system_var_in_name.

#check variable datatypes FILTER(CONTAINS(?system_var_out_name, ?system_var_in_name)) ?sim_block mm:Simulates ?source; mm:hasBlockParam ?q. ?q a mm:InputParameter. ?q mm:representedBy ?b. ?b mm:hasVariableName ?d. ?b mm:hasVariableDataType ?jj. ?ii rdfs:subClassOf* mm:VariableDataType. ?jj a ?sim_var_class. ?d mm:hasVariableNameString ?sim_var_in_name. FILTER(CONTAINS(?sim_var_in_name, ?system_var_in_name) ) ?ii rdfs:subClassOf* mm:VariableDataType. ?jj a ?sim_var_class. } GROUP BY ?sim_block ?iclist ?source ?dest ?system_var_out_name ?system_var_in_name ?sim_var_in_name


Sébastien Imbert

Lauraine Gauthier

Isabelle Joly

Jean-Yves Brossas

Madalina Uzunov

Feriel Touafek

Sophie Brun

Dominique Mazier

Annick Datry

Frédérick Gay

Arnaud Fekkar
email: arnaud.fekkar@aphp.fr

Aspergillus PCR in serum for the diagnosis, follow-up and prognosis of invasive aspergillosis in neutropenic and non-neutropenic patients

Keywords: Aspergillus fumigatus, Fungal Infection, Solid Organ Transplant, Haematology, Galactomannan

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

ABSTRACT.

We evaluated the usefulness of a serum Aspergillus PCR assay for the diagnosis and prognosis of invasive aspergillosis in a study involving 941 patients for a total of 5,146 serum samples. Fifty-one patients had proven/probable aspergillosis. We compared galactomannan, PCR, and mycological analysis of pulmonary samples in both neutropenic and nonneutropenic patients. PCR performed in serum yielded 66.7% sensitivity, 98.7% specificity, 75.6% positive predictive value and 98.0% negative predictive value while the galactomannan index yielded 78.4% sensitivity, 87.5% specificity, 27% positive predictive value and 98.6% negative predictive value. The inclusion of PCR in the EORTC/MSG mycological criteria permitted the reclassification of nine other cases from possible to probable aspergillosis and increased sensitivity to 71.7%. Combining the galactomannan index with serum PCR increased the detection rate of invasive aspergillosis with 88.2% sensitivity. PCR was systematically negative in 16 patients with non-invasive forms of aspergillosis (namely aspergilloma and chronic aspergillosis). Remaining PCR-positive after a period of 14 to 20 days of treatment was related to poor outcome at 30 and 90 days. Our results also indicate that, unlike the determination of the galactomannan index, the initial fungal load as determined by PCR was highly predictive of 90-day mortality, with the rate of this latter being 15.8% for patients with less than 150 copies/mL vs 73.2% for patients at or above that cut-off (p<0.0001). Therefore, PCR appears to be a very powerful and interesting tool for the identification of patients with invasive aspergillosis who might benefit from more intense care.

INTRODUCTION.

Invasive aspergillosis (IA) is a major threat for immunocompromised hosts. Early diagnosis and initiation of appropriate antifungal therapy are essential to improve the prognosis of the disease [START_REF] Von Eiff | Pulmonary aspergillosis: Early diagnosis improves survival[END_REF][START_REF] Walsh | Treatment of aspergillosis: Clinical practice guidelines of the infectious diseases society of america[END_REF]. However, the diagnosis of IA remains difficult. Currently, it is frequently based on a set of host, clinical and mycological criteria, such as those defined jointly by the European Organisation for Research and Treatment of Cancer (EORTC) and the Mycosis Study Group (MSG) [START_REF] De Pauw | Revised definitions of invasive fungal disease from the european organization for research and treatment of cancer/invasive fungal infections cooperative group and the national institute of allergy and infectious diseases mycoses study group (eortc/msg) consensus group[END_REF]. In the EORTC/MSG criteria, galactomannan (GM) or β-D-glucan assays are the only non-invasive mycological diagnostic methods currently available.

Although these assays are of interest and widely used, they lack sensitivity, especially in nonneutropenic patients [START_REF] Meersseman | Galactomannan in bronchoalveolar lavage fluid: A tool for diagnosing aspergillosis in intensive care unit patients[END_REF]. They also may be influenced by the use of certain medical devices [START_REF] Marty | Role of (1-->3)-beta-d-glucan in the diagnosis of invasive aspergillosis[END_REF] or antibiotics [START_REF] Mennink-Kersten | 1,3-beta-d-glucan in patients receiving intravenous amoxicillin-clavulanic acid[END_REF][START_REF] Sulahian | Use and limits of (1-3)-beta-d-glucan assay (fungitell), compared to galactomannan determination (platelia Aspergillus), for diagnosis of invasive aspergillosis[END_REF], leading to a large number of false positive results. GM detection has also been reported in invasive fungal diseases not due to Aspergillus [START_REF] Fekkar | Serum cross-reactivity with Aspergillus galactomannan and cryptococcal antigen during fatal disseminated Trichosporon dermatis infection[END_REF][START_REF] Wheat | Histoplasmosis-associated cross-reactivity in the biorad platelia Aspergillus enzyme immunoassay[END_REF][START_REF] Dalle | Cryptococcus neoformans galactoxylomannan contains an epitope(s) that is cross-reactive with Aspergillus galactomannan[END_REF]. Moreover, β-Dglucan is present in many fungal species and is therefore not specific to aspergillosis. Other tools have been developed to improve diagnosis, including in particular real-time PCR in blood samples to detect circulating Aspergillus DNA. Several studies have shown the potential interest of this PCR approach, but due to a lack of standardisation, it has not yet been included in the EORTC/MSG mycological criteria. Very recently however, a panel of experts argued for its inclusion in those criteria [START_REF] White | Aspergillus polymerase chain reaction: Systematic review of evidence for clinical use in comparison with antigen testing[END_REF].

For the present study, we aimed at evaluating the performance of an in-house A. fumigatus real-time PCR assay using 1 mL volume of serum for the diagnosis of IA in at-risk patients, both neutropenic and non-neutropenic, and comparing the PCR results with those of the GM assay to determine their 30 and 90-day prognostic contributions.

MATERIALS AND METHODS.

Design. A retrospective single-centre analysis was performed between February 2012 and October 2014 in La Pitié-Salpêtrière hospital, in Paris, France.

Patients. All patients who are at risk of IA are routinely subjected to monitoring with a serologic assay for the detection of GM antigen and serological PCR for the detection of A. fumigatus DNA. For the present study, we focused on patients with proven/probable IA according to EORTC/MSG criteria [START_REF] De Pauw | Revised definitions of invasive fungal disease from the european organization for research and treatment of cancer/invasive fungal infections cooperative group and the national institute of allergy and infectious diseases mycoses study group (eortc/msg) consensus group[END_REF], extended to include the additional criteria of alcoholic liver cirrhosis, a long stay in the ICU and severe acute respiratory distress syndrome as host factors, as already reported [START_REF] Prodanovic | Invasive pulmonary aspergillosis in patients with decompensated cirrhosis: Case series[END_REF][START_REF] Azoulay | Acute respiratory distress syndrome in patients with malignancies[END_REF]. GM either in serum or in broncho-alveolar lavage (BAL) was used as mycological criterion for probable case. GM determination and PCR results were also available for some patients experiencing non-invasive aspergillosis.

PCR. We used a real time PCR that targets a previously described 67-bp segment of a 28S ribosomal RNA coding DNA [START_REF] Suarez | Detection of circulating Aspergillus fumigatus DNA by real-time pcr assay of large serum volumes improves early diagnosis of invasive aspergillosis in high-risk adult patients under hematologic surveillance[END_REF]15], for which the primer sequences were 5'CTCGCAATGTATCACCTCTCGG3' and 5'TCCTCGGTCCAGGCAGG3' and the probe was 5'(6FAM)TGTCTTATAGCCGAGGGTGCAATGGG(TAMRA)3'. DNA extraction was performed on 1 mL of serum with the MagNA Pure Compact large volume kit on a MagNA Pure device (Roche). Elution volume was 50 µL. Amplification was performed on the 7500 Fast Real-Time PCR System (Applied Biosystems). Quantification was achieved using 5 serial ten-fold dilutions of the plasmid PGEMT Easy-Afu28S containing the target. The final PCR result was expressed in numbers of copies per mL of serum. We used an internal control in our assay for all wells (TaqMan exogenous Internal Positive Control) as well as an extraction control (albumin gene) for each sample. All PCRs were performed in duplicate. A single positive well was considered as a positive result.

Galactomannan determination. The GM index was determined by enzyme immuno-assay (BioRad) according to the manufacturer's recommendations. A result was considered positive after two determinations, performed on two different assays but on the same sample, showing both an index above 0.5 for serum and 1 for BAL. Statistical analysis. Tests were performed using GraphPad Prism 5 and the free online site BiostaTGV (http://marne.u707.jussieu.fr/biostatgv/).

RESULTS.

Patients.

Over the study period, GM assay and A. fumigatus PCR were performed in 970 patients (Figure 1). Clinical data were available for 941 patients (5,146 serum samples). A diagnosis of proven or probable IA was made respectively for 6 and 45 patients according to the extended EORTC/MSG criteria. Moreover, a non-invasive form of aspergillosis was diagnosed in 16 patients. Also, although the study was not focused on an exhaustive identification of all possible cases, we did register diagnoses of IA based on host factors and clinical evaluation for nine others patients, classifying them as possible IA according to the EORTC/MSG criteria. These patients had positive serum PCR.

Characteristics and outcomes of patients with aspergillosis (Table 1).

Among the 51 patients with proven/probable aspergillosis, 19 were female and 32 male. Their median age was 56 years (range 20-82). Twenty-two patients (43%) were neutropenic (absolute neutrophil count <500/µL) at the time of diagnosis. Underlying conditions were haematopoietic stem cell transplant (n=17; 33.3%), haematological malignancies (n=13; 25.5%), heart transplantation (n=9; 17.7%), liver transplantation (n=3; 5.9%), kidney transplantation (n=1; 2%) and liver/kidney transplantation (n=1; 2%). Others risk factors were present in seven patients as follows: severe acute respiratory distress syndrome (n=2; 3.8%), oncological diseases (n=2; 3.8%), alcoholic liver cirrhosis (n=1; 2%), cardiogenic shock (n=1; 2%), multi-organ failure and long stay in intensive care unit (n=1; 2%). Overall three-month mortality was 49%. Neutropenic patients had a rate of mortality of 54.5% (12/22) vs 44.8% (13/29) for non-neutropenic patients (p=0.49 by Fischer exact test).

Performance of the galactomannan index, mycological examination of respiratory sample and PCR for the diagnosis of proven/probable aspergillosis.

A diagnosis of proven/probable IA (according to the extended EORTC/MSG criteria; n=51) was made for 40 patients by a positive GM index (Figure 2). PCR was positive in 34 patients (including 11 with a positive GM in serum, 4 with a positive mycological analysis of respiratory samples, 18 with both positive GM in serum and positive mycological analysis of respiratory samples and 1 with an isolated positive GM in BAL), including five with a negative serum GM index. In one patient with a positive PCR, the diagnosis of probable aspergillosis was based only on a positive GM index in a bronchoalveolar lavage sample.

Sensitivities of the serum GM index, mycological examination and serum PCR were 78.4% (95% confidence interval [CI], 67.1-89.7%), 62.7% (95% CI, 49.4-76%), and 66.7% (95% CI, 53.8-79.6%) respectively. Of note, sensitivity of GM and PCR in case of proven aspergillosis was similar (83.3%; 5/6 patients). Among the patients for whom the diagnosis of any clinical form of aspergillosis was excluded by clinicians, there were 108 with positive GM indices, 11 with positive PCR results, and 2 with positive GM indices and positive PCR results. Specificities of GM and PCR were 87.5% (95% CI, 85.3-89.7%) and 98.7% (95% CI, 97.9-99.5%), respectively. PCR yielded 75.6% positive predictive value (PPV) (95% CI, 63.1-88.1%) and 98.0% negative predictive value (NPV) (95% CI, 97.1-98.9%) while the galactomannan index yielded 27% PPV (95% CI, 19.8-34.2%) and 98.6% NPV (95% CI, 97.8-99.4%). No differences were observed between PCR and GM as concerns the precocity of diagnosis (data not shown).

PCR results for non-invasive forms of aspergillosis.

Among the 16 non-invasive aspergillosis patients, 10 were immunocompetent, 3 had metastatic malignancy, 2 had solid organ transplantation and one had alcoholic liver cirrhosis.

Clinical forms were simple aspergilloma (n=7), colonisation (n=4), chronic cavitary aspergillosis for (n=4) and chronic bronchitis (n=1). In addition to clinical condition, host history and radiological features, the patient diagnoses were further supported by mycological examination (n=11), the presence of anti-Aspergillus antibodies (n=1) or both (n=4). None of these patients had positive serum GM or PCR.

Addition of PCR to the mycological criteria.

Of the 9 patients treated for IA but classified as possible cases due to a lack of EORTC/MSG mycological criteria, 7 were male and 2 were female. Six patients were neutropenic (five haematological malignancies and one haematopoietic stem cell transplant) and 3 were not (one liver transplantation, one kidney transplantation and one severe acute respiratory distress syndrome). All 9 of these patients had positive PCR results. Thus, considering PCR as a mycological criterion would have enabled the reclassification of 9 patients from possible to probable IA, increasing the number of patients with proven/probable IA to 60 (Table 1 and2). In this scenario, GM determination and PCR would have been positive in 40 and 43 of the 60 patients respectively, conferring sensitivities of 66.7% (95% CI, 54.8-78.6%) and 71.7% (95% CI, 60.3-83.1%) and specificities of 87.7% (95% CI, 85.5-89.9%) and 98.8% (95% CI, 98.1-99.5%) for GM and PCR respectively. PCR would yield 79.6% PPV (95% CI, 68.9-90.3%) and 98.0% NPV (95% CI, 97.1-98.9%) while the galactomannan index would yield 27% PPV (95% CI, 19.8-34.2%) and 97.5% NPV (95% CI, 96.4-98.6%).

Neutropenic versus non-neutropenic.

Considering these 60 patients, PCR sensitivity tended to be better in neutropenic patients (82.1%) than in non-neutropenic patients (62.5%). The difference did not reach statistical significance (p=0.09) but the power of the test (P=39%) was insufficient to conclude. PCR specificity did not differ between the two groups. As for the GM index, sensitivity did not differ between the two groups but specificity was significantly lower in neutropenic patients (83.9%) compared to non-neutropenic patients (91.2%) (p<0.005).

Effect of antifungal therapy.

Among our 60 patients with IA, 49 have at least one sample before the targeted antifungal therapy initiation. All patients had a sample after treatment initiation except for 5 patients who died early. At the time of diagnosis, 24 patients received antifungal therapy: 12 had empirical therapy with caspofungin and 12 have more targeted anti-Aspergillus therapy with voriconazole or amphotericin B. Among the patients who received antifungal drugs, PCR and GM yield similar sensitivity of 70.8% (17/24) and there was also no difference according to the antifungal drug that was used. Among the 36 patients without antifungal therapy, sensitivity was 72.2% (26/36) for PCR and 63.9% for GM (23/36). The difference is not statistically significant (p=0.6). When comparing patients with antifungal therapy and patients without, there were also no statistically significant difference of sensitivity either for PCR, or for GM.

Variation of the fungal load after treatment initiation and prediction of outcome.

Real time PCR allows the quantification of fungal load with results expressed as the number of Aspergillus gene copies per mL of serum. GM results are presented as an index which can be interpreted quantitatively. Considering this, we assessed variations (decreases or increases) in PCR and the GM index to determine their ability to predict treatment efficacy and 30-and 90-day mortality.

Concerning the response to targeted antifungal therapy, we observed no early variations in PCR results as fungal loads did not decrease quickly after the initiation of treatment, even when outcomes were favourable. Moreover, an increase or decrease in the number of copies in the first week was not related with day 30 or day 90 outcome. However, patients who become PCR negative (and one who had cleared more than 99% of the initial load) between day 14 and day 20 after treatment initiation were all alive at day 30 (n=9) while those who remained PCR positive during this period had poor outcomes, with 80% (4/5) mortality at day 30 (Table 3). This result was highly significant (p<0.005 by Fischer exact test). PCR results between day 14 and day 20 were also predictive of day 90 outcome (Table 3).

Initial fungal load and prediction of outcome.

We also assessed initial fungal loads as predictors of outcome. ROC curves for the evaluation of fungal load as a marker of 90-day mortality in invasive aspergillosis indicated that the 150 copies/mL cut-off offered the most efficient value (Figure 3a). Patients with PCR results strictly below 150 copies/mL had a higher probability of survival 90 days after the diagnosis (n=30/41; 73.2% survival), compared to those with PCR results equal to or above this cut-off (n=3/19; 15.8% survival; median survival 20 days) (Figure 3b). This result was highly statistically significant (p<0.0001 with Log-rank test). The test had a hazard ratio of 0.14 (95% CI of ratio: 0.05 to 0.34). Similar results were obtained when all patients were considered i.e. including the 17 patients with negative PCR in the '<150 copies/mL' group.

There were no differences between these two groups in terms of age, sex ratio, underlying diseases or antifungal therapy (Table 1). There were also no differences concerning the interval between sampling and the start of targeted antifungal therapy. In comparison, among patients with initial positive GM, patients with GMs below 2 (the most efficient value) appear to have more favourable outcomes than others but the difference was not statistically significant (p=0.19 by Log-rank test) (Figure 3c).

DISCUSSION.

PCR was originally excluded from the 2008 EORTC/MSG definitions of invasive fungal diseases, but expert consensus now considers that this method is 'mature' enough for its inclusion [START_REF] White | Aspergillus polymerase chain reaction: Systematic review of evidence for clinical use in comparison with antigen testing[END_REF]. Meta-analyses have indeed demonstrated that PCR offers sensitivity ranging from 77% to 88% and specificity from 75% to 94.1%, rates similar to those attained with GM assays [START_REF] Mengoli | Use of PCR for diagnosis of invasive aspergillosis: Systematic review and meta-analysis[END_REF][START_REF] Arvanitis | PCR in diagnosis of invasive aspergillosis: A meta-analysis of diagnostic performance[END_REF][START_REF] Arvanitis | Galactomannan and polymerase chain reaction-based screening for invasive aspergillosis among high-risk hematology patients: A diagnostic meta-analysis[END_REF]. Moreover, PCR permits the use of quality control and inter-laboratory checks, and provides a more robust quantification assessment than does the determination of the GM index.

The results of the present study add to this evidence, with PCR sensitivity reaching 71.7% and specificity 98.7%. In our study, PCR appeared to offer an interesting PPV, i.e., 79.6% for all patients, in stark contrast to the GM index, where a high false positive rate, particularly in neutropenic patients, led to a poor overall PPV of 27%. PCR and GM both offered interesting negative predictive values above 97%. PCR was more sensitive in neutropenic patients (82.1%) than it was in non-neutropenic patients (62.5%) but the difference was not significant (p=0.09). Increasing the number of patients could lead to a significant result. Nevertheless, our study shows that PCR is also useful in non-neutropenic patients and non-haematological populations such as solid organ transplant recipients. As previously reported, our result indicate that performing both GM determination and PCR on the same sample increases the sensitivity [START_REF] Arvanitis | Galactomannan and polymerase chain reaction-based screening for invasive aspergillosis among high-risk hematology patients: A diagnostic meta-analysis[END_REF].

In 2010, Koo et al reported a relation between GM and outcome [START_REF] Koo | Prognostic features of galactomannan antigenemia in galactomannan-positive invasive aspergillosis[END_REF] and in 2012 Bergeron et al reported that patients with poor outcomes 45 days after the initiation of antifungal treatment had high baseline serum GM [START_REF] Bergeron | Prospective evaluation of clinical and biological markers to predict the outcome of invasive pulmonary aspergillosis in hematological patients[END_REF]. We however found only a non-statistically-significant trend for the GM index in the determination of outcomes. Nevertheless, Bergeron et al also found no association between outcome and Aspergillus DNA detection while our work supports that the initial level of Aspergillus DNA is highly predictive of the 90-day mortality rate. As concerns our results more specifically, we found that a PCR threshold of 150 copies/mL could discriminate patients with low (below the threshold) or high (above) probabilities of 90-day mortality. PCR thus has a much greater discriminative capacity than does the determination of the GM index. This result could be useful for identifying patients who may benefit from more intensive care and designing further clinical study. We also showed that PCR quantification is useful in follow-up to predict outcomes. After only one week of therapy, an increase or decrease in the number of copies is not relevant for the assessment of treatment efficacy or outcome. However, PCR results after 2 weeks of treatment did appear to have relevance. In our study, patients who had a negative PCR (to which we add one patient who had cleared more than 99% of the initial load) between days 14 and 20 were all alive at day 30 and 7 of them were still alive at day 90 vs none of the five patients who remained PCR positive.

In a work initiated by the European Aspergillus PCR Initiative, White et al clearly demonstrated that PCR performed on plasma gives better results than PCR performed on serum [START_REF] White | Clinical performance of Aspergillus PCR when testing serum and plasma-a study by the european Aspergillus PCR initiative[END_REF]. In consideration of this evidence, we are now changing our methods. The PCR that we used in the study was specific to the fumigatus species and thus it would not have amplified the genomes of others species such as flavus, niger or nidulans. This may explain at least in part the relative lack of sensitivity for PCR in our study, as non-fumigatus species may account for more than 25% of disease [START_REF] Steinbach | Clinical epidemiology of 960 patients with invasive aspergillosis from the path alliance registry[END_REF]. We did however retrieve the causative species in 32 of our cases and, strikingly, all were fumigatus. Going forward, the utility of PCR in IA and the determination of cut-offs will have to be evaluated in other laboratory specimens, such as cerebrospinal fluid and bronchoalveolar lavage.

CONCLUSION.

Aspergillus DNA detection in serum by PCR is a interesting tool for the diagnosis of invasive aspergillosis in both neutropenic and non-neutropenic patients and should be used concomitantly with the galactomannan index in at-risk populations. Moreover, PCR allows quantification of fungal load and our results indicate that a threshold of 150 copies/mL is very powerful to discriminate patients with low (below the threshold) or high (above) probabilities of 90-day mortality. So, initial fungal load and variations in fungal load during treatment as determined by PCR are robust predictive markers of mortality that may be used to identify patients who might benefit from closer and more attentive care. Diagram shows data for patients for whom PCR products and galactomannan in sera as well as mycological analysis of respiratory samples were available. Among the 10 patients with only positive PCR, one was positive for galactomannan in bronchoalveolar lavage samples. 3a. ROC curve for the evaluation of PCR (square) or GM (triangle) as a marker of 90-day mortality in invasive aspergillosis. The 150 copies/mL cut-off offers the most efficient value and an area under the curve of 0.837. For GM index cut-off of 2 (the most efficient value) is related to a small area under the curve of 0.546. 3b. Patients with initial fungal loads below 150 copies/mL (n=41; 73.2% survival) have more favourable outcomes than other patients (n=19; 15.8% survival): p<0.0001 by Log-rank (Mantel-Cox) test; hazard ratio 0.14 (95% CI of ratio 0.05 to 0.34). 3c. Patients with initial GMs below 2 (n=28; 50% survival) appear to have more favourable outcomes than others

ACKNOWLEDGEMENTS.

Part of the data was presented in

Figure 1 :

 1 FIGURES CAPTION

Figure 2 :

 2 Figure 2: Venn diagram showing data for patients with invasive aspergillosis (n=60).

Figure 3 :

 3 Figure 3: serum Aspergillus PCR is highly predictive of 90-day mortality in invasive

  Figure 2

Table 1 : characteristics of patients with proven/probable invasive aspergillosis according to EORTC/MSG criteria and the Aspergillus PCR level

 1 : two patients in the ">150 copies/mL" group died before the start of antifungal therapy

			Characteristics of patients with proven/probable invasive
					aspergillosis
			EORTC/MSG criteria	According to the Aspergillus PCR level
			without PCR	with PCR	Number of copies <150 ≥150	p
	Patients; n		51	60	41	19
	Haematological malignancies (%)	13 (25.5)	18 (30)	10 (24,4)	5 (26,3)	0.87
	Haematopoietic stem cell transplant (%)	17 (33.3)	18 (30)	10 (24,4)	6 (31,6)	0.78
	Antifungal therapy; n (%) a	Azole based Non-azole based	36 (70.5) 13 (25.5)	42 (70) 16 (26.7)	31 (75,6) 10 (24,4)	11 (57,9) 6 (31,6)	0.6
	SOT recipient (%)		14 (27.4)	16 (26.7)	9 (22)	5 (26,3)	0.96
	Others (%)		7 (13.7)	8 (13.3)	5 (12.2)	3 (15.8)	0.7
	Interval between time of sample
	and start of targeted antifungal	0.65	0.67	0.8	0.37	0.7
	therapy in days; mean
	3-months mortality (%)	25 (49)	27 (45)	11 (26.8)	16 (84.2)	<0.0001

a

Table 2 :

 2 Performance of PCR to detect Aspergillus fumigatus in serum, determination of galactomannan index in serum and mycological examination of respiratory samples for the diagnosis of invasive aspergillosis in 60 patients treated for proven/probable invasive aspergillosis according to extended EORTC/MSG criteria with the addition of PCR in the mycological criteria a IA: invasive aspergillosis; criteria used for classification were those defined jointly by the EORTC/MSG consensus and published in 2008 with, additionally, the inclusion of PCR as a mycological criteria and minor modifications for host factors (e.g. inclusion of alcoholic liver cirrhosis) b as calculated by the Chi-square test between neutropenic and non-neutropenic patients c not available


R A Ferreira

M E Dos Santos

; C Morilla-Santos

; R Lebullenger

; O Peña

; P N Lisboa-Filho

Effect of Eu substitution on the crystallographic and magnetic properties of the BiMn 2 O 5 oxide obtained by urea combustion

Keywords: A. Powders: chemical preparation, B. X-ray methods, C. Magnetic properties ; D. Perovskites

Polycrystalline oxide materials with nominal compositions BiMn 2 O 5 and Bi 0.9 Eu 0.1 Mn 2 O 5 were obtained by urea combustion. Crystallographic studies using X-ray diffraction and Rietveld Refinement techniques showed the formation of single-phase samples for both compositions, crystallizing in a mullite-type orthorhombic perovskite structure, space group Pbam (Z=4).

Replacement of Bi +3 by Eu +3 promoted a slight distortion and a unit cell contraction due to a decrease of the c-parameter for Bi 0.9 Eu 0.1 Mn 2 O 5 . The SEM/EDX techniques confirmed the formation of single-phase materials with excellent mapping distribution. Magnetic measurements showed different behaviors: BiMn 2 O 5 is an antiferromagnet (AFM) with a Néel temperature (T N ) of 41 K and a Curie-Weiss temperature CW of -277 K, while Bi 0.9 Eu 0.1 Mn 2 O 5 displayed a ferrimagnetic behavior with a T C of 44K and CW of -270 K. The role of Eu +3 and the mechanisms responsible for the magnetic transition are discussed on the basis of chemical bonds and exchange interactions.

Introduction

Multiferroics is a material class which simultaneously presents two or more primary ferroic orders such as ferroelectricity, ferroelasticity and (anti-) ferromagnetism when an electric field or a magnetic field are applied. The capacity of a material to couple two or more ferroic properties is due to the presence of two different types of cations: i) cations with electronic configuration ns 2 for which the lone pair is responsible for ferroelectricity; ii) cations with d n or f n partially-filled subshells, accountable for the magnetic ordering. Thus, these materials may present various properties such as magnetoelectricity, magnetooptical response, magneto-elasticity and ferroelasticity. Research on these materials attracts a great interest from the scientific and technological points of view since they can be used in spintronic and magneto-optical devices or as low-field magnetic sensors [1][2][3][4][5][6][7] .

Although these materials are subject of fascination in basic physics and of great interest in applied research, only few oxides can be classified as multiferroics with potential possibilities in technological applications. Manganite oxides of the RMn 2 O 5 family (R a lanthanide, Yttrium or Bismuth), crystallizing with a mullite-type perovskite structure and space group Pbam, have attracted much attention due to their multiferroic properties such as magnetoelectricity and magnetodielectricity [8][9][10] .

Among this family, BiMn 2 O 5 has been extensively investigated. The two manganese ions have different oxidation states: Mn 4+ occupies the octahedral sites (4f site) coordinated by six oxygen anions, while Mn 3+ is located in a distorted tetragonal pyramidal site (4h site) coordinated by five oxygen anions.

The Bi 3+ ions (4g site) are coordinated to eight oxygen atoms and form BiO 8 polyhedra. These polyhedra are responsible for the interconnection between both manganese cations localized at the octahedral and tetragonal pyramidal sites [11] . The mixed-valent manganese ions occupying different crystallographic sites lead to the formation of a magnetic structure with two different magnetic sublattices. BiMn 2 O 5 orders below 39 K in a noncollinear commensurate magnetic structure with a propagation vector k = (½,0,½), in contrast to the RMn 2 O 5 series, incommensurate with k = (½,0,k z ) [12,13] . The magnetization curves for all members of the RMn 2 O 5 family present a typical antiferromagnetic behavior with Néel temperatures close to 40 K [11][12][13][14][15] . This paper aims to demonstrate, first, that the urea combustion is an adequate synthesis method to obtain multiferroic materials and, secondly, to investigate the role of the Eu 3+ ions as improving dopants for the BiMn 2 O 5 properties. In particular, the X-ray diffraction analysis and Rietveld refinement should allow correlating the modifications of the magnetic behavior of the RMn 2 O 5 family when the Bi element is partially substituted by Eu.

Materials and Methods

The oxide materials BiMn 2 O 5 and Bi 0.9 Eu 0.1 Mn 2 O 5 were obtained by the urea combustion reaction method [16,17] . In a first step, MnO, Bi 2 O 3 and Eu 2 O 3 precursors (99,99% purity Aldrich) were weighted and dissolved separately in a concentrated solution of nitric acid. Then, these solutions were mixed under agitation with appropriate amounts of urea dissolved in water. The ratio of the urea amount (R) to the corresponding nitrates was obtained using equation 1 [16] : (1) where is a parameter which defines the oxidize/reduction reaction conditions and, for this work, taken as 3. Numbers in numerator and denominator are the oxidation state of elements which for nitrogen was taken as zero.

The obtained products were then heated to 500°C until complete combustion. Powders were progressively calcined in air at 600 °C for 4 hours and then at 700 °C and 800 °C for 4 hours in each temperature with intermediate grinding after each calcination temperature. Similar procedure was adopted for the europium-doped sample with a final calcination at 900°C for 4 hours. The final products were grinded in an agate mortar and characterized by different techniques.

The homogeneity of the samples was confirmed by energy dispersive Xray mapping (EDX) and scanning electronic microscopy SEM observations (Philips model FEI Inspect S50). X-ray diffraction (XRD) patterns were registered with a Rigaku Rint DMAX-2100/PC diffractometer using Cu K radiation (1.5406 Å). The 2 scans were carried out at room temperature from 10° to 90°, with fixed time mode of 1.6 s and a step of 0.02°. Structural refinements were performed by the Rietveld method [18,19] using the GSAS program [START_REF] Larson | General Structure Analysis System (GSAS)[END_REF] , the JCPDS phase identification and the ICSD (2003) structural database. The diffraction profiles were modeled using the pseudo-Voigt Thompson-Cox-Hastings (TCH) function [21] . Magnetization measurements were performed using a PPMS Quantum Design 6000 magnetometer with zero field cooling (ZFC) and field cooling (FC) data collected between 2K and 300K under an applied field of 1 kOe.

Results and Discussions

SEM Analysis and Elements Mapping

Figures 1(a) and 1(c) present SEM images of BiMn 2 O 5 and Bi 0.9 Eu 0.1 Mn 2 O 5 powder specimens. A homogeneous distribution of grains can be observed with average sizes close to 200 nm and 300 nm, for the non-doped and the doped samples, respectively. The difference in the particle sizes can be attributed to the calcination procedure: T (max) = 800°C (calcination total time = 12h) for BiMn 2 O 5 and T (max) = 900°C (calcination total time = 16h) for Bi 0.9 Eu 0.1 Mn 2 O 5 . These two parameters (temperature and time) are known to enhance the sintering phenomena and induce an increase of particles size, as it occurred in the case of the Bi 0.9 Eu 0.1 Mn 2 O 5 material Heat-treatments performed at 900°C for the Eu doped sample was made considering it crystallized in a monophasic solid solution at this temperature.

Otherwise, BiMn 2 O 5 sample was crystallized like a monophasic solid solution in 800°C. This were observed using the XRD associated with Rietveld Refinement and will be discussed on the section 3.2.

The elements distribution was checked by EDX analysis by mapping a large surface of compacted powders. Figures 1(b) and 1(d) show a uniform distribution of all elements (Bi, Eu, Mn and O). Quantitative analyses of both samples by SEM-EDX confirmed the expected stoichiometric ratios of the constituent elements and the results are given in Table 1.

X-Ray Powder diffraction and Rietveld Refinement

The X-ray diffraction data (XRD) of both samples were refined by the Rietveld method. Figures 2(a) and 3 show a good agreement between the calculated and observed data for BiMn 2 O 5 and Bi 0.9 Eu 0.1 Mn 2 O 5 , respectively.

Both materials crystallize in a mullite-type orthorhombic structure, space group Pbam and Z=4. In this structure, Mn ions are found in two different crystallographic sites: Mn 4+ ions occupy the octahedral pyramidal site (4f) coordinated to six oxygen atoms, whereas the Mn 3+ ions occupy the tetragonal pyramidal site (4h) coordinated to Þve oxygen atoms. The Bi 3+ ions sit at the 4g sites and are coordinated to eight oxygen atoms forming BiO 8 polyhedra [11][12][13][14] .

Based on our refined data, we may suppose that the Eu 3+ ions replace Bi 3+ at the 4g site and do not modify the crystal structure of the host material. The average Mn-O distances obtained in this work (<Mn +4 -O> = 1.897 Å and <Mn +3 -O> = 1.920 Å (Table 3)) are slightly lower than those reported by Muñoz et al from Neutron Powder Diffraction data (<Mn +4 -O> = 1.916 Å and <Mn +3 -O> = 1.948 Å) [11] . This fact is associated to a contraction of the octahedral and pyramidal sites. Reported results of structural parameters for samples synthesized by other methods, such as hydrothermal [22] and solid state reactions [8,10] are in agreement with the data found in this work. However, we should recall the fact that the heat-treatment parameters (T° and total time) adopted in this work (800-900°C and 12h and 16h) are lower than those mentioned by others authors, and it confirms that urea stress the fact that the heat-treatment adopted in this work is lower than all others mentioned above, and it confirms that urea combustion is an excellent method to obtain single-phase oxide materials at low temperatures.

The Eu 3+ substitution at the Bi 3+ site leads to a slight contraction of the unit cell (Table 2) due to the smaller ionic radius of Eu +3 (1.06 Å) compared to Bi +3 (1.17 Å). The lattice contraction occurs predominantly on the lattice parameters a and c, whereas no significant variation is observed for the b-parameter. Slight variations in the positions of the (001), (002), (003) and (004) planes explain the modifications of the octahedral sites occupied by Mn +4 ions that form chains along the c axis [23][24][25] .

The average distance Mn1-O for sample BiMn 2 O 5 (<Mn1-O> = 1.897 Å) is larger than the (<Mn1-O>= 1.887 Å), value obtained for the Eu-doped sample suggesting that the insertion of the Eu 3+ ions in the 4g position promotes a contraction of this site. The distorted pyramidal polyhedra Mn 3+ O 5 of the Bi 0.9 Eu 0.1 Mn 2 O 5 suffers a contraction due to the Mn 3+ located at the 4h position, the Mn2-O4 distance changing from 2.111 Å to 2.088 Å, for the non-doped and Eu-doped compounds, respectively (Tables 3 and4). Such stretching of the Mn2-O distance in the Mn 3+ O 5 pyramid tetrahedral is probably associated to the Jahn-Teller effect of the Mn 3+ ion.

Other distances related to the chemical bonds, such as <Bi-O> and <Mn1-Mn1>, slightly decrease in the doped Bi 0.9 Eu 0.1 Mn 2 O 5 sample, whereas the <Mn2-Mn2> average distance stays invariant. This fact confirms the larger distortion of the tetragonal pyramidal site, since the average distances <Mn1-O> and <Mn2-O> also decrease with doping.

Magnetic Measurements

Figure 4 shows the ZFC/FC cycles of BiMn 2 O 5 and Bi 0.9 Eu 0.1 Mn 2 O 5 measured under an applied magnetic field of 1 kOe. Both samples present an antiferromagnetic-type ZFC magnetization, with a maximum at T = T N of 41 K and 37 K, respectively. During the FC mode, the magnetization increases abruptly at the ferromagnetic ordering temperature T C , defined in this work as the intersection of two straight lines sketched just above and just below T C . The T C values for both systems are identical, 44.3(0.2)K, meaning that Mn 3+ -Mn 4+ ferromagnetic interactions have been optimized through the coupling between the tetrahedral and octahedral sublattices.

Two experimental facts are worth to be noticed in Figure 4: firstly, the large difference in magnetization values between both samples, in the ordered state; secondly, the quite constant evolution of the FC magnetization below 20-30 K for the Eu-doped sample. Knowing that Eu 3+ is a VanVleck ion, no intrinsic magnetism may be expected from it at these temperatures [START_REF] Van Vleck | The Theory of Electric and Magnetic Susceptibilities[END_REF] ; in consequence, the large M FC magnetization in the Bi 0.9 Eu 0.1 Mn 2 O 5 system must be attributed to a better ferromagnetic coupling between the manganese moments, probably due to the contraction of the unit cell when replacing Bi by Eu. The observed phenomena for Eu substitution is attributed to a Morin-type transition, usually observed in Eu doped systems [START_REF] Allia | [END_REF] .

Modifications of the lattice parameters and probably the accompanying distortion of the lattice due to a Jahn-Teller effect of the Mn 3+ ion [9] , may also create some AFM canting of the magnetic moments, bringing about an additional ferromagnetic component in the Eu-doped case, thus increasing the ferromagnetic contribution at low temperatures.

The second fact pointed above, that is the quite constant thermal evolution of M FC magnetization at low temperature in the case of the Eu-doped material, may be attributed to anisotropic features which are quite important in the case of the full-europium mullite-type perovskite structure, EuMn 2 O 5 , in contrast to BiMn 2 O 5 which presents none [15] . Our experimental conditions for powdered samples may provoke some reorientation by the applied field, of grains and/or particles due to the magnetic anisotropy, contrary to the reports made by Golovenchits et al in single crystal samples [15] . This anisotropy may also be responsible of the much smaller hysteresis between the ZFC and FC modes in the case of BiMn 2 O 5 , even though this system was reported by Golovenchits et al as isotropical from the magnetic point of view. Other authors have also found weak hysteretic behavior in BiMn 2 O 5 samples [11] . Magnetic anisotropy may also be the explanation of small differences in the values of T N (39 K in ref. [11], ~40 K in ref. [15], 41 K in this work).

The inset, Figure 4, shows the inverse magnetic susceptibility for both samples, at the paramagnetic state. Analysis of 1/ -vs-T by linear regression in the range [100-300K] allows finding the Curie-Weiss temperature WC and the magnetic effective moment eff for both samples, with values of WC = -277 K and -270 K, and eff = 6.69 B and 6.90 B , for BiMn 2 O 5 and Bi 0.9 Eu 0.1 Mn 2 O 5 , respectively. These values are quite similar to those reported in the literature for a BiMn 2 O 5 perovskite obtained by hydrothermal, solid-state and citrate techniques, WC ~ -270 K and eff = 6.9 B [8][9][10][11] , but somewhat higher than the magnetic moment of 6.24 B expected from the contributions of non-interacting Mn 3+ and Mn 4+ ions in the paramagnetic state.

Some considerations must be forwarded regarding the coexistence of an AFM and a FM ordering behaviors. It is quite evident that the quite large negative Curie-Weiss temperature (~-270 and -277 K found in this work) is indicative of a ferrimagnetic nature of both materials, which may be extrapolated to other components of the RMn 2 O 5 family [8][9][10][11][12][13][14] .

Conclusions

In

Figure 2 (

 2 Figure 2(b) is a schematic representation of the crystallographic structure along the c-axis. In this representation distorted Mn 4+ O 6 octahedral are environments of the Mn 4+ (Mn1 atoms) which form infinite linear chains along the c-axis, sharing edges via the O 3 and O 4 oxygen. Mn 3+ (Mn2 atoms) occupy Mn 3+ O 5 distorted tetragonal pyramids and have as neighbors five oxygen atoms. These units present four oxygen atoms in different positions (two O 1 and two O 2 ) are in a square planar configuration and fifth oxygen (O 4 ) is in an axial position at a longer distance (2.111 Å). The connections between pyramids and octahedrons are along the c-axis via O 4 and O 2 .

Figure 2 .

 2 Figure 1: a) Scanning electron micrograph of a representative samples of BiMn 2 O 5 manganite powders obtained by the urea combustion method, followed by heat treatment in air at 800°C for 4 hours. b) Scanning elements analysis of the BiMn 2 O 5 sample showing a homogenous elements distribution for Bismuth, Manganese and Oxygen. c) Scanning electron micrograph of representative samples of Bi 0.9 Eu 0.1 Mn 2 O 5 powders obtained by the urea combustion method, followed by heat treatment in air at 900°C for 4 hours. d) Scanning elements analysis of the Bi 0.9 Eu 0.1 Mn 2 O 5 sample showing a homogenous elements distribution for Europium, Bismuth, Manganese and Oxygen.Figure 2. a) XRD Rietveld refinement for the BiMn 2 O 5 sample heat-treated in air at 800°C for 4h. b) The structural models of BiMn 2 O 5 with chain of Mn atoms along the c-axis.

Figure 3 .

 3 Figure 3. XRD Rietveld refinement for the Bi 0,90 Eu 0,10 Mn 2 O 5 sample heattreated in air at 800°C for 4h.

Figure 4 .

 4 Figure 4. Magnetic susceptibility versus temperature at a constant applied field of 1 kOe. (Inset) Inverse of magnetic susceptibility vs temperature for an applied field of 1 kOe for the BiMn 2 O 5 Bi 0,90 Eu 0,10 Mn 2 O 5 samples.

Figure 2 FCFigure 4 .

 24 Figure 2.

Table 1 .

 1 this work we demonstrated that single phase materials BiMn 2 O 5 and Bi 0.9 Eu 0.1 Mn 2 O 5 were successfully obtained by the urea combustion method, excellent technique to obtain complex oxide materials at intermediate temperatures with good particle size and uniform element distribution. Rietveld refinement of the XRD data agrees with a substitution of Bi +3 by Eu +3 in the same crystallographic sites, both cations having similar ionic radius. The magnetic behavior changes significantly when Bi +3 ions are substituted by Eu +3 .Both compositions order below 40 K, with BiMn 2 O 5 in an antiferromagnetic state while Bi 0.9 Eu 0.1 Mn 2 O 5 presents a ferrimagnetic behavior. This difference seems to be related to the commensurate magnetic structure of BiMn 2 O 5 eventually becoming incommensurate with the substitution of Bi +3 by Eu+3 . One of the key mechanisms is the lattice distortion, which modifies the strength of the exchange chains responsible of the ferrimagnetic behavior. Stoichiometric Compositions of BiMn 2 O 5 and Bi 0.9 Eu 0.1 Mn 2 O 5 samples obtained by EDS-analysis.

Table 2 .

 2 Rietveld XPD analysis result data for the samples obtained by combustion urea method. Data were collected at room temperature and the space group used was Pbam. Bi 0.9 Eu 0.1 Mn 2 O 5 Specimen interatomic distances (in Å) and select bonding angles (in degree) obtained by Rietveld Refinement Method at 298 K. Scanning electron micrograph of a representative samples of BiMn 2 O 5 manganite powders obtained by the urea combustion method, followed by heat treatment in air at 800°C for 4 hours. b) Scanning elements analysis of the BiMn 2 O 5 sample showing a homogenous elements distribution for Bismuth, Manganese and Oxygen. c) Scanning electron micrograph of representative samples of Bi 0.9 Eu 0.1 Mn 2 O 5 powders obtained by the urea combustion method, followed by heat treatment in air at 900°C for 4 hours. d) Scanning elements analysis of the Bi 0.9 Eu 0.1 Mn 2 O 5 sample showing a homogenous elements distribution for Europium, Bismuth, Manganese and Oxygen.

	Table3. BiMn 2 O 5 Specimen interatomic distances (in Å) and select bonding
	angles (in degree) obtained by Rietveld Refinement Method at 298 K.
	Table4.

Acknowledgments

Authors are thankful to FAPESP GRANT 2007/08072-0 and 2013/07296-2. The authors also acknowledge the bilateral exchange programs France-Brazil CAPES-COFECUB, project no. 706/11. R.A. Ferreira and M.E. Santos in a Joint Ph.D. International Program, UNESP-Université de Rennes 1.