Jonathan Pepin

Pascal André

Christian Attiogbé

Erwan Breton
email: ebreton@sodifrance.fr

Virtual Extension of Meta-models with Facet Tools

Keywords: Meta-model, Weaving, Mapping, Facet, Transformation Tools

In the software industry, Model Driven Engineering (MDE) techniques have proven useful not only for developing new software applications but for re-engineering legacy systems. However the stakeholders face costly maintenance operations due to frequent new standards and upgraded releases. Therefore, solutions ensuring a better adaptability and flexibility of modelling tools are needed. We propose an improved technique of virtual extension of meta-models with Facets that enables one to modify meta-models already in use without rebuilding completely the software product. This technique has been implemented and experimented for model alignment and evolution.

Introduction

The importance of modelling in software engineering and the popularity of the UML notation result in the dissemination of model-based tools and the emergence of a modelling language industry based on the model-driven approach (MDA). The Meta-Object Facility (MOF) language is then the foundation for an ecosystem of Domain Specific Languages (DSL) [START_REF] Brambilla | Model-Driven Software Engineering in Practice[END_REF]. Some of them are standards like BPMN, BMM, SoaML, SysML, UPDM while others are specific. Model Driven Engineering (MDE) emphasizes the use of models and meta-models to improve the software productivity and some aspects of the software quality such as maintainability or interoperability. MDE techniques have proven useful not only for developing new software applications but for re-engineering legacy systems and dynamically configuring running systems [START_REF] Cuadrado | Applying model-driven engineering in small software enterprises[END_REF]. Meta-models are essential in MDE since many model processing are described at the meta-model level through transformation rules or operations. However this is also a pitfall since both model and meta-models evolve separately. This is even more complex when models (including metamodels) are parts of larger models. As mentioned by El Kouhen in [START_REF] El Kouhen | Panorama : A Unified Framework for Model Composition[END_REF], MDE promotes the separation of concerns to deal with the design's complexity and maintainability. This practice implies the creation of several heterogeneous models using different notations. Since the semantics of each individual model is limited, the model's consistency and completeness are easier to prove. Model composition is the symmetric paradigm of separation of concerns. It is then necessary to compose models to reason on the overall designed system for many purposes such as: checking the global consistency of the models, understanding the interactions between models, generating code, etc.

In the context of software maintenance, including model-driven reverse engineering of a legacy system, it is a primary requirement to have techniques to build new models without modifying the source models (to preserve source property), this is a nonintrusive model composition. A typical scenario for a software provider is to deliver customised release to some clients but customisation leads to a tricky maintenance problem when models and meta-models evolve. This includes the case of (old) legacy code built using various DSL with various development methods at different level of abstract (from implementation code up to business processes). In order to represent specific (or customised) aspects we extend meta-models with new concepts, attributes and relations. It is always possible to create the new metamodel with references to the initial meta-model but any modification requires to rebuild the delivery product. This is also the case when the modelling language evolves; the new release requiring tool adaptation.

Paige et al. mentioned several challenges of evolving models in MDE [START_REF] Paige | Evolving models in model-driven engineering: State-of-theart and future challenges[END_REF]. The work presented in this paper is a practical contribution to the dependency heterogeneity challenge. We focus on model mappings that support several semantic links, models (or meta-models) evolution and persistence, while staying non-intrusive by preserving their parts.

A mapping model

We propose an improved version of virtual extension of meta-models that uses Facets and enables one to modify meta-models already in use without rebuilding the software product. The extension is called virtual because it does not directly impact the initial meta-models.

A mapping technique Taking this industrial point of view, the question is not only to find a mapping meta-model, which is usually depending on the context (the models we work with), but also to implement mapping techniques which are compliant with the existing MDA tools (e.g. tools based on the Eclipse EMF Frameworks) and efficient for large-scale applications. EMF enables to define meta-models, load, persist and manipulate the compliant models. The EMF Facet tool is based on EMF to extend virtually a meta-model. This solution is non intrusive, it supports link semantics but it does not have a mechanism for persistence (the values are calculated by queries) and an adequate technique and tools for mapping models.

A mapping tool We contribute here to this limitation by improving the EMF Facet technique by modifying the meta-model and implement several tools to manage model mapping in practice. At the implementation level, maintaining a link between model elements can also be seen as an object relation mapping (ORM) problem [START_REF] Ambler | Building Object Applications That Work: Your Step-by-Step Handbook for Developing Robust Systems with Object Technology[END_REF] preserving the link multiplicities and a bi-directional navigation. This problem needs a robust algorithm engine to maintain the constraints imposed by the multiplicities.

The paper mainly targets a tool set; it is structured as follows. In Section 2 we review model mapping techniques and motivate our choices. Section 3 overviews EMF Facet and introduces our improvements. Persistence, navigation and testing issues are discussed in Section 4. New user interface facilities are presented on the illustrating example in Section 5 while larger case studies are reported in Section 6. Finally, Section 7 summarises the contribution and draws perspectives.

Background and Requirements

Model mapping is one of the "Model manipulation and management challenges" of [START_REF] France | Model-driven development of complex software: A research roadmap[END_REF] and in particular to the points (2) and (3) mentioned by its authors: (2) maintaining traceability links among model elements to support model evolution and round trip engineering and (3) maintaining consistency among viewpoints. Model mapping techniques are useful for model composition, decomposition or synchronization. Model composition is usually applied at the meta-model level.

Non-intrusive Model Mapping (NI-MM for short), a composition that preserves the components, is called the "model-based correspondence" by Clavreul who identified 88 techniques for different purposes in his systematic review of models composition [START_REF] Clavreul | Model and Metamodel Composition: Separation of Mapping and Interpretation for Unifying Existing Model Composition Techniques[END_REF]). Clavreul's mapping language is a merging approach and the architects must learn a DSL. We opted for a mechanized approach by providing a simple tool which applies at both compile and run time. A mapping tool is proposed by Limyr et al. [START_REF] Limyr | Semaphore -a model-based semantic mapping framework[END_REF] but the goal is to prepare patterns for model transformation, not to map heterogeneous models. El Kouhen [START_REF] El Kouhen | Panorama : A Unified Framework for Model Composition[END_REF] proposed an unified methodology to compose models based on meta-model extensions. The composition operators are symmetric (commutative e.g. merge, parallel) or asymmetric (weaving in the meaning of AOP, sequential integration). His work is largely inspired by [START_REF] Marchand | A categorical model of model merging and weaving[END_REF] who gave a formal semantics for weaving and merging through morphisms of a category theory. NI-MM can be seen as an ad hoc model composition in their classification since the mapping uses semantic information of the source models. Their mapping approaches enable the separation of concerns, but the merging and weaving do not preserve the legacy models but also tool support because their result is a new model.

NIP-MM is close to model weaving as defined by [START_REF] Jouault | Inter-DSL Coordination Support by Combining Megamodeling and Model Weaving[END_REF] which was inspired by Aspect Oriented Modelling. "Model weaving operations are performed between two or more metamodels, or between models. They aim to specify the links, and their associated semantics, between elements of source and target models" [START_REF] Jouault | Inter-DSL Coordination Support by Combining Megamodeling and Model Weaving[END_REF]. Models are woven by establishing different kinds of links that contains the semantics of weaving: merge operations, traceability links, data translation mappings, text to graphical representation, etc. Atlas Model Weaver (AMW) includes a transformation mechanism with ATL1 to create an automatic weaving. Virtual EMF [START_REF] Brunelière | Virtual EMF -transparent composition, weaving and linking of models[END_REF] provides a visual assistant to edit two models from different meta-models and to create links between concepts with drag and drop. Unfortunately, editors are no longer updated and no more supported for the recent Eclipse 4.x versions. The existing tools did not suit to our requirements but we got inspired by them to create our own weaving assistant, including some improvements and new features (see Section 5). Didonet et al. (Del Fabro and Valduriez, 2007) propose an approach that uses matching transformations and weaving models to semi-automate the development of transformations, which is not our goal.

NI-MM has also been explored in the context of Domain Specific Languages (DSL) to define new languages from existing ones in a non-invasive way without re-creating the tool support. Bruneliere et al. [START_REF] Brunelière | On lightweight metamodel extension to support modeling tools agility[END_REF] define a textual DSL with extension operators to extend metamodel semantics. It is independent from modelling tooling. Similarly, Greifenberg et al. propose DSL-specific tag language [START_REF] Greifenberg | Engineering tagging languages for dsls[END_REF]. [START_REF] Kolovos | Constructing and Navigating Non-invasive Model Decorations[END_REF] propose decorator extraction and injection operators based on GMF notes to ensure the non-invasive property but requires manual transformations. Also conflicting specializations of GMF notes may appear. Langer et al. [START_REF] Langer | EMF profiles: A lightweight extension approach for EMF models[END_REF] propose EMF profiles, a lightweight adaptation of UML profiles to extend meta-models with annotations, constraints and stereotypes. An advantage is to add new information. These DSL extension approaches have in common to work on the (binary) inheritance relation (one model is more specialised than another) while we target any kind of n-ary relations between models such as aggregation, composition, inheritance, dependency e.g. traceability... However they are complementary.

As mentioned in a previous work [START_REF] Pepin | Using ontologies for enterprise architecture integration and analysis[END_REF], mapping approaches such as merging, weaving or annotation do not support properly mandatory properties for model maintenance and evolution. These properties issue from lessons learned during model maintenance activities. A software system sustains several releases that can even co-exist for different users with different hardware. The semantics of models needs enhancements: new attributes, new entities, new classification, and new links....

Revisiting the Facet Approach

Eclipse EMF Facet is a runtime meta-model extension framework composed of four parts: Facet, Customization, Widgets and Query. The Facet part offers the possibility to virtually extend (at runtime) existing meta-models and models. The Customization part adds UI enhancements on a meta-model. The Widgets part can be used to apply your own customization to model editors. The Query part enables to compute attribute, reference and operation value. The query can be written in Java or OCL. With Facet one can extend models by adding virtual features to existing models and also weave models by linking their concepts (Figure 1). Let us remind here the limitations of the Facet approach for NIP-MM:

-A new feature of the Facet systematically calls a query. However the queries cannot access to the model to map and it is necessary to store the values. Also queries must be executed during the model loading; if the model is voluminous, the calculation times can impact the response time. -New features cannot be valued manually, as the attributes or references of an ordinary meta-model. -In independent model composition, the mapping links must be persistent, consequently the values of the features is to be serializable. To fix the above limitations, we first improve the meta-model, then we modify the existing Facet manager and serialization mechanisms. We improve the meta-model by allowing dynamic structure instead of the static one: we add FacetAttribute and FacetReference getters and setters instead of recomputing systematically the attached query (Figure 2). Since the meta-model constraints have an impact on the behaviour of the current Facet manager, we upgraded its implementation to support the new behaviour. Thus the multiplicity of the eReference query on De-rivedTypedElement is changed from 1..1 to 0..1. This feature will permit to get the values of FacetReference and FacetAttribute without using a query. Furthermore we add a new eReference named fOpposite to create a reflexive reference mechanism like eOpposite on EReference in the Ecore meta-model. The improved EMF Facet enables now to manually extend and weave models. As a matter of fact, the serialization makes the improved Facet be the most effective approach among all the mapping techniques of Section 2. Consequently we submitted it as a contribution to the open-source Eclipse EMF Facet2 which has been approved.

After the modification of the meta-model, we turn to the Java implementation of the new behaviour of the Facet engine called FacetManager. At first, we supported the two simplest multiplicities of the bidirectional references type: one-to-one and many-tomany. But, this was insufficient to cover all the cases so we proceeded with all the possible cases. In the next section, we present the cases and their implementation in order to obtain a complete weaving engine.

Persistence and Navigation

We can now create FacetReference links to map concepts from different meta-models. In this section, we describe the mechanism that supports bidirectional mappings and a two-way navigation. Mappings are represented here by UML associations, which is bi-directional by default.

Similarly to object relation mapping (ORM) or UML2Code transformations, a (bi-directional) association is represented by a pair of uni-directional (oneway) associations as shown in the Library example of Figure 3. Thus any (instance) link modification requires a propagation on the opposite link. An association between classes is represented by a set of links at the instance level. We distinguish four cases: • one to one: an instance is linked to one instance only, • one to many: an instance can be linked to several instances, • many to one: several instances can be linked to one instance, • many to many: several instances can be linked to several instances.

Multiplicity is an interval of value represented by a lower and an upper bound. We precise that one to many and many to one cases are not symmetric because the relation is directional. Then, these two cases have different algorithms to preserve the multiplicity.

The problem is to preserve the symmetry constraint (prefacedBy.reverse() = prefaces) of the opposite association ends. Updating only one side usually leads to a symmetry constraint violation. Next we illustrate each case by giving a figure and the algorithm we implemented in FacetManager 3 . One to one. In Fig. 4, a book is prefaced by one author and an author prefaces only one book. To check the multiplicity consistency, the implementation in the FacetManager involve to keep only one opposite link during the set operation. Thanks to the automatic management of bidirectional links, the instance mapping is transparent for users. Our implementation allows one to weave the instances without a slave-master semantics. The user can draw a link in any order: from the source to the target or reverse. Henceforth EMF Facet enables one to create links between any models following a definition at meta-model level. Section 5 presents additional tools to handle the instance level links.

Checking Link Conformity by Testing

To test and verify code coverage our new implementation for the mapping added in the FacetManager, we wrote JUnit tests following the Test-Driven Development approach. Let's illustrate it with two simple examples of the Library. For each multiplicity case, we set facetReference between different instances of books and writers, then we check if the reference setting is conform to the reference and its opposite.

Example 1 In the many-to-one case, writer1 and writer2 write the preface of book1. The test case of Listing 1 assigns the value, establishes the mapping and checks the reverse link. It succeeds. . c l a s s ) ; A s s e r t . a s s e r t E q u a l s ( msg (BOOK1, " w r i t e r 1 and w r i t e r 2 " ) , A r r a y s . a s L i s t ( w r i t e r 1 , w r i t e r 2 ) , w r i t e r s 1 a n d 2 ) ;

The above examples are specific to the Library case study but generic tests, written at the meta-model level, can be shared by all applications.

End-User Running the Mapping

The mapping framework includes an API, a user interface and tests. The mapping API and the tests have been presented in section 3 and 4. In this section, we illustrate the additional implemented tools with the Library example. We extend Book and Writer meta-classes by creating new FacetSet for each link mapping cases containing all facets which defines the new virtual references with the specific multiplicity: prefaces and prefaced. A mapping process includes the following activities: facet mapping definition, instance valuation, instance linking, mapping navigation and evaluation.

Mapping Definition

Each facet defines at least a name and the meta-class type. A facet optionally extends an existing Facet. The facet refers to the original meta-class by its absolute Universal Resource Identifier (URI). The facet defines three kinds of features: FacetAttribute, Face-tReference and FacetOperation. We can create as many new features as required. A FacetReference defines at least a name, a multiplicity, a type and an opposite reference if the association is bidirectional. The type is a meta-class from any Ecore reachable meta-model or another predefined FacetReference in the current FacetSet. A FacetAttribute defines at least a name, a multiplicity and the meta-class type as in Example a Book meta-model describing the books and a Writer describing authors. In Fig. 8, we create four FacetSets, one for each mapping multiplicity: many to many (MToM), many to one (MToO), one to many (OToM), and one to one (OToO). For each case, a new reference preface links 'writers to books' and the opposite reference prefaced links 'books to writers'. The multiplicity differs through the upper and lower bound. In the example of Fig. 8, 'many' has 0 lower bound and -1 upper bound.

The purpose of this new definition is to extend the existing classification of a library of books and authors to obtain an enriched cataloguing.

Setting links with property value

Property editors set the value of the attributes and references that will be used by queries. Example To experiment the different multiplicity links, we create a library with books and writers. We apply a specific FacetSet at a time: MToM, MToO, OToM, or OToO. In Fig. 9 the FacetSet OToM is applied, the book "2001 Space Odyssey" have two prefaced writers "Isaac Asimov" and "Arthur C. Clarke". The property field is different according to the multiplicity: one, a pop-up menu selector ; many, a two columns chooser. We can check the opposite link is correctly set in Fig. 10: "Isaac Asimov" preface "2001 Space Odyssey", and "Arthur C. Clarke" preface "2001 Space Odyssey".

Setting links with model weaver

Setting a lot of links between instances with the property values is a fastidious work and editors are really useless in this case. We developed a specific weaving editor with multiple views (drag-and-drop). This design allows us to drag and drop elements from right to left to link elements by references corresponding to the FacetSet definition. In this example, we drag and drop two writers "Ray Bradbury" and "George Orwell" on reference prefaced of the book "Fahrenheit 451".

Exploring links with OCL query

Using a model mapping by FacetReferences makes possible the navigation through the models, from one FacetReference to another. The TreeEditor is compatible with EMF Facet and enables one to browse hierarchically the models by the FacetReference, but it is still not very convenient; architects would like to get some statistics to measure which elements from models are mapped or not. Since EMF offers OCL expressions parser on Ecore models, we have produced an engine extension in order to make Facet virtual feature compatible and used as property in OCL statements. This extension is also integrated in a plug-in to EMF Facet project.

Large Scale Experimentations

The Library example is a toy example for illustrating this paper. We applied our improved Facet in the context of the Business-IT alignment in Enterprise Architecture. The problem was to align models from different points of view, in the context of Information System maintenance. It covers a wider field than the scope of this paper but we will focus only on the mapping issue. We briefly report these experimentations here but the reader will find details in [START_REF] Pepin | Using ontologies for enterprise architecture integration and analysis[END_REF].

In this context, we experimented our Facet approach with real size case studies provided by insurance companies with heterogeneous information supports: lots java source files, MEGA repositories, databases,... First, we defined different meta-models (business process, functional, application) and we implemented reverse-engineering techniques to feed the corresponding models. The mapping technique has been implemented to establish the alignment links between the models. The experiments showed that big mappings are hardly manageable by humans and tool assistance is mandatory. Our tool support handled efficiently big models. Even in bulky case with manual mapping, our Weaving Editor (cf. figure 11) provides an optimized user interface with search engine to find concepts and highlight concepts matching. However additional tool is needed to visualize big mappings, to evaluate the mapping properties (consistency, completeness) and quality (misalignment, evolution). Our Facet query engine is a first step to reach this goal.

Applications

The improved Facet tooling is available at the opensource Eclipse EMF Facet https://wiki.eclipse. org/EMF_Facet.

Application use cases include implementation of heterogeneous modelling (e.g. static vs dynamic aspects), traceability links, evolution links (between releases), refinement, roundtrip representation (e.g. code vs models), ...

Conclusion

We proposed an improved technique of virtual extension of meta-models that uses Facets; it enables one to modify meta-models already in use by software, without rebuilding completely the legacy tool support. The extension takes account of the multiplicity of associations and considers two-way references between the involved entities. The proposed technique has been implemented, then experimented on various case studies and integrated in the open-source Eclipse EMF Facet project. We have then contributed to solve an important model and software evolution issue.

The next step will provide more assistance to the user; we target the implementation of heuristics to propose a list of possible model mappings to the modeller: he can then choose the desired ones. These heuristics will depend on the nature and the semantics of the mappings. For example, when mapping two releases of the same model, it is usually easier to detect equality mapping. In specific cases, one can detect patterns or naming conventions.

Figure 1 :

 1 Figure 1: Facet and Customization A Facet provides a new viewpoint on a model which is helpful to categorize model elements with new classification, to add information on model element, to navigate between model elements easily with new derived links. A Facet provides a virtual mechanism to add more attributes, references or operations on a model without modifying the initial meta-

Figure 2 :

 2 Figure 2: EMF Facet meta-model modifications Technically, enabling the access of values turns to weakening the multiplicity of FacetAttribute and FacetReference which extend DerivedTypeElement. Thus the multiplicity of the eReference query on De-rivedTypedElement is changed from 1..1 to 0..1. This feature will permit to get the values of FacetReference and FacetAttribute without using a query. Furthermore we add a new eReference named fOpposite to create a reflexive reference mechanism like eOpposite on EReference in the Ecore meta-model. The improved EMF Facet enables now to manually extend and weave models. As a matter of fact, the serialization makes the improved Facet be the most effective

  Figure 3: Bi-directionnal link example

Figure 4 :Figure 5 :Figure 6 :Figure 7 :

 4567 Figure 4: One to one relation One to many. In Fig. 5, a book is prefaced by many authors and author prefaces one book. To check the multiplicity consistency, the implementation in the FacetManager involves to replace the opposite link during the set operation. 1 @remove o l d r e f e r e n c e from e x i s t i n g s @ 2 &d e l e t e o l d o p p o s i t e r e f e r e n c e& 3 &c r e a t e new o p p o s i t e r e f e r e n c e& 4 @add new r e f e r e n c e i n t o e x i s t i n g @ Book Author 0..1 prefaces prefacedBy 0..*

Listing 1 :

 1 Many-to-one: the direct preface reference f i n a l F a c e t R e f e r e n c e p r e f a c e = g e t F a c e t R e f ( MANY_TO_ONE, WRITER_EXT , PREFACE ) ; f i n a l Book b o o k 1 w r i t e r 1 = t h i s . f a c e t M g r . g e t O r I n v o k e ( w r i t e r 1 , p r e f a c e , Book . c l a s s ) ; A s s e r t . a s s e r t E q u a l s ( msg ( WRITER1 , BOOK1) , book1 , b o o k 1 w r i t e r 1 ) ; f i n a l Book b o o k 1 w r i t e r 2 = t h i s . f a c e t M g r . g e t O r I n v o k e ( w r i t e r 2 , p r e f a c e , Book . c l a s s ) ; A s s e r t . a s s e r t E q u a l s ( msg ( WRITER2 , BOOK1) , book1 , b o o k 1 w r i t e r 2 ) ; Example 2 The test case of Listing 2 checks the automatic setting of the opposite reference prefaced: book1 is prefaced by writer1 and writer2. Its execution also led to success. Listing 2: Many-to-one opposite: the prefaced reference f i n a l F a c e t R e f e r e n c e p r e f a c e d = g e t F a c e t R e f ( MANY_TO_ONE, BOOK_EXT, PREFACED) ; f i n a l L i s t < W r i t e r > w r i t e r s 1 a n d 2 = t h i s . f a c e t M g r . g e t O r I n v o k e M u l t i V a l u e d ( book1 , p r e f a c e d , W r i t e r

Figure 8 :

 8 Figure 8: FacetSet definition example

Figure 9 :

 9 Figure 9: Editing multi-valued reference

Figure 10 :

 10 Figure 10: Editing monovalued references

Figure 11 :

 11 Figure 11: Model weaver editor Example In the right part of Fig. 11, an outline displays the different models to weave i.e. a first model 'library with books' and a second model 'library with writer'. On the left, a specific view organizes the weaving result by facets. The MToM facet is loaded.This design allows us to drag and drop elements from right to left to link elements by references corresponding to the FacetSet definition. In this example, we drag and drop two writers "Ray Bradbury" and "George Orwell" on reference prefaced of the book "Fahrenheit 451".

Figure 12 :

 12 Figure 12: OCL Facet Console Example In Fig. 12, we use the previous model weaving result. We open the model 'library with books' and write an OCL query on the console to know all writers who have prefaced the books: "self.books.prefaced". The query writing is helped with autocomplete, and result appear on the console. Most presented tools and wizards have been integrated in the EMF Facet opensource project. They are free available for installation on Eclipse IDE.

http://eclipse.org/atl/

https://bugs.eclipse.org/bugs/show_bug. cgi?id=463898


Emilien Bourdy
email: emilien.bourdy@univ-reims.fr

Kandaraj Piamrat

| Michel Herbin

Big Data: an incoming challenge for VANET

Keywords: Big Data, VANET, ITS, IoT

Big Data is gaining lots of attentions from various research communities as massive data are becoming real issues and processing such data is now possible thanks to available high-computation capacity of today's equipment. This paper focuses especially on the network community where currently huge amount of data have to be processed in real time. In the meanwhile, it is also the beginning of Vehicular Adhoc Networking (VANET) era. Connected vehicles are being manufactured and will become an important part of vehicle market. Topology in this type of network is in constant evolution, accompanied by massive data coming from increasing volume of connected vehicles in the network. Therefore, we will handle this interesting topic by providing backgrounds on Big Data concerning vehicular networks. Key aspects of Big Data related to VANET (e.g., data generation, preprocessing, and communication) are discussed. Finally, we will also illustrate how to analyze data in VANET by presenting a real experimentation and some representatives results.

INTRODUCTION

Over the past few years, the volume of data has increased tremendously and in a very large scale within various domains, particularly, in vehicles with cooperative Intelligent Transport Systems (ITS). We can observe the emergence of connected vehicles, which will generate a huge amount of data in the near future [START_REF] Yunshu | Traffic big data analysis supporting vehicular network access recommendation[END_REF][START_REF] Cheng Nan | Big data driven vehicular networks[END_REF] . In Vehicular Ad-hoc Network (VANET), the data are generated either from the infrastructure by the Road Side Unit (RSU), or from the vehicles themselves by the On-Board Unit (OBU). RSU and OBU communicate with each other using IEEE 802.11p (vehicular Wi-Fi) [START_REF]IEEE Draft Standard for Information Technology -Telecommunications and Information Exchange Between Systems -Local and Metropolitan Area Networks -Specific Requirements -Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications[END_REF] standard, which is called ITS-G5 in Europe. With a prediction of 35 % of market share among vehicles that will be marketed in 2022 along with revenues of around 113 billion euros [START_REF]Reportlinker . Connected Car Market by Hardware, Application, and Geography -Global Forecast to[END_REF][START_REF] Viereckl | The bright future of connected cars[END_REF] , ITS data volume is becoming massive in such a way that they can generally be considered as Big Data [START_REF] Sharief | Big Sensed Data: Evolution, Challenges, and a Progressive Framework[END_REF][START_REF] Juan | Solving vehicular ad hoc network challenges with big data solutions[END_REF] , and they will have the same problematic. The VANET topology is a highly dynamic topology, which is an important challenge for the underlying mechanisms and algorithms, and so, classical algorithms may be inefficient.

Big Data comes generally with the 4 V (see the Fig. 1 ): Variety, Velocity, Veracity and Volume [START_REF] Min | Big data: a survey[END_REF] . Variety means that generated data change from a station to another. Velocity means that data from a station change quickly. Veracity means that data are coherent with the situation (for example, a vehicle located in Paris will not indicate in position in New York). Finally, Volume means that there is a huge amount of data, so they cannot be analyzed by classical computers. Big Data analysis consists in four main steps 8 : 1-data generation (section 2); 2-preprocessing to reduce volume of data (section 3); 3-communication of the data to the infrastructure (section 4); 4-storage and analysis in the data-centers, which is not the focus in this paper, because it is very closed to "classical" Big Data mechanisms. We will also illustrate a practical example of using Big Data methodology with VANET in section 5 before concluding in section 6.

DATA GENERATION

In general, there are two types of data in VANET: message data and sensor data (e.g. raw data from camera, radar, etc.). Message data are deployed for cooperative driving and sensor data for helping driver to make a decision or to analyze driver's behavior within a specific situation. If a vehicle hidden by an obstacle makes an emergency braking, the other vehicles will receive the event (through a Decentralized Environmental Notification Message (DENM 9 )) sent by the braking vehicle and brake automatically before the driver reacts. Message data also enable drivers to be aware of invisible information like transportation of specific objects (i.e. eggs, animals, baby, etc.). A simple example is a truck carrying dangerous goods that will be displayed in Cooperative Awareness Messages (CAM [START_REF]Intelligent Transport Systems (ITS); Vehicular Communications[END_REF] , an application layer beacon used to inform neighbors about the ITS station (ITS-S) position). These are few examples among so many others of VANET's usage. Automatic Breaking System (ABS) and cruise control are examples of driving assistance systems that can be activated in this situation. As for sensor data, if a vehicle knows that the road is slippery by noticing that the braking is not efficient or by observing high level of humidity, this vehicle can send a message to other vehicles that do not have this information, and hence all the neighborhoods can be aware of the situation.

Data generation from VANET according to the 4V is summarized in Fig. 2 : 1. Variety: Different ITS manufacturers implement standard in different ways (e.g. some manufacturers have access to CAN bus and some others not).

2. Velocity: With a frequency of 1 to 10 messages per second for each vehicle, and for each message, some data will change from one message to another like position, timestamp, speed, etc.

3. Veracity: All data need to be coherent with the situation to have an efficient cooperative ITS.

4.

Volume: With all data generated from messages and sensors and the number of vehicles, data volume increases fast. In [START_REF]Reportlinker . Connected Car Market by Hardware, Application, and Geography -Global Forecast to[END_REF][START_REF] Viereckl | The bright future of connected cars[END_REF] , the authors predict that connected vehicle market will grow from 5 million units to 35 % of the vehicle marketed in 2022 along with revenues of around 113 billion euros.

PREPROCESSING

• Message preprocessing According to CAM and DENM Structure, there are two ways that we can eliminate redundancy and make aggregations of data. First, we can make an aggregation of CAM data by creating a lighter CAM that contains only average data (speed, heading, acceleration, length, etc.) in a given observation interval and transmit only these aggregated data to the Big Data platforms. Second, we can also select the predefined useful data in the DENM, because some information (e.g. height and position of the longitudinal career left or right) are here to improve the impact reduction in pre-and post-crash use cases, but are a priori not useful for other statistics.

• Sensor preprocessing

In order to prevent Internet of Things (IoT), where many sensors are deployed, from sending useless data or too much amount of data too often, the preprocessing can be set to send data only when significant changes occur. For example, a temperature sensor does not need to communicate engine temperature every second, but only when it is hot enough to become dangerous or to prove that it is still alive. If the sensor does not send other data than "alive data", it means that data has not evolved. Some types of thresholds can be set for this.

• Implicit preprocessing Fortunately, some preprocessing is implicitly made by many duplicate packet detection (DPD) [START_REF]Intelligent Transport Systems (ITS); Vehicular Communications[END_REF] in the VANET communication protocol: if a message is sent many times by multi-hop broadcasting or by sending many times the same event when new neighbors appears, the protocol discards these messages, and reduce the redundancy. The preprocessing is necessary when we have a huge volume of data. For the moment, there is not enough OBU, RSU, and sensors deployed; however, it will be necessary in very near future.

The preprocessing raises issues in Volume and Veracity.

COMMUNICATION

Fig. 3 presents the communication in VANET, there exist two types of communications called commonly V2X (Vehicle to X): 1) Vehicle to Vehicle (V2V): data are sent from a vehicle to another in order to cooperate or to warn of an event; 2) Vehicle to Infrastructure (V2I/I2V): data are sent from a vehicle to an RSU (and vice-versa) in order to share information about events. The V2X communication operates using the ITS-G5 (the vehicular Wi-Fi) technology, but if needed, the cellular network can be used to ensure the QoS. On backbone, we use classical wired communication between the different architectural elements. According to [START_REF] Xiaomeng | Building a network highway for big data: architecture and challenges[END_REF] , there are three layers in a Big Data network architecture:

1. Access Network. It is the user side, where devices communicate with the network infrastructure. This is our V2X communication.

Internet Backbone.

It is where data is transported to the data center. This means the communications from RSU to data center (and vice versa).

Inter-and intra-data center network.

It is the network formed by the data centers. Intra-data center when data center is inside the same building or farms and inter-data center when data center is situated in different locations.

FIGURE 3 VANET communication

In VANET communication, there are two types of dissemination: single and multi-hop. Single hop is used to communicate with the neighborhood and multi-hop to send messages to a destination (can be both an ITS-S or a geographic area) [START_REF]Intelligent Transport Systems (ITS); Vehicular Communications[END_REF] . Because of multi-hop mechanism, ITS-S will forward these messages, and they can become redundant for the observer. As mentioned before in 3, this known messages of an ITS-S will not be forwarded again thanks to the implicit preprocessing. For example, in Fig. 3 , the left car sends a message, the right car forwards it to the left car and infrastructure, and the left car will not forward the message again, because it already knows this message.

When we use multi-hop dissemination, the denser is the traffic, faster is the dissemination. In 11 , they send an echo to a predefined distance from a traffic light. Vehicles receive the echo and reply with multi-hop dissemination. Traffic light, then waits for the response. The traffic light will then estimate traffic density with the delay of reply (shorter is the delay, heavier is the traffic density).

Since we are in a cooperative environment, messages sent by ITS-Ss are not encrypted. However, this raises security challenges: everyone can be tracked, and everyone can send what he wants. To overcome these problematic, ITS-Ss add their certificate and sign the messages. By doing this, we ensure the authenticity and the integrity of the message. For the privacy, ITS-Ss use pseudonyms which are periodically changed, and so, we can not track them. To perform these security mechanisms, we use a Public Key Infrastructure (PKI) to manage all certificates and pseudonyms of ITS-Ss, and is responsible for the revocation list to blacklist malicious ITS-Ss. However, this security mechanism may triple the size of packet in case of small CAM.

Therefore, VANET communication raises issues in Volume (with the number of interconnections), Veracity (due to privacy, authenticity, and integrity challenges), and Velocity (by the topography).

EXPERIMENTATION OF BIG DATA WITH VANET

From the 23 rd to the 26 th of April 2018, the University of Reims Champagne-Ardenne (URCA), in France, organized the second InterCor TestFest event [START_REF] Yunshu | Traffic big data analysis supporting vehicular network access recommendation[END_REF] . During this week, 18 European brands tested their connected vehicles on a 30 km track inside the city of Reims and on roadway around the city. Unlike the first event in Dordrecht in The Netherlands, the security layer was added. We took advantage of this event to collect real VANET data, and try to extract driver's behavior. To do that, we provided 9 tablets to 9 drivers from the InterCor project. These tablets saved periodically the vehicular position using the GPS.

At the end of the event, we got back the tablets, and preprocessed the data, by selecting 24 strategic points on the track: the dangerous positions where a deceleration is needed. By doing this, we want to see if drivers react in the same way with dangerous situations. For each point, we took the timestamp from the beginning of the track, the vehicle speed, acceleration, and heading from the North. Since the first point is the start of track, its timestamp is set to 0, we did not use it, and we had 24 × 4 -1 = 95 variables per record. With these 9 drivers, we got 303 observations, which correspond to 303 records with the distribution of records per driver as follows (21, 8, 10, 12, 28, 36, 56, 15, 117).

We applied our methodology [START_REF] Bourdy Emilien | New Method for Selecting Exemplars Application to Roadway Experimentation[END_REF] with these data. The methodology uses local density to extract samples from dataset. Samples are representative data, and so, with our methodology, we extract these samples and their neighborhood (i.e. the represented data by this sample). The methodology uses a value, which corresponds to the selected density to compute the samples. the higher is the value of , the fewer we have samples, because they gather more data. In Fig. 4 , we present the resulting sample selection and their neighborhood for a value of 75. Each node shape corresponds to a driver, and each driver has its own color, so we can see that there is no sample which gather other drivers (i.e. drivers have different behaviors). In the Table 1 , we can see different values of . When ⩾ 100, samples gather too many data, and so drivers are mixed together; and with ⩾ 250 some drivers are not represented (0 value in the distribution).

FIGURE 4 Exemplar neighborhood

Number of exemplars

If we can extract driver's behavior, it can raises privacy problem. Because even if the ITS-S changes its pseudonym along the journey, the driver still keeps his behavior, and it would still be possible to track him.

CONCLUSION

In this paper, we have seen that data in VANET can be considered as Big Data because VANET complies with the 4V: Variety, with the number of stations that uses different data; Velocity, with data that evolve quickly; Veracity, to ensure a good cooperative ITS; and Volume, with the number of stations that produces a lot of data. Some Big Data preprocessing mechanisms need to be used to improve data quality and utility.

We have presented a practical example of using Big Data mechanisms with VANET data from an urban and roadway experimentation from the InterCor TestFest event. The methodology made sampling by selecting the exemplars with the highest neighborhood. With this experimentation, we can retrieve driver behavior, and we can see that they are well distinguished. This raises the questions on privacy, because even if the ITS-S pseudonym can evolve during the time, the driver's behavior will not, and so they are still potentially traceable. Finally, we can conclude that autonomous car needs VANET in addition to their sensors, and Big Data mechanisms are needed to extract common or specific pattern of behavior in order to improve car's predictions and recommendations.

Apart from data analysis, there are also many challenging issues raised from VANET. In order to provide a good solution, issues such as regulations and law need to be addressed along with VANET evolution to ensure anonymity and responsibility for each usage. Anonymity concerns essentially Europe where there is a strong culture of privacy. Each country has its own definition and ways to ensure privacy and so they will need a consortium to decide these issues together.

Sampling distribution Class mixing

	40
	50	57	(7, 7, 5, 2, 4, 9, 7, 3, 13)	No
	75
	100	53	(7, 6, 5, 2, 4, 9, 7, 2, 11)
	150 200	44 41	(7, 6, 4, 2, 3, 7, 5, 1, 9) (5, 6, 4, 1, 3, 6, 5, 1, 10)	Yes
	250	35	(7, 4, 3, 0, 3, 6, 4, 2, 6)

TABLE 1

 1 Different values of with Intercor TestFest data

The InterCor project is funded by the European Commission: https://intercor-project.eu/

ACKNOWLEDGMENTS

This work was made possible by EC Grant No. INEA/CEF/TRAN/A2014/1042281 from the INEA Agency for the SCOOP project. The statements made herein are solely the responsibility of the authors.


Paolo Arosio

Julie Thevenot

Tomas Orlando

Francesco Orsini

Maurizio Corti

Manuel Mariani

Lorenzo Bordonali

Claudia Innocenti

Claudio Sangregorio

Hugo De Oliveira

Paulo Arosio

Julie Thévenot

Hugo Oliveira

Sébastien Lecommandoux

Alessandro Lascialfari

Olivier Sandre

Hybrid iron

Introduction

The recent years have seen the development of the so-called "theranostic" concept, which purpose is the integration in the same medical device of diagnostic tools and therapeutic agents.

More particularly, there was a recent focus on the development of multifunctional nanoparticle drug delivery systems (DDS) that simultaneously serve as medical imaging contrast agents. 1 Indeed, medical imaging techniques play a key role in clinical diagnostics as they provide spatially resolved anatomical information on diseases. All the current imaging methods (CT, PET/SPECT, MRI, ultrasound imaging, optical imaging) have their advantages and drawbacks/limits, but, among these technologies, MRI stands as one of the best non-invasive imaging modalities thanks to its good spatial and temporal resolution. [2][3][4] Magnetic nanoparticles (NPs) have drawn a lot of attention in the biomedicine field 5 due to their unique magnetic properties and the possibility to functionalize their surface. 6,7 Thank to their small crystal size, they are magnetic mono-domains with either superparamagnetic or ferromagnetic behavior. Iron oxide crystals 45 (magnetite or maghemite) have been the most extensively studied for biomedical applications. 8 Magnetic iron oxide phases (ferrites) show a superparamagnetic behaviour for crystal sizes smaller than e.g. 12 nm (called ultrasmall superparamagnetic iron oxide, USPIO): below this limit, the magnetic moments are free 50 to rotate within the crystal core (Néel's relaxation), so that they do not retain any remanent magnetization in the absence of external magnetic field. When a magnetic field is applied, the propensity of superparamagnetic particles to align their magnetic moment in the direction of the field (i.e. their large magnetic 55 susceptibility) is at the origin of their use as MRI contrast agents. [START_REF] Laurent | NMR-MRI, mSR and Mossbauer Spectroscopies in Molecular Magnets[END_REF][START_REF] Lascialfari | NMR-MRI,mSR and Mossbauer Spectroscopies in Molecular Magnets[END_REF] Their magnetic dipoles (arising from electronic spins) interact strongly with the nuclear moments of water protons, enhancing the relaxation rates in tissues where the NPs have accumulated. They predominantly reduce the transverse 60 relaxation time, T 2 (or T 2 * in presence of field heterogeneity), of protons, locally lowering the MRI signal intensity as compared to the background signal. Therefore superparamagnetic NPs are most often used as "negative" contrast agents (CAs), in contrast with paramagnetic compounds, used as "positive" CAs, because they increase the signal of the tissues. At the bottom of the size scale, ultra-ultrasmall superparamagnetic iron oxide (UUSPIO) cores with diameters below 5 nm were recently evidenced as positive CAs (for T 1 -weighted MRI sequences) due to their minimal value of r 2 /r 1 as long as clustering is prevented by a repulsive shell. 7,[START_REF] Tromsdorf | [END_REF]12 Usually, to establish a comparison between MRI CAs, their efficiency as contrast enhancers is measured by the longitudinal (respectively transverse) relaxivity r 1 (respectively r 2 ), defined as 10 the relaxation rate R 1 =1/T 1 (respectively R 2 =1/T 2 ) of water protons in presence of CA (minus the value of solvent alone) normalized by the concentration of CA (in equivalent iron mmolL -1 ). Most of the negative MRI CAs developed so far are hybrid nanoparticles (HNPs): 13,14 these superparamagnetic HNPs consist of an inorganic core made of several iron oxide crystals surrounded by an organic shellfrequently Dextranleading to a hydrodynamic diameter larger than 40 nm. 1 Apart from carbohydrate homopolymers adsorbed by weak H-bonds at the iron oxide surface, other polymers were introduced to prepare 20 HNPs by self-assembly with amphiphilic block copolymers into micelles 15 or by electrostatic complexation with charged-neutral double-hydrophilic copolymers into polyion complex micelles. 16 HNPs are of interest for the following reasons: first, the organic part can be loaded with a drug and provide a substrate for (bio)functionalization through a wide range of coupling reactions 17 ; then the clustering effect (when several iron oxide crystals are embedded in the organic matrix) both enhances the value of transverse relaxivity 15,16 and reduces renal clearance as compared to uncoated USPIOs. 8, 18 However, the whole HNP size (including coating) needs to remain below 150-200 nm, because r 2 starts to decrease for diameters larger than this optimum 13 and due to sequestration of bigger particles by the mononuclear phagocyte system (MPS). 18 On the contrary, HNPs with hydrodynamic diameters below 40 nm, made of single USPIOs 35 wrapped by a thin hydrophilic repulsive coating, exhibit rapid renal clearance and thus were introduced mainly for brain tumor studies, when the brain-blood-barrier is damaged. 18 When designing hybrid nanoparticles (HNPs) as efficient MRI CAs from non toxic copolymers loaded with maghemite NPs (γ-Fe 2 O 3 ), numerous parameters remain in the hands of chemists: structure of the cluster, size of the magnetic cores and of the whole objects, composition... The recent concept of theranostic prompted the development of particles with dual functions, acting as both drug delivery system and imaging agent. The numerous nanosized drug delivery systems proposed so far exhibit a variety of morphologies and compositions. One can then wonder if a particular geometry should be preferred in order to obtain high relaxivities: is there any advantage of the micelle geometry (filled sphere) compared to the less commonly used vesicle structure (hollow shell)? Moreover, is it a better option to increase the core sizes of the USPIOs within the HNPs or to use smaller USPIOs that enable higher loadings? Or will intermediate values of both core size and iron content be the best solution?

To address these fundamentally relevant questions, two types of self-assembled structuresrespectively vesicles and micelleswere obtained by a solvent-displacement self-assembly process with two different amphiphilic block copolymers, respectively poly(trimethylene carbonate)-block-poly(L-glutamic acid) (PTMC-b-PGA) and poly(ethylene glycol)-block-poly(γ-benzyl-60 L-glutamate) (PEG-b-PBLG), sharing common features e.g. biocompatibility, biodegradability and presence of one artificially synthesized polypeptide block (respectively PGA and PBLG). In previous studies, we already evidenced the formation of magnetic vesicles with PGA as hydrophilic block, 19 and magnetic micelles 65 with PBLG as hydrophobic block. 20 Measurements of the NMR-Dispersion (NMRD) curves of the nuclear longitudinal (r 1 ) and transverse (r 2 ) relaxivities are reported, from very low frequency to high frequencies used by clinical imagers (21 MHz and 64 MHz). We show here that both types of HNPs behave like 70 efficient "negative" contrast agents increasing the transverse relaxivity of the protons. They offer improved properties as negative CAs (higher r 2 and r 2 /r 1 ) as compared to Endorem ® , a standard superparamagnetic agent composed of maghemite NPs clustered by Dextran yielding aggregates with undefined shapes 75 and diameters ranging from 50 to 100 nm. 21,22 Furthermore, we demonstrate that the (micellar or vesicular) morphology has a lower impact on the transverse relaxivities than the core size of the maghemite crystals to obtain the best relaxometric properties. Following a universal method to derive the transverse relaxivity 80 from the outer size of the HNP, its magnetization, and the internal iron oxide volume fraction, 23 we evidence here that the relevant parameter for r 2 is the number of encapsulated USPIOs normalized by the hydrodynamic diameter, whenever the particle is a micelle or a vesicle. 85

Experimental section

Reference material

Used as reference material, Endorem ® was purchased from Guerbet Group (Roissy, France) and used without further purification. Thermogravimetric analysis (TGA, TA Instruments) 90 yielded an inorganic content of 17.3 gL -1 iron oxide, which is coherent with the iron concentration of 11.2 gL -1 (200 mM Fe ) claimed by the supplier. From the organic content also measured by TGA, iron oxide represents 63.8 wt% of the total solid matter or, equivalently, a volume fraction of 26 % v/v in the composite.

95

Synthesis of iron oxide superparamagnetic nanoparticles

Superparamagnetic maghemite (-Fe 2 O 3 ) nanoparticles were synthesized in water according to Massart's procedure. [START_REF] Massart | Magnetics[END_REF] Details of the process are described in Supporting Info. Briefly, magnetite Fe 3 O 4 nanocrystals (also called ferrous ferrite 100 FeOFe 2 O 3 ) were prepared from an alkaline coprecipitation of a mixture of iron +II and iron +III chloride salts in HCl solution. After acidification, magnetite was oxidized into maghemite by immersion into boiling ferric nitrate solution. The "ionic ferrofluid" obtained, which presents a wide size-dispersity, was 105 treated with a size-sorting procedure based on fractionated phaseseparation. 25 After repeating this process three times (see Fig. SI-1 in Supporting Information), three fractions with well defined sizes were selected for this work. In order to disperse them in a chlorinated solvent, 26 these fractions were coated with Beycostat 110 NB09 (CECA, Arkema group, France), an anionic surfactant composed of a mixture of mono-and di-esters of phosphoric acid with alkylphenol chains containing nine ethoxy groups. 27 The grafting was performed by reacting 200 g of surfactant per mole of iron oxide (around 20 mol%) in acidic conditions (HNO 3 2 molL -1 ) for 30 min at 60 °C under vigorous stirring and subsequent removal of the surfactant in excess by 5 washing cycles with methanol. The obtained precipitates of hydrophobic USPIOs were readily suspended in dichloromethane by simple vortexing, leading to the hydrodynamic diameters measured by dynamic light scattering listed in Table SI-I.

Copolymers synthesis

PEG 45 -b-PBLG 13 was synthesized according to a protocol described elsewhere with minor modifications. 28 Briefly, CH 3 O-10 PEG-NH 2 (2000 gmol -1 , RAPP Polymere, Germany; 2 g, 1 mM) was dissolved in 2 mL dioxane, freeze-dried and dissolved in dry DMF (0.1 gmL -1 ). In a glove-box, γ-benzyl-L-glutamate Ncarboxyanhydride (Isochem, France; 4.20 g, 16 mM) was introduced into a flame-dried Schlenk flask and dissolved in anhydrous DMF (0.1 gmL -1 ). This solution was then added under vacuum to the first flask. The mixture was stirred for 48 hours at 40 °C in an oil bath. The polymerization medium was concentrated by cryo-distillation and the copolymer was recovered by precipitation into cold diethyl ether. The white powdery solid was then washed three times with diethyl ether and finally dried under dynamic vacuum for 24 hours. The degree of polymerization of PBLG measured by proton NMR was DP=13±1, corresponding to a molar mass of 2.8±0.2 kgmol -1 . For some samples, we used a 1:1 mixture of PEG-b-PBLG batches with the same polypeptide block length but differing by the molar mass of the PEG chains (respectively 2000 and 12000 gmol -1 ).

PTMC 25 -b-PGA 12 diblock copolymer was synthesized following a previously published method. 29 Briefly, amino endfunctionalised PTMC (PTMC-NH 2 ) was synthesized by ringopening polymerization (ROP) of TMC (kindly provided as a gift by Labso Chimie Fine, Boerhinger Ingelheim, France) in the presence of diethyl zinc and 3-(Fmoc-amino)-1-propanol as initiator, followed by the deprotection of the amine group with piperidin. PTMC-b-PBLG was then prepared by ROP of γbenzyl-L-glutamate N-carboxyanhydride (Isochem) initiated by the PTMC-NH 2 macroinitiator and subsequently deprotected into PTMC-b-PGA amphiphilic diblock copolymer by catalytic hydrogenation.

Preparation of hybrid (copolymer / iron oxide) particles

Hybrid nanoparticles (HNPs) were obtained by the nanoprecipitation method also called solvent displacement or solvent assisted dispersion. 30 For PTMC-b-PGA hybrid vesicles, Tris buffer 50 mM pH 7.4 (9 mL) was added in 10 seconds under magnetic stirring (500 rpm) at room temperature to a solution in DMSO of PTMCb-PGA block copolymer (10 mg, 1 mL) and the volume of iron oxide NPs in dichloromethane corresponding to the desired feed weight ratio (FWR, defined as the weight of iron oxide divided by the copolymer weight multiplied by 100). The traces of dichloromethane became negligible after rapid evaporation. The buffering at neutral pH was necessary to deprotonate the acid moieties of the PGA blocks and provide them negative charges.

In the case of hybrid PEG-b-PBLG particles, ultrapure water Total solid content of the final particles dispersions was assessed by differential weighting after water evaporation using a 65 TGA Q50 instrument (TA Instruments).

Particles size and morphology

Hybrid particles sizes were measured by Dynamic Light Scattering (DLS) using a NanoZS90 apparatus from Malvern Instruments working at a scattering angle of 90°. The sample was 70 kept at constant temperature (25 °C) during all the experiment. Data acquisition was carried out using the automatic adjustment of position, attenuation and measurement time. Three measurements were averaged to obtain mean values of the Zaverage hydrodynamic diameter (D H ) and polydispersity index 75 (PDI) obtained from the ratio of the 2 nd order cumulant divided by the square of the 1 st order one.

For transmission electron microscopy (TEM), samples were prepared by spraying 1 gL -1 suspensions of particles onto a copper grid (200 mesh coated with carbon or carbon/Formvar™) 80 using a homemade spraying tool. After complete drying, samples were stained with a 1% w/v uranyl acetate solution in water. Bright field images were recorded at the Bordeaux Imaging Center (Bordeaux University) on a Hitachi H7650 microscope working at 80 kV and equipped with a GATAN ORIUS SC1100 85 11 Megapixel camera.

Iron content determination

Total iron titration providing the value of equivalent ferric ions concentration, C Fe , is of uppermost importance for precise relaxometric studies. An absorption spectrum curve between 200 90 and 800 nm wavelengths calibrated by atomic emission spectroscopy was used for rapid and non invasive assessment of iron oxide amount in HNP suspensions (Figure SI-4). To avoid uncertainty coming from a turbidity baseline in case of large size HNPs inducing a strong light scattering effect, a more precise 95 measurement involving the total disruption of the USPIOs and their conversion into dissolved Fe 3+ ions was used. Each aliquot was incubated for 1 hour at 60 °C in HCl 5 molL -1 in a capped vial, and the iron hexachloride complex concentration C Fe =[FeCl 6

3-] was read using Beer-Lambert's law at the 100 wavelength of the absorption peak of FeCl 6 3-(350 nm). According to calibration with a series of ferric chloride solutions in HCl 5 molL -1 (Figure SI-5), the extinction coefficient of the

ferric chloride complex is  350nm =2800 mol -1 Lcm -1 .
Zero-Field-Cooled /Field-Cooled magnetization curves 105 ZFC and FC curves were measured with a SQUID magnetometer (Quantum Design Ltd.) upon increasing temperature, with a 5 mT probing field. Before each procedure, samples were cooled with (FC) or without (ZFC) applying the probe field. The blocking temperatures, T B , identified at the maxima of the ZFC curves, are

NMR relaxometric measurements

The 1 H NMR relaxometry characterization (NMR-dispersion profile) was performed at physiological (37 °C) and room temperature by measuring the longitudinal and the transverse nuclear relaxation times T 1 and T 2 , in the frequency range 10 kHz-60 MHz. It should be noticed that the measurements at room and physiological temperatures gave the same results within 10 % (corresponding to viscosity change of water). The range of frequency  has been chosen in order to cover the most used clinical fields i.e. 0.2 T (8.5 MHz), 0.5 T (21 MHz) and 1.5 T (64 MHz) and to study the mechanisms that lead to the nuclear relaxation, through the analysis of curves of r 1 and r 2 vs.  (NMRD profiles). The NMR signal detection and generation was obtained with a Smartracer ® Fast-Field-Cycling relaxometer (Stelar, Mede, Italy) for 10 kHz-10 MHz, 31 and with a Stelar Spinmaster spectrometer for >10 MHz. In the second case, standard radio frequency excitation sequences were used for T 1 and T 2 measurements, respectively inversion-recovery and Carr Purcell Meiboom Gill (CPMG).

To determine the efficiency of MRI contrast agents, we calculated the longitudinal (r 1 ) and transverse (r 2 ) nuclear relaxivities. The relaxivity values are defined as the increase of relaxation rates of the solvent induced by 1 mmolL -1 of iron:

    C T T r Fe dia meas i i i 1 1 _  (Eq.1)
where is the relaxation rate measured for a sample of iron concentration C Fe (mmol•L -1 ) (see Table 1 andTable 2), and represents the diamagnetic contribution to nuclear 30 relaxation rate of the host solution (≈4 s -1 for ultrapure water).

Results and Discussion

Hybrid particles characterization

Self-assembly of both copolymers with iron oxide nanoparticles 35 led to stable, well defined, colloidal dispersions in water-based media for the feed weigh ratios (FWRs) indicated in Tables 1 and2. By carefully choosing the nanoprecipitation parameters, 29 vesicles of comparable sizes (measured by DLS) were prepared with PTMC 25 -b-PGA 12 and various amounts of the three USPIOs 40 sizes available (Table 1). By the same nanoprecipitation process, PEG 45 -b-PBLG 13 formed micelle-like aggregates with the USPIOs (Table 2). These two geometries are depicted schematically on Figure 1. Clearly, for PTMC-b-PGA, the amount of USPIO has no impact on the size of the vesicles (as 45 showed in Figure 2 for vesicles loaded with 6-7 nm USPIOs), since the preparation process mainly governs this characteristic. 30 Indeed, for iron oxide contents ranging from 5 to 70 %, the mean size and polydispersity indexes of the vesicles are very similar, with narrow and symmetrical size distributions typical of fairly 50 monodisperse samples. The hydrodynamic diameters and PDI values in a cell culture medium supplemented with up to 10% foetal bovine serum do not change significantly compared to pure water (in vitro stability and toxicity assays on M10-20 and V10-20 will be published in a forthcoming paper). Thus we can infer a Measured iron contents in the hybrid particles are in good agreement with the targeted FWR. Moreover, both methodsfit of the whole absorption spectrum of intact HNPs or colorimetric assay of the FeCl 6 3-complex content after acidic degradationgave similar results within their respective experimental uncertainties (Table 1 and 2, columns 3 and 4). and filled micelle with a hydrophobic (orange) and magnetic core surrounded by a hydrophilic shell (blue) (b). For both geometries, we took into account the theoretical factor between the hydrodynamic and the physical sizes (respectively 1 for a spherical shell and 0.775 for a filled sphere. 32,[START_REF] Burchard | Light Scattering from Polymers[END_REF] At low FWRs, USPIOs do not cover the entire surface of the vesicles (Figure 3.b & 4.a) and form patches, letting some areas uncovered. 30 Looking at the TEM images of much highly loaded vesicles (Figure 4.b), it appears that the maximum FWR accessible before the destabilization of the system, 70 wt%, also corresponds to the saturation of the membrane where USPIOs are closely packed, leaving very few uncovered areas. 35 Moreover, as observed by Hickey et al., [START_REF] Hickey | [END_REF] the incorporation of USPIOs in the membrane of polymersomes visibly affects their mechanical properties. Indeed, at low FWR, vesicles are spread on the TEM grid and their apparent diameter is higher than the diameter measured by DLS. On the contrary, at high FWR like in 40 the V6-70 sample, nanoparticles rigidify the vesicles that maintain their spherical shape (Figure 4). The number of USPIOs per hybrid particle can be estimated roughly using the following formulas involving experimental parameters such as the hydrodynamic diameter of the hybrids, D H , membrane thickness,  mb (vesicles only), FWR, and using simple geometric considerations (Figure 1):

5 Φ V N D HNP O Fe USPI O m b 2 H USPI O / ve s i c l e 3 2     
for vesicles 30 (Eq.2a) where  Fe2O3 =5 gcm -3 and  copo are the mass densities of respectively the inorganic and the organic parts of the HNPs, with  copo =1 gcm -3 for PTMC-b-PGA and  copo =1.28 gcm -3 for PEGb-PBLG (density of PBLG reported in literature, 35 which appears to be close to that of PEG). Values calculated from Eq. 2a and Eq. 2b are reported in Table 1 and Table 2, respectively. These estimates are pretty well correlated with the approximate numbers of USPIOs per object counted on the TEM pictures. T 1 and T 2 relaxivities of hybrid copolymer/iron oxide particles 30 In the following, we aim at separating the intrinsic relaxometric properties of the USPIOs (due to their weight-average core size d w and specific magnetization m spe as individual nanoparticles) and the benefits of their self-assembly into a cluster of given geometry, either as filled micelles or hollow vesicles. The 35 USPIO's size is indeed directly correlated to relaxometric properties for two reasons. At first, the saturation magnetization of USPIOshence their T 1 and T 2 reducing capabilitydecreases for lower sizes due to the increase of surface-to-volume ratio that reduces the number of atoms that contribute efficiently 40 to the particle's magnetic moment. 36 In other words, smaller USPIOs are less magnetized (in terms of volume magnetization, that is nothing else than the volume concentration of Bohr magnetons within the magnetic core), a tendency appearing clearly on the data reported in Supporting Information for the 45 three USPIO batches (Table SI-I). The second reason is related to the so-called "outer sphere" mechanism that is invoked generally to explain the higher relaxation rates of protons in the vicinity of superparamagnetic CAs. 13,14,[37][38][39] In this theory also called "Motional Averaging Regime" (MAR), the relaxation rates 50 R i =1/T i (i=1,2) are estimated from the fluctuations of the interaction between the magnetic particle considered as static and the water protons exploring by translational diffusion all the values of dipolar magnetic field lines around the particle. The parameters are the volume magnetization M v and the sphere 55 radius R NMR , which is the minimal approach distance between the protons and the centre of the particle, thus R NMR includes the thickness of any organic coating impermeable to water around the particle. A characteristic time  D of proton diffusion is defined as  D =(R NMR ) 2 /D where D stands for the diffusion constant of the 60 water molecules (that slightly varies with temperature). As long as the particle keeps a high value of M v , increasing the outer shell radius R NMR leads to a longer time during which the proton interacts with the magnetic field created by the particles. Larger USPIOs should thus bring higher intrinsic relaxometric properties 65 to the hybrid particle. But from the previous structural study by TEM and DLS, the maximal FWRhence the number of USPIOs per object that can be accommodated within the selfassembled polymer structuresis much lower for larger magnetic cores: 20 wt% for 10-15 nm USPIOs vs. 70 wt% for 6- The shape of these r 1 () NMRD curves can be explained through the known mechanisms of nuclear longitudinal relaxation in superparamagnetic particles. 13,14 The first mechanism is the Néel relaxation, dominating at low frequency, induced by the reversal of the magnetic moments through the anisotropy energy barrier. The second mechanism is the Curie relaxation that predominates at higher frequencies and is ascribed to the progressive orientation of the magnetic moments with an increasing field. According to the Langevin's law, this mechanism is a function of the USPIOs' parameters d w and m spe and temperature. While Néel's mechanism flattens the curve at low frequencies, Curie's mechanism is responsible for the maximum of r 1 () at higher frequencies. In particular, the inflection point at the right foot of the peak corresponds to  D ≈1. 20 Consequently, the shift of the peak from 8 MHz for the 6-7 nm USPIOs to 2 MHz for the 10-15 nm USPIOs is directly explained by the two-fold size increase of the USPIOs' size (thus, respective values of  D varying in a ratio 4).

  Φ V N D HNP O Fe US P I O
From a theoretical point of view, the longitudinal relaxivity can be written following a heuristic model first introduced by Roch et al [37][38][39] . The fitting expression of r 1 () reported by Laurent et al. in a popular review paper 14 (Eq. ( 27)) has been obtained by linear combination of the cases of null and infinite anisotropy and by approximation of a more complete model, which starts from the spin Hamiltonian, [37][38][39] but is not applicable to particles with diameters higher than 5-6 nm due to un-practicable calculation times.

With the above heuristic model, we fitted only the data of micelle-like samples, as the vesicular samples would require a more refined model due to the particular shell topology of the magnetic part. The simulated curves reported on Figure 5 fit relatively well the experimental NMRD profiles. It should be noted that the values of specific saturation magnetization m spe resulting from the fits are very close to those measured by 40 magnetometry. From the fitting parameters of Figure 5, we see that the value  Néel =17 ns deduced for the 6-7 nm USPIOs is typical of superparamagnetic iron oxide. The value  Néel =47 ns for the 10-15 nm USPIOs is obviously underestimated, since the dispersion of r 1 () at low frequency is too weak to get a reliable 45 estimate of  Néel from the fit. 14 Another interpretation could be the strong inter-USPIO interactions in these dense micelles, lowering  Néel compared to individual USPIOs. Concerning the distance of minimum approach of the protons, the values of 2R NMR (11.2 and 20.1 nm, respectively) are larger than the weight-average 50 diameters of the magnetic cores d w (7.5 and 14.8 nm, respectively), but remain well below the hydrodynamic diameter D H values (Table 2). The diffusion constant of water molecules obtained from the fits are twice as lower than the value in the bulk, D(H 2 O)=310 -9 m 2 s -1 at 37 °C. The decrease of D(H 2 O) 55 can be ascribed simply to an excluded-volume effect experienced by water molecules near hydrophobic solutes, 40,41 which hampers the rotations around hydrogen bonds and divides the water diffusion constant by a geometrical factor around 2. Therefore we can conclude that the PBLG cores of the micelles are partially 60 penetrated by water molecules. For transverse relaxivities, the analysis of 1/T 2 data for the PTMC-b-PGA vesicles was complicated by a non mono-In fact, for all these samples, at frequencies higher than 10 MHz, two exponentials contribute to the total transverse nuclear magnetization, corresponding to two different nuclear transverse relaxation times T 2 . The fast T 2 relaxation mode might be ascribed to the contribution of water protons located inside the vesicles, whose diffusion is confined and which are thus maintained at close vicinity of the USPIOs. Since the shortest T 2 was always lower than 1-3 ms and considering that in standard clinical MRI T 2 -weighted sequences the parameters which affect the signal intensity (repetition time, TR, and echo time, TE) are usually longer than 1-5 ms, we reported on Figure 6 only the relaxivity r 2 () values obtained from the slow T 2 relaxation time, which ranges from 4 to 60 ms.

Based on the measured T 1 and the long T 2 components at the clinical relevant frequency =60 MHz (Table 3), micellar and vesicular samples loaded approximately at the same FWR with USPIOs are saturated at more than 95%, whereas the magnetization of 6-7 nm USPIOs continue to grow, according to Langevin's superparamagnetism law. Since the transverse relaxivity is related to the square of particles' magnetization, 13,14 the saturation of r 2 for M8 and M10 micelles and on the contrary the continuous increase with the M6 ones are totally understood by the differences of USPIOs' size and thus of the saturation degree of the magnetic moments in the corresponding magnetic field values. But in the case of vesicles (Figure 6), the Langevin's function alone cannot explain the r 2 () profiles, since r 2 continue to grow for ≥10 MHz (B≥0.24 T) not only for V6 vesicles but also for the V8 and V10 ones, whereas 8-10 nm and 10-15 nm USPIOs are saturated at more than 85% at these fields. A plausible explanation for this discrepancy between NMRD profiles of micelles and vesicles could be that, unlike micelles, magnetic shells become anisotropic in strong magnetic fields.

15

Neutron scattering experiments reported on similar magnetic polymersomes showed that they deform indeed under magnetic field intensities as low as B=0.1 T (=4 MHz). 19,30 Thus the continuous increase of the r 2 () profiles could be due to the combined effects of the magnetization curve of the USPIOs (until 20 the total moment saturates) and the deformation of the vesicles that continue to deform further under increasing fields. The non spherical symmetry of such soft magnetic shells under a strong applied magnetic field should be deeper investigated in order to try fitting the full experimental T 2 NMRD profiles, but this is 25 beyond the scope of the present article. a The intra-aggregate volume fraction occupied by -Fe2O3 was used to calculate the magnetization over the whole object (micelle or vesicle) and to estimate the transverse relaxivity limit at high frequency within the frame of the MAR model of protons spins relaxation. Other values come from Monte-Carlo simulations of the static dephasing regime (SDR) for peculiar values of Mv. 23,42 On the structural point of view, there is a discrepancy between micelles and vesicles in term of the "intra-aggregate" volume fraction occupied by iron oxide inside the HNP. 23 For the micelles which internal core is filled with USPIOs and only marginally permeable to water, the volume fraction is identical to the dry volume fraction given by Eq. ( 3) and measurable by TGA. For vesicles, the internal aqueous core needs to be taken into account to estimate the effective intra-aggregate volume fraction through  intra = HNP 6 mb /D H , as reported on column two of Table 3. Effective intra-aggregate volume fractions  intra are needed in order to calculate the volume magnetizations at saturation M v = intra m spe of the entire HNPs. From these values, it is possible to compute estimated r 2 of the HNPs using either the MAR theory (for D H values sufficiently small) or Monte-Carlo simulations when the size of the clusters falls in the validity range of another model called "static dephasing regime" (SDR), as shown in recent studies intended to rationalize the comparison between experimental r 2 values and the available models. 23,42 In practical, for the smaller HNPs we used the scaling law 50 r 2  intra 11.610 -12 (M v D H ) 2 , characteristic of the MAR model. 23 For micelles or vesicles that are either larger or more magnetic, we used the results of Monte Carlo simulations made in the closest range of magnetization M v (see Figure 2 in Vuong et al. 23 ); this method yields less precise results since simulations of 55 the curve r 2  intra vs. D H are needed for each value of M v. Nevertheless, the models (either MAR or SDR) lead to estimates of the high field transverse relaxivity that are reasonably close to the experimental data (Table 3), a plausible source of discrepancy being the size-dispersity, suggesting the possibility in future of 60 improved theory-experiment matching by size grading. Another indicator of the relaxometric properties of a given MRI CA is the ratio between the transverse and longitudinal relaxation rates (r 2 /r 1 ), being 1-2 in the case of traditional paramagnetic (positive) CAs and above 10 and up to 50 or more in the case of 65 superparamagnetic (negative) CAs. 13 Negative CAs with a high r 2 /r 1 ratio enable obtaining contrasted images of tissues with T 2 -weighted or T 2 *-weighted MRI sequences. For both particle types, the measured r 2 /r 1 ratios (Table 3, column 6) are always greater than 10, which is the value obtained for the negative contrast agent Endorem ® . Two samples (V10-20, M10-20) elicit remarkably higher values, one order of magnitude above the Endorem ® , suggesting an optimal configuration with the most strongly magnetized 10-15 nm USPIOs confined inside a hydrophobic environment that is either a spherical core for PEGb-PBLG micelles or between the leaflets of a bilayer membrane for PTMC-b-PGA vesicles. Then, we tentatively aimed at deciphering which one from USPIOs' size, FWR, and micelle vs. vesicle geometry is the most influent parameter to increase the transverse relaxivity. We show here that for a given size of iron oxide core, the value of r 2 at 20 60 MHz depends mainly on the number of encapsulated USPIOs (N USPIO ) divided by the diameter of the cluster (D H ). This parameter is relevant not only for relaxometry, but also to estimate the magnetophoretic velocity of magnetic objects in a field gradient. 30 In this case, the morphology (vesicle or micelle) 25 has only a minor influence, as shown on Figure 7 where r 2 is plotted vs. N USPIO /D H , for both types of samples. A linear relation is observed for each size of USPIOs, independently of the HNP morphology (all the data overlap in case of the 8-10 nm USPIOs). Indeed, the parameter that has the greatest influence on the final 30 relaxivity of the assemblies is the individual size of the USPIOs. For a given USPIOs' size range, r 2 is related to both the external diameter D H and the intra-aggregate volume fraction of magnetic material  intra , which variation law with the FWR used during the synthesis is dependent upon the geometry. But when designing magnetic clusters, an easy thumb-rule for the chemists consists in increasing the number of USPIO per cluster (N USPIO ) while keeping the size of the assemblies (D H ) at a moderate value.

From these results, one can conclude that the structure of the cluster (micelle or vesicle) has a weak influence on transverse relaxivity, mostly by the shape of the profile of r 2 vs. field or frequency as measured by fast-field cycling. Nevertheless, it was observed in a recent study that the morphology can impact the relaxivity of magnetic assemblies, a particular magneto-core/shell structure exhibiting a higher relaxivity than usual dense magneto-45 micelles and magneto-polymersomes. [START_REF] Hickey | [END_REF] The authors ascribed this phenomenon to a better water accessibility when USPIOs are located at intermediate distance in the corona of the micelles rather than distributed homogeneously or buried deeply in the hydrophobic cores, where they are separated from water protons 50 by a thick shell of hydrophobic polymer, in that case polystyrene.

This particular magneto-core/shell morphology was obtained neither with PTMC-b-PGA nor with PEG-b-PBLG copolymers, presumably due to a better compatibility between the hydrophobic blocks and the surfactant-coated USPIOs (thus no 55 micro-phase separation in the assemblies). For both morphologies tested in this work (magnetic micelles or magnetic vesicles), the thickness of the non-magnetic layer covering the USPIOs was small enough or sufficiently permeable to water molecules. Thus all magnetic material introduced in the particles efficiently 60 contributes to the measured r 2 . Our results are also in accordance with recent Monte-Carlo simulations of dense spherical micelles and hollow shells of USPIOs' clusters, leading to comparable r 2 values for both geometries. 42 In this study, the sample maximizing the transverse relaxivity 65 was the batch of M10-20 micelles prepared at high feed weight ratio (20 wt%) from the biggest USPIOs (10-15 nm) clustered in a compact fashion (meaning maximizing the ratio N USPIO /D H ).

With r 2 =500 s -1 mM Fe -1 , they compete with the samples from the class of magnetic hydrogels, the best T 2 contrast agents reported 70 so far, whose superior transverse relaxivity was ascribed to their hydrophilic nature. 43,44 The world record of transverse relaxivity for iron oxide HNPs was recently pushed higher, slightly above 600 s -1 mM Fe -1 at 1.4 T for a series of USPIOs clusters chemically cross-linked by hydrophilic polymer chains. 45 Nevertheless, this 75 type of high r 2 magnetic hydrogels follows the MAR model: the universal scaling law 23 r 2 (m spe D H ) 2 • intra is still valid, but the volume fraction of iron oxide in the composite measured by TGA (thus in dry state) must be normalized by the swelling ratio of the hydrogel (volume of water divided by volume of solid matter), 80 which is totally equivalent to the interpretation by the authors that the surface-to-surface separation between the USPIOs within the clusters is enlarged, giving space for the entry of water. 45

Conclusions

A series of hybrid particles was prepared by self-assembly of 85 amphiphilic copolymers and various amounts of hydrophobically coated USPIOs of three different sizes. Depending on the copolymer used, these HNPs exhibited either a micellar or vesicular morphology. Their relaxometric properties were studied on a large frequency range and results compared to theoretical 90 models. Focusing on geometrical and composition parameters, we found that, for a given USPIO's size, for a fixed frequency (60 MHz) and for the spherical geometry, r 2 varies linearly with the ratio of the number of embedded USPIOs over the hydrodynamic diameter of the cluster (r 2  N USPIO /D H ), whatever 95 the HNP morphology. This plot also clearly shows that the individual USPIOs' size has the uppermost effect onto the final efficiency of these hybrid NPs as contrast agents. The cluster geometry (micelle or vesicle) has a mild influence on the final properties as contrast agent, but a strong influence on the physical 100 properties, as seen by comparing the transverse relaxivity curves r 2 (); further efforts to interpret these data quantitatively using more complex models should be done. Thanks to their relatively small size and the nature of the polymer hydrophobic blocks (poly(ester) and poly(amino acid)) used indeed, the particles studied in this work have partially hydrated cores. Therefore, all the magnetic material embedded in the HNPsbe it micelle or vesiclecontributes efficiently to the final contrast agent properties. According to our results, it is likely that the structure of polymer/iron oxide assemblies will have an impact on their efficiency as MRI contrast agents in the case of larger objects 10 prepared with highly hydrophobic material where USPIOs buried in the dehydrated core will not have any impact on water protons. Consequently, for small particles prepared from biodegradable polymers, the choice of geometry can be determined by other requirements, for instance the type of bio-active compounds that needs to be loaded: for a hydrophobic drug (e.g. Paclitaxel), a micelle geometry will be preferred, whereas for a hydrophilic one (e.g. nucleic acids) the encapsulation within the aqueous core of a vesicle would be best suited. When designing nanocarriers for theranostic applications from magnetic polymer HNPs, the choice 20 of nanostructure (spherical micelles, polymersomes, core-shells, hydrogels...) can be dictated by other requirements (drug loading efficiency, grafting of biological ligands for tumor targeting...) since MRI contrast efficiency can always be optimized, whatever the particulate structure, by choosing appropriate magnetic core 25 size, localization and feed weight ratio of the USPIOs, a good hint being to maximize the N USPIO /D H ratio. com-puted tomography; DDS, drug delivery systems; DLS, dynamic light scattering; FFC, fast-field-cycling; FWR, feed weight ratio; HNP, hybrid nanoparticle; MAR, motional averaging regime; MPS, mononuclear phagocyte system; MRI, magnetic resonance imaging; NANOTHER, integration of novel PBLG, poly(γ-benzyl-L-glutamate); PDI, polydispersity index; PEG, poly(ethylene glycol); PET, positron emission tomography; PGA, poly(L-glutamic acid); PTMC-,

55(

  120 mL) was added in 25 seconds under magnetic stirring at room temperature (22 °C) to a solution of PEG-b-PBLG block copolymer (45 mg; 40 mL) and iron oxide NPs in DMSO. Both solvent and aqueous solutions were filtered (0.22 μm) prior to the addition. After nanoprecipitation, DMSO was eliminated from 60 the suspensions by dialysis against ultrapure water for PEG-b-PBLG HNPs and Tris 50 mM pH 7.4 for PTMC-b-PGA HNPs (35 L).

Figure 3

 3 shows representative bright field TEM images of PEG-b-PBLG (a) and PTMC-b-PGA (b) HNPs loaded with 10 wt% FWR of 10-15 nm USPIOs, where their respective micellar and vesicular geometry are clearly evidenced. For M10-10 hybrid micelles (a), around 1 to 10 USPIOs per particle are fully wrapped by the polymer material and located in the hydrophobic core of the objects. On the contrary, in the case of V10-10 (b), the location of the iron oxide NPs solely at the periphery of the objects confirms their incorporation within the membrane of polymersomes, this behavior being identical for V6-10 vesicles which are loaded at the same FWR but with the smallest USPIOs (Figure 4.a).

Fig. 1

 1 Fig.1 Sketches of the two geometries of hybrid particles, respectively hollow vesicle with USPIOs (red spheres) embedded in the membrane (a) and filled micelle with a hydrophobic (orange) and magnetic core surrounded by a hydrophilic shell (blue) (b). For both geometries, we took

Fig. 2

 2 Fig.2 Autocorrelation curves and corresponding size distributions45

  volume fraction occupied by iron oxide in the composite and the weight-average volume of the USPIOs:

Fig. 3

 3 Fig.3 Transmission electron microscopy images of (a) PEG-b-PBLG and (b) PTMC-b-PGA HNPs loaded with 10 wt% FWR of 10-15 nm USPIOs. Negative staining: 1% uranyl acetate. The approximate numbers of USPIOs per object calculated by (Eq.2a) are respectively 5 per M10-10 micelle (a) from (Eq. 2b) and 2 per V10-10 vesicle (b) from (Eq. 2a), in agreement with the TEM pictures.

70 7 nmFigure 5

 75 Figure 5 shows typical examples of NMRD longitudinal relaxivity curves (r 1 vs. ) obtained for hybrid micelles with the smallest USPIOs (M6-5, Figure 5.a) and the largest ones (M10-20, Figure 5.b). Both profiles are flat for low frequencies, then reach a maximum and finally decrease rapidly at higher 80

Fig. 4

 4 Fig.4 Transmission electron microscopy images of PTMC-b-PGA HNPs loaded with (a) 10 wt% and (b) 70 wt% FWR of 6-7 nm USPIOs. The approximate numbers of USPIOs per vesicle calculated by (Eq. 2a) are respectively ≈30 for V6-10 (a) and ≈235 for V6-70 (b), in agreement with the TEM pictures.

Fig. 5

 5 Fig.5 Fitting of the T1 NMRD curves with the "outer shell" model for the micelle samples M6-5 (a) and M10-20 (b). The parameters of the fits are:65

  USPIOs of the same size such as M8-30 and V8-35 are relatively equivalent in term of r 2 and r 2 /r 1 . However when looking at the full curves of transverse relaxivity r 2 (), the two geometries respectively PEG-b-PBLG micelles (Figure SI-6) and PTMC-b-25 PGA vesicles (Figure 6) display somehow different behaviours. Indeed, while HNPs loaded with the smallest USPIOs' size (M6-5, V6-5, V6-10, V6-50, V6-70) show a continuous increase of r 2 () at high frequencies (≥10 MHz, Figure 6.b), all the other samples (M8-20, M8-25, M8-30, M10-20, V8-35, V8-50, V10-5, 30 and V10-10) reach a saturation plateau at frequencies ≥32 MHz, i.e. magnetic field values above 0.75 T (Figure 6.d). These two behaviours could be ascribed either to individual magnetic properties of the USPIOs or to the global morphology of the clusters: as seen on the magnetization curves (Figure SI-2), at 35 0.75 T and above, the magnetization of 8-10 nm and 10-15 nm

Fig. 7

 7 Fig. 7 Master curve representing the relavixity r2 at high field (60 MHz for the proton resonance) vs. the ratio of the number of USPIOs divided by the hydrodynamic diameter for the majority of samples reported in this study, both vesicles (V6-5, V6-10, V6-50, V6-70, V8-35, V8-50, V10-5, V10-10, V10-20) and micelles (M8-20, M8-25, M8-30).

40 NANOparticle

 40 based technol-ogy for THERapeutics and diagnosis of different types of cancer; NMRD, nuclear resonance dispersion;

Table 1

 1 Characteristics of the studied hybrid PTMC-b-PGA vesicles, named according to: V(USPIOs' size)-(theoretical -Fe2O3 FWR).

	Vesicles			Inorganic part			Organic part	DLS results
	Sample code	USPIOs	CFe (mM) (spectrum fit)	CFe (mM) HCl) a (OD350nm c after degradation in	Effective (wt%) FWR iron oxide	Approximate HNP d number of USPIOs per	[Copolymer] (mgmL -1 )	DH (nm)	PDI
	V6-5	6-7 nm	0.4	0.5	5.0	22	0.8	125	0.07
	V6-10	6-7 nm	0.8	0.95	9.5	29	0.8	102	0.14
	V6-50	6-7 nm	4.0	5.8	51.6	154	0.9	109	0.11
	V6-70	6-7 nm	6.0	7.7	68.1	235	0.9	116	0.09
	V8-35	8-10 nm	5.4	5.9	33.8	16	1.4	67	0.14
	V8-50	8-10 nm	7.4	7.6	50.5	31	1.2	79	0.15
	V10-5	10-15 nm	0.8	0.77	5.1	1	1.2	87	0.11
	V10-10	10-15 nm	1.5	1.4	10.1	2	1.1	73	0.14
	V10-20	10-15 nm		50 b	20 b	16	20.0	148	0.07

a The typical errors for iron assay by UV-V are ±5% with degradation into ions and only ±15% without. b calculated value (experimental value not determined), c supporting info, d from (Eq. 2a), assuming a membrane thickness mb=10 nm.

30

Table 2

 2 Characteristics of the studied hybrid PEG-b-PBLG micelles, named according to: M(USPIOs' size)-(theoretical -Fe2O3 FWR).

	Micelles	Inorganic part

b calculated value (experimental value not determined), c supporting info, d from (Eq. 2b).

Table 3

 3 Intra-aggregate volume fractions and magnetizations, estimates and measured values of r2 and r2/r1 at 60 MHz.

	Sample	intra	Mv (Am -1	)	r 2 predicted value from MAR or SDR model a (s -1 mM Fe -1 )	r 2 measured at 60MHz (1.41 T) (s -1 mM Fe -1 )	r2/r1 at 60MHz
	V6-5	0.5%	1.410 3	67 (MAR)	71	14
	V6-10	1.1%	3.310 3	108 (MAR)	80	17
	V6-50	4.9%	1.410 4	120 (SDR)	114	25
	V8-35	5.8%	1.810 4	140 (SDR)	128	22
	V8-50	6.8%	2.110 4	180 (SDR)	167	25
	V10-5	7%	2.210 3	75 (MAR)	219	71
	V10-10	1.6%	5.310 3	125 (MAR)	205	51
	V10-20	1.5%	5.510 3	270 (SDR)	280	103
	M6-5	1.2%	3.510 3	170 (SDR)	180	90
	M8-20	4.8%	1.510 4	125 (SDR)	95	20
	M8-25	5.9%	1.910 4	135 (SDR)	90	19
	M8-30	7%	2.210 4	140 (SDR)	105	30
	M10-20	4.8%	1.710 4	400 (SDR)	500	126
	Endorem ®	26%	7.310 4	96 (SDR)	98	10

reported inTable SI-I. As expected, T B increases with USPIOs' size: the plot of 25k B T B vs. volume d w 3 /6 leads to an anisotropy constant K a =2.410 4 Jm -3 , typical for -Fe 2 O 3 (Figure SI-7b).

good stability of these samples also in vivo.

exponential behavior of the decay of the transverse nuclear magnetization M xy .

Acknowledgements

This work was supported by NANOTHER project (Seventh Framework Programme: Theme NMP-2007, Large Scale 30 Integrating Collaborative Project n° CP-IP 213631-2) and by Italian MIUR project FIRB "RINAME" (RBAP114AMK). Authors also thank RNP-ESF P2M project for support.

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J. Mater. Chem. B, 2013, 1, 5317-5328 | 8 Fig. 6 Longitudinal r1 (left) and transverse r2 (right) relaxivities for PTMC-b-PGA HNPs at different USPIOs' diameter and FWRs. For a given USPIOs' size, the r1() curves are roughly superimposed, being only slightly translated vertically when increasing the FWR. On the contrary the r2() curves vary much more significantly with the FWR. Like with the micelles, at high fields/frequencies r2 reaches a plateau for vesicles loaded with 8-10 nm or 10-15 nm USPIOs, while it continues to increase with the 6-7 nm ones.

Abbreviations